bluuebunny/crossref_metadata_2025 huggingface dataset:
Oceanic Precipitation Nowcasting Using a UNet-Based Residual and Attention Network and Real-Time Himawari-8 Images
Authors: Ji Xianpu , Song Xiaojiang , Guo Anboyu , Liu Kai , Cao Haijin , Feng Tao
Published: August 2024

Qualitative precipitation forecasting plays a vital role in marine operational services. However, predicting heavy precipitation over the open ocean presents a significant challenge due to the limited availability of ground-based radar observations far from coastal regions. Recent advancements in deep learning models offer potential for oceanic precipitation nowcasting using satellite images. This study implemented an enhanced UNet model with an attention mechanism and a residual architecture (RA-UNet) to predict the precipitation rate within a 90 min time frame. A comparative analysis with the standard UNet and UNet with an attention algorithm revealed that the RA-UNet method exhibited superior accuracy metrics, such as the critical ratio index and probability of detection, with fewer false alarms. Two typical cases demonstrated that RA-UNet had a better ability to forecast monsoon precipitation as well as intense precipitation in a tropical cyclone. These findings indicate the greater potential of the RA-UNet approach for nowcasting heavy precipitation over the ocean using satellite imagery.
Evaluation of Traffic Sign Occlusion Rate Based on a 3D Point Cloud Space
Authors: Wang Ziqi , Wang Xiaofei , Li Jun , Yao Jiangbei
Published: August 2024

The effectiveness of road signs is hindered by obstructions, such as vegetation, mutual obstruction of signs, or the road alignment itself. The traditional evaluation of road sign recognition effectiveness is conducted through in-vehicle field surveys. However, this method has several drawbacks, including discontinuous identification, unclear positioning, incomplete coverage, and being time-consuming. Consequently, it is unable to effectively assess the recognition status of road signs at any arbitrary point within the road space. Therefore, this study employed laser scanning to construct a point-surface model, which was based on a point cloud algorithm and SLAM (Simultaneous Localization and Mapping), integrated LiDAR and inertial navigation system data, and optimized the point model after processing steps such as denoising, resampling, and three-dimensional model construction. Furthermore, a method for assessing the highway sign occlusion rate based on the picking algorithm was proposed. The algorithm was applied to an actual road environment, and the occlusion by other items was simulated. The results demonstrated the effectiveness of the method. This new method provides support for the fast and accurate calculation of road sign occlusion rates, which is of great importance for ensuring the safe traveling of vehicles.
Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
Authors: Rodríguez-Esparragón Dionisio , Gamba Paolo , Marcello Javier
Published: August 2024

The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines the use of various vegetation indices with clustering algorithms (bisecting k-means and k-means) to analyze images before and after fires, with the aim of improving the precision of the burned area and severity assessments. The results demonstrate the potential of using this PlanetScope sensor, showing that the methodology effectively delineates burned areas and classifies them by severity level, in comparison with data from the Copernicus Emergency Management Service (CEMS). Thus, the potential of the SuperDove satellite sensor constellation for fire monitoring is highlighted, despite its limitations regarding radiometric distortion and the absence of Short-Wave Infrared (SWIR) bands, suggesting that the methodology could contribute to better fire management strategies.
A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning
Authors: Ma Linjuan , Zhang Fuquan
Published: August 2024

In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics.
Review of Methods for Automatic Plastic Detection in Water Areas Using Satellite Images and Machine Learning
Authors: Danilov Aleksandr , Serdiukova Elizaveta
Published: August 2024

Ocean plastic pollution is one of the global environmental problems of our time. “Rubbish islands” formed in the ocean are increasing every year, damaging the marine ecosystem. In order to effectively address this type of pollution, it is necessary to accurately and quickly identify the sources of plastic entering the ocean, identify where it is accumulating, and track the dynamics of waste movement. To this end, remote sensing methods using satellite imagery and aerial photographs from unmanned aerial vehicles are a reliable source of data. Modern machine learning technologies make it possible to automate the detection of floating plastics. This review presents the main projects and research aimed at solving the “plastic” problem. The main data acquisition techniques and the most effective deep learning algorithms are described, various limitations of working with space images are analyzed, and ways to eliminate such shortcomings are proposed.
Large-Area Film Thickness Identification of Transparent Glass by Hyperspectral Imaging
Authors: Huang Shuan-Yu , Karmakar Riya , Chen Yu-Yang , Hung Wei-Chin , Mukundan Arvind , Wang Hsiang-Chen
Published: August 2024

This study introduces a novel method for detecting and measuring transparent glass sheets using hyperspectral imaging (HSI). The main goal of this study is to create a conversion technique that can accurately display spectral information from collected images, particularly in the visible light spectrum (VIS) and near-infrared (NIR) areas. This technique enables the capture of relevant spectral data when used with images provided by industrial cameras. The next step in this investigation is using principal component analysis to examine the obtained hyperspectral images derived from different treated glass samples. This analytical procedure standardizes the magnitude of light wavelengths that are inherent in the HSI images. The simulated spectral profiles are obtained using the generalized inverse matrix technique on the normalized HSI images. These profiles are then matched with spectroscopic data obtained from microscopic imaging, resulting in the observation of distinct dispersion patterns. The novel use of images coloring methods effectively displays the thickness of the glass processing sheet in a visually noticeable way. Based on empirical research, changes in the thickness of the glass coating in the NIR-HSI range cause significant changes in the transmission of infrared light at different wavelengths within the NIR spectrum. This phenomenon serves as the foundation for the study of film thickness. The root mean square error inside the NIR area is impressively low, calculated to be just 0.02. This highlights the high level of accuracy achieved by the technique stated above. Potential areas of investigation that arise from this study are incorporating the proposed approach into the design of a real-time, wide-scale automated optical inspection system.
PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment
Authors: Razfar Najmeh , Kashef Rasha , Mohammadi Farah
Published: August 2024

The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing smart rehabilitation in various healthcare systems. Maintaining patient privacy is paramount in healthcare while providing smart insights and recommendations. This study proposed the adoption of federated learning to develop a scalable AI model for post-stroke assessment while protecting patients’ privacy. This research compares the centralized (PSA-MNMF) model performance with the proposed scalable federated PSA-FL-CDM model for sensor- and camera-based datasets. The computational time indicates that the federated PSA-FL-CDM model significantly reduces the execution time and attains comparable performance while preserving the patient’s privacy. Impact Statement—This research introduces groundbreaking contributions to stroke assessment by successfully implementing federated learning for the first time in this domain and applying consensus models in each node. It enables collaborative model training among multiple nodes or clients while ensuring the privacy of raw data. The study explores eight different clustering methods independently on each node, revolutionizing data organization based on similarities in stroke assessment. Additionally, the research applies the centralized PSA-MNMF consensus clustering technique to each client, resulting in more accurate and robust clustering solutions. By utilizing the FedAvg federated learning algorithm strategy, locally trained models are combined to create a global model that captures the collective knowledge of all participants. Comparative performance measurements and computational time analyses are conducted, facilitating a fair evaluation between centralized and federated learning models in stroke assessment. Moreover, the research extends beyond a single type of database by conducting experiments on two distinct datasets, wearable and camera-based, broadening the understanding of the proposed methods across different data modalities. These contributions develop stroke assessment methodologies, enabling efficient collaboration and accurate consensus clustering models and maintaining data privacy.
Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios
Authors: Tian Yuan , Wen Hong , Zhou Jiaxin , Duan Zhiqiang , Li Tao
Published: August 2024

With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC−α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.
Determination of Gb3 and Lyso-Gb3 in Fabry Disease-Affected Patients by LC-MRM/MS
Authors: Battaglia Gennaro , Pinto Gabriella , Fontanarosa Carolina , Spinelli Michele , Illiano Anna , Serpico Stefania , Chiariotti Lorenzo , Risoluti Roberta , Materazzi Stefano , Amoresano Angela
Published: August 2024

Limited or absent activity of the enzyme α-galactosidase A (α-Gal A), due to mutation in the related gene on the X chromosome, leads to the development of a rare hereditary and genetic disease known as Fabry disease (FD). This pathology involves a progressive accumulation in various organs of the substrates of the enzyme e.g., globotriaosylceramide (Gb3) and its deacylated form, globotriaosylsphingosine (Lyso-Gb3), suggesting these molecules as biomarkers of Fabry disease. The present paper describes the development of an analytical strategy for the identification and quantification of Gb3 and Lyso-Gb3, in serum and blood samples by using liquid chromatography (LC) coupled to mass spectrometry in multiple reaction monitoring (MRM/MS) ion mode. The best experimental conditions were obtained by extracting the glycolipids with chloroform/methanol/H2O (2/1/0.3) and by separating them on a C4 column with a linear gradient (A: H2O with 2 mM ammonium formate. B: methanol with 1 mM ammonium formate, both acidified with 0.2% formic acid). The best transitions (a combination of precursor and fragment ions—m/z) were 786.8 m/z > 268.3 m/z for Lyso-GB3, 1137.3 m/z > 264.3 m/z for Gb3, 1039.3 m/z > 264.4 m/z for N-heptadecanoyl-ceramide trihexoside, and 843.5 m/z > 264.3 m/z for N-glycinated lyso-ceramide trihexoside, the latter being used as an internal standard. The developed method provided a reliable, fast, and effective procedure for direct measurements of GB3 and Lyso-GB3 in serum and blood for diagnosis of Fabry disease, suggesting this method as a complementary assay to the current enzymatic test. Therefore, this approach could open new insights into the clinical diagnostics of lysosomal storage disorders.
Research on a Metal–Organic Framework (MOF)-Derived Carbon-Coated Metal Cathode for Strengthening Bioelectrochemical Salt Resistance and Norfloxacin Degradation
Authors: Fan Mengjie , Li Hui , Wang Liuhong , Chen Zhixuan , Liu Jining , Chen Yingwen
Published: August 2024

Microbial fuel cells (MFCs) can realize the conversion of chemical energy to electrical energy in high-salt wastewater, but the easily deactivated cathode seriously affects the performance of MFCs. To enhance the stability and sustainability of MFC in such circumstances, a bimetallic organic framework ZIF-8/ZIF-67 was utilized for the synthesis of a carbon cage-encapsulated metal catalysts in this study. Catalysts with different Co and Ce ratio (Co@C (without the Ce element), CoCe0.25@C, CoCe0.5@C, and CoCe1@C) were employed to modify the activated carbon cathodes of MFCs. The tests demonstrated that the MFCs with the CoCe0.5@C cathode catalyst obtained the highest maximum power density (188.93 mW/m2) and the smaller polarization curve slope, which boosted the electrochemical activity of microorganisms attached to the anode. The appropriate addition of the Ce element was conductive to the stability of the catalyst’s active center, which is beneficial for the stability of catalytic performance. Under the function of the CoCe0.5@C catalyst, the MFCs exhibited superior and stable norfloxacin (NOR) degradation efficiency. Even after three cycles, the NOR degradation rate remained at 68%, a negligible 5.6% lower than the initial stage. Furthermore, based on the analysis of microbial diversity, the abundance of electrogenic microorganisms on a bioanode is relatively high with CoCe0.5@C as the cathode catalyst. This may be because the better cathode oxygen reduction reaction (ORR) performance can strengthen the metabolic activity of anode microorganisms. The electrochemical performance and NOR degradation ability of MFC were enhanced in a high-salt environment. This paper provides an approach to address the challenge of the poor salt tolerance of cathode catalysts in MFC treatment, and presents a new perspective on resource utilization, low carbon emissions, and the sustainable treatment of high-salt wastewater.
How Business–Government Relationships Drive Digital Innovation and Entrepreneurship: A Study of 292 Cities in China Using NCA and TDQCA
Authors: Hu Shuigen , Cang Yilin , Jie Yulong , Wang Xianbo , Weng Lie’en
Published: August 2024

Innovation is the driving force for achieving sustainable economic development, and healthy business–government relationships are the foundation and guarantee for promoting the sustainability of digital innovation and entrepreneurship. However, current academic research on the impact of business–government relations on digital innovation and entrepreneurship often neglects the configurational effects of various factors. Therefore, this study constructed an analytical framework from the new dimension of “close” and “clean” business–government relationships, selected 292 Chinese cities as research subjects, and employed the Necessary Condition Analysis (NCA) and Time-Differencing Qualitative Comparative Analysis (TDQCA) methods. From a configurational perspective, it explored the relationship between business–government relations and digital innovation and entrepreneurship. The results showed the following: Firstly, the various business–government relationship factors did not have a single linear impact on digital innovation and entrepreneurship, and configuration was more crucial than a single factor. Secondly, based on the integration of research findings and the theoretical framework, five successful configurations were proposed. However, these configurations possess certain adaptability and need to be tailored to local conditions. Thirdly, analyzing the three “non” condition variables in these five configurations, including “clean” business–government relationships, government efficiency, and new infrastructure, also contributed to enhancing the sustainability of digital innovation and entrepreneurship outcomes. Additionally, the study analyzed the implications of these critical configurations for five key stakeholders: government, enterprises, research institutions and academia, policymakers, and the public. Specifically, the government can implement policies tailored to local conditions to promote the sustainable development of digital innovation and entrepreneurship. These policies include increasing investment in digital infrastructure, simplifying approval processes, and enhancing the efficiency of government services.
BIM-Based Strategies for the Revitalization and Automated Management of Buildings: A Case Study
Authors: Cascone Stefano , Parisi Giuliana , Caponetto Rosa
Published: August 2024

This study explores the transformative potential of integrating Building Information Modeling (BIM) and Generative Design methodologies in heritage conservation and building management. By utilizing BIM, detailed architectural, structural, and MEP models were created, facilitating precise design and effective stakeholder collaboration. Generative Design enabled the exploration of multiple design solutions, optimizing spatial layouts and structural integrity. The project also integrated automated management systems and IoT sensors to enhance real-time monitoring, energy efficiency, and user comfort through the development of a digital twin. Despite encountering challenges such as technical complexities and budget constraints, the project successfully preserved the cinema’s historical essence while incorporating modern functionalities. The findings highlight the contributions of BIM and Generative Design to the AEC industry, emphasizing their role in improving design accuracy, operational efficiency, and sustainability. This research provides valuable insights for future projects in heritage conservation, offering a blueprint for balancing historical preservation with contemporary needs. The revitalization of the “Ex Cinema Santa Barbara” in Paternò exemplifies these advancements, demonstrating how these technologies can restore and modernize culturally significant historical buildings effectively.
Benefit Distribution Mechanism of a Cooperative Alliance for Basin Water Resources from the Perspective of Cooperative Game Theory
Authors: Zhang Kai , Lu Haishu , Wang Bin
Published: August 2024

At present, global water resource security is facing serious threats, and the construction of a cooperative, open, and mutually beneficial water resource community is a potential solution to the global water resource crisis and water resource hegemony. Previous studies on the formation and beneficial distribution of water resources in cooperative alliances have more often focused on the idea that participants take all of their water resources when they join a cooperative alliance (i.e., a crisp cooperative alliance), while fewer studies have focused on participants including different proportions of their water resources and joining multiple cooperative alliances (i.e., fuzzy cooperative alliances), and even fewer comparative studies concern the use of different benefit-sharing mechanisms. In this paper, in order to improve the efficiency of water use, allocate water resources more optimally, and generate higher returns for water users in a given basin, we propose the establishment of a traditional crisp and improved fuzzy cooperative alliance for water resources in the basin from the perspective of cooperative game theory; we examine the water resource allocation mechanism within the alliance based on the principle of priority; we construct a benefit allocation mechanism for the cooperative alliance based on the core, least core, weak least core, and Shapley value method; and we carry out empirical research using the example of the Tarim River Basin. Our findings are as follows: (1) A cooperative alliance based on the perspective of cooperative game theory can effectively improve overall benefits and individual benefits, and a fuzzy cooperative alliance is more effective than a crisp cooperative alliance in improving the overall water benefits of the region. (2) The participants in the fuzzy cooperative alliance can obtain more benefits than in the crisp cooperative alliance, and the benefit distribution mechanism of the cooperative alliance helps the participants to determine the object of cooperation while maintaining the sustainable existence of the alliance. (3) The different methods of benefit distribution within the cooperative alliance directly affect the overall water benefits of the region. (4) The different methods of benefit distribution directly affect the stability of the alliance, and the players in the game have heterogeneous preferences for different distribution schemes. The resource-sharing mechanism and benefit distribution mechanism of a water resource cooperative alliance have good applicability as solutions to the problem of water resource optimization and allocation in river basins, and they may provide policy references for the efficient use of water resources and optimization of water resource allocation and management in areas with a shortage of water resources, such as arid and semi-arid zones.
Analysis of Solid Waste Treatment and Management in Typical Chinese Industrial Parks with the Goal of Sustainable Development and Future Suggestions
Authors: Yu Lu , Chen Sichen , Tan Zhe
Published: August 2024

Solid waste disposal and management have become a global problem, which is particularly tricky in China with its large population and rapid urbanization. This study focused on the disposal status of multi-source solid waste as well as the park management of some typical cities of China. Firstly, the main technical methods for solid waste disposal were summarized as follows: landfill, incineration, anaerobic digestion and aerobic compost. Secondly, the network analysis method was applied to seek an optimized method for solid waste disposal and management. Thirdly, typical demonstration parks for solid waste disposal and management were analyzed to study their respective operating modes and strategies for synergistic development in terms of resources, environment and economy and to explore the sustainable development potential of the Guangdong–Hong Kong–Macao operating mode. The results showed that the collaborative disposal and recycling of solid waste are important for cities (especially megacities) to achieve resource conservation and environmental protection. The NIMBY effect and environmental pollution risks caused by decentralized construction could be reduced through the construction of circular industrial parks. Advanced technologies and the national policy for solid waste disposal and recycling in industrial parks of small–medium cities, large–medium cities and megacities were systematically analyzed so as to explore a self-operating management mode of industrial parks. Finally, reasonable suggestions, such as sharing, saving and cycling and propaganda education, as well as green and low-carbon solutions were put forward for solid waste disposal and management in typical industrial parks, effectively resolving the contradiction between economic development and environmental protection so as to help urban sustainable development.
Institutional Performance and Carbon Reduction Effect of High-Quality Development of New Energy: China’s Experience and Policy Implication
Authors: Zhang Li-chen , Dong Zheng-ai , Tan Zhi-xiong , Luo Jia-hui , Yan De-kui
Published: August 2024

Based on the policy text from 1999 to 2022, this paper quantitatively analyzes photovoltaic power, wind power and new energy policies in mainland China by keyword capture and policy strength and establishes a spatial Durbin model to study the carbon reduction effects. The results show the following: (1) The development of new energy is primarily project-based and concentrated in rural areas. (2) Financial support is a double-edged sword. (3) After the implementation of the Renewable Energy Law in 2015, the change trend in policy quantity, total policy intensity, and average policy intensity is generally consistent. (4) The increase in the strength of new energy policies has effectively reduced the intensity of provincial carbon dioxide emissions. (5) New energy policies introduced in a single region are less effective in reducing carbon emissions than joint regional regulation. Therefore, it is necessary to improve the quality and coordination of new energy policies through the effective convergence of policies and regular evaluations to enhance the positive guidance of the policies. Policy measures for new energy are refined in three areas: improving the amount and quality of new energy policies, strengthening new energy policies and establishing cooperation mechanisms for the cross-regional consumption of new energy.
Spatiotemporal Distribution and Regional Imbalance of China’s Digital Economy
Authors: Fan Ruina , Nie Chong , Zhao Yuanhao , Hao Chunxu , Peng Chen
Published: August 2024

The digital economy is an important driving force for promoting national economic growth and achieving high-quality economic transformation, and is a key force in achieving the sustainable development goals (SDGs). This paper measures the digital economy development level of 30 provinces in China from 2013 to 2021 utilizing the entropy of weighting approach, and in order to further reveal its intrinsic laws and differences, it uses three-dimensional kernel density analysis, the Dagum Gini coefficient, spatial autocorrelation analysis, and cold hot spot analysis to explore the spatial and temporal evolution characteristics of the digital economy and its regional imbalance. The empirical results show that ① China’s digital economy has been steadily increasing, spatially showing the characteristics of gradual decrease from east to west and from coast to inland, with obvious spatial agglomeration characteristics and an increasing trend. ② There are obvious regional imbalances in the digital economy, with the southeast coastal region leading significantly. ③ The overall regional differences are large but reduced, mainly from intra-regional differences, the and inter-regional contribution is low. The research in this paper provides data support for revealing the spatial and temporal evolution characteristics of digital economic development and provides new path support for the balanced development of China’s regional digital economy.
Transgenerational Sub-Lethal Pyrethroid Exposure Affects Shape Variation and Fluctuating Asymmetry in Seed Beetles
Authors: Budečević Sanja , Predojević Dragana , Đorđević Mirko , Vlajnić Lea , Stojković Oliver , Stojković Biljana , Savković Uroš
Published: August 2024

One method of crop protection is the application of a widely used group of pesticides—pyrethroids. As xenobiotics, sub-lethal doses of insecticides cause stress in pests, resulting in a change in the shape and size of their organs or bodies. The stress caused by pesticides may lead to acute destabilization of development, but also to transgenerational canalization through the process of genetic assimilation. Fluctuating asymmetry (FA), small random deviations between the right and left sides of bilaterally symmetrical traits, is an outcome of developmental instability and is a measurable indicator of phenotypic response to stress. We exposed four populations of the seed beetle Acanthoscelides obtectus to sub-lethal doses of cypermethrin for ten generations in a laboratory evolution experiment. Using geometric morphometrics, we analyzed size and shape changes and the level of fluctuating asymmetry in untreated beetles and in samples from the fifth and tenth generation. Exposure over ten generations led to an increase in the body size of the beetles, shortening of their pronotum and elongation of their thorax and abdomen. After ten generations of exposure to cypermethrin, FA levels decreased, indicating a canalization of development. This study provides new insights into the phenotypic markers of environmental pollution from agricultural activities.
Group Theoretic Approach towards the Balaban Index of Catacondensed Benzenoid Systems and Linear Chain of Anthracene
Authors: Yaseen Muhammad , Alkahtani Badr S. , Min Hong , Anjum Mohd
Published: August 2024

In this work, we present the analytical closed forms of the Balaban index for anthracene and catacondensed benzenoid systems using group theoretic techniques. The Balaban index is a distance-based topological index that provides valuable information about the properties of chemical structures. We emphasize the importance of determining analytical closed forms of the Balaban index for catacondensed benzenoid systems and linear chains of anthracene, as it enables a deeper understanding of these systems and their behavior. Our analysis utilizes the group action of the automorphism group of these chains on the set of vertices, which refer to the points where the chains intersect. In future work, we plan to determine the Balaban index of other polymeric linear chains using group theoretic techniques and extend the applications of this index to other fields, such as materials science and biology. It is clear that the Balaban index remains a valuable tool in theoretical and computational chemistry, and its applications are constantly evolving.
Revisit Birkhoff’s Theorem: The Post-Newtonian Metric of a Self-Gravitating and Collapsing Thin Spherical Shell
Authors: Lin Wenbin
Published: August 2024

We calculate the metric of a self-gravitating and collapsing infinitely thin spherical shell in the weak-field and slow-motion limits, and we demonstrate that Birkhoff’s theorem is not consistent with the theory of the post-Newtonian approximation. More importantly, it is illustrated that performing a coordinate transformation in solving Einstein field equations may change the matter energy-momentum tensor, making the resultant solution not correspond to the original problem.
Energy Efficiency for Faster-than-Nyquist Data Transmission Using Processing Algorithms with Decision Feedback
Authors: Shang Wenjing , Lavrenyuk Ilya , Makarov Sergey , Ovsyannikova Anna , Zavjalov Sergey , Yu Dingfeng , Xue Wei
Published: August 2024

One of the ways to increase the volume of transmitted information is to increase the bit rate above the Nyquist barrier. However, an increase in bit rate in the case of FTN (Faster-Than-Nyquist) signals leads to an increase in energy costs for receiving information on channels with limited bandwidth, for example, in Digital Video Broadcasting satellite systems like DVB-S2/S2X. It is possible to minimize energy losses by using the processing algorithm “maximum likelihood sequence estimation”. However, the computational complexity of this algorithm is extremely high, which limits its use, especially in terrestrial mobile satellite terminals. We propose a new bit-by-bit decision feedback algorithm with maximum likelihood ratio estimation of subsequent symbols in the observation interval. This algorithm provides minimal energy costs comparable to the method “maximum likelihood sequence estimation” at speeds 2–3 times higher than the Nyquist barrier. At the same time, the complexity is two orders of magnitude less. It is shown by simulation for a channel with additive noise that energy losses in relation to the potential bit error rate (BER) are less than 4.5 dB. In the presence of Rayleigh fading, the application of the proposed algorithm makes it possible to provide the processing of FTN signals for double bit rates in urban areas with energy costs equal to 12 dB when using an equalizer. We give numerical estimations of the increase in computational complexity for the proposed processing algorithm. It is shown that an increase in the bit rate by 1.5 times leads to an increase in the computational complexity by more than an order of magnitude. The same conclusion can be reformulated in another form: for the proposed algorithm, each decibel of energy gain is achieved by increasing the number of computational operations by 1.5×105. It is experimentally shown that additional energy losses due to non-ideal phase and timing synchronization are no more than 1 dB when the proposed algorithm is applied in a fading channel. The energy costs in fading channels relative to a stationary channel for twice the Nyquist rate are equal to 13.8 dB when using an equalizer.
Complexity in Systemic Cognition: Theoretical Explorations with Agent-Based Modeling
Authors: Secchi Davide , Gahrn-Andersen Rasmus , Neumann Martin
Published: August 2024

This paper presents a systemic view of human cognition that suggests complexityis an essential feature of such a system. It draws on the embodied, distributed, and extended cognition paradigms to outline the elements and the mechanisms that define cognition. In doing so, it uses an agent-based computational model (the TS 1.0.5Model) with a focus on learning mechanisms as they reflect on individual competence to gain insights on how cognition works. Results indicate that cognitive dynamics do not depend solely on macro structural elements, nor do they depend uniquely on individual characteristics. Instead, more insights and understanding are available through the consideration of all elements together as they co-evolve and interact over time. This perspective illustrates the essential role of how we define the meso domain and constitutes a clear indication that cognitive systems are indeed complex.
Feedback Collection and Nearest-Neighbor Profiling for Recommendation Systems in Healthcare Scenarios
Authors: António João , Malheiro Ricardo , Jardim Sandra
Published: August 2024

The rise in the dimension and complexity of information generated in the clinical field has motivated research on the automation of tasks in personalized healthcare. Recommendation systems are a filtering method that utilizes patterns and data relationships to generate items of interest for a particular user. In healthcare, these systems can be used to potentiate physical therapy by providing the user with specific exercises for rehabilitation, albeit facing issues pertaining to low accuracy in earlier iterations (cold-start) and a lack of gradual optimization. In this study, we propose a physical activity recommendation system that utilizes a K-nearest neighbor (KNN) sampling strategy and feedback collection modules to improve the adequacy of recommendations at different stages of a rehabilitation period when compared to traditional collaborative filtering (CF) or human-constrained methods. The results from a trial show significant improvements in the quality of initial recommendations, achieving 81.2% accuracy before optimization. Moreover, the introduction of short-term adjustments based on frequent player feedback can be an efficient manner of improving recommendation accuracy over time, achieving overall better convergence periods than those of human-based systems, topping at a measured 98.1% accuracy at K = 7 cycles.
Chromatin Regulator SMARCA4 Is Essential for MHV-Induced Inflammatory Cell Death, PANoptosis
Authors: Malireddi R. K. Subbarao , Kanneganti Thirumala-Devi
Published: August 2024

The innate immune system serves as the first line of defense against β-coronaviruses (β-CoVs), a family of viruses that includes SARS-CoV-2. Viral sensing via pattern recognition receptors triggers inflammation and cell death, which are essential components of the innate immune response that facilitate viral clearance. However, excessive activation of the innate immune system and inflammatory cell death can result in uncontrolled release of proinflammatory cytokines, resulting in cytokine storm and pathology. PANoptosis, innate immune, inflammatory cell death initiated by innate immune sensors and driven by caspases and RIPKs through PANoptosome complexes, has been implicated in the pathology of viral infections. Therefore, understanding the molecular mechanisms regulating PANoptosis in response to β-CoV infection is critical for identifying new therapeutic targets that can mitigate disease severity. In the current study, we analyzed findings from a cell death-based CRISPR screen with archetypal β-CoV mouse hepatitis virus (MHV) as the trigger to characterize host molecules required for inflammatory cell death. As a result, we identified SMARCA4, a chromatin regulator, as a putative host factor required for PANoptosis in response to MHV. Furthermore, we observed that gRNA-mediated deletion of Smarca4 inhibited MHV-induced PANoptotic cell death in macrophages. These findings have potential translational and clinical implications for the advancement of treatment strategies for β-CoVs and other infections.
Prevention of Meningococcal Disease: Knowledge, Attitudes, and Practices of General Practitioners and Primary Care Pediatricians in South Italy
Authors: Angelillo Silvia , Pelullo Concetta Paola , Licata Francesca , Lanzano Raffaele , Napolitano Francesco , Di Giuseppe Gabriella
Published: August 2024

Background: The purpose of this study was to evaluate the knowledge, attitude, and current practices about prevention of meningococcal disease among general practitioners (GPs) and primary care pediatricians (PCPs) in Italy. Methods: A cross-sectional survey was carried out between February 2022 and July 2023 among a random sample of GPs and PCPs in Southern Italy. The data were collected using a questionnaire accessible via an internet link with the free software Google Forms®. Results: Regarding the participants’ knowledge toward meningococcal vaccinations, 84.2% of the PCPs and more than half of the GPs (55.2%) knew that the meningococcal B (MenB) vaccination is recommended for infants from the second month of life and 84.2% and 82.7% of the PCPs were aware that quadrivalent meningococcal ACWY (MenACWY) vaccine is recommended for children in the second year of life and adolescents, respectively. The GPs and PCPs considered vaccination against meningococcal disease to be very effective and safe with average values of 8.8 and 8.7, respectively, on a scale ranging from 1 to 10. Those with an older age, those who knew the medical conditions that expose patients to a higher risk of contracting meningococcal disease, and those who self-rated their knowledge on meningococcal disease as excellent/very good were more likely to consider the vaccination to be very effective and safe. Only 15.5% of the GPs and more than half of the PCPs (54.3%) administered anti-meningococcal vaccines to their patients. GPs and females were less likely to administer anti-meningococcal vaccines to their patients, whereas those who acquired information on meningococcal vaccinations by scientific journals were more likely to administer meningococcal vaccines. Conclusions: The findings of the survey highlighted the need of a greater engagement of GPs and PCPs in the immunization campaigns in order to increase meningococcal vaccination coverage.
Exploring T-Cell Immunity to Hepatitis C Virus: Insights from Different Vaccine and Antigen Presentation Strategies
Authors: Costa Gabriel L. , Sautto Giuseppe A.
Published: August 2024

The hepatitis C virus (HCV) is responsible for approximately 50 million infections worldwide. Effective drug treatments while available face access barriers, and vaccine development is hampered by viral hypervariability and immune evasion mechanisms. The CD4+ and CD8+ T-cell responses targeting HCV non-structural (NS) proteins have shown a role in the viral clearance. In this paper, we reviewed the studies exploring the relationship between HCV structural and NS proteins and their effects in contributing to the elicitation of an effective T-cell immune response. The use of different vaccine platforms, such as viral vectors and virus-like particles, underscores their versability and efficacy for vaccine development. Diverse HCV antigens demonstrated immunogenicity, eliciting a robust immune response, positioning them as promising vaccine candidates for protein/peptide-, DNA-, or RNA-based vaccines. Moreover, adjuvant selection plays a pivotal role in modulating the immune response. This review emphasizes the importance of HCV proteins and vaccination strategies in vaccine development. In particular, the NS proteins are the main focus, given their pivotal role in T-cell-mediated immunity and their sequence conservation, making them valuable vaccine targets.
Licorice Extract Supplementation Benefits Growth Performance, Blood Biochemistry and Hormones, Immune Antioxidant Status, Hindgut Fecal Microbial Community, and Metabolism in Beef Cattle
Authors: Liang Sunzhen , Meng Jinzhu , Tang Zining , Xie Xinxin , Tian Miaomiao , Ma Xiaowan , Yang Xiao , Xiao Dingfu , Wang Shuilian
Published: August 2024

This study aimed to evaluate the effects of licorice extract (LE) on growth performance, nutrient apparent digestibility, serum index (biochemistry, hormones, humoral immunity, and antioxidant function), hindgut fecal microbiota, and metabolism in beef cattle. In total, 12 male yellow cattle aged 12 months were divided into two groups (6 cattle per group): the basal diet (CK group) and the basal diet supplemented with 2 g/kg LE (CHM group). The entire experimental phase lasted for 120 days, including a 30-day pre-feeding period. Compared to the CK group, the average daily gain, crude fiber, calcium, and crude protein nutrient digestibility were greater on d 30 than d 60 (p < 0.05) and the feed meat ratio was lower for LE addition (p < 0.01). In terms of serum indexes, the insulin and nitric oxide contents were enhanced on d 30, the alkaline phosphatase level was improved on d 60, and the levels of albumin, immunoglobulin A, and catalase were increased on d 90 (p < 0.05). In contrast, the cholesterol content was lower on d 60 for LE addition compared with the CK group (p < 0.05). The higher enrichment of [Eubacterium]-oxidoreducens-group, p-2534-18b5-gut-group, and Ileibacterium were observed in the CHM group (p < 0.05), while the relative abundances of Gallibacterium and Breznakia in the CHM group were lower compared with the CK group (p < 0.05). In addition, the differential metabolites related to healthy growth in the CHM group were increased compared with the CK group. And there was a close correlation between hindgut microbiota and metabolic differentials. In general, LE has a promoting effect on the growth performance and health status of beef cattle over a period (30 to 60 days).
Spatial and Temporal Variability in Bioswale Infiltration Rate Observed during Full-Scale Infiltration Tests: Case Study in Riga Latvia
Authors: Kondratenko Jurijs , Boogaard Floris C. , Rubulis Jānis , Maļinovskis Krišs
Published: August 2024

Urban nature-based solutions (NBSs) are widely implemented to collect, store, and infiltrate stormwater. This study addressed infiltration rate as a measure of the performance of bioretention solutions. Quick scan research was conducted, starting with mapping over 25 locations of implemented green infrastructure in Riga, Latvia. Basic information, such as location, characteristics, as well as photos and videos, has been uploaded to the open-source database ClimateScan. From this, eight bioswales installed in the period 2017–2022 were selected for hydraulic testing, measuring the infiltration capacity of bio-retention solutions. The results show a high temporal and spatial variation of infiltration rate for the bioswales, even those developed with similar designs: 0.1 to 7.7 m/d, mean 2.0 m/d, coefficient of variation 1.0. The infiltration capacity decreased after saturation: a 30% to 58% decrease in infiltration rate after refilling storage volume. The variation in infiltration rate as well as infiltration rate decrease on saturation is similar to other full-scale studies done internationally. The infiltration rate of most bioswales falls within the range specified by international guidelines, all swales empty within 48 h. Most bioswales empty several times within one day, questioning the effectiveness of water retention and water availability for dry periods. The results are of importance for stakeholders involved in the implementation of NBS and will be used to set up Latvian guidelines for design, construction, and maintenance.
A ‘Nuclear Bomb’ or Just ‘a Joke’? Groundwater Models May Help Communicate Nuanced Risks to the Great Salt Lake
Authors: LaPlante Matthew D. , Dahal Piyush , Wang Shih-Yu Simon , Hakala Kirsti , Mukherjee Avik
Published: August 2024

The Great Salt Lake entered the zeitgeist of environmental concern in 2022 when a coalition of scientists and activists warned in a highly publicized report that the lake might be just five years away from complete desiccation, a possibility one state official warned was tantamount to an “environmental nuclear bomb”. Shortly thereafter, an unpredicted and unprecedented pluvial winter resulted in an increase in inflow, temporarily halting the lake’s decline and prompting Utah’s governor to mock the dire prediction as “a joke”, an outcome that speaks to the tension between agenda-setting and trust-building that researchers face when sharing worst-case warnings, particularly those based on short-term variability. Here, we describe a robust relationship between the lake and groundwater in the surrounding region and demonstrate how coupled models can thus be used to improve lake elevation predictions, suggesting that while the situation may not be as dire as some have warned, the lake remains at long-term risk as a result of climate warming. We further suggest that efforts to communicate the risk of future desiccation should be informed by stochastic variability and guided by long-term fluctuations in the total water storage of the endorheic lake’s watershed.