bluuebunny/crossref_metadata_2025 huggingface dataset:
CAMLLA-YOLOv8n: Cow Behavior Recognition Based on Improved YOLOv8n
Authors: Jia Qingxiang , Yang Jucheng , Han Shujie , Du Zihan , Liu Jianzheng
Published: October 2024

Cow behavior carries important health information. The timely and accurate detection of standing, grazing, lying, estrus, licking, fighting, and other behaviors is crucial for individual cow monitoring and understanding of their health status. In this study, a model called CAMLLA-YOLOv8n is proposed for Holstein cow behavior recognition. We use a hybrid data augmentation method to provide the model with rich Holstein cow behavior features and improve the YOLOV8n model to optimize the Holstein cow behavior detection results under challenging conditions. Specifically, we integrate the Coordinate Attention mechanism into the C2f module to form the C2f-CA module, which strengthens the expression of inter-channel feature information, enabling the model to more accurately identify and understand the spatial relationship between different Holstein cows’ positions, thereby improving the sensitivity to key areas and the ability to filter background interference. Secondly, the MLLAttention mechanism is introduced in the P3, P4, and P5 layers of the Neck part of the model to better cope with the challenges of Holstein cow behavior recognition caused by large-scale changes. In addition, we also innovatively improve the SPPF module to form the SPPF-GPE module, which optimizes small target recognition by combining global average pooling and global maximum pooling processing and enhances the model’s ability to capture the key parts of Holstein cow behavior in the environment. Given the limitations of traditional IoU loss in cow behavior detection, we replace CIoU loss with Shape–IoU loss, focusing on the shape and scale features of the Bounding Box, thereby improving the matching degree between the Prediction Box and the Ground Truth Box. In order to verify the effectiveness of the proposed CAMLLA-YOLOv8n algorithm, we conducted experiments on a self-constructed dataset containing 23,073 Holstein cow behavior instances. The experimental results show that, compared with models such as YOLOv3-tiny, YOLOv5n, YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s, the improved CAMLLA-YOLOv8n model achieved increases in Precision of 8.79%, 7.16%, 6.06%, 2.86%, 2.18%, and 2.69%, respectively, when detecting the states of Holstein cows grazing, standing, lying, licking, estrus, fighting, and empty bedding. Finally, although the Params and FLOPs of the CAMLLA-YOLOv8n model increased slightly compared with the YOLOv8n model, it achieved significant improvements of 2.18%, 1.62%, 1.84%, and 1.77% in the four key performance indicators of Precision, Recall, mAP@0.5, and mAP@0.5:0.95, respectively. This model, named CAMLLA-YOLOv8n, effectively meets the need for the accurate and rapid identification of Holstein cow behavior in actual agricultural environments. This research is significant for improving the economic benefits of farms and promoting the transformation of animal husbandry towards digitalization and intelligence.
Mechanisms of Social Attachment Between Children and Pet Dogs
Authors: Reilly Olivia T. , Somerville Leah H. , Hecht Erin E.
Published: October 2024

An increasing body of evidence indicates that owning a pet dog is associated with improvements in child health and well-being. Importantly, the degree of the social bond between child and dog may mediate the beneficial outcomes of dog ownership. The formation of social bonds is an intrinsically dyadic, interactive process where each interactor’s behavior influences the other’s behavior. For this reason, it is critical to evaluate the biological mechanisms of attachment in both children and their pet dogs as a socially bonded pair. Here, we review the physical, mental, and emotional outcomes that are associated with pet dog ownership or interaction in children. We then discuss the evidence that suggests that the strength of a social bond between a child and their pet dog matters for maximizing the beneficial outcomes associated with pet dog ownership, such as possible stress-buffering effects. We review the existing literature on the neural and endocrinological mechanisms of social attachment for inter-species social bonds that form between human children and dogs, situating this emerging knowledge within the context of the mechanisms of intra-species bonds in mammals. Finally, we highlight the remaining open questions and point toward directions for future research.
The Effect of Parasitization by Trichodinid Ciliates on the Mortality of Cultured Pacific Fat Sleeper larvae (Dormitator latifrons)
Authors: Reyes-Mero Byron , Cruz-Quintana Yanis , Rodríguez-Canul Rossanna , Gisbert Enric , Santana-Piñeros Ana
Published: October 2024

Trichodinidae, a prevalent group of protozoan ectoparasites in aquaculture, cause rapid mortality in fish hatcheries. Despite their significance, knowledge about these parasites in farmed fish in South America, especially in native species that are currently being domesticated for aquaculture, remains limited. This study morphologically characterized the Trichodinid species that are infecting Pacific fat sleeper (Dormitator latifrons) larvae and evaluated their impact on larval rearing. Four pairs of broodstock were induced with GnRHA implants and placed in tanks containing 200 L of freshwater, with a water temperature of 28 ± 1.0 °C and a dissolved oxygen level of 4.00 ± 1.23 mg L−1, with partial water exchanges being performed daily. The larvae hatched 7 to 8 h after fertilization and were transferred to tanks containing water with the same quality parameters. Twelve hours post-hatching, the presence of Trichodina was observed. Every 24 h, 60 larvae per tank (n = 180 per treatment) were sedated, and larval wet mounts were prepared, air-dried at room temperature, and impregnated with silver nitrate. Infection parameters and daily mortality were calculated. Trichodina was observed to parasitize the pelvic fins, caudal fins, and heads of fish larvae, which showed lethargy and erratic swimming movements. The Trichodina species showed a daily increase in the infection parameters, and a 58% rate of larval mortality was observed at the fourth day post-hatching (dph) in the infected tanks. In captivity, D. latifrons larvae typically survive up to 7 days post-hatching (dph) before reaching their point of no return due to the lack of adequate diet and feeding regimes. However, our study indicates that Trichodina infestation accelerates mortality, causing infected larvae to die more quickly than uninfected ones.
Does Exposure to Summer Season at Different Stages of Intrauterine Development and Maternal Parity Affect Health and First-Lactation Milk Production of Female Offspring of Holstein Cows?
Authors: Beiranvand Hamed , Mahnani Abolfazl , Kahyani Ali , Dunshea Frank R. , Ahmadi Farhad
Published: October 2024

A suboptimal intrauterine environment during gestation may result in the programming of long-lasting structural and physiological alterations in the developing fetus, leading to health and production complications in adulthood. This observational study aimed to identify the impact of exposure to the summer season at different trimesters of gestation, dam parity, and their interaction on the postpartum disease incidence, first-lactation milk production, and herd lifespan of the offspring (F1 generation). Using a dataset collected from two commercial herds, the female offspring were categorized into three groups based on the trimester their dams experienced summer season during pregnancy: (1) first trimester (n = 2345), (2) second trimester (n = 3513), and (3) final trimester (n = 4988). The estimated 305-day milk production was lower in daughters (as a first-lactation cow) born to dams exposed to summer season during the first vs. third trimester. Summer season exposure during the first vs. third trimester resulted in the offspring that were less likely to remain in the herd (hazard ratio = 0.90; 95% CI = 0.84–0.95). Daughters of parous vs. nulliparous dams were more likely to experience dystocia and metritis but less likely to experience retained placenta. The risk of culling was higher in daughters of parous vs. nulliparous dams. Our preliminary findings suggest that the first trimester is a critical determinant of the female progeny’s future productive performance and survivability. Dam parity was also identified as an influential factor affecting offspring health, as dystocia and metritis were more prevalent, and the culling risk was greater in daughters born to parous dams.
Anti-MET Antibody Therapies in Non-Small-Cell Lung Cancer: Current Progress and Future Directions
Authors: Wang Kinsley , Hsu Robert
Published: October 2024

Background/Objectives: Non-small-cell lung cancer (NSCLC) remains a leading cause of cancer mortality globally, though advances in targeted therapies have improved treatment outcomes. The mesenchymal–epithelial transition (MET) gene plays a significant role in NSCLC, often through protein overexpression, exon 14 skipping mutations, and gene amplification, many of which arise as resistance mechanisms to other oncogenic drivers like epidermal growth factor receptor (EGFR) mutations. This review examines the development and clinical efficacy of anti-MET antibody therapies. Methods: A comprehensive literature search was conducted using major medical databases looking at key relevant studies on anti-MET antibody studies. Both authors reviewed the literature, assessed study quality, and interpreted the results from each study. Results: Amivantamab, a bispecific EGFR/MET antibody was approved to treat EGFR exon 20 insertion and now has recently been extended to target classical EGFR mutations with progression on osimertinib. Other important anti-MET targeted therapies in development include antibody drug conjugates such as telisotuzumab vedotin, REGN5093-M114, and AZD9592 and emibetuzumab, which is a humanized immunoglobulin G4 monoclonal bivalent MET antibody. Conclusions: MET plays a significant role in NSCLC and amivantamab along with other anti-MET targeted therapies play a role in directly targeting MET and addressing acquired resistance to oncogenic drivers. Future research should focus on developing novel MET antibody drugs and exploring new therapeutic combinations to enhance treatment efficacy and overcome resistance in NSCLC. Refining biomarker-driven approaches to ensure precise patient selection is also critical to optimizing treatment outcomes.
Effect of Printing Orientation on the Mechanical Properties of Low-Force Stereolithography-Manufactured Durable Resin
Authors: Martínez Raya Antonio , Aranda-Ruiz Josué , Sal-Anglada Gastón , Jaureguizahar Sebastián Martín , Braun Matías
Published: October 2024

This study presents the results of fracture toughness tests conducted on specimens obtained by additive manufacturing techniques, specifically using low-force stereolithography. The samples were manufactured from a transparent 3D printing material for biocompatible applications, the so-called BioMed Durable Resin, which is a Formlabs-patented polymer material that simulates the strength and rigidity of polyethylene. The selected toughness tests in this context were performed following the ASTM D5045-99 guidelines. All tests were conducted under controlled laboratory conditions at 23 °C and 50% relative humidity, ensuring adherence to the standard and the replicability of the experimental results. To investigate the influence of printing plane orientation, specimens were produced at three printing orientation angles (0, 45, and 90 degrees). These angles were selected to provide a comprehensive evaluation of the anisotropy effects in the material. They cover both extreme orientations (0° and 90°) and include an intermediate value (45°), allowing us to assess variations in mechanical behavior across a representative range of printing orientations, consistent with prior research in the field. The experimental tests yielded data on the crack resistance and energy release rate for each angle of orientation. There are various implications of the findings, beyond materials engineering, for applications in biomedicine. Indeed, this same approach opens the door to new research methods for manufacturing certified biocompatible materials from such durable resins. Finally, complementary issues such as related medical applications have been slightly addressed for future work, since biomedicine innovation clusters can contribute to accelerating growth in this crucial field for productive sector activity and the local business environment.
Parametric Study on the Effect of Rail Dampers on Track Decay Rate
Authors: Fologea Dorina , Mazilu Traian , Gheți Marius-Alin , Apostol Ioana-Izabela
Published: October 2024

Track decay rate (TDR), meaning the rate of attenuation of bending waves through the rail, is the most important indicator of a track’s dynamic characteristic impacting the rail noise emission. TDR depends on various parameters related to the construction of the track, and it can be increased using rail dampers. These are mechanical devices working on the principle of dynamic absorbers and are attached to the rail. This paper addresses the track with light rails needing improvements to reduce the rail noise emission using a particular rail damper with a mixed damping system (rubber–oil). The bending waves that propagate through the rail, the frequency response function of the rail, and TDR are investigated considering different scenarios regarding the parameters of the track: soft/stiff rail pad, tampered/settled ballast, and sleeper bay. To this end, an analytic model of the track featuring rail dampers consisting of an infinite Timoshenko beam with discrete attached oscillators is used. Numerical results show the possibility to increase TDR of railway track with light rails for both soft/stiff rail pads from 4 to 500 Hz up to 1250–1600 Hz using rail dampers with a mixed damping system.
Numerical Modeling, Analysis, and Optimization of RFID Tags Functioning at Low Frequencies
Authors: Constantinescu Claudia , Andreica Sergiu , Laszlo Rapolti , Giurgiuman Adina , Gliga Marian , Munteanu Calin , Pacurar Claudia
Published: October 2024

RFID (radio frequency identification) tags play a crucial role in a wide range of applications, from wireless communications to personal tracking and smart city infrastructure. These tags come in various shapes and sizes, prompting the authors to review the specialized literature and focus on optimizing planar designs with different geometries. This study prioritizes reducing the size of the most commonly used tags while enhancing their reliability. The primary objective of this article is to understand and improve the performance of planar RFID tags operating at 13.56 MHz through numerical simulations based on structures generated by algorithms developed in MATLAB. Building on previous research, the methodology is validated, followed by a detailed description of the algorithm designed and implemented by the authors in MATLAB to identify all possible structures that meet the design criteria. The authors compared various analyzed structures, considering different inductor shapes, dielectric materials, and thicknesses while examining their effects on gain and resonant frequency. The study also provides thermal analysis of the structures, and experimental validation of the studied designs. Finally, the researchers conclude with recommendations on the optimal structure for RFID tags.
Spatiotemporal Sensitive Network for Non-Contact Heart Rate Prediction from Facial Videos
Authors: Su Liying , Wang Yitao , Zhai Dezhao , Shi Yuping , Ding Yinghao , Gao Guohua , Li Qinwei , Yu Ming , Wu Hang
Published: October 2024

Heart rate (HR) is an important indicator reflecting the overall physical and mental health of the human body, playing a crucial role in diagnosing cardiovascular and neurological diseases. Recent research has revealed that variations in the light absorption of human skin captured through facial video over the cardiac cycle, due to changes in blood volume, can be utilized for non-contact HR estimation. However, most existing methods rely on single-modal video sources (such as RGB or NIR), which often yield suboptimal results due to noise and the limitations of a single information source. To overcome these challenges, this paper proposes a multimodal information fusion architecture named the spatiotemporal sensitive network (SS-Net) for non-contact heart rate estimation. Firstly, spatiotemporal feature maps are utilized to extract physiological signals from RGB and NIR videos effectively. Next, a spatiotemporal sensitive (SS) module is introduced to extract useful physiological signal information from both RGB and NIR spatiotemporal maps. Finally, a multi-level spatiotemporal context fusion (MLSC) module is designed to fuse and complement information between the visible light and infrared modalities. Then, different levels of fused features are refined in task-specific branches to predict both remote photoplethysmography (rPPG) signals and heart rate (HR) signals. Experiments conducted on three datasets demonstrate that the proposed SS-Net achieves superior performance compared to existing methods.
Hyperspectral Image Classification Algorithm for Forest Analysis Based on a Group-Sensitive Selective Perceptual Transformer
Authors: Shi Shaoliang , Li Xuyang , Fan Xiangsuo , Li Qi
Published: October 2024

Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (PGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model’s spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model’s representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model’s versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markededly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing.
Research on Ultra-Fast Transient Overvoltage Characteristics of Electric Locomotive
Authors: Guo Yike , Tian Fuqiang , Liu Hongqi , Yin Yufeng
Published: October 2024

Operating overvoltage occurs when the pantograph or main breaker of an electric locomotive is operated, which is prone to causing insulation failure of high-voltage equipment. The HXD1 electric locomotive is taken as the research object in this paper to explore the characteristics and influencing factors of operating overvoltage. Under pantograph lifting, main breaker closing, main breaker opening, and pantograph dropping, operating overvoltage waveform in the high-voltage system is recorded by a high-speed oscilloscope and resistance–capacitance voltage divider to analyze the overvoltage characteristics and distribution law. Tested data show that the amplitude of operating overvoltage is in the range of 80 to 330 kV with ultra-high steepness, which is similar to the Very Fast Transient Overvoltage (VFTO) in power systems. The maximum overvoltage during the entire test occurred during the main breaker closing and its amplitude is 328.60 kV with a steepness of 4.21 × 104 kV/μs. The max overvoltage of the other operations (pantograph lifting, main breaker opening, and pantograph dropping) are 280.60 kV, 194.73 kV, and 305.56 kV with ultra-high steepness. High-amplitude overvoltage is predominantly located at the pantograph, while the low-amplitude sort is mainly observed around other high-voltage equipment. The result indicates that operating overvoltage belongs to ultra-fast transient overvoltage and its amplitude and steepness are higher than existing research.
Experimental Study of Erosion Prevention Model by Bio-Cement Sand
Authors: Chen Ren , Li Guoying , Mi Zhankuan , Wei Kuangming
Published: October 2024

Microbially induced carbonate precipitation (MICP) technology is employed to reinforce the surface soil of a dam, aiming to prevent erosion caused by water flow and damage to the dam slope. The relationship between penetration depth, calcium carbonate content, and bonding depth was investigated at eight measuring points on the sand slope surface of a mold under different reinforcement durations. It was observed that as grouting reinforcement times increased, there was a gradual increase in calcium carbonate content but a rapid rise in penetration resistance. Moreover, the bonding depth of sand on the bio-reinforced sand slope increased with higher levels of calcium carbonate content. Microbial grouting reinforcement enhanced soil particle bonding force, requiring water flow to overcome this force for activation of sand particles. Consequently, microbial grouting reinforcement significantly improved shear strength and critical starting flow velocity on sand slope surfaces. The experimental results demonstrated that after MICP surface treatment through spraying, a dense and water-stable hard shell layer composed of bonded calcium carbonate and soil particles formed continuously on sample surfaces, effectively enhancing the strength and erosion resistance of sandy soils. These findings provide reliable evidence for silt slope reinforcement and dam erosion prevention.
Simulation Research on the Dual-Electrode Current Excitation Method for Distance Measurements While Drilling
Authors: Dou Xinyu , Yan Xiaoping , Hu Longyu , Liang Huaqing
Published: October 2024

Based on a comprehensive analysis of the existing methods for measuring adjacent well distances, along with their advantages and disadvantages, this study employs theoretical analysis, simulation experiments, and other comprehensive research methods to investigate a distance measurement method based on current excitation. In response to the need for measuring and controlling the connection of relief wells, a method utilizing dual-electrode current excitation during drilling is proposed. This approach facilitates synchronous excitation measurements while drilling, significantly reducing both time and costs while ensuring safety and efficiency, making it particularly suitable for the connection operation of relief wells that involve safety risks. Firstly, this paper establishes a drilling with measurement model corresponding to the excitation mode, which derives the calculation formulas for the target casing current amplitude attenuation, as well as the induced magnetic field distribution within the formation. Additionally, it provides the calculation methods for determining the target well distance and azimuth direction. Lastly, the impact levels of various key factors are verified through numerical calculations and simulation analyses, which confirm the correctness and effectiveness of this distance measurement method. The findings from this research establish both a core theoretical foundation and a technological basis for the real-time measurement of adjacent well distances during relief well operations.
Enhancing Printability Through Design Feature Analysis for 3D Food Printing Process Optimization
Authors: Alghamdy Mohammed , He Iris , Satsangee Guru Ratan , Keramati Hadi , Ahmad Rafiq
Published: October 2024

We present a novel, systematic method for evaluating design printability in 3D food printing using a scoring system based on the Design for Additive Manufacturing (DfAM) guidelines. This study addresses a gap in the current literature by proposing a structured approach to assess and enhance the printability of 3D food designs. Our framework consists of a set of nine critical questions derived from the multi-level DfAM guidelines, focusing on key printability factors including unsupported features, geometric accuracy, and surface finish. The evaluation process converts qualitative assessments into numerical values, resulting in a comprehensive printability score that categorizes designs into high, moderate, or low printability levels. To validate the effectiveness of this method, we conducted a case study involving five different designs. The scoring system successfully explores the design space and maximizes the printability of 3D food products. This method alleviates the challenges in design evaluation compared with traditional trial-and-error approaches. The results demonstrate the practicality and efficiency of our framework’s output. The proposed methodology provides a structured approach to design evaluation, offering practical insights and a valuable tool for improving the success rate of 3D printed food products. This research contributes to the field by offering a systematic framework for assessing and enhancing the printability of 3D food designs, potentially accelerating the adoption and effectiveness of 3D food printing technology in various applications.
The Environmental Stake of Bitcoin Mining: Present and Future Challenges
Authors: Arfelli Francesco , Coralli Irene , Cespi Daniele , Ciacci Luca , Fabbri Daniele , Passarini Fabrizio , Spada Lorenzo
Published: October 2024

The environmental impact of Bitcoin mining has raised severe concerns considering the expected growth of 30% by 2030. This study aimed to develop a Life Cycle Assessment model to determine the carbon dioxide equivalent emissions associated with Bitcoin mining, considering material requirements and energy demand. By applying the impact assessment method IPCC 2021 GWP (100 years), the GHG emissions associated with electricity consumption were estimated at 51.7 Mt CO2 eq/year in 2022 and calculated by modelling real national mixes referring to the geographical area where mining takes place, allowing for the determination of the environmental impacts in a site-specific way. The estimated impacts were then adjusted to future energy projections (2030 and 2050), by modelling electricity mixes coherently with the spatial distribution of mining activities, the related national targeted goals, the increasing demand for electricity for hashrate and the capability of the systems to recover the heat generated in the mining phase. Further projections for 2030, based on two extrapolated energy consumption models, were also determined. The outcomes reveal that, in relation to the considered scenarios and their associated assumptions, breakeven points where the increase in energy consumption associated with mining nullifies the increase in the renewable energy share within the energy mix exist. The amount of amine-based sorbents hypothetically needed to capture the total CO2 equivalent emitted directly and indirectly for Bitcoin mining reaches up to almost 12 Bt. Further developments of the present work would rely on more reliable data related to future energy projections and the geographical distribution of miners, as well as an extension of the environmental categories analyzed. The Life Cycle Assessment methodology represents a valid tool to support policies and decision makers.
Research on the Optimization Method of Bus Travel Safety Considering Drivers’ Risk Characteristics
Authors: Dou Yue , Deng Shejun , Yu Hongru , Li Tingting , Yu Shijun , Zhang Jun
Published: October 2024

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers’ risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses.
UPI-LT: Enhancing Information Propagation Predictions in Social Networks Through User Influence and Temporal Dynamics
Authors: Huang Zexia , Gu Xu , Hu Jinsong , Chen Xiaoliang
Published: October 2024

The TEST pervasive use of social media has highlighted the importance of developing sophisticated models for early information warning systems within online communities. Despite the advancements that have been made, existing models often fail to adequately consider the pivotal role of network topology and temporal dynamics in information dissemination. This results in suboptimal predictions of content propagation patterns. This study introduces the User Propagation Influence-based Linear Threshold (UPI-LT) model, which represents a novel approach to the simulation of information spread. The UPI-LT model introduces an innovative approach to consider the number of active neighboring nodes, incorporating a time decay factor to account for the evolving influence of information over time. The model’s technical innovations include the incorporation of a homophily ratio, which assesses the similarity between users, and a dynamic adjustment of activation thresholds, which reflect a deeper understanding of social influence mechanisms. Empirical results on real-world datasets validate the UPI-LT model’s enhanced predictive capabilities for information spread.
Analysis of the Response Relationship Between PWV and Meteorological Parameters Using Combined GNSS and ERA5 Data: A Case Study of Typhoon Lekima
Authors: Gao Ying , Wang Xiaolei
Published: October 2024

Precipitable water vapor (PWV) is a crucial parameter of Earth’s atmosphere, with its spatial and temporal variations significantly impacting Earth’s energy balance and weather patterns. Particularly during meteorological disasters such as typhoons, PWV and other meteorological parameters exhibit dramatic changes. Studying the response relationship between PWV and typhoon events, alongside other meteorological parameters, is essential for meteorological and climate analysis and research. To this end, this paper proposes a method for analyzing the response relationship between PWV and meteorological parameters based on Wavelet Coherence (WTC). Specifically, PWV and relevant meteorological parameters were obtained using GNSS and ERA5 data, and the response relationships between PWV and different meteorological parameters before and after typhoon events were studied in time–frequency domain. Considering that many GNSS stations are not equipped with meteorological monitoring equipment, this study interpolated meteorological parameters based on ERA5 data for PWV retrieval. In the experimental section, the accuracy of ERA5 meteorological parameters and the accuracy of PWV retrieval based on ERA5 were first analyzed, verifying the feasibility and effectiveness of this approach. Subsequently, using typhoon Lekima as a case study, data from six GNSS stations affected by the typhoon were selected, and the corresponding PWV was retrieved using ERA5. The WTC method was then employed to analyze the response relationship between PWV and meteorological parameters before and after the typhoon’s arrival. The results show that the correlation characteristics between PWV and pressure can reveal different stages before and after the typhoon passes, while the local characteristics between PWV and temperature better reflect regional precipitation trends.
Machine Learning for Optimising Renewable Energy and Grid Efficiency
Authors: Oladapo Bankole I. , Olawumi Mattew A. , Omigbodun Francis T.
Published: October 2024

This research investigates the application of machine learning models to optimise renewable energy systems and contribute to achieving Net Zero emissions targets. The primary objective is to evaluate how machine learning can improve energy forecasting, grid management, and storage optimisation, thereby enhancing the reliability and efficiency of renewable energy sources. The methodology involved the application of various machine learning models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), and ARIMA, to predict energy generation and demand patterns. These models were evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Key findings include a 15% improvement in grid efficiency after optimisation and a 10–20% increase in battery storage efficiency. Random Forest achieved the lowest MAE, reducing prediction error by approximately 8.5%. The study quantified CO2 emission reductions by energy source, with wind power accounting for a 15,000-ton annual reduction, followed by hydropower and solar reducing emissions by 10,000 and 7500 tons, respectively. The research concludes that machine learning can significantly enhance renewable energy system performance, with measurable reductions in errors and emissions. These improvements could help close the “ambition gap” by 20%, supporting global efforts to meet the 1.5 °C Paris Agreement targets.
Ground-Based MAX-DOAS Observations for Spatiotemporal Distribution and Transport of Atmospheric Water Vapor in Beijing
Authors: Ren Hongmei , Li Ang , Hu Zhaokun , Zhang Hairong , Xu Jiangman , Wang Shuai
Published: October 2024

Understanding the spatiotemporal distribution and transport of atmospheric water vapor in urban areas is crucial for improving mesoscale models and weather and climate predictions. This study employs Multi-Axis Differential Optical Absorption Spectroscopy to monitor the dynamic distribution and transport flux of water vapor in Beijing within the tropospheric layer (0–4 km) from June 2021 to May 2022. The seasonal peaks in precipitable water occur in August, reaching 39.13 mm, with noticeable declines in winter. Water vapor was primarily distributed below 2.0 km and generally decreases with increasing altitude. The largest water vapor transport flux occurs in the southeast–northwest direction, whereas the smallest occurs in the southwest–northeast direction. The maximum flux, observed at about 1.2 km in the southeast–northwest direction during summer, reaches 31.77 g/m2/s (transported towards the southeast). Before continuous rainfall events, water vapor transport, originating primarily from the southeast, concentrates below 1 km. Backward trajectory analysis indicates that during the rainy months, there was a higher proportion of southeasterly winds, especially at lower altitudes, with air masses from the southeast at 500 m accounting for 69.11%. This study shows the capabilities of MAX-DOAS for remote sensing water vapor and offers data support for enhancing weather forecasting and understanding urban climatic dynamics.
Simulation of the Neutral Atmospheric Flow Using Multiscale Modeling: Comparative Studies for SimpleFoam and Fluent Solver
Authors: Zhao Zihan , Tang Lingxiao , Xiao Yiqing
Published: October 2024

The reproduced planetary boundary layer (PBL) wind is commonly applied in downscaled simulations using commercial CFD codes with Reynolds-averaged Navier–Stokes (RANS) turbulence modeling. When using the turbulent inlets calculated by numerical weather prediction models (NWP), adjustments of the turbulence eddy viscosity closures and wall function formulations are concerned with maintaining the fully developed wind profiles specified at the inlet of CFD domains. The impact of these related configurations is worth discussing in engineering applications, especially when commercial codes restrict the internal modifications. This study evaluates the numerical performances of open-source OpenFOAM 2.3.0 and commercial Fluent 17.2 codes as supplementary scientific comparisons. This contribution focuses on the modified turbulence closures to incorporate turbulent profiles produced from mesoscale PBL parameterizations and the modified wall treatments relating to the roughness length. The near-ground flow features are evaluated by selecting the flat terrains and the classical Askervein benchmark case. The improvement in near-ground wind flow under the downscaled framework shows satisfactory performance in the open-source CFD platform. This contributes to engineers realizing the micro-siting of wind turbines and quantifying terrain-induced speed-up phenomena under the scope of wind-resistant design.
Climatology of Cirrus Clouds over Observatory of Haute-Provence (France) Using Multivariate Analyses on Lidar Profiles
Authors: Mandija Florian , Keckhut Philippe , Alraddawi Dunya , Khaykin Sergey , Sarkissian Alain
Published: October 2024

This study aims to achieve the classification of the cirrus clouds over the Observatory of Haute-Provence (OHP) in France. Rayleigh–Mie–Raman lidar measurements, in conjunction with the ERA5 dataset, are analyzed to provide geometrical morphology and optical cirrus properties over the site. The method of cirrus cloud climatology presented here is based on a threefold classification scheme based on the cirrus geometrical and optical properties and their formation history. Principal component analysis (PCA) and subsequent clustering provide four morphological cirrus classes, three optical groups, and two origin-related categories. Cirrus clouds occur approximately 37% of the time, with most being single-layered (66.7%). The mean cloud optical depth (COD) is 0.39 ± 0.46, and the mean heights range around 10.8 ± 1.35 km. Thicker tropospheric cirrus are observed under higher temperature and humidity conditions than cirrus observed in the vicinity of the tropopause level. Monthly cirrus occurrences fluctuate irregularly, whereas seasonal patterns peak in spring. Concerning the mechanism of the formation, it is found that the majority of cirrus clouds are of in situ origin. The liquid-origin cirrus category consists nearly entirely of thick cirrus. Overall results suggest that in situ origin thin cirrus, located in the upper tropospheric and tropopause regions, have the most noteworthy occurrence over the site.
An Extensive Analysis of Tsallis Statistics: π±, K± Mesons, and pp¯ Baryon in Inelastic pp Collisions
Authors: Tabassam Uzma , Abidin Zain Ul , Gul Khadija , Siddique Irfan
Published: October 2024

This study explores the inelastic doubly differential transverse momentum spectra of the primary charged particles, (π++π−), (K++K−) and (pp¯), as a function of observables associated with underlying event (UE) at s=13TeV. The particle production is measured on the basis of different angular regions like toward, transverse and away, elucidated with respect to the direction of leading particle of an event. To study the thermal freeze-out parameters, the non-extensive Tsallis distribution function is used to extract the temperature Teff and chemical potential μ, which provide a basis to explain the QCD matter. The Tsallis distribution function describes transverse momentum spectra in pseudorapidity region of |η|<0.8. It is observed that effective temperature Teff changes from away to towards and forward region.
Estimation of Lifetime Performance Index for Generalized Inverse Lindley Distribution Under Adaptive Progressive Type-II Censored Lifetime Test
Authors: Xiao Shixiao , Hu Xue , Ren Haiping
Published: October 2024

The lifetime performance index (LPI) is an important metric for evaluating product quality, and research on the statistical inference of the LPI is of great significance. This paper discusses both the classical and Bayesian estimations of the LPI under an adaptive progressive type-II censored lifetime test, assuming that the product’s lifetime follows a generalized inverse Lindley distribution. At first, the maximum likelihood estimator of the LPI is derived, and the Newton–Raphson iterative method is adopted to solve the numerical solution due to the log-likelihood equations having no analytical solutions. If the exact distribution of the LPI is not available, then the asymptotic confidence interval and bootstrap confidence interval of the LPI are constructed. For the Bayesian estimation, the Bayesian estimators of the LPI are derived under three different loss functions. Due to the complex multiple integrals involved in these estimators, the MCMC method is used to draw samples and further construct the HPD credible interval of the LPI. Finally, Monte Carlo simulations are used to observe the performance of these estimators in terms of the average bias and mean squared error, and two practical examples are used to illustrate the application of the proposed estimation method.
LSTM-CRP: Algorithm-Hardware Co-Design and Implementation of Cache Replacement Policy Using Long Short-Term Memory
Authors: Wang Yizhou , Meng Yishuo , Wang Jiaxing , Yang Chen
Published: October 2024

As deep learning has produced dramatic breakthroughs in many areas, it has motivated emerging studies on the combination between neural networks and cache replacement algorithms. However, deep learning is a poor fit for performing cache replacement in hardware implementation because its neural network models are impractically large and slow. Many studies have tried to use the guidance of the Belady algorithm to speed up the prediction of cache replacement. But it is still impractical to accurately predict the characteristics of future access addresses, introducing inaccuracy in the discrimination of complex access patterns. Therefore, this paper presents the LSTM-CRP algorithm as well as its efficient hardware implementation, which employs the long short-term memory (LSTM) for access pattern identification at run-time to guide cache replacement algorithm. LSTM-CRP first converts the address into a novel key according to the frequency of the access address and a virtual capacity of the cache, which has the advantages of low information redundancy and high timeliness. Using the key as the inputs of four offline-trained LSTM network-based predictors, LSTM-CRP can accurately classify different access patterns and identify current cache characteristics in a timely manner via an online set dueling mechanism on sampling caches. For efficient implementation, heterogeneous lightweight LSTM networks are dedicatedly constructed in LSTM-CRP to lower hardware overhead and inference delay. The experimental results show that LSTM-CRP was able to averagely improve the cache hit rate by 20.10%, 15.35%, 12.11% and 8.49% compared with LRU, RRIP, Hawkeye and Glider, respectively. Implemented on Xilinx XCVU9P FPGA at the cost of 15,973 LUTs and 1610 FF registers, LSTM-CRP was running at a 200 MHz frequency with 2.74 W power consumption.
Characterization of MSC Growth, Differentiation, and EV Production in CNF Hydrogels Under Static and Dynamic Cultures in Hypoxic and Normoxic Conditions
Authors: Nikolits Ilias , Chariyev-Prinz Farhad , Egger Dominik , Liebner Falk , Mytzka Nicolas , Kasper Cornelia
Published: October 2024

Mesenchymal stem cells (MSCs) hold immense therapeutic potential due to their regenerative and immunomodulatory properties. However, to utilize this potential, it is crucial to optimize their in vitro cultivation conditions. Three-dimensional (3D) culture methods using cell-laden hydrogels aim to mimic the physiological microenvironment in vitro, thus preserving MSC biological functionalities. Cellulosic hydrogels are particularly promising due to their biocompatibility, sustainability, and tunability in terms of chemical, morphological, and mechanical properties. This study investigated the impact of (1) two physical crosslinking scenarios for hydrogels derived from anionic cellulose nanofibers (to-CNF) used to encapsulate adipose-derived MSCs (adMSCs) and (2) physiological culture conditions on the in vitro proliferation, differentiation, and extracellular vesicle (EV) production of these adMSCs. The results revealed that additional Ca2+-mediated crosslinking, intended to complement the self-assembly and gelation of aqueous to-CNF in the adMSC cultivation medium, adversely affected both the mechanical properties of the hydrogel spheres and the growth of the encapsulated cells. However, cultivation under dynamic and hypoxic conditions significantly improved the proliferation and differentiation of the encapsulated adMSCs. Furthermore, it was demonstrated that the adMSCs in the CNF hydrogel spheres exhibited potential for scalable EV production with potent immunosuppressive capacities in a bioreactor system. These findings underscore the importance of physiological culture conditions and the suitability of cellulosic materials for enhancing the therapeutic potential of MSCs. Overall, this study provides valuable insights for optimizing the in vitro cultivation of MSCs for various applications, including tissue engineering, drug testing, and EV-based therapies.