bluuebunny/crossref_metadata_2025 huggingface dataset:
Suspended Particles in Water and Energetically Sustainable Solutions of Their Removal—A Review
Authors: Zezulka Štěpán , Maršálek Blahoslav , Maršálková Eliška , Odehnalová Klára , Pavlíková Marcela , Lamaczová Adéla
Published: November 2024

Solid particles (SP) suspended in water represent a common contamination that degrades the water quality, not only in drinking water sources. Particles differ in size, nature, and related features like surface charge. Thus, various methods can be utilized for their removal—physical approaches including settling or filtration, chemical coagulation/flocculation, biological microbial degradation, and others. This paper aims to summarize currently available methods for SP removal with special attention devoted to alternative, cost-effective, sustainable, and eco-friendly approaches with low energetic demands where the power of renewable energy sources can be utilized. Besides SP properties, the selection of the proper method (or a sequence of methods) for their separation also depends on the purpose of water treatment. Drinking water production demands technologies with immediate effect and high throughputs, like conventional filtration and coagulation/flocculation (electro- or chemical with alternative coagulant/flocculant agents) or some hybrid approaches to ensure quick and cost-effective decontamination. Such technologies usually imply heavy machinery with high electricity consumption, but current progress allows the construction of smaller facilities powered by solar or wind power plant systems. On the other hand, water decontamination in rivers or ponds can include slower processes based on phytoremediation, being long-term sustainable with minimal energy and cost investments.
Assessment of the Influences of Numerical Models on Aerodynamic Performances in Hypersonic Nonequilibrium Flows
Authors: Zhang Wenqing , Zhang Zhijun , Yang Hualin
Published: November 2024

In this paper, the aerodynamic performances including shock wave standoff distance (SSD) and heat flux of ELECTRE vehicle at 53.3 km and 4230 m/s for several types of numerical models are investigated. The numerical models include thermal equilibrium/nonequilibrium (1T/2T) assumption, three surface boundary conditions (no-slip/non-catalytic, slip/non-catalytic, slip/fully-catalytic), four chemical kinetic models (DK, Park, Gupta, and No Reaction (NR)) and two controlling temperatures (Ttr0.7Tve0.3, Ttr0.5Tve0.5). The results show that the chemical kinetic model significantly affects the SSD, and its value gradually decreases with the increase in chemical reaction rate. The SSD predicted by the NR model is 20.7% larger than that of the Park model. The SSD is also affected by the proportion of vibro-electronic temperature (Tve) in the controlling temperature, and the higher the proportion, the larger the SSD. Regarding the heat flux, the catalytic surface setting is crucial, where the value predicted by the fully-catalytic model is 62.2% higher than that by the non-catalytic model. As the chemical reaction rate of Gupta, DK, and Park models increases sequentially, the calculated heat flux decreases in turn. The heat flux predicted by the 2T model is lower than that by the 1T model, and the higher Tve proportion in the controlling temperature, the smaller the heat flux. The fundamental reason is that the trans-rotational convective heat flux of the 2T model is much lower than that of the 1T model, and the trans-rotational convective heat flux decreases with an increase in the Tve proportion.
Comprehensive Analysis of the Annulus Pressure Buildup in Wells with Sustained Gas Leakage Below the Liquid Level
Authors: Yang Siqi , Fu Jianglong , Zhao Nan , Xu Changfeng , Han Lihong , Wang Jianjun , Liu Hailong , Zhang Yuhang , Liu Jun
Published: November 2024

During the process of natural gas development, sustained casing pressure (SCP) frequently occurs within the annulus of the gas wells; we specifically referred to the “A” annular space located between the tubing and the production casing in this paper. SCP in an annulus poses a paramount safety challenge, universally acknowledged as a significant threat to gas field development and production, jeopardizing well integrity, personnel safety, and environmental protection. There are multiple factors that contribute to this issue. Due to the multitude of factors contributing to SCP in an annulus and the unclear mechanisms underlying the pressure buildup in wells, an early assessment of downhole leakage risks remains challenging. Hence, this study focused on a comprehensive analysis of the SCP in the annulus of gas wells. A detailed experimental study on the pressure buildup in an annulus due to tubing leakage below the liquid level was conducted, and the variation patterns of the annulus pressure under various leakage conditions were explored. The findings indicated that the equilibrium attainment time of annulus pressure at the wellhead subsequent to tubing leakage decreases with the increase in the pressure difference between the tubing and the casing, the liquid level height, the leakage orifice diameter, and the quantity, while it increases with the increase in the leakage position and gas temperature. According to the theory of gas fluid dynamics, a predictive model of the annulus pressure buildup with sustained gas leakage below the liquid level was proposed, which was well-validated against experimental results, achieving a model accuracy of over 95%. This study provided a theoretical framework for diagnosing SCP in the annulus of gas wells and developing mitigation strategies, thereby contributing to the advancement of the research field and ensuring the safety of industrial operations.
The Non-Linear Profile of Aging: U-Shaped Expression of Myostatin, Follistatin and Intermediate Signals in a Longitudinal In Vitro Murine Cell Sarcopenia Model
Authors: Alonso-Puyo Janire , Izagirre-Fernandez Oihane , Crende Olatz , Seco-Calvo Jesús , Fernandez-Atutxa Ainhoa , Fernandez-Lazaro Diego , Garcia-Gallastegi Patricia , Sanz Begoña
Published: November 2024

Sarcopenia is linked to the decline in muscle mass, strength and function during aging. It affects the quality and life expectancy and can lead to dependence. The biological process underlying sarcopenia is unclear, but the proteins myostatin and follistatin are involved in the balance between muscle breakdown and synthesis. While myostatin promotes muscle breakdown, follistatin promotes muscle growth, but several works have shown an inconsistent association of these proteins with aging-related parameters in serum of older people. We aimed to know the evolution of these putative sarcopenia biomarkers along muscle aging in an in vitro model. We created and phenotyped a longitudinal murine model (C2C12 cells). Then, we analyzed the protein and genetic expression of myostatin and follistatin as well as the signaling pathway regulators mTOR and RPS6KB1. Myostatin and RPS6KB1 showed a similar tendency in both protein and genetic expression with aging (basal–up–down). Follistatin, on the other hand, shows the opposite tendency (basal–down–up). Regarding mTOR, the tendencies differ when analyzing proteins (basal–up–down) or genes (basal–down–down). Our work demonstrates a U-shape tendency for myostatin and follistatin and for the signaling pathway regulators. These results could be of the utmost importance when designing further research on seeking molecular biomarkers and/or targets for sarcopenia.
Developing a Reclamation Framework to Promote Circularity in Demolition Projects
Authors: Saad Mariah , Ruparathna Rajeev , Biswas Nihar , Tam Edwin
Published: November 2024

The construction, demolition, and renovation industries are among the largest contributors to global carbon emissions and waste. With decreased landfill capacities, increased waste diversion targets, resource shortages, and the recognition that material waste is critical to climate change, diverting demolition waste is now a significant priority in waste management. Deconstructing a structure and reusing its building components can significantly reduce the environmental burdens imposed. However, to optimize the reuse of building materials and components for their environmental, societal, and economical benefits, the reclamation procedure must be undertaken in a more rational and robust manner. There are currently gaps in frameworks and tools that involve the assessment of reusable building components in demolition projects. This paper develops a reclamation framework to assess the viability of recovering and reusing building components. The framework first describes a process for conducting a technical audit and uses an assessment tool to suggest a level of deconstruction based on the physical parameters of the building circumstances. The framework complements this initial outcome by then assessing additional comprehensive parameters, such as the cost, the heritage value, and the available timeframe to arrive at a suggested outcome of actions, which can range from complete demolition and basic material recovery to deliberately removing salvageable items. The framework is then applied to an older, detached office building as a conceptual case study for demonstration. The recommended level of deconstruction appears appropriate based on the visual assessment of the structure. The result of this paper promotes the circular economy and supports the United Nations Sustainable Development Goals (UN SDGs) by presenting a notably more insightful and guided approach to capturing deconstruction waste.
Preparation of Polypropylene Composites with Pyrolyzed Carbon Fibers Using an Internal Mixer
Authors: Müller Dennis , Bruchmüller Matthias , Puch Florian
Published: November 2024

The use of pyrolyzed carbon fibers (pCFs) in the secondary raw material market is growing, but potential applications for pCFs are limited by their wool-like appearance. Common solutions are further processing into fiber mats or shredding and adding the fibers during compounding in twin-screw extruders (TSEs). In the latter process, the initial fiber length is usually reduced to less than 1 mm during compounding and further reduced during injection molding. Hence, this paper presents an alternative compounding approach by investigating if internal mixers (IMs) are suitable for retaining pCFs after compounding longer. First, the influence of the mixing sequence for adding pCFs to the mixing process of the resulting fiber length was investigated. Second, a design of experiments was carried out using a laboratory IM, considering the process parameters of rotational speed, mixing time, coupling agent content, initial fiber length, and chamber filling level. Third, the results obtained were scaled up and applied to a production-scale IM. Important findings are that the melting of the matrix polymer should occur before fibers are added. This results in fiber contents of 20 wt.%. To achieve higher fiber contents, small amounts of carbon fiber must be added during the melting process. The process parameters investigated had no significant influence on the resulting fiber length. Compounding with IM is suitable for an initial fiber length of up to 24 mm. A composite with carbon fibers from industrial offcuts (rCFs) prepared by TSE compounding was used to compare the mechanical properties of the injection-molded samples due to the non-availability of composites with pyrolyzed fibers. Compounding resulted in an improvement in the weight-average fiber length from 226 µm (TSE) to 540 µm (IM). However, this fiber length could not be preserved during injection molding, resulting in similar mechanical properties of both, the pCF composites prepared by an IM and the commercially available rCF composites.
A Review of Lidar Technology in China’s Lunar Exploration Program
Authors: Huang Genghua , Xu Weiming
Published: November 2024

Lidar technology plays a pivotal role in lunar exploration, particularly in terrain mapping, 3D topographic surveying, and velocity measurement, which are crucial for guidance, navigation, and control. This paper reviews the current global research and applications of lidar technology in lunar missions, noting that existing efforts are primarily focused on 3D terrain mapping and velocity measurement. The paper also discusses the detailed system design and key results of the laser altimeter, laser ranging sensor, laser 3D imaging sensor, and laser velocity sensor used in the Chang’E lunar missions. By comparing and analyzing similar foreign technologies, this paper identifies future development directions for lunar laser payloads. The evolution towards multi-beam single-photon detection technology aims to enhance the point cloud density and detection efficiency. This manuscript advocates that China actively advance new technologies and conduct space application research in areas such as multi-beam single-photon 3D terrain mapping, lunar surface water ice measurement, and material composition analysis, to elevate the use of laser pay-loads in lunar and space exploration.
A Real-Time Signal Measurement System Using FPGA-Based Deep Learning Accelerators and Microwave Photonic
Authors: Zhang Longlong , Zhou Tong , Yang Jie , Li Yin , Zhang Zhiwen , Hu Xiang , Peng Yuanxi
Published: November 2024

Deep learning techniques have been widely investigated as an effective method for signal measurement in recent years. However, most existing deep learning-based methods still face difficulty in deploying on embedded platforms and perform poorly in real-time applications. To address this, this paper develops two accelerators, as the core of the signal measurement system, for intelligent signal processing. Firstly, by introducing the idea of automated framework, we propose a simplest deep neural network (DNN)-based hardware structure, which automatically maps algorithms to hardware modules, supports configurable parameters, and has the advantage of low latency, with an average inference time of only 3.5 μs. Subsequently, another accelerator is designed with the efficient hardware structure of the long short-term memory (LSTM) + DNN model, demonstrating outstanding performance with a classification accuracy of 98.82%, mean absolute error (MAE) of 0.27°, and root mean square errors (RMSE) of 0.392° after model compression. Moreover, parallel optimization strategies are exploited to further reduce latency and support simultaneous frequency and direction measurement tasks. Finally, we test the actual collected signal data on the XCVU13P field programmable gate array (FPGA). The results show that the time of inference saves 28–31% for the DNN model and 71–73% for the LSTM + DNN model compared to running on graphic processing unit (GPU). In addition, the parallel strategies further decrease the delay by 23.9% and 37.5% when processing continuous data. The FPGA-based and deep learning-assisted hardware accelerators significantly improve real-time performance and provide a promising solution for signal measurement.
Analysis of High-Frequency Sea-State Variability Using SWOT Nadir Measurements and Application to Altimeter Sea State Bias Modelling
Authors: Mazaleyrat Estelle , Tran Ngan , Amarouche Laïba , Vandemark Douglas , Feng Hui , Dibarboure Gérald , Bignalet-Cazalet François
Published: November 2024

The 1-day fast-sampling orbit phase of the Surface Water Ocean Topography (SWOT) satellite mission provides a unique opportunity to analyze high-frequency sea-state variability and its implications for altimeter sea state bias (SSB) model development. Time series with 1-day repeat sampling of sea-level anomaly (SLA) and SSB input parameters—comprising the significant wave height (SWH), wind speed (WS), and mean wave period (MWP)—are constructed using SWOT’s nadir altimeter data. The analyses corroborate the following key SSB modelling assumption central to empirical developments: the SLA noise due to all factors, aside from sea state change, is zero-mean. Global variance reduction tests on the SSB model’s performance using corrected SLA differences show that correction skill estimation using a specific (1D, 2D, or 3D) SSB model is unstable when using short time difference intervals ranging from 1 to 5 days, reaching a stable asymptotic limit after 5 days. It is proposed that this result is related to the temporal auto- and cross-correlations associated with the SSB model’s input parameters; the present study shows that SSB wind-wave input measurements take time (typically 1–4 days) to decorrelate in any given region. The latter finding, obtained using unprecedented high-frequency satellite data from multiple ocean basins, is shown to be consistent with estimates from an ocean wave model. The results also imply that optimal time-differencing (i.e., >4 days) should be considered when building SSB model data training sets. The SWOT altimeter data analysis of the temporal cross-correlations also permits an evaluation of the relationships between the SSB input parameters (SWH, WS, and MWP), where distinct behaviors are found in the swell- and wind-sea-dominated areas, and associated time scales are less than or on the order of 1 day. Finally, it is demonstrated that computing cross-correlations between the SLA (with and without SSB correction) and the SSB input parameters offers an additional tool for evaluating the relevance of candidate SSB input parameters, as well as for assessing the performance of SSB correction models, which, so far, mainly rely on the reduction in the variance of the differences in the SLA at crossover points.
Pest Detection in Citrus Orchards Using Sentinel-2: A Case Study on Mealybug (Delottococcus aberiae) in Eastern Spain
Authors: Della Bellver Fàtima , Franch Gras Belen , Moletto-Lobos Italo , Guerrero Benavent César José , San Bautista Primo Alberto , Rubio Constanza , Vermote Eric , Saunier Sebastien
Published: November 2024

The Delottococcus aberiae is a mealybug pest known as Cotonet de les Valls in the province of Castellón (Spain). This tiny insect is causing large economic losses in the Spanish agricultural sector, especially in the citrus industry. The European Copernicus program encourages the progress of Earth observation (EO) in relation to the development of agricultural monitoring tools. In this context, this work is based on the analysis of the temporal evolution of spectral surface reflectance data from Sen2Like, analyzing healthy and fields affected by the mealybug. The study area is focused on the surroundings of Vall d’Uixó (Castellón, Spain), involving an approximate area of 25 ha distributed in a total of 21 fields of citrus trees with different mealybug incidence, classified as healthy or unhealthy, during the 2020–2021 season. The relationship between the mealybug infestation level and the Normalized Difference Vegetation Index (NDVI) and other optical bands (Red, NIR, SWIR, derived from Sen2Like) were analyzed by studying the time-series evolution of each parameter across the time period 2017–2022. In this study, we also demonstrate that evergreen fruit trees such as citrus, show a seasonality across the EO-based time series, which is linked to directional effects caused by the sensor–sun geometry. This can be mitigated by using a Bidirectional Reflectance Distribution Function (BRDF) model such as the High-Resolution Adjusted BRDF Algorithm (HABA). To study the infested fields separately from healthy ones and avoid mixing fields with very different spectral responses caused by field type, separation between rows, or age, we studied the evolution of each parcel separately using monthly linear regressions, considering the 2017–2018 seasons as a reference when the pest had not developed yet. The observations indicate the feasibility of the distinction between affected and healthy plots during a year utilizing specific spectral ranges, with SWIR proving a notably effective channel, enabling separability from mid-summer to the fall. Furthermore, the anomaly inspection demonstrates an increase in the effects of the pest from 2020 to 2022 in all spectral regions and enables a first approximation for identifying healthy and affected fields based on negative anomalies in the red and SWIR channels and positive anomalies in the NIR and NDVI. This work contributes to the development of new monitoring tools for efficient and sustainable action in pest control.
The Influence of Local Climatic Factors and Water Vapor Transport from North Atlantic Ocean on Winter Snow-Cover Variation on Western Kunlun Mountains and Eastern Pamir Plateau
Authors: Xue Xiaoying , Xu Xiangde , Ren Guoyu , Sun Xiubao , Zhang Panfeng
Published: November 2024

Snow cover days (SCD) have increased significantly in winter on the Western Kunlun Mountains and Eastern Pamir Plateau (hereafter referred to as KMPP for short), however the causes have not been well understood so far. Here, we use remote sensing data to analyze the abnormal increase in SCD on the KMPP and explore its causes from the perspective of the local factors and water vapor transport caused by sea surface temperatures (SST) warming. We discover that the winter SCD on the KMPP increased significantly at a rate of 4.75 days/decade (significant at the 0.01 level) during 1989–2020, while there has been a significant decrease on the Tibetan Plateau (TP), with a rate of −1.50 days/decade (significant at the 0.1 level). Based on ERA5, GPCP, GHCN, and station data, we find that, in contrast to the significant warming observed on the TP, temperature changes on the KMPP are negligible, while precipitation is increasing, differing from the decreasing precipitation trend observed on the TP. The differences in local temperature and precipitation changes cause different variations in SCD between the KMPP and the TP. The increase in SCD on the KMPP is primarily driven by increased precipitation (over 97% contribution), with minimal impact from the more or less unchanged temperature. In contrast, the decline in SCD on the TP results from decreased precipitation and significantly increased temperature. Furthermore, we found that changes in SCD on the KMPP are significantly correlated with SST in the northern North Atlantic Ocean. Based on the correlation vector, the anomaly field in the high/low SCD years of water vapor transport, and the FLEXPART model, we show that the northern North Atlantic Ocean is one of the major water vapor sources affecting the SCD on the KMPP. The warming SST in the northern North Atlantic Ocean enhances water vapor transport to the KMPP in winter, leading to an abnormal increase in the SCD that differs from the overall trend on the TP. The findings are conductive to further understand the peculiarity of winter precipitation and SCD on the KMPP, and the “Western Kunlun Mountains Oddity” in mountain glacial change.
Remote Sensing Technologies Using UAVs for Pest and Disease Monitoring: A Review Centered on Date Palm Trees
Authors: Alsadik Bashar , Ellsäßer Florian J. , Awawdeh Muheeb , Al-Rawabdeh Abdulla , Almahasneh Lubna , Oude Elberink Sander , Abuhamoor Doaa , Al Asmar Yolla
Published: November 2024

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases.
An Approach to Predicting Urban Carbon Stock Using a Self-Attention Convolutional Long Short-Term Memory Network Model: A Case Study in Wuhan Urban Circle
Authors: Zhou Zhi , Wu Xueling , Peng Bo
Published: November 2024

To achieve the regional goal of “double carbon”, it is necessary to map the carbon stock prediction for a wide area accurately and in a timely fashion. This paper introduces a long- and short-term memory network algorithm called the Self-Attention Convolutional Long and Short-Term Memory Network (SA-ConvLSTM). This paper takes the Wuhan urban circle of China as the research object, establishes a carbon stock AI prediction model, constructs a carbon stock change evaluation system, and investigates the correlation between carbon stock change and land use change during urban expansion. The results demonstrate that (1) the overall accuracy of the ConvLSTM and SA-ConvLSTM models improved by 4.68% and 4.70%, respectively, when compared to the traditional metacellular automata prediction methods (OS-CA, Open Space Cellular Automata Model), and for small sample categories such as barren land, shrubs, and grassland, the accuracy of SA-ConvLSTM increased by 17.15%, 43.12%, and 51.37%, respectively; (2) from 1999 to 2018, the carbon stock in the Wuhan urban area showed a decreasing trend, with an overall decrease of 6.49 × 106 MgC. The encroachment of arable land due to rapid urbanization is the main reason for the decrease in carbon stock in the Wuhan urban area. From 2018 to 2023, the predicted value of carbon stock in the Wuhan urban area was expected to increase by 9.17 × 104 MgC, mainly due to the conversion of water bodies into arable land, followed by the return of cropland to forest; (3) the historical spatial error model (SEM) indicates that for each unit decrease in carbon stock change, the Single Land Use Dynamic Degree (SLUDD) of water bodies and impervious surfaces will increase by 119 and 33 units, respectively. For forests, grasslands, and water bodies, the future spatial error model (SEM) indicated that for each unit increase in carbon stock change, the SLUDD would increase by 55, 7, and −305 units, respectively. This study demonstrates that we can use deep neural networks as a new method for predicting land use expansion, revealing the key impacts of land use change on carbon stock change from both historical and future perspectives and providing valuable insights for policymakers.
Noise Variation Characteristics of the Superconducting Gravimeter at Jiufeng Station in Wuhan (China)
Authors: Li Hang , Chen Xiaodong , Zhang Miaomiao , Niu Xiaowei , Xu Jianqiao , Sun Heping
Published: November 2024

The noise level of gravity stations is an important indicator for measuring the operating status of a station and is a prerequisite for evaluating whether the station’s observations can be used to extract weak geodynamic signals. With the continuous expansion of areas of human activity, gravity stations originally located in the wild may become increasingly closer to cities. Whether their noise levels change is an important issue that is worthy of attention. Based on power spectrum analyses and probability density function methods, the noise level of the superconducting gravimeter (SG) at Jiufeng station in Wuhan in the seismic frequency band of 0.001–0.04 Hz was calculated, and its time-varying characteristics were analyzed. The noise level of Jiufeng station did not change significantly before and after the lockdown of Wuhan due to the COVID-19 epidemic in 2020. No significant changes in the noise level were found before and after the official operation of Wuhan Metro Line 19 at the end of 2023. From October 2016 to April 2017, the noise level showed an abnormal trend of suddenly rapidly rising and then slowly declining, which was found to be caused by a tilt problem in the gravity sensor. Overall, in the seismic frequency band of 0.001–0.04 Hz, the noise level at Jiufeng station showed seasonal variation characteristics, and the noise was stronger in winter than in summer, which is consistent with the characteristics of Earth’s hum. Since January 2022, the noise level has shown an increasing trend year by year. The results of this study can provide an important reference for the operation of gravity stations and the extraction of weak geodynamic signals.
Determining Tyre Adhesion Characteristics Based on the Road Tests of Automobiles
Authors: Reński Andrzej , Brukalski Mateusz , Sar Hubert , Abramowski Michał , Fundowicz Piotr , Rokicki Krzysztof
Published: November 2024

The motion of automobiles significantly depends on the conditions of interaction between a tyre and a road surface. One of the most frequently used ways of presenting the conditions of cooperation between a tyre and a road surface is a characteristic showing a longitudinal adhesion coefficient as a function of a longitudinal slip of a tyre. One of the methods for determining tyre-to-road adhesion characteristics is to use a special trailer combined with a towing vehicle. This type of method is commonly used to determine adhesion characteristics for a braked wheel. This article presents a method for determining adhesion characteristics for a driven wheel based on the road tests of automobiles. For this purpose, vehicle wheel velocity signals from a vehicle CAN network and a vehicle velocity signal from a GPS receiver were used. The signals from the CAN network were recorded using a special measurement card and an application developed in LabVIEW environment. The application developed in LabVIEW also allowed for simultaneous recording of automobile velocity from the GPS receiver. In this paper, the courses of a wheel velocity, longitudinal acceleration of automobile, longitudinal slip of the front wheels in time domain, as well as the coefficient of tyre-to-road longitudinal adhesion as a function of the longitudinal slip of the wheel are presented.
ReLU, Sparseness, and the Encoding of Optic Flow in Neural Networks
Authors: Layton Oliver W. , Peng Siyuan , Steinmetz Scott T.
Published: November 2024

Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.
Effect of Post-Heat Treatment on the Mechanical and Residual Stress Behavior of Pulsed Wave S316L Fabricated by Directed Energy Deposition
Authors: Yan Zhou , Guo Jia , Zou Xi
Published: November 2024

The influence of annealing at various temperatures on the phase stability and microstructure of pulsed-wave laser mode (PW) 316L stainless steel fabricated via Directed Energy Deposition (DED) was systematically investigated. The microstructural alterations resulting from heat treatment were examined to clarify their influence on the mechanical properties of the specimens subjected to tensile loading. The results showed that cell size increased with annealing temperature, with the cellular microstructure disappearing at higher temperatures (T ≥ 1000 °C). A decrease in the mechanical strength of the specimens was observed as annealing temperature increased. Additionally, the influence of different laser pulse frequencies and duty cycles on residual stresses was examined, revealing that moderate laser frequencies and duty cycles effectively reduced residual stress levels.
An Automated Feature-Based Image Registration Strategy for Tool Condition Monitoring in CNC Machine Applications
Authors: Lazar Eden , Bennett Kristin S. , Hurtado Carreon Andres , Veldhuis Stephen C.
Published: November 2024

The implementation of Machine Vision (MV) systems for Tool Condition Monitoring (TCM) plays a critical role in reducing the total cost of operation in manufacturing while expediting tool wear testing in research settings. However, conventional MV-TCM edge detection strategies process each image independently to infer edge positions, rendering them susceptible to inaccuracies when tool edges are compromised by material adhesion or chipping, resulting in imprecise wear measurements. In this study, an MV system is developed alongside an automated, feature-based image registration strategy to spatially align tool wear images, enabling a more consistent and accurate detection of tool edge position. The MV system was shown to be robust to the machining environment, versatile across both turning and milling machining centers and capable of reducing tool wear image capturing time up to 85% in reference to standard approaches. A comparison of feature detector-descriptor algorithms found SIFT, KAZE, and ORB to be the most suitable for MV-TCM registration, with KAZE presenting the highest accuracy and ORB being the most computationally efficient. The automated registration algorithm was shown to be efficient, performing registrations in 1.3 s on average and effective across a wide range of tool geometries and coating variations. The proposed tool reference line detection strategy, based on spatially aligned tool wear images, outperformed standard methods, resulting in average tool wear measurement errors of 2.5% and 4.5% in the turning and milling tests, respectively. Such a system allows machine tool operators to more efficiently capture cutting tool images while ensuring more reliable tool wear measurements.
Carbon-Efficient Scheduling in Fresh Food Supply Chains with a Time-Window-Constrained Deep Reinforcement Learning Model
Authors: Zou Yuansu , Gao Qixian , Wu Hao , Liu Nianbo
Published: November 2024

Intelligent Transportation Systems (ITSs) leverage Internet of Things (IoT) technology to facilitate smart interconnectivity among vehicles, infrastructure, and users, thereby optimizing traffic flow. This paper constructs an optimization model for the fresh food supply chain distribution route of fresh products, considering factors such as carbon emissions, time windows, and cooling costs. By calculating carbon emission costs through carbon taxes, the model aims to minimize distribution costs. With a graph attention network structure adopted to describe node locations, accessible paths, and data with collection windows for path planning, it integrates to solve for the optimal distribution routes, taking into account carbon emissions and cooling costs under varying temperatures. Extensive simulation experiments and comparative analyses demonstrate that the proposed time-window-constrained reinforcement learning model provides effective decision-making information for optimizing fresh product fresh food supply chain transportation and distribution, controlling logistics costs, and reducing carbon emissions.
Eyelid Dynamics Characterization with 120 GHz mmW Radar
Authors: Patscheider Dominik , Wu Ruochen , Broquetas Antoni , Aguasca Albert , Romeu Jordi
Published: November 2024

This paper presents a new approach to measuring eyelid movement using millimeter wave (mmW) radar technology. A two-step method is proposed, involving the observation of a small resolution cell corresponding to the monitored eye and the evaluation of the phase evolution over the measurement period. Simulations are conducted to support radar system optimization and data interpretation with a focus on detecting eyelid movement patterns and compensating for interference from other parts of the body. The feasibility of using this method with eyeglasses is also explored. The proposed technique’s advantages and limitations are discussed in comparison with existing measurement alternatives. The characteristics of eyelid dynamics, including blink frequency, regularity, duration, and velocity can be used to assess neurological conditions and driver drowsiness.
DIMScern: A Framework for Discerning DIMSE Services on Remote Medical Devices
Authors: Kim Gunhee , Kim Dohyun , Seo Jeonghun , Lee Seyoung , Song Wonjun
Published: November 2024

In the medical domain, computer systems in digital healthcare have increased connectivity continuously and the DICOM Message Service Element (DIMSE) protocol has a critical role in exchanging biomedical imaging data among different digital healthcare systems. As the data communication technology is used to handle sensitive information such as patient information (e.g., patient’s name, date of birth, and address) and medical images (e.g., ultrasound, X-ray, and MRI), it has emerged as a major target for security attacks. In this work, we study security concerns on the message exchange method used in the DIMSE protocol. It is important to know which DIMSE services are available on a given healthcare IT system to an adversary and we observe that the DIMSE protocol can be implemented in various ways across products, with each supporting different DIMSE services as well. We present DIMScern, a framework for discerning DIMSE services on remote medical devices. To show the effectiveness of DIMScern, we evaluate our framework on multiple DIMSE implementations, including commercial products and libraries, and identify the supported DIMSE services of them. We demonstrate that DIMScern successfully identifies medical services that are supported differently across 22 healthcare IT systems in a remote environment.
Enhancing Deep Sleep Induction Through a Wireless In-Ear EEG Device Delivering Binaural Beats and ASMR: A Proof-of-Concept Study
Authors: Hestermann Elke , Schreve Kristiaan , Vandenheever David
Published: November 2024

This study presents the development of a wireless in-ear EEG device designed to monitor brain activity during sleep and deliver auditory stimuli aimed at enhancing deep sleep. The device records EEG signals and plays a combined auditory stimulus consisting of autonomous sensory meridian response (ASMR) and 3 Hz binaural beats at a 60:30 dB ratio, intended to promote delta wave activity and non-rapid eye movement (NREM) stage 3 sleep. Fifteen participants completed this study, which included two consecutive nights: a baseline night and a testing night. Participants were divided into an experimental group, which received the combined ASMR and binaural beat stimulus, and a control group, which received only ASMR. The combined stimulus was delivered upon entering NREM stage 2 and replaced by ASMR when NREM stage 3 was reached. Results showed that the experimental group experienced an increase in NREM 3 sleep, a decrease in NREM 2 sleep, and a slight increase in NREM 3 latency compared to the baseline night. Although the findings are promising, further testing with a larger sample size is required to confirm the device’s potential to enhance sleep quality and promote delta activity in the brain.
Human Resource Practices and Job Performance: Insights from Public Administration
Authors: Ouabi Zakaria , Douayri Khadija , Barboucha Fatine , Boubker Omar
Published: November 2024

The Moroccan government has taken several initiatives to improve HR practices in the public sector, with the aim of enhancing the performance of civil servants and thereby improving the quality of public administration services. Therefore, this study employs the PLS-SEM technique to investigate the influence of HRM practices on job satisfaction, affective commitment, and in-role job performance within public administrations. The dataset was collected online from 390 Moroccan civil servants. The results indicate that recruitment and selection (RSE), training (TRA), compensation (COM), employment security (ESE), and internal career opportunities (ICOs) had a positive impact on job satisfaction (JSA). Likewise, RSE, TRA, and ICOs improved affective commitment (ACO). In addition, JSA and ACO enhanced in-role job performance (RJP). These findings provide valuable theoretical implications and practical guidelines for policymakers on how to adapt HR practices to enhance civil servants’ job satisfaction and affective commitment, ultimately improving their in-role job performance levels.
When Readers Do Not Fight Falsehood: An Exploration of Factors Influencing the Perceived Realism of False News on International Disputes
Authors: Sui Mingxiao , Luo Yunjuan , Paul Newly
Published: November 2024

This study examines the effects of misleading news—one type of false information presented by news media in the U.S. and China—in the context of international disputes. Through a web-based survey experiment, we tested how Chinese readers’ perception of false news is affected by the source of the news, the presence of visual elements, and general trust in mainstream Chinese media and that in mainstream U.S. media, as well as news literacy. Our results suggested false news reported by domestic media was perceived to better represent the reality of the covered issue than news presented by foreign media. This relationship was moderated by readers’ general trust in U.S. media and news literacy, which indicated media literacy training as a possible solution to counteract the effect of the news source. These findings not only advance current scholarship on misinformation by incorporating perspectives from non-Western media systems but also provide both foreign and domestic readers with timely and relevant methods to combat false information.
Canine and Human Red Blood Cells: Biochemical Mechanisms for the Control of Heat Dissipation
Authors: Russo Annamaria , Patanè Giuseppe Tancredi , Putaggio Stefano , Tellone Ester , Ficarra Silvana , Barreca Davide , Laganà Giuseppina
Published: November 2024

Dogs, unlike humans, are equipped with a reduced number of sweat glands, which makes it difficult for them to dissipate heat, especially in conditions of intense activity that lead to a significant increase in body temperature. The study aims to investigate the metabolic differences between canine and human red blood cells (RBCs) and the hemoglobin (Hb) functionality focusing on their roles in heat dissipation. In detail, we evaluated the Band 3 protein (AE1) kinetic flux by observing that in canine red blood cells the anion exchange rate is higher than in humans (Rate Constant: 0.0438 min−1 and 0.012 min−1, respectively). Furthermore, we investigated the rate of ATP production and release to evaluate the possible variation of nucleotide concentration in the two species, observing a lower intracellular ATP concentration (101.80 μM and 297.90 μM) but a higher ATP release (3 μM and 2.65 μM) in canine RBCs compared to humans respectively. Subsequently, we evaluated the involvement of canine hemoglobin in heat dispersion; in detail, the ΔH= −5.15 Kcal/mol recorded in dog hemolysate at pH 7.5 shows an exothermic Hb-O2 bond that may be useful for further dispersing heat from the lungs. The peculiar oxygen-binding properties of dog Hb may also promote oxygen release in hyperventilation characterized by alkaline pH.
Latent Class Analysis of Environmental Behavior and Psychological Well-Being: Insights into Sustainable Well-Being Practices
Authors: Bostan Ayşe , Balcıoğlu Yavuz Selim , Elçi Meral
Published: November 2024

This study explores the relationship between environmental behavior, job satisfaction, performance, and burnout using latent class analysis (LCA) to identify distinct behavioral patterns. Data were collected from 537 participants in various sectors, including education and health. Five latent classes were identified based on the participation of participants in sustainable practices. The analysis revealed significant differences in job satisfaction, with higher participation in sustainable behaviors generally associated with greater job satisfaction. Although performance remained stable across classes, burnout levels varied significantly, with some groups showing higher levels of stress and burnout and others demonstrating better psychological outcomes. The results confirm that sustainable behaviors are linked to psychological well-being, emphasizing the importance of fostering sustainability in the workplace to enhance employee well-being. The study highlights the need for targeted interventions to address burnout and promote sustainability in organizational settings. Future research should explore these dynamics longitudinally and across diverse occupational and cultural contexts.
Technical Management of Residential Buildings—The Idea of Implementing Predictive Approach Elements for Sustainable Maintenance
Authors: Dziadosz Agnieszka , Gajzler Marcin , Nowotarski Piotr , Wloch-Surowka Kamila
Published: November 2024

This article presents the issue of sustainably managing of the technical maintenance of multi-family residential buildings with diverse characteristics. The results of this research conducted on entities managing real estate in a real estate market in the city of Poznan (0.5 million inhabitants) are presented along with comments, with attention drawn to the commonness of the approaches used in building maintenance management. Of particular note is the reactive approach (which, in extreme cases, turns out to be unreliable and does not guarantee the absence of restrictions in the possibility of using housing resources) and the practical lack of application of the proactive-predictive approach. The main features of the approaches used in management are presented, and selected examples are used to demonstrate the advantage of proactive approaches in the economic, social, and environmental aspects. This article presents arguments for the implementation of proactive management strategies, including predictive maintenance. Due to financial and technical limitations, our model is presented in support of the technical management of buildings with predictive elements. The model is based on case-based reasoning (CBR). An important element of the model is the knowledge acquisition system, which uses mandatory technical condition inspections resulting from the applicable law and inspection protocols created as a result. The proposed model is not as expensive as using full predictive maintenance using sensor infrastructure devices, and at the same time allows advanced reasoning and solves the practical problems of maintaining housing resources.