bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Mechanical Properties of Composite Material with a Thermoplastic Matrix Reinforced with Cellulose Acetate Microfibers
Authors: Rodríguez Sandoval Pedro , Rubiano-Navarrete Andres Felipe , Gómez-Pachón Edwin Yesid , Vera-Graziano Ricardo
Published: September 2024

Low-density polyethylene (LDPE) has been widely used in various applications due to its flexibility, lightness, and low production cost. However, its massive use in disposable products has raised environmental concerns, prompting the search for more sustainable alternatives. This study aims to investigate the mechanical properties achievable in a composite material utilizing low-density polyethylene (LDPE), potato starch (PS), and cellulose microfibrils (MFCA) at loadings of 0.05%, 0.15%, and 0.30%. Initially, the cellulose acetate microfibrils (MFCA) were produced via an electrospinning process. Subsequently, a dispersive mixture of the aforementioned materials was created through the extrusion and pelletizing process to form pellets. These pellets were then molded by injection molding to produce test specimens in accordance with ASTM D 638, the standard for tensile strength testing. The evaluation of the properties was conducted through mechanical tensile tests (ASTM D638), hardness tests (ASTM D 2240), melt flow index (ASTM D1238), and scanning electron microscopy (SEM). This study determined the influence of cellulose acetate microfibril loadings below 0.3% as reinforcement within a thermoplastic LDPE matrix. It was demonstrated that these microfibrils, due to their length-to-diameter ratio, contribute to an enhancement in the mechanical properties.
Phenolic Foam Preparation Using Hydrofluoroolefin Blowing Agents and the Toughening Effect of Polyethylene Glycol
Authors: Sarika P. R. , Nancarrow Paul , Ibrahim Taleb H.
Published: September 2024

In this work, a new class of fourth-generation, zero ozone depletion potential, hydrofluoroolefin-based blowing agents were used to prepare phenolic foam. While hydrofluoroolefin blowing agents have been used previously to prepare polyurethane foams, few studies have been reported on their use in phenolic foams. We introduce an effective method for foam preparation using two low-boiling blowing agents, cis-1,1,1,4,4,4-hexafluoro-2-butene and trans-1,1,1,4,4,4-hexafluoro-2-butene, and their combinations with hexane. Traditionally, phenolic foams have been prepared using chlorofluorocarbons and hydrochlorofluorocarbons, which can have harmful effects on the environment due to their high ozone depletion potential or global warming potential. Conductor-like screening model for real solvents (COSMO-RS) modeling studies were performed to understand the effects of different blowing agent combinations on their boiling points. A series of phenolic foams were prepared by varying the concentration of the hydrofluoroolefin and the hydrofluoroolefin–hexane blowing agent combinations. The concentrations of the surfactant, Agnique CSO 30, and the toughening agent, polyethylene glycol, were also varied to yield a formulation with the optimal properties. The foams formulated with the hydrofluoroolefin–hexane mixture displayed a higher compressive strength and a lower thermal conductivity than those prepared with either hydrofluoroolefin or hexane alone. The cell microstructure of all the foams was examined using scanning electron microscopy. By introducing flexible chains into the resin matrix, PEG facilitates proper distribution of hydrofluoroolefin–hexane blowing agents and other reagents and thereby increases the mechanical strength of the foam.
Effect of Cutting Conditions on the Size of Dust Particles Generated during Milling of Carbon Fibre-Reinforced Composite Materials
Authors: Dvořáčková Štěpánka , Kroisová Dora , Knápek Tomáš , Váňa Martin
Published: September 2024

Conventional dry machining (without process media) of carbon fibre composite materials (CFRP) produces tiny chips/dust particles that float in the air and cause health hazards to the machining operator. The present study investigates the effect of cutting conditions (cutting speed, feed per tooth and depth of cut) during CFRP milling on the size, shape and amount of harmful dust particles. For the present study, one type of cutting tool (CVD diamond-coated carbide) was used directly for machining CFRP. The analysis of harmful dust particles was carried out on a Tescan Mira 3 (Tescan, Brno, Czech Republic) scanning electron microscope and a Keyence VK-X 1000 (Keyence, Itasca, IL, USA) confocal microscope. The results show that with the combination of higher feed per tooth (mm) and lower cutting speed, for specific CFRP materials, the size and shape of harmful dust particles is reduced. Particles ranging in size from 2.2 to 99 μm were deposited on the filters. Smaller particles were retained on the tool body (1.7 to 40 μm). Similar particle sizes were deposited on the machine and in the work area.
Gelatin–Sodium Alginate Hydrogels Cross-Linked by Squaric Acid and Dialdehyde Starch as a Potential Bio-Ink
Authors: Skopinska-Wisniewska Joanna , Tuszynska Marta , Kaźmierski Łukasz , Bartniak Mateusz , Bajek Anna
Published: September 2024

Hydrogels as biomaterials possess appropriate physicochemical and mechanical properties that enable the formation of a three-dimensional, stable structure used in tissue engineering and 3D printing. The integrity of the hydrogel composition is due to the presence of covalent or noncovalent cross-linking bonds. Using various cross-linking methods and agents is crucial for adjusting the properties of the hydrogel to specific biomedical applications, e.g., for direct bioprinting. The research subject was mixtures of gel-forming polymers: sodium alginate and gelatin. The polymers were cross-linked ionically with the addition of CaCl2 solutions of various concentrations (10%, 5%, 2.5%, and 1%) and covalently using squaric acid (SQ) and dialdehyde starch (DAS). Initially, the polymer mixture’s composition and the hydrogel cross-linking procedure were determined. The obtained materials were characterized by mechanical property tests, swelling degree, FTIR, SEM, thermal analysis, and biological research. It was found that the tensile strength of hydrogels cross-linked with 1% and 2.5% CaCl2 solutions was higher than after using a 10% solution (130 kPa and 80 kPa, respectively), and at the same time, the elongation at break increased (to 75%), and the stiffness decreased (Young Modulus is 169 kPa and 104 kPa, respectively). Moreover, lowering the concentration of the CaCl2 solution from 10% to 1% reduced the final material’s toxicity. The hydrogels cross-linked with 1% CaCl2 showed lower degradation temperatures and higher weight losses than those cross-linked with 2.5% CaCl2 and therefore were less thermally stable. Additional cross-linking using SQ and DAS had only a minor effect on the strength of the hydrogels, but especially the use of 1% DAS increased the material’s elasticity. All tested hydrogels possess a 3D porous structure, with pores of irregular shape and heterogenic size, and their swelling degree initially increased sharply to the value of approx. 1000% during the first 6 h, and finally, it stabilized at a level of 1200–1600% after 24 h. The viscosity of 6% gelatin and 2% alginate solutions with and without cross-linking agents was similar, and they were only slightly shear-thinning. It was concluded that a mixture containing 2% sodium alginate and 6% gelatin presented optimal properties after gel formation and lowering the concentration of the CaCl2 solution to 1% improved the hydrogel’s biocompatibility and positively influenced the cross-linking efficiency. Moreover, chemical cross-linking by DAS or SQ additionally improved the final hydrogel’s properties and the mixture’s printability. In conclusion, among the tested systems, the cross-linking of 6% gelatin–2% alginate mixtures by 1% DAS addition and 1% CaCl2 solution is optimal for tissue engineering applications and potentially suitable for 3D printing.
Opportunities and Challenges in the Application of Bioplastics: Perspectives from Formulation, Processing, and Performance
Authors: Negrete-Bolagay Daniela , Guerrero Víctor H.
Published: September 2024

Tremendously negative effects have been generated in recent decades by the continuously increasing production of conventional plastics and the inadequate management of their waste products. This demands the production of materials within a circular economy, easy to recycle and to biodegrade, minimizing the environmental impact and increasing cost competitiveness. Bioplastics represent a sustainable alternative in this scenario. However, the replacement of plastics must be addressed considering several aspects along their lifecycle, from bioplastic processing to the final application of the product. In this review, the effects of using different additives, biomass sources, and processing techniques on the mechanical and thermal behavior, as well as on the biodegradability, of bioplastics is discussed. The importance of using bioplasticizers is highlighted, besides studying the role of surfactants, compatibilizers, cross-linkers, coupling agents, and chain extenders. Cellulose, lignin, starch, chitosan, and composites are analyzed as part of the non-synthetic bioplastics considered. Throughout the study, the emphasis is on the use of well-established manufacturing processes, such as extrusion, injection, compression, or blow molding, since these are the ones that satisfy the quality, productivity, and cost requirements for large-scale industrial production. Particular attention is also given to fused deposition modeling, since this additive manufacturing technique is nowadays not only used for making prototypes, but it is being integrated into the development of parts for a wide variety of biomedical and industrial applications. Finally, recyclability and the commercial requirements for bioplastics are discussed, and some future perspectives and challenges for the development of bio-based plastics are discussed, with the conclusion that technological innovations, economic incentives, and policy changes could be coupled with individually driven solutions to mitigate the negative environmental impacts associated with conventional plastics.
Experimental and Statistical Analysis of Concrete Eco-Cobble Using Organic and Synthetic Fibers
Authors: García-León Ricardo Andrés , Trigos-Caceres Jorge , Castilla-Quintero Natalia , Afanador-García Nelson , Gómez-Camperos July
Published: September 2024

The environmental impact of traditional construction materials necessitates the development of sustainable alternatives. This study evaluates eco-cobbles as novel building materials designed to reduce environmental footprint while maintaining performance standards. The objectives were to develop an eco-friendly cobble alternative and assess its effectiveness through laboratory tests. Eco-cobbles were synthesized using recycled and bio-based materials and tested for compressive strength, flexural strength, and water absorption at 14 and 28 days. The compressive strength ranged from 11.5 MPa to 26.8 MPa, with a maximum value observed at 28 days in a mixture containing 95% concrete and 5% polyethylene terephthalate (PET). Flexural strength varied from 9.1 MPa to 28.7 MPa, with the highest value achieved in a mixture of 95% concrete and 0% fibers. Water absorption rates ranged from 2.1% to 6.6%, demonstrating an effective balance between performance and durability. Environmental assessments indicated a 30% reduction in resource consumption and a 40% decrease in carbon footprint compared to traditional cobble production methods. The findings demonstrate that eco-cobbles not only meet performance standards but also offer significant environmental benefits with a 99% compliance from the results obtained by response surface methodology plots, confirming that eco-cobbles offer a viable, sustainable alternative to conventional materials, with the potential for broader application in eco-friendly construction practices.
Mechanism of Improving Etching Selectivity for E-Beam Resist AR-N 7520 in the Formation of Photonic Silicon Structures
Authors: Fetisenkova Ksenia , Melnikov Alexander , Kuzmenko Vitaly , Miakonkikh Andrey , Rogozhin Alexander , Tatarintsev Andrey , Glaz Oleg , Kiselevsky Vsevolod
Published: September 2024

The selectivity of the reactive ion etching of silicon using a negative electron resist AR-N 7520 mask was investigated. The selectivity dependencies on the fraction of SF6 in the feeding gas and bias voltage were obtained. To understand the kinetics of passivation film formation and etching, the type and concentration of neutral particles were evaluated and identified using plasma optical emission spectroscopy. Electron temperature and electron density were measured by the Langmuir probe method to interpret the optical emission spectroscopy data. A high etching selectivity of 8.0 ± 1.8 was obtained for the etching process. The optimum electron beam exposure dose for defining the mask was 8200 pC/m at 30 keV.
Identifying Rare Earth Elements Using a Tripod and Drone-Mounted Hyperspectral Camera: A Case Study of the Mountain Pass Birthday Stock and Sulphide Queen Mine Pit, California
Authors: Qasim Muhammad , Khan Shuhab D. , Sisson Virginia , Greer Presley , Xia Lin , Okyay Unal , Franco Nicole
Published: September 2024

As the 21st century advances, the demand for rare earth elements (REEs) is rising, necessitating more robust exploration methods. Our research group is using hyperspectral remote sensing as a tool for mapping REEs. Unique spectral features of bastnaesite mineral, has proven effective for detection of REE with both spaceborne and airborne data. In our study, we collected hyperspectral data using a Senop hyperspectral camera in field and a SPECIM hyperspectral camera in the laboratory settings. Data gathered from California’s Mountain Pass district revealed bastnaesite-rich zones and provided detailed insights into bastnaesite distribution within rocks. Further analysis identified specific bastnaesite-rich rock grains. Our results indicated higher concentrations of bastnaesite in carbonatite rocks compared to alkaline igneous rocks. Additionally, rocks from the Sulphide Queen mine showed richer bastnaesite concentrations than those from the Birthday shonkinite stock. Results were validated with thin-section studies and geochemical data, confirming the reliability across different hyperspectral data modalities. This study demonstrates the potential of drone-based hyperspectral technology in augmenting conventional mineral mapping methods and aiding the mining industry in making informed decisions about mining REEs efficiently and effectively.
A Target Detection Algorithm Based on Fusing Radar with a Camera in the Presence of a Fluctuating Signal Intensity
Authors: Yang Yanqiu , Wang Xianpeng , Wu Xiaoqin , Lan Xiang , Su Ting , Guo Yuehao
Published: September 2024

Radar point clouds will experience variations in density, which may cause incorrect alerts during clustering. In turn, it will diminish the precision of the decision-level fusion method. To address this problem, a target detection algorithm based on fusing radar with a camera in the presence of a fluctuating signal intensity is proposed in this paper. It introduces a snow ablation optimizer (SAO) for solving the optimal parameters of the density-based spatial clustering of applications with noise (DBSCAN). Subsequently, the enhanced DBSCAN clusters radar point clouds, and the valid clusters are fused with monocular camera targets. The experimental results indicate that the suggested fusion method can attain a Balance-score ranging from 0.97 to 0.99, performing outstandingly in preventing missed detections and false alarms. Additionally, the fluctuation range of the Balance-score is within 0.02, indicating the algorithm has an excellent robustness.
Compensating Acquisition Footprint for Amplitude-Preserving Angle Domain Common Image Gathers Based on 3D Reverse Time Migration
Authors: Liu Hongwei , Fu Liyun , Li Qingqing , Liu Lu
Published: September 2024

Angle domain common image gathers (ADCIGs) play a crucial role in seismic exploration, offering prestack underground illumination information that aids in validating migration velocity and conducting prestack amplitude versus angle (AVA) analysis for reservoir characterization. This paper introduces an innovative approach for compensating amplitude errors caused by irregular seismic acquisition geometries in ADCIGs. By incorporating an angle domain illumination compensation factor, the proposed method effectively modifies these errors, preserving the amplitude of seismic reflectivity in the prestack angle domain. The effectiveness of the proposed approach is validated through comprehensive tests conducted on synthetic and field data examples. The results demonstrate the capability of the method to enhance the quality of ADCIGs derived from 3D reverse time migration (RTM), yielding accurate and reliable amplitude preservation. While the illumination compensation factor assumes a vertically linear velocity model, the method holds promise for extension to more complex media and diverse migration techniques. This suggests its applicability and adaptability beyond the specific assumptions considered in this study. In conclusion, this paper presents an innovative angle domain illumination compensation factor that significantly improves the quality of ADCIGs by addressing amplitude errors arising from irregular seismic acquisition geometries. The experimental validation using synthetic and field data confirms the effectiveness of the proposed method within the context of 3D RTM. Furthermore, the technique holds potential for broader application in more complex subsurface scenarios and various migration methodologies.
Remote Sensing and Geospatial Approaches for Studying the Environment Affected by Human Activities
Authors: Zhang Chengye , Li Jun , Shen Xinyi , Wu Qiusheng
Published: September 2024

In the past few decades, drastic human activities such as urban expansion, agricultural and pastoral activities, water conservancy construction, deforestation, mining, and tourism have significantly altered regional ecological environments [...]
Estimation of Aerosol Characteristics from Broadband Solar Radiation Measurements Carried Out in Southern Algeria
Authors: Zaiani Mohamed , Irbah Abdanour , Delanoë Julien
Published: September 2024

Aerosols in the atmosphere significantly reduce the solar radiation reaching the Earth’s surface through scattering and absorption processes. Knowing their properties becomes essential when we are interested in measuring solar radiation at a given location on the ground. The commonly used parameters that characterize their effects are the Aerosol Optical Depth τ, the Angstrom exponent α, and the Angstrom coefficient β. One method for estimating these parameters is to fit ground-based measurements of clear-sky direct solar radiation using a model on which it depends. However, the choice of model depends on its suitability to the atmospheric conditions of the site considered. Eleven empirical solar radiation models depending on α and β were thus chosen and tested with solar radiation measurements recorded between 2005 and 2014 in Tamanrasset in southern Algeria. The results obtained were compared to measurements made with the AERONET solar photometer on the same site during the same period. Among the 11 models chosen, the best performing ones are REST2 and CPCR2. They proved to be the best suited to estimate β with approximately the same RMSE of 0.05 and a correlation coefficient R with respect to AERONET of 0.95. The results also highlighted good performances of these models for the estimation of τ with an RMSE of 0.05 and 0.04, and an R of 0.95 and 0.96, respectively. The values of α obtained from the fitting of these models were, however, less good, with R around 0.38. Additional treatments based on a Recurrent Neural Network (RNN) were necessary to improve its estimation. They provided promising results showing a significant improvement in α estimates with R reaching 0.7 when referring to AERONET data. Furthermore, this parameter made it possible to identify different types of aerosols in Tamanrasset such as the presence of maritime, dust, and mixed aerosols representing, respectively, 31.21%, 3.25%, and 65.54%, proportions calculated over the entire period studied. The seasonal analysis showed that maritime aerosols are predominant in the winter in Tamanrasset but decrease with the seasons to reach a minimum in the summer (JJA). Dust aerosols appear in February and persist mainly in the spring (MAM) and summer (JJA), then disappear in September. These results are also consistent with those obtained from AERONET.
Three-Dimensional Dense Reconstruction: A Review of Algorithms and Datasets
Authors: Lee Yangming
Published: September 2024

Three-dimensional dense reconstruction involves extracting the full shape and texture details of three-dimensional objects from two-dimensional images. Although 3D reconstruction is a crucial and well-researched area, it remains an unsolved challenge in dynamic or complex environments. This work provides a comprehensive overview of classical 3D dense reconstruction techniques, including those based on geometric and optical models, as well as approaches leveraging deep learning. It also discusses the datasets used for deep learning and evaluates the performance and the strengths and limitations of deep learning methods on these datasets.
AVaTER: Fusing Audio, Visual, and Textual Modalities Using Cross-Modal Attention for Emotion Recognition
Authors: Das Avishek , Sarma Moumita Sen , Hoque Mohammed Moshiul , Siddique Nazmul , Dewan M. Ali Akber
Published: September 2024

Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for audio, video and textual emotion recognition (i.e., AVaTER) that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model’s ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods.
Microfibrous Carbon Paper Decorated with High-Density Manganese Dioxide Nanorods: An Electrochemical Nonenzymatic Platform of Glucose Sensing
Authors: Ahmed Khawtar Hasan , Mohamedi Mohamed
Published: September 2024

Nanorod structures exhibit a high surface-to-volume ratio, enhancing the accessibility of electrolyte ions to the electrode surface and providing an abundance of active sites for improved electrochemical sensing performance. In this study, tetragonal α-MnO2 with a large K+-embedded tunnel structure, directly grown on microfibrous carbon paper to form densely packed nanorod arrays, is investigated as an electrocatalytic material for non-enzymatic glucose sensing. The MnO2 nanorods electrode demonstrates outstanding catalytic activity for glucose oxidation, showcasing a high sensitivity of 143.82 µA cm−2 mM−1 within the linear range from 0.01 to 15 mM, with a limit of detection (LOD) of 0.282 mM specifically for glucose molecules. Importantly, the MnO2 nanorods electrode exhibits excellent selectivity towards glucose over ascorbic acid and uric acid, which is crucial for accurate glucose detection in complex samples. For comparison, a gold electrode shows a lower sensitivity of 52.48 µA cm−2 mM−1 within a linear range from 1 to 10 mM. These findings underscore the superior performance of the MnO2 nanorods electrode in both sensitivity and selectivity, offering significant potential for advancing electrochemical sensors and bioanalytical techniques for glucose monitoring in physiological and clinical settings.
AI-Enabled Sensor Fusion of Time-of-Flight Imaging and mmWave for Concealed Metal Detection
Authors: Kaul Chaitanya , Mitchell Kevin J. , Kassem Khaled , Tragakis Athanasios , Kapitany Valentin , Starshynov Ilya , Villa Federica , Murray-Smith Roderick , Faccio Daniele
Published: September 2024

In the field of detection and ranging, multiple complementary sensing modalities may be used to enrich information obtained from a dynamic scene. One application of this sensor fusion is in public security and surveillance, where efficacy and privacy protection measures must be continually evaluated. We present a novel deployment of sensor fusion for the discrete detection of concealed metal objects on persons whilst preserving their privacy. This is achieved by coupling off-the-shelf mmWave radar and depth camera technology with a novel neural network architecture that processes radar signals using convolutional Long Short-Term Memory (LSTM) blocks and depth signals using convolutional operations. The combined latent features are then magnified using deep feature magnification to reveal cross-modality dependencies in the data. We further propose a decoder, based on the feature extraction and embedding block, to learn an efficient upsampling of the latent space to locate the concealed object in the spatial domain through radar feature guidance. We demonstrate the ability to detect the presence and infer the 3D location of concealed metal objects. We achieve accuracies of up to 95% using a technique that is robust to multiple persons. This work provides a demonstration of the potential for cost-effective and portable sensor fusion with strong opportunities for further development.
Discrimination of Explosive Residues by Standoff Sensing Using Anodic Aluminum Oxide Microcantilever Laser Absorption Spectroscopy with Kernel-Based Machine Learning
Authors: Jeong Ho-Jung , Park Chang-Ju , Kim Kihyun , Park Yangkyu
Published: September 2024

Standoff laser absorption spectroscopy (LAS) has attracted considerable interest across many applications for environmental safety. Herein, we propose an anodic aluminum oxide (AAO) microcantilever LAS combined with machine learning (ML) for sensitive and selective standoff discrimination of explosive residues. A nanoporous AAO microcantilever with a thickness of <1 μm was fabricated using a micromachining process; its spring constant (18.95 mN/m) was approximately one-third of that of a typical Si microcantilever (53.41 mN/m) with the same dimensions. The standoff infrared (IR) spectra of pentaerythritol tetranitrate, cyclotrimethylene trinitramine, and trinitrotoluene were measured using our AAO microcantilever LAS over a wide range of wavelengths, and they closely matched the spectra obtained using standard Fourier transform infrared spectroscopy. The standoff IR spectra were fed into ML models, such as kernel extreme learning machines (KELMs), support vector machines (SVMs), random forest (RF), and backpropagation neural networks (BPNNs). Among these four ML models, the kernel-based ML models (KELM and SVM) were found to be efficient learning models able to satisfy both a high prediction accuracy (KELM: 94.4%, SVM: 95.8%) and short hyperparameter optimization time (KELM: 5.9 s, SVM: 7.6 s). Thus, the AAO microcantilever LAS with kernel-based learners could emerge as an efficient sensing method for safety monitoring.
Looping Flexible Fluoropolymer Microcapillary Film Extends Analysis Times for Vertical Microfluidic Blood Testing
Authors: Sarıyer Rüya Meltem , Gill Kirandeep K. , Needs Sarah H. , Reis Nuno M. , Jones Chris I. , Edwards Alexander Daniel
Published: September 2024

The microfluidic measurement of capillary flow can be used to evaluate the response of biological samples to stimulation, where distance and velocity are altered. Melt-extruded multi-bored microfluidic capillaries allow for high-throughput testing with low device cost, but simple devices may limit control over sample flow when compared to the more complex “lab-on-a-chip” devices produced using advanced microfluidic fabrication methods. Previously, we measured the dynamics of global haemostasis stimulated by thrombin by dipping straight vertical microcapillaries into blood, but only the most rapid response could be monitored, as flow slowed significantly within 30 s. Here, we show an innovative method to extend both the stimulation process and flow measurement time without increasing the cost of the device by adding simple loops to the flexible extruded device. The loops enable longer time-scale measurements by increasing resistance to flow, thereby reducing the dependence on high stimulus concentrations for rapid reactions. The instantaneous velocity and equilibrium heights of straight and looped vertical microcapillary films were assessed with water, plasma and whole blood, showing that the loops create additional frictional resistances, reduce flow velocity and prolong residence times for increased time scales of the stimulation process. A modified pressure balance model was used to capture flow dynamics with the added loop. Looped devices loaded with thrombin and collagen showed an improved detection of blood stimulation responses even with lower stimulus concentrations, compared to straight vertical capillaries. Thrombin-activated blood samples in straight capillaries provided a maximum measurement zone of only 4 mm, while the looped design significantly increased this to 11 mm for much longer time scale measurements. Our results suggest that extending stimulation times can be achieved without complex microfluidic fabrication methods, potentially improving concentration–response blood stimulation assays, and may enhance the accuracy and reliability. We conclude adding a loop to low-cost extruded microfluidic devices may bring microfluidic devices closer to delivering on their promise of widespread, decentralized low-cost evaluation of blood response to stimulation in both research and clinical settings.
Asymmetric Stereo High Dynamic Range Imaging with Smartphone Cameras
Authors: Russell Finn , Midgley William J. B.
Published: September 2024

Stereo high dynamic range imaging (SHDRI) offers a more temporally stable solution to high dynamic range (HDR) imaging from low dynamic range input images compared to bracketing and removes the loss of accuracy that single-image HDR solutions offer. However, few solutions currently exist that take advantage of the different (asymmetric) lenses, commonly found on modern smartphones, to achieve SHDRI. This paper presents a method that achieves single-shot asymmetric HDR fusion via a reference-based deep learning approach. Results demonstrate a system that is more robust to aperture and image signal processing pipeline differences than existing solutions.
Investigation of Aircraft Conflict Resolution Trajectories under Uncertainties
Authors: Dudoit Anrieta , Rimša Vytautas , Bogdevičius Marijonas
Published: September 2024

As air traffic intensity increases and stochastic uncertainties, such as wind direction and speed, continue to impact air traffic controllers’ workload significantly, airlines are increasingly pressured to reduce costs by flying via straighter/more direct trajectories. Due to these changes, it is important to search for new means/solutions for aircraft conflict resolution to ensure the required level of safety and rational flight trajectory. Such a solution could be the implementation of Dubin’s method of flight trajectories. This paper aims to propose and deeply analyze a new mathematical model for two-aircraft conflict resolution where the Dubins method is applied in a dynamic conflict scenario. In this model, at a certain moment, the flight trajectory of one aircraft follows a path similar to a moving circle’s tangential line. Upon that, the conflict detection and resolution (CDR) model considers wind uncertainty. The proposed CDR method could be applied when uncertainty such as wind direction and speed are inconstant (stochastic) throughout the simulation.
Method of Motion Planning for Digital Twin Navigation and Cutting of Shearer
Authors: Miao Bing , Ge Shirong , Li Yunwang , Guo Yinan
Published: September 2024

To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. This study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of the shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital–model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.
Electronic Nose Humidity Compensation System Based on Rapid Detection
Authors: Cai Minhao , Xu Sai , Zhou Xingxing , Lu Huazhong
Published: September 2024

In this study, we present an electronic nose (e-nose) humidity compensation system based on rapid detection to solve the issue of humidity drift’s potential negative impact on the performance of electronic noses. First, we chose the first ten seconds of non-steady state (rapid detection mode) sensor data as the dataset, rather than waiting for the electronic nose to stabilize during the detection process. This was carried out in the hope of improving the detection efficiency of the e-nose and to demonstrate that the e-nose can collect gasses efficiently in rapid detection mode. The random forest approach is then used to optimize and reduce the dataset’s dimensionality, filtering critical features and improving the electronic nose’s classification capacity. Finally, this study builds an electronic nose humidity compensation system to compensate for the datasets generated via rapid real-time detection, efficiently correcting the deviation of the sensor response caused by humidity variations. This method enhanced the average resolution of the electronic nose in this trial from 87.7% to 99.3%, a 12.4% improvement, demonstrating the efficacy of the humidity compensation system based on rapid detection for the electronic nose. This strategy not only improves the electronic nose’s anti-drift and classification capabilities but also extends its service life, presenting a new solution for the electronic nose in practical detecting applications.
Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks
Authors: Mohajelin Fatemeh , Sheykhivand Sobhan , Shabani Abbas , Danishvar Morad , Danishvar Sebelan , Lahijan Lida Zare
Published: September 2024

Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions—the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition. In the proposed model, firstly, a comprehensive database based on musical stimuli is provided to induce two and three emotional classes, including positive, negative, and neutral emotions. Generative adversarial networks (GANs) are used to supplement the recorded data, which are then input into the suggested deep network for feature extraction and classification. The suggested deep network can extract the dynamic information from the EEG data in an optimal manner and has 4 GConv layers. The accuracy of the categorization for two classes and three classes, respectively, is 99% and 98%, according to the suggested strategy. The suggested model has been compared with recent research and algorithms and has provided promising results. The proposed method can be used to complete the brain-computer-interface (BCI) systems puzzle.
Social Identity and Voting Behavior in a Deeply Divided Society: The Case of Israel
Authors: Zeedan Rami
Published: September 2024

This paper examines what best explains political behavior in a deeply divided society. Despite the democratic nature of the Israeli political system, we suggest that Israel’s society is characterized by social affiliations mainly defined by social identity that include race, ethnicity, and religion and amount to tribalism. Based on the results of the 2020 election, we examined whether these social affiliations or other socioeconomic characteristics better explain voting behavior. First, we found a significant correlation between tribalism and voting behavior in Israel and socioeconomic characteristics and voting behavior in Israel. When comparing tribalism’s correlation versus other socioeconomic characteristics, we found that the correlation between tribalism and voting behavior in Israel trumps the correlation between socioeconomic characteristics and voting behavior in Israel, except for the Ashkenazi Jews. In other words, voting behavior is better explained by affiliation to the social group, that is, by kinship, race, ethnicity, and religion, than by other socioeconomic characteristics such as education level, economic status, or geographic location. This extends the idea of “in-group favoritism”.
A Cognitive Map of Sexual Violence Victims’ Decision-Making: Understanding the Preference for Social Media over Formal Legal Avenues—Insights from Media Consultants
Authors: Nadav-Carmel Hila , Lev-On Azi
Published: September 2024

This study delves into the complex decision-making processes of sexual violence survivors regarding public disclosure of their experiences. By constructing a cognitive map from interviews with ten media consultants, this research highlights that survivors often prefer seeking justice through social media rather than legal channels. This study was conducted in Israel, and it underscores the importance survivors place on the disclosure process itself, valuing voice and respect over the outcome. This research, part of a larger project that includes interviews with victims, focuses on the choice between social media and institutional channels, analyzing cognitive processes through the lens of media consultants. While the constructed cognitive map provides a clear framework for understanding the decisions made by survivors of sexual violence, it simplifies complex emotional and psychological factors. Future research may enhance this map by incorporating a deeper analysis of survivors’ internal conflicts and the interplay between emotional coping mechanisms and external pressures to disclose their experiences. From background conversations with survivors, it was understood that two main factors influence their decision: the process versus the outcome and whether to turn to social media or institutional channels. This nuanced approach provides valuable insights into the specific choices made by survivors when determining where to disclose their assaults. By designing this cognitive map, this study helps understand the needs and priorities of the survivors, such as the importance of voice, respect, and the nature of the disclosure process, and aims to aid future research and decision makers in better understanding and supporting the decision-making processes of sexual violence survivor.
Who Is Buying Green Products? The Roles of Sustainability Consciousness, Environmental Attitude, and Ecotourism Experience in Green Purchasing Intention at Tourism Destinations
Authors: Gulzar Yonis , Eksili Nisa , Koksal Kemal , Celik Caylak Pinar , Mir Mohammad Shuaib , Soomro Arjumand Bano
Published: September 2024

The main purpose of this study was to reveal the relationships between green purchasing intention, sustainability consciousness, environmental attitude, and ecotourism experience within the scope of the theory of planned behaviour. The results of the analysis showed that there are positive relationships between sustainability consciousness, environmental attitude, ecotourism experience, and green purchasing intention. The model was tested using SPSS with 435 survey responses. Tourists with a higher consciousness of sustainability develop positive attitudes toward the environment. Tourists with positive attitudes toward the environment intend to purchase more green products. However, while tourists with more ecotourism experience have stronger green purchasing intentions, less ecotourism experience does not affect green purchasing intentions. This study highlights the critical role of sustainability consciousness in shaping environmental attitudes and behaviours.
Adaptation of the Revised Environmental Identity Scale to Adult Portuguese Native Speakers: A Validity and Reliability Study
Authors: Ferrajão Paulo , Torres Nuno , Martins Amadeu Quelhas
Published: September 2024

Environmental identity is a dimension of personal identity that reflects an individual’s sense of connection with nature and personal view of nature as part of her/his identity. There is a need to adapt and validate measures that assess environmental identity, namely in Portuguese. This study analyzed the validity and reliability of the Revised Environmental Identity Scale (Revised EID) in a sample of native speakers of Portuguese from 18 to 83 years old. The survey included the Portuguese translation of the Revised EID, measures of eco-anxiety, adult attachment, affective neurobiological systems, psychological symptoms, and sociodemographic information. Exploratory and confirmatory factor analyses were conducted to assess the construct validity of the scale. It was tied to a two-factor solution (Connectedness with Nature and Protection of Nature) found in prior research. The Revised EID was positively related to feelings of anxiety about personal impact on climate change and positive emotional systems (SEEKING, CARE, and PLAY). Older participants reported higher levels in the overall environmental identity score and the Protection of Nature factor. The results indicate that the Revised EID is an appropriate measure to assess environmental identity in adult Portuguese native speakers and should be used to conduct cross-cultural studies to drive environmental and health policies.
Impact of Urbanization on Carbon Dioxide Emissions—Evidence from 136 Countries and Regions
Authors: Ma Bingying , Ogata Seiichi
Published: September 2024

Urbanization affects economic production activities and energy demand, as well as lifestyle and consumption behavior, affecting carbon dioxide emissions. This study constructs the System Generalized Method of Moments (Sys-GMM) model of the impact of urbanization rate on carbon dioxide emissions based on panel data of 136 countries and regions in the world from 1990 to 2020, grounded on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. This study found that (1) there is a negative relationship between urbanization rate and CO2 emissions from 1990 to 2020. (2) The impact of the urbanization rate on CO2 emissions is heterogeneous. An increase in urbanization rate in non-OECD countries significantly reduces CO2 emissions, while the effect is not significant in OECD countries. (3) The carbon intensity of fossil energy consumption moderates the relationship between urbanization rate and CO2 emissions, weakening the effect of urbanization rate on CO2 emissions. Based on these findings, policy recommendations such as promoting urbanization and increasing the regulation and control of fossil energy carbon intensity are proposed.
Simulation of a Hazardous Chemical Cascading Accident Using the Graph Neural Network
Authors: Cui Wenqi , Chen Xinwu , Li Weisong , Li Kunjing , Liu Kaiwen , Feng Zhanyun , Chen Jiale , Tian Yueling , Chen Boyu , Chen Xianfeng , Cui Wei
Published: September 2024

In the storage of hazardous chemicals, due to space limitations, various hazardous chemicals are usually mixed stored when their chemical properties do not conflict. In a fire or other accidents during storage, the emergency response includes two key steps: first, using fire extinguishers like dry powder and carbon dioxide to extinguish the burning hazardous chemicals. In addition, hazardous chemicals around the accident site are often watered to cool down to prevent the spread of the fire. But both the water and extinguishers may react chemically with hazardous chemicals at the accident site, potentially triggering secondary accidents. However, the existing research about hazardous chemical domino accidents only focuses on the pre-rescue stage and ignores the simulation of rescue-induced accidents that occur after rescue. Aiming at the problem, a quantitative representation algorithm for the spatial correlation of hazardous chemicals is first proposed to enhance the understanding of their spatial relationships. Subsequently, a graph neural network is introduced to simulate the evolution process of hazardous chemical cascade accidents. By aggregating the physical and chemical characteristics, the initial accident information of nodes, and bi-temporal node status information, deep learning models have gained the ability to accurately predict node states, thereby improving the intelligent simulation of hazardous chemical accidents. The experimental results validated the effectiveness of the method.