bluuebunny/crossref_metadata_2025 huggingface dataset:
With Great Ecosystem Services Comes Great Responsibility: Benefits Provided by Urban Vegetation in Brazilian Cities
Authors: Nunes Candido Helder Marcos , Endreny Theodore A. , Alvim Carvalho Fabrício
Published: January 2025

Ecosystem services (ESs) are extremely important, specifically in urban areas. Urban forests, even representing a pivotal role in global sustainability, have been converted into different human-modified landscapes. This paper aims to analyze the ES provided by the urban areas of 25 cities of the Atlantic Forest in Brazil. We used i-Tree Canopy v.7.1 to classify the land use. We quantified the monetary benefits of the urban vegetation and used socioeconomic variables (i.e., total population, population density, Human Development Index (HDI), and Gross Domestic Product (GDP) per capita) to analyze if the ecosystem services or the land uses are associated with this. Our data reveal that together, the cities studied sequester a significant total of 235.3 kilotonnes of carbon and a substantial 864.82 kilotonnes of CO2 Equivalent (CO2 Equiv.) annually. Furthermore, together, they also store a total of 4861.19 kilotonnes of carbon and 17,824.32 kilotonnes of CO2 Equiv. We found out that the average monetary estimate of annual carbon sequestration was USD 3.57 million, while the average stored estimate was USD 73.76 million. Spearman’s correlogram showed a strong positive correlation between density and the percentage of impervious cover non-plantable no trees (IN) in urban areas (p < 0.001). IN was also positively correlated with HDI (p = 0.01), indicating that urban areas with higher HDI tend to have larger impervious areas. Our data suggest essential insights about the ecosystem services provided by urban areas and can serve as significant findings to drive policymakers’ attention to whether they want to provide more ecosystem services in cities.
Metabolic and Photosynthesis Analysis of Compound-Material-Mediated Saline and Alkaline Stress Tolerance in Cotton Leaves
Authors: An Mengjie , Zhu Yongqi , Chang Doudou , Wang Xiaoli , Wang Kaiyong
Published: January 2025

Soil salinization and alkalization can cause great losses to agricultural production in arid regions. Cotton, a common crop in arid and semi-arid regions in China, often encounters saline stress and alkaline stress. In this study, NaCl (8 g·kg−1), Na2CO3 (8 g·kg−1), and a compound material (an organic polymer compound material) were mixed with field soil before cotton sowing, and the ion content, photosynthetic characteristics, and metabolite levels of the new cotton leaves were analyzed at the flowering and boll-forming stage, aiming to clarify the photosynthetic and metabolic mechanisms by which compound material regulates cotton’s tolerance to saline stress and alkaline stress. The results showed that the application of the compound material led to an increase in the K+/Na+ ratio, stomatal conductance (Gs), efficiency of PSII photochemistry (ψPSⅡ), potential activity (Fv/Fo), and chlorophyll content (Chla and Chlb), as well as the abundances of D-xylonic acid and DL-phenylalanine in the NaCl treatments. Additionally, there were increases in the K+ content, K+/Na+ ratio, Chla/b ratio, net photosynthetic rate (Pn), transpiration rate (Tr), ψPSⅡ, and D-saccharic acid abundance in the Na2CO3 treatments. A correlation analysis and a metabolic pathway analysis revealed that the compound material mainly regulated the photosynthetic characteristics of and the ion balance in the new leaves through regulating the abundance of key metabolites when the cotton was under NaCl stress or Na2CO3 stress. Furthermore, the positive impact of the compound material on the cotton’s NaCl stress tolerance was stronger than that on the cotton’s Na2CO3 stress tolerance.
Predicting the Key Properties of a Modified Product to Pre-Select a Pluronic F127 Modification Scheme for Preparing High-Quality Nano-Micelles
Authors: Song Jizheng , Hu Yu , Yang Shiyu , Liu Dexue , Tseng Yiider , Li Lingjun
Published: January 2025

Hydrophobic modification alters the properties of Pluronic F127 to form micelles more efficiently and enhances its drug-loading capacity. However, selecting the appropriate hydrophobic group for modification is laborious. In this paper, we propose an efficient approach for predicting key parameters to select hydrophobic groups for F127 modification prior to synthesis, in order to improve the formability and stability of the micelles. The results of nuclear magnetic resonance and isothermal titration calorimetry were utilized to establish a function for predicting the hydrophile–lipophile balance, critical micelle concentration, and Gibbs free energy of the products based on the structure of raw material. These predicted values can assist us in selecting suitable hydrophobic groups for F127 modification. Subsequently, we successfully tested our method and validated our work using pharmaceutical evaluation methods, such as appearance observation, particle size measurement, drug loading determination, equilibrium binding rate assessment, storage stability testing, and the plotting of accumulation release curves. Therefore, we suggest that our work could provide a model linking the molecular structure to properties, with the purpose of pre-selecting modification products that have advantages in micelle preparation. This can facilitate the application of F127 in preparing nano-micelles.
Concentrated Pre-Vulcanized Natural Rubber Latex Without Additives for Fabricating High Mechanical Performance Rubber Specimens via Direct Ink Write 3D Printing
Authors: Liu Lin , Zhang Jizhen , Luo Zirong , Kong Na , Zhao Xu , Ji Xu , Li Jihua , Huang Shenbo , Zhao Pengfei , Li Shuang , Shao Yanqiu , Tao Jinlong
Published: January 2025

Direct ink writing (DIW) is an economical, straightforward, and relatively energy-efficient 3D printing technique that has been used in various domains. However, the utilization of rubber latex for DIW remains limited due to its high fluidity and inadequate support, which makes it challenging to meet the required ink rheological characteristics for DIW. In this study, a concentrated pre-vulcanized natural rubber latex (CPNRL) ink with a high solid content of 73% without additives is developed for DIW 3D printing. The CPNRL ink is concentrated using superabsorbent polymer (SAP) beads, which demonstrates good colloidal stability, favorable rheological properties, and superior printability. The impact of printing angles on the mechanical properties of the rubber specimens based on the CPNRL-73 ink is explored in detail, wherein the tensile strength of the specimen printed at a 90° angle reaches an impressive 26 MPa and a strain of approximately 800%, which surpasses the majority of 3D-printed rubber latex specimens. Additionally, the CPNRL ink can be used to print a wide range of intricate shapes, demonstrating its advantages in excellent formability. The preparation of 3D printable ink using the absorption method will expand the application of elastomers in fields such as customized flexible sensing and personalized rubber products.
Polymer Composite Films with P(VDF-TrFE) and Molecular Ferroelectric Tris(hydroxymethyl) Nitromethane: Improvement of Their Ferroelectric Properties
Authors: Escobar-Castillo Marianela , Duman Samet , Lupascu Doru C.
Published: January 2025

Polymer composites of P(VDF-TrFE) and Tris(hydroxymethyl) nitromethane as filler material with different concentrations have been prepared. Tris(hydroxymethyl) nitromethane is an organic ferroelectric material with low preparation cost and easy processing, and it is also lightweight. Its properties enable it to be a potential candidate for use as filler material in polymers to improve their ferroelectric, dielectric, and piezoelectric properties. We investigated the effect of filler content on the ferroelectric and dielectric properties of the polymer. Our results show that Tris(hydroxymethyl) nitromethane retains its crystallinity after embedding it in the polymer matrix. It does not alter the crystalline ferroelectric β-phase of the polymer. All composites possess higher polarization compared to pure P(VDF-TrFE). Up to 11.4 µC/cm2 remnant polarization and a dielectric constant of 14 at 1000 Hz have been obtained with the free-standing 10 wt% composite film.
Application of a New Carbon Black Filler in SBR Composites
Authors: Zhou Junan , Tang Bainan , Yu Liangchen , Song Junping , Wang Zepeng
Published: January 2025

The microstructure and properties of a new type of carbon black produced by a domestic company through a new process were systematically characterized by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), Raman spectroscopy, and X-ray diffraction (XRD). The vulcanization properties, mechanical properties, and electrical conductivity of the new carbon black with different filler amounts were investigated in styrene butadiene rubber (SBR), using the traditional reinforcing filler N660 carbon black as a control. The experimental results demonstrate that the new carbon black exhibits a stratified structure with a specific surface area of 345.96 m2/g, and its particle size distribution is primarily concentrated within the 0.1–1 μm range. When the filling ratio was 30 phr/100 phr, the tensile strength of SBR composites filled with the new carbon black increased by 12.3% and the tear strength increased by 9.6% compared with those filled with N660 carbon black. In summary, the new carbon black can significantly improve the comprehensive performance of SBR composites and reduce the production cost. This provides a new type of material for the rubber industry that takes into account both economy and performance while also providing reference data for basic research in the field of SBR.
Novel Inlay Methodology with Thermoplastic and Heating System for Durable Road Markings
Authors: Kim Kwan Kyu , Jun Chul Soo , Lee Hee Jun , Rodrigazo Shanelle Aira , Yeon Jaeheum
Published: January 2025

Road markings, such as lane dividers and pedestrian crossings, are integral in ensuring the safety of road users. However, traditional markings frequently exhibit limitations, including short lifespans, diminished visibility, and significant maintenance costs, particularly as traffic volumes increase. To address these persistent challenges, this study presents a thermoplastic road marking system that combines material innovation and advanced application techniques. Central to this approach is the portable heating system, equipped with ceramic heaters and precise temperature controls, which facilitates uniform heating while mitigating fire risks. The thermoplastic blend, processed into pre-formed sheets, was integrated with this heating technology. Together, these components enabled a two-phase process, engraving asphalt surfaces followed by sheet integration, that ensured robust adhesion and seamless bonding. Field trials conducted on various asphalt types validated the system’s reliability, demonstrating its durability under traffic loads and consistent visibility. By integrating durable materials with advanced application methods, this methodology significantly enhances the efficiency, longevity, and safety of road markings. It presents a practical and scalable solution for modern infrastructure needs. Future research will focus on evaluating the system’s long-term performance under extreme weather conditions to further optimize its applicability.
Liquid–Solid Coupled Internal Flow Field Analysis of Natural Gas Hydrate Spiral-Swirling Downhole In Situ Separator
Authors: He Yufa , Jiang Xiaohui , Tang Yang , Zhou Yunjian , Xie Na , Wang Guorong
Published: January 2025

This study aims to solve the problem of the recovery efficiency of natural gas hydrate being affected by a large amount of mud and sand in the solid fluidization mining of natural gas hydrate. Therefore, a new method for the separation of in situ sediment and natural gas hydrate in a spiral-swirling downhole is proposed, and the corresponding numerical simulation model is established to realize the analysis and verification of key flow field parameters such as flow velocity, pressure, sediment, and natural gas hydrate phase distribution. The results show that the flow velocity field of the mixed slurry presents an ‘M’-shaped symmetrical distribution, and the slurry near the wall of the separator can obtain a larger flow velocity, which is beneficial to the separation of mud–sand and natural gas hydrate. The static pressure field shows an axisymmetric distribution that decreases first and then increases, indicating that the pressure of the mixed slurry increases with the increase in the radial position, and the closer to the wall, the greater the static pressure of the mixed slurry. Near the wall of the separator, the volume fraction of the sediment phase reaches the maximum. In contrast, the volume fraction of the natural gas hydrate phase reaches the minimum, which confirms the separation effect of the sediment and the natural gas hydrate. The results show that the separation of sediments and natural gas hydrate can be realized, thereby improving the exploitation efficiency of natural gas hydrate. The designed spiral cyclone coupling separator provides a new solution to solving the problem of sand removal in natural gas hydrate exploitation.
Recovery and Reuse of Acetone from Pharmaceutical Industry Waste by Solar Distillation
Authors: Tarango Brito Eva Carina , Barrera Díaz Carlos Eduardo , Ávila Córdoba Liliana Ivette , Frontana Uribe Bernardo Antonio , Solís Casados Dora Alicia
Published: January 2025

Solvents are particularly hazardous among the mixture of pollutants found in the air, as their low vapor pressure allows them to reach the atmosphere, causing damage to ecosystems, and producing secondary deleterious effects on living organisms through a wide variety of possible reactions. In response, innovative, sustainable, and ecological methods are being developed to recover solvents from industrial wastewater, which is typically contaminated with other organic compounds. This study describes the procedure for recovering acetone from a residue from the pharmaceutical industry. This compound contains a high amount of solid organic compounds, which are generated during the manufacture of medicines. The treatment consisted of performing a simple solar distillation using a single-slope glass solar still, which separated the acetone from the mother solution. Under ideal circumstances, the use of solar radiation allowed an efficiency rate of 80% using solar concentration by means of mirrors to increase the temperature and 85% without the use of mirrors in the production of distilled acetone, which was characterized to evaluate its quality using instrumental analytical techniques: NMR, IR, and GC. The results obtained indicate that the acetone recovered by this procedure has a good quality of 84%; however, due to this percentage obtained, its reuse is limited for certain applications where a high degree of purity is required, such as its reuse for pharmaceutical use; for this reason, it was proposed to use said compound to eliminate the organic impurities contained in the catalyst waste granules used in a Mexican oil refinery. The resulting material was examined by SEM and EDS, revealing a high initial carbon content that decreased by 29% after treatment. Likewise, as an additional study, a study was carried out to evaluate the characteristics of the residues obtained at the end of the distillation where rubidium, silicon, carbon, nitrogen, oxygen, and chlorine contents were observed.
Mental Models of the London Missionary Society’s Scientific Discourse in Southeast Asia (1815–1842)—A Socio-Cognitive Discourse Analysis Perspective
Authors: Shu Ai
Published: January 2025

In the early nineteenth century, the London Missionary Society (LMS) established missionary outposts and printing presses in Southeast Asia, publishing a number of scientific periodicals and books, which subsequently informed the preaching policy on the Chinese mainland. The motivation and discursive strategies of the LMS’s dissemination of scientific knowledge in Southeast Asia are worthy of in-depth investigation. This study employs the socio-cognitive discourse analysis approach to investigate the mental model and discursive strategies of LMS’s two representative scientific publications in Southeast Asia, namely the Chinese Monthly Magazine and Memoir of Things Seen and Heard by Travelling West around the World, thus providing insights into the rationale behind the scientific discourse of Protestant missionaries in nineteenth-century China. It is found that the scientific discourse of the LMS in Southeast Asia was to modify the comprehension of world formation held by local Chinese migrants, thereby influencing their perception of European scientific strength and Christian belief. This study is an interdisciplinary investigation of Protestant history in Southeast Asia, and the methodology of critical discourse analysis offers a new way of explaining history, which complements the conventional historical analysis.
Criteria for the Acceptability of Scientific Theories as Locus theologicus: A Methodological Analysis of Catholic Church’s Reactions to the Cases of Galileo and Darwin (Bellarmine—Pius XII—John Paul II)
Authors: Liana Zbigniew
Published: January 2025

The categories of demonstration and hypothesis appear constantly in the texts of Catholic theologians confronted with the new science of Copernicus and Darwin. They were used by Robert Bellarmine to reject Galileo’s scientific and realistic interpretation of Copernican theory. They were equally used by some neo-scholastic theologians against Darwin’s theory of evolution and reappeared in the official texts of Pope Pius XII and John Paul II. My paper will analyze these selected historical texts to show whether and how the epistemology and methodology adopted by their authors determined their acceptance or rejection of scientific theories as potential Loci theologici. Moreover, the historical-comparative approach should reveal in this theological tradition, for all its officially declared continuity, the progressive evolution of views on the meaning and role of demonstration and hypothesis in science, as well as official Catholic theology’s dependence on the historically changing notion of science and its method. This will allow me to discuss the idea of a changing scientific methodology as another very specific locus theologicus, this time metatheoretical. I will propose the idea of a non-absolute autonomy or relative dependence of theology on available meta-scientific solutions.
Signal Preprocessing in Instrument-Based Electronic Noses Leads to Parsimonious Predictive Models: Application to Olive Oil Quality Control
Authors: Fernandez Luis , Oller-Moreno Sergio , Fonollosa Jordi , Garrido-Delgado Rocío , Arce Lourdes , Martín-Gómez Andrés , Marco Santiago , Pardo Antonio
Published: January 2025

Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with the common requirement for chemical identification, has led to the adaptation and incorporation of analytical chemistry instruments into the e-nose framework. Although instrument-based e-noses exhibit greater specificity to gasses than traditional ones, they still produce data that require correction in order to build reliable predictive models. In this work, we introduce the use of a multivariate signal processing workflow for datasets from a multi-capillary column ion mobility spectrometer-based e-nose. Adhering to the electronic nose philosophy, these workflows prioritized untargeted approaches, avoiding dependence on traditional peak integration techniques. A comprehensive validation process demonstrates that the application of this preprocessing strategy not only mitigates overfitting but also produces parsimonious models, where classification accuracy is maintained with simpler, more interpretable structures. This reduction in model complexity offers significant advantages, providing more efficient and robust models without compromising predictive performance. This strategy was successfully tested on an olive oil dataset, showcasing its capability to improve model parsimony and generalization performance.
A Double Extended Kalman Filter Algorithm for Weakening Non-Line-of-Sight Errors in Complex Indoor Environments Based on Ultra-Wideband Technology
Authors: Xu Sheng , Liu Qianyun , Lin Min , Wang Qing , Chen Kaile
Published: January 2025

In complex indoor environments, target tracking performance is impacted by non-line-of sight (NLOS) noises and other measurement errors. In order to fix NLOS errors, a double extended Kalman filter (DEKF) algorithm is proposed, which refers to a kind of cascaded structure composed of two Kalman filters. In the proposed algorithm, the first filter is a classic Kalman filter (KF) and the second is an extended Kalman filter (EKF). Time of arrival (TOA) measurements collected by multiple stationary ultra-wideband (UWB) sensors are used. The residual errors between the measured TOA and that of the first KF are predicted, and the covariance of the first KF is adjusted correspondingly. Then, we use the estimated distance state of the first KF as a measurement vector for the second EKF in order to obtain a smoother observation. One of the advantages of the proposed algorithm is that it is able to perform target tracking with good accuracy even without or with only one LOS TOA measurement for a period of time without prior information about the NLOS noise, which may be difficult to obtain in practical applications. Another advantage is that the accuracy does not greatly decrease when NLOS noises exist for a long period of time. Finally, the proposed DEKF can maintain the high-precision positioning characteristics in both the constant velocity (CV) model and the constant acceleration (CA) model in the LOS/NLOS environment. Our simulation and experimental results show that the proposed algorithm performs much better than other algorithms in SOTA, particularly in severe mixed LOS/NLOS environments.
Detection of Lunar Regolith Acquired by Excavator Using Radiofrequency (RF) Sensors
Authors: Kurek Krzysztof , Seweryn Karol , Tkacz Arkadiusz , Just Gunter
Published: January 2025

This paper presents the concept of a radiofrequency (RF) sensor designed to estimate the mass of the regolith acquired by a sampling device or excavator in planetary environments. The sensor utilizes a microstrip line with an open end as the sensing element, with the mass estimation based on measurements of the phase of the reflection coefficient (S11 of the scattering matrix) for the line immersed in the regolith. The Rotary Clamshell Excavator (RCE) was employed for the experimental evaluation of the sensor’s performance. The RCE successfully passed an environmental test campaign, demonstrating its suitability for future lunar missions. The test results indicate that the RF sensor can estimate the mass of the acquired regolith with reasonable accuracy, approximately 15%, making it a viable solution for rough mass estimation in sampling devices and excavators.
Clinical Validation of a Video-Otoscopy-Based Medical Device for the Remote Diagnosis of Ear Complaints
Authors: Pannonhalmi Ádám , Posta Bálint , Perényi Ádám , Rovó László , Bende Balázs , Katona Gábor , Csóka Ildikó , Kemény Lajos , Szakács László
Published: January 2025

Telemedicine brings several benefits to patients, healthcare providers, and the wider society, including reductions in the need for hospitalizations or readmissions, as well as in overall healthcare costs and the length of inpatient stay. In addition, these services may provide psychological benefits to patients, including excellent satisfaction and medication adherence. The present study aimed to investigate an in-house-developed otorhinolaryngologic remote diagnostic system (mobile app). The basis of the comparison was the incidence between the diagnoses and therapies made by remote diagnosticians and on-site specialists based on static images and videos captured by a smartphone otoscope device. In the study, 103 patients were involved. After registering demographic data, the telemedicine software was evaluated by comparing the matching of physically established diagnoses and/or therapies with remotely established diagnoses and/or therapies. The most remarkable result was in concordance with the diagnoses, with 79 matches identified of the 103 cases examined; the rate of the matching cases was 76.7% (95% CI: 68.5–84.9%). These results support that telemedicine-based otorhinolaryngological remote diagnostics could play a significant role in future healthcare.
Selective Scale-Aware Network for Traffic Density Estimation and Congestion Detection in ITS
Authors: Jian Cheng , Lin Chenxi , Hu Xiaojian , Lu Jian
Published: January 2025

Traffic congestion detection in surveillance video is crucial for road traffic condition monitoring and improving traffic operation efficiency. Currently, traffic congestion is often characterized through traffic density, which is obtained by detecting vehicles or using holistic mapping methods. However, these traditional methods are not effective in dealing with the vehicle scale variation in surveillance video. This prompts us to explore density-map-based traffic density detection methods. Considering the dynamic characteristics of traffic flow, relying solely on the spatial feature of traffic density is overly limiting. To address these limitations, we propose a multi-task framework that simultaneously estimates traffic density and dynamic traffic congestion. Specifically, we firstly propose a Selective Scale-Aware Network (SSANet) to generate a traffic density map. Secondly, we directly generate a static congestion level from a traffic density map through a linear layer, which can characterize the spatial occupancy of traffic congestion in each frame. In order to further describe dynamic congestion, we simultaneously consider the dynamic characteristics of traffic flow, using the overall traffic flow velocity integrated with static congestion estimation for a dynamic assessment of congestion. On the collected dataset, our method achieves state-of-the-art results on both congestion detection and density estimation task. SSANet also obtains 99.21% accuracy on the UCSD traffic flow classification dataset, which outperforms other state-of-the-art methods.
Integrating an Extended-Gate Field-Effect Transistor in Microfluidic Chips for Potentiometric Detection of Creatinine in Urine
Authors: De Brito Oliveira Dhaniella Cristhina , Costa Fernando Henrique Marques , Beraldo Renato Massaroto , da Silva José Alberto Fracassi , Diniz José Alexandre
Published: January 2025

Monitoring creatinine levels in urine helps to recognize kidney dysfunction. In this research, we developed a photocurable membrane for the detection of serum creatinine. Using a system based on field-effect transistors, we carried out creatinine quantification in synthetic urine. The device was able to cover values between 3 and 27 mmol L−1. The current sensitivity was 0.8529 (mA)1/2 mmol−1 L with 91.8% linearity, with the LOD and LOQ being 5.3 and 17.5 mmol L−1, respectively. The voltage sensitivity was 0.71 mV mmol−1 L with a linearity of 96.2%, with the LOD and LOQ being 4.2 and 14.0 mmol L−1, respectively. These data were obtained under flow conditions. The system performed very well during the measurements, with a hysteresis of about 1.1%. Up to 90 days after manufacture, the sensor still maintained more than 70% of its initial response. Even when used periodically during the first week and then stored unused at −18 °C, it was able to maintain 96.7% of its initial response. The device used in the flow setup only had a useful life of three days due to membrane saturation, which was not reversible. In the interference test, the membrane was also shown to respond to the urea molecule, but in a different response window, which allowed us to discriminate urea in synthetic urine. EGFETs can be used to identify variations in the creatinine concentration in urine and can help in therapeutic decision-making.
Node Selection and Path Optimization for Passive Target Localization via UAVs
Authors: Xing Xiaoyou , Zhong Zhiwen , Li Xueting , Yue Yiyang
Published: January 2025

The performance of passive target localization is affected by the positions of unmanned aerial vehicles (UAVs) at a large scale. In this paper, to improve resource utilization efficiency and localization accuracy, the node selection problem and the path optimization problem are jointly investigated. Firstly, the target passive localization model is established and the Chan-based time difference of arrival (TDOA) localization method is introduced. Then, the Cramer–Rao lower bound (CRLB) for Chan-TDOA localization is derived, and the problems of node selection and path optimization are formulated. Secondly, a CRLB-based node selection method is proposed to properly divide the UAVs into several groups, localizing different targets, and a CRLB-based path optimization method is proposed to search for the optimal UAV position configuration at each time step. The proposed path optimization method also effectively handles no-fly-zone (NFZ) constraints, ensuring operational safety while maintaining optimal target tracking performance. Also, to improve the efficiency of path optimization, particle swarm algorithm (PSO) is applied to accelerate the searching process. Finally, numerical simulations are performed to verify the validity and effectiveness of the proposed methods in this paper.
Electrode Setup for Electromyography-Based Silent Speech Interfaces: A Pilot Study
Authors: Salomons Inge , del Blanco Eder , Navas Eva , Hernáez Inma
Published: January 2025

This paper describes a series of pilot experiments developed to define the electrode setup in order to record a novel parallel electromyography (EMG)–audio database. The main purpose of the database is to provide data useful for the development of an EMG-based silent speech interface for Spanish laryngectomized speakers. Motivated by the scarcity of information in related studies regarding this important decision-making process, we decided to carry out a set of experiments with multiple recording sessions and different setups. We included different electrode types (paired and concentric) and locations targeting different muscles in the face and neck involved in the speech production process. We then analyzed the results obtained in a phone classification task using frame-based phone accuracy. The final setup consists of eight channels with bipolar single-electrode pairs targeting eight specific muscles crucial for capturing speech-related information: the digastric, the depressor anguli oris, the risorius, the levator labii superioris, the masseter, the zygomaticus major, the depressor labii superioris, and the stylohyoid. This setup has been used for the final recordings in the database. By providing insight into the electrode setups that were used in related studies and the optimal setup that resulted from this pilot study, we hope that this research will help future researchers in the field in determining their experimental setup.
Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning
Authors: Vahidi Milad , Shafian Sanaz , Frame William Hunter
Published: January 2025

Accurately estimating soil moisture at multiple depths is essential for sustainable farming practices, as it supports efficient irrigation management, optimizes crop yields, and conserves water resources. This study integrates a drone-mounted hyperspectral sensor with machine learning techniques to enhance soil moisture estimation at 10 cm and 30 cm depths in a cornfield. The primary aim was to understand the relationship between root zone water content and canopy reflectance, pinpoint the depths where this relationship is most significant, identify the most informative wavelengths, and train a machine learning model using those wavelengths to estimate soil moisture. Our results demonstrate that PCA effectively detected critical variables for soil moisture estimation, with the ANN model outperforming other machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (XGBoost). Model comparisons between irrigated and non-irrigated treatments showed that soil moisture in non-irrigated plots could be estimated with greater accuracy across various dates. This finding indicates that plants experiencing high water stress exhibit more significant spectral variability in their canopy, enhancing the correlation with soil moisture in the root zone. Moreover, over the growing season, when corn exhibits high chlorophyll content and increased resilience to environmental stressors, the correlation between canopy spectrum and root zone soil moisture weakens. Error analysis revealed the lowest relative estimation errors in non-irrigated plots at a 30 cm depth, aligning with periods of elevated water stress at shallower levels, which drove deeper root growth and strengthened the canopy reflectance relationship. This correlation corresponded to lower RMSE values, highlighting improved model accuracy.
An Infrared and Visible Image Fusion Network Based on Res2Net and Multiscale Transformer
Authors: Tan Binxi , Yang Bin
Published: January 2025

The aim of infrared and visible image fusion is to produce a composite image that can highlight the infrared targets and maintain plentiful detailed textures simultaneously. Despite the promising fusion performance of current deep-learning-based algorithms, most fusion algorithms highly depend on convolution operations, which limits their capability to represent long-range contextual information. To overcome this challenge, we design a novel infrared and visible image fusion network based on Res2Net and multiscale Transformer, called RMTFuse. Specifically, we devise a local feature extraction module based on Res2Net (LFE-RN) in which dense connections are adopted to reuse the information that might be lost in convolution operation and a global feature extraction module based on multiscale Transformer (GFE-MT) which is composed of a Transformer module and a global feature integration module (GFIM). The Transformer module extracts the coarse-to-fine semantic features of the source images, while GFIM is used to further aggregate the hierarchical features to strengthen contextual feature representations. Furthermore, we employ the pre-trained VGG-16 network to compute the loss of features with different depths. Massive experiments on mainstream datasets indicate that RMTFuse is superior to the state-of-the-art methods in both subjective and objective assessments.
Enhancing Road Safety with AI-Powered System for Effective Detection and Localization of Emergency Vehicles by Sound
Authors: Banchero Lucas , Vacalebri-Lloret Francisco , Mossi Jose M. , Lopez Jose J.
Published: January 2025

This work presents the design and implementation of an emergency sound detection and localization system, specifically for sirens and horns, aimed at enhancing road safety in automotive environments. The system integrates specialized hardware and advanced artificial intelligence algorithms to function effectively in complex acoustic conditions, such as urban traffic and environmental noise. It introduces an aerodynamic structure designed to mitigate wind noise and vibrations in microphones, ensuring high-quality audio capture. In terms of analysis through artificial intelligence, the system utilizes transformer-based architecture and convolutional neural networks (such as residual networks and U-NET) to detect, localize, clean, and analyze nearby sounds. Additionally, it operates in real-time through sliding windows, providing the driver with accurate visual information about the direction, proximity, and trajectory of the emergency sound. Experimental results demonstrate high accuracy in both controlled and real-world conditions, with a detection accuracy of 98.86% for simulated data and 97.5% for real-world measurements, and localization with an average error of 5.12° in simulations and 10.30° in real-world measurements. These results highlight the effectiveness of the proposed approach for integration into driver assistance systems and its potential to improve road safety.
Experimental Study on the Anti-Scouring Characteristics of Bedrock in Engineering Reservoir Areas That Are Conducive to Sustainable Development
Authors: Li Zhijing , Wang Yisen , Yao Shiming , Jin Zhongwu , Hu Zhixinghua
Published: January 2025

High-speed water flow conditions can cause erosion of the bedrock in engineering areas. Due to the lack of accurate evaluation of bedrock scour and erosion rates, there has been a consumption of manpower and resources without achieving satisfactory engineering outcomes. Therefore, studying the scouring and erosion effects of water flow on bedrock is of significant importance for maintaining the sustainable development and safety of engineering projects. Using the bedrock prototype from the Xiaonanhai site in the upper reaches of the Yangtze River, a model test device was developed to conduct anti-scour tests on the bedrock. The study quantitatively examined the basic physical properties, incipient erosion velocity, and erosion rates of different types of bedrock. The study found that the prototype bedrock under natural exposure, submerged immersion, and alternating wet and dry conditions showed a trend of decreased tensile strength, with the alternating wet and dry conditions being the most detrimental to maintaining the physical properties of the rock mass. The anti-scour velocity of silty claystone and clayey siltstone samples increased with the increase in tensile strength, and the erosion rate increased with the increase in shear stress. If the shear stress is kept constant, the erosion rate decreases with the increase in tensile strength. The erosion rate is inversely proportional to the ratio of the bedrock’s tensile strength to the riverbed shear stress, with the fitting relationship showing a piecewise linear distribution. The research results can provide guidance for the safe production of engineering involving bedrock erosion in engineering reservoir areas that are conducive to sustainable development.
An Investigation of the Catalytic Activity of Inconel and Stainless Steel Powders in Reforming Primary Syngas
Authors: Bezerra Silva Claudia , Lugo-Pimentel Michael , Ceballos Carlos M. , Lavoie Jean-Michel
Published: January 2025

Biomass is perhaps the only renewable resource on the planet capable of delivering molecules similar to those derived from petroleum, and one of the most developed technologies to achieve this is gasification. When it comes to biomass conversion into fuels and commodities, supercritical water gasification (SCWG) could offer promising solution for producing hydrogen-rich syngas. However, the presence of methane (CH4) and carbon dioxide (CO2) in the syngas could negatively impact downstream processes, particularly when carbon monoxide is also required. Hence, improving the quality of the syngas produced from biomass gasification is essential for promoting the sustainability of several industrial processes. In this context, understanding the principles of the dry reforming of methane (DRM) becomes essential for upgrading syngas with high CH4 and CO2 content, especially when the carbon monoxide content is low. In addition to the experimental conditions used in such process, it has been reported that the material composition of the reactor can impact on reforming performance. Hence, this work aims at comparing the catalytic efficacy of Inconel and stainless steel for reforming syngas derived from SCWG under standard DRM conditions. In this specific work, the metals were directly used as catalyst and results showed that when using Inconel powder, CH4 conversion increased from 3.03% to 37.67% while CO2 conversion went from 23.16% to 51.48% when compared to stainless steel. Elemental and structural analyses revealed that the Inconel’s superior performance might be due to its high nickel content and the formation of active oxide compounds, such as FeNiO, FeCrO3, Fe3O4, Cr2O3, and Cr2NiO4, during the reaction. In contrast, Fe3O4 was the only oxide found in stainless steel post-reaction. Additionally, increasing the total gas feed flow rate was shown to reduce CH4 and CO2 conversions, supporting the known impact of residency time on catalytic efficiency.
Navigating Sustainable Mobility in Taiwan: Exploring the Brand-Specific Effects of Perceived Green Attributes on the Green Purchase Intention for Battery Electric Vehicles
Authors: Tsai Chih-Ming , Kao Wen-Yang , Liu Wei-Chi
Published: January 2025

In recent years, increased environmental awareness has led consumers to adopt more eco-friendly lifestyles, including choosing green products to reduce their impact on the environment. With a particular focus on the green consumption behavior with regard to battery electric vehicles (BEVs) from two leading brands, Tesla (automotive) and Gogoro (scooter), in Taiwan, this study investigated the relationships among green perceived quality (GPQ), green perceived value (GPV), green brand image (GBI), green trust (GTR), and green purchase intention (GPI). After collecting 203 environmentally conscious respondents who are familiar with Tesla and Gogoro BEVs, the PLS-SEM was used to verify the research framework. The findings demonstrate that GPQ significantly impacts GBI and GPV; GBI significantly impacts GPV and GTR; and GPV significantly impacts GTR. As the key determinants of purchasing BEVs, GPQ, GBI, and GTR have significant effects on GPI, whereas the effect of GPV on GPI is not significant. In addition, this study also explored the moderation effect on green purchase behavior, revealing that the brand-specific cognition of Tesla and Gogoro moderates the relationships between green attributes (such as GPQ, GBI, and GTR) and GPI. This study contributes a more comprehensive understanding of the psychological and behavioral drivers behind green consumption behavior within the context of BEVs. This study not only sheds light on consumer behavior within dual-market dynamics in Taiwan but also offers a framework for other markets with similar environmental and infrastructural challenges. These insights can assist BEV companies to develop their sustainable marketing strategies, emphasizing the importance of developing a strong green brand image and green trust to boost BEV sales from the perspective of green perceived quality.
How Enterprise Resilience Affects Enterprise Sustainable Development—Empirical Evidence from Listed Companies in China
Authors: Zhang Lingfu , Dou Yongfang , Wang Hailing
Published: January 2025

With the frequent occurrence of various emergencies, the stable operation of enterprises has been seriously affected, and the research of resilience has received more and more attention in various fields. Enterprise resilience (En_RES) is not only related to corporate survival but is also the key to determining whether a company can realize long-term development. To explore the impact of En_RES on enterprise sustainable development (En_SD), this paper conducts an empirical test using panel regression models based on the data of A-share listed companies in China from 2004 to 2022. It is found that En_RES has a significant positive contribution to En_SD, which is more obvious when the degree of environmental uncertainty is lower, the degree of information sharing is higher, and the degree of business complexity is higher. The mechanism test analysis finds that En_RES can further contribute to En_SD by reducing the bankruptcy risk, improving credit availability, and optimizing resource allocation efficiency. This paper innovatively analyzes and verifies the impact of En_RES on En_SD and its functioning mechanism from the perspective of microenterprises, which not only enrich the theoretical relationship between En_RES and En_SD but also provide important references for enterprises to pay attention to and develop resilience in practice, which can help enterprises better cope with challenges, grasp opportunities, and make contributions to the sustainable development of enterprises.
Effect of an Educational Initiative for Sustainability on Pre-Service Teachers’ Ethical Decision-Making Skills, Motivation to Learn Science, and Learning Atmosphere in the Classroom
Authors: Khazen Manal , Asli Sare , Hofstein Avi , Hugerat Muhamad
Published: January 2025

Using a pre-post design, this study investigated an educational sustainability initiative’s impact on ethical decision-making, science motivation, and classroom environment perceptions among 60 pre-service teachers at Sakhnin College in northern Israel. Data were collected through self-report questionnaires and analyzed with multivariate analysis of covariance (MANCOVA), structural equation modeling (SEM), and hierarchical linear modeling (HLM). The results indicated significant improvements in ethical decision-making skills, moderated by baseline moral reasoning. Motivation to learn science also increased, influenced by perceived content relevance and intrinsic motivation, with socioeconomic status and teaching experience acting as moderators. A three-way interaction of gender, religiosity, and collaborative group work involvement predicted improvements in the classroom atmosphere. These findings suggest that sustainability education enhances ethical reasoning, motivation, and classroom dynamics, with demographic factors playing important roles. The study highlights the value of collaborative learning environments in teacher education and the relevance of sustainability topics in fostering critical thinking about ethical issues. It demonstrates how sustainability can inspire teachers to embed values of responsibility and cooperation into their practice, enhancing the quality of the educational environment and encouraging a broader commitment to sustainability within the educational system. These findings highlight the value of sustainability education in enhancing pre-service teachers’ ethical reasoning, motivation, and classroom dynamics, offering insights for teacher-education programs to foster responsibility and cooperation among future educators.
Prediction of Short-Term Solar Irradiance Using the ProbSparse Attention Mechanism for a Sustainable Energy Development Strategy
Authors: Zhuang Zhenyuan , Wang Huaizhi , Yu Cilong
Published: January 2025

Sustainability refers to a development approach that meets the needs of the present generation without compromising the ability of future generations to meet their own needs. Solar energy is an inexhaustible and renewable resource. From the perspective of resource utilization, solar power generation has a high degree of sustainability. Therefore, solar power generation is one of the most important ways to transform the energy structure and promote the sustainable development of the economy and society, and it is of great significance for promoting the construction of a resource-conserving and environmentally friendly society. However, solar energy resources also exhibit strong unpredictability; therefore, this paper proposes a novel artificial intelligence (AI) model for short-term solar irradiance prediction in photovoltaic power generation. Leveraging the ProbSparse attention mechanism within an encoder-decoder architecture, the AI model efficiently captures both short- and long-term dependencies in the input sequence. The dingo algorithm is innovatively redesigned to optimize the hyperparameters of the proposed AI model, enhancing model convergence. Data preprocessing involves feature selection based on mutual information, multiple imputations for data cleaning, and median filtering. Evaluation metrics include the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The proposed AI model demonstrates improved efficiency and robust performance in solar irradiance prediction, contributing to advancements in energy management for electrical power and energy systems.