bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Investigation of Durability Properties of Polymer Coated Pumice Aggregate Lightweight Concretes
Authors: Tuncer Metin , Bideci Alper , Çomak Bekir , Durmuş Gökhan , Sallı Bideci Özlem
Published: January 2025

Pumice aggregates with low density and high porosity are widely used in lightweight concrete. The high water retention ability of pumice aggregates adversely affects the properties of fresh concrete. Additionally, pumice aggregates’ inadequate mechanical strength and durability hinder concrete performance. In recent years, research on coated aggregates has gained traction to improve the physical properties, mechanical strength, and durability characteristics of concrete. In this study, coarse pumice aggregates were coated with polyester and partially substituted with uncoated aggregates at ratios of 0%, 25%, 50%, 75%, and 100% in lightweight concrete formulations. Specific weight and water absorption tests were performed on the aggregates, while slump and unit weight tests were performed on fresh concrete mixtures. SEM-EDX analyses, unit weight, water absorbing capacity, sorptivity, compressive strength, freeze-thaw resistance, and sulfate resistance tests were performed on concrete specimens. The results indicated that the polyester coating significantly increased the specific weight of the aggregates and decreased the water absorption rates by up to 85%. Despite the coated aggregates resulting in decreased compressive strength of concrete specimens, they demonstrated reduced water absorbing capacity and sorptivity characteristics relative to reference concrete. Moreover, concrete made with coated aggregates exhibited better results in freeze-thaw and sulphate resistance tests.
Effect of Emotional Valence on Text Comprehension by French Fourth and Fifth Graders
Authors: Ballenghein Ugo , Lachaud Léa , Aparicio Xavier , Alamargot Denis
Published: January 2025

Background: Emotional valence can be used to describe the pleasant or unpleasant nature of information such as images, words, sentences, or texts. The possible existence of a bias favoring the comprehension of positive versus negative information remains a controversial subject. The aim of the present study was to explore whether the emotional valence of a text influences its comprehension by students in the fourth or fifth grade. Methods: To this end, 55 French students with a mean age of 10.11 years answered surface, semantic, and inferential questions after reading texts with a positive, negative, or neutral valence. Results: The results showed that surface comprehension scores were higher for texts with a positive or negative valence, rather than a neutral one. They also showed that semantic comprehension scores were lower for negatively valenced texts. Conclusion: These observations suggest that emotional valence influences text comprehension in young readers. The existence of a positivity bias in children’s reading comprehension and the educational implications in the classroom are discussed.
Estimation of Characteristics, Methane Generated and Sustainability of Municipal Landfill Waste in Urban City, Thailand
Authors: Boongla Yaowatat , Changphuek Supachai , Saehuang Anuwat
Published: January 2025

Many developed and developing countries are concerned about climate change and greenhouse gas (GHG) reduction, with landfills being a major contributor due to the presence of important GHGs such as carbon dioxide (CO2) and methane (CH4) in landfill emissions. The appropriate technology or suitable innovation could result in the extraction of significant amounts of energy from CH4 in landfills. This work used landfill gas emissions (LandGEM) software modeling to analyze the distribution patterns of the gas emissions of two urban landfills in Chonburi and Phuket Provinces, Thailand, from 2013 to 2023. The methane emissions from the Chonburi landfill were 1.063 × 104 Mg/year, and they were 1.077 × 103 Mg/year for the Phuket landfill, in 2023. According to estimates, the Chonburi landfill emitted 2.916 × 104 Mg/year of CO2 in 2023, while Phuket emitted 2.955 × 103 Mg/year. The Chonburi landfill generated 8.67 MWh/year and 195.74 MWh/year of electrical energy potential from CH4 in 2014 and 2023. In 2014 and 2023, the electrical energy potential from CH4 was 1.00 MWh/year and 19.83 MWh/year for the Phuket landfill. This study’s results show that landfills can produce CH4 and that it is possible to collect this gas and stop GHG emissions from entering the atmosphere. This would be beneficial for local authorities considering the potential of landfill gas.
Phytochemical Composition and Effects of Aqueous Extracts from Moringa oleifera Leaves on In Vitro Ruminal Fermentation Parameters
Authors: Oliveira Inessa Steffany Torres de , Fernandes Tatiane , Santos Aylpy Renan Dutra , González Aquino Carolina , Vega Britez Gustavo Daniel , Vargas Junior Fernando Miranda de
Published: January 2025

This study evaluated the phytochemical composition of aqueous extracts of Moringa oleifera (MO) obtained by maceration, decoction, and infusion of fresh or dried leaves and their effects on in vitro ruminal fermentation parameters. Phytochemical prospecting analyses were conducted to determine the bioactive compounds in each aqueous extract. Regarding the in vitro ruminal fermentation study, the seven treatments were the following: no addition of extract or control (CON); extract obtained by maceration of fresh leaves (MFL); extract obtained by maceration of dry leaves (MDL); extract obtained by decoction of the fresh leaves (DFL); extract obtained by decoction of dry leaves (DDL); extract obtained by infusion of fresh leaves (IFL) and extract obtained by infusion of dry leaves (IDL). The concentration of all bioactives (saponins, flavonoids, tannins, and alkaloids) quantified was higher when fresh MO leaves were used (p < 0.001). DFL and DDL provided less elimination of azino-bis radicals. On the other hand, MFL resulted in a greater elimination of these radicals. Extracts obtained from fresh leaves resulted in a greater total production of short-chain fatty acids, acetate, and butyrate (p < 0.05). Compared to the control treatment, the inclusion of extracts obtained from fresh leaves provided a higher concentration of propionate (p = 0.049). It is thereby concluded that the use of fresh MO leaves for the production of aqueous extracts is the most recommended, as it results in a higher concentration of bioactive compounds. The use of aqueous extracts of fresh MO leaves increases the total production of fatty acids but does not change their proportion.
Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US
Authors: Sarkar Sayantan , Osorio Leyton Javier M. , Noa-Yarasca Efrain , Adhikari Kabindra , Hajda Chad B. , Smith Douglas R.
Published: January 2025

Efficient and reliable corn (Zea mays L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers. Therefore, the objective of our study was to determine the best combination of vegetation indices and abiotic factors for predicting corn yield in a rain-fed, production-scale area, identify the most suitable corn growth stage for yield estimation using machine learning, and identify the most effective machine learning model for corn yield estimation. Our study used high-resolution (6 cm) aerial multispectral imagery. Sixty-two different predictors, including soil properties (sand, silt, and clay percentages), slope, spectral bands (red, green, blue, red-edge, NIR), vegetation indices (GNDRE, NDRE, TGI), color-space indices, and wavelengths were derived from the multispectral data collected at the seven (V4, V5, V6, V7, V9, V12, and V14/VT) growth stages of corn. Four regression and machine learning algorithms were evaluated for yield prediction: linear regression, random forest, extreme gradient boosting, and gradient boosting regressor. A total of 6865 yield values were used for model training and 1716 for validation. Results show that, using random forest method, the V14/VT stage had the best yield predictions (RMSE of 0.52 Mg/ha for a mean yield of 10.19 Mg/ha), and yield estimation at V6 stage was still feasible. We concluded that integrating abiotic factors, such as slope and soil properties, significantly improved model accuracy. Among vegetation indices, TGI, HUE, and GNDRE performed better. Results from this study can help farmers or crop consultants plan ahead for future logistics through enhanced early-season yield predictions and support farm profitability and sustainability.
Is the EnodePro® a Valid Tool to Determine the Bar Velocity in the Bench Press and Barbell Back Squat? A Comparative Analysis
Authors: Behrmann Nina , Hillebrecht Martin , Afonso José , Warneke Konstantin
Published: January 2025

In recent years, the EnodePro® device has been one of the most frequently used velocity sensors to track the bar velocity in resistance training, with the aim of providing load–velocity profiles. However, recent articles highlight a lack of reliability and validity in the estimated maximal strength, which can cause a serious health risk due to the overestimation of the bar velocity. With this study, we aimed to investigate whether imprecision in the measurement could explain the variance in this measurement error. Methods: The research question was evaluated by comparing the integrated velocities from the EnodePro® with the velocities from a high-resolution displacement sensor for the squat and bench press. The velocity was measured with loads corresponding to 30%, 50%, and 70% of the one-repetition maximum (1RM) in moderately trained participants (n = 53, f = 16, m = 37). Intraclass correlation coefficients (ICC) for agreement were supplemented by an exploration of the systematic bias and the random error (mean absolute error (MAE), mean absolute percentage error (MAPE)). Results: The results indicated movement specificity, with the ICC values for the squat ranging from 0.204 to 0.991 and with ICC = 0.678–0.991 for the bench press. Systematically higher velocities were reported by the EnodePro® sensor (p < 0.001–0.176), with an MAE = 0.036–0.198 m/s, which corresponds to an MAPE of 4.09–42.15%. Discussion: The EnodePro® seems to provide overly high velocities, which could result in the previously reported overestimation of the 1RM. Despite the validity problems of force/load–velocity profiles, we suggest evaluating the bar velocity with accurate measurement devices, which is, contrary to previous reports, not the case with the EnodePro®.
Key Fundamentals and Examples of Sensors for Human Health: Wearable, Non-Continuous, and Non-Contact Monitoring Devices
Authors: Guarducci Sara , Jayousi Sara , Caputo Stefano , Mucchi Lorenzo
Published: January 2025

The increasing demand for personalized healthcare, particularly among individuals requiring continuous health monitoring, has driven significant advancements in sensor technology. Wearable, non-continuous monitoring, and non-contact sensors are leading this innovation, providing novel methods for monitoring vital signs and physiological data in both clinical and home settings. However, there is a lack of comprehensive comparative studies assessing the overall functionality of these technologies. This paper aims to address this gap by presenting a detailed comparative analysis of selected wearable, non-continuous monitoring, and non-contact sensors used for health monitoring. To achieve this, we conducted a comprehensive evaluation of various sensors available on the market, utilizing key indicators such as sensor performance, usability, associated platforms functionality, data management, battery efficiency, and cost-effectiveness. Our findings highlight the strengths and limitations of each sensor type, thus offering valuable insights for the selection of the most appropriate technology based on specific healthcare needs. This study has the potential to serve as a valuable resource for researchers, healthcare providers, and policymakers, contributing to a deeper understanding of existing user-centered health monitoring solutions.
Monte Carlo Simulation of the Effect of Melanin Concentration on Light–Tissue Interactions in Reflectance Pulse Oximetry
Authors: Al-Halawani Raghda , Qassem Meha , Kyriacou Panicos A.
Published: January 2025

Over the past ten years, there has been an increasing demand for reliable consumer wearables as users are inclined to monitor their health and fitness metrics in real-time, especially since the COVID-19 pandemic. Reflectance pulse oximeters in fitness trackers and smartwatches provide convenient, non-invasive SpO2 measurements but face challenges in achieving medical-grade accuracy, particularly due to difficulties in capturing physiological signals, which may be affected by skin pigmentation. Hence, this study sets out to investigate the influence of skin pigmentation, particularly in individuals with darker skin, on the accuracy and reliability of SpO2 measurement in consumer wearables that utilise reflectance pulse oximeters. A Monte Carlo model is developed to assess the effect on simulated reflectance pulse oximetry measurements across light, moderate, and dark skin types for oxygen saturation levels between 70 and 100%. The results indicate that a one-algorithm-fits-all calibration approach may be insufficient, and root mean square errors (RMSEs) of at least 0.3956%, 0.9132%, and 8.4111% for light, moderate, and dark skin are observed when compared to transmittance calibration algorithms. Further research is required to validate these findings and improve the performance of reflectance pulse oximeters in real-world applications, particularly in the context of consumer wearables.
An Improved Detecting Algorithm of Moving Targets for Airborne Maritime Surveillance Radar
Authors: Wu Zhefeng , Zhang Gang , Wang Xinchun , Sun Peng , Lin Yanchao
Published: January 2025

The traditional method is capable of detecting and tracking stationary and slow-moving targets in a sea surface environment. However, the signal focusing capability of such a method could be greatly reduced especially for those variable-speed targets. To solve this problem, a novel tracking algorithm combining range envelope alignment and azimuth phase filtering is proposed. In this method, the motion of the airborne maritime surveillance radar platform is firstly compensated for target echoes. Secondly, range envelope alignment is performed to correct the unpredictable range migration of the target after pulse compression. The higher-order phase difference between the adjacent pulses is estimated and compensated. Ultimately, such pulse series are accumulated through azimuth Fourier transform. Traditional methods compensate only for platform motion, limiting their ability to handle variable-speed targets. The proposed algorithm addresses this limitation by compensating for both platform and target motion, significantly improving signal focusing and tracking accuracy. A detailed analysis shows that our algorithm can significantly increase the signal accumulating gain and improve the focusing effect. The simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
Performance and Energy Consumption Analysis for UWSNs with Priority Scheduling Based on Access Probability and Wakeup Threshold
Authors: Li Ning , Xiang Zhiyu , Feng Liang , Gao Zhiqiang , Liu Jiaqi , Gu Haitao
Published: January 2025

As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. To address these challenges, a novel mixed packet forwarding strategy is developed, which incorporates a wakeup threshold and a dynamically adjusted access probability for the cluster head (CH). This approach aims to conserve energy while maintaining acceptable network latency levels. The wakeup threshold restricts the frequency of state switching for the CH, thereby reducing energy consumption. Meanwhile, the dynamic access probability regulates the influx of packets to mitigate system congestion based on current network conditions. Furthermore, to accommodate the network’s varied transmission demands, packets generated by sensor nodes (SNs) are categorized into two types according to their sensitivity to latency. A discrete−time queueing model with preemptive priority is then established to evaluate the performance of different packets and the CH. Numerical results show how different parameters affect network performance and demonstrate that the proposed mixed packet forwarding mechanism can effectively manage the trade−off between latency and energy consumption, outperforming the traditional mechanism within a specific range of parameters.
Sensorless Junction Temperature Estimation of Onboard SiC MOSFETs Using Dual-Gate-Bias-Triggered Third-Quadrant Characteristics
Authors: Lu Yansong , Ding Yijun , Li Jia , Yin Hao , Li Xinlian , Zhu Chong , Zhang Xi
Published: January 2025

Silicon carbide (SiC) metal oxide semiconductor field-effect transistors (MOSFETs) are a future trend in traction inverters in electric vehicles (EVs), and their thermal safety is crucial. Temperature-sensitive electrical parameters’ (TSEPs) indirect detection normally requires additional circuits, which can interfere with the system and increase costs, thereby limiting applications. Therefore, there is still a lack of cost-effective and sensorless thermal monitoring techniques. This paper proposes a high-efficiency datasheet-driven method for sensorless estimation utilizing the third-quadrant characteristics of MOSFETs. Without changing the existing hardware, the closure degree of MOS channels is controlled through a dual-gate bias (DGB) strategy to achieve reverse conduction in different patterns with body diodes. This method introduces a MOSFET operating current that TSEPs are equally sensitive to into the two-argument function, improving the complexity and accuracy. A two-stage current pulse is used to decouple the motor effect in various conduction modes, and the TSEP-combined temperature function is built dynamically by substituting the currents. Then, the junction temperature is estimated by the measured bus voltage and current. Its effectiveness was verified through spice model simulation and a test bench with a three-phase inverter. The average relative estimation error of the proposed method is below 7.2% in centigrade.
Development of a Sustainable Flexible Humidity Sensor Based on Tenebrio molitor Larvae Biomass-Derived Chitosan
Authors: Nettey-Oppong Ezekiel Edward , Muhammad Riaz , Ackah Emmanuel , Yang Hojun , Ali Ahmed , Jeong Hyun-Woo , Kim Seong-Wan , Seok Young-Seek , Choi Seung Ho
Published: January 2025

This study presents the fabrication of a sustainable flexible humidity sensor utilizing chitosan derived from mealworm biomass as the primary sensing material. The chitosan-based humidity sensor was fabricated by casting chitosan and polyvinyl alcohol (PVA) films with interdigitated copper electrodes, forming a laminate composite suitable for real-time, resistive-type humidity detection. Comprehensive characterization of the chitosan film was performed using Fourier-transform infrared (FTIR) spectroscopy, contact angle measurements, and tensile testing, which confirmed its chemical structure, wettability, and mechanical stability. The developed sensor exhibited a broad range of measurements from 6% to 97% relative humidity (RH), a high sensitivity of 2.43 kΩ/%RH, and a rapid response time of 18.22 s with a corresponding recovery time of 22.39 s. Moreover, the chitosan-based humidity sensor also demonstrated high selectivity for water vapor when tested against various volatile organic compounds (VOCs). The superior performance of the sensor is attributed to the structural properties of chitosan, particularly its ability to form reversible hydrogen bonds with water molecules. This mechanism was further elucidated through molecular dynamics simulations, revealing that the conductivity in the sensor is modulated by proton mobility, which operates via the Grotthuss mechanism under high-humidity and the packed-acid mechanism under low-humidity conditions. Additionally, the chitosan-based humidity sensor was further seamlessly integrated into an Internet of Things (IoT) framework, enabling wireless humidity monitoring and real-time data visualization on a mobile device. Comparative analysis with existing polymer-based resistive-type sensors further highlighted the superior sensing range, rapid dynamic response, and environmental sustainability of the developed sensor. This eco-friendly, biomass-derived, eco-friendly sensor shows potential for applications in environmental monitoring, smart agriculture, and industrial process control.
The Effect of Electrical Stimulation Strength Training on Lower Limb Muscle Activation Characteristics During the Jump Smash Performance in Badminton Based on the EMS and EMG Sensors
Authors: Lin Xinyu , Hu Yimin , Sheng Yi
Published: January 2025

This study investigates the effects of electrical stimulation (EMS) combined with strength training on lower limb muscle activation and badminton jump performance, specifically during the “jump smash” movement. A total of 25 male badminton players, with a minimum of three years of professional training experience and no history of lower limb injuries, participated in the study. Participants underwent three distinct conditions: baseline testing, strength training, and EMS combined with strength training. Each participant performed specific jump tests, including the jump smash and static squat jump, under each condition. Muscle activation was measured using electromyography (EMG) sensors to assess changes in the activation of key lower limb muscles. The EMS intervention involved targeted electrical pulses designed to stimulate both superficial and deep muscle fibers, aiming to enhance explosive strength and coordination in the lower limbs. The results revealed that the EMS + strength condition significantly improved performance in both the jump smash and static squat jump, as compared to the baseline and strength-only conditions (F = 3.39, p = 0.042; F = 3.67, p = 0.033, respectively). Additionally, increased activation of the rectus femoris (RF) was observed in the EMS + strength condition, indicating improved muscle recruitment and synchronization, likely due to the activation of fast-twitch fibers. No significant differences were found in the eccentric-concentric squat jump (F = 0.59, p = 0.561). The findings suggest that EMS, when combined with strength training, is an effective method for enhancing lower limb explosiveness and muscle activation in badminton players, offering a promising training approach for improving performance in high-intensity, explosive movements.
Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization
Authors: Henkmann Jannik , Memmolo Vittorio , Moll Jochen
Published: January 2025

This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model. To reduce the number of trainable parameters, a physical signal processing approach is applied to the raw data before passing the data to the model. Starting from current state of the art in algorithms used for damage detection and localization, an AI-based technique is developed and validated on an experimental benchmark dataset before tiny ML implementation on a low-cost development board. A discussion of the need for a balance between the reduction in computational resources and increasing the precision of the models is also reported. It is shown that by extracting simple features of the signal, the models required to predict the damage locations can be significantly reduced in size while still having high accuracies of over 90%. In addition, it is possible to use these predictions to construct a fairly accurate heat map indicating the likely damage locations. Finally, a convenient edge/cloud visualization of the results can be achieved by simplifying the heat map.
A Precise Oxide Film Thickness Measurement Method Based on Swept Frequency and Transmission Cable Impedance Correction
Authors: Li Yifan , Xiao Qi , Peng Lisha , Huang Songling , Ye Chaofeng
Published: January 2025

Accurately measuring the thickness of the oxide film that accumulates on nuclear fuel assemblies is critical for maintaining nuclear power plant safety. Oxide film thickness typically ranges from a few micrometers to several tens of micrometers, necessitating a high-precision measurement system. Eddy current testing (ECT) is commonly employed during poolside inspections due to its simplicity and ease of on-site implementation. The use of swept frequency technology can mitigate the impact of interference parameters and improve the measurement accuracy of ECT. However, as the nuclear assembly is placed in a pool for inspection, a cable several dozen meters in length is used to connect the ECT probe to the instrument. The measurement is affected by the transmission line and its effect is a function of the operating frequencies, resulting in errors for swept frequency measurements. This paper proposes a method for precisely measuring oxide film thickness based on the swept frequency technique and long transmission line impedance correction. The signals are calibrated based on a transmission line model of the cable, effectively eliminating the influence of the transmission cable. A swept frequency signal-processing algorithm is developed to separate the parameters and calculate oxide film thickness. To verify the feasibility of the method, measurements are conducted on fuel cladding samples with varying conductivities. It is found that the method can accurately assess oxide film thickness with varying conductivity. The maximum error is 3.42 μm, while the average error is 1.82 μm. The impedance correction reduces the error by 66%. The experimental results indicate that this method can eliminate the impact of long transmission cables, and the algorithm can mitigate the influence of material conductivity. This method can be utilized to measure oxide film thickness in nuclear power maintenance inspections following extensive testing and engineering optimization.
Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm
Authors: Susilo Bambang , Muis Abdul , Sari Riri Fitri
Published: January 2025

The Internet of Things (IoT) has emerged as a crucial element in everyday life. The IoT environment is currently facing significant security concerns due to the numerous problems related to its architecture and supporting technology. In order to guarantee the complete security of the IoT, it is important to deal with these challenges. This study centers on employing deep learning methodologies to detect attacks. In general, this research aims to improve the performance of existing deep learning models. To mitigate data imbalances and enhance learning outcomes, the synthetic minority over-sampling technique (SMOTE) is employed. Our approach contributes to a multistage feature extraction process where autoencoders (AEs) are used initially to extract robust features from unstructured data on the model architecture’s left side. Following this, long short-term memory (LSTM) networks on the right analyze these features to recognize temporal patterns indicative of abnormal behavior. The extracted and temporally refined features are inputted into convolutional neural networks (CNNs) for final classification. This structured arrangement harnesses the distinct capabilities of each model to process and classify IoT security data effectively. Our framework is specifically designed to address various attacks, including denial of service (DoS) and Mirai attacks, which are particularly harmful to IoT systems. Unlike conventional intrusion detection systems (IDSs) that may employ a singular model or simple feature extraction methods, our multistage approach provides more comprehensive analysis and utilization of data, enhancing detection capabilities and accuracy in identifying complex cyber threats in IoT environments. This research highlights the potential benefits that can be gained by applying deep learning methods to improve the effectiveness of IDSs in IoT security. The results obtained indicate a potential improvement for enhancing security measures and mitigating emerging threats.
Time-Series Change Detection Using KOMPSAT-5 Data with Statistical Homogeneous Pixel Selection Algorithm
Authors: Waqar Mirza Muhammad , Yang Heein , Sukmawati Rahmi , Chae Sung-Ho , Oh Kwan-Young
Published: January 2025

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives.
Field-Programmable Gate Array (FPGA)-Based Lock-In Amplifier System with Signal Enhancement: A Comprehensive Review on the Design for Advanced Measurement Applications
Authors: Galaviz-Aguilar Jose Alejandro , Vargas-Rosales Cesar , Falcone Francisco , Aguilar-Avelar Carlos
Published: January 2025

Lock-in amplifiers (LIAs) are critical tools in precision measurement, particularly for applications involving weak signals obscured by noise. Advances in signal processing algorithms and hardware synthesis have enabled accurate signal extraction, even in extremely noisy environments, making LIAs indispensable in sensor applications for healthcare, industry, and other services. For instance, the electrical impedance measurement of the human body, organs, tissues, and cells, known as bioelectrical impedance, is commonly used in biomedical and healthcare applications because it is non-invasive and relatively inexpensive. Also, due to its portability and miniaturization capabilities, it has great potential for the development of new point-of-care and portable testing devices. In this document, we highlight existing techniques for high-frequency resolution and precise phase detection in LIA reference signals from field-programmable gate array (FPGA) designs. A comprehensive review is presented under the key requirements and techniques for single- and dual-phase digital LIA architectures, where relevant insights are provided to address the LIAs’ digital precision in measurement system configurations. Furthermore, the document highlights a novel method to enhance the spurious-free dynamic range (SFDR), thereby advancing the precision and effectiveness of LIAs in complex measurement environments. Finally, we summarize the diverse applications of impedance measurement, highlighting the wide range of fields that can benefit from the design of high performance in modern measurement technologies.
The Fluorescent Detection of Alkaline Phosphatase Based on Iron Nanoclusters and a Manganese Dioxide Nanosheet
Authors: Zhao Liang , Liu Xinyue , Zhang Xinwen , Liu Siyu , Wu Jiazhen
Published: January 2025

Fluorescent iron nanoclusters are emerging fluorescent nanomaterials. Herein, we synthesized hemoglobin-coated iron nanoclusters (Hb−Fe NCs) with a significant fluorescence emission peak at 615 nm and investigated the inner-filter effect of fluorescence induced by a manganese dioxide nanosheet (MnO2 NS). The fluorescence quenching of Hb−Fe NCs by a MnO2 NS can be significantly reversed by the addition of ascorbic acid. On the basis of fluorescent recovery by ascorbic acid, we proposed a system that consisted of Hb−Fe NCs, a MnO2 NS and ascorbate phosphate, and the proposed system was successfully used for alkaline phosphatase (ALP) detection in the range of 0–20 μg/mL based on the significant fluorescence recovery achieved.
Compact Quantum Cascade Laser-Based Noninvasive Glucose Sensor Upgraded with Direct Comb Data-Mining
Authors: Song Liying , Han Zhiqiang , Nie Hengyong , Lau Woon-Ming
Published: January 2025

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available. This work reports on two compact sensor system designs, both reaching the FDA accuracy benchmark. Each design commonly comprises a mid-infrared QCL for emission, a multiple attenuation total reflection prism (MATR) for data acquisition, and a computer-controlled infrared detector for data analysis. The first design translates the comb-like signals into conventional spectra, and then data-mines the resultant spectra to yield blood glucose concentrations. When a pressure actuator is employed to press the patient’s hypothenar against the MATR, the sensor accuracy is considered to reach the FDA accuracy benchmark. The second design abandons the data processing step of translating combs-to-spectra and directly data-mines the “first-hand” comb signal. Beyond increasing the measurement accuracy to the FDA accuracy benchmark, even without a pressure actuator, direct comb data-mining upgrades the sensor system with speed and data integrity, which can impact the healthcare of diabetic patients. Specifically, the sensor performance is validated with 492 glucose absorption scans in the time domain, each with 20 million datapoints measured from four subjects with glucose concentrations of 3.9–7.9 mM. The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. These concentrations are correlated with those measured with a standard finger-pricking glucometer. An accuracy of 99.6% is confirmed from the 164 measurements with errors not more than 15% from the reference of the standard glucometer.
Low-Complexity Timing Correction Methods for Heart Rate Estimation Using Remote Photoplethysmography
Authors: Chen Chun-Chi , Lin Song-Xian , Jeong Hyundoo
Published: January 2025

With the rise of modern healthcare monitoring, heart rate (HR) estimation using remote photoplethysmography (rPPG) has gained attention for its non-contact, continuous tracking capabilities. However, most HR estimation methods rely on stable, fixed sampling intervals, while practical image capture often involves irregular frame rates and missing data, leading to inaccuracies in HR measurements. This study addresses these issues by introducing low-complexity timing correction methods, including linear, cubic, and filter interpolation, to improve HR estimation from rPPG signals under conditions of irregular sampling and data loss. Through a comparative analysis, this study offers insights into efficient timing correction techniques for enhancing HR estimation from rPPG, particularly suitable for edge-computing applications where low computational complexity is essential. Cubic interpolation can provide robust performance in reconstructing signals but requires higher computational resources, while linear and filter interpolation offer more efficient solutions. The proposed low-complexity timing correction methods improve the reliability of rPPG-based HR estimation, making it a more robust solution for real-world healthcare applications.
Diagnosis of Reverse-Connection Defects in High-Voltage Cable Cross-Bonded Grounding System Based on ARO-SVM
Authors: Ai Yuhao , Song Bin , Wu Shaocheng , Li Yongwen , Lu Li , Wang Linong
Published: January 2025

High-voltage (HV) cables are increasingly used in urban power grids, and their safe operation is critical to grid stability. Previous studies have analyzed various defects, including the open circuit in the sheath loop, the flooding in the cross-bonded link box, and the sheath grounding fault. However, there is a paucity of research on the defect of the reverse direction between the inner core and the outer shield of the coaxial cable. Firstly, this paper performed a theoretical analysis of the sheath current in the reversed-connection state and established a simulation model for verification. The outcomes of the simulation demonstrate that there are significant variations in the amplitudes of the sheath current under different reversed-connection conditions. Consequently, a feature vector was devised based on the amplitude of the sheath current. The support vector machine (SVM) was then applied to diagnose the reversed-connection defects in the HV cable cross-bonded grounding system. The artificial rabbits optimization (ARO) algorithm was adopted to optimize the SVM model, attaining an impressively high diagnostic accuracy rate of 99.35%. The effectiveness and feasibility of the proposed algorithm are confirmed through the analysis and validation of the practical example.
Tocilizumab in Extracranial Giant-Cell Arteritis and Takayasu Arteritis: A Multicentric Observational Comparative Study
Authors: Lasa-Teja Carmen , Loricera Javier , Prieto-Peña Diana , López-Gutiérrez Fernando , Bernabéu Pilar , Freire-González María Mercedes , González-Alvarez Beatriz , Solans-Laqué Roser , Mínguez Mauricio , Ferraz-Amaro Iván , Castañeda Santos , Blanco Ricardo ,   on behalf of the Tocilizumab in Large-Vessel Vasculitis Spanish Collaborative Group
Published: January 2025

Tocilizumab (TCZ) has demonstrated potential efficacy in managing large-vessel (LV) vasculitis such as giant-cell arteritis (GCA) and Takayasu arteritis (TAK). Despite the shared characteristics between the LV-GCA phenotype and TAK, there are differences between both entities that may affect therapeutic responses to TCZ. We aim to assess and compare the effectiveness and safety of TCZ in patients with LV-GCA and TAK. Multicenter, observational study on 70 LV-GCA patients and 57 TAK patients treated with TCZ. Outcomes were assessed at baseline and at 1, 3, 6 and 12 months post-treatment initiation. The variables analyzed included the following: (a) the achievement of clinical remission and improvement in laboratory markers; (b) imaging-based disease activity; (c) a glucocorticoid (GC)-sparing effect; and (d) side events and a safety profile. At the treatment initiation, TAK patients were younger, exhibited longer disease duration, had received more prior biologics, and were on higher doses of prednisone compared to LV-GCA patients. While TAK patients showed a slower initial clinical response, remission rates at 12 months were comparable between groups (74.5% for LV-GCA vs. 76.9% for TAK). Both groups experienced rapid laboratory marker improvement and a significant GC-sparing effect. However, complete imaging resolution was observed in only 18.9% of LV-GCA patients and 21.1% of TAK patients. The safety profile was similar in both groups, with severe infections leading to TCZ discontinuation in four LV-GCA and three TAK patients. In clinical practice, TCZ demonstrates similar efficacy in promoting remission and reducing GC dependency in both LV-GCA and TAK patients. Nonetheless, discrepancies between clinical outcomes and imaging improvement highlight the need for further investigation into disease monitoring and management strategies.
Acceptability of Children Road Safety Education in Pakistan: A Mixed-Method Approach to Exploring Parents’ and Teachers’ Perspectives
Authors: Nawaz Imran , Cuenen Ariane , Wets Geert , Janssens Davy
Published: January 2025

In Pakistan, implementing road safety education (RSE) initiatives is vital in tackling the concerning rates of road accidents. Since parents and teachers are crucial in moulding children’s road safety behaviours, this study investigated the perspectives of parents and teachers regarding the acceptability of RSE programs in Pakistan. Using a mixed-methods approach, the research combines quantitative data from questionnaires (n = 63 teachers, n = 97 parents) with qualitative insights from interviews (five teachers, four parents). The study reveals significant gaps in RSE implementation across educational levels (i.e., primary, secondary, and high school), with not even half of the teachers reporting dedicated RSE programs in their curriculum, majorly in secondary and high schools. Both parents and teachers express dissatisfaction with current RSE effectiveness, highlighting a critical need for improvement. Key barriers to RSE implementation include cultural norms, inadequate infrastructure, and limited teacher training. However, the study also identifies a strong interest from parents and teachers in participating in effective RSE programs. Parents favour a mixed approach to RSE delivery, combining online and physical formats, and prefer short, frequent sessions for their children. The research underscores the need for a multidimensional RSE approach, addressing educational content, societal perceptions, and infrastructure improvements. These findings provide valuable insights for policymakers and educators to enhance RSE and improve children’s road safety knowledge in Pakistan.
Casilda Iturrizar: A Case of Overcoming the Invisibilization of Women Relevant for Their Religiosity
Authors: Crespo-López Alba , Cañaveras Paula , Álvarez-Guerrero Garazi , Olabarria Ane , Lopez de Aguileta Garazi , Alzaga Aitor , Bordanoba Lidia , Puigvert Lidia , Flecha Ramón , Soler-Gallart Marta
Published: January 2025

Feminist studies have increasingly highlighted the contributions of women from various backgrounds; however, a gap remains in the research on conservative religious women. This article presents findings on Casilda Iturrizar, a bourgeois and religiously conservative woman who significantly influenced 19th-century Bilbao. Three surveys with 442 participants and five communicative interviews with educators were conducted using a communicative methodology, which has pioneered the current criteria for social impact and cocreation in the Horizon Europe scientific program. The results revealed that most people in Bilbao were unaware of Casilda’s contributions, with feminists and institutions neglecting her achievements. The findings from the communicative interviews suggested that Casilda’s contributions have been overlooked, contradicting recent efforts in education to address the invisibility of notable women. Such findings potentially have social impact by opening feminism to all women, including those made invisible for having conservative religious thinking.
Revegetation and Quality Indicators of Technosols in Restored Mine Fields with Saline Mine Spoils
Authors: Bosch-Serra Àngela D. , Sorribas Mónica , Gómez-Reig Pere , Poch Rosa M.
Published: January 2025

The European Union prioritizes nature restoration, particularly in semiarid Mediterranean regions where integrating degraded coal mining areas into the landscape is essential. This involves maximizing water use and controlling runoff. A rehabilitation project in a former mining quarry was conducted with the objective of constructing suitable Technosols to support vegetation, limit erosion, and reduce rehabilitation costs. To prepare the substrate, mine spoils (saline materials) were mixed with residual materials, including discarded lignite powder, sewage sludge, pig slurry, and straw. Pig slurry was also introduced as a mulch in the experiment. A complete randomized block design with three replicates was set up, with each block containing two plots of the prepared substrate. In one of the plots, pig slurry was applied on the surface as a mulch to enhance infiltration and promote plant establishment. The quality of the newly created Technosols and the benefits of mulch application were evaluated 2 and 4 years after the rehabilitation. After two years, salt-tolerant plant species colonized the rehabilitated areas, providing sufficient vegetation cover to control water, soil, and nutrient losses, keeping soil losses below a 2.2 Mg ha−1 yr−1 threshold. Four years later, the new Technosols showed a fourfold increase in soluble organic-C content (up to 0.59 g kg−1) and higher soil respiration rates compared to the mine spoils and lignite powder in the surrounding degraded quarry areas. No significant differences were observed in any parameters due to superficial slurry application. Addressing salinity and optimizing vegetation cover are crucial for the successful formation and sustainability of Technosols in these environments.
Neonicotinoid Insecticides in the Qiantang River: Occurrence, Sources, and Ecological Risks for Sustainable Water Resources
Authors: Zhang Xilong , Shi Sha , Jiang Jianming , Liu Hui , Hong Wenjun , Li Minjie , Guo Lianghong , Zhou Xinyi , Ye Cheng , Wu Haigang , Luo Haiwei
Published: January 2025

Neonicotinoid insecticides (NNIs) can harm non-target species. This study examines NNIs’ distribution, sources, human exposure risks, and ecological risks in the Qiantang River’s water and sediment. NNIs concentrations ranged from 8.92 to 88.0 ng/L in water and 0.55 to 16.6 ng/g dw in sediment. The concentrations showed a trend of middle reaches > lower reaches > upper reaches. Agricultural activities were the main NNIs source, followed by wastewater treatment plants. Infants had the highest NNIs intake, with a daily intake of 15.4 ng/kg body weight, about 5.4-times higher than adolescents. Using the species sensitivity distribution and relative potency factor, the acute and chronic toxicity thresholds for aquatic organisms were estimated at HC5 values of 3636 ng/L and 40 ng/L, respectively. Species at lower trophic levels were more sensitive to NNIs. While acute risks to aquatic fauna are low, prolonged exposure poses potential risks to human and ecological health. These findings contribute to the enhancement of the regulatory framework for NNIs and serve as a reference for policymakers in formulating water pollution management strategies, thereby advancing the sustainable development of water resources in the region.