bluuebunny/crossref_metadata_2025 huggingface dataset:
Lidar-Based Spatial Large Deflection Measurement System for Wind Turbine Blades
Authors: Hu Yue , Zhu Yutian , Zhou Aiguo , Liu Penghui
Published: March 2024

With the advancement of China’s wind power industry, research into full-scale structural testing of wind turbine blades, including static testing and fatigue testing, has shown increasing significance. Static testing measures the deflection at fixed points, using pull-wire sensors in industrial practice. However, the demerits of this method involve single dimension, excessive deviation, costly experiment, and complex installment. Given the advantages that lidar provides, correspondingly, high data density, precision, and convenience, we proposed a simple and efficient spatial large deflection measurement system for wind turbine blades with multi lidars. For point clouds collected from lidar scanners, registration based on point primitives and geometric primitives, dynamic radius DBSCAN clustering, spatial line clustering, and line integrals are applied to calculate the 3D coordinates of measured points on the blade. Experimentally validated, the proposed method demonstrates its effectiveness in serving as a viable alternative to the traditional pull-wire sensor measurement approach. In the minimum oscillation direction test, the measurement error is controlled within 3% compared to the theoretical value. Simultaneously, in the maximum swing direction test, the 3D coordinates of the measured point remain consistent with the changing trend observed under small deformation. These results confirm the feasibility of the system and its potentials to be generalized.
Infant Non-Secretor Histoblood Group Antigen Phenotype Reduces Susceptibility to Both Symptomatic and Asymptomatic Rotavirus Infection
Authors: Lee Benjamin , Kader Md Abdul , Alam Masud , Dickson Dorothy M. , Harvey Patrick , Colgate E. Ross , Taniuchi Mami , Petri William A. , Haque Rashidul , Kirkpatrick Beth D.
Published: March 2024

The infant non-secretor histoblood group antigen phenotype is associated with reduced risk of symptomatic rotavirus diarrhea, one of the leading global causes of severe pediatric diarrheal disease and mortality. However, little is known regarding the role of secretor status in asymptomatic rotavirus infections. Therefore, we performed a nested case–control study within a birth cohort study previously conducted in Dhaka, Bangladesh, to determine the association between infant secretor phenotype and the odds of asymptomatic rotavirus infection, in addition to the risk of rotavirus diarrhea, in unvaccinated infants. In the parent cohort, infants were enrolled in the first week of life and followed through the first two years of life with multiple clinic visits and active surveillance for diarrheal illness. Secretor phenotyping was performed on saliva. Eleven surveillance stools collected over the first year of life were tested for rotavirus by real-time RT-PCR, followed by conventional PCR and amplicon sequencing to identify the infecting P-type of positive specimens. Similar to findings for symptomatic diarrhea, infant non-secretors experienced significantly fewer primary episodes of asymptomatic rotavirus infection through the first year of life in a likely rotavirus P-genotype-dependent manner. These data suggest that non-secretors experienced reduced risk from rotavirus due to decreased susceptibility to infection rather than reduced infection severity.
Effect of Arthrospira (Spirulina) maxima on Cadmium-Chloride-Induced Alterations in Sexual Behavior and Fertility in Male Wistar Rats
Authors: Candelaria Galván-Colorado , Rosa Virginia García-Rodríguez , María Angélica Mojica-Villegas , Yuliana García-Martínez , José Melesio Cristóbal-Luna , Germán Chamorro-Cevallos
Published: March 2024

Chronic exposure to potentially toxic elements (PTEs) such as cadmium (Cd) leads to male reproductive toxicity through the generation of oxidative stress. Spirulina Arthrospira maxima (AM) is a cyanobacterium that has been consumed since ancient times for its high nutritional value, and in recent years for its antiviral, hepatoprotective, hypoglycemic, anticancer, and antioxidant effects, among others. This study aimed to evaluate the effects of AM against the damage to reproductive health induced by Cd. A total of 48 10-week-old sexually experienced male Wistar rats were distributed in five groups (n = 8): control; vehicle (tween-water); cadmium chloride (CdCl2) 5 mg/kg; and three doses of AM (100, 200 and 400 mg/kg) + CdCl2 5 mg/kg. All treatments were orally administered once a day for 36 consecutive days. At the end, sexual behavior was evaluated, and semen, testicle, and blood samples were obtained to analyze sperm quality, enzymatic activity, and testosterone levels, respectively. Rats exposed to Cd showed a decrease in sexual behavior, as well as in the quality of reproductive health, and an increase in oxidative stress; while rats exposed simultaneously to AM + Cd showed an improvement in all this parameters. Based on our results, we believe that the mechanism by which AM exerts its effect could be attributed to the presence of phycobiliproteins. These compounds are responsible for exerting an antioxidant effect and chelating effect on elements such as Cd.
New MoS2/Tegafur-Containing Pharmaceutical Formulations for Selective LED-Based Skin Cancer Photo-Chemotherapy
Authors: Campos Miguel T. , Silva Filipa A. L. S. , Fernandes José Ramiro , Santos Susana G. , Magalhães Fernão D. , Oliveira Maria J. , Pinto Artur M.
Published: March 2024

Non-melanoma skin cancer (NMSC) is one of the most common types of cancer worldwide. Despite the low mortality rate, rising incidence and recurrence rates are a burden on healthcare systems. Standard treatments such as chemotherapy, radiotherapy, and surgery are either invasive or toxic to healthy tissues; therefore, new, alternative, selective treatments are needed. In this work, a combined photothermal and chemotherapeutic approach is proposed. MoS2 was used as photothermal agent. It was prepared by a liquid-phase exfoliation and intercalation method using polyvinylpyrrolidone (PVP), followed by recirculation through a custom-built high-power ultrasonication probe. After 6 h of ultrasonication treatment, the average particle size was 165 ± 170 nm. Near-infrared (NIR) irradiation assays (810 nm, 0.1 W/cm2, 30 min, 180 J/cm2) confirmed that MoS2 nanosheets can efficiently convert NIR light into heat and reach 52 °C. The therapeutic doses of MoS2 (125 µg/mL) and Tegafur (50 µg/mL) were optimized and both were simultaneously incorporated into a Carbopol hydrogel. The cells were brought into contact with the hydrogel and irradiated with a custom-built NIR LED system. In HFF-1 cells (normal human fibroblasts), the metabolic activity was 78% (above the 70% toxicity limit—ISO 10993-5:2009(E)), while in A-431 skin cancer cells, it was 28%. In addition, the MoS2 + Tegafur hydrogels led to a 1.9-fold decrease in A-431 cancer cell metabolic activity, 72 h after irradiation, in comparison to MoS2 hydrogels, indicating a combined effect of photothermal and chemotherapy.
Computational Model for Analysing the Tooth Deflection of Polymer Gears
Authors: Ignatijev Aljaž , Glodež Srečko , Kramberger Janez
Published: March 2024

A computational model for analysing the tooth deflection of polymer gears is presented in this paper. Because polymer gears have less stiffness compared to metal gears, the proposed approach considers a comprehensive analysis to determine the most suitable numerical model, i.e., the number of teeth that should be modelled for a given gear’s geometry and material. The developed computational model has been evaluated using a spur gear pair, where the pinion made of POM was meshed with a support gear made of steel. Material properties were assigned with linear elastic characteristics for the gear, while the pinion was characterised by hyperelastic properties using POM material. Furthermore, a nonlubricated frictional contact between the gear and pinion was considered in the numerical computations. The computational results that were obtained were compared to the empirical results according to VDI 2736 guidelines. Here, the computational approach showed more accurate results due to the hyperelastic material characteristics of POM and the simulation of multiple teeth meshing. However, VDI 2736 calculation showed comparability with the computational results, with a slightly larger deviation at higher loads. In this respect, the proposed computational approach is more suitable for analysing the tooth deflection of polymer gears under higher loads.
Mechanistic Insights of Ethylene Polymerization on Phillips Chromium Catalysts
Authors: Nifant’ev Ilya , Komarov Pavel , Sadrtdinova Guzelia , Safronov Vadim , Kolosov Nikolay , Ivchenko Pavel
Published: March 2024

Silica-supported chromium oxide catalysts, also named Phillips chromium catalysts (PCCs), provide more than half of the world’s production of high- and medium-density polyethylenes. PCCs are usually prepared in the Cr(VI)/SiO2 form, which is subjected to reductive activation. It has been explicitly proven that CO reduces Cr(VI) to Cr(II) species that initiate ethylene polymerization; ethylene activates Cr(VI) sites as well, but the nature of the catalytic species is complicated by the presence of the ethylene oxidation products. It is widely accepted that the catalytic species are of a Cr(III)–alkyl nature, but this common assumption faces the challenge of “extra” hydrogen: the formation of similar species under the action of even-electron reducing agents requires an additional H atom. Relatively recently, it was found that saturated hydrocarbons can also activate CrOx/SiO2, and alkyl fragments turn out to be bonded with a polyethylene chain. In recent years, there have been numerous experimental and theoretical studies of the structure and chemistry of PCCs at the different stages of preparation and activation. The use of modern spectral methods (such as extended X-ray absorption fine structure (EXAFS), X-ray absorption near-edge structure (XANES), and others); operando IR, UV–vis, EPR, and XAS spectroscopies; and theoretical approaches (DFT modeling, machine learning) clarified many essential aspects of the mechanisms of CrOx/SiO2 activation and catalytic behavior. Overall, the Cosse–Arlman mechanism of polymerization on Cr(III)–alkyl centers is confirmed in many works, but its theoretical support required the development of nontrivial and contentious mechanistic concepts of Cr(VI)/SiO2 or Cr(II)/SiO2 activation. On the other hand, conflicting experimental data continue to be obtained, and certain mechanistic concepts are being developed with the use of outdated models. Strictly speaking, the main question of what type of catalytic species, Cr(II), Cr(III), or Cr(IV), comes into polymerization still has not received an unambiguous answer. The role of the chemical nature of the support—through the prism of the nature, geometry, and distribution of the active sites—is also not clear in depth. In the present review, we endeavored to summarize and discuss the recent studies in the field of the preparation, activation, and action of PCCs, with a focus on existing contradictions in the interpretation of the experimental and theoretical results.
Processing Method and Performance Evaluation of Flame-Retardant Corrugated Sandwich Panel
Authors: Sun Yiliang , Li Jingwen , Zhang Boming
Published: March 2024

In this study, in order to expand the engineering application range of thermoplastic corrugated sheets, flame-retardant thermoplastic corrugated sheets were prepared by the thermoplastic molding method. Based on our previous research results, we prepared flame-retardant prepreg tapes with the flame retardant addition accounting for 15%, 20%, and 25% of the resin matrix. Then, we prepared flame-retardant thermoplastic corrugated sandwich panels with corresponding flame retardant addition amounts. The limiting oxygen index test, vertical combustion test, cone calorimetry test, and mechanical property test were carried out on each group of samples and control group samples. The results showed that when the flame retardant was added at 25%, the flame retardant level could reach the V0 level. Compared with the control group, the flexural strength and flexural modulus decreased by 2.6%, 14.1%, and 19.9% and 7.3%, 16.1%, and 21.9%, respectively. When the amount of flame retardant was 15%, 20%, and 25%, respectively, the total heat release decreased by 16.3%, 23.5%, and 34.1%, and the maximum heat release rate decreased by 12.5%, 32.4%, and 37.4%, respectively.
A Study on the Properties of Composite Modified Mortar with Styrene–Butadiene Rubber Latex and Silica Fume
Authors: Yan Renwei , Wang Laifa , Ni Yongjun , Zhang Shuowen , He Zhenqing , Guan Bowen
Published: March 2024

To solve the problem of the poor abrasion resistance of concrete pavement surface mortar, this study substituted cement with equal amounts of styrene–butadiene rubber (SBR) latex and silica fume (SF) to investigate the effects of organic/inorganic material composite modification on the fluidity, drying shrinkage, mechanical properties, and abrasion resistance of cement mortar. Also in this study, the microstructure, product, and pore structure characteristics of the composite modified cement mortar were investigated using scanning electron microscope (SEM), X-Ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), and the Brunauer–Emmett–Teller (BET) method. This research found that the sole substitution of SF negatively impacted the mortar’s fluidity and drying shrinkage yet enhanced its mechanical strength and abrasion resistance; the incorporation of SBR latex improved fluidity, reduced shrinkage, and increased flexural strength but adversely affected the compressive strength of the mortar. Additionally, the enhancement of the mortar’s abrasion resistance with SBR latex was significantly greater than that with SF. When SBR latex and SF were used together as substitutes, the latex struggled to offset the negative impact of SF on mortar fluidity but effectively reduced shrinkage; SF compensated for the detrimental effect of the latex on compressive strength. Moreover, the primary role in enhancing the mortar’s abrasion resistance was played by the latex. Microscopic tests showed that SBR latex and SF could increase the content of calcium silicate hydrate (C-S-H) gel, inhibit the formation of ettringite (AFt) and reduce carbonation, refine the pore size of cement mortar, and effectively improve the microstructure of mortar.
PVDF-Based Piezo-Catalytic Membranes—A Net-Zero Emission Approach towards Textile Wastewater Purification
Authors: Siddique Amna , Nawaz Hifza , Razzaque Shumaila , Tabasum Anila , Gong Hugh , Razzaq Humaira , Umar Muhammad
Published: March 2024

Among the various water purification techniques, advancements in membrane technology, with better fabrication and analysis, are receiving the most research attention. The piezo-catalytic degradation of water pollutants is an emerging area of research in water purification technology. This review article focuses on piezoelectric polyvinylidene difluoride (PVDF) polymer-based membranes and their nanocomposites for textile wastewater remediation. At the beginning of this article, the classification of piezoelectric materials is discussed. Among the various membrane-forming polymers, PVDF is a piezoelectric polymer discussed in detail due to its exceptional piezoelectric properties. Polyvinylidene difluoride can show excellent piezoelectric properties in the beta phase. Therefore, various methods of β-phase enhancement within the PVDF polymer and various factors that have a critical impact on its piezo-catalytic activity are briefly explained. This review article also highlights the major aspects of piezoelectric membranes in the context of dye degradation and a net-zero approach. The β-phase of the PVDF piezoelectric material generates an electron–hole pair through external vibrations. The possibility of piezo-catalytic dye degradation via mechanical vibrations and the subsequent capture of the resulting CO2 and H2 gases open up the possibility of achieving the net-zero goal.
Design and Analysis of Porous Elastomeric Polymer Based on Electro-Mechanical Coupling Characteristics for Flexible Pressure Sensor
Authors: Bu Yingxuan , Wu Jian , Zhang Zheming , Wei Qiandiao , Su Benlong , Wang Youshan
Published: March 2024

Elastomeric polymers have gained significant attention in the field of flexible electronics. The investigation of the electro-mechanical response relationship between polymer structure and flexible electronics is in increasing demand. This study investigated the factors that affect the performance of flexible capacitive pressure sensors using the finite element method (FEM). The sensor employed a porous elastomeric polymer as the dielectric layer. The results indicate that the sensor’s performance was influenced by both the structural and material characteristics of the porous elastomeric polymer. In terms of structural characteristics, porosity was the primary factor influencing the performance of sensors. At a porosity of 76%, the sensitivity was 42 times higher than at a porosity of 1%. In terms of material properties, Young’s modulus played a crucial role in influencing the performance of the sensors. In particular, the influence on the sensor became more pronounced when Young’s modulus was less than 1 MPa. Furthermore, porous polydimethylsiloxane (PDMS) with porosities of 34%, 47%, 67%, and 72% was fabricated as the dielectric layer for the sensor using the thermal expansion microsphere method, followed by sensing capability testing. The results indicate that the sensor’s sensitivity was noticeably influenced within the high porosity range, aligning with the trend observed in the simulation.
Dynamic Load Balancing in Cloud Computing: Optimized RL-Based Clustering with Multi-Objective Optimized Task Scheduling
Authors: Khan Ahmad Raza
Published: March 2024

Dynamic load balancing in cloud computing is crucial for efficiently distributing workloads across available resources, ensuring optimal performance. This research introduces a novel dynamic load-balancing approach that leverages a deep learning model combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to calculate load values for each virtual machine (VM). The methodology aims to enhance cloud performance by optimizing task scheduling and stress distribution. The proposed model employs a dynamic clustering mechanism based on computed loads to categorize VMs into overloaded and underloaded clusters. To improve clustering efficiency, the approach integrates Reinforcement Learning (RL) with a sophisticated Hybrid Lyrebird Falcon Optimization (HLFO) algorithm. HLFO merges the Lyrebird Optimization Algorithm (LOA) and Falcon Optimization Algorithm (FOA), enhancing the effectiveness of load balancing. A Multi-Objective Hybrid Optimization model is introduced to optimize task scheduling while considering Quality of Service (QoS) parameters, including makespan minimization, energy consumption reduction, balanced CPU utilization, efficient memory usage, and task prioritization. The implementation, conducted in Python and CloudSim, demonstrates the model’s ability to effectively allocate work between virtual machines (VMs) and physical machines (PMs), resulting in improved resource utilization, shortened makespan, enhanced CPU usage, and rigorous assessments affirming its efficacy. This research addresses the complexity of dynamic load balancing in cloud environments by combining deep learning, reinforcement learning, and hybrid optimization techniques, offering a comprehensive solution to optimize cloud performance under varying workloads and resource conditions.
To Gain One’s Soul: Kierkegaard and the Hermeneutical Virtue of Patience
Authors: Bowen Amber
Published: March 2024

In his 1843–1844 Upbuilding Discourses on patience, Søren Kierkegaard makes the claim that one gains one’s soul in patience. Philosophically speaking, this claim seems to be a meshing together of two unrelated topics: the virtue of patience, which usually falls under moral philosophy, and the topic of the soul, which belongs to metaphysics or religious discourse. Rather than interpreting Kierkegaard’s talk about the soul as merely poetic or religious rather than properly philosophical, in this essay I attempt to take his connection between the virtue of patience and the constitution of the person seriously. I do so by arguing that the constitutive elements of the Kierkegaardian self can be understood hermeneutically as a proto-fundamental ontology. I then identify how Kierkegaard describes the virtue of patience in distinctly hermeneutical terms not as qualities or traits that adhere to the person but as a particular way of inhabiting space and time in relation to God. In patience, the self remains rooted in the present, bearing the weight of the loss and lack therein, while maintaining an anticipatory openness toward the future—a future that ultimately only God can provide. Patience, I conclude, is a way of being in time that is necessary at the constitutive level of the hermeneutical self.
The Regime-Switching Structural Default Risk Model
Authors: Milidonis Andreas , Chisholm Kevin
Published: March 2024

We develop the regime-switching default risk (RSDR) model as a generalization of Merton’s default risk (MDR) model. The RSDR model supports an expanded range of asset probability density functions. First, we show using simulation that the RSDR model incorporates sudden changes in asset values faster than the MDR model. Second, we empirically implement the RSDR, MDR and an extension of the MDR model with changes in drift parameters, using maximum likelihood estimation. Focusing on the period before and after corporate rating downgrades used primarily for investment advice, we find that the RSDR model uses changes in equity mean returns and volatility to produce higher estimated default probabilities, faster, than both benchmark models.
Single-Temporal Sentinel-2 for Analyzing Burned Area Detection Methods: A Study of 14 Cases in Republic of Korea Considering Land Cover
Authors: Lee Doi , Son Sanghun , Bae Jaegu , Park Soryeon , Seo Jeongmin , Seo Dongju , Lee Yangwon , Kim Jinsoo
Published: March 2024

Forest fires are caused by various climatic and anthropogenic factors. In Republic of Korea, forest fires occur frequently during spring when the humidity is low. During the past decade, the number of forest fire incidents and the extent of the damaged area have increased. Satellite imagery can be applied to assess damage from these unpredictable forest fires. Despite the increasing threat, there is a lack of comprehensive analysis and effective strategies for addressing these forest fires, particularly considering the diverse topography of Republic of Korea. Herein, we present an approach for the automated detection of forest fire damage using Sentinel-2 images of 14 areas affected by forest fires in Republic of Korea during 2019–2023. The detection performance of deep learning (DL), machine learning, and spectral index methods was analyzed, and the optimal model for detecting forest fire damage was derived. To evaluate the independent performance of the models, two different burned areas exhibiting distinct characteristics were selected as test subjects. To increase the classification accuracy, tests were conducted on various combinations of input channels in DL. The combination of false-color RNG (B4, B8, and B3) images was optimal for detecting forest fire damage. Consequently, among the DL models, the HRNet model achieved excellent results for both test regions with intersection over union scores of 89.40 and 82.49, confirming that the proposed method is applicable for detecting forest fires in diverse Korean landscapes. Thus, suitable mitigation measures can be promptly designed based on the rapid analysis of damaged areas.
A Novel Rain Identification and Rain Intensity Classification Method for the CFOSAT Scatterometer
Authors: Quan Meixuan , Zhang Jie , Zhang Rui
Published: March 2024

The China–France oceanography satellite scatterometer (CSCAT) is a rotating fan-beam scanning observation scatterometer operating in the Ku-band, and its product quality is affected by rain contamination. The multiple azimuthal NRCS measurements provided by CSCAT L2A, the retrieved wind speed and wind direction provided by CSCAT L2B, as well as the rain data provided by GPM, are used to construct a new rain identification and rain intensity classification model for CSCAT. The EXtreme Gradient Boosting (XGBoost) model, optimized by the Dung Beetle Optimizer (DBO) algorithm, is developed and evaluated. The performance of the DBO-XGBoost exceeds that of the CSCAT rain flag in terms of rain identification ability. Also, compared with XGBoost without parameter optimization, K-nearest Neighbor with K = 5 (KNN5) and K-nearest Neighbor with K = 3 (KNN3), the performance of DBO-XGBoost is better. Its rain identification achieves an accuracy of about 90% and a precision of about 80%, which enhances the quality control of rain. DBO-XGBoost has also shown good results in the classification of rain intensity. This ability is not available in traditional rain flags. In the global regional and local regional tests, most of the accuracy and precision in rain intensity classification have reached more than 80%. This technology makes full use of the rich observed information of CSCAT, realizes rain identification, and can also classify the rain intensity so as to further evaluate the degree of rain contamination of CSCAT products.
Spatio-Temporal Deep Learning-Based Forecasting of Surface Solar Irradiance: Leveraging Satellite Data and Feature Selection
Authors: Kim Jinyong , Kim Eunkyeong , Jung Seunghwan , Kim Minseok , Kim Baekcheon , Kim Sungshin
Published: March 2024

This paper proposes a method for forecasting surface solar irradiance (SSI), the most critical factor in solar photovoltaic (PV) power generation. The proposed method uses 16-channel data obtained by the GEO-KOMPSAT-2A (GK2A) satellite of South Korea as the main data for SSI forecasting. To determine feature variables related to SSI from the 16-channel data, the differences and ratios between the channels were utilized. Additionally, to consider the fundamental characteristics of SSI originating from the sun, solar geometry parameters, such as solar declination (SD), solar elevation angle (SEA), and extraterrestrial solar radiation (ESR), were used. Deep learning-based feature selection (Deep-FS) was employed to select appropriate feature variables that affect SSI from various feature variables extracted from the 16-channel data. Lastly, spatio-temporal deep learning models, such as convolutional neural network–long short-term memory (CNN-LSTM) and CNN–gated recurrent unit (CNN-GRU), which can simultaneously reflect temporal and spatial characteristics, were used to forecast SSI. Experiments conducted to verify the proposed method against conventional methods confirmed that the proposed method delivers superior SSI forecasting performance.
Mapping Soil Organic Carbon Stock Using Hyperspectral Remote Sensing: A Case Study in the Sele River Plain in Southern Italy
Authors: Francos Nicolas , Nasta Paolo , Allocca Carolina , Sica Benedetto , Mazzitelli Caterina , Lazzaro Ugo , D’Urso Guido , Belfiore Oscar Rosario , Crimaldi Mariano , Sarghini Fabrizio , Ben-Dor Eyal , Romano Nunzio
Published: March 2024

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO2) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R2 = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R2 = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R2 = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R2 = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks.
Dual-Use Strain Sensors for Acoustic Emission and Quasi-Static Bending Measurements
Authors: Stiefvater Jason , Kang Yuhong , de Clerck Albrey , Mao Shuo , Jones Noah , Deem Josh , Wicks Alfred , Ruan Hang , Ng Wing
Published: March 2024

In this paper, a MEMS piezoresistive ultrathin silicon membrane-based strain sensor is presented. The sensor’s ability to capture an acoustic emission signal is demonstrated using a Hsu–Nielsen source, and shows comparable frequency content to a commercial piezoceramic ultrasonic transducer. To the authors’ knowledge, this makes the developed sensor the first known piezoresistive strain sensor which is capable of recording low-energy acoustic emissions. The improvements to the nondestructive evaluation and structural health monitoring arise from the sensor’s low minimum detectable strain and wide-frequency bandwidth, which are generated from the improved fabrication process that permits crystalline semiconductor membranes and advanced polymers to be co-processed, thus enabling a dual-use application of both acoustic emission and static strain sensing. The sensor’s ability to document quasi-static bending is also demonstrated and compared with an ultrasonic transducer, which provides no significant response. This dual-use application is proposed to effectively combine the uses of both strain and ultrasonic transducer sensor types within one sensor, making it a novel and useful method for nondestructive evaluations. The potential benefits include an enhanced sensitivity, a reduced sensor size, a lower cost, and a reduced instrumentation complexity.
Secure Enhancement for MQTT Protocol Using Distributed Machine Learning Framework
Authors: Alotaibi Nouf Saeed , Sayed Ahmed Hassan I. , Kamel Samah Osama M. , ElKabbany Ghada Farouk
Published: March 2024

The Message Queuing Telemetry Transport (MQTT) protocol stands out as one of the foremost and widely recognized messaging protocols in the field. It is often used to transfer and manage data between devices and is extensively employed for applications ranging from smart homes and industrial automation to healthcare and transportation systems. However, it lacks built-in security features, thereby making it vulnerable to many types of attacks such as man-in-the-middle (MitM), buffer overflow, pre-shared key, brute force authentication, malformed data, distributed denial-of-service (DDoS) attacks, and MQTT publish flood attacks. Traditional methods for detecting MQTT attacks, such as deep neural networks (DNNs), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and fuzzy logic, may exist. The increasing prevalence of device connectivity, sensor usage, and environmental scalability become the most challenging aspects that novel detection approaches need to address. This paper presents a new solution that leverages an H2O-based distributed machine learning (ML) framework to improve the security of the MQTT protocol in networks, particularly in IoT environments. The proposed approach leverages the strengths of the H2O algorithm and architecture to enable real-time monitoring and distributed detection and classification of anomalous behavior (deviations from expected activity patterns). By harnessing H2O’s algorithms, the identification and timely mitigation of potential security threats are achieved. Various H2O algorithms, including random forests, generalized linear models (GLMs), gradient boosting machine (GBM), XGBoost, and the deep learning (DL) algorithm, have been assessed to determine the most reliable algorithm in terms of detection performance. This study encompasses the development of the proposed algorithm, including implementation details and evaluation results. To assess the proposed model, various evaluation metrics such as mean squared error (MSE), root-mean-square error (RMSE), mean per class error (MCE), and log loss are employed. The results obtained indicate that the H2OXGBoost algorithm outperforms other H2O models in terms of accuracy. This research contributes to the advancement of secure IoT networks and offers a practical approach to enhancing the security of MQTT communication channels through distributed detection and classification techniques.
Highly Sensitive Pressure Sensor Based on Elastic Conductive Microspheres
Authors: Li Zhangling , Guan Tong , Zhang Wuxu , Liu Jinyun , Xiang Ziyin , Gao Zhiyi , He Jing , Ding Jun , Bian Baoru , Yi Xiaohui , Wu Yuanzhao , Liu Yiwei , Shang Jie , Li Runwei
Published: March 2024

Elastic pressure sensors play a crucial role in the digital economy, such as in health care systems and human–machine interfacing. However, the low sensitivity of these sensors restricts their further development and wider application prospects. This issue can be resolved by introducing microstructures in flexible pressure-sensitive materials as a common method to improve their sensitivity. However, complex processes limit such strategies. Herein, a cost-effective and simple process was developed for manufacturing surface microstructures of flexible pressure-sensitive films. The strategy involved the combination of MXene–single-walled carbon nanotubes (SWCNT) with mass-produced Polydimethylsiloxane (PDMS) microspheres to form advanced microstructures. Next, the conductive silica gel films with pitted microstructures were obtained through a 3D-printed mold as flexible electrodes, and assembled into flexible resistive pressure sensors. The sensor exhibited a sensitivity reaching 2.6 kPa−1 with a short response time of 56 ms and a detection limit of 5.1 Pa. The sensor also displayed good cyclic stability and time stability, offering promising features for human health monitoring applications.
Planar Thermoelectric Microgenerators in Application to Power RFID Tags
Authors: Dziedzic Andrzej , Wójcik Szymon , Gierczak Mirosław , Bernik Slavko , Brguljan Nana , Reinhardt Kathrin , Körner Stefan
Published: March 2024

This paper presents an innovative approach to the integration of thermoelectric microgenerators (μTEGs) based on thick-film thermopiles of planar constantan–silver (CuNi-Ag) and calcium cobaltite oxide–silver (Ca3Co4O9-Ag) thick-film thermopiles with radio frequency identification (RFID) technology. The goal was to consider using the TEG for an active or semi-passive RFID tag. The proposed implementation would allow the communication distance to be increased or even operated without changing batteries. This article discusses the principles of planar thermoelectric microgenerators (μTEGs), focusing on their ability to convert the temperature difference into electrical energy. The concept of integration with active or semi-passive tags is presented, as well as the results of energy efficiency tests, considering various environmental conditions. On the basis of the measurements, the parameters of thermopiles consisting of more thermocouples were simulated to provide the required voltage and power for cooperation with RFID tags. The conclusions of the research indicate promising prospects for the integration of planar thermoelectric microgenerators with RFID technology, opening the way to more sustainable and efficient monitoring and identification systems. Our work provides the theoretical basis and practical experimental data for the further development and implementation of this innovative technology.
Design and Experimental Study of a Hybrid Micro-Vibration Isolation System Based on a Strain Sensor for High-Precision Space Payloads
Authors: Guo Qiwei , Zhou Jian , Li Liang , Xu Minglong , Tang Guoan
Published: March 2024

Micro-vibrations significantly influence the imaging quality and pointing accuracy of high-precision space-borne payloads. To mitigate this issue, vibration isolation technology must be employed to reduce the transmission of micro-vibrations to payloads. In this paper, a novel active–passive hybrid isolation (APHI) system based on a strain sensor is proposed for high-precision space payloads, and corresponding theoretical and experimental studies are implemented. First, a theoretical analysis model of the APHI system is established using a two-degrees-of-freedom system, and an integral control method based on strain sensing is presented. Then, an electromagnetic damper, active piezoelectric actuator, and strain sensor are designed and manufactured. Finally, an APHI experimental system is implemented to validate the effectiveness of electromagnetic damping and strain-sensing active control. Additionally, the control effects of acceleration, displacement, and strain sensors are compared. The results demonstrate that strain sensors can achieve effective active damping control, and the control method based on strain sensors can effectively suppress the payload response while maintaining stability. Both displacement and strain sensors exhibit superior suppression effects compared with the acceleration sensor, with the strain sensor showing greater potential for practical engineering applications than the displacement sensor.
Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine
Authors: Jafarzadeh Hamid , Mahdianpari Masoud , Gill Eric W. , Mohammadimanesh Fariba
Published: March 2024

Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination (R2) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies.
Optical pH Sensor Based on a Long-Period Fiber Grating Coated with a Polymeric Layer-by-Layer Electrostatic Self-Assembled Nanofilm
Authors: Pereira José M. , Mendes João P. , Dias Bernardo , Almeida José M. M. M. de , Coelho Luís C. C.
Published: March 2024

An optical fiber pH sensor based on a long-period fiber grating (LPFG) is reported. Two oppositely charged polymers, polyethylenimine (PEI) and polyacrylic acid (PAA), were alternately deposited on the sensing structure through a layer-by-layer (LbL) electrostatic self-assembly technique. Since the polymers are pH sensitive, their refractive index (RI) varies when the pH of the solution changes due to swelling/deswelling phenomena. The fabricated multilayer coating retained a similar property, enabling its use in pH-sensing applications. The pH of the PAA dipping solution was tuned so that a coated LPFG achieved a pH sensitivity of (6.3 ± 0.2) nm/pH in the 5.92–9.23 pH range. Only two bilayers of PEI/PAA were used as an overlay, which reduces the fabrication time and increases the reproducibility of the sensor, and its reversibility and repeatability were demonstrated by tracking the resonance band position throughout multiple cycles between different pH solutions. With simulation work and experimental results from a low-finesse Fabry–Perot (FP) cavity on a fiber tip, the coating properties were estimated. When saturated at low pH, it has a thickness of 200 nm and 1.53 ± 0.01 RI, expanding up to 310 nm with a 1.35 ± 0.01 RI at higher pH values, mostly due to the structural changes in the PAA.
Student Grouping: Investigating a Socio-Educational Practice in a Public School in Portugal
Authors: Loureiro Armando , Rodrigues Marta de Oliveira
Published: March 2024

Social inclusion of children and young people and the curbing of school failure, dropout, and early school leaving is are central concerns of the European educational policy and are seen as means to prevent unemployment, poverty, and social exclusion. In Portugal, as in other European countries, to address this issue, several programs have been designed and developed. This article aims to investigate how teachers, technicians, and parents involved in a socio-educational practice of student grouping envisage processes that contribute to overcoming school failure, dropout, and early school leaving. This qualitative case study took place in a Portuguese municipality within a school-based nationwide intervention programme. From the voices of the educational agents consulted, some vectors of institutional, dispositional, and situational dimensions were identified that contribute to overcoming ‘barriers to access and participation in education’, which promote some changes among students at an individual level. The research findings highlight processes, factors, and logics of action that allow teachers to work closely with their students and tailor the pace and curriculum contents to their needs, thereby enhancing students’ academic performance.
Assessing Coastal Vulnerability to Climate Change: A Case Study of Nakhon Si Thammarat and Krabi
Authors: Cheewinsiriwat Pannee , Langkulsen Uma , Lertwattanamongkol Vanida , Poompongthai Wanlee , Lambonmung Augustine , Chamchan Chalermpol , Boonmanunt Suparee , Nakhapakorn Kanchana , Moses Cherith
Published: March 2024

Climate change is increasingly impacting both environments and human communities. Coastal regions in Thailand are experiencing more severe impacts, which vary based on the unique physical and socio-economic characteristics of each area. To assess the vulnerability of coastal regions in Thailand, this study focused on two provinces, Nakhon Si Thammarat (NST) and Krabi, each representing distinct coastal environments. NST, situated on the Gulf of Thailand’s east coast, has an agriculture-based economy, while Krabi, on the Andaman Sea’s west coast, relies heavily on tourism. The study utilized a multi-criteria decision analysis approach (MCDA) and GIS to analyze the Coastal Vulnerability Index at the sub-district level. The results revealed that, although NST was more vulnerable than Krabi to socio-economic factors such as the poverty rate and the number of fishery households, Krabi was much more vulnerable in the physical environment, including wave height, tidal level, coastal erosion, and slope. However, overall, Krabi exhibited high to the highest levels of coastal vulnerability, while NST displayed moderate to high levels. These findings provide valuable insights for policymakers and government agencies, aiding in the development of strategies to mitigate vulnerability and enhance the quality of life for local residents in both provinces.
Achieving Neighborhood-Level Collaborative Governance through Participatory Regeneration: Cases of Three Residential Heritage Neighborhoods in Shanghai
Authors: Wang Han , Xu Yueli
Published: March 2024

Residential heritage neighborhoods in China are experiencing a transformation from large-scale demolishment, which is associated with relocating residents, to small-scale neighborhood regeneration, and distinctive models of participatory regeneration are emerging. Participatory regeneration is increasingly considered to be an effective way to achieve multiple goals in urban development; however, little research has investigated the impacts of participatory regeneration on achieving neighborhood-based collaborative governance. This study aims to explore which mechanisms associated with participatory regeneration facilitate or constrain neighborhood-based collaborative governance, using case studies in Shanghai. Based on the investigation of three nuanced pathways in participatory regeneration, this study compares four dimensions associated with participatory regeneration (i.e., participatory decision-making, inclusion, collective problem-solving, and power-sharing) and explores key mechanisms that are applicable for achieving collaborative governance in different scenarios, aiming to enhance social development and social sustainability in future. The findings indicate that although grassroots government played vital roles in participatory heritage neighborhood regeneration through financial support and supervision strategies, facilitating mechanisms could still be observed for achieving collaborative governance. Furthermore, this study provides suggestions for planners in building collaborative governance in other developing areas which are experiencing rapid urbanization with strong state interventions.