bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Graphene Oxide Localization on Morphology Development and Rheological and Mechanical Properties of Poly(lactic acid)/ethylene vinyl Alcohol Copolymer Blend Composites: A Comprehensive Study
Authors: Dadashi Parsa , Ray Suprakas Sinha , Babaei Amir
Published: April 2024

This study investigates the rheological, morphological, and mechanical properties of melt-processed polylactide/ethylene vinyl alcohol (70PLA/30EVOH) blend composites containing 0.25, 0.5, and 1 wt.% of graphene oxide (GO) nanoplates. Thermodynamic-based suggested the localization of nanoparticles in EVOH, SEM studies showed that the introduction of GO to the blend increased dispersed droplet size, which was attributed to the localization of GO within EVOH, as confirmed by TEM. The rheology results indicated a decrease in the elasticity for the composite containing 0.25 wt.% of GO compared to the neat blend, which was attributed to the sliding effect of the added GO nanoplatelets. However, samples containing higher amounts of GO nanoplatelets exhibited more excellent elasticity than the neat blend. The increased elasticity was suggestively attributed to the dominance of hydrodynamic interactions, the physical network of added nanoplatelets, and polymer/GO interactions over the sliding role of the GO nanoplatelets at higher loadings. In addition, the effect of the order of mixing was investigated, and the premixing of PLA and GO exhibited a decrease in the droplet radius compared to the neat blend. It was ascribed to the localization of GO nanosheets in the PLA and interface, which was confirmed by rheological results and mechanical assessments.
Mechanical, Thermal, and Physicochemical Properties of Filaments of Poly (Lactic Acid), Polyhydroxyalkanoates and Their Blend for Additive Manufacturing
Authors: Mondragón-Herrera L. Itzkuautli , Vargas-Coronado R. F. , Carrillo-Escalante H. , Cauich-Rodríguez J. V. , Hernández-Sánchez F. , Velasco-Santos C. , Avilés F.
Published: April 2024

Polymeric blends are employed in the production of filaments for additive manufacturing to balance mechanical and processability properties. The mechanical and thermal properties of polymeric filaments made of poly (lactic acid) (PLA), polyhydroxyalkanoates (PHA), and its blend (PLA–PHA) are investigated herein and correlated to their measured structural and physicochemical properties. PLA exhibits the highest stiffness and tensile strength, but lower toughness. The mechanical properties of the PLA–PHA blend were similar to those of PLA, but with a significantly higher toughness. Despite the lower mechanical properties of neat PHA, incorporating a small amount (12 wt.%) of PHA into PLA significantly enhances toughness (approximately 50%) compared to pure PLA. The synergistic effect is attributed to the spherulitic morphology of blended PHA in PLA, promoting interactions between the amorphous regions of both polymers. Thermal stability is notably improved in the PLA–PHA blend, as determined by thermogravimetric analysis. The blend also exhibits lower cold crystallization and glass transition temperatures as compared to PLA, which is beneficial for additive manufacturing. Following additive manufacturing, X-ray photoelectron spectroscopic showed that the three filaments present an increase in C–C and C=O bonds associated with the loss of C–O bonds. The thermal process induces a slight increase in crystallinity in PHA due to chain reorganization. The study provides insights into the thermal and structural changes occurring during the melting process of additive manufacturing.
Recent Progresses in Pyrolysis of Plastic Packaging Wastes and Biomass Materials for Conversion of High-Value Carbons: A Review
Authors: Cheng Youliang , Wang Jinpeng , Fang Changqing , Du Yanli , Su Jian , Chen Jing , Zhang Yingshuan
Published: April 2024

The recycling of plastic packaging wastes helps to alleviate the problems of white pollution and resource shortage. It is very necessary to develop high-value conversion technologies for plastic packaging wastes. To our knowledge, carbon materials with excellent properties have been widely used in energy storage, adsorption, water treatment, aerospace and functional packaging, and so on. Waste plastic packaging and biomass materials are excellent precursor materials of carbon materials due to their rich sources and high carbon content. Thus, the conversion from waste plastic packaging and biomass materials to carbon materials attracts much attention. However, closely related reviews are lacking up to now. In this work, the pyrolysis routes of the pyrolysis of plastic packaging wastes and biomass materials for conversion to high-value carbons and the influence factors were analyzed. Additionally, the applications of these obtained carbons were summarized. Furthermore, the limitations of the current pyrolysis technology are put forward and the research prospects are forecasted. Therefore, this review can provide a useful reference and guide for the research on the pyrolysis of plastic packaging wastes and biomass materials and the conversion to high-value carbon.
Concentrated O/W Emulsion Stability of Non-Ionic Chitosan Oligomer Surfactants Modified by Epoxidized Fatty Chains at pH7: Influence of Emulsification Conditions
Authors: Berthalon Steve , Frugier Jérémy , Azema Nathalie , Negrell Claire , David Ghislain
Published: April 2024

In this study, chitosan-based surfactants were synthesized by epoxy–amine chemistry to stabilize concentrated O/W emulsions at pH7. Chitosan was first depolymerized by nitrous deamination to obtain chitooligosaccharides (COS) with degrees of polymerization of 10 (DP10) and 20 (DP20). Then, three different epoxidized fatty chains, i.e., octyl/decyl glycidyl ether (C9), hexadecyl glycidyl ether (C16) and epoxidized cardanol (card), were grafted onto the amine groups of chitosan to form six amphiphilic structures. NMR measurements revealed grafting efficiencies ranging from 1 to 30% while HLB values ranged from 13 to 20. The relationships between these surfactant structures and their adsorption properties were investigated by tensiometric measurements, highlighting the need for a short hydrophilic moiety and high grafting efficiency to obtain the best adsorption. Subsequently, concentrated O/W emulsions (66% of oil) at pH7 were produced using COS-based surfactants and the impact of stirring time and speed during the emulsification process was described through rheological, droplet size and microscopy measurements. Finally, emulsions were stored over 2 months in order to study the destabilization phenomenon into the mixture, i.e., coalescence and creaming, by using laser granulometry and Turbiscan. Results demonstrated that stability could be enhanced by increasing emulsion viscosity, reducing droplet size or optimizing the adsorption layer at the O/W interfaces.
Effect of Silicon–Manganese Deoxidation on Oxygen Content and Inclusions in Molten Steel
Authors: Song Tianle , Wang Zhongliang , Bao Yanping , Gu Chao , Zhang Zefeng
Published: April 2024

In order to improve the cleanliness of steel, non-aluminum deoxidation processes have begun to replace aluminum deoxidation processes. Although the aluminum deoxidation process can reduce the oxygen content in steel to <10 × 10−6, this deoxidation method causes fatigue failure resulting from the formation of large-grained spherical (Ds-type) inclusions composed of calcium–aluminate. It also tends to lead to nozzle blockage during casting. Given the above problems, this study conducted an in-depth investigation of silicon–manganese deoxidation. Thermal experiments and thermodynamic calculations were used to assess the impact of different Mn–Si ratios on the oxygen content and inclusion characteristics during the deoxidation process of molten steel with different initial oxygen contents. The experimental samples were analyzed using an oxygen–nitrogen–hydrogen analyzer, a direct reading spectrometer, and an automatic scanning electron microscope. After that, the samples were electrolyzed to observe the 2D morphology and 3D morphology of the inclusions using scanning electron microscopy. Finally, thermodynamic calculations were carried out using FactSage to verify the experimental results. The results indicated that, regardless of the initial oxygen content, silicon–manganese deoxidation maintained the total oxygen content at 35 × 10−6. It effectively managed the plasticization of inclusions in molten steel, predominantly yielding spherical silicates while minimizing Al-containing inclusions. Nevertheless, as the initial content of [O] increased, the size and density of the silicate inclusions in the steel also increased. An optimal point in the number and size of inclusions was observed with an increased Mn–Si ratio. Moreover, the combined utilization of silicon–manganese deoxidation, diffusion deoxidation, and vacuum deoxidation enabled ultra-low oxygen content control of molten steel.
Preparation, Characterization, and Scattering Characteristics of Mixed Aerosol of Fly Ash and Ammonium Sulfate
Authors: Li Jiaxin , Li Shengji , Zhang Minqi , Wang Zhangtao , Shu Yanbokang , Liu Jie , Huang Xuefeng
Published: April 2024

The mixed aerosols formed by fly ash and ammonium sulfate have a vital impact on the scattering characteristics of the atmosphere. This paper proposes to investigate the scattering characteristics of an individual optically levitated mixed aerosol of fly ash and ammonium sulfate using a coupled laser levitation and scattering measuring apparatus. The mixed aerosols were first prepared and characterized by multiple techniques. The results demonstrated that mixed aerosol particles completely encapsulated ammonium sulfate crystals on the rough porous surface of fly ash, resembling the “core-shell” structure. Moreover, the surface formed columnar ammonium sulfate crystals that exhibit the highest regularity when the solid mass concentration of fly ash was 1000 mg/L. The scattering intensity of mixed aerosols was measured, and the comparisons among fly ash aerosol and mixed aerosols were made to evaluate the effect of fly ash concentration on scattering. The measurements demonstrated that the mixed aerosols exhibited a lower overall scattering intensity compared to fly ash alone. The higher regularity of ammonium sulfate crystals formed on the surface of mixed aerosols at different solid mass concentrations of fly ash corresponds to higher scattering intensity. These findings will be helpful for recognizing the scattering characteristics of real atmospheric aerosols in depth.
Solvent Extraction of Gold(III) by 2-Ethylhexanol and Modeling of Facilitated Transport across a Supported Liquid Membrane
Authors: Alguacil Francisco Jose , Robla Jose Ignacio
Published: April 2024

The solvent extraction of gold(III) by undiluted 2-ethylhexanol or dissolved in toluene from a HCl solution has been investigated in this paper. The numerical analysis of gold distribution data suggests the formation of HAuCl4·L and HAuCl4·2L (L = 2-ethylhexanol) species in the organic phase, with formation constants K11 = 38 and K12 = 309, respectively. The results derived from gold(III) distribution have been implemented in a solid-supported liquid membrane system. The influence of several variables on gold transport has been considered: feed and receiving phases’ stirring speeds, HCl and gold concentrations in the feed phase, and carrier concentration in the membrane phase as well as the presence of base metals (Fe, Cu, Ni) and platinum-group metals (PGMs) in the feed phase. Gold transport is influenced by the stirring speed of the feed phase and the variation in HCl and gold (feed phase) and carrier (membrane phase) concentrations. Also, diffusional resistances to mass transfer are estimated, and the contribution of each resistance to gold transport is estimated. Gold is recovered as zero-valent nanoparticles.
“Violent Times Call for Violent Prayers”: “Divine Violence” during the COVID-19 Pandemic in the Mountain of Fire and Miracles Ministries, Nigeria
Authors: Igboin Benson Ohihon
Published: April 2024

Interest in studying prayer has significantly increased because of the belief that it helps humanity to cope, particularly in times of crisis. Prayer is not just a communication with God, it is also an instrument of bond and embodied ritual among prayer litigants or people who belong to the same religious community. This article argues that divine violence, a sovereign act of God, was crucially needed by the litigants in order to guarantee human flourishing in the face of existential threat. The article studied how violent prayer—a genre of prayer that is targeted at the spirits underlying physical manifestation of suffering, pain, or crisis—was utilised by the Mountain of Fire and Miracle Ministries (MFMM) in Nigeria to cope with the fear and uncertainties occasioned by the COVID-19 pandemic. This article is part of an ongoing ethnographic research on “The Politics and Poetics of Violent Prayer in the Nigerian Pentecostal Churches”, which began in 2021. I utilised ethnographic engagement, particularly interviews and participant observation, to attempt to understand what these prayer litigants are doing when they pray violently.
Neglected Agents and the Cultural Nexus of Power within Protestant Churches and Associated Institutions in the Latter Half of the Twentieth Century in Hong Kong
Authors: Chen Haocheng
Published: April 2024

In the latter half of the twentieth century, Protestant churches in Hong Kong and institutions associated with them played a significant role in the daily lives of the people of Hong Kong. However, the role of ordinary people in the social functions of Protestant churches in Hong Kong during this period has rarely been explored in the previous literature. Interpreting several cases from written archives and oral history archives, this study aims to demonstrate how Protestant churches in Hong Kong held special significance as a public place for the ordinary people of the local community, who played an active role in these churches to serve the local community and derived self-satisfaction from doing so. This study also reveals how these local Protestant churches and the associated institutions emerged as a cultural nexus of power along with individuals who contributed to them to provide different types of welfare and social services to the community, which the colonial government in Hong Kong failed to provide.
Reconstruction of Single-Bay Buddhist Architecture Based on Stylistic Comparisons in Northeast Fujian, the Core Hinterland of the Changxi River Basin—Using Gonghoulong Temple as an Example
Authors: Ding Yu , Cai Yuqing , Liu Jie
Published: April 2024

In the Changxi River Basin in eastern Fujian, a few stone elements remain and Buddhist buildings with one bay in width and three bays in depth have been preserved dating from the timespan of the Tang to the Song dynasty. These features are characterized by a regional form of early Buddhist architecture seldom seen in Chinese history. The article focuses on the reconstruction of a Song dynasty Buddhist building at the Gonghoulou Temple site in Huotong Town, Jiaocheng District, Ningde City, and aims to analyze the potential characteristics and rules of single-bay Buddhist architecture. From the styles of the remaining stone columns, the direction of the lotus carving at the column base, and the mortises of the plinth stone, a spatial arrangement is indicated that includes an open front corridor and a closed rear section. A “reconstruction” of the ruler used in the original building reveals the possibility that a local Fujian ruler was used, shorter than the standard measurement device employed elsewhere. The analysis of the frame construction indicates that this hip-gable roof-covered Buddhist hall utilizes the horizontally layered logic of multi-storied palatial-style halls. Key elements include its gentle roof slope, restraint from the practice of shortening the roof ridge, use of the traditional chuji method, and the interior columns use of internal longitudinal architraves secured to beam supporting brackets. This research brings to light a unique architectural type that has disappeared in the course of history and was previously unknown to the academic community. It holds significant importance and value for deepening the understanding of the history of timber frame architecture technology in Fujian.
South Korean Christian Communities Supporting Women in Need
Authors: Kovacs Ramona F.
Published: April 2024

Christianity does not have as long of a history as other monotheistic religions and traditional ideologies in Korea, but—especially from the end of the 19th century—its new concepts have had a huge impact on the basic thoughts of Korean society. This paper focuses on the effects of Christianity and the activity of Christian communities on women’s lives. According to my preliminary findings, in the late 1800s, the Christian missionaries and their newly formed communities offered opportunities for girls and women to get education, a profession, access to better health care, and learn self-care. After the complicated decades between 1910 and 1950, South Korea was experiencing remarkable changes, and Christians were an active part of this rebuilding, helping the lives of those who were struggling with poverty, lack of daily necessities, education, and health care. This paper aims to examine the thoughts and actions of Korean Christian communities towards decisions on childbirth, children out of wedlock, adoption, and single motherhood. This study investigates the approaches of South Korean Christian communities towards women related to the above-mentioned circumstances, focusing on the early examples and the last five to six decades. It is assumed that even though South Korea is now considered a modernized country, the government sometimes fails to cope with current problems, and traditional notions are still strong in familial matters. Therefore, not obeying conventional forms may cause social conflicts, or the decisions are hidden because of taboos and stigmas. This research is based on the publications of Christian communities and involves documentaries and case studies, including the baby box operation and adoption. This paper contributes to the expanding studies on Korean Christian communities, highlighting the social norms and their changes generated by new religious thoughts, while giving an insight into the daily struggles of Korean women’s lives when it comes to decision-making about their motherhood.
Wide-Angle Repeat Pass Bistatic SAR Interferometry: A Geometrical Introduction
Authors: Rocca Fabio
Published: April 2024

Positioning a Synthetic Aperture Radar (SAR) receiver at a significant distance from an illuminator in a Low Earth Orbit (LEO) enables wide-angle repeat-pass bistatic interferometry. It’s crucial to define the positioning limits of the receiver with respect to the illuminator to ensure reasonable coherences, altitudes of ambiguity, and a suitable common wavenumber support. I extended the standard monostatic formula, which yields the change in the interferometric travel path due to the vertical baseline. In the wide-angle bistatic case, the range-azimuth coordinate system is no longer orthogonal and we must consider the full 2D vector nature of the LOS orthogonal components. The two vertical baselines and the horizontal baseline of the receiver are needed to parameterize the system. Their impact on the interferometric coherence is quantified and bistatic limits are seen to be more flexible than those in the monostatic case due to the combination of independent baselines. I estimated the coherence, and then extended the analysis to the wavenumber domain, to quantify the common spectral support due to the wavenumber shifts due to the three baselines. The analysis carried out geometrically allows for combining together the effects of the transmitter’s and receiver’s vertical baselines and thus representing the residual spectral fraction with two instead of three parameters. Finally, I examined the achievable spatial resolution of the position of the interferer obtainable from the delay of the arrival of the interference between the illuminator (supposed to be silent) and the receiver.
Shadow-Aware Point-Based Neural Radiance Fields for High-Resolution Remote Sensing Novel View Synthesis
Authors: Li Li , Zhang Yongsheng , Wang Ziquan , Zhang Zhenchao , Jiang Zhipeng , Yu Ying , Li Lei , Zhang Lei
Published: April 2024

Novel view synthesis using neural radiance fields (NeRFs) for remote sensing images is important for various applications. Traditional methods often use implicit representations for modeling, which have slow rendering speeds and cannot directly obtain the structure of the 3D scene. Some studies have introduced explicit representations, such as point clouds and voxels, but this kind of method often produces holes when processing large-scale scenes from remote sensing images. In addition, NeRFs with explicit 3D expression are more susceptible to transient phenomena (shadows and dynamic objects) and even plane holes. In order to address these issues, we propose an improved method for synthesizing new views of remote sensing images based on Point-NeRF. Our main idea focuses on two aspects: filling in the spatial structure and reconstructing ray-marching rendering using shadow information. First, we introduce hole detection, conducting inverse projection to acquire candidate points that are adjusted during training to fill the holes. We also design incremental weights to reduce the probability of pruning the plane points. We introduce a geometrically consistent shadow model based on a point cloud to divide the radiance into albedo and irradiance, allowing the model to predict the albedo of each point, rather than directly predicting the radiance. Intuitively, our proposed method uses a sparse point cloud generated with traditional methods for initialization and then builds the dense radiance field. We evaluate our method on the LEVIR_NVS data set, demonstrating its superior performance compared to state-of-the-art methods. Overall, our work provides a promising approach for synthesizing new viewpoints of remote sensing images.
Explainable Automatic Detection of Fiber–Cement Roofs in Aerial RGB Images
Authors: Omarzadeh Davoud , González-Godoy Adonis , Bustos Cristina , Martín-Fernández Kevin , Scotto Carles , Sánchez César , Lapedriza Agata , Borge-Holthoefer Javier
Published: April 2024

Following European directives, asbestos–cement corrugated roofing tiles must be eliminated by 2025. Therefore, identifying asbestos–cement rooftops is the first necessary step to proceed with their removal. Unfortunately, asbestos detection is a challenging task. Current procedures for identifying asbestos require human exploration, which is costly and slow. This has motivated the interest of governments and companies in developing automatic tools that can help to detect and classify these types of materials that are dangerous to the population. This paper explores multiple computer vision techniques based on Deep Learning to advance the automatic detection of asbestos in aerial images. On the one hand, we trained and tested two classification architectures, obtaining high accuracy levels. On the other, we implemented an explainable AI method to discern what information in an RGB image is relevant for a successful classification, ensuring that our classifiers’ learning process is guided by the right variables—color, surface patterns, texture, etc.—observable on asbestos rooftops.
Geomorphological Response of Alluvial Streams to Flood Events during Base-Level Lowering: Insights from Drone-Based Photogrammetric Surveys in Dead Sea Tributaries
Authors: Ben Moshe Liran , Lensky Nadav G.
Published: April 2024

The geomorphological impact of base-level lowering on ephemeral alluvial streams has been extensively investigated through fieldwork, experimentation, and modeling. Yet, the understanding of hydrological parameters governing the dynamics of the stream’s geometry during discrete flood events is lacking due to limited direct measurements of flood-scale erosion/deposition. The emergence of novel remote sensing methods allows for quantifying morphological modifications caused by floods in alluvial streams. This study utilizes drone surveys and hydrological data to quantitatively investigate the relation between channel evolution in alluvial tributaries draining to the receding Dead Sea and the hydrological characteristics of flash floods. Drone-based photogrammetric surveys were conducted before and after 25 floods, over a period of four years, to generate centimeter-scale Digital Elevation Models (DEM) and orthophoto maps of two major streams. The outcomes of these DEMs are maps of ground elevation changes (erosion/deposition), thalweg longitudinal profiles, and channel cross sections, revealing the incision/aggradation along and across the streams. Statistical comparison of results with flow hydrographs identified potential relations linking the hydrological characteristics of each flood and the corresponding geomorphological modifications. Peak discharge emerged as the primary factor influencing sediment removal, leading to more efficient sediment evacuation and a negative sediment budget with increased discharge. Water volumes of floods also exhibited a secondary effect on the sediment budget. The chronological order of floods, whether first or later in the season, was identified as the primary factor determining incision magnitude. Knickpoints formed at the streams’ outlets during the dry period, when lake-level drops, amplifying the impact of the first flood. These findings have potential implications for infrastructure planning and environmental management in the context of climate change and altered water runoff. The research highlights the efficiency of drone-based photogrammetry for cost-effective and timely data collection, providing invaluable flexibility for field research.
Adaptive and Anti-Drift Motion Constraints for Object Tracking in Satellite Videos
Authors: Fan Junyu , Ji Shunping
Published: April 2024

Object tracking in satellite videos has garnered significant attention due to its increasing importance. However, several challenging attributes, such as the presence of tiny objects, occlusions, similar objects, and background clutter interference, make it a difficult task. Many recent tracking algorithms have been developed to tackle these challenges in tracking a single interested object, but they still have some limitations in addressing them effectively. This paper introduces a novel correlation filter-based tracker, which uniquely integrates attention-enhanced bounding box regression and motion constraints for improved single-object tracking performance. Initially, we address the regression-related interference issue by implementing a spatial and channel dual-attention mechanism within the search area’s region of interest. This enhancement not only boosts the network’s perception of the target but also improves corner localization. Furthermore, recognizing the limitations in small size and low resolution of target appearance features in satellite videos, we integrate motion features into our model. A long short-term memory (LSTM) network is utilized to create a motion model that can adaptively learn and predict the target’s future trajectory based on its historical movement patterns. To further refine tracking accuracy, especially in complex environments, an anti-drift module incorporating motion constraints is introduced. This module significantly boosts the tracker’s robustness. Experimental evaluations on the SatSOT and SatVideoDT datasets demonstrate that our proposed tracker exhibits significant advantages in satellite video scenes compared to other recent trackers for common scenes or satellite scenes.
Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks
Authors: Frid Alan , Ben-Shimol Yehuda , Manor Erez , Greenberg Shlomo
Published: April 2024

The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of −10 dB using the LSTM network and fused features.
Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach
Authors: Aoun Charbel Geryes , Mansour Noura , Dornaika Fadi , Lagadec Loic
Published: April 2024

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices’ hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems’ development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity’s complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs.
Optimal Microphone Array Placement Design Using the Bayesian Optimization Method
Authors: Zhang Yuhan , Li Zhibao , Yiu Ka Fai Cedric
Published: April 2024

In addition to the filter coefficients, the location of the microphone array is a crucial factor in improving the overall performance of a beamformer. The optimal microphone array placement can considerably enhance speech quality. However, the optimization problem with microphone configuration variables is non-convex and highly non-linear. Heuristic algorithms that are frequently employed take a long time and have a chance of missing the optimal microphone array placement design. We extend the Bayesian optimization method to solve the microphone array configuration design problem. The proposed Bayesian optimization method does not depend on gradient and Hessian approximations and makes use of all the information available from prior evaluations. Furthermore, Gaussian process regression and acquisition functions make up the Bayesian optimization method. The objective function is given a prior probabilistic model through Gaussian process regression, which exploits this model while integrating out uncertainty. The acquisition function is adopted to decide the next placement point based upon the incumbent optimum with the posterior distribution. Numerical experiments have demonstrated that the Bayesian optimization method could find a similar or better microphone array placement compared with the hybrid descent method and computational time is significantly reduced. Our proposed method is at least four times faster than the hybrid descent method to find the optimal microphone array configuration from the numerical results.
Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers
Authors: Crognale Marianna , Rinaldi Cecilia , Potenza Francesco , Gattulli Vincenzo , Colarieti Andrea , Franchi Fabio
Published: April 2024

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.
Advancing ADAS Perception: A Sensor-Parameterized Implementation of the GM-PHD Filter
Authors: Bader Christian , Schwieger Volker
Published: April 2024

Modern vehicles equipped with Advanced Driver Assistance Systems (ADAS) rely heavily on sensor fusion to achieve a comprehensive understanding of their surrounding environment. Traditionally, the Kalman Filter (KF) has been a popular choice for this purpose, necessitating complex data association and track management to ensure accurate results. To address errors introduced by these processes, the application of the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter is a good choice. This alternative filter implicitly handles the association and appearance/disappearance of tracks. The approach presented here allows for the replacement of KF frameworks in many applications while achieving runtimes below 1 ms on the test system. The key innovations lie in the utilization of sensor-based parameter models to implicitly handle varying Fields of View (FoV) and sensing capabilities. These models represent sensor-specific properties such as detection probability and clutter density across the state space. Additionally, we introduce a method for propagating additional track properties such as classification with the GM-PHD filter, further contributing to its versatility and applicability. The proposed GM-PHD filter approach surpasses a KF approach on the KITTI dataset and another custom dataset. The mean OSPA(2) error could be reduced from 1.56 (KF approach) to 1.40 (GM-PHD approach), showcasing its potential in ADAS perception.
Wearable Biosensor Technology in Education: A Systematic Review
Authors: Hernández-Mustieles María A. , Lima-Carmona Yoshua E. , Pacheco-Ramírez Maxine A. , Mendoza-Armenta Axel A. , Romero-Gómez José Esteban , Cruz-Gómez César F. , Rodríguez-Alvarado Diana C. , Arceo Alejandro , Cruz-Garza Jesús G. , Ramírez-Moreno Mauricio A. , Lozoya-Santos Jorge de J.
Published: April 2024

Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012–2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.
An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder
Authors: Yang Qiying , Guo Rongzuo
Published: April 2024

Existing industrial image anomaly detection techniques predominantly utilize codecs based on convolutional neural networks (CNNs). However, traditional convolutional autoencoders are limited to local features, struggling to assimilate global feature information. CNNs’ generalizability enables the reconstruction of certain anomalous regions. This is particularly evident when normal and abnormal regions, despite having similar pixel values, contain different semantic information, leading to ineffective anomaly detection. Furthermore, collecting abnormal image samples during actual industrial production poses challenges, often resulting in data imbalance. To mitigate these issues, this study proposes an unsupervised anomaly detection model employing the Vision Transformer (ViT) architecture, incorporating a Transformer structure to understand the global context between image blocks, thereby extracting a superior representation of feature information. It integrates a memory module to catalog normal sample features, both to counteract anomaly reconstruction issues and bolster feature representation, and additionally introduces a coordinate attention (CA) mechanism to intensify focus on image features at both spatial and channel dimensions, minimizing feature information loss and thereby enabling more precise anomaly identification and localization. Experiments conducted on two public datasets, MVTec AD and BeanTech AD, substantiate the method’s effectiveness, demonstrating an approximate 20% improvement in average AUROC% at the image level over traditional convolutional encoders.
Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning
Authors: Sunal Cem Ekin , Velisavljevic Vladan , Dyo Vladimir , Newton Barry , Newton Jake
Published: April 2024

The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy.
Identification and Positioning of Abnormal Maritime Targets Based on AIS and Remote-Sensing Image Fusion
Authors: Wang Xueyang , Song Xin , Zhao Yong
Published: April 2024

The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only provides positional and morphological characteristics without detailed identity information, presenting limitations as a sole data source. To address this issue, this paper proposes a method for enhancing maritime target identification and positioning accuracy through the fusion of Automatic Identification System (AIS) data and satellite remote-sensing imagery. The AIS utilizes radio communication to acquire multidimensional feature information describing targets, serving as an auxiliary data source to complement the limitations of image data and achieve maritime target identification. Additionally, the positional information provided by the AIS can serve as maritime control points to correct positioning errors and enhance accuracy. By utilizing data from the Jilin-1 Spectral-01 satellite imagery with a resolution of 5 m and AIS data, the feasibility of the proposed method is validated through experiments. Following preprocessing, maritime target fusion is achieved using a point-set matching algorithm based on positional features and a fuzzy comprehensive decision method incorporating attribute features. Subsequently, the successful fusion of target points is utilized for positioning error correction. Experimental results demonstrate a significant improvement in maritime target positioning accuracy compared to raw data, with over a 70% reduction in root mean square error and positioning errors controlled within 4 pixels, providing relatively accurate target positions that essentially meet practical requirements.
A New Denoising Method for Belt Conveyor Roller Fault Signals
Authors: Hao Xuedi , Zhang Jiajin , Gao Yingzong , Zhu Chenze , Tang Shuo , Guo Pengfei , Pei Wenliang
Published: April 2024

In the process of the intelligent inspection of belt conveyor systems, due to problems such as its long duration, the large number of rollers, and the complex working environment, fault diagnosis by acoustic signals is easily affected by signal coupling interference, which poses a great challenge to selecting denoising methods of signal preprocessing. This paper proposes a novel wavelet threshold denoising algorithm by integrating a new biparameter and trisegment threshold function. Firstly, we elaborate on the mutual influence and optimization process of two adjustment parameters and three wavelet coefficient processing intervals in the BT-WTD (the biparameter and trisegment of wavelet threshold denoising, BT-WTD) denoising model. Subsequently, the advantages of the proposed threshold function are theoretically demonstrated. Finally, the BT-WTD algorithm is applied to denoise the simulation signals and the vibration and acoustic signals collected from the belt conveyor experimental platform. The experimental results indicate that this method’s denoising effectiveness surpasses that of traditional threshold function denoising algorithms, effectively addressing the denoising preprocessing of idler roller fault signals under strong noise backgrounds while preserving useful signal features and avoiding signal distortion problems. This research lays the theoretical foundation for the non-contact intelligent fault diagnosis of future inspection robots based on acoustic signals.
Influence on Sample Determination for Deep Learning Electromagnetic Tomography
Authors: Zhao Pengfei , Liu Ze
Published: April 2024

Deep learning (DL) has been frequently applied in the image reconstruction of electromagnetic tomography (EMT) in recent years. It offers the potential to achieve higher-quality image reconstruction. Among these, research on samples is relatively scarce. Samples are the cornerstone for both large and small models, which is easy to ignore. In this paper, a deep learning electromagnetic tomography (DL-EMT) model with nine elements is established. Complete simulation and experimental samples are obtained based on this model. On the sample sets, the reconstruction quality is observed by adjusting the size and configuration of the training set. The Mann–Whitney U test shows that beyond a certain point, the addition of more samples to the training data fed into the deep learning network does not result in an obvious improvement statistically in the quality of the reconstructed images. This paper proposes a CC-building method for optimizing a sample set. This method is based on the Pearson correlation coefficient calculation, aiming to establish a more effective sample base for DL-EMT image reconstruction. The statistical analysis shows that the CC-building method can significantly improve the image reconstruction effect in a small and moderate sample size. This method is also validated by experiments.