bluuebunny/crossref_metadata_2025 huggingface dataset:
Bond Strength of Sandblasted PEEK with Dental Methyl Methacrylate-Based Cement or Composite-Based Resin Cement
Authors: Hata Kentaro , Komagata Yuya , Nagamatsu Yuki , Masaki Chihiro , Hosokawa Ryuji , Ikeda Hiroshi
Published: April 2023

Poly-ether-ether-ketone (PEEK) is commonly employed in dental prostheses owing to its excellent mechanical properties; however, it is limited by its low bond strength with dental resin cement. This study aimed to clarify the type of resin cement most suitable for bonding to PEEK: methyl methacrylate (MMA)-based resin cement or composite-based resin cement. For this purpose, two MMA-based resin cements (Super-Bond EX and MULTIBOND II) and five composite-based resin cements (Block HC Cem, RelyX Universal Resin Cement, G-CEM LinkForce, Panavia V5, and Multilink Automix) were used in combination with appropriate adhesive primers. A PEEK block (SHOFU PEEK) was initially cut, polished, and sandblasted with alumina. The sandblasted PEEK was then bonded to resin cement with adhesive primer according to the manufacturer’s instructions. The resulting specimens were immersed in water at 37 °C for 24 h, followed by thermocycling. Subsequently, the tensile bond strengths (TBSs) of the specimens were measured; the TBSs of the composite-based resin cements after thermocycling were found to be zero (G-CEM LinkForce, Panavia V5, and Multilink Automix), 0.03 ± 0.04 (RelyX Universal Resin Cement), or 1.6 ± 2.7 (Block HC Cem), whereas those of Super-Bond and MULTIBOND were 11.9 ± 2.6 and 4.8 ± 2.3 MPa, respectively. The results demonstrated that MMA-based resin cements exhibited stronger bonding to PEEK than composite-based resin cements.
Electrospun Fibrous Nanocomposite Sensing Materials for Monitoring Biomarkers in Exhaled Breath
Authors: Chang Yin-Hsuan , Hsieh Ting-Hung , Hsiao Kai-Chi , Lin Ting-Han , Hsu Kai-Hsiang , Wu Ming-Chung
Published: April 2023

Human−exhaled breath mainly contains water, oxygen, carbon dioxide, and endogenous gases closely related to human metabolism. The linear relationship between breath acetone and blood glucose concentration has been revealed when monitoring diabetes patients. Considerable attention has been directed toward developing a highly sensitive volatile organic compounds (VOCs) sensing material that can detect breath acetone. In this study, we propose a tungsten oxide/tin oxide/silver/poly (methyl methacrylate) (WO3/SnO2/Ag/PMMA) sensing material fabricated using the electrospinning technique. By monitoring the evolution of sensing materials’ extinction spectra, low concentrations of acetone vapor can be detected. Moreover, the interfaces between SnO2 and WO3 nanocrystals construct n−n junctions, which generate more electron–hole pairs than those without such structure when the light strikes. This helps to improve the sensitivity of sensing materials when they are subjected to acetone surroundings. The established sensing materials (WO3/SnO2/Ag/PMMA) exhibit a sensing limit of 20 ppm for acetone vapor and show specificity for acetone even in ambient humidity.
Recent Advances in Nanoparticle Development for Drug Delivery: A Comprehensive Review of Polycaprolactone-Based Multi-Arm Architectures
Authors: El Yousfi Ridouan , Brahmi Mohamed , Dalli Mohammed , Achalhi Nafea , Azougagh Omar , Tahani Abdesselam , Touzani Rachid , El Idrissi Abderrahmane
Published: April 2023

Controlled drug delivery is a crucial area of study for improving the targeted availability of drugs; several polymer systems have been applied for the formulation of drug delivery vehicles, including linear amphiphilic block copolymers, but with some limitations manifested in their ability to form only nanoaggregates such as polymersomes or vesicles within a narrow range of hydrophobic/hydrophilic balance, which can be problematic. For this, multi-arm architecture has emerged as an efficient alternative that overcame these challenges, with many interesting advantages such as reducing critical micellar concentrations, producing smaller particles, allowing for various functional compositions, and ensuring prolonged and continuous drug release. This review focuses on examining the key variables that influence the customization of multi-arm architecture assemblies based on polycaprolactone and their impact on drug loading and delivery. Specifically, this study focuses on the investigation of the structure–property relationships in these formulations, including the thermal properties presented by this architecture. Furthermore, this work will emphasize the importance of the type of architecture, chain topology, self-assembly parameters, and comparison between multi-arm structures and linear counterparts in relation to their impact on their performance as nanocarriers. By understanding these relationships, more effective multi-arm polymers can be designed with appropriate characteristics for their intended applications.
Influence of Fabrication Technique on Adhesion and Biofilm Formation of Candida albicans to Conventional, Milled, and 3D-Printed Denture Base Resin Materials: A Comparative In Vitro Study
Authors: Osman Reham B. , Khoder Ghalia , Fayed Bahgat , Kedia Reena Arora , Elkareimi Yaser , Alharbi Nawal
Published: April 2023

The aim of this study was to evaluate the adhesion and biofilm formation of Candida albicans (C. albicans) on conventionally fabricated, milled, and 3D-printed denture base resin materials in order to determine the susceptibility of denture contamination during clinical use. Specimens were incubated with C. albicans (ATCC 10231) for 1 and 24 h. Adhesion and biofilm formation of C. albicans were assessed using the field emission scanning electron microscopy (FESEM). The XTT (2,3-(2-methoxy-4-nitro-5-sulphophenyl)-5-[(phenylamino) carbonyl]-2H-tetrazolium hydroxide) assay was used for the quantification of fungal adhesion and biofilm formation. The data were analyzed using GraphPad Prism 8.02 for windows. One-way ANOVA with Tukey’s post hoc testing were performed with a statistical significance level set at α = 0.05. The quantitative XTT biofilm assay revealed significant differences in the biofilm formation of C. albicans between the three groups in the 24 h incubation period. The highest proportion of biofilm formation was observed in the 3D-printed group, followed by the conventional group, while the lowest candida biofilm formation was observed in the milled group. The difference in biofilm formation among the three tested dentures was statistically significant (p < 0.001). The manufacturing technique has an influence on the surface topography and microbiological properties of the fabricated denture base resin material. Additive 3D-printing technology results in increased candida adhesion and the roughest surface topography of maxillary resin denture base as compared to conventional flask compression and CAD/CAM milling techniques. In a clinical setting, patients wearing additively manufactured maxillary complete dentures are thus more susceptible to the development of candida-associated denture stomatitis and accordingly, strict oral hygiene measures and maintenance programs should be emphasized to patients.
Biofuel Blends for Desalination Units: Comparison and Assessments
Authors: Elfasakhany Ashraf
Published: April 2023

Although desalinations with renewables were introduced some time ago, conventional desalination units are still applied. Conventional desalinations account for 90% of desalinations worldwide. Yet, they have two significant issues: a high demand for energy and a high level of environmental contaminants. Such issues are studied and remedies are suggested in the current study. Varieties of biofuel blends in dual and ternary bases are investigated experimentally for indirect desalination. Results showed that ternary blends can introduce lower desalination potentials than fossil fuels by about 4–7%. The best ternary blends for the indirect desalination process are iBE, followed by niB, and finally EM. The EGT of iBE is greater than niB and EM by about 1.1 and 1.2%, respectively. Both n-butanol/iso-butanol–gasoline dual blends introduced an almost similar desalination potential as the ternary blends (e.g., lower desalination by about 4.4 and 4.7%). Nevertheless, bio-ethanol/bio-methanol–gasoline dual blends introduced greater desalination potentials than the fossil fuel by 3.2 and 3%, respectively. Regarding environmental issues, both ternary and dual blends introduced lower CO and UHC emissions than fossil fuels in varying degrees. M presented the lowest CO by about 30%, followed by EM by about 21%, and lastly E by about 20%, compared to G. However, the lowest UHC is presented by EM followed by nB and niB with rates of 18, 16.2, and 13.5%. Results also showed that the engine speed has a considerable effect on the desalination process and environment; low engine speed is recommended in the case of applying ternary blends, as well as dual n-butanol/iso-butanol–gasoline blends. Alternatively, in the case of applying bio-ethanol/bio-methanol–gasoline dual blends, moderate engine speed is preferable.
Portable NIR Spectroscopic Application for Coffee Integrity and Detection of Adulteration with Coffee Husk
Authors: Boadu Vida Gyimah , Teye Ernest , Amuah Charles L. Y. , Lamptey Francis Padi , Sam-Amoah Livingstone Kobina
Published: April 2023

Reliable and user-friendly discrimination of coffee bean integrity and quantification of adulteration in the coffee bean processing value chain would be vital for ensuring consumer trust in quality control and traceability management. In this research, a portable short-wave NIR spectroscopy coupled with chemometric data analysis was employed under different pre-treatments to develop a rapid detection technique. Different pre-processing treatments (multiplicative scatter correction; MSC, standard normal variant; SNV, first derivative; FD) together with multivariate techniques; support vector machine (SVM), linear discriminant analysis (LDA), neural network (NN), and random forest (RF) were comparatively assessed using accuracy and correlation coefficient (R) for discrimination and quantification. The results showed that the FD-LDA model had 97.78% and 100 % in both the calibration set and prediction set. In comparison, the SPA-PLS model had R = 0.9711 and 0.9897 in both the calibration set and prediction set. The outcome of this study showed portable short-wave NIR spectroscopic techniques could be used for examining the integrity of coffee.
Study and Validation of a Novel Grouting Clamp Type Deepwater Oilfield Pipeline Repair Method Based on Computational Fluid Dynamics
Authors: Lu Yuliang , Liu Dongtao , Wei Xinjie , Xiao Qiaogang , Song Jiming , Yu Yajun
Published: April 2023

In order to handle the corrosion of underwater production pipe sinks in deepwater oil fields, a non-solid phase silicone plugging agent, an external clamp, and an underwater injection tool are combined in this paper’s innovative pipeline repair technique proposal. The optimal main agent to curing the agent ratio for non-solid phase silicone plugging agents was found to be 100:25, which was achieved through an experimental examination of the curing process. The compressive and cementing strength changes in the curd plugging agent were disclosed by testing and evaluating the mechanical behavior of the plugging agent. In addition, the limits of the compressive and cementing strength were found to be 143 MPa and 11.6 MPa, respectively. Based on this, a computational-fluid-dynamics(CFD)-based analytical approach of the complicated flow field in a deep sea environment on the eroding impact of a plugging agent was developed. Through numerical simulation testing, the mathematical relationship between the plugging agent’s maximum pressure and the flow field’s velocity was established. The limits of saltwater and petroleum oil on the erosion of the plugging agent were found to be 4.19 MPa and 3.29 MPa, respectively, which are values that are far below their strength limits. In this way, the viability and efficacy of this novel pipeline repair technique were confirmed. The study presented in this paper sets the groundwork for an in-depth analysis and optimization of novel corrosion control solutions for deepwater oil field subsea process pipes.
Multi-Objective Multi-Stage Optimize Scheduling Algorithm for Nonlinear Virtual Work-Flow Based on Pareto
Authors: Luo Zhiyong , Liu Xintong , Tan Shanxin , Xu Haifeng , Liu Jiahui
Published: April 2023

Work-flow scheduling is for finding the allocation method to achieve optimal resource utilization. In the scheduling process, constraints, such as time, cost and quality, need to be considered. How to balance these parameters is a NP-hard problem, and the nonlinear manufacturing process increases the difficulty of scheduling, so it is necessary to provide an effective heuristic algorithm. Aiming at these problems, a multi-objective nonlinear virtual work-flow model was set up, and a multi-objective staged scheduling optimization algorithm with the objectives of minimizing cost and time and maximizing quality was proposed. The algorithm includes three phases: the virtualization phase abstracts tasks and services into virtual nodes to generate a virtual work-flow model; the virtual scheduling phase divides optimized segments and obtains the solution set through reverse iteration; the generation phase obtains the scheduling path according to the Pareto dominance. The proposed algorithm performed 10.5% better in production quality than the minimum critical path algorithm, reduced the time to meet the time constraint by 9.1% and saves 13.7% more of the cost than the production accuracy maximization algorithm.
Progress in Electrodeposited Copper Catalysts for CO2 Conversion to Valuable Products
Authors: Maniam Kranthi Kumar , Maniam Madhuri , Diaz Luis A. , Kukreja Hari K. , Papadopoulos Athanasios I. , Kumar Vikas , Seferlis Panos , Paul Shiladitya
Published: April 2023

Carbon capture, utilisation and storage (CCUS) is a key area of research for CO2 abatement. To that end, CO2 capture, transport and storage has accrued several decades of development. However, for successful implementation of CCUS, utilisation or conversion of CO2 to valuable products is important. Electrochemical conversion of the captured CO2 to desired products provides one such route. This technique requires a cathode “electrocatalyst” that could favour the desired product selectivity. Copper (Cu) is unique, the only metal “electrocatalyst” demonstrated to produce C2 products including ethylene. In order to achieve high-purity Cu deposits, electrodeposition is widely acknowledged as a straightforward, scalable and relatively inexpensive method. In this review, we discuss in detail the progress in the developments of electrodeposited copper, oxide/halide-derived copper, copper-alloy catalysts for conversion of CO2 to valuable products along with the future challenges.
Research on a Multi-Agent Cooperative Control Method of a Distributed Energy Storage System
Authors: Shi Tao , Qiu Xuan , Tang Chunsheng , Qu Linan
Published: April 2023

For the flexible regulation requirements of new power systems with a high proportion of new energy, this paper proposes a multi-point distributed energy storage system control method based on the idea of multi-agent cooperative control. On the one hand, the method transforms and upgrades the strategies of each distributed battery energy storage control system to make it a terminal agent with active response and control functions; on the other hand, a coordination agent is set between the power grid and each terminal agent, the coordinated control of the distributed energy storage system is realized by the information interaction between the coordination agent and the terminal agent. The simulation results show that the method can fully exploit the aggregate regulation ability of the distributed energy storage system, quickly stabilize the frequency fluctuation of new energy, and support the safe and economical operation of the system.
Research on Adaptive Distribution Control Strategy of Braking Force for Pure Electric Vehicles
Authors: Liu Jingang , Bu Lei , Fu Bing , Zheng Jianyun , Wang Gaosheng , He Lihong , Hu Yuliang
Published: April 2023

The actual driving conditions of electric vehicles (EVs) are complex and changeable. Limited by road adhesion conditions, it is necessary to give priority to ensuring safety, taking into account the energy recovery ratio of the vehicle during braking to obtain better braking quality. In this work, an electric vehicle with an EHB (electro-hydraulic braking) system whose braking force adaptive distribution control strategy is studied. Firstly, the vehicle dynamics model, including seven degrees of freedom, tire, drive motor, main reducer, battery pack, and braking system, was constructed, which is attributed to the vehicle configuration and braking system scheme. Second, based on curve I and ECE regulations, the adaptive braking force distribution control strategy was formulated by taking the maximum regenerative braking torque as the inflection point, the synchronous adhesion coefficient as the desired point, and the battery SOC, road adhesion coefficient, and braking strength as the threshold. Finally, the vehicle dynamics simulation model was built on the Matlab/Simulink platform, and the simulation results verified the feasibility of the proposed braking force adaptive allocation control strategy. The research shows that the adaptive distribution control strategy can better adapt to the complex and variable driving conditions of the vehicle by combining the inflection point and the desired point. The braking energy recovery ratios of the vehicle under the NEDC and NYCC cycle conditions on a high adhesion road are 52.62% and 47.45%. The braking force distribution curve is close to curve I under the low adhesion extreme road.
Mathematical Modeling of Microbial Electrolysis Cells for Enhanced Urban Wastewater Treatment and Hydrogen Generation
Authors: Rahimi Narges , Eicker Ursula
Published: April 2023

Conventional wastewater treatment plants (CWTPs) are intensive energy consumers. New technologies are emerging for wastewater treatment such as microbial electrolysis cells (MECs) that can simultaneously treat wastewater and generate hydrogen as a renewable energy source. Mathematical modeling of single and dual-chamber microbial electrolysis cells (SMEC and DMEC) has been developed based on microbial population growth in this study. The model outputs were validated successfully with previous works, and are then used for comparisons between the SMEC and DMEC regarding the hydrogen production rate (HPR). The results reveal that the daily HPR in DMEC is higher than in SMEC, with about 0.86 l H2 and 0.52 l H2, respectively, per 1 L of wastewater. Moreover, the results have been used to compare the HPR in water electrolysis (WE) processes and MECs. WE consume 51 kWh to generate 1 kg of hydrogen, while SMEC and DMEC require only 30 kWh and 24.5 kWh, respectively.
Effect of Soaking and Proteolytic Microorganisms Growth on the Protein and Amino Acid Content of Jack Bean Tempeh (Canavalia ensiformis)
Authors: Yarlina Vira Putri , Djali Mohammad , Andoyo Robi , Lani Mohd Nizam , Rifqi Muhammad
Published: April 2023

Soaking is an important step in making tempeh. Tempeh fermentation normally involves the natural presence of proteolytic bacteria capable of producing protease enzymes to break down peptide bonds in protein molecules. This study evaluated the protein and amino acid content of Jack bean tempeh (Canavalia ensiformis) soaked in distilled water during natural fermentation for 12, 24, 36, and 48 h. In this study, the crude proteins were determined using the Kjeldahl technique, amino acids were determined from protein hydrolysis, and proteolytic bacteria were enumerated for Total Plate Counts and further identified using Vitek 2.0 Compact System. The results showed that soaked Jack beans have higher protein and amino acid content, with sixteen essential and non-essential amino acids required for human bodies. The protein content of soaked Jack beans varied from 35% at 12 h to 32% at 24 and 36 h and 33% at 48 h. Soaking for 12 h yielded the highest amino acid concentration of 38,000 mg/kg L-glutamate, and the lowest of 14,000 mg/kg L-proline. Seven isolated bacteria showed proteolytic activity on Skim Milk Agar with a clear zone of 3.00 mm to 10.65 mm surrounding the colony. The bacteria identified were Pediococcus pentosaceus, Stenorophomonas maltophilia, Cronobacter sakazakii, and Klebsiella pneumonia ssp. In summary, Lactobacillaceae and Enterobacteriaceae were the predominant bacteria during tempeh fermentation, indicating the synergistic interaction between these microflorae during soaking conditions as part of their survival in this hostile environment.
Considering Waste Generation in the Energy Sector during the Transition to a Circular Economy
Authors: Karaeva Anzhelika , Magaril Elena
Published: April 2023

The need to reduce the negative environmental impact of energetics with the growing energy demand in the world is the core objective of the industry for the upcoming decades. The global agenda in the field of environmental protection increases the requirements for evaluation of the energy investment projects, in particular for their environmental efficiency. Currently, much attention is paid to assessing the impact of the project on atmospheric air, water bodies and land resources, including the formation of industrial waste during the operational stage. The formation of production and consumption waste at other stages of the project life cycle is not considered either when generating cash flows or when conducting the assessment pf environmental efficiency. It might significantly reduce the reliability of assessment results. The purpose of this study is to develop a system of integrated accounting for all types of waste generated and the environmental costs incurred for handling them during the environmental and economic assessment of energy projects. The paper discusses modern waste management practices at energy enterprises, waste generation at various stages of investment projects implementation, and provides recommendations on waste accounting when assessing their environmental and economic efficiency.
Give Them Time to Ponder
Authors: Irwin Anne-Marie T
Published: April 2023

The current educational climate poses challenges for both the educator and learner when it comes to deep learning. A significant part of the solution lies in providing both time and space wherein a deep learning can occur, despite the challenges. After situating the SALT Approach (acronym for Scripture and Liturgy Teaching Approach), progressing from its doctoral study roots, the paper unpacks ways that the SALT approach harnesses time to facilitate deep thinking. The pedagogical ways discussed can also be adapted for use across the curriculum, as the ‘toolbox’ of ideas in the final section of the paper summarizes elements that can facilitate a classroom climate and setting leading to peace and productivity in the learning space.
The Realm of Tianfang Advocated by the Daoist Philosophy of Naturalism
Authors: Wang Shaojun
Published: April 2023

More and more people talk about so-called Daoist ecological thought. Actually, Daoism does not have a ready-made ecological thought. However, it indeed can act as a vital theoretical resource for constructing such thought. In this article, I will argue how this is possible, and what realm Daoism can attain in the relation between human and nature. I will mainly employ such methods as original problem research, literature analysis and comparative research in the inquiry. Compared with traditional Western metaphysics, Daoist ontology can provide stronger philosophical support for the value and significance of empirical things. In addition, Dao does not dominate things like a personal god with will, but gives them the chance to grow and develop according to their own nature. Lao-Zhuang called on people to imitate this character of Dao. They believed that the primitive nature itself was worthy of respect, and urged us to set limits for ourselves and never to distort things’ natural propensities to suit us. Consequently, natural things are neither overshadowed by a noumenon, nor are they subject to humans’ conquering and abuse, so they are capable of flourishing freely. This is precisely the realm of tianfang which Daoism seeks.
Hydrochemical Indicator Analysis of Seawater Intrusion into Coastal Aquifers of Semiarid Areas
Authors: Wurl Jobst , Imaz-Lamadrid Miguel Angel , Mendez-Rodriguez Lía Celina , Hernández-Morales Pablo
Published: April 2023

Saltwater intrusion into groundwater systems is a problem worldwide and is induced mainly by human activities, such as groundwater overexploitation and climate change. The coastal Los Planes aquifer in the southern part of the Baja California Peninsula (Mexico) is affected by seawater intrusion due to more than 40 years of groundwater overexploitation. A dataset of 55 samples was compiled, including 18 samples from our campaigns between 2014 and 2016. Several methods exist to define the impact of seawater in a coastal aquifer, such as the “seawater fraction”, the “Chloro-Alkaline Indices”, the “Hydrochemical Facies Evolution Diagram”, and the “Saltwater Mixing Index”. These methods provide reasonable results for most of the coastal zone of the Los Planes aquifer. A slight increase in mineralization was observed from 2014 to 2016 compared with the situation in 2003. However, in its northwestern part, samples from hydrothermal wells were not recognized by these methods. Here, the aquifer is affected mainly by thermal water with elevated mineralization, introduced through the El Sargento fault, a main fault, which cuts through the study area in the north–south direction. By considering known hydrothermal manifestations in the interpretation, samples could be classified as a combination of four end-members: fresh groundwater, seawater, and the composition of two types of thermal water. One thermal endmember with very low mineralization coincides with the thermal water described from the Los Cabos Block, where meteoric water represents the source (found in the Sierra la Laguna). The second endmember is comparable to coastal thermal manifestations where seawater represents the main source. Therefore, the higher mineralization in the northwestern zone is the result of the mobilization of thermal groundwater and direct mixing with seawater, which is introduced locally at the coast due to overextraction. This finding is important for future management strategies of the aquifer.
Whoops! It Happened Again: Demand for Insurance That Covers Multiple Risks
Authors: Hong Liang , Schlesinger Harris , Zhuang Boyi
Published: April 2023

This article studies insurance demand in a two-period framework in which an individual faces risks in both current and future periods. Models for insurance with and without the presence of endogenous saving are both discussed. In contrast to what most literature suggests, when decisions on insurance and saving are made separately, insurance alone does not always unambiguously reduce risk, and decision makers might demand more insurance when there is a positive loading on the premium than when the insurance price is actuarially fair. We compare the demand for insurance in our framework with that in a two-period model where risk is concentrated in the second period and derive the conditions under which these demands differ. We examine the effects of risk aversion and derive the conditions under which a more risk-averse individual demands more or less insurance.
Mapping Wheat Take-All Disease Levels from Airborne Hyperspectral Images Using Radiative Transfer Models
Authors: Wang Jian , Shi Lei , Fu Yuanyuan , Si Haiping , Liu Yi , Qiao Hongbo
Published: April 2023

Take-all is a root disease that can severely reduce wheat yield, and wheat leaves with take-all disease show a large amount of chlorophyll loss. The PROSAIL model has been widely used for the inversion of vegetation physiological parameters with a clear physical meaning of the model and high simulation accuracy. Based on the chlorophyll deficiency characteristics, the reflectance data under different canopy chlorophyll contents were simulated using the PROSAIL model. In addition, inverse models of spectral reflectance profiles and canopy chlorophyll contents were constructed using a one-dimensional convolutional neural network (1D-CNN), and a transfer learning approach was used to detect the take-all disease levels. The spectral reflectance data of winter wheat acquired by an airborne imaging spectrometer during the filling period were used as input parameters of the model to obtain the chlorophyll content of the canopy. Finally, the results of the distribution of winter wheat take-all disease were mapped based on the relationship between take-all disease and the chlorophyll content of the canopy. The results showed that classification based on the deep learning model performed well for winter wheat take-all monitoring. This study can provide some reference basis for high-precision winter wheat take-all disease monitoring and can also provide some technical method references and ideas for remote sensing crop pest and disease remote sensing mapping.
Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure
Authors: Billi Dario , Croce Valeria , Bevilacqua Marco Giorgio , Caroti Gabriella , Pasqualetti Agnese , Piemonte Andrea , Russo Michele
Published: April 2023

The reconstruction of 3D geometries starting from reality-based data is challenging and time-consuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition.
Adaptive Local Cross-Channel Vector Pooling Attention Module for Semantic Segmentation of Remote Sensing Imagery
Authors: Wang Xiaofeng , Kang Menglei , Chen Yan , Jiang Wenxiang , Wang Mengyuan , Weise Thomas , Tan Ming , Xu Lixiang , Li Xinlu , Zou Le , Zhang Chen
Published: April 2023

Adding an attention module to the deep convolution semantic segmentation network has significantly enhanced the network performance. However, the existing channel attention module focusing on the channel dimension neglects the spatial relationship, causing location noise to transmit to the decoder. In addition, the spatial attention module exemplified by self-attention has a high training cost and challenges in execution efficiency, making it unsuitable to handle large-scale remote sensing data. We propose an efficient vector pooling attention (VPA) module for building the channel and spatial location relationship. The module can locate spatial information better by performing a unique vector average pooling in the vertical and horizontal dimensions of the feature maps. Furthermore, it can also learn the weights directly by using the adaptive local cross-channel interaction. Multiple weight learning ablation studies and comparison experiments with the classical attention modules were conducted by connecting the VPA module to a modified DeepLabV3 network using ResNet50 as the encoder. The results show that the mIoU of our network with the addition of an adaptive local cross-channel interaction VPA module increases by 3% compared to the standard network on the MO-CSSSD. The VPA-based semantic segmentation network can significantly improve precision efficiency compared with other conventional attention networks. Furthermore, the results on the WHU Building dataset present an improvement in IoU and F1-score by 1.69% and 0.97%, respectively. Our network raises the mIoU by 1.24% on the ISPRS Vaihingen dataset. The VPA module can also significantly improve the network’s performance on small target segmentation.
Rockfall Magnitude-Frequency Relationship Based on Multi-Source Data from Monitoring and Inventory
Authors: Janeras Marc , Lantada Nieves , Núñez-Andrés M. Amparo , Hantz Didier , Pedraza Oriol , Cornejo Rocío , Guinau Marta , García-Sellés David , Blanco Laura , Gili Josep A. , Palau Joan
Published: April 2023

Quantitative hazard analysis of rockfalls is a fundamental tool for sustainable risk management, even more so in places where the preservation of natural heritage and people’s safety must find the right balance. The first step consists in determining the magnitude-frequency relationship, which corresponds to the apparently simple question: how big and how often will a rockfall be detached from anywhere in the cliff? However, there is usually only scarce data on past activity from which to derive a quantitative answer. Methods are proposed to optimize the exploitation of multi-source inventories, introducing sampling extent as a main attribute for the analysis. This work explores the maximum possible synergy between data sources as different as traditional inventories of observed events and current remote sensing techniques. Both information sources may converge, providing complementary results in the magnitude-frequency relationship, taking advantage of each strength that overcomes the correspondent weakness. Results allow characterizing rockfall detachment hazardous conditions and reveal many of the underlying conditioning factors, which are analyzed in this paper. High variability of the hazard over time and space has been found, with strong dependencies on influential external factors. Therefore, it will be necessary to give the appropriate reading to the magnitude-frequency scenarios, depending on the application of risk management tools (e.g., hazard zoning, quantitative risk analysis, or actions that bring us closer to its forecast). In this sense, some criteria and proxies for hazard assessment are proposed in the paper.
Friction Velocity and Aerodynamic Drag Coefficient Retrieval from Sentinel-1 IW Cross-Polarization C-SAR Images under Hurricane Conditions
Authors: Ermakova Olga , Rusakov Nikita , Poplavsky Evgeny , Sergeev Daniil , Troitskaya Yuliya
Published: April 2023

This study presents an approach for friction velocity and aerodynamic drag coefficient retrieval utilizing C-band VH SAR observations from Sentinel-1. The dataset contained 14 SAR images collected under six hurricane scenes co-analyzed with stepped frequency microwave radiometer (SFMR) measurements. The basis for creating this approach utilizes the results proposed earlier linking the parameters of the atmospheric boundary layer from GPS-dropsondes data to the ocean surface emissivity from SFMR measurements. The obtained dependencies of the ocean surface emissivity on surface friction velocity, aerodynamic drag coefficient, and surface wind speed are analyzed together with the collocated SAR data leading to the new GMF valid for the retrieval of friction velocities ranging from 0.55–1.56 m/s and drag coefficient values ranging from 0.00076–0.00232 for all sub swaths. Within the framework of the proposed approach, dependences of the normalized radar cross-section on the surface wind speed were also obtained and used for comparison with existing GMFs to show that the proposed approach is valid. A good consistency was obtained when comparing our results with H14E and MS1A. As an example the distributions of friction velocity, drag coefficient, and surface wind speed retrieved from the Hurricane Maria SAR image (23 September 2017) were considered.
China’s Largest City-Wide Lockdown: How Extensively Did Shanghai COVID-19 Affect Intensity of Human Activities in the Yangtze River Delta?
Authors: Jiang Luguang , Liu Ye
Published: April 2023

COVID-19 has been the most widespread and far-reaching public health emergency since the beginning of the 21st century. The Chinese COVID-19 lockdown has been the most comprehensive and strict in the world. Based on the Shanghai COVID-19 outbreak in 2022, we analyzed the heterogeneous impact of the COVID-19 lockdown on human activities and urban economy using monthly nighttime light data. We found that the impact of lockdown on human activities in the Yangtze River Delta is very obvious. The number of counties in Shanghai, Jiangsu, Zhejiang and Anhui showing a downward trend of MNLR (Mean of Nighttime Light Radiation) is 100%, 97%, 99% and 85%, respectively. Before the outbreak of COVID-19, the proportion of counties with a downward trend of MNLR was 19%, 67%, 22% and 33%, respectively. Although the MNLR of some counties also decreased in 2019, the scope and intensity was far less than 2022. Under regular containment (2020 and 2021), MNLR in the Yangtze River Delta also showed a significant increase (MNLR change > 0). According to NLRI (Nighttime Light Radiation Influence), the Shanghai lockdown has significantly affected the surrounding provinces (Average NLRI < 0). Jiangsu is the most affected province other than Shanghai. At the same time, Chengdu-Chongqing, Guangdong–Hong Kong–Macao and the Triangle of Central China have no obvious linkage effect.
The Crustal Dynamics and Its Geological Explanation of the Three-Dimensional Co-Seismic Deformation Field for the 2021 Maduo MS7.4 Earthquake Based on GNSS and InSAR
Authors: Li Xiaobo , Chen Yanling , Wang Xiaoya , Xiong Renwei
Published: April 2023

Three-dimensional deformation is an important input to explore seismic mechanisms and geodynamics. The GNSS and InSAR technologies are commonly used to obtain the co-seismic three-dimensional deformation field. This paper focused on the effect of calculation accuracy caused by the deformation correlation between the reference point and the points involved in the solution, to build a high-accuracy three-dimensional deformation field for a detailed geological explanation. Based on the variance component estimation (VCE) method, the InSAR LOS, azimuthal deformation, and the GNSS horizontal and vertical deformation were integrated to solve the three-dimensional displacement of the study area in combination with the elasticity theory. The accuracy of the three-dimensional co-seismic deformation field of the 2021 Maduo MS7.4 earthquake obtained by the method proposed in this paper, was compared with that obtained from the only InSAR measurements obtained using a multi-satellite and multi-technology approach. The results showed the difference in root-mean-square errors (RMSE) of the integration and GNSS displacement was 0.98 cm, 5.64 cm, and 1.37 cm in the east–west, north–south and vertical direction respectively, which was better than the RMSE of the method using only InSAR and GNSS displacement, which was 5.2 cm and 12.2 cm in the east–west, north–south, and no vertical direction. With the geological field survey and aftershocks relocation, the results showed good agreement with the strike and the position of the surface rupture. The maximum slip displacement was about 4 m, which was consistent with the result of the empirical statistical formula. It was firstly found that the pre-existing fault controlled the vertical deformation on the south side of the west end of the main surface rupture caused by the Maduo MS7.4 earthquake, which provided the direct evidence for the theoretical hypothesis that large earthquakes could not only produce surface rupture on seismogenic faults, but also trigger pre-existing faults or new faults to produce surface rupture or weak deformation in areas far from seismogenic faults. An adaptive method was proposed in GNSS and InSAR integration, which could take into account the correlation distance and the efficiency of homogeneous point selection. Meanwhile, deformation information of the decoherent region could be recovered without interpolation of the GNSS displacement. This series of findings formed an essential supplement to the field surface rupture survey and provided a novel idea for the combination of the various spatial measurement technologies to improve the seismic deformation monitoring.
An Integrated Framework for Data Quality Fusion in Embedded Sensor Systems
Authors: Scholl Christoph , Spiegler Maximilian , Ludwig Klaus , Eskofier Bjoern M. , Tobola Andreas , Zanca Dario
Published: April 2023

The advancement of embedded sensor systems allowed the monitoring of complex processes based on connected devices. As more and more data are produced by these sensor systems, and as the data are used in increasingly vital areas of applications, it is of growing importance to also track the data quality of these systems. We propose a framework to fuse sensor data streams and associated data quality attributes into a single meaningful and interpretable value that represents the current underlying data quality. Based on the definition of data quality attributes and metrics to determine real-valued figures representing the quality of the attributes, the fusion algorithms are engineered. Methods based on maximum likelihood estimation (MLE) and fuzzy logic are used to perform data quality fusion by utilizing domain knowledge and sensor measurements. Two data sets are used to verify the proposed fusion framework. First, the methods are applied to a proprietary data set targeting sample rate inaccuracies of a micro-electro-mechanical system (MEMS) accelerometer and second, to the publicly available Intel Lab Data set. The algorithms are verified against their expected behavior based on data exploration and correlation analysis. We prove that both fusion approaches are capable of detecting data quality issues and providing an interpretable data quality indicator.
Gait-CNN-ViT: Multi-Model Gait Recognition with Convolutional Neural Networks and Vision Transformer
Authors: Mogan Jashila Nair , Lee Chin Poo , Lim Kian Ming , Ali Mohammed , Alqahtani Ali
Published: April 2023

Gait recognition, the task of identifying an individual based on their unique walking style, can be difficult because walking styles can be influenced by external factors such as clothing, viewing angle, and carrying conditions. To address these challenges, this paper proposes a multi-model gait recognition system that integrates Convolutional Neural Networks (CNNs) and Vision Transformer. The first step in the process is to obtain a gait energy image, which is achieved by applying an averaging technique to a gait cycle. The gait energy image is then fed into three different models, DenseNet-201, VGG-16, and a Vision Transformer. These models are pre-trained and fine-tuned to encode the salient gait features that are specific to an individual’s walking style. Each model provides prediction scores for the classes based on the encoded features, and these scores are then summed and averaged to produce the final class label. The performance of this multi-model gait recognition system was evaluated on three datasets, CASIA-B, OU-ISIR dataset D, and OU-ISIR Large Population dataset. The experimental results showed substantial improvement compared to existing methods on all three datasets. The integration of CNNs and ViT allows the system to learn both the pre-defined and distinct features, providing a robust solution for gait recognition even under the influence of covariates.
Smartphone Sensors for Indoor Positioning
Authors: Ashraf Imran , Park Yongwan , Zikria Yousaf Bin , Din Sadia
Published: April 2023

The explosive growth and wide proliferation of mobile devices, the majority of which are smartphones, led to the inception of several novel and intuitive services, including on-the-go services, online customer services, and location-based services (LBS) [...]
A Dynamic Trust-Related Attack Detection Model for IoT Devices and Services Based on the Deep Long Short-Term Memory Technique
Authors: Alghofaili Yara , Rassam Murad A.
Published: April 2023

The integration of the cloud and Internet of Things (IoT) technology has resulted in a significant rise in futuristic technology that ensures the long-term development of IoT applications, such as intelligent transportation, smart cities, smart healthcare, and other applications. The explosive growth of these technologies has contributed to a significant rise in threats with catastrophic and severe consequences. These consequences affect IoT adoption for both users and industry owners. Trust-based attacks are the primary selected weapon for malicious purposes in the IoT context, either through leveraging established vulnerabilities to act as trusted devices or by utilizing specific features of emerging technologies (i.e., heterogeneity, dynamic nature, and a large number of linked objects). Consequently, developing more efficient trust management techniques for IoT services has become urgent in this community. Trust management is regarded as a viable solution for IoT trust problems. Such a solution has been used in the last few years to improve security, aid decision-making processes, detect suspicious behavior, isolate suspicious objects, and redirect functionality to trusted zones. However, these solutions remain ineffective when dealing with large amounts of data and constantly changing behaviors. As a result, this paper proposes a dynamic trust-related attack detection model for IoT devices and services based on the deep long short-term memory (LSTM) technique. The proposed model aims to identify the untrusted entities in IoT services and isolate untrusted devices. The effectiveness of the proposed model is evaluated using different data samples with different sizes. The experimental results showed that the proposed model obtained a 99.87% and 99.76% accuracy and F-measure, respectively, in the normal situation, without considering trust-related attacks. Furthermore, the model effectively detected trust-related attacks, achieving a 99.28% and 99.28% accuracy and F-measure, respectively.
An Eye Movement Study in Unconventional Usage of Different Software Tools
Authors: Katona Jozsef
Published: April 2023

One of the main challenges of Human-Computer Interaction is the creation of UIs that enable the use of different systems in an easy and understandable method. The study analyses the student audience who uses software tools differently from the basis. In the research, two languages supporting UI implementation related to .NET technology, XAML and classic C#, were compared in terms of the cognitive load of test subjects. The results of the traditional knowledge level assessment tests and the answers to the questionnaires show that the UI implementation described in XAML is easier to read and understand than the same description in classic C#. When viewing the source codes, the eye movement parameters of the test subjects were also recorded and then evaluated, where a significant difference in the number and duration of fixations was observed, i.e., the interpretation of the classic C# source code showed a larger cognitive load. Overall, the results of the eye movement parameters supported the results of the other two measurement methods when comparing the different types of UI descriptions. The results established in the study and its conclusion may have an impact on programming education as well as industrial software development in the future, and also clearly shows the importance of choosing the development technology that best suits the person or development team.