bluuebunny/crossref_metadata_2025 huggingface dataset:
Improvement of Heat Release Rate Measurement from Woods Based on Their Combustion Products Temperature Rise
Authors: Martinka Jozef , Rantuch Peter , Martinka Filip , Wachter Igor , Štefko Tomáš
Published: April 2023

Heat release rate (HRR) is the principal fire characteristic of materials. There are three known methods for the measurement of HRR (based on oxygen consumption, mass loss rate, and combustion products temperature rise). The method based on oxygen consumption is considered to be the reference. However, this method is expensive and for a large part of laboratories and universities unavailable. The simplest method is based on combustion products’ temperature rise. However, this method has a fundamental problem with the temperature dependence of the heat capacity of combustion products and the thermal inertia of the measurement system. This problem has been solved by training neural networks to predict molar heat capacity and the amount of substance (chemical amount) flow rate of combustion products in the cone calorimeter exhaust duct. Data were obtained for six different wood species: birch (Betula verrucosa Ehrh.), oak (Quercus robur L.) spruce (Picea abies (L.) H. Karst.), locust (Robinia pseudoacacia L.), poplar (Populus nigra × P. maximowiczii L.), and willow (Salix alba L.) woods at heat fluxes from 25 to 50 kW m−2 have been used for neural network training. Data from three other wood species iroko (Milicia excelsa (Welw.) C.C. Berg), pine (Pinus sylvestris L.), and paulownia (Paulownia tomentosa (Thunb.) Steud.) woods have been used for testing of trained neural network. The average percentage ratio of the predicted to the true value of HRR (during the test) has been 103.8%. In addition to that, some key average fire characteristics of wood have been determined: critical heat flux 20.7 kW m−2, effective heat of combustion 14.01 MJ kg−1, and the average value of molar heat capacity of combustion products 0.045 kJ mol−1 K−1.
Lean-and-Green Strength Performance Optimization of a Tube-to-Tubesheet Joint for a Shell-and-Tube Heat Exchanger Using Taguchi Methods and Random Forests
Authors: Boulougouras Panagiotis , Besseris George
Published: April 2023

The failing tube-to-tubesheet joint is identified as a primary quality defect in the fabrication of a shell-and-tube heat exchanger. Operating in conditions of high pressure and temperature, a shell-and-tube heat exchanger may be susceptible to leakage around faulty joints. Owing to the ongoing low performance of the adjacent tube-to-tubesheet expansion, the heat exchanger eventually experiences malfunction. A quality improvement study on the assembly process is necessary in order to delve into the tight-fitting of the tube-to-tubesheet joint. We present a non-linear screening and optimization study of the tight-fitting process of P215NL (EN 10216-4) tube samples on P265GH (EN 10028-2) tubesheet specimens. A saturated fractional factorial scheme was implemented to screen and optimize the tube-to-tubesheet expanded-joint performance by examining the four controlling factors: (1) the clearance, (2) the number of grooves, (3) the groove depth, and (4) the tube wall thickness reduction. The adopted ‘green’ experimental tactic required duplicated tube-push-out test trials to form the ‘lean’ joint strength response dataset. Analysis of variance (ANOVA) and regression analysis were subsequently employed in implementing the Taguchi approach to accomplish the multifactorial non-linear screening classification and the optimal setting adjustment of the four investigated controlling factors. It was found that the tube-wall thickness reduction had the highest influence on joint strength (55.17%) and was followed in the screening hierarchy by the number of grooves (at 30.47%). The groove depth (at 7.20%) and the clearance (at 6.84%) were rather weaker contributors, in spite of being evaluated to be statistically significant. A confirmation run showed that the optimal joint strength prediction was adequately estimated. Besides exploring the factorial hierarchy with statistical methods, an algorithmic (Random Forest) approach agreed with the leading effects line-up (the tube wall thickness and the number of grooves) and offered an improved overall prediction for the confirmation-run test dataset.
Development and Experimental Evaluation of Some Silver Nanoparticles with Antimicrobial Potential
Authors: Velescu Bruno Ștefan , Ilie Marina Ionela , Amzăr Anca Ioana , Lupașcu Raluca Elisabeta , Marandiuc Ilinca Mihaela , Apetroaei Miruna-Maria , Arsene Andreea Letiția , Blejan Emilian Ionuț , Nedea Octvian Alexandru , Fistos Toma , Fierăscu Radu Claudiu , Bărbuceanu Florica , Țoca Cristina , Fierăscu Irina , Udeanu Denisa Ioana , Ghica Manuela , Drăgănescu Doina , Cobelschi Pavel Călin
Published: April 2023

By adjusting the synthesis process, silver nanoparticles (AgNp) of various shapes, sizes, and structures can be obtained, all of which have a substantial impact on the biological effect, notably, the regulation of antibacterial activity in the present circumstances of growing bacterial resistance. Due to their relatively small size, nanoparticles may be disseminated evenly throughout the body of the experimental animal, even at low doses, and exert more potent antibacterial activities. Our research was centered on the synthesis, production, and biological evaluation of antibacterial silver nanoparticles. Using the Turkevich method, we were able to effectively synthesize and characterize nanoscale silver particles, with an average crystallite size of 9.49 nm. We examined their acute toxicity and pharmacokinetic characteristics in rats after administering a single dosage. In addition, we evaluated the biological effect of topical AgNp suspension on the progression of burn-type lesions in the experimental animals. The pharmacokinetic profile demonstrated that the plasma concentration of silver nanoparticles, as well as their clearance rate, and dispersion throughout the body, are significantly enhanced in large rodent species. The restorative effect of synthesized silver nanoparticles in the form of a suspension in distilled water was corroborated by the values of the hematological parameters. These results demonstrated an intense stimulation of the cellular and molecular processes of the local immune defense, which has resulted in significantly faster regeneration in the AgNp-treated group.
Development of a Novel Fabrication Process for Application in Glass Gas Electron Multiplier Detectors
Authors: Wu Xiaomeng , Sun Xilei , Cao Liqiang , Wang Qidong
Published: April 2023

Gas electron multipliers (GEMs) have been widely used for particle collection and signal amplification. Because of the advantages of glass, such as high hardness, aging resistance, and dielectric strength, research into its application as a substrate material in GEM design and process has attracted extensive attention in recent years. This paper compares two commonly used glass GEM structural designs and shows that the optical transparency of the hexagonal symmetric structure is superior to that of the rectangle structure. An electric field model is developed to characterize the negative correlation between the hole diameter and the electric field strength. The structure of glass GEM is designed according to the feasibility of the process. A new process method of surface metal patterning using hole filling to form a mask is proposed, which can meet the high alignment and shaping requirements of the perfect match between the opening of metal layer and the aperture shape of the substrate. Combined with the advanced laser hole modification process, a glass GEM sample with a hole diameter of 70 μm, a spacing of 140 μm, a substrate thickness of 240 μm, and a metal thickness of 13 μm is obtained. Finally, particle trajectories, avalanche region coverage, and electron energy are discussed based on the prototype structure.
Using Structural Equation Modeling to Reproduce and Extend ANOVA-Based Generalizability Theory Analyses for Psychological Assessments
Authors: Vispoel Walter P. , Lee Hyeryung , Chen Tingting , Hong Hyeri
Published: April 2023

Generalizability theory provides a comprehensive framework for determining how multiple sources of measurement error affect scores from psychological assessments and using that information to improve those assessments. Although generalizability theory designs have traditionally been analyzed using analyses of variance (ANOVA) procedures, the same analyses can be replicated and extended using structural equation models. We collected multi-occasion data from inventories measuring numerous dimensions of personality, self-concept, and socially desirable responding to compare variance components, generalizability coefficients, dependability coefficients, and proportions of universe score and measurement error variance using structural equation modeling versus ANOVA techniques. We further applied structural equation modeling techniques to continuous latent response variable metrics and derived Monte Carlo-based confidence intervals for those indices on both observed score and continuous latent response variable metrics. Results for observed scores estimated using structural equation modeling and ANOVA procedures seldom varied. Differences in reliability between raw score and continuous latent response variable metrics were much greater for scales with dichotomous responses, thereby highlighting the value of doing analyses on both metrics to evaluate gains that might be achieved by increasing response options. We provide detailed guidelines for applying the demonstrated techniques using structural equation modeling and ANOVA-based statistical software.
Review on Deep Learning Algorithms and Benchmark Datasets for Pairwise Global Point Cloud Registration
Authors: Zhao Yang , Fan Lei
Published: April 2023

Point cloud registration is the process of aligning point clouds collected at different locations of the same scene, which transforms the data into a common coordinate system and forms an integrated dataset. It is a fundamental task before the application of point cloud data. Recent years have witnessed the rapid development of various deep-learning-based global registration methods to improve performance. Therefore, it is appropriate to carry out a comprehensive review of the more recent developments in this area. As the developments require access to large benchmark point cloud datasets, the most widely used public datasets are also reviewed. The performance of deep-learning-based registration methods on the benchmark datasets are summarized using the reported performance metrics in the literature. This forms part of a critical discussion of the strengths and weaknesses of the various methods considered in this article, which supports presentation of the main challenges currently faced in typical global point cloud registration tasks that use deep learning methods. Recommendations for potential future studies on this topic are provided.
Reconstructing Digital Terrain Models from ArcticDEM and WorldView-2 Imagery in Livengood, Alaska
Authors: Zhang Tianqi , Liu Desheng
Published: April 2023

ArcticDEM provides the public with an unprecedented opportunity to access very high-spatial resolution digital elevation models (DEMs) covering the pan-Arctic surfaces. As it is generated from stereo-pairs of optical satellite imagery, ArcticDEM represents a mixture of a digital surface model (DSM) over a non-ground areas and digital terrain model (DTM) at bare grounds. Reconstructing DTM from ArcticDEM is thus needed in studies requiring bare ground elevation, such as modeling hydrological processes, tracking surface change dynamics, and estimating vegetation canopy height and associated forest attributes. Here we proposed an automated approach for estimating DTM from ArcticDEM in two steps: (1) identifying ground pixels from WorldView-2 imagery using a Gaussian mixture model (GMM) with local refinement by morphological operation, and (2) generating a continuous DTM surface using ArcticDEMs at ground locations and spatial interpolation methods (ordinary kriging (OK) and natural neighbor (NN)). We evaluated our method at three forested study sites characterized by different canopy cover and topographic conditions in Livengood, Alaska, where airborne lidar data is available for validation. Our results demonstrate that (1) the proposed ground identification method can effectively identify ground pixels with much lower root mean square errors (RMSEs) (<0.35 m) to the reference data than the comparative state-of-the-art approaches; (2) NN performs more robustly in DTM interpolation than OK; (3) the DTMs generated from NN interpolation with GMM-based ground masks decrease the RMSEs of ArcticDEM to 0.648 m, 1.677 m, and 0.521 m for Site-1, Site-2, and Site-3, respectively. This study provides a viable means of deriving high-resolution DTM from ArcticDEM that will be of great value to studies focusing on the Arctic ecosystems, forest change dynamics, and earth surface processes.
Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints
Authors: Peng Zihan , Gao Yang , Gao Chengfa , Shang Rui , Gan Lu
Published: April 2023

To improve smartphone GNSS positioning performance using extra inequality information, an inequality constraint method was introduced and verified in this study. Firstly, the positioning model was reviewed and three constraint applications were derived from it, namely, vertical velocity, direction, and distance constraints. Secondly, we introduced an estimator based on the density function truncation method to solve the inequality constraint problem. Finally, the performance of the method was investigated using datasets from three smartphones, including a Huawei P30, a Huawei P40, and a Xiaomi MI8. The results indicate that the position and velocity accuracy can be improved in the up component using a vertical velocity constraint. The horizontal positioning accuracy was increased using a heading direction constraint with dynamic datasets. Numerically, the root mean square error (RMSE) improvement percentages were 16.77%, 14.57%, and 31.09% for HP40, HP30, and XMI8, respectively. Using an inter-smartphone distance constraint could enhance the horizontal positioning of all participating smartphones, with improvement percentages of 34.27%, 75.58%, and 23.66% for HP40, HP30, and XMI8, respectively, in the static dataset. Additionally, the improvement percentages were 15.90%, 5.55%, and 0.17% in dynamic datasets. In summary, this study demonstrates that utilizing inequality constraints can significantly improve smartphone GNSS positioning.
Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach
Authors: Handschuh Jana , Erbertseder Thilo , Baier Frank
Published: April 2023

The latest epidemiological studies have revealed that the adverse health effects of PM2.5 have impacts beyond respiratory and cardio-vascular diseases and also affect the development of the brain and metabolic diseases. The need for accurate and spatio-temporally resolved PM2.5 data has thus been substantiated. While the selective information provided by station measurements is mostly insufficient for area-wide monitoring, satellite data have been increasingly applied to comprehensively monitor PM2.5 distributions. Although the accuracy and reliability of satellite-based PM2.5 estimations have increased, most studies still rely on a single sensor. However, several datasets have become available in the meantime, which raises the need for a systematic analysis. This study presents the first systematic evaluation of four satellite-based AOD datasets obtained from different sensors and retrieval methodologies to derive ground-level PM2.5 concentrations. We apply a random forest approach and analyze the effect of the resolution and coverage of the satellite data and the impact of proxy data on the performance. We examine AOD data from the Moderate resolution Imaging spectroradiometer (MODIS) onboard Terra and Aqua satellites, including Dark Target (DT) algorithm products and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. Additionally, we explore more recent datasets from the Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3a and from the Tropospheric Monitoring Instrument (TROPOMI) operating on the Sentinel-5 precursor (S5p). The method is demonstrated for Germany and the year 2018, where a dense in situ measurement network and relevant proxy data are available. Overall, the model performance is satisfactory for all four datasets with cross-validated R2 values ranging from 0.68 to 0.77 and excellent for MODIS AOD reaching correlations of almost 0.9. We find a strong dependency of the model performance on the coverage and resolution of the AOD training data. Feature importance rankings show that AOD has less weight compared to proxy data for SLSTR and TROPOMI.
Monitoring Grassland Growth Based on Consistency-Corrected Remote Sensing Image
Authors: Ren Yuejuan , Wen Qingke , Xi Fengjiang , Ge Xiaosan , Yuan Yixin , Hu Bo
Published: April 2023

Monitoring grassland growth in large areas usually needs multiple images from different sensors or on different dates to cover the study area completely. Images from different sensors or on different dates need consistency correction to eliminate the sharp differences between images. The main contribution of this study is to promote a method for consistency correction of images on different days by constructing a linear regression equation of land cover types and the classification pixel mean. Taking a prefecture-level area in China as a test area, the consistency corrected images were applied for monitoring grassland growth. The results showed the following. First, compared with the normal correction equation constructed for two images, taking all features into account, the coefficient of determination of the equation corrected by the land cover types was improved, and the root mean square error was also significantly reduced. Secondly, the areas of consistency in the corrected image were improved compared with the original image, with an improvement rate of 21% for images from the same sensor and 25% for images from different sensors. The pixel average was much closer to the benchmark images, indicating that the corrected image was more consistent than the original image. Thirdly, when applied for monitoring grassland growth, consistency correction can solve the problem of misjudging grassland degradation. Grassland that was judged to be degraded using direct imagery, in fact, showed stable growth after consistency correction, and this type accounted for 7.33% of the regional grassland area. The seasonal characteristics of grass growth in the region were also obtained by monitoring the growth of grass in the region throughout the year. The application test showed that an effective image consistency correction method can improve the accuracy of grassland growth monitoring across a large area.
Mapping the Age of Subtropical Secondary Forest Using Dense Landsat Time Series Data: An Ensemble Model
Authors: Zhang Shaoyu , Yu Jun , Xu Hanzeyu , Qi Shuhua , Luo Jin , Huang Shiming , Liao Kaitao , Huang Min
Published: April 2023

Quantifying secondary forest age (SFA) is essential to evaluate the carbon processes of forest ecosystems at regional and global scales. However, the successional stages of secondary forests remain poorly understood due to low-frequency thematic maps. This study aimed to estimate SFA with higher frequency and more accuracy by using dense Landsat archives. The performances of four time-series change detection algorithms—moving average change detection (MACD), Continuous Change Detection and Classification (CCDC), LandTrendr (LT), and Vegetation Change Tracker (VCT)—for detecting forest regrowth were first evaluated. An ensemble model was then developed to determine more accurate timings for forest regrowth based on the evaluation results. Finally, after converting the forest regrowth year to the SFA, the spatiotemporal and topographical distributions of the SFA were analyzed. The proposed ensemble model was validated in Jiangxi province, China, which is located in a subtropical region and has experienced drastic forest disturbances, artificial afforestation, and natural regeneration. The results showed that: (1) the developed ensemble model effectively determined forest regrowth time with significantly decreased omission and commission rates compared to the direct use of the four single algorithms; (2) the optimal ensemble model combining the independent algorithms obtained the final SFA for Jiangxi province with the lowest omission and commission rates in the spatial domain (14.06% and 24.71%) and the highest accuracy in the temporal domain (R2 = 0.87 and root mean square error (RMSE) = 3.17 years); (3) the spatiotemporal and topographic distribution from 1 to 34 years in the 2021 SFA map was analyzed. This study demonstrated the feasibility of using change detection algorithms for estimating SFA at regional to national scales and provides a data foundation for forest ecosystem research.
Multi-Scale Ship Detection Algorithm Based on YOLOv7 for Complex Scene SAR Images
Authors: Chen Zhuo , Liu Chang , Filaretov V. , Yukhimets D.
Published: April 2023

Recently, deep learning techniques have been extensively used to detect ships in synthetic aperture radar (SAR) images. The majority of modern algorithms can achieve successful ship detection outcomes when working with multiple-scale ships on a large sea surface. However, there are still issues, such as missed detection and incorrect identification when performing multi-scale ship object detection operations in SAR images of complex scenes. To solve these problems, this paper proposes a complex scenes multi-scale ship detection model, according to YOLOv7, called CSD-YOLO. First, this paper suggests an SAS-FPN module that combines atrous spatial pyramid pooling and shuffle attention, allowing the model to focus on important information and ignore irrelevant information, reduce the feature loss of small ships, and simultaneously fuse the feature maps of ship targets on various SAR image scales, thereby improving detection accuracy and the model’s capacity to detect objects at several scales. The model’s optimization is then improved with the aid of the SIoU loss function. Finally, thorough tests on the HRSID and SSDD datasets are presented to support our methodology. CSD-YOLO achieves better detection performance than the baseline YOLOv7, with a 98.01% detection accuracy, a 96.18% recall, and a mean average precision (mAP) of 98.60% on SSDD. In addition, in comparative experiments with other deep learning-based methods, in terms of overall performance, CSD-YOLO still performs better.
Statistical Assessment of Some Interpolation Methods for Building Grid Format Digital Bathymetric Models
Authors: Amoroso Pier Paolo , Aguilar Fernando J. , Parente Claudio , Aguilar Manuel A.
Published: April 2023

As far as the knowledge of the seabed is concerned, both for safe navigation and for scientific research, 3D models, particularly digital bathymetric models (DBMs), are nowadays of fundamental importance. This work aimed to evaluate the quality of DBMs according to the interpolation methods applied to obtain grid format 3D surfaces from scattered sample points. Other complementary factors affecting DBM vertical accuracy, such as seabed morphological complexity and surveyed points sampling density, were also analyzed by using a factorial ANOVA experimental design. The experiments were performed on a multibeam dataset provided by the Italian Navy Hydrographic Institute (IIM) with an original resolution of 1 m × 1 m grid spacing, covering a surface of 0.24 km2. Six different sectors comprising different seabed morphologies were investigated. Eight sampling densities were randomly extracted from every sector, each with four repetitions. Finally, four different interpolation methods were tested, including: radial basis multiquadric function (RBMF), ordinary kriging (OK), universal kriging (UK) and Gaussian Markov random fields (GMRF). The results demonstrated that both RBMF and OK produced very accurate DBM in areas characterized by low levels of seabed ruggedness at sampling densities of only 0.0128 points/m2 (equivalent grid spacing of 8.84 m). In contrast, a higher density of 0.1024 points/m2 (3.13 m grid spacing) was required to produce accurate DBM in areas with more complex seabed topography. On the other hand, UK and GMRF were strongly influenced by morphology and sampling density, yielding higher vertical random errors and more prone to slightly overestimate seabed depths. In addition, sampling density and morphology were the factors that most influenced the vertical accuracy of the interpolated DBM. In this sense, the highly statistically significant influence of the interaction between sampling density and morphology on the vertical accuracy of the interpolated DBM confirms the need to perform a preliminary analysis of seabed morphological complexity in order to increase, if necessary, the number of surveyed points in cases of complex morphologies.
A New Fast Control Strategy of Terminal Sliding Mode with Nonlinear Extended State Observer for Voltage Source Inverter
Authors: Zhang Chunguang , Xu Donglin , Ma Jun , Chen Huayue
Published: April 2023

To overcome the sensitivity of voltage source inverters (VSIs) to parameter perturbations and their susceptibility to load variations, a fast terminal sliding mode control (FTSMC) method is proposed as the core and combined with an improved nonlinear extended state observer (NLESO) to resist aggregate system perturbations. Firstly, a mathematical model of the dynamics of a single-phase voltage type inverter is constructed using a state-space averaging approach. Secondly, an NLESO is designed to estimate the lumped uncertainty using the saturation properties of hyperbolic tangent functions. Finally, a sliding mode control method with a fast terminal attractor is proposed to improve the dynamic tracking of the system. It is shown that the NLESO guarantees convergence of the estimation error and effectively preserves the initial derivative peak. The FTSMC enables the output voltage with high tracking accuracy and low total harmonic distortion and enhances the anti-disturbance ability.
A Preliminary Study of the Efficacy of Using a Wrist-Worn Multiparameter Sensor for the Prediction of Cognitive Flow States in University-Level Students
Authors: Graft Josephine , Romine William , Watts Brooklynn , Schroeder Noah , Jawad Tawsik , Banerjee Tanvi
Published: April 2023

Engagement is enhanced by the ability to access the state of flow during a task, which is described as a full immersion experience. We report two studies on the efficacy of using physiological data collected from a wearable sensor for the automated prediction of flow. Study 1 took a two-level block design where activities were nested within its participants. A total of five participants were asked to complete 12 tasks that aligned with their interests while wearing the Empatica E4 sensor. This yielded 60 total tasks across the five participants. In a second study representing daily use of the device, a participant wore the device over the course of 10 unstructured activities over 2 weeks. The efficacy of the features derived from the first study were tested on these data. For the first study, a two-level fixed effects stepwise logistic regression procedure indicated that five features were significant predictors of flow. In total, two were related to skin temperature (median change with respect to the baseline and skewness of the temperature distribution) and three were related to acceleration (the acceleration skewness in the x and y directions and the kurtosis of acceleration in the y direction). Logistic regression and naïve Bayes models provided a strong classification performance (AUC > 0.7, between-participant cross-validation). For the second study, these same features yielded a satisfactory prediction of flow for the new participant wearing the device in an unstructured daily use setting (AUC > 0.7, leave-one-out cross-validation). The features related to acceleration and skin temperature appear to translate well for the tracking of flow in a daily use environment.
Reparameterizable Multibranch Bottleneck Network for Lightweight Image Super-Resolution
Authors: Shen Ying , Zheng Weihuang , Huang Feng , Wu Jing , Chen Liqiong
Published: April 2023

Deployment of deep convolutional neural networks (CNNs) in single image super-resolution (SISR) for edge computing devices is mainly hampered by the huge computational cost. In this work, we propose a lightweight image super-resolution (SR) network based on a reparameterizable multibranch bottleneck module (RMBM). In the training phase, RMBM efficiently extracts high-frequency information by utilizing multibranch structures, including bottleneck residual block (BRB), inverted bottleneck residual block (IBRB), and expand–squeeze convolution block (ESB). In the inference phase, the multibranch structures can be combined into a single 3 × 3 convolution to reduce the number of parameters without incurring any additional computational cost. Furthermore, a novel peak-structure-edge (PSE) loss is proposed to resolve the problem of oversmoothed reconstructed images while significantly improving image structure similarity. Finally, we optimize and deploy the algorithm on the edge devices equipped with the rockchip neural processor unit (RKNPU) to achieve real-time SR reconstruction. Extensive experiments on natural image datasets and remote sensing image datasets show that our network outperforms advanced lightweight SR networks regarding objective evaluation metrics and subjective vision quality. The reconstruction results demonstrate that the proposed network can achieve higher SR performance with a 98.1 K model size, which can be effectively deployed to edge computing devices.
Possibilities of Physical-Chemical Sensors’ Use for Optimizing the Processing of Metallurgical Melting Based on Computer Systems
Authors: Ioana Adrian , Constantin Nicolae , Istrate Alexandra , Paunescu Lucian , Pasare Vili
Published: April 2023

This article presents aspects regarding the possibilities of optimizing the quality of the elaboration of metallurgical melts by determining their physical-chemical properties. Thus, the article analyzes and presents methods for determining the viscosity and electrical conductivity related to metallurgical melts. Among the viscosity determination methods, two methods are presented, namely: the rotary viscometer method and the electro-vibratory viscometer method. Determining the electrical conductivity of a metallurgical melt is also of particular importance for ensuring the quality of the elaboration and refining of the melt. The article also presents the possibilities of using and implementing computer systems that ensure the accuracy of determining the physical-chemical properties of metallurgical melts, as well as examples of the use of physical-chemical sensors and specific computer systems for determining the analyzed parameters. The specific electrical conductivity measurements of oxide melts are performed by direct methods (by contact), with Ohm’s law as a starting point. Thus, the article presents the voltmeter-ammeter method and the point method (or the zero method). The novelty of this article is the description and the use of specific methods and sensors for certain determinations (viscosity and electrical conductivity) for metallurgical melts. The motivation here is the presentation of the authors’ research in the addressed field. The article presents an original contribution of the adaptation and use of some methods for determining some physico-chemical parameters, including specific sensors, in the field of the elaboration of metal alloys, with the aim of optimizing their quality.
Electrical Parameters as Diagnostics of Fresh Engine Oil Condition—Correlation with Test Voltage Frequency
Authors: Wolak Artur , Żywica Ryszard , Molenda Jarosław , Banach Joanna Katarzyna
Published: April 2023

The aim of this study was to assess whether electrical parameters (capacitance and conductivity) of fresh engine oils—tested over a wide range of measurement voltage frequencies—can be used for oil quality assessment and its identification, based on physicochemical properties. The study encompassed 41 commercial engine oils with different quality ratings (American Petroleum Institute (API) and European Automobile Manufacturers’ Association (ACEA)). As part of the study, the oils were tested for their total base number (TBN) and total acid number (TAN), as well as their electrical parameters, including impedance magnitude, phase shift angle, conductance, susceptance, capacitance and quality factor. Next, the results for all of the samples were examined for correlations between the mean electrical parameters and the test voltage frequency. A statistical analysis (k-means and agglomerative hierarchical clustering) was applied to group oils with similar readings, drawing on the values for all electrical parameters to produce group oils with the highest similarity to each other into clusters. The results show that the electrical-based diagnostics of fresh engine oils can serve as a highly selective method for identifying oil quality, offering much higher resolution than assessments based on the TBN or the TAN. This is further supported by the cluster analysis, with five clusters generated for electrical parameters of the oils, compared to only three generated for TAN- and TBN-based measurements. Out of all the tested electrical parameters, capacitance, impedance magnitude and quality factor were found to be the most promising for diagnostic purposes. The value of electrical parameters of fresh engine oils is mostly dependent on the test voltage frequency (with the exception of capacitance). The correlations identified in the course of the study can be used to select for those frequency ranges that offer the highest diagnostic utility.
A Lightweight Authentication and Key Agreement Protocol for IoT-Enabled Smart Grid System
Authors: Chen Chen , Guo Hua , Wu Yapeng , Shen Bowen , Ding Mingyang , Liu Jianwei
Published: April 2023

The IoT-enabled Smart Grid uses IoT smart devices to collect the private electricity data of consumers and send it to service providers over the public network, which leads to some new security problems. To ensure the communication security in a smart grid, many researches are focusing on using authentication and key agreement protocols to protect against cyber attacks. Unfortunately, most of them are vulnerable to various attacks. In this paper, we analyze the security of an existent protocol by introducing an insider attacker, and show that their scheme cannot guarantee the claimed security requirements under their adversary model. Then, we present an improved lightweight authentication and key agreement protocol, which aims to enhance the security of IoT-enabled smart grid systems. Furthermore, we proved the security of the scheme under the real-or-random oracle model. The result shown that the improved scheme is secure in the presence of both internal attackers and external attackers. Compared with the original protocol, the new protocol is more secure, while keeping the same computation efficiency. Both of them are 0.0552 ms. The communication of the new protocol is 236 bytes, which is acceptable in smart grids. In other words, with similar communication and computation cost, we proposed a more secure protocol for smart grids.
Energy Consumption Optimization of a Fluid Bed Dryer in Pharmaceutical Manufacturing Using EDA (Exploratory Data Analysis)
Authors: Barriga Roberto , Romero Miquel , Hassan Houcine , Nettleton David F.
Published: April 2023

In this paper, a data preprocessing methodology, EDA (Exploratory Data Analysis), is used for performing an exploration of the data captured from the sensors of a fluid bed dryer to reduce the energy consumption during the preheating phase. The objective of this process is the extraction of liquids such as water through the injection of dry and hot air. The time taken to dry a pharmaceutical product is typically uniform, independent of the product weight (Kg) or the type of product. However, the time it takes to heat up the equipment before drying can vary depending on different factors, such as the skill level of the person operating the machine. EDA (Exploratory Data Analysis) is a method of evaluating or comprehending sensor data to derive insights and key characteristics. EDA is a critical component of any data science or machine learning process. The exploration and analysis of the sensor data from experimental trials has facilitated the identification of an optimal configuration, with an average reduction in preheating time of one hour. For each processed batch of 150 kg in the fluid bed dryer, this translates into an energy saving of around 18.5 kWh, giving an annual energy saving of over 3.700 kWh.
Synthesis and Integration of Hybrid Metal Nanoparticles Covered with a Molecularly Imprinted Polymer Nanolayer by Photopolymerization
Authors: Khitous Amine , Molinaro Céline , Thomas Constance , Haupt Karsten , Soppera Olivier
Published: April 2023

Interfacing recognition materials with transducers has consistently presented a challenge in the development of sensitive and specific chemical sensors. In this context, a method based on near-field photopolymerization is proposed to functionalize gold nanoparticles, which are prepared by a very simple process. This method allows in situ preparation of a molecularly imprinted polymer for sensing by surface-enhanced Raman scattering (SERS). In a few seconds, a functional nanoscale layer is deposited by photopolymerization on the nanoparticles. In this study, the dye Rhodamine 6G was chosen as a model target molecule to demonstrate the principle of the method. The detection limit is 500 pM. Due to the nanometric thickness, the response is fast, and the substrates are robust, allowing regeneration and reuse with the same performance level. Finally, this method of manufacturing has been shown to be compatible with integration processes, allowing the future development of sensors integrated in microfluidic circuits and on optical fibers.
Utilization of Waste Biomaterial as an Efficient and Eco-Friendly Adsorbent for Solid-Phase Extraction of Pantoprazole Contaminants in Wastewater
Authors: Haq Nazrul , Iqbal Muzaffar , Hussain Afzal , Shakeel Faiyaz , Ahmad Ashfaq , Alsarra Ibrahim A. , AlAjmi Mohamed Fahad , Mahfooz Asra , Abouzadeh M. Ali
Published: April 2023

The objective of this analysis is to establish the potential of biodegradable agro-industrial waste materials as biosorbents in the solid-phase extraction (SPE) technique for sample preparation. In this regard, waste coffee husk (CH) powder was collected, washed, treated chemically, characterized, and applied as an SPE adsorbent to extract pantoprazole from the wastewater samples. Sample detection was accomplished using the UPLC-MS/MS system. The positive mode of electrospray ionization was exploited for the ionization of the sample, and quantification of the target analyte was performed by the multiple reaction monitoring modes. The precursor to product ion transition of 384.02→1380.05 and 384.02→200.05 was used as qualifiers and quantifiers, respectively. Optimization of the particle size, adsorbent dose, and contact time were evaluated to select the best combination of features. The efficiency and regeneration capability of the CH were compared with respect to a commercially available silica-based C18 SPE adsorbent, and it was found that CH possessed comparable (~50%) extraction, as well as regeneration capacity (~95%). The developed biosorbent was applied in a wastewater sample spiked with the target analyte and recovery studies were performed, which found a range of 93.0 to 102.0% with a %RSD of 3.72 to 12.7%. Thus, CH can be exploited as a ‘greener’ replacement for the commercially available adsorbents for the extraction/retention of active pharmaceutical ingredients present in water/wastewater samples.
Immigrant Victimization: Centering Language in Theory, Data and Method
Authors: Ballard Meghan Maree , Kubrin Charis E.
Published: April 2023

Compared to immigrant criminality, relatively less attention is paid to immigrant victimization, even as extensive scholarship on criminal victimization exists more generally. This is curious in light of research showing that certain immigrant groups are at increased risk of victimization with respect to certain crimes. In this essay, we set out to answer the following questions: How do leading theories of victimization explain the risk of immigrant victimization? Are there aspects of immigrant victimization that would benefit from further theorization and empirical inquiry? How do challenges associated with data collection of immigrant populations impact the advancement of theorizing and research on immigrant victimization? What insights about immigrant victimization may be gained by better integrating theory, data, and method in this research area? To answer these questions, we first provide an overview of classic frameworks used to explain criminal victimization in general, mapping their development to broader discussions in victimology. We then review how victimization theories are used to explain immigrant victimization, discuss the possibility of using culturally integrated theories of offending in immigrant victimization research, and examine data impediments associated with studying immigrant crime victims. With an aim toward integrating theory, data, and method in this research area, we next propose that scholars center language in research on immigrant victimization, offering examples of where such an approach could yield important theoretical and empirical advancements. We conclude by identifying policies and practices that are consistent with this approach.
Sheet Metal Design Approach for 3D Shaped Facade Elements with Integrated Solar Thermal Functionality
Authors: Scholz Peter , Weise Dieter , Schmidt Linda , Dembski Martin , Stahr Alexander , Dix Martin , Duminica Florin , Le Craz Sebastien , Koziorek Jiri
Published: April 2023

This paper provides an overview of the development of a 3D formed and metal-based facade element that combines a custom design and solar thermal functionality. To achieve this, a novel simplified solar thermal collector structure was developed using formed sheet metal half-shells with an integrated channel structure on the inside and a special absorber coating on the outside. The sheet metal half-shells were manufactured by highly innovative incremental sheet forming (ISF), which allows seamless integration into existing facades. As a part of this paper, the initial test results on thermal efficiency and the energy accumulation of the new collector type are presented.
The Dynamic Correlation and Volatility Spillover among Green Bonds, Clean Energy Stock, and Fossil Fuel Market
Authors: Tang Chaofeng , Aruga Kentaka , Hu Yi
Published: April 2023

This study employs mainly the Bayesian DCC-MGARCH model and frequency connectedness methods to respectively examine the dynamic correlation and volatility spillover among the green bond, clean energy, and fossil fuel markets using daily data from 30 June 2014 to 18 October 2021. Three findings arose from our results: First, the green bond market has a weak negative correlation with the fossil fuel (WTI oil, Brent oil, natural gas, heating oil, and gasoline) and clean energy markets, which means that green bonds play a critical hedging role against fossil fuel and clean energy. Second, the green bond and clean energy are net volatility receivers from WTI crude oil and heating oil for the short term, indicating that investors and policymakers need to pay attention to the WTI oil volatility spillover risk when promoting green bonds and clean energy. Third, the correlation and volatility spillover from WTI crude oil to green bonds and clean energy is stronger than that of Brent oil, which implies that investors and policymakers need to consider the price movements of WTI crude oil more than Brent oil when investing in the green bond market. In summary, our conclusion is that investors should be aware that green bond investing addresses the two-pronged investment strategy of (i) risk diversification and (ii) carbon mitigation. Thus, this study can provide essential information for energy investors and policymakers to achieve sustainable investment.
Optimal Planning of Electric Vehicle Fast-Charging Stations Considering Uncertain Charging Demands via Dantzig–Wolfe Decomposition
Authors: Wang Luyun , Zhou Bo
Published: April 2023

This study investigates the planning problem of fast-charging stations for electric vehicles with the consideration of uncertain charging demands. This research aims to determine where to build fast-charging stations and how many charging piles to be installed in each fast-charging station. Based on the multicommodity flow model, a chance-constrained programming model is established to address this planning problem. A scenario-based approach as well as a big-M coefficients generation algorithm are applied to reformulate the programming model into tractable one, then the Dantzig–Wolfe decomposition method is leveraged to find its optimal solution. Finally, a numerical experiment is conducted in a 25-node network to assess the efficiency of the proposed model and solution approach.
Analyzing the Attractiveness of Businesses to Receive Investments for a Creative and Innovative Transition to a Circular Economy: The Case of the Textile and Fashion Industry
Authors: Silva Wesley Douglas Oliveira , Fontana Marcele Elisa , de Almeida Bianca Maria Jacinto , Carmona Marques Pedro , Vidal Raphaela
Published: April 2023

Excessive and often compulsive consumption has given the textile–fashion industry the reputation of being one of the industries causing the most pollution in today’s world. For this reason, there is a necessity for a transition from a linear to a circular approach in the textile–fashion industry. However, this is not an easy task, especially when considering the investments that must be made to put a circular economy structure into practice. In this sense, the transition to a circular economy in the textile–fashion industry presents a unique opportunity for businesses to attract investments to support this transition by leveraging creativity and innovation to reduce waste, minimize resource consumption, and increase the longevity of products and materials. Therefore, this study sets out to develop a multicriteria decision support model to measure the attractiveness of businesses to receive investments that aim at aiding the transition to the circular economy. The model uses the “play card” from Simos’ procedure and the Normalize software that provide a comprehensive, consistent, and transparent approach to decision making, which can help investors to evaluate the attractiveness of investment opportunities and identify businesses that have the potential for long-term success in the circular economy. Hence, catalyzing and obstructing factors of the circular economy discussed in the literature were selected to underpin the analysis model and to draw up robust investment recommendations to the investors. In addition to the scientific contributions of the model, indications are also provided to the private sector, public policy makers, and society on how sustainability can be driven by the circular economy.