bluuebunny/crossref_metadata_2025 huggingface dataset:
Pyrolysis of Chilean Southern Lignocellulosic Biomasses: Isoconversional Kinetics Analysis and Pyrolytic Products Distribution
Authors: Cerda-Barrera Cristian , Fernández-Andrade Kevin J. , Alejandro-Martín Serguei
Published: June 2023

Biomass provides potential benefits for obtaining value-added compounds instead of straight burning; as Chile has forestry potential that supports such benefits, it is crucial to understand the biomasses’ properties and their thermochemical behaviour. This research presents a kinetic analysis of thermogravimetry, and pyrolysis of representative species in the biomass of southern Chile, heating biomasses at 5 to 40 °C·min−1 rates before being subjected to thermal volatilisation. The activation energy (Ea) was calculated from conversion using model-free methods (Flynn–Wall–Ozawa (FWO), Kissinger–Akahira–Sunose (KAS), and Friedman (FR)), as well as the Kissinger method based on the maximum reaction rate. The average Ea varied between KAS 117 and 171 kJ·mol−1, FWO 120–170 kJ·mol−1, and FR 115–194 kJ·mol−1 for the five biomasses used. Pinus radiata (PR) was identified as the most suited wood for producing value-added goods based on the Ea profile for the conversion (α), along with Eucalyptus nitens (EN) for its high value of reaction constant (k). Each biomass demonstrated accelerated decomposition (an increase in k relative to α). The highest concentration of bio-oil containing phenolic, ketonic, and furanic compounds was produced by the forestry exploitation biomasses PR and EN, demonstrating the viability of these materials for thermoconversion processes.
Effects of Pre-Curing on the Structure and Properties of Paper-Based Materials
Authors: Lin Mingcen , Zhou Wenling , Yao Ye , Chen Jingxiang , Zhang Chunhui
Published: June 2023

Paper-based friction material is a typical paper-based composite that is usually cured via hot-pressing. This curing method does not account for the effect of pressure on the matrix resin, resulting in uneven distribution of resin in the material and reducing the mechanical properties of friction materials. To overcome the above shortcomings, a pre-curing method was introduced before hot-pressing, and the effects of different pre-curing degrees on the surface morphology and mechanical properties of paper-based friction materials were studied. The pre-curing degree significantly affected the resin distribution and interfacial bonding strength of the paper-based friction material. When the material was cured at 160 °C for 10 min, the pre-curing degree reached 60%. At this point, most of the resin was in a gel state, which could retain abundant pore structures on the material surface without causing mechanical damage to the fiber and resin matrix during hot-pressing. Ultimately, the paper-based friction material exhibited improved static mechanical properties, decreased permanent deformation, and reasonable dynamic mechanical properties.
An Experimental Investigation of the Mechanical Performance of EPS Foam Core Sandwich Composites Used in Surfboard Design
Authors: Crameri Sam , Stojcevski Filip , Usma-Mansfield Clara
Published: June 2023

Surfboard manufacturing has begun to utilise Expanded Polystyrene as a core material; however, surf literature relatively ignores this material. This manuscript investigates the mechanical behaviour of Expanded Polystyrene (EPS) sandwich composites. An epoxy resin matrix was used to manufacture ten sandwich-structured composite panels with varying fabric reinforcements (carbon fibre, glass fibre, PET) and two foam densities. The flexural, shear, fracture, and tensile properties were subsequently compared. Under common flexural loading, all composites failed via compression of the core, which is known in surfing terms as creasing. However, crack propagation tests indicated a sudden brittle failure in the E-glass and carbon fibre facings and progressive plastic deformation for the recycled polyethylene terephthalate facings. Testing showed that higher foam density increased the flex and fracture mechanical properties of composites. Overall, the plain weave carbon fibre presented the highest strength composite facing, while the single layer of E-glass was the lowest strength composite. Interestingly, the double-bias weave carbon fibre with a lower-density foam core presented similar stiffness behaviour to standard E-glass surfboard materials. The double-biased carbon also improved the flexural strength (+17%), material toughness (+107%), and fracture toughness (+156%) of the composite compared to E-glass. These findings indicate surfboard manufacturers can utilise this carbon weave pattern to produce surfboards with equal flex behaviour, lower weight and improved resistance to damage in regular loading.
The Mechanical Properties of Silicone Rubber Composites with Shear Thickening Fluid Microcapsules
Authors: Wei Chun , Hao Xiaofei , Mao Chaoying , Zhong Fachun , Liu Zhongping
Published: June 2023

In this study, Sylgard 184 silicone rubber (SylSR) matrix composites with shear thickening fluid (STF) microcapsules (SylSR/STF) were fabricated. Their mechanical behaviors were characterized by dynamic thermo-mechanical analysis (DMA) and quasi-static compression. Their damping properties increased with the addition ofSTF into the SR in DMA tests and the SylSR/STF composites presented decreased stiffness and an obvious positive strain rate effect in the quasi-static compression test. Moreover, the impact resistance behavior of the SylSR/STF composites was tested by the drop hammer impact test. The addition of STF enhanced the impact protective performance of silicone rubber, and the impact resistance increased with the increase of STF content, which should be ascribed to the shear thickening and energy absorption of STF microcapsules in the composites. Meanwhile, in another matrix, hot vulcanized silicone rubber (HTVSR) with a mechanical strength higher than Sylgard 184, the impact resistance capacity of its composite with STF (HTVSR/STF) was also examined by the drop hammer impact test. It is interesting to note that the strength of the SR matrix obviously influenced the enhancement effect of STF on the impact resistance of SR. The stronger the strength of SR, the better the effect of STF on improving the impact protective performance of SR. This study not only provides a new method for packaging STF and improving the impact resistance behavior of SR, but is also beneficial for the design of STF-related protective functional materials and structures.
Wet Ball Milling Applied to Production of Composites and Coatings Based on Ti, W, and Nb Carbides
Authors: Eryomina Marina , Lomayeva Svetlana
Published: June 2023

The paper demonstrates the potential of wet ball milling of metals for the synthesis of various carbides and carbohydrides. The work reports on multicomponent carbides formed in Ti-(Cu/Fe/Si)-C, W-Fe-C, and Nb-(Cu/Fe/Si/Al)-C systems, as well as metastable or high-temperature intermetallics formed in Ti-Si, Nb-Si, Nb-Al, and Nb-Cu-Fe systems, which are stabilized with interstitial carbon. The formation of phase composition of powders fabricated under mechanochemical synthesis and subsequent thermal treatment has been studied. The as-fabricated powders have been used to produce bulk compacts and to apply wear-resistant coatings on steel (iron).
Using Lean-and-Green Supersaturated Poly-Factorial Mini Datasets to Profile Energy Consumption Performance for an Apartment Unit
Authors: Zarkadas Spyridon , Besseris George
Published: June 2023

The Renovation Wave for Europe initiative aspires to materialize the progressive greening of 85–95% of the continental older building stock as part of the European Green Deal objectives to reduce emissions and energy use. To realistically predict the energy performance even for a single apartment building is a difficult problem. This is because an apartment unit is inherently a customized construction which is subject to year-round occupant use. We use a standardized energy consumption response approach to accelerate the setting-up of the problem in pertinent energy engineering terms. Nationally instituted Energy Performance Certification databases provide validated energy consumption information by taking into account an apartment unit’s specific shell characteristics along with its installed electromechanical system configuration. Such a pre-engineered framework facilitates the effect evaluation of any proposed modifications on the energy performance of a building. Treating a vast building stock requires a mass-customization approach. Therefore, a lean-and-green, industrial-level problem-solving strategy is pursued. The TEE-KENAK Energy Certification database platform is used to parametrize a real standalone apartment. A supersaturated mini dataset was planned and collected to screen as many as 24 controlling factors, which included apartment shell layout details in association with the electromechanical systems arrangements. Main effects plots, best-subsets partial least squares, and entropic (Shannon) mutual information predictions—supplemented with optimal shrinkage estimations—formed the recommended profiler toolset. Four leading modifications were found to be statistically significant: (1) the thermal insulation of the roof, (2) the gas-sourced heating systems, (3) the automatic control category type ‘A’, and (4) the thermal insulation of the walls. The optimal profiling delivered an energy consumption projection of 110.4 kWh/m2 (energy status ‘B’) for the apartment—an almost 20% reduction in energy consumption while also achieving upgrading from the original ‘C’ energy status. The proposed approach may aid energy engineers to make general empirical screening predictions in an expedient manner by simultaneously considering the apartment unit’s structural configuration as well as its installed electromechanical systems arrangement.
Development of Hindi-Translated Version of the Female Sexual Function Index for Evaluating Sexual Dysfunctions among Individuals with Anxiety, Depression, and Other Common Mental Health Conditions: Experiential Account and Preliminary Findings from India
Authors: Jain Jyotsna , Ghosh Sanjukta , Gupta Snehil , Vullanki Sai Sreeja
Published: June 2023

Sexual dysfunctions are common among females with common mental health conditions, such as anxiety and depression. However, discussion on this topic remains taboo in many cultures, including India. Worldwide, the Female Sexual Function Index (FSFI) is the most used tool to evaluate sexual functioning in females. Researchers across the globe attempted to translate and validate this scale in native languages (e.g., Japanese and Spanish); however, such translations are not available in Hindi. Strikingly, although there is literature informing us about the regional validation process of this scale, little has been discussed about qualitative nuances of the process of translation, the expert panel discussion, and cognitive interviewing during pilot testing. The lack of such procedural qualitative data limits the replicability of such translations in different cultures; furthermore, it can adversely influence the parameters of the validation study. Hence, the current study was conducted to highlight the process of Hindi translation of the FSFI and illustrate the challenges faced at various stages of translation and testing. The study was conducted in the multidisciplinary hospital of Central India by involving five translators, five expert panel discussants, and 15 patients with common mental health conditions or their caregivers. The significant findings of the current work include the requirement of semantic and grammatical changes and rephrasing of the sentences for improving comprehensibility and applicability in the Indian population (during the panel discussion). Additionally, it informed us to use alternate or more than one word to capture a concept, including English words written in Hindi (during preliminary pilot testing). Critical roadblocks were inadequate awareness of the issues, lack of typical/comprehensive terminologies to capture the ideas, and significant stigma attached to the subject.
Immobilization of Cellulolytic Enzymes in Accurel® MP1000
Authors: Baruque Julia R. S. , Carniel Adriano , Sales Júlio C. S. , Ribeiro Bernardo D. , do Nascimento Rodrigo P. , Itabaiana Ivaldo
Published: June 2023

Cellulases are a class of enzymes of great industrial interest that present several strategic applications. However, the high cost of enzyme production, coupled with the instabilities and complexities of proteins required for hydrolytic processes, still limits their use in several protocols. Therefore, enzyme immobilization may be an essential tool to overcome these issues. The present work aimed to evaluate the immobilization of cellulolytic enzymes of the commercial enzyme cocktail Celluclast® 1.5 L in comparison to the cellulolytic enzyme cocktail produced from the wild strain Trichoderma harzianum I14-12 in Accurel® MP1000. Among the variables studied were temperature at 40 °C, ionic strength of 50 mM, and 72 h of immobilization, with 15 m·L −1 of proteins generated biocatalysts with high immobilization efficiencies (87% for ACC-Celluclast biocatalyst and 95% for ACC-ThI1412 biocatalyst), high retention of activity, and specific activities in the support for CMCase (DNS method), FPase (filter paper method) and β-glucosidase (p-nitrophenyl-β-D-glucopyranoside method). Presenting a lower protein concentration (0.32 m·L−1) than the commercial Celluclast® 1.5 L preparation (45 m·L−1), the ACC-ThI1412-derived immobilized biocatalyst showed thermal stability at temperatures higher than 60 °C, maintaining more than 90% of the residual activities of FPase, CMCase, and β-glucosidase. In contrast, the commercial-free enzyme presented a maximum catalytic activity at only 40 °C. Moreover, the difference in molecular weight between the component enzymes of the extract was responsible for different hydrophobic and lodging interactions of proteins on the support, generating a robust and competitive biocatalyst.
Christian Ethics and Liberation from Below: A Way of Doing Theological Ethics in Brazil
Authors: Martins Alexandre
Published: June 2023

This essay offers a Latin American perspective of theological ethics developed from the preferential option for the poor, marked by dialogue and encounter with the poor in their reality. Considering the theological diversity of the region, the author focuses on a theological ethics developed in Brazil, especially the dialogical methods of Brazilian Catholic ethicists gathered by the Brazilian Society of Moral Theology. This essay presents an account on dialogue in theological ethics as a means of creation and liberation from the encounter with the poor in their reality and with other partners in a praxis of faith and struggle for justice. Then, the author stresses their reality as a theological locus and their voices as interlocutors for developing theological ethics, showing an experience of this method from below in theological bioethics and global health challenges.
Same Incision for Simultaneous Laparoscopic Hand-Assisted Native Nephrectomy Contralateral to the Site of the Kidney Transplant
Authors: Angelico Roberta , Keçi Laura , Tariciotti Laura , Anselmo Alessandro , Favi Evaldo , Manzia Tommaso Maria , Tisone Giuseppe , Cacciola Roberto
Published: June 2023

Native nephrectomies in patients scheduled for a kidney transplant may represent a major challenge. The timing of the procedures as well as the magnitude of both surgical procedures require a risk mitigation strategy that may be restricted by the specific condition of the patients. We report a case of a simultaneous laparoscopic hand-assisted native nephrectomy contralateral to the site of the living donor kidney transplant.
Multilayer Data and Artificial Intelligence for the Delineation of Homogeneous Management Zones in Maize Cultivation
Authors: Gallardo-Romero Diego José , Apolo-Apolo Orly Enrique , Martínez-Guanter Jorge , Pérez-Ruiz Manuel
Published: June 2023

Variable rate application (VRA) is a crucial tool in precision agriculture, utilizing platforms such as Google Earth Engine (GEE) to access vast satellite image datasets and employ machine learning (ML) techniques for data processing. This research investigates the feasibility of implementing supervised ML models (random forest (RF), the support vector machine (SVM), gradient boosting trees (GBT), classification and regression trees (CART)) and unsupervised k-means clustering in GEE to generate accurate management zones (MZs). By leveraging Sentinel-2 satellite imagery and yielding monitor data, these models calculate vegetation indices to monitor crop health and reveal hidden patterns. The achieved classification accuracy values (0.67 to 0.99) highlight the potential of GEE and ML models for creating precise MZs, enabling subsequent VRA implementation. This leads to enhanced farm profitability, improved natural resource efficiency, and reduced environmental impact.
Performance Assessment of Structural Monitoring of a Dedicated High-Speed Railway Bridge Using a Moving-Base RTK-GNSS Method
Authors: Xi Ruijie , Jiang Weiping , Xuan Wei , Xu Dongsheng , Yang Jian , He Lihua , Ma Jun
Published: June 2023

At present, high-precision GNSS positioning technology is an important means to monitor the health of bridges and other structures. However, the GNSS signal of reference stations and monitoring stations used for bridge monitoring can easily be blocked by bridge towers, vehicles, or other objects, resulting in low positioning accuracy and insufficient availability of GNSS, which affects the effectiveness of bridge structural health monitoring. Therefore, according to the characteristics of bridge structure, this paper proposes to take the bridge tower monitoring station as a moving-base station to build the baselines with other monitoring stations and use the moving-base RTK-GNSS method to realize the relative positioning, so as to improve the availability of GNSS in the application of bridge structure health monitoring. In this paper, the moving-base RTK-GNSS model is derived and verified via GNSS monitoring data of the Ganzhou dedicated high-speed railway bridge. The results show that the ambiguity in the fixing rate can be improved using the moving-base RTK-GNSS method with the tower monitoring station as the reference station. The deformation and vibration characteristics of each monitoring point can be reflected, and the displacement and vibration amplitude estimation accuracy can achieve results better than 4 mm. Therefore, the moving-base RTK-GNSS method can be used as an alternative scheme when the observation environment of the base station is poor or the banded engineering monitoring is applied, so as to improve the monitoring capability of GNSS.
Spatio-Temporal Evolution and Coupled Coordination of LUCC and ESV in Cities of the Transition Zone, Shenmu City, China
Authors: Xie Yuyang , Zhu Qiuchen , Bai Hua , Luo Pingping , Liu Jifa
Published: June 2023

This study investigates the spatial-temporal evolution and the interconnectedness of land use/cover change (LUCC) and ecosystem service value (ESV). Such analysis can offer theoretical guidance and support decision-making for sustainable land resource development and ecological preservation in ecologically vulnerable cities within the Loess Plateau-Maowusu Desert transition zone. Utilizing Landsat data spanning 2000–2020, the paper examines the synergistic relationship between ESV and land use intensity in Shenmu City through bivariate spatial autocorrelation and the coupled coordination degree (CCD) model. Our findings indicate that the area of construction land in Shenmu City experienced the most significant change between 2000 and 2020, with a dynamism rate of 76.8%. This shift resulted in a decrease in the total ESV, from RMB 10.059 billion in 2000 to RMB 9.906 billion in 2020. The bivariate spatial autocorrelation analysis reveals a significant positive spatial correlation between ESV and land use intensity, while the CCD levels for both demonstrate a fluctuating yet overall upward trend over the 20-year period. The paper uncovers the spatial-temporal evolution of LUCC and ESV in Shenmu City along with their interconnected dynamics. The research outcomes can contribute valuable insights for reinforcing land resource utilization and promoting sustainable regional development within cities in the Loess Plateau-Maowusu Desert transition zone.
Pixel-Wise Attention Residual Network for Super-Resolution of Optical Remote Sensing Images
Authors: Chang Yali , Chen Gang , Chen Jifa
Published: June 2023

The deep-learning-based image super-resolution opens a new direction for the remote sensing field to reconstruct further information and details from captured images. However, most current SR works try to improve the performance by increasing the complexity of the model, which results in significant computational costs and memory consumption. In this paper, we propose a lightweight model named pixel-wise attention residual network for optical remote sensor images, which can effectively solve the super-resolution task of multi-satellite images. The proposed method consists of three modules: the feature extraction module, feature fusion module, and feature mapping module. First, the feature extraction module is responsible for extracting the deep features from the input spatial bands with different spatial resolutions. Second, the feature fusion module with the pixel-wise attention mechanism generates weight coefficients for each pixel on the feature map and fully fuses the deep feature information. Third, the feature mapping module is aimed to maintain the fidelity of the spectrum by adding the fused residual feature map directly to the up-sampled low-resolution images. Compared with existing deep-learning-based methods, the major advantage of our method is that for the first time, the pixel-wise attention mechanism is incorporated in the task of super-resolution fusion of remote sensing images, which effectively improved the performance of the fusion network. The accuracy assessment results show that our method achieved superior performance of the root mean square error, signal-to–reconstruction ratio error, universal image quality index, and peak signal noise ratio compared to competing approaches. The improvements in the signal-to-reconstruction ratio error and peak signal noise ratio are significant, with a respective increase of 0.15 and 0.629 dB for Sentinel-2 data, and 0.196 and 1 dB for Landsat data.
Observations of the Impacts of Hong Kong International Airport on Water Quality from 1986 to 2022 Using Landsat Satellite
Authors: Wang Zhengyi , Mao Zhihua , Zhang Longwei , Zhang Xianliang , Yuan Dapeng , Li Youzhi , Wu Zhongqiang , Huang Haiqing , Zhu Qiankun
Published: June 2023

Hong Kong International Airport (HKIA) is an important sea airdrome in China. The aim of this study is to evaluate the impacts of this reclamation on the water quality of the Northwestern Bay of Hong Kong (NWBHK). In all, 117 Landsat 5 TM and 44 Landsat 8 OLI images were preprocessed and matched with the marine water data of 18 in situ monitoring points, acquiring 458 and 119 sets of data, respectively. This study adopted BPNN Machine Learning methods to establish the retrieval algorithm. Based on the images, the construction of HKIA was divided into three stages: (1) the construction of the first and second runways from 1992 to 1995; (2) the construction of the Hong Kong Port from 2013 to 2016; and (3) the construction of the third runway from 2017 to 2020. The concentrations of suspended particulate matter, orthophosphate phosphorus, and dissolved inorganic nitrogen from 1986 to 2022 were retrieved. In this paper, it was found that (1) the construction activities led to an increase in SPM, PO4P, and DIN concentrations in adjacent water bodies; (2) the impact of the Tuen Mun River on the NWBHK increased, while the impact of the Tung Chung River on the NWBHK decreased; and (3) the interception impact of HKIA on the transportation of the Pearl River water became stronger.
Ground Deformation and Permafrost Degradation in the Source Region of the Yellow River, in the Northeast of the Qinghai-Tibet Plateau
Authors: Li Chengye , Zhao Lin , Wang Lingxiao , Liu Shibo , Zhou Huayun , Li Zhibin , Liu Guangyue , Du Erji , Zou Defu , Hou Yingxu
Published: June 2023

The source region of the Yellow River (SRYR) is situated on the permafrost boundary in the northeast of the Qinghai-Tibet Plateau (QTP), which is an area highly sensitive to climate change. As a result of increasing global temperatures, the permafrost in this region has undergone significant degradation. In this study, we utilized Sentinel-1 to obtain ground surface deformation data in the SRYR from June 2017 to January 2022. We then analyzed the differences in terrain deformation under various environmental conditions. Our findings indicated an overall subsidence trend in the SRYR, with a long-term deformation velocity of −4.2 mm/a and seasonal deformation of 8.85 mm. Furthermore, the results showed that terrain deformation varied considerably from region to region, and that the Huanghe’ yan sub-basin with the highest permafrost coverage among all sub-basins significantly higher subsidence rates than other regions. Topography strongly influenced ground surface deformation, with flat slopes exhibiting much higher subsidence rates and seasonal deformation. Moreover, the ground temperature and ground ice richness played a certain role in the deformation pattern. This study also analyzed regional deformation details from eight boreholes and one profile line covering different surface conditions, revealing the potential for refining the permafrost boundary. Overall, the results of this study provide valuable insights into the evolution of permafrost in the SRYR region.
CACTUS: Content-Aware Compression and Transmission Using Semantics for Automotive LiDAR Data
Authors: Mari Daniele , Camuffo Elena , Milani Simone
Published: June 2023

Many recent cloud or edge computing strategies for automotive applications require transmitting huge amounts of Light Detection and Ranging (LiDAR) data from terminals to centralized processing units. As a matter of fact, the development of effective Point Cloud (PC) compression strategies that preserve semantic information, which is critical for scene understanding, proves to be crucial. Segmentation and compression have always been treated as two independent tasks; however, since not all the semantic classes are equally important for the end task, this information can be used to guide data transmission. In this paper, we propose Content-Aware Compression and Transmission Using Semantics (CACTUS), which is a coding framework that exploits semantic information to optimize the data transmission, partitioning the original point set into separate data streams. Experimental results show that differently from traditional strategies, the independent coding of semantically consistent point sets preserves class information. Additionally, whenever semantic information needs to be transmitted to the receiver, using the CACTUS strategy leads to gains in terms of compression efficiency, and more in general, it improves the speed and flexibility of the baseline codec used to compress the data.
Prediction of Continuous Emotional Measures through Physiological and Visual Data
Authors: Joudeh Itaf Omar , Cretu Ana-Maria , Bouchard Stéphane , Guimond Synthia
Published: June 2023

The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments.
Federated Learning-Based Insulator Fault Detection for Data Privacy Preserving
Authors: Luan Zhirong , Lai Yujun , Xu Zhicong , Gao Yu , Wang Qian
Published: June 2023

Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy.
Ground Radioactivity Distribution Reconstruction and Dose Rate Estimation Based on Spectrum Deconvolution
Authors: Xu Hang , Ai Xianyun , Wang Ying , Chen Wenzhuo , Li Zikun , Guan Xian , Wei Xing , Xie Jianming , Chen Ye
Published: June 2023

Estimating the gamma dose rate at one meter above ground level and determining the distribution of radioactive pollution from aerial radiation monitoring data are the core technical issues of unmanned aerial vehicle nuclear radiation monitoring. In this paper, a reconstruction algorithm of the ground radioactivity distribution based on spectral deconvolution was proposed for the problem of regional surface source radioactivity distribution reconstruction and dose rate estimation. The algorithm estimates unknown radioactive nuclide types and their distributions using spectrum deconvolution and introduces energy windows to improve the accuracy of the deconvolution results, achieving accurate reconstruction of multiple continuous distribution radioactive nuclides and their distributions, as well as dose rate estimation of one meter above ground level. The feasibility and effectiveness of the method were verified through cases of single-nuclide (137Cs) and multi-nuclide (137Cs and 60Co) surface sources by modeling and solving them. The results showed that the cosine similarities between the estimated ground radioactivity distribution and dose rate distribution with the true value were 0.9950 and 0.9965, respectively, which could prove that the proposed reconstruction algorithm would effectively distinguish multiple radioactive nuclides and accurately restore their radioactivity distribution. Finally, the influences of statistical fluctuation levels and the number of energy windows on the deconvolution results were analyzed, showing that the lower the statistical fluctuation level and the more energy window divisions, the better the deconvolution results.
A DFT Study of Phosphate Ion Adsorption on Graphene Nanodots: Implications for Sensing
Authors: Shtepliuk Ivan
Published: June 2023

The optical properties of graphene nanodots (GND) and their interaction with phosphate ions have been investigated to explore their potential for optical sensing applications. The absorption spectra of pristine GND and modified GND systems were analyzed using time-dependent density functional theory (TD-DFT) calculation investigations. The results revealed that the size of adsorbed phosphate ions on GND surfaces correlated with the energy gap of the GND systems, leading to significant modifications in their absorption spectra. The introduction of vacancies and metal dopants in GND systems resulted in variations in the absorption bands and shifts in their wavelengths. Moreover, the absorption spectra of GND systems were further altered upon the adsorption of phosphate ions. These findings provide valuable insights into the optical behavior of GND and highlight their potential for the development of sensitive and selective optical sensors for phosphate detection.
NISQE: Non-Intrusive Speech Quality Evaluator Based on Natural Statistics of Mean Subtracted Contrast Normalized Coefficients of Spectrogram
Authors: Zafar Shakeel , Nizami Imran Fareed , Rehman Mobeen Ur , Majid Muhammad , Ryu Jihyoung
Published: June 2023

With the evolution in technology, communication based on the voice has gained importance in applications such as online conferencing, online meetings, voice-over internet protocol (VoIP), etc. Limiting factors such as environmental noise, encoding and decoding of the speech signal, and limitations of technology may degrade the quality of the speech signal. Therefore, there is a requirement for continuous quality assessment of the speech signal. Speech quality assessment (SQA) enables the system to automatically tune network parameters to improve speech quality. Furthermore, there are many speech transmitters and receivers that are used for voice processing including mobile devices and high-performance computers that can benefit from SQA. SQA plays a significant role in the evaluation of speech-processing systems. Non-intrusive speech quality assessment (NI-SQA) is a challenging task due to the unavailability of pristine speech signals in real-world scenarios. The success of NI-SQA techniques highly relies on the features used to assess speech quality. Various NI-SQA methods are available that extract features from speech signals in different domains, but they do not take into account the natural structure of the speech signals for assessment of speech quality. This work proposes a method for NI-SQA based on the natural structure of the speech signals that are approximated using the natural spectrogram statistical (NSS) properties derived from the speech signal spectrogram. The pristine version of the speech signal follows a structured natural pattern that is disrupted when distortion is introduced in the speech signal. The deviation of NSS properties between the pristine and distorted speech signals is utilized to predict speech quality. The proposed methodology shows better performance in comparison to state-of-the-art NI-SQA methods on the Centre for Speech Technology Voice Cloning Toolkit corpus (VCTK-Corpus) with a Spearman’s rank-ordered correlation constant (SRC) of 0.902, Pearson correlation constant (PCC) of 0.960, and root mean squared error (RMSE) of 0.206. Conversely, on the NOIZEUS-960 database, the proposed methodology shows an SRC of 0.958, PCC of 0.960, and RMSE of 0.114.
A Sensitive, Simple, and Fast LC–MS/MS Method for Quantification of Remifentanil in Human Plasma: Applications in Critically Ill Patients’ Plasma during Extracorporeal Membrane Oxygenation
Authors: Chae Soon Uk , Kim Ji Seon , Lee Chae Bin , Jo Seong Jun , Min Kyung Lok , Chang Min Jung , Bae Soo Kyung
Published: June 2023

A sensitive, simple, and fast liquid chromatography–tandem mass spectrometry (LC–MS/MS) bioanalytical method was developed to determine remifentanil in human plasma and prevent its instability by esterases during quantification. A 20 μL sample of human plasma, acidified with formic acid, was precipitated using 100 μL of acetonitrile. Chromatography was carried out on an Agilent Poroshell 120 EC-C18 column (4.6 × 50 mm, 2.7 μm) with an isocratic elution by acetonitrile and distilled water containing 0.1% formic acid (65:35, v/v) at a flow rate of 0.5 mL/min. Transition ions were detected as m/z 377.10→113.20 and m/z 277.00→111.00 for remifentanil and chlorpropamide (internal standard), respectively, with positive electrospray ionization in mass spectrometry. The run time was only 3 min per sample. We evaluated the selectivity, linearity, carry-over, accuracy, precision, extraction recovery, absolute matrix effect, stability, and incurred sample reanalysis and found that all these parameters were within acceptable limits. The calibration curve range for remifentanil was 0.05–50 ng/mL with regression coefficient (r) values higher than 0.9939. Given the simple and fast sample preparation and the lower LLOQ concentration compared to those in other methods, this method was successfully used to quantify plasma levels after intravenous infusion of remifentanil to intensive-care-unit patients during extracorporeal membrane oxygenation.
Experimental Study on Mechanical Properties of Hemp Fibers Influenced by Various Parameters
Authors: Ribeiro João , Bueno Guilherme , Martín Manuel Rodríguez , Rocha João
Published: June 2023

Hemp fibers produced by different extraction techniques that were cultivated in the Portuguese districts of Bragança and Mirandela under various nitrogen fertilization and planting density conditions are examined and mechanically described in this paper. The objective of this study is to evaluate the influence of sowing density, nitrogen fertilization, sample location, and extraction method in order to improve the mechanical properties of hemp fibers. To achieve this, we determined the modulus of elasticity, the tensile strength, and the density. The mean value for the modulus of elasticity was 92.44 ± 7.44 GPa, the mean of tensile strength was 564.98 ± 167.03 MPa, and the mean of the density was 1.64 ± 0.24 g cm−3. We performed a statistical analysis of all parameters using ANOVA and found that the retting method had the greatest influence among all parameters. The associated effects of nitrogen fertilization and sowing density revealed an important influence on tensile strength and specific tensile strength, respectively.
Technical and Economic Assessment of Energy Efficiency of Electrification of Hydrocarbon Production Facilities in Underdeveloped Areas
Authors: Marinina Oksana , Nechitailo Anna , Stroykov Gennady , Tsvetkova Anna , Reshneva Ekaterina , Turovskaya Liudmila
Published: June 2023

The relevance of the technical and economic evaluation of options for the optimization of electrification projects of hydrocarbon production facilities is due to the growing need for the development of new fields in undeveloped and hard-to-reach territories. Development of new fields requires the construction of large amounts of infrastructure energy facilities, new solutions to improve energy efficiency, reducing capital intensity of projects, solutions to improve the efficiency of resource use in the circular economy, and the use of renewable energy sources (RES). Analysis of the technological directions of electrification of hydrocarbon production facilities proves that the low level of application of RES for energy supply purposes is due to the lack of experimental data on the implementation of this kind of project. This study considers features of technological solutions, practical recommendations, and the main limitations of the application of a hybrid automated system based on RES for the electrification of gas production facilities located in poorly-developed territories. A comparative technical and economic analysis of electrification options using autonomous RES and construction of a power transmission line (PTL) to a remote section of an oil and gas condensate field located in the Arctic zone was carried out. In order to justify the implementation of the electrification project with the use of RES sources, the climatic potential of the region was assessed, and the calculation of energy supply needs and a comparison of alternatives on the basis of specific total capital and operating costs were provided. Assessment of the specific indicator of costs for the proposed variant of the autonomous energy complex based on wind generation indicated savings of RUB 2.24 per kilowatt-hour of energy used. The results of the study can be used in project planning and evaluation of proposed technological solutions based on the rational choice of energy sources and optimization of cost indicators for the construction and operation of energy supply systems.
Exploration of Solar Panel Damage and Service Life Reduction Using Condition Assessment, Dust Accumulation, and Material Testing
Authors: Bodnár István , Matusz-Kalász Dávid , Boros Ruben Rafael
Published: June 2023

This paper presents a comprehensive inspection of a 10.044 MWp solar power plant that had operational problems. The condition assessment was necessary because the power plant was constantly producing electricity significantly below the expected peak power. The research confirmed that the cause of the decrease in performance is the pollution from the industrial park and the conventional power plant located next to the solar system. The accuracy of the results is confirmed using on-site thermal imaging at the solar power plant and laboratory tests. During the laboratory tests, the electrical performance of 1063 solar panels was examined as well as their damaging and life-reducing effect with further thermography tests. Surface dirt deposition cause several significant problems during energy generation. Power outages are a minor problem, and sustainable production is the main goal in the long term. If the failure and destruction of the solar modules cause more serious damage, the lifetime of the photovoltaic power plants can be shortened.
Development of a New Model for the Formation of Wax Deposits through the Passage of Crude Oil within the Well
Authors: Ilushin Pavel , Vyatkin Kirill , Kozlov Anton
Published: June 2023

Wax deposits related to flow assurance are a costly problem in oil production in many fields around the world. Modeling of this process is the main tool for creating and optimizing methods to deal with this problem. This paper considers a new empirical model for the formation of these deposits, based on the results of an array of laboratory studies, theoretical data and technological calculations are presented. The created technique takes into account the conditions of oil flow, data from laboratory studies, and the water cut of the product. The experience of the industrial operation of the technique showed a high convergence of the calculated and actual deposit profiles. Based on a comparison of the calculated and actual deposit profiles, it was concluded that the standard deviation of maximum wax thickness is 6.0%, and the depth with the greatest wax thickness is 3.5%, which is a fairly high result. The use of this technique makes it possible to optimize the depth of mechanical cleaning of the well, the installation of heating cables, as well as the parameters of hot flushing, which increases their efficiency and reduces the cost of combating the formation of wax deposition.
Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance
Authors: Sandamal Kelum , Shashiprabha Sachini , Muttil Nitin , Rathnayake Upaka
Published: June 2023

Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R2) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.
Bus Bunching and Bus Bridging: What Can We Learn from Generative AI Tools like ChatGPT?
Authors: Voß Stefan
Published: June 2023

Regarding tools and systems from artificial intelligence (AI), chat-based ones from the area of generative AI have become a major focus regarding media coverage. ChatGPT and occasionally other systems (such as those from Microsoft and Google) are discussed with hundreds if not thousands of academic papers as well as newspaper articles. While various areas have considerably gone into this discussion, transportation and logistics has not yet come that far. In this paper, we explore the use of generative AI tools within this domain. More specifically, we focus on a topic related to sustainable passenger transportation, that is, the handling of disturbances in public transport when it comes to bus bunching and bus bridging. The first of these concepts is related to analyzing situations where we observe two or more buses of the same line following close to each other without being planned deliberately and the second is related to the case where buses are used to replace broken connections in other systems, such as subways. Generative AI tools seem to be able to provide meaningful entries and a lot of food for thought while the academic use may still be classified as limited.