bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Advancements and Strategies for Overcoming the Blood–Brain Barrier Using Albumin-Based Drug Delivery Systems to Treat Brain Cancer, with a Focus on Glioblastoma
Authors: Tincu (Iurciuc) Camelia-Elena , Andrițoiu Călin Vasile , Popa Marcel , Ochiuz Lăcrămioara
Published: October 2023

Glioblastoma multiforme (GBM) is a highly aggressive malignant tumor, and the most prevalent primary malignant tumor affecting the brain and central nervous system. Recent research indicates that the genetic profile of GBM makes it resistant to drugs and radiation. However, the main obstacle in treating GBM is transporting drugs through the blood–brain barrier (BBB). Albumin is a versatile biomaterial for the synthesis of nanoparticles. The efficiency of albumin-based delivery systems is determined by their ability to improve tumor targeting and accumulation. In this review, we will discuss the prevalence of human glioblastoma and the currently adopted treatment, as well as the structure and some essential functions of the BBB, to transport drugs through this barrier. We will also mention some aspects related to the blood–tumor brain barrier (BTBB) that lead to poor treatment efficacy. The properties and structure of serum albumin were highlighted, such as its role in targeting brain tumors, as well as the progress made until now regarding the techniques for obtaining albumin nanoparticles and their functionalization, in order to overcome the BBB and treat cancer, especially human glioblastoma. The albumin drug delivery nanosystems mentioned in this paper have improved properties and can overcome the BBB to target brain tumors.
Evaluation of the Shear Bond Strength of Immediate and Delayed Restorations of Various Calcium Silicate-Based Materials with Fiber-Reinforced Composite Resin Materials
Authors: Candan Merve , Altinay Karaca Fatıma Kübra , Öznurhan Fatih
Published: October 2023

Due to significant tissue loss in teeth requiring pulp treatments, hermetic restoration of the remaining dental tissues is one of the most crucial factors in determining the treatment’s success. The adhesion of composite resins to calcium silicate cements (CSCs) is considered challenging. Consequently, it is crucial to identify the optimal method for obtaining optimal adhesion. The aim of the present study is to evaluate the shear bond strength (SBS) values of immediate and delayed restorations with fiber-reinforced composites on powder–liquid, premixed, and resin-containing flowable CSCs. In the present study, the SBS values obtained after immediate (14 min) and delayed (7 days) restorations of three different CSCs (NeoMTA2, NeoPutty, and TheraCal PT) with three different resin composite materials (EverX FlowTM, EverX PosteriorTM, and Filtek Z550) were compared. The fracture types were evaluated using a stereomicroscope and SEM. TheraCal PT had the highest SBS values for both immediate and delayed restorations, and the comparison with other materials showed a statistically significant difference (p = 0.001). In contrast, there was no statistically significant difference between the SBS values of NeoMTA and NeoPutty (p > 0.05). In both immediate and delayed restorations, there was no statistically significant difference between nanohybrid and fiber-reinforced composites (p > 0.05). The simple use and strong bonding ability of TheraCal PT with composite resins may provide support for the idea that it is suitable for pulpal interventions. Nevertheless, due to the in vitro nature of this study, additional in vitro and clinical studies are required to investigate the material’s physical, mechanical, and biological properties for use in clinical applications.
Enzymatic Treatment of Ferulated Arabinoxylans from Distillers Dried Grains with Solubles: Influence on the Fabrication of Covalent Electro-Sprayed Nanoparticles
Authors: De Anda-Flores Yubia , Lizardi-Mendoza Jaime , Rascón-Chu Agustín , Tanori-Cordova Judith , Martínez-López Ana Luisa , Carvajal-Millan Elizabeth
Published: October 2023

Arabinoxylans (AXs) extracted from distillers dried grains with solubles (DDGSs) were treated with amylase, amyloglucosidase, and protease, to evaluate their effect on the polysaccharide capability to form covalent electro-sprayed nanoparticles. Enzymatically treated arabinoxylans (AXPPs) presented a significant decrease in protein content and molecular weight (31 and 37%, respectively), while the ferulic acid content and the arabinose-to-xylose ratio (A/X) were not statistically modified. The Fourier transform infrared spectra of the AXPPs showed a diminution in the intensity of amide I and amide II bands concerning AXs. The AXPP gels (1% w/v) induced via laccase registered a slight increase in the dimers of ferulic acid cross-linking content (9%) and the G’ value (27%) about the AX gels. The electro-sprayed nanoparticles of AXs and AXPPs (NAXs and NAXPPs, respectively) revealed a spherical and regular morphology via transmission electron microscopy. The nanoparticle diameter was not different for the NAXs and NAXPPs, while the NAXPPs show a significant reduction in Z potential value compared to NAXs. Confocal laser microscopy observations were conducted, to analyze the protein content in the AX network, and a decrease in illuminated areas was observed in the AXPP gels and the NAXPPs. These results indicate that the enzymatical treatment of an AX improves the polysaccharide gelling capability, but does not influence the fabrication of electro-sprayed covalent nanoparticles. NAXs and NAXPPs could be attractive biomaterials for diverse pharmaceutical and biomedical applications.
Small-Sample Underwater Target Detection: A Joint Approach Utilizing Diffusion and YOLOv7 Model
Authors: Cheng Chensheng , Hou Xujia , Wen Xin , Liu Weidong , Zhang Feihu
Published: October 2023

Underwater target detection technology plays a crucial role in the autonomous exploration of underwater vehicles. In recent years, significant progress has been made in the field of target detection through the application of artificial intelligence technology. Effectively applying AI techniques to underwater target detection is a highly promising area of research. However, the difficulty and high cost of underwater acoustic data collection have led to a severe lack of data, greatly restricting the development of deep-learning-based target detection methods. The present study is the first to utilize diffusion models for generating underwater acoustic data, thereby effectively addressing the issue of poor detection performance arising from the scarcity of underwater acoustic data. Firstly, we place iron cylinders and cones underwater (simulating small preset targets such as mines). Subsequently, we employ an autonomous underwater vehicle (AUV) equipped with side-scan sonar (SSS) to obtain underwater target data. The collected target data are augmented using the denoising diffusion probabilistic model (DDPM). Finally, the augmented data are used to train an improved YOLOv7 model, and its detection performance is evaluated on a test set. The results demonstrate the effectiveness of the proposed method in generating similar data and overcoming the challenge of limited training sample data. Compared to models trained solely on the original data, the model trained with augmented data shows a mean average precision (mAP) improvement of approximately 30% across various mainstream detection networks. Additionally, compared to the original model, the improved YOLOv7 model proposed in this study exhibits a 2% increase in mAP on the underwater dataset.
Analysis of BDS/GPS Deformation Monitoring for the Lake Sarez Dam
Authors: Han Junqiang , Tu Rui , Lu Xiaochun , Fan Lihong , Zhuang Wenquan , Wang Weisheng , Zhao Feng , Dalai Bayin , Shonazarovich Gulayozov Majid , Safarov Mustafo
Published: October 2023

The Sarez Dam, currently recognized as the world’s highest natural dam, holds immense economic significance, necessitating the reliable monitoring and early detection of potential deformations. This study utilizes the Beidou high-precision deformation monitoring system for the dam. Employing baseline network solutions, precise point positioning, and real-time kinematic positioning, the monitoring data from October 2021 to March 2023 were meticulously processed and comparatively analyzed. The results reveal varied degrees of displacement deformation at all sites, with horizontal deformation towards the lake center and vertical deformation showing subsidence. The three-dimensional vector average displacement is 29.1 mm, with an average monthly rate of 4.3 mm/month, and the maximum deformation is 41.4 mm. The cause of the horizontal deformation towards the lake center may be a slow collapse of the coastal soil and rock towards the lake center. Additionally, the monitoring detected a 7.2-magnitude earthquake on 23 February 2023, 52 km from the dam, causing an average displacement of 22.5 mm towards the south, which is 20 times the monthly deformation rate. In summary, deformation is present within the dam monitoring area, with a relatively stable deformation rate, warranting continued tracking and monitoring. Furthermore, earthquakes significantly impact dam deformation, necessitating heightened attention to the potential for seismic-induced dam damage in the future.
Measurements of Complex Free Water Surface Topography Using a Photogrammetric Method
Authors: Pleterski Žan , Hočevar Marko , Bizjan Benjamin , Kolbl Repinc Sabina , Rak Gašper
Published: October 2023

This paper presents a photogrammetry-based system for capturing turbulent aerated flow topography in a laboratory environment, especially for complex hydraulic phenomena character-ised by turbulent, non-stationary, and non-homogeneous aerated flows. It consists of ten high-resolution cameras equipped with monochromatic sensors and custom-built LED lights, all synchronised for accurate data acquisition. Post processing involves Structure-from-Motion and Multi-View Stereo techniques to calculate exterior and interior orientation parameters that ensure accurate alignment within a desired coordinate system, and conversion to point clouds. The proposed method showed great potential for capturing free water surface topography of turbulent aerated flows with high spatial and temporal resolution over the entire field of view of the cameras. Due to the unique capabilities of this system, direct comparisons with existing benchmarks were not possible. Instead, average free water surface profiles were derived from selected control cross sections, using 2D LIDAR measurements for verification. Both the LIDAR and photogrammetry averaged profiles showed remarkably good agreement, with deviations within ±20 mm. Validation showed that photogrammetry can be used to measure the complex aerated turbulent free water surface. In this way, this approach, involving consecutive image dataset acquisition at predefined intervals, is proving to be a valuable tool for observing, visualising, analysing, investigating, and gaining a comprehensive understanding of the dynamics of the free water surface.
Assessing the Water Status and Leaf Pigment Content of Olive Trees: Evaluating the Potential and Feasibility of Unmanned Aerial Vehicle Multispectral and Thermal Data for Estimation Purposes
Authors: Marques Pedro , Pádua Luís , Sousa Joaquim J. , Fernandes-Silva Anabela
Published: October 2023

Global warming presents a significant threat to the sustainability of agricultural systems, demanding increased irrigation to mitigate the impacts of prolonged dry seasons. Efficient water management strategies, including deficit irrigation, have thus become essential, requiring continuous crop monitoring. However, conventional monitoring methods are laborious and time-consuming. This study investigates the potential of aerial imagery captured by unmanned aerial vehicles (UAVs) to predict critical water stress indicators—relative water content (RWC), midday leaf water potential (ΨMD), stomatal conductance (gs)—as well as the pigment content (chlorophyll ab, chlorophyll a, chlorophyll b and carotenoids) of trees in an olive orchard. Both thermal and spectral vegetation indices are calculated and correlated using linear and exponential regression models. The results reveal that the thermal vegetation indices contrast in estimating the water stress indicators, with the Crop Water Stress Index (CWSI) demonstrating higher precision in predicting the RWC (R2 = 0.80), ΨMD (R2 = 0.61) and gs (R2 = 0.72). Additionally, the Triangular Vegetation Index (TVI) shows superior accuracy in predicting the chlorophyll ab (R2 = 0.64) and chlorophyll a (R2 = 0.61), while the Modified Chlorophyll Absorption in Reflectance Index (MCARI) proves most effective for estimating the chlorophyll b (R2 = 0.52). This study emphasizes the potential of UAV-based multispectral and thermal infrared imagery in precision agriculture, enabling assessments of the water status and pigment content. Moreover, these results highlight the vital importance of this technology in optimising resource allocation and enhancing olive production, critical steps towards sustainable agriculture in the face of global warming.
A New Method for Reconstructing Tree-Level Aboveground Carbon Stocks of Eucalyptus Based on TLS Point Clouds
Authors: Fan Guangpeng , Lu Feng , Cai Huide , Xu Zhanyong , Wang Ruoyoulan , Zeng Xiangquan , Xu Fu , Chen Feixiang
Published: October 2023

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation.
Unraveling Effect of Snow Cover on Spring Vegetation Phenology across Different Vegetation Types in Northeast China
Authors: Ren Chong , Zhang Lijuan , Fu Bin
Published: October 2023

Snow cover has significantly changed due to global warming in recent decades, causing large changes in the vegetation ecosystem. However, the impact of snow cover changes on the spring phenology of different vegetation types in Northeast China remains unclear. In this study, we investigated the response of the start of the growing season (SOS) to different snow cover indicators using partial correlation analysis and stepwise regression analysis in Northeast China from 1982 to 2015 based on multiple remote sensing datasets. Furthermore, we revealed the underlying mechanisms using a structural equation model. The results show that decreased snow cover days (SCD) and an advanced snow cover end date (SCED) led to an advanced SOS in forests. Conversely, an increased SCD and a delayed SCED led to an advanced SOS in grasslands. The trends of SCD and SCED did not exhibit significant changes in rainfed cropland. The maximum snow water equivalent (SWEmax) increased in most areas. However, the proportion of the correlation between SWEmax and SOS was small. The impact of snow cover changes on the SOS varied across different vegetation types. Snow cover indicators mainly exhibited positive correlations with the SOS of forests, including deciduous broadleaf forests and deciduous coniferous forests, with positive and negative correlations of 18.61% and 2.58%, respectively. However, snow cover indicators mainly exhibited negative correlations in the SOS of grasslands and rainfed croplands, exhibiting positive and negative correlations of 4.87% and 13.06%, respectively. Snow cover impacted the SOS through the “temperature effect” in deciduous broadleaf forests, deciduous coniferous forests, and rainfed croplands, while it affected SOS through the “moisture effect” in grasslands. These results provide an enhanced understanding of the differences in snow cover changes affecting SOS in different vegetation types under climate change in Northeast China.
A Multifactor-Based Random Forest Regression Model to Reconstruct a Continuous Deformation Map in Xi’an, China
Authors: Guo Xinxin , Zhao Chaoying , Li Guangrong , Peng Mimi , Zhang Qin
Published: October 2023

The synthetic aperture radar interferometry (InSAR) technique is an effective means to monitor ground deformation with high spatial resolution over large areas. However, it is still difficult to obtain the spatially continuous deformation map due to SAR decorrelation or SAR distortion, which greatly limits the usage of the InSAR deformation map, especially for spatiotemporal characterizing and mechanism inversion. Some conventional methods (e.g., spatial interpolation) rely only on the deformation measurements without considering the influence factors, leading to the inaccuracy of the deformation prediction. So, we propose a multifactor-based machine learning model, namely the K-RFR model, that combines K-means clustering and random forest regression algorithm to reconstruct a continuous deformation map, where the influence factors on ground deformation are considered, such as land use, geological engineering, and under groundwater extraction. We take the city of Xi’an, China, as the study area where SBAS-InSAR was used to obtain the ground deformation maps from 2012 to 2015. Fourteen influence factors are employed, including confined water level, change of confined water, phreatic water level, change of phreatic water, rainfall, ground fissures, stratigraphic lithology, landform, hydrogeology, engineering geology, type of land use, soil type, GDP, and DEM, where the K-means clustering method is used to reduce the influence of spatial heterogeneity. The study area is divided into three homogeneous regions and modeled independently, where the mean squared errors of region I–III are 2.9 mm, 2.3 mm, and 3.9 mm, respectively, and the mean absolute errors are 2.5 mm, 1.0 mm, and 2.8 mm, respectively. Finally, the continuous ground deformation maps of Xi’an from 2012 to 2015 are reconstructed. We compared the new method with two interpolation methods. Results show that the correlation coefficient between prediction and InSAR measurements of the new model is 0.94, whereas the ordinary Kriging method is 0.69, and the IDW method is only 0.63. This study provides an effective means to predict the continuous surface deformation over a large area.
Sofia Airport Visibility Estimation with Two Machine-Learning Techniques
Authors: Penov Nikolay , Guerova Guergana
Published: October 2023

Fog is a weather phenomenon with visibility below 1 km. Fog heavily influences ground and air traffic, leading to accidents and delays. The main goal of this study is to use two machine-learning (ML) techniques—the random forest (RF) and long short-term memory (LSTM) models—to estimate visibility using 11 meteorological parameters. Several meteorological elements related to fog are investigated, including pressure, temperature, wind speed, and direction. The seasonal cycle shows that fog in Sofia has a peak in winter, but a small secondary peak in spring was found in this study. Fog occurrence has a tendency to decrease during the studied period, with the peak of fog observations being shifted towards the higher visibility range. The input parameters in the models are day of year, hour, wind speed, wind direction, first-cloud-layer coverage, first-cloud-layer base height, temperature, dew point, dew-point deficit, pressure, and fog stability index (FSI). The FSI and dew-point deficit are evaluated as the most important input parameters by the RF model. Post-processing was performed with double linear regression for the correction of the predictions by the models, which led to a significant improvement in performance. Both models were found to describe the complexity of fog well.
A New Architecture of a Complex-Valued Convolutional Neural Network for PolSAR Image Classification
Authors: Ren Yihui , Jiang Wen , Liu Ying
Published: October 2023

Polarimetric synthetic aperture radar (PolSAR) image classification has been an important area of research due to its wide range of applications. Traditional machine learning methods were insufficient in achieving satisfactory results before the advent of deep learning. Results have significantly improved with the widespread use of deep learning in PolSAR image classification. However, the challenge of reconciling the complex-valued inputs of PolSAR images with the real-valued models of deep learning remains unsolved. Current complex-valued deep learning models treat complex numbers as two distinct real numbers, providing limited assistance in PolSAR image classification results. This paper proposes a novel, complex-valued deep learning approach for PolSAR image classification to address this issue. The approach includes amplitude-based max pooling, complex-valued nonlinear activation, and a cross-entropy loss function based on complex-valued probability. Amplitude-based max pooling reduces computational effort while preserving the most valuable complex-valued features. Complex-valued nonlinear activation maps feature into a high-dimensional complex-domain space, producing the most discriminative features. The complex-valued cross-entropy loss function computes the classification loss using the complex-valued model output and dataset labels, resulting in more accurate and robust classification results. The proposed method was applied to a shallow CNN, deep CNN, FCN, and SegNet, and its effectiveness was verified on three public datasets. The results showed that the method achieved optimal classification results on any model and dataset.
Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis
Authors: Thapa Aakash , Horanont Teerayut , Neupane Bipul , Aryal Jagannath
Published: October 2023

Remote sensing image scene classification with deep learning (DL) is a rapidly growing field that has gained significant attention in the past few years. While previous review papers in this domain have been confined to 2020, an up-to-date review to show the progression of research extending into the present phase is lacking. In this review, we explore the recent articles, providing a thorough classification of approaches into three main categories: Convolutional Neural Network (CNN)-based, Vision Transformer (ViT)-based, and Generative Adversarial Network (GAN)-based architectures. Notably, within the CNN-based category, we further refine the classification based on specific methodologies and techniques employed. In addition, a novel and rigorous meta-analysis is performed to synthesize and analyze the findings from 50 peer-reviewed journal articles to provide valuable insights in this domain, surpassing the scope of existing review articles. Our meta-analysis shows that the most adopted remote sensing scene datasets are AID (41 articles) and NWPU-RESISC45 (40). A notable paradigm shift is seen towards the use of transformer-based models (6) starting from 2021. Furthermore, we critically discuss the findings from the review and meta-analysis, identifying challenges and future opportunities for improvement in this domain. Our up-to-date study serves as an invaluable resource for researchers seeking to contribute to this growing area of research.
Calf Health, Feeding and Social Behaviours within Groups Fed on Automatic Milk Feeders
Authors: Mahendran Sophie A. , Wathes D. Claire , Blackie Nicola , Booth Richard E.
Published: October 2023

Automatic calf feeders (AMFs) that supply milk for pre-weaning calves require group housing. This observational study assessed calf growth, health, feeding patterns and social behaviours prior to, during and following weaning in groups on the AMF. Calves were recruited from a single UK dairy farm and placed into two groups on the AMF between 3 and5 weeks of age (n = 19 and 13). They were fed milk near-ad libitum until 8 weeks of age when they entered a 2-week step-down weaning period. Daily milk consumption and the numbers of successful and refusal visits were collected from the AMF, along with weekly weights and health checks. Calf behaviour around the AMF was monitored through video analysis, and activity was assessed using tri-axial accelerometers. On average, the calves consumed approximately 3 L of milk three times a day from the AMF, confirming that limiting calves to twice daily feeding will restrict their desired milk consumption. The ADLG pre-weaning was 0.74 kg/d (SD 0.41 kg), rising to 0.99 kg/d (SD 0.46 kg) over weaning and 1.05 kg/d (SD 0.12 kg) post weaning. Refusal visits to the AMF, social interactions, displacements and cross sucking all increased over the weaning periods, suggesting that the calves were experiencing frustration due to a lack of expected milk rewards rather than hunger as ADLG continued to increase. Female calves also had more successful and refusal visits to the AMF than males, along with higher displacement indexes (p = 0.052), suggesting single-sex groups may be beneficial to AMFs. Calf activity had a diurnal pattern, with the highest standing times being during mid-morning and early evening, which may put pressure on limited resources if all calves are active during these particular time periods.
Effects of Feeding 60% Dried Corn Distillers’ Grains or the Equivalent Sulfur as CaSO4 on DNA Integrity and Gene Expression in Yearling Angus Bull Sperm
Authors: Kassetas Cierrah J. , Geary Tom W. , Zezeski Abby L. , Caton Joel S. , Kirsch James D. , Dorsam Sheri T. , Diniz Wellison J. S. , McCarthy Kacie L. , Crouse Matthew S. , Sedivec Kevin K. , Neville Bryan W. , Dahlen Carl R.
Published: October 2023

We evaluated the effects of feeding 60% dried corn distillers’ grains plus solubles (DDGS) or the equivalent sulfur as CaSO4 on sperm characteristics and transcript abundance. Thirty-six half-sibling Angus bulls (256 ± 8.5 d; initial BW = 320 ± 2.7 kg) were assigned to one of three treatments: (1) 60% concentrate as corn (CON); (2) 60% DDGS as corn replacement (60DDGS); and (3) CON diet + equivalent sulfur of 60DDGS added as CaSO4 (SULF). The acrosome/cell membrane integrity, mitochondrial energy potential, oxidation status, DNA integrity, and zinc signatures were analyzed via flow cytometry. Sperm-specific gene expression was assessed via RNA sequencing. The flow cytometry data were analyzed using PROC MIXED in SAS to determine the effects of treatment. Pairwise comparisons based on edgeR were used to identify differentially expressed genes. The percentage of polarized mitochondria tended to be greater (p = 0.08) for SULF compared with CON and 60DDGS. Protamine 1 was upregulated (p < 0.01; FDR = 0.10) in 60DDGS compared to CON. Zinc signature 1 in 60DDGS and SULF was reduced (p = 0.03) compared to CON. This study suggests that feeding bulls diets containing 60% DDGS had little effect on DNA integrity and gene expression.
Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data
Authors: Al Jawarneh Isam Mashhour , Foschini Luca , Bellavista Paolo
Published: October 2023

The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer–Douglas–Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy.
A Multi-Task Road Feature Extraction Network with Grouped Convolution and Attention Mechanisms
Authors: Zhu Wenjie , Li Hongwei , Cheng Xianglong , Jiang Yirui
Published: October 2023

To cope with the challenges of autonomous driving in complex road environments, the need for collaborative multi-tasking has been proposed. This research direction explores new solutions at the application level and has become a hot topic of great interest. In the field of natural language processing and recommendation algorithms, the use of multi-task learning networks has been proven to reduce time, computing power, and storage usage in various task coupling cases. Due to the characteristics of the multi-task learning network, it has also been applied to visual road feature extraction in recent years. This article proposes a multi-task road feature extraction network that combines group convolution with transformer and squeeze excitation attention mechanisms. The network can simultaneously perform drivable area segmentation, lane line segmentation, and traffic object detection tasks. The experimental results of the BDD-100K dataset show that the proposed method performs well for different tasks and has a higher accuracy than similar algorithms. The proposed method provides new ideas and methods for the autonomous road perception of vehicles and the generation of highly accurate maps in visual-based autonomous driving processes.
A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors
Authors: Barrera-Llanga Kevin , Burriel-Valencia Jordi , Sapena-Bañó Ángel , Martínez-Román Javier
Published: October 2023

Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.
A Method for the Precise Coordinate Determination of an Inaccessible Location
Authors: Osada Edward , Owczarek-Wesołowska Magdalena , Karsznia Krzysztof , Becek Kazimierz , Muszyński Zbigniew
Published: October 2023

Surveyors are occasionally tasked to with determining the coordinates of inaccessible locations or points in civil engineering applications, ground control points for photogrammetry or LiDAR data acquisition, among others. The present work outlines and investigates a novel method for estimating the GNSS coordinates of an inaccessible location where a surveying instrument cannot be set up. The procedure is based on the well-known surveying intersection method and data extracted from an Earth Gravity Model (e.g., EGM 2008). The location’s coordinates are obtained from the least-squares adjustment of the angles and distances measured from at least two sites to the unknown point using a total station, within the framework of the Gauss–Helmert method. Field tests confirmed that the accuracy of the determined coordinates of the inaccessible point is at the level of 1 cm. The proposed method bypasses standard coordinate transformation steps performed with the traditional approach, directly producing geocentric coordinates of the unknown points.
Using Sparfloxacin-Capped Gold Nanoparticles to Modify a Screen-Printed Carbon Electrode Sensor for Ethanol Determination
Authors: Magesh Vasanth , Kothari Vishaka S. , Ganapathy Dhanraj , Atchudan Raji , Arya Sandeep , Nallaswamy Deepak , Sundramoorthy Ashok K.
Published: October 2023

Alcohol is a dangerous substance causing global mortality and health issues, including mental health problems. Regular alcohol consumption can lead to depression, anxiety, cognitive decline, and increased risk of alcohol-related disorders. Thus, monitoring ethanol levels in biological samples could contribute to maintaining good health. Herein, we developed an electrochemical sensor for the determination of ethanol in human salivary samples. Initially, the tetra-chloroauric acid (HAuCl4) was chemically reduced using sparfloxacin (Sp) which also served as a stabilizing agent for the gold nanoparticles (AuNPs). As-prepared Sp-AuNPs were comprehensively characterized and confirmed by UV-visible spectroscopy, X-ray diffraction, field emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDS), and elemental mapping analysis. The average particle size (~25 nm) and surface charge (negative) of Sp-AuNPs were determined by using dynamic light scattering (DLS) and Zeta potential measurements. An activated screen-printed carbon electrode (A-SPE) was modified using Sp-AuNPs dispersion, which exhibited greater electrocatalytic activity and sensitivity for ethanol (EtOH) oxidation in 0.1 M sodium hydroxide (NaOH) as studied by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). DPV showed a linear response for EtOH from 25 µM to 350 µM with the lowest limit of detection (LOD) of 0.55 µM. Reproducibility and repeatability studies revealed that the Sp-AuNPs/A-SPEs were highly stable and very sensitive to EtOH detection. Additionally, the successful electrochemical determination of EtOH in a saliva sample was carried out. The recovery rate of EtOH spiked in the saliva sample was found to be 99.6%. Thus, the incorporation of Sp-AuNPs within sensors could provide new possibilities in the development of ethanol sensors with an improved level of precision and accuracy.
Spatial and Channel Aggregation Network for Lightweight Image Super-Resolution
Authors: Wu Xianyu , Zuo Linze , Huang Feng
Published: October 2023

Advanced deep learning-based Single Image Super-Resolution (SISR) techniques are designed to restore high-frequency image details and enhance imaging resolution through the use of rapid and lightweight network architectures. Existing SISR methodologies face the challenge of striking a balance between performance and computational costs, which hinders the practical application of SISR methods. In response to this challenge, the present study introduces a lightweight network known as the Spatial and Channel Aggregation Network (SCAN), designed to excel in image super-resolution (SR) tasks. SCAN is the first SISR method to employ large-kernel convolutions combined with feature reduction operations. This design enables the network to focus more on challenging intermediate-level information extraction, leading to improved performance and efficiency of the network. Additionally, an innovative 9 × 9 large kernel convolution was introduced to further expand the receptive field. The proposed SCAN method outperforms state-of-the-art lightweight SISR methods on benchmark datasets with a 0.13 dB improvement in peak signal-to-noise ratio (PSNR) and a 0.0013 increase in structural similarity (SSIM). Moreover, on remote sensing datasets, SCAN achieves a 0.4 dB improvement in PSNR and a 0.0033 increase in SSIM.
A Review of Parking Slot Types and their Detection Techniques for Smart Cities
Authors: Kumar Kamlesh , Singh Vijander , Raja Linesh , Bhagirath Swami Nisha
Published: October 2023

Smart parking system plays a critical role in the overall development of the cities. The capability to precisely detect an open parking space nearby is necessary for autonomous vehicle parking for smart cities. Finding parking spaces is a big issue in big cities. Many of the existing parking guidance systems use fixed IoT sensors or cameras that are unable to offer information from the perspective of the driver. Accurately locating parking spaces can be difficult since they come in a range of sizes and colors that are blocked by objects that seem different depending on the environmental lighting. There are numerous auto industry players engaged in the advanced testing of driverless cars. A vacant parking space must be found, and the car must be directed to park there in order for the operation to succeed. The machine learning-based algorithms created to locate parking spaces and techniques and methods utilizing dashcams and fish-eye cameras are reviewed in this study. In response to the increase in dashcams, neural network-based techniques are created for identifying open parking spaces in dashcam videos. The paper proposed the review of the existing parking slot types and their detection techniques. The review will highlight the importance and scope of a smart parking system for smart cities.
Evaluating Phonon Characteristics by Varying the Layer and Interfacial Thickness in Novel Carbon-Based Strained-Layer Superlattices
Authors: Talwar Devki N. , Becla Piotr
Published: October 2023

Systematic results of lattice dynamical calculations are reported as a function of m and n for the novel (SiC)m/(GeC)n superlattices (SLs) by exploiting a modified linear-chain model and a realistic rigid-ion model (RIM). A bond polarizability method is employed to simulate the Raman intensity profiles (RIPs) for both the ideal and graded (SiC)10-Δ/(Si0.5Ge0.5C)Δ/(GeC)10-Δ/(Si0.5Ge0.5C)Δ SLs. We have adopted a virtual-crystal approximation for describing the interfacial layer thickness, Δ (≡0, 1, 2, and 3 monolayers (MLs)) by selecting equal proportions of SiC and GeC layers. Systematic variation of Δ has initiated considerable upward (downward) shifts of GeC-(SiC)-like Raman peaks in the optical phonon frequency regions. Our simulated results of RIPs in SiC/GeC SLs are agreed reasonably well with the recent analyses of Raman scattering data on graded short-period GaN/AlN SLs. Maximum changes in the calculated optical phonons (up to ±~47 cm−1) with Δ = 3, are proven effective for causing accidental degeneracies and instigating localization of atomic displacements at the transition regions of the SLs. Strong Δ-dependent enhancement of Raman intensity features in SiC/GeC are considered valuable for validating the interfacial constituents in other technologically important heterostructures. By incorporating RIM, we have also studied the phonon dispersions [ωjSLq→] of (SiC)m/(GeC)n SLs along the growth [001] as well as in-plane [100], [110] directions [i.e., perpendicular to the growth]. In the acoustic mode regions, our results of ωjSLq→  have confirmed the formation of mini-gaps at the zone center and zone edges while providing strong evidences of the anti-crossing and phonon confinements. Besides examining the angular dependence of zone-center optical modes, the results of phonon folding, confinement, and anisotropic behavior in (SiC)m/(GeC)n are compared and contrasted very well with the recent first-principles calculations of (GaN)m/(AlN)n strained layer SLs.
Revolutionizing Chinese Manufacturing: Uncovering the Nexus of Intelligent Transformation and Capital Market Information Efficiency
Authors: Zhang Qiuyue , Cao Yu
Published: October 2023

Intelligent transformation plays a crucial role in advancing sustainable development in manufacturing while also enhancing the information environment. This study examines the role of intelligent transformation in China’s manufacturing sector, spanning theoretical and empirical dimensions and being anchored in the context of capital market information efficiency. The theoretical framework highlights how intelligent transformation mitigates information asymmetry, aligning a firm’s valuation with its intrinsic value, thereby elevating the information efficiency of capital markets. Leveraging annual reports from China’s A-share manufacturing firms, this study employs textual analysis to construct indicators assessing the extent of intelligent transformation across these entities. The empirical findings of this study harmonize with the theoretical constructs. Notably, intelligent transformation emerges as a pivotal driver in enhancing information efficiency in capital markets, substantiated by a negative correlation between intelligent transformation and stock price synchronicity within the manufacturing domain. This correlation withstands a battery of robustness tests and endogeneity treatment. The mechanism driving this transformative impact lies in intelligent transformation’s ability to enhance productivity and magnify market attention, thereby positively influencing capital market information efficiency. The insights not only provide empirical support but also offer practical guidance for improving real-world company operations and developing high-quality capital markets.
Climate Concern and Pro-Environmental Behaviour in the Light of Trust
Authors: Grünhut Zoltán , Varjú Viktor , Bodor Ákos
Published: October 2023

When exploring individual motivations of pro-climate behaviour, researchers investigate a multitude of factors, including the role of trust. Based on the empirical results obtained to date, the literature does not paint a uniform picture regarding the strength of the relationship between trust and environmental awareness. Using a large-scale, representative survey conducted in Hungary in June–July 2022, the present study—based on a data analysis—provides an answer to the question of how much the trust of the individual motivates the given individual to take pro-climate action. The results of the study show that most of the respondents cannot be viewed as pro-climate actors in spite of the fact that, in the meantime, they are concerned about climate change. Most of them are mistrustful of their fellow human beings, in other words they think that protecting the climate and taking action against climate change is not particularly important for others. One of the most important conclusions that may be drawn from the analyses is that trust influences both pro-environment/pro-climate action, and the intent to act. Research results confirm the general conclusion that supporting and increasing social trust would have a beneficial effect on more effective action against environmental challenges.
Implications of ICT for the Livelihoods of Women Farmers: A Study in the Teesta River Basin, Bangladesh
Authors: Rahman Md. Mizanur , Huq Hamidul
Published: October 2023

Rural agrarian societies, like Bangladesh, rely substantially on women as primary contributors to crop production. Their involvement covers a broad spectrum, from the first stage of seed sowing to the ultimate phase of marketing agricultural products. Information and communication technology (ICT) in agriculture could be a transformative tool for women’s agricultural involvement. Despite the inherent challenges associated with ICT adoption, it has emerged as an effective catalyst for improving the livelihoods of rural women in Bangladesh. This study investigates the impacts of ICT on the livelihoods of rural women. This study concurrently addresses the challenges that infringe upon its sustainability. The study was conducted within Oxfam Bangladesh’s ICT interventions implemented upon the women farmers in Dimla Upazila, Nilphamari, Bangladesh. We employed a mixed-methods research approach to examine the multilayered impacts of ICT on women farmers’ livelihoods. Our findings indicate that ICT support has improved the livelihoods of rural women through a comprehensive capital-building process encompassing human capital, social capital, financial capital, physical capital, and political capital, facilitated by creating an enabling environment. The study also unfolded several challenges stemming from aspects of ICT integration, including the disappearance of indigenous agroecological knowledge and the disruption of traditional multicropping practices. In light of the study’s outcomes, a key recommendation emerges, emphasizing the importance of integrating indigenous agroecological knowledge in the widescale implementation of ICT initiatives. Acknowledging and accommodating indigenous knowledge can enhance the sustainability of ICT-driven livelihood enhancements for rural women in Bangladesh.