bluuebunny/crossref_metadata_2025 huggingface dataset:
Optical Bistability and Thermal Mode Hopping in External Cavity Feedback Semiconductor Lasers
Authors: Meng Da , Yu Haiyang , Zhang Yejin , Liu Jianguo
Published: September 2023

The external cavity feedback semiconductor laser shows obvious optical bistability in practice. That is, at the same operating temperature, different temperature tuning directions will lead to different laser mode outputs. This will lead to optical mode hopping and unstable output wavelength of the laser. In this work, we measured this phenomenon and explained it through a theoretical model with thermal synchronization considered. Our measured results show obvious bistability and temperature tuning direction dependence. In order to explain this phenomenon, we considered the physical characteristics of each optoelectronic component in the external cavity, and found that the temperature changes of each component were not synchronized over time. After establishing a model that takes into account the thermal asynchrony phenomenon, we have effectively explained this phenomenon.
Enhanced Circular Dichroism by F-Type Chiral Metal Nanostructures
Authors: Luo Yuyuan , Liu Jin , Yang Haima , Liu Haishan , Zeng Guohui , Huang Bo
Published: September 2023

Circular dichroism (CD) effects have broad applications in fields including biophysical analysis, analytical chemistry, nanoscale imaging, and nanosensor design. Herein, a novel design of a tilted F-type chiral metal nanostructure composed of circular nanoholes with varying radii has been proposed to achieve remarkable CD effects, and the results demonstrate the generation of a significant current oscillation at the sharp edges where the nanoholes overlap under circularly polarized light, resulting in a strong CD effect. The CD effect can reach up to 7.5%. Furthermore, spectral modulation of the resonant wavelength can be achieved by adjusting the structural parameters, which enhances the tunability of the structure. Overall, these results provide theoretical or practical guidance for enhancing the circular dichroism signal strength of chiral metal nanostructures and designing new types of two-dimensional chiral structures.
Reactive Oxygen Species in Drought-Induced Stomatal Closure: The Potential Roles of NPR1
Authors: Li Xin-Cheng , Chang Claire , Pei Zhen-Ming
Published: September 2023

Stomatal closure is a vital, adaptive mechanism that plants utilize to minimize water loss and withstand drought conditions. We will briefly review the pathway triggered by drought that governs stomatal closure, with specific focuses on salicylic acid (SA) and reactive oxygen species (ROS). We propose that the non-expressor of PR Gene 1 (NPR1), a protein that protects plants during pathogen infections, also responds to SA during drought to sustain ROS levels and prevent ROS-induced cell death. We will examine the evidence underpinning this hypothesis and discuss potential strategies for its practical implementation.
Fruit Quality and Metabolomic Analyses of Fresh Food Accessions Provide Insights into the Key Carbohydrate Metabolism in Blueberry
Authors: Zhang Chunhong , Li Jie , Wang Jialuan , Lyu Lianfei , Wu Wenlong , Li Weilin , Wu Yaqiong
Published: September 2023

Blueberry is a nutrient-rich berry, and its taste and flavor directly determine the consumer preference. Until now, few studies have focused on the comparison of fresh food quality and the key metabolites in superior fresh-eating blueberry cultivars. Herein, fruit quality indicators of 10 highbush blueberry cultivars were evaluated using ‘Bluerain’ as the control. Appearance quality analysis of fruits showed that ‘Brigitta’ had a larger fruit size and ‘Anna’ was the smallest. ‘Anna’ fruits, followed by ‘O′Neal’, had the highest ratio of soluble solids to acidity because of their lowest titratable acidity content. Despite the high soluble sugar content, the antioxidants in ‘Anna’ fruits such as total flavonoids, anthocyanins and vitamin C were lowest among all cultivars, while ‘Duke’ seemed to have opposite patterns. Furthermore, a total of 553 and 557 metabolites were identified by non-targeted metabolomics liquid chromatography-tandem mass spectrometry (LC-MS/MS) in positive and negative ion mode, respectively. Particularly, the numbers of differentially accumulated metabolites (DAMs) were the most between the ‘O′Neal’ vs. ‘Bluerain’ group. The DAMs involved in the metabolic pathways, sesquiterpenoid and triterpenoid biosynthesis, monoterpenoid biosynthesis, galactose metabolism, starch and sucrose metabolism, may be mainly related to the synthesis of flavor and carbohydrate substances. Moreover, the expression patterns of genes involved in sugar metabolism were verified by quantitative real-time PCR (qRT-PCR) analysis in different cultivars. Therefore, the systematical comparison of the quality characteristics, metabolites and expression profiles of related genes in highbush blueberries with good flavor could provide some basis for further research on fresh fruit breeding of blueberries.
Comparative Study of Flax and Pineapple Leaf Fiber Reinforced Poly(butylene succinate): Effect of Fiber Content on Mechanical Properties
Authors: Amornsakchai Taweechai , Duangsuwan Sorn , Mougin Karine , Goh Kheng Lim
Published: September 2023

In this study, we compare the reinforcing efficiency of pineapple leaf fiber (PALF) and cultivated flax fiber in unidirectional poly(butylene succinate) composites. Flax, known for robust mechanical properties, is contrasted with PALF, a less studied but potentially sustainable alternative. Short fibers (6 mm) were incorporated at 10 and 20% wt. levels. After two-roll mill mixing, uniaxially aligned prepreg sheets were compression molded into composites. At 10 wt.%, PALF and flax exhibited virtually the same stress–strain curve. Interestingly, PALF excelled at 20 wt.%, defying its inherently lower tensile properties compared to flax. PALF/PBS reached 70.7 MPa flexural strength, 2.0 GPa flexural modulus, and 107.3 °C heat distortion temperature. Comparable values for flax/PBS were 57.8 MPa, 1.7 GPa, and 103.7 °C. X-ray pole figures indicated similar matrix orientations in both composites. An analysis of extracted fibers revealed differences in breakage behavior. This study highlights the potential of PALF as a sustainable reinforcement option. Encouraging the use of PALF in high-performance bio-composites aligns with environmental goals.
Enhanced Biodegradability in Soil of Chicken Feather by Steam Explosion for Potential Application in Agricultural Biodegradable Plastics
Authors: Vadillo Julen , Montes Sarah , Grande Hans-Jürgen , Verstichel Steven , Almqvist Jonna , Wrześniewska-Tosik Krystyna
Published: September 2023

Feather waste is a major issue from an economic and environmental point of view. Even though there are already routes for the valorisation of feathers into fertilisers and feather meal, these are considered to have low added value. For more attractive applications, for example in agricultural biodegradable plastics, higher and faster degradability in soil is required. To face this challenge alternative approaches to accelerate biodegradation and disintegration processes are needed. In this context, steam explosion appears as an effective technology to modify the structure of feather and improve its soil degradability. In this work, chicken feathers were treated by steam explosion and the effect of treatment on their structure and physico-chemical and thermal properties were evaluated. Finally, the effect of the process conditions on the disintegration and biodegradation in soil of feathers was also investigated, finding an increased degradation in soil of steam explosion treated feathers. These results open up the possibilities of using feather waste as a component for environmentally friendly agricultural bioplastics that can be degraded in-situ in soil.
Classification and Evaluation of Tight Sandstone Reservoirs Based on MK-SVM
Authors: Lu Xuefei , Xing Xin , Hu Kelai , Zhou Bin
Published: September 2023

It is difficult to determine the main microscopic factors controlling reservoir quality due to the strong microscopic heterogeneity of tight sandstone reservoirs, which also makes it difficult to distinguish dominant reservoirs. At the same time, there are fewer experimental samples available, and data collected from relevant research are thus worth paying attention to. In this study, based on the experimental results of high-pressure mercury injection of 25 rock samples from Chang 6 reservoir in the Wuqi area, Lasso dimensionality reduction was used to reduce the dimensionality of 14 characteristic parameters to 6, which characterize the microscopic pore structure, while a combination of different kernel functions was used to construct the multi-kernel function of the multi-kernel model to be determined. A multi-kernel support vector machine (MK-SVM) model was established for unsupervised learning of microscopic pore structure characteristic parameters that affect reservoir quality. By optimizing the hyperparameters of the model and obtaining the optimal decision function, the tight sandstone reservoirs were classified into classes I, II and III, and the classification results were verified. The results show that the accuracy of the proposed reservoir classification method is as high as 86%, which can effectively reduce the time loss and save labor costs. It provides an effective method for the comprehensive evaluation of tight sandstone reservoirs.
Detection of Cotton Seed Damage Based on Improved YOLOv5
Authors: Liu Zhicheng , Wang Long , Liu Zhiyuan , Wang Xufeng , Hu Can , Xing Jianfei
Published: September 2023

The quality of cotton seed is of great significance to the production of cotton in the cotton industry. In order to reduce the workload of the manual sorting of cotton seeds and improve the quality of cotton seed sorting, this paper proposed an image-detection method of cotton seed damage based on an improved YOLOv5 algorithm. Images of cotton seeds with different degrees of damage were collected in the same environment. Cotton seeds of three different damage degrees, namely, undamaged, slightly damaged, and seriously damaged, were selected as the research objects. Labeling software was used to mark the images of these cotton seeds and the marked images were input into the improved YOLOv5s detection algorithm for appearance-based damage identification. The algorithm added the lightweight upsampling operator CARAFE to the original YOLOv5s detection algorithm and also improved the loss function. The experimental results showed that the mAP_0.5 value of the improved algorithm reached 99.5% and the recall rate reached 99.3% when the uncoated cotton seeds were detected. When detecting coated cotton seeds, the mAP_0.5 value of the improved algorithm reached 99.2% and the recall rate reached 98.9%. Compared with the traditional appearance-based damage detection approach, the improved YOLOv5s proposed in this paper improved the recognition accuracy and processing speed, and exhibited a better adaptability and generalization ability. Therefore, the proposed method can provide a reference for the appearance detection of crop seeds.
Optimal Pathways for Nitric Acid Synthesis Using P-Graph Attainable Region Technique (PART)
Authors: Sitoh Yiann , Tan Viggy Wee Gee , Tapia John Frederick D. , Tan Raymond R. , Foo Dominic C. Y.
Published: September 2023

Developing a chemical reaction network is considered the first and most crucial step of process synthesis. Many methods have been employed for process synthesis, such as the attainable region (AR) theory. AR states that a region of all possible configurations can be defined with all the potential products and reactants. The second method is process network synthesis (PNS), a technique used to optimise a flowsheet based on the feasible materials and energy flow. P-graph is an algorithmic framework for PNS problems. P-graph attainable region technique (PART) is introduced here as an integration of both AR and P-graph to generate optimal reaction pathways for a given process. A descriptive AR plot is also developed to represent all the possible solution structures or reaction pathways. A case study of a conventional nitric acid synthesis process was used to demonstrate this technique.
Integration of Renewable-Energy-Based Green Hydrogen into the Energy Future
Authors: Marouani Ismail , Guesmi Tawfik , Alshammari Badr M. , Alqunun Khalid , Alzamil Ahmed , Alturki Mansoor , Hadj Abdallah Hsan
Published: September 2023

There is a growing interest in green hydrogen, with researchers, institutions, and countries focusing on its development, efficiency improvement, and cost reduction. This paper explores the concept of green hydrogen and its production process using renewable energy sources in several leading countries, including Australia, the European Union, India, Canada, China, Russia, the United States, South Korea, South Africa, Japan, and other nations in North Africa. These regions possess significant potential for “green” hydrogen production, supporting the transition from fossil fuels to clean energy and promoting environmental sustainability through the electrolysis process, a common method of production. The paper also examines the benefits of green hydrogen as a future alternative to fossil fuels, highlighting its superior environmental properties with zero net greenhouse gas emissions. Moreover, it explores the potential advantages of green hydrogen utilization across various industrial, commercial, and transportation sectors. The research suggests that green hydrogen can be the fuel of the future when applied correctly in suitable applications, with improvements in production and storage techniques, as well as enhanced efficiency across multiple domains. Optimization strategies can be employed to maximize efficiency, minimize costs, and reduce environmental impact in the design and operation of green hydrogen production systems. International cooperation and collaborative efforts are crucial for the development of this technology and the realization of its full benefits.
Using Adsorption Energy Distribution for Parameter Estimation of Competitive Cofactor Coupled Enzyme Reaction
Authors: Waluga Thomas , Skiborowski Mirko
Published: September 2023

The chemical and biotechnology industries are facing new challenges in the use of renewable resources. The complex nature of these materials requires the use of advanced techniques to understand the kinetics of reactions in this context. This study presents an interdisciplinary approach to analyze cofactor coupled enzymatic two-substrate kinetics and competitive two-substrate kinetics in a fast and efficient manner. By studying the adsorption energy distribution (AED), it is possible to determine the individual parameters of the reaction kinetics. In the case of a single alcohol reaction, the AED is able to identify parameters in agreement with the literature with few experimental data points compared to classical methods. In the case of a competitive reaction, AED analysis can automatically determine the number of competing substrates, whereas traditional nonlinear regression requires prior knowledge of this information for parameter identification.
Response Surface Methodology (RSM) Optimization of Pulsed Electric Field (PEF) Pasteurization Process of Milk-Date Beverage
Authors: Younis Mahmoud , Ahmed Khaled A. , Ahmed Isam A. Mohamed , Yehia Hany M. , Abdelkarim Diaeldin O. , Alhamdan Abdulla , Elfeky Ahmed , Ibrahim Mansour N.
Published: September 2023

Milk beverage with added natural sweetener is well appreciated by consumers as a nutritious and healthy product with unique sensorial quality attributes. However, this product requires a suitable pasteurization method without significant impact on the sensorial and physicochemical quality characteristics of the product. This study optimizes the pulsed electric filed (PEF) conditions for the pasteurization of a milk-date beverage with conserved physicochemical quality properties. The effect of process variables, such as pulse off time (20, 30, and 40 μs), number of pulses (20, 50, and 80), powder ratio (10, 15, 20, and 25% w/w), storage time (2, 4, and 6 days), and storage temperature (5, 15, and 25 °C) on the responses of total viable count (TVC), color difference (∆E), pH, and total soluble solids (TSS) was evaluated using the RSM central composite design (CCD). Pulse off time, number of pulses, date powder/milk ratio (w/w), storage time, and storage temperature greatly impacted the microbial and physical properties of the beverage. The optimal conditions for decreasing the microbiological load and physical change of beverages were a pulse off time of 40 μs, number of pulses of 80, and storage temperature of 5 °C for all powder ratios. These variables gave a safe beverage for up to six days. At optimal conditions, the values of pH, TSS, ∆E, and TVC were 7.51, 15.44 °Brix, 18.01, and 0.138 Log 10 CFU/mL, respectively, for the powder ratio of 10% (w/w); 7.66, 18.6 °Brix, 21.46, and 0.284 Log 10 CFU/mL, respectively, for the powder ratio of 15% (w/w); 7.56, 21.52 °Brix, 25.24, and 0.577 Log 10 CFU/mL, respectively, for the powder ratio of 20% (w/w); and 7.2, 24.2 °Brix, 29.34, and 0.741 Log 10 CFU/mL, respectively, for the powder ratio of 25% (w/w).
Study on Flow Characteristics of a Single Blade Breakage Fault in a Centrifugal Pump
Authors: Li Huairui , Huang Qian , Li Sihan , Li Yunpeng , Fu Qiang , Zhu Rongsheng
Published: September 2023

The precise identification of faults in centrifugal pumps is crucial for ensuring their safe and stable operation, given their significance as vital industrial equipment. This article aims to rigorously examine and analyze the flow characteristics of centrifugal pumps under two specific conditions: normal blade operation and a single blade breakage fault. Through systematic comparison and in-depth study, this article sheds light on distinguishing flow patterns exhibited by these pumps under both normal and fault scenarios. Utilizing validated numerical simulation methods, a thorough analysis is conducted to explore the flow condition and energy characteristics of the impeller channel following the breakage of a single blade. Additionally, the article investigates changes in the pressure pulsation characteristics of the pump volute as a result. The numerical simulation results reveal that the head of the centrifugal pump decreases at all flow points when a single blade breaks. However, there is no significant change in efficiency at small flow points. As the flow rate exceeds 0.9Qd, efficiency experiences a substantial decrease. Furthermore, the efficiency decline becomes even more pronounced as the flow rate continues to increase. At 1.5Qd, efficiency plummets by 14.33%. The flow pattern undergoes significant changes as well. The breakage of the blade induces noteworthy secondary flow in adjacent impeller channels, resulting in heightened turbulence dissipation. Additionally, it was observed that blade fracture causes alterations in the main frequency of pressure pulsation within the volute. This is characterized by an increase in shaft passing frequency and a decrease in blade passing frequency. Notably, near the tongue monitoring point, the shaft frequency escalates by over 20 times.
Real-Time Segmentation of Artificial Targets Using a Dual-Modal Efficient Attention Fusion Network
Authors: Shen Ying , Liu Xiancai , Zhang Shuo , Xu Yixuan , Zeng Dawei , Wang Shu , Huang Feng
Published: September 2023

The fusion of spectral–polarimetric information can improve the autonomous reconnaissance capability of unmanned aerial vehicles (UAVs) in detecting artificial targets. However, the current spectral and polarization imaging systems typically suffer from low image sampling resolution, which can lead to the loss of target information. Most existing segmentation algorithms neglect the similarities and differences between multimodal features, resulting in reduced accuracy and robustness of the algorithms. To address these challenges, a real-time spectral–polarimetric segmentation algorithm for artificial targets based on an efficient attention fusion network, called ESPFNet (efficient spectral–polarimetric fusion network) is proposed. The network employs a coordination attention bimodal fusion (CABF) module and a complex atrous spatial pyramid pooling (CASPP) module to fuse and enhance low-level and high-level features at different scales from the spectral feature images and the polarization encoded images, effectively achieving the segmentation of artificial targets. Additionally, the introduction of the residual dense block (RDB) module refines feature extraction, further enhancing the network’s ability to classify pixels. In order to test the algorithm’s performance, a spectral–polarimetric image dataset of artificial targets, named SPIAO (spectral–polarimetric image of artificial objects) is constructed, which contains various camouflaged nets and camouflaged plates with different properties. The experimental results on the SPIAO dataset demonstrate that the proposed method accurately detects the artificial targets, achieving a mean intersection-over-union (MIoU) of 80.4%, a mean pixel accuracy (MPA) of 88.1%, and a detection rate of 27.5 frames per second, meeting the real-time requirement. The research has the potential to provide a new multimodal detection technique for enabling autonomous reconnaissance by UAVs in complex scenes.
A Multi-Channel Attention Network for SAR Interferograms Filtering Applied to TomoSAR
Authors: Li Jie , Li Zhiyuan , Zhang Bingchen , Wu Yirong
Published: September 2023

Tomographic synthetic aperture radar (TomoSAR) is an advanced synthetic aperture radar (SAR) interferometric technique that can retrieve 3-D spatial information. However, the performances of 3-D reconstruction could be degraded due to the noise in interferograms, which makes the filtering crucial before the tomographic reconstruction. As known, filters for single-channel interferograms are common, but those for multi-channel interferograms are still rare. In this paper, we propose a multi-channel attention network to denoise the multi-channel interferograms applied for TomoSAR, which is built on the basis of multi-channel attention blocks. An important feature of the block is the local context mixing before the computation of attention maps across channels, which explores the intra-channel local information and the inter-channel relationship of the multi-channel interferograms. Based on this architecture, the proposed method can effectively filter the noise while preserving the structures in interferograms, thus improving the performance of tomographic reconstruction. The network is trained by simulated data and the promising results of both simulated and real data validate the effectiveness of our proposed method.
TCUNet: A Lightweight Dual-Branch Parallel Network for Sea–Land Segmentation in Remote Sensing Images
Authors: Xiong Xuan , Wang Xiaopeng , Zhang Jiahua , Huang Baoxiang , Du Runfeng
Published: September 2023

Remote sensing techniques for shoreline extraction are crucial for monitoring changes in erosion rates, surface hydrology, and ecosystem structure. In recent years, Convolutional neural networks (CNNs) have developed as a cutting-edge deep learning technique that has been extensively used in shoreline extraction from remote sensing images, owing to their exceptional feature extraction capabilities. They are progressively replacing traditional methods in this field. However, most CNN models only focus on the features in local receptive fields, and overlook the consideration of global contextual information, which will hamper the model’s ability to perform a precise segmentation of boundaries and small objects, consequently leading to unsatisfactory segmentation results. To solve this problem, we propose a parallel semantic segmentation network (TCU-Net) combining CNN and Transformer, to extract shorelines from multispectral remote sensing images, and improve the extraction accuracy. Firstly, TCU-Net imports the Pyramid Vision Transformer V2 (PVT V2) network and ResNet, which serve as backbones for the Transformer branch and CNN branch, respectively, forming a parallel dual-encoder structure for the extraction of both global and local features. Furthermore, a feature interaction module is designed to achieve information exchange, and complementary advantages of features, between the two branches. Secondly, for the decoder part, we propose a cross-scale multi-source feature fusion module to replace the original UNet decoder block, to aggregate multi-scale semantic features more effectively. In addition, a sea–land segmentation dataset covering the Yellow Sea region (GF Dataset) is constructed through the processing of three scenes from Gaofen-6 remote sensing images. We perform a comprehensive experiment with the GF dataset to compare the proposed method with mainstream semantic segmentation models, and the results demonstrate that TCU-Net outperforms the competing models in all three evaluation indices: the PA (pixel accuracy), F1-score, and MIoU (mean intersection over union), while requiring significantly fewer parameters and computational resources compared to other models. These results indicate that the TCU-Net model proposed in this article can extract the shoreline from remote sensing images more effectively, with a shorter time, and lower computational overhead.
An Integrated Approach between Multispectral Satellite Images and Geophysical and Morpho-Topographic Surveys for the Detection of Water Stress Associated with Coastal Dune Erosion
Authors: Scardino Giovanni , Mancino Saverio , Romano Gerardo , Patella Domenico , Scicchitano Giovanni
Published: September 2023

Coastal erosion occurs due to different processes involving physical and ecological systems. One of these factors is the degree of water stress experienced by dune vegetation. While healthy dune vegetation can help to stabilize the dune systems, water-stressed vegetation can instead enhance dune erosion. In this study, remote sensing techniques were used to monitor the water stress affecting the dune vegetation in dune systems along the alluvial plain of the Chiatona coast (Apulia, Southern Italy) located on the Ionian Arc. Multispectral satellite data from Landsat 8/9 and Sentinel-2 were used to assess the water stress at different spatial scales over a 4-year monitoring period from 2019 to 2023. The normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were used to identify dune surfaces that were experiencing water stress. Furthermore, a terrestrial laser scanner and LiDAR data were acquired at different temporal ranges in areas affected by water stress to highlight coastal changes in areas associated with unhealthy dune vegetation. A large drop in NDVI values was observed in May 2020 due to the occurrence of coastal fires in some parts of the Chiatona coast. Geoelectrical surveys were conducted to investigate if coastal fires were capable of saline groundwater contamination, potentially enhancing dune erosion in these areas. The joint analysis of remote sensing, topographical, and geoelectric data showed that water stress reduced the amount of healthy dune vegetation, triggering dune deflation processes that resulted in increased coastal erosion rates, while also leading to the saline contamination of groundwater.
Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model
Authors: Bouras El houssaine , Olsson Per-Ola , Thapa Shangharsha , Díaz Jesús Mallol , Albertsson Johannes , Eklundh Lars
Published: September 2023

Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.
Point CNN:3D Face Recognition with Local Feature Descriptor and Feature Enhancement Mechanism
Authors: Wang Qi , Lei Hang , Qian Weizhong
Published: September 2023

Three-dimensional face recognition is an important part of the field of computer vision. Point clouds are widely used in the field of 3D vision due to the simple mathematical expression. However, the disorder of the points makes it difficult for them to have ordered indexes in convolutional neural networks. In addition, the point clouds lack detailed textures, which makes the facial features easily affected by expression or head pose changes. To solve the above problems, this paper constructs a new face recognition network, which mainly consists of two parts. The first part is a novel operator based on a local feature descriptor to realize the fine-grained features extraction and the permutation invariance of point clouds. The second part is a feature enhancement mechanism to enhance the discrimination of facial features. In order to verify the performance of our method, we conducted experiments on three public datasets: CASIA-3D, Bosphorus, and Lock3Dface. The results show that the accuracy of our method is improved by 0.7%, 0.4%, and 0.8% compared with the latest methods on these three datasets, respectively.
Detection and Recognition of Tilted Characters on Railroad Wagon Wheelsets Based on Deep Learning
Authors: Xu Fengxia , Xu Zhenyang , Lu Zhongda , Peng Chuanshui , Yan Shiwei
Published: September 2023

The quality of railroad wheelsets is an important guarantee for the safe operation of wagons, and mastering the production information of wheelsets plays a vital role in vehicle scheduling and railroad transportation safety. However, when using objection detection methods to detect the production information of wheelsets, there are situations that affect detection such as character tilting and unfixed position. Therefore, this paper proposes a deep learning-based method for accurately detecting and recognizing tilted character information on railroad wagon wheelsets. It covers three parts. Firstly, we construct a tilted character detection network based on Faster RCNN for generating a wheelset’s character candidate regions. Secondly, we design a tilted character correction network to classify and correct the orientation of flipped characters. Finally, a character recognition network is constructed based on convolutional recurrent neural network (CRNN) to realize the task of recognizing a wheelset’s characters. The result shows that the method can quickly and effectively detect and identify the information of tilted characters on wheelsets in images.
Comparative Study of Leak Detection in PVC Water Pipes Using Ceramic, Polymer, and Surface Acoustic Wave Sensors
Authors: Hamamed Najah , Mechri Charfeddine , Mhammedi Taoufik , Yaakoubi Nourdin , El Guerjouma Rachid , Bouaziz Slim , Haddar Mohamed
Published: September 2023

The detection and location of pipeline leakage can be deduced from the time arrival leak signals measured by acoustic sensors placed at the pipe. Ongoing research in this field is primarily focused on refining techniques for accurately estimating the time delays. This enhancement predominantly revolves around the application of advanced signal processing methods. Additionally, researchers are actively immersed in the utilization of machine learning approaches on vibro-acoustic data files, to determine the presence or absence of leaks. Less attention has been given to evaluating the sensitivity, performance, and overall effectiveness of these sensors in leak detection; although acoustic methods have been successfully used for leak detection in metallic pipes, they are less effective in plastic pipes due to the high attenuation of leak noise signals. The primary thrust of this research centers on identifying sensors that not only possess sensitivity but also exhibit high efficiency. To accomplish this goal, we conducted an exhaustive evaluation of the performance of three distinct categories of acoustic sensors employed for detecting water leaks in plastic pipes: specifically, lead zirconate titanate (PZT) sensors, polyvinylidene fluoride (PVDF) sensors, and surface acoustic wave (SAW) sensors. Our evaluation encompassed the performance of PVDF and SAW sensors in leak detection, comparing them to PZT sensors under a variety of conditions, including different leak sizes, flow rates, and distances from the leak. The results showed that all three sensors, when they were placed in the same position, were able to detect water leaks in plastic pipes with different sensitivities. For small leaks (1 mm, 2 mm), the PVDF sensor showed the greatest sensitivity (0.4 dB/L/h, 0.33 dB/L/h), followed by the SAW sensor (0.16 dB/L/h, 0.14 dB/L/h), and finally the PZT (0.13 dB/L/h, 0.12 dB/L/h). Similarly, for larger leaks (4 mm, 10 mm), the PVDF sensor continued to show superior sensitivity (0.2 dB/L/h, 0.17 dB/L/h), followed by the SAW sensor (0.13 dB/L/h, 0.11), and finally the PZT sensor (0.12 dB/L/h, 0.1 dB/L/h), outperforming the PZT sensor. This suggests that SAW and PVDF sensors, have the potential to serve as valuable, cost-effective alternatives to traditional commercial leak noise transducers. The outcomes of this comparative study involving three acoustic sensors hold the potential to advance the development of robust and dependable systems for the detection of water leaks in plastic pipelines.
Miniaturized Non-Contact Heating and Transmitted Light Imaging Using an Inexpensive and Modular 3D-Printed Platform for Molecular Diagnostics
Authors: Laman Alex , Das Debayan , Priye Aashish
Published: September 2023

The ability to simultaneously heat and image samples using transmitted light is crucial for several biological applications. However, existing techniques such as heated stage microscopes, thermal cyclers equipped with imaging capabilities, or non-contact heating systems are often bulky, expensive, and complex. This work presents the development and characterization of a Miniaturized Optically-clear Thermal Enclosure (MOTE) system—an open-source, inexpensive, and low-powered modular system—capable of convectively heating samples while simultaneously imaging them with transmitted light. We develop and validate a computational fluid dynamics (CFD) model to design and optimize the heating chamber. The model simulates velocity and temperature profiles within the heating chamber for various chamber materials and sizes. The computational model yielded an optimal chamber dimension capable of achieving a stable temperature ranging from ambient to 95 °C with a spatial discrepancy of less than 1.5 °C, utilizing less than 8.5 W of power. The dual-functionality of the MOTE system, enabling synchronous heating and transmitted light imaging, was demonstrated through the successful execution of paper-based LAMP reactions to detect λ DNA samples in real-time down to 10 copies/µL of the target concentration. The MOTE system offers a promising and flexible platform for various applications, from molecular diagnostics to biochemical analyses, cell biology, genomics, and education.
Evaluating the Performance of Video-Based Automated Passenger Counting Systems in Real-World Conditions: A Comparative Study
Authors: Pronello Cristina , Garzón Ruiz Ximena Rocio
Published: September 2023

Automatic passenger counting (APC) systems in public transport are useful in collecting information that can help improve the efficiency of transport networks. Focusing on video-based passenger counting, the aim of this study was to evaluate and compare an existing APC system, claimed by its manufacturer to be highly accurate (98%), with a newly developed low-cost APC system operating under the same real-world conditions. For this comparison, a low-cost APC system using a Raspberry Pi with a camera and a YOLOv5 object detection algorithm was developed, and an in-field experiment was performed in collaboration with the public transport companies operating in the cities of Turin and Asti in Italy. The experiment shows that the low-cost system was able to achieve an accuracy of 72.27% and 74.59%, respectively, for boarding and alighting, while the tested commercial APC system had an accuracy, respectively, of 53.11% and 55.29%. These findings suggest that current APC systems might not meet expectations under real-world conditions, while low-cost systems could potentially perform at the same level of accuracy or even better than very expensive commercial systems.
Swimmers’ Effective Actions during the Backstroke Start Technique
Authors: de Jesus Karla , de Jesus Kelly , Mourão Luís , Roesler Hélio , Fernandes Ricardo J. , Vaz Mário A. P. , Vilas-Boas João Paulo , Machado Leandro J.
Published: September 2023

The analysis of the external forces of swimming starts has revealed how swimmers propel themselves out of the block, but data should be properly interpreted to fully understand force-generation mechanisms. This study aimed to assess horizontal and vertical forces in the backstroke start based on swimmers’ structural and propulsive actions. Firstly, a simulated structural force was estimated by two transient backstroke-start inter-segmental realistic body positions: a maximally tucked position and an extended one (just before the hands-off and the take-off, respectively). Secondly, 10 competitive backstroke swimmers performed four 15 m maximal backstroke starts with the external forces estimated. Thirdly, the simulated structural force was subtracted from raw horizontal and vertical force data, measured between hands-off and take-off instants, resulting in the propulsive forces. The application of the algorithm has evidenced that backstrokers’ horizontal and vertical simulated-structural-force components contributed to ~40% of total force during start propulsion (~0.2–0.12 s before the take-off), followed by the propulsive horizontal force increment and a progressive vertical component reduction (~0.05 s) with ~20° take-off angle. Based on these findings, researchers and coaches can better guide swimmers as to the proper mechanical strategies to achieve effectiveness in the backstroke start, and to improve direct transfer of resistance training programs.
IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques
Authors: Ahamed Md. Faysal , Syfullah Md. Khalid , Sarkar Ovi , Islam Md. Tohidul , Nahiduzzaman Md. , Islam Md. Rabiul , Khandakar Amith , Ayari Mohamed Arselene , Chowdhury Muhammad E. H.
Published: September 2023

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.
Special Issue on Acoustic Sensors and Their Applications (Vol. 1)
Authors: Sattar Farook , Puhan Niladri Bihari , Fazel-Rezai Reza
Published: September 2023

Acoustic sensors have been in commercial use for more than 60 years [...]
A Smart Grid Overvoltage Identification System Associated with Partial Discharge Signal and Dielectric Loss Detection
Authors: Chen Guojin , Zhu Yucheng , Meng Zihao , Fang Weixing , Xie Wei , Xu Ming , Li Wenxin
Published: September 2023

Capacitive equipment refers to its insulation design using the principle of capacitance of electrical equipment, mainly by a variety of different capacitive components in series. Most of the equipment in the substation is capacitive equipment. Once an insulation failure occurs, it will lead to extremely serious consequences. Monitoring grid overvoltage and insulation degradation of capacitive equipment is an effective means to ensure the stable operation of the power system. Therefore, in order to enhance the health management of capacitive equipment, including transformers, bushings, and current transformers, and to mitigate the risk of severe failures, it is imperative to conduct broad-spectrum frequency-domain online monitoring of overvoltages, dielectric losses, and partial discharge. However, the current monitoring work requires the utilization of multiple detection apparatuses. Aiming at the disadvantage that the existing inspection is not well integrated and requires a combination of multiple devices. This paper proposes a smart grid overvoltage identification system that utilizes partial discharge (PD) signals in correlation with dielectric loss detection. The system achieves synchronous detection of dielectric loss and high-frequency partial discharge by synchronously and in real-time acquiring four current signals from the power grid, enhancing the integration level of the hardware system.
TiO2/MWCNT/Nafion-Modified Glassy Carbon Electrode as a Sensitive Voltammetric Sensor for the Determination of Hydrogen Peroxide
Authors: de Oliveira Rafael Henrique , Gonçalves Daniel A. , dos Reis Diogo Duarte
Published: September 2023

In this work we describe a straightforward approach for creating a nanocomposite comprising multiwalled carbon nanotubes (MWCNTs) and titanium dioxide (TiO2) using the hydrothermal technique, which is then characterized by scanning electron microscope (SEM), energy-dispersive X-ray spectrometer (EDS), X-ray diffraction analysis (XRD), Fourier transform infrared spectroscopy (FTIR), and thermal gravimetric analysis (TGA) to assess its properties. Nafion is employed as a reticular agent for the nanocomposite on the glassy carbon electrode (GCE), creating the MWCNT/TiO2/Nafion/GCE system. The electrochemical behavior of the system was evaluated using cyclic voltammetry, revealing its remarkable electrocatalytic activity for detecting hydrogen peroxide in water. The developed sensor showcased a broad linear response range of 14.00 to 120.00 μM, with a low detection limit of 4.00 μM. This electrochemical sensor provides a simple and highly sensitive method for detecting hydrogen peroxide in aqueous solutions and shows promising potential for various real-world applications, particularly in H2O2 monitoring.
A Unified Multimodal Interface for the RELAX High-Payload Collaborative Robot
Authors: Muratore Luca , Laurenzi Arturo , De Luca Alessio , Bertoni Liana , Torielli Davide , Baccelliere Lorenzo , Del Bianco Edoardo , Tsagarakis Nikos G.
Published: September 2023

This manuscript introduces a mobile cobot equipped with a custom-designed high payload arm called RELAX combined with a novel unified multimodal interface that facilitates Human–Robot Collaboration (HRC) tasks requiring high-level interaction forces on a real-world scale. The proposed multimodal framework is capable of combining physical interaction, Ultra Wide-Band (UWB) radio sensing, a Graphical User Interface (GUI), verbal control, and gesture interfaces, combining the benefits of all these different modalities and allowing humans to accurately and efficiently command the RELAX mobile cobot and collaborate with it. The effectiveness of the multimodal interface is evaluated in scenarios where the operator guides RELAX to reach designated locations in the environment while avoiding obstacles and performing high-payload transportation tasks, again in a collaborative fashion. The results demonstrate that a human co-worker can productively complete complex missions and command the RELAX mobile cobot using the proposed multimodal interaction framework.
A Sensor-Fault-Estimation Method for Lithium-Ion Batteries in Electric Vehicles
Authors: Lan Tianyu , Gao Zhi-Wei , Yin Haishuang , Liu Yuanhong
Published: September 2023

In recent years, electric vehicles powered by lithium-ion batteries have developed rapidly, and the safety and reliability of lithium-ion batteries have been a paramount issue. Battery management systems are highly dependent on sensor measurements to ensure the proper functioning of lithium-ion batteries. Therefore, it is imperative to develop a suitable fault diagnosis scheme for battery sensors, to realize a diagnosis at an early stage. The main objective of this paper is to establish validated electrical and thermal models for batteries, and address a model-based fault diagnosis scheme for battery sensors. Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, current sensor fault, and temperature sensor fault. To verify the estimation effect of the proposed scheme, various types of faults are utilized for simulation experiments. Battery experimental data are used for battery modeling and observer-based fault diagnosis in battery sensors.