bluuebunny/crossref_metadata_2025 huggingface dataset:
Effective Detergency Determination for Single Polymeric Fibers Using Confocal Microscopy
Authors: Hu Qian , Wu Jindan , Qin Zhiqiang , Wei Xuanxiang , Jiang Chenchen , Wu Minghua , Yu Deyou , Wang Jiping
Published: August 2023

Detergency determination for single polymeric fibers is of significant importance to screening effective detergents for laundry, but remains challenging. Herein, we demonstrate a novel and effective method to quantify the detergency for single polymeric fibers using a confocal laser scanning microscope (CLSM). It was applied to visualize the oil-removing process of single polymeric fibers and thus assess the detergency of various detergents. Four typical surfactants were selected for comparison, and a compounded detergent containing multiple components (e.g., anionic and nonionic surfactants, enzymes) was demonstrated to be the most effective and powerful soil-removing detergent because more than 50% of oil on the cotton fiber could be easily removed. Moreover, the oil removal process of three kinds of fibers (i.e., cotton, viscose, and polyester) was imaged and monitored by confocal microscopy. It was found that the percentage of the detergency of a single polyester fiber exceeded 70%, which is much higher than that of cotton and viscose fibers (~50%), which may be due to its relatively smooth surface. Compared to traditional methods, the CLSM imaging method is more feasible and effective to determine the detergency of detergents for single polymeric fibers.
Predicting Shear Wave Velocity Using a Convolutional Neural Network and Dual-Constraint Calculation for Anisotropic Parameters Incorporating Compressional and Shear Wave Velocities
Authors: Liu Jiaqi , Gui Zhixian , Gao Gang , Li Yonggen , Wei Qiang , Liu Yizhuo
Published: August 2023

As the exploration of unconventional reservoirs progresses, characterizing challenging formations like tight sandstone becomes increasingly complex. Anisotropic parameters play a vital role in accurately characterizing these unconventional reservoirs. In light of this, this paper introduces an approach that uses a dual-constraint anisotropic rock physics model based on compressional and shear wave velocities. The proposed method aims to enhance the precision of anisotropic parameter calculations, thus improving the overall accuracy of reservoir characterization. The paper begins by applying a convolutional neural network (CNN) to predict shear wave velocity, effectively resolving the issue of incomplete shear wave logging data. Subsequently, an anisotropic rock physics model is developed specifically for tight sandstone. A comprehensive analysis is conducted to examine the influence of quartz, clay porosity aspect ratio, and fracture density on compressional and shear wave velocities. Trial calculations using the anisotropic model data demonstrated that the accuracy of calculating anisotropic parameters significantly improved when both compressional and transverse wave velocity constraints were taken into account, as opposed to relying solely on the compressional wave velocity constraint. Furthermore, the rationality of predicting anisotropic parameters using both the shear wave velocity predicted by the convolutional neural network and the measured compressional wave velocity was confirmed using the example of deep tight sandstone in the Junggar Basin.
Doxycycline Removal by Solar Photo-Fenton on a Pilot-Scale Composite Parabolic Collector (CPC) Reactor
Authors: Bensaibi Faiza , Chabani Malika , Bouafia Souad , Djelal Hayet
Published: August 2023

In this study, the solar photo-Fenton (SPF) process was investigated for the degradation of doxycycline (DOX) using a solar compound parabolic collector (CPC) reactor and a borosilicate serpentine tube with an irradiated volume of 1.8 L. The influence of the operating parameters, such as H2O2, Fe2+ dosage, and DOX concentration, was investigated. The optimum H2O2, Fe2+ dosage, and DOX concentration were found to be 4, 0.1, and 0.06 mM, respectively. The results of photo-Fenton experiments fitted the pseudo-first-order kinetic equation (R2 = 0.99). The efficiency of the treatment under optimized conditions was analyzed by an HPLC analysis of the samples, chemical oxygen demand (COD), and total organic carbon (TOC). The results obtained showed that the solar photo-Fenton process achieved a DOX degradation of 95.07%, a COD elimination of 81.43%, and a TOC elimination of 73.05%. The phytotoxicity tests revealed a 73.32% decrease in the germination index of watercress seeds, demonstrating that the SPF process minimizes the toxicity of the chemical and did not have any negative impact on plant growth. Overall, the results of this study suggest that SPF is a promising technology for the removal of doxycycline from wastewater.
High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data
Authors: Liu Chunhui , Wu Sensen , Dai Zhen , Wang Yuanyuan , Du Zhenhong , Liu Xingyu , Qiu Chunxia
Published: August 2023

This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.
D-VINS: Dynamic Adaptive Visual–Inertial SLAM with IMU Prior and Semantic Constraints in Dynamic Scenes
Authors: Sun Yang , Wang Qing , Yan Chao , Feng Youyang , Tan Rongxuan , Shi Xiaoqiong , Wang Xueyan
Published: August 2023

Visual–inertial SLAM algorithms empower robots to autonomously explore and navigate unknown scenes. However, most existing SLAM systems heavily rely on the assumption of static environments, making them ineffective when confronted with dynamic objects in the real world. To enhance the robustness and localization accuracy of SLAM systems in dynamic scenes, this paper introduces a visual–inertial SLAM framework that integrates semantic and geometric information, called D-VINS. This paper begins by presenting a method for dynamic object classification based on the current motion state of features, enabling the identification of temporary static features within the environment. Subsequently, a feature dynamic check module is devised, which utilizes inertial measurement unit (IMU) prior information and geometric constraints from adjacent frames to calculate dynamic factors. This module also validates the classification outcomes of the temporary static features. Finally, a dynamic adaptive bundle adjustment module is developed, utilizing the dynamic factors of the features to adjust their weights during the nonlinear optimization process. The proposed methodology is evaluated using both public datasets and a dataset created specifically for this study. The experimental results demonstrate that D-VINS stands as one of the most real-time, accurate, and robust systems for dynamic scenes, showcasing its effectiveness in challenging real-world scenes.
TPH-YOLOv5-Air: Airport Confusing Object Detection via Adaptively Spatial Feature Fusion
Authors: Wang Qiang , Feng Wenquan , Yao Lifan , Zhuang Chen , Liu Binghao , Chen Lijiang
Published: August 2023

Airport detection in remote sensing scenes is a crucial area of research, playing a key role in aircraft blind landing procedures. However, airport detection in remote sensing scenes still faces challenges such as class confusion, poor detection performance on multi-scale objects, and limited dataset availability. To address these issues, this paper proposes a novel airport detection network (TPH-YOLOv5-Air) based on adaptive spatial feature fusion (ASFF). Firstly, we construct an Airport Confusing Object Dataset (ACD) specifically tailored for remote sensing scenarios containing 9501 instances of airport confusion objects. Secondly, building upon the foundation of TPH-YOLOv5++, we adopt the ASFF structure, which not only enhances the feature extraction efficiency but also enriches feature representation. Moreover, an adaptive spatial feature fusion (ASFF) strategy based on adaptive parameter adjustment module (APAM) is proposed, which improves the feature scale invariance and enhances the detection of airports. Finally, experimental results based on the ACD dataset demonstrate that TPH-YOLOv5-Air achieves a mean average precision (mAP) of 49.4%, outperforming TPH-YOLOv5++ by 2% and the original YOLOv5 network by 3.6%. This study contributes to the advancement of airport detection in remote sensing scenes and demonstrates the practical application potential of TPH-YOLOv5-Air in this domain. Visualization and analysis further validate the effectiveness and interpretability of TPH-YOLOv5-Air. The ACD dataset is publicly available.
Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data
Authors: Kui Mengyun , Xu Yunna , Wang Jinliang , Cheng Feng
Published: August 2023

Photon-counting light detection and ranging (LiDAR) emits and receives weak photon signals, which are easily mixed with background noise caused by the sun, the atmosphere, etc., and is thus difficult to distinguish. Therefore, point-cloud denoising is a key step in point-cloud data processing of photon-counting LiDAR. To explore the adaptability of different denoising algorithms for photon-counting LiDAR data in different times and spaces, in this paper, NASA’s official differential, regressive and Gaussian adaptive nearest neighbor (DRAGANN) algorithm; Herzfeld’s radial basis function (RBF) denoising algorithm; and the density-based spatial clustering of applications with noise (DBSCAN) algorithm based on density clustering are used to denoise the ICESat-2 ATL03 photon point-cloud data. Airborne LiDAR data are used to verify the denoising accuracy, and then the adaptability of the three algorithms is discussed. The results show that the DRAGANN algorithm is suitable for data with moderate Fraction Vegetation Coverage (FVC) (45–75%) at night and is less affected by slope; therefore, it is not limited to terrain slope. The denoising accuracy of the RBF algorithm decreases with increasing FVC and decreases with increasing slope. It is suitable for data with low terrain slope (0~55°) and low FVC (0~220°), which is less affected by observation time; therefore, it is suitable for all-day data. The DBSCAN algorithm is suitable for data with moderate FVC (45~75%) at night, regardless of terrain slope. Unlike the DRAGANN algorithm, the DBSCAN algorithm is greatly affected by solar noise photons, but at night, its denoising accuracy is higher than that of the DRAGANN algorithm. The research results have certain reference significance for the subsequent processing and application of ICESat-2 data.
The Ionospheric Three-Dimensional Electron Density Variations Induced by the 21 August 2017 Total Solar Eclipse by Using Global Ionospheric Specification
Authors: Lin Chi-Yen , Liu Jann-Yenq , Lin Charles Chien-Hung , Chou Min-Yang
Published: August 2023

Global Ionospheric Specification (GIS) is based on the Gauss–Markov Kalman filter to assimilate the slant total electron content (TEC) observed from ground-based GPS receivers and space-based radio occultation instrumentations in order to reconstruct three-dimensional (3D) ionospheric electron density structure, and it can remotely sense and monitor the weather condition in space. In this study, five minutes of high temporal resolution GIS is implemented in order to reconstruct the 3D electron density structure on the 21 August 2017 total solar eclipse and analyze the variations induced by the moon’s shadow. To obtain more information of the ionosphere, from the extend 2200 GPS stations on the continental United States, are added for assimilation. The results show the ionosphere peak height (hmF2) uplift was 30–50 km altitude in latitude 25–40°N, and that the electron density depletion at higher altitudes (400 km) has a more noticeable time delay than at low altitudes (200 km), especially in low-latitude regions.
Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions
Authors: Yang Zheng , Chen Fei , Xu Binbin , Ma Boquan , Qu Zege , Zhou Xin
Published: August 2023

The lack of labeled data and variable working conditions brings challenges to the application of intelligent fault diagnosis. Given this, extracting labeled information and learning distribution-invariant representation provides a feasible and promising way. Enlightened by metric learning and semi-supervised architecture, a triplet-guided path-interaction ladder network (Tri-CLAN) is proposed based on the aspects of algorithm structure and feature space. An encoder–decoder structure with path interaction is built to utilize the unlabeled data with fewer parameters, and the network structure is simplified by CNN and an element additive combination activation function. Metric learning is introduced to the feature space of the established algorithm structure, which enables the mining of hard samples from extremely limited labeled data and the learning of working condition-independent representations. The generalization and applicability of Tri-CLAN are proved by experiments, and the contribution of the algorithm structure and the metric learning in the feature space are discussed.
Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery
Authors: Zanoli Silvia Maria , Pepe Crescenzo
Published: August 2023

In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs.
Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
Authors: Brito Jorge Andrés , Moreno José Ignacio , Contreras Luis Miguel , Alvarez-Campana Manuel , Blanco Caamaño Marta
Published: August 2023

The rapid evolution of 5G and beyond technologies has sparked an unprecedented surge in the need for networking infrastructure that can deliver high speed, minimal latency, and remarkable flexibility. The programmable data plane, which enables the dynamic reconfiguration of network functions and protocols, is becoming increasingly important in meeting these requirements. This paper provides an overview of the current state of the art in programmable data planes implemented in 5G and beyond architectures. It proposes a classification of the reviewed studies based on system architecture and specific use cases. Furthermore, the article surveys the primary applications of programmable devices in emerging telecommunication networks, such as tunneling and forwarding, network slicing, cybersecurity, and in-band telemetry. Finally, this publication summarizes the open research challenges and future directions. In addition to offering a comprehensive review of programmable data plane applications in telecommunication networks, this article aims to guide further research in this promising field for network operators and researchers alike.
FCCD-SAR: A Lightweight SAR ATR Algorithm Based on FasterNet
Authors: Dong Xiang , Li Dong , Fang Jiandong
Published: August 2023

In recent times, the realm of remote sensing has witnessed a remarkable surge in the area of deep learning, specifically in the domain of target recognition within synthetic aperture radar (SAR) images. However, prevailing deep learning models have often placed undue emphasis on network depth and width while disregarding the imperative requirement for a harmonious equilibrium between accuracy and speed. To address this concern, this paper presents FCCD-SAR, a SAR target recognition algorithm based on the lightweight FasterNet network. Initially, a lightweight and SAR-specific feature extraction backbone is meticulously crafted to better align with SAR image data. Subsequently, an agile upsampling operator named CARAFE is introduced, augmenting the extraction of scattering information and fortifying target recognition precision. Moreover, the inclusion of a rapid, lightweight module, denoted as C3-Faster, serves to heighten both recognition accuracy and computational efficiency. Finally, in cognizance of the diverse scales and vast variations exhibited by SAR targets, a detection head employing DyHead’s attention mechanism is implemented to adeptly capture feature information across multiple scales, elevating recognition performance on SAR targets. Exhaustive experimentation on the MSTAR dataset unequivocally demonstrates the exceptional prowess of our FCCD-SAR algorithm, boasting a mere 2.72 M parameters and 6.11 G FLOPs, culminating in an awe-inspiring 99.5% mean Average Precision (mAP) and epitomizing its unparalleled proficiency.
Real-Time Bucket Pose Estimation Based on Deep Neural Network and Registration Using Onboard 3D Sensor
Authors: Xu Zijing , Bi Lin , Zhao Ziyu
Published: August 2023

Real-time and accurate bucket pose estimation plays a vital role in improving the intelligence level of mining excavators, as the bucket is a crucial component of the excavator. Existing methods for bucket pose estimation are realized by installing multiple non-visual sensors. However, these sensors suffer from cumulative errors caused by loose connections and short service lives caused by strong vibrations. In this paper, we propose a method for bucket pose estimation based on deep neural network and registration to solve the large registration error problem caused by occlusion. Specifically, we optimize the Point Transformer network for bucket point cloud semantic segmentation, significantly improving the segmentation accuracy. We employ point cloud preprocessing and continuous frame registration to reduce the registration distance and accelerate the Fast Iterative Closest Point algorithm, enabling real-time pose estimation. By achieving precise semantic segmentation and faster registration, we effectively address the problem of intermittent pose estimation caused by occlusion. We collected our own dataset for training and testing, and the experimental results are compared with other relevant studies, validating the accuracy and effectiveness of the proposed method.
Beam Steering 3D Printed Dielectric Lens Antennas for Millimeter-Wave and 5G Applications
Authors: Piroutiniya Asrin , Rasekhmanesh Mohamad Hosein , Masa-Campos José Luis , López-Hernández Javier , García-Marín Eduardo , Tamayo-Domínguez Adrián , Sánchez-Olivares Pablo , Ruiz-Cruz Jorge A.
Published: August 2023

Two types of cost-efficient antennas based on dielectric gradient index dielectric lens have been designed for 5G applications at 28 GHz. The first is a linearly polarized flat lens antenna (LP-FLA) for terrestrial 5G communications. The second is a novel circularly polarized stepped lens antenna (CP-SLA) for 5G satellite services. An efficient design method is presented to optimize and conform the lens topology to the radiation pattern coming from the antenna feeder. The LP-FLA is fed by a traditional linearly polarized pyramidal horn antenna (PHA). The CP-SLA is fed by an open-ended bow-tie waveguide cavity (BCA) antenna. This cavity feeder (BCA), using cross-sections with bow-tie shapes, allows having circular polarization at the desired frequency bandwidth. The two types of presented antennas have been manufactured in order to verify their performance by an easy, low-cost, three-dimensional (3D) printing technique based on stereolithography. The peak realized gain value for the flat (LP-FLA) and stepped (CP-SLA) lens antennas have been increased at 28 GHz to 25.2 and 24.8 dBi, respectively, by disposing the lens structures at the appropriated distance from the feeders. Likewise, using an array of horns (PHA) or open-ended bow-tie waveguide cavity (BCA) antenna feeders, it is possible to obtain a maximum steering angle range of 20° and 35°, for a directivity over 15 dBi and 10 dBi, in the planar and stepped lens antennas, respectively.
Pain Management Mobile Applications: A Systematic Review of Commercial and Research Efforts
Authors: Koumpouros Yiannis , Georgoulas Aggelos
Published: August 2023

Shared decision making is crucial in the pain domain. The subjective nature of pain demands solutions that can facilitate pain assessment and management. The aim of the current study is to review the current trends in both the commercial and the research domains in order to reveal the key issues and guidelines that could further help in the effective development of pain-focused apps. We searched for scientific publications and commercial apps in 22 databases and the two major app stores. Out of 3612 articles and 336 apps, 69 met the requirements for inclusion following the PRISMA guidelines. An analysis of their features (technological approach, design methodology, evaluation strategy, and others) identified critical points that have to be taken into consideration in future efforts. For example, commercial and research efforts target different types of pain, while no participatory design is followed in the majority of the cases examined. Moreover, the evaluation of the final apps remains a challenge that hinders their success. The examined domain is expected to experience a substantial increase. More research is needed towards the development of non-intrusive wearables and sensors for pain detection and assessment, along with artificial intelligence techniques and open data.
A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises
Authors: Ye Xiangzhou , Wang Jian , Wu Dongjie , Zhang Yong , Li Bing
Published: August 2023

The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model’s high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target’s maneuvering turn. Second, an improved Sage–Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.
Flow Ripple Reduction in Reciprocating Pumps by Multi-Phase Rectification
Authors: Özkayar Gürhan , Wang Zhilin , Lötters Joost , Tichem Marcel , Ghatkesar Murali Krishna
Published: August 2023

Reciprocating piezoelectric micropumps enable miniaturization in microfluidics for lab-on-a-chip applications such as organs-on-chips (OoC). However, achieving a steady flow when using these micropumps is a significant challenge because of flow ripples in the displaced liquid, especially at low frequencies or low flow rates (<50 µL/min). Although dampers are widely used for reducing ripples in a flow, their efficiency depends on the driving frequency of the pump. Here, we investigated multi-phase rectification as an approach to minimize ripples at low flow rates by connecting piezoelectric micropumps in parallel. The efficiency in ripple reduction was evaluated with an increasing number (n) of pumps connected in parallel, each actuated by an alternating voltage waveform with a phase difference of 2π/n (called multi-phase rectification) at a chosen frequency. We introduce a fluidic ripple factor (RFfl.), which is the ratio of the root mean square (RMS) value of the fluctuations present in the rectified output to the average fluctuation-free value of the discharge flow, as a metric to express the quality of the flow. The fluidic ripple factor was reduced by more than 90% by using three-phase rectification when compared to one-phase rectification in the 2–60 μL/min flow rate range. Analytical equations to estimate the fluidic ripple factor for a chosen number of pumps connected in parallel are presented, and we experimentally confirmed up to four pumps. The analysis shown can be used to design a frequency-independent multi-phase fluid rectifier to the desired ripple level in a flow for reciprocating pumps.
Robust Fusion Kalman Estimator of the Multi-Sensor Descriptor System with Multiple Types of Noises and Packet Loss
Authors: Zheng Jie , Cui Wenxia , Sun Sian
Published: August 2023

Under the influence of multiple types of noises, missing measurement, one-step measurement delay and packet loss, the robust Kalman estimation problem is studied for the multi-sensor descriptor system (MSDS) in this paper. Moreover, the established MSDS model describes uncertain-variance noises, multiplicative noises, time delay and packet loss phenomena. Different types of noises and packet loss make it more difficult to build the estimators of MSDS. Firstly, MSDS is transformed to the new system model by applying the singular value decomposition (SVD) method, augmented state and fictitious noise approach. Furthermore, the robust Kalman estimator is constructed for the newly deduced augmented system based on the min-max robust estimation principle and Kalman filter theory. In addition, the given estimator consists of four parts, which are the usual Kalman filter, predictor, smoother and white noise deconvolution estimator. Then, the robust fusion Kalman estimator is obtained for MSDS according to the relation of augmented state and the original system state. Simultaneously, the robustness is demonstrated for the actual Kalman estimator of MSDS by using the mathematical induction method and Lyapunov’s equation. Furthermore, the error variance of the obtained Kalman estimator is guaranteed to the upper bound for all admissible uncertain noise variance. Finally, the simulation example of a circuit system is examined to illustrate the performance and effectiveness of the robust estimators.
Mitigating Under-Sampling Artifacts in 3D Photoacoustic Imaging Using Res-UNet Based on Digital Breast Phantom
Authors: Huo Haoming , Deng Handi , Gao Jianpan , Duan Hanqing , Ma Cheng
Published: August 2023

In recent years, photoacoustic (PA) imaging has rapidly grown as a non-invasive screening technique for breast cancer detection using three-dimensional (3D) hemispherical arrays due to their large field of view. However, the development of breast imaging systems is hindered by a lack of patients and ground truth samples, as well as under-sampling problems caused by high costs. Most research related to solving these problems in the PA field were based on 2D transducer arrays or simple regular shape phantoms for 3D transducer arrays or images from other modalities. Therefore, we demonstrate an effective method for removing under-sampling artifacts based on deep neural network (DNN) to reconstruct high-quality PA images using numerical digital breast simulations. We constructed 3D digital breast phantoms based on human anatomical structures and physical properties, which were then subjected to 3D Monte-Carlo and K-wave acoustic simulations to mimic acoustic propagation for hemispherical transducer arrays. Finally, we applied a 3D delay-and-sum reconstruction algorithm and a Res-UNet network to achieve higher resolution on sparsely-sampled data. Our results indicate that when using a 757 nm laser with uniform intensity distribution illuminated on a numerical digital breast, the imaging depth can reach 3 cm with 0.25 mm spatial resolution. In addition, the proposed DNN can significantly enhance image quality by up to 78.4%, as measured by MS-SSIM, and reduce background artifacts by up to 19.0%, as measured by PSNR, even at an under-sampling ratio of 10%. The post-processing time for these improvements is only 0.6 s. This paper suggests a new 3D real time DNN method addressing the sparse sampling problem based on numerical digital breast simulations, this approach can also be applied to clinical data and accelerate the development of 3D photoacoustic hemispherical transducer arrays for early breast cancer diagnosis.
Can Plants Sense Humans? Using Plants as Biosensors to Detect the Presence of Eurythmic Gestures
Authors: de la Cal Luis , Gloor Peter A. , Weinbeer Moritz
Published: August 2023

This paper describes the preliminary results of measuring the impact of human body movements on plants. The scope of this project is to investigate if a plant perceives human activity in its vicinity. In particular, we analyze the influence of eurythmic gestures of human actors on lettuce and beans. In an eight-week experiment, we exposed rows of lettuce and beans to weekly eurythmic movements (similar to Qi Gong) of a eurythmist, while at the same time measuring changes in voltage between the roots and leaves of lettuce and beans using the plant spikerbox. We compared this experimental group of vegetables to a control group of vegetables whose voltage differential was also measured while not being exposed to eurythmy. We placed a plant spikerbox connected to lettuce or beans in the vegetable plot while the eurythmist was performing their gestures about 2 m away; a second spikerbox was connected to a control plant 20 m away. Using t-tests, we found a clear difference between the experimental and the control group, which was also verified with a machine learning model. In other words, the vegetables showed a noticeably different pattern in electric potentials in response to eurythmic gestures.
A CNN Sound Classification Mechanism Using Data Augmentation
Authors: Chu Hung-Chi , Zhang Young-Lin , Chiang Hao-Chu
Published: August 2023

Sound classification has been widely used in many fields. Unlike traditional signal-processing methods, using deep learning technology for sound classification is one of the most feasible and effective methods. However, limited by the quality of the training dataset, such as cost and resource constraints, data imbalance, and data annotation issues, the classification performance is affected. Therefore, we propose a sound classification mechanism based on convolutional neural networks and use the sound feature extraction method of Mel-Frequency Cepstral Coefficients (MFCCs) to convert sound signals into spectrograms. Spectrograms are suitable as input for CNN models. To provide the function of data augmentation, we can increase the number of spectrograms by setting the number of triangular bandpass filters. The experimental results show that there are 50 semantic categories in the ESC-50 dataset, the types are complex, and the amount of data is insufficient, resulting in a classification accuracy of only 63%. When using the proposed data augmentation method (K = 5), the accuracy is effectively increased to 97%. Furthermore, in the UrbanSound8K dataset, the amount of data is sufficient, so the classification accuracy can reach 90%, and the classification accuracy can be slightly increased to 92% via data augmentation. However, when only 50% of the training dataset is used, along with data augmentation, the establishment of the training model can be accelerated, and the classification accuracy can reach 91%.
Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy
Authors: Sathish Kaveripakam , Chinthaginjala Ravikumar , Kim Wooseong , Rajesh Anbazhagan , Corchado Juan M. , Abbas Mohamed
Published: August 2023

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object’s position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.
Biomechanical Load of Neck and Lumbar Joints in Open-Surgery Training
Authors: Zhang Ce , Roossien Charlotte Christina , Verkerke Gijsbertus Jacob , Houdijk Han , Hijmans Juha M. , Greve Christian
Published: August 2023

The prevalence of musculoskeletal symptoms (MSS) like neck and back pain is high among open-surgery surgeons. Prolonged working in the same posture and unfavourable postures are biomechanical risk factors for developing MSS. Ergonomic devices such as exoskeletons are possible solutions that can reduce muscle and joint load. To design effective exoskeletons for surgeons, one needs to quantify which neck and trunk postures are seen and how much support during actual surgery is required. Hence, this study aimed to establish the biomechanical profile of neck and trunk postures and neck and lumbar joint loads during open surgery (training). Eight surgical trainees volunteered to participate in this research. Neck and trunk segment orientations were recorded using an inertial measurement unit (IMU) system during open surgery (training). Neck and lumbar joint kinematics, joint moments and compression forces were computed using OpenSim modelling software and a musculoskeletal model. Histograms were used to illustrate the joint angle and load distribution of the neck and lumbar joints over time. During open surgery, the neck flexion angle was 71.6% of the total duration in the range of 10~40 degrees, and lumbar flexion was 68.9% of the duration in the range of 10~30 degrees. The normalized neck and lumbar flexion moments were 53.8% and 35.5% of the time in the range of 0.04~0.06 Nm/kg and 0.4~0.6 Nm/kg, respectively. Furthermore, the neck and lumbar compression forces were 32.9% and 38.2% of the time in the range of 2.0~2.5 N/kg and 15~20 N/kg, respectively. In contrast to exoskeletons used for heavy lifting tasks, exoskeletons designed for surgeons exhibit lower support torque requirements while additional degrees of freedom (DOF) are needed to accommodate combinations of neck and trunk postures.
Survey of Time Series Data Generation in IoT
Authors: Hu Chaochen , Sun Zihan , Li Chao , Zhang Yong , Xing Chunxiao
Published: August 2023

Nowadays, with the rapid growth of the internet of things (IoT), massive amounts of time series data are being generated. Time series data play an important role in scientific and technological research for conducting experiments and studies to obtain solid and convincing results. However, due to privacy restrictions, limited access to time series data is always an obstacle. Moreover, the limited available open source data are often not suitable because of a small quantity and insufficient dimensionality and complexity. Therefore, time series data generation has become an imperative and promising solution. In this paper, we provide an overview of classical and state-of-the-art time series data generation methods in IoT. We classify the time series data generation methods into four major categories: rule-based methods, simulation-model-based methods, traditional machine-learning-based methods, and deep-learning-based methods. For each category, we first illustrate its characteristics and then describe the principles and mechanisms of the methods. Finally, we summarize the challenges and future directions of time series data generation in IoT. The systematic classification and evaluation will be a valuable reference for researchers in the time series data generation field.
Healthism vis-à-vis Vaccine Hesitancy: Insights from Parents Who Either Delay or Refuse Children’s Vaccination in Portugal
Authors: Mendonça Joana , Hilário Ana Patrícia
Published: August 2023

Although healthism appears to be at the heart of the decision-making process of vaccine hesitancy, this matter has been understudied. We believe that the concept of healthism may be key to lessen the polarization of discourses around vaccination, offering a broad understanding of parents’ decision to not vaccinate their children. This article aims to deepen the knowledge on the relation between healthism and vaccine hesitancy, using Portugal as a case study. A qualitative research approach was adopted, and therefore, in-depth interviews were conducted with 31 vaccine-hesitant parents. The findings showed that vaccine-hesitant parents usually adopt several strategies based on natural living to prevent and tackle their children’s potential health issues. There appears to be a common approach towards health and life (i.e., healthism) among vaccine-hesitant parents. Drawing upon the healthism ideology, vaccine-hesitant parents make choices to ensure the good health of their child. These choices nevertheless represent a privileged position as the pursuit of healthfulness is constrained by sociodemographic aspects. Using vaccine hesitancy as the starting point, our findings show that healthism and its focus on personal accountability under the umbrella of neoliberalism may jeopardize global public health. Healthcare professionals should pay particular attention to healthism when addressing vaccine hesitancy in Portugal and elsewhere. Research evidence advocates the need to be sensitive to the broad spectrum of vaccine hesitancy as this encompasses multiple views on the subject.
Life-Cycle Assessments of Meat-Free and Meat-Containing Diets by Integrating Sustainability and Lean: Meat-Free Dishes Are Sustainable
Authors: Mannheim Viktoria , Avató Judit Lovasné
Published: August 2023

Nowadays, sustainable food choices are taking on an increasingly central role. This paper assesses the environmental loads and energy resources of meat-free (vegan and pescovegetarian) and meat-containing (traditional) restaurant soups and main dishes. The applied life-cycle assessment focuses on determining environmental loads and energy resources in restaurant products’ preparation, cooking, and end-of-life phases. Mann–Whitney and Kruskal–Wallis statistical methods were applied to investigate restaurant products’ distribution and carbon footprints. Furthermore, a sustainability assessment model was developed by integrating green-lean and life-cycle assessment approaches called “GreenCycLEAN”. Based on the analysis results, the whole life cycle of meat-free dishes has a lower environmental impact. However, the primary energy requirement of a vegetable soup is less favorable than that of a meat-containing soup. The preparation phase has higher burdens, and the cooking phase is the most energy intensive. Research results are helpful for the sustainability of catering establishments.
Unveiling Sustainability in Ecommerce: GPT-Powered Software for Identifying Sustainable Product Features
Authors: Roumeliotis Konstantinos I. , Tselikas Nikolaos D. , Nasiopoulos Dimitrios K.
Published: August 2023

In recent years, the concept of sustainability has gained significant attention across various industries. Consumers are increasingly concerned about the environmental impact of the products they purchase, leading to a growing demand for sustainable options. However, identifying sustainable product features can be a complex and time-consuming task. This paper presents a novel approach to address this challenge by utilizing GPT (Generative Pre-trained Transformer) powered software for automatically identifying sustainable product features from product descriptions, titles, and product specifications. The software leverages the power of natural language processing and machine learning to classify products into different sustainability categories. By analyzing the textual information provided, the software can extract key sustainability indicators, such as eco-friendly materials, energy efficiency, recyclability, and ethical sourcing. This automated process eliminates the need for manual assessment and streamlines the evaluation of product sustainability. The proposed software not only empowers consumers to make informed and sustainable purchasing decisions but also facilitates businesses in showcasing their environmentally friendly offerings. The experimental results demonstrate the effectiveness and accuracy of the software in identifying sustainable product features. The primary objective of this article is to assess the suitability of the GPT model for the domain of sustainability assessment. By collecting a real-life dataset and employing a specific methodology, four hypotheses are formulated, which will be substantiated through the experimental outcomes. This research contributes to the field of sustainability assessment by combining advanced language models with product classification, paving the way for a more sustainable and eco-conscious future.
Effects of Water—Binder Ratio on Strength and Seismic Behavior of Stabilized Soil from Kongshavn, Port of Oslo
Authors: Lindh Per , Lemenkova Polina
Published: August 2023

In many civil engineering problems, soil is stabilized by a combination of binders and water. The success of stabilization is evaluated using seismic tests with measured P-wave velocities. Optimization of process, laboratory testing and data modelling are essential to reduce the costs of the industrial projects. This paper reports the optimized workflow of soil stabilization through evaluated effects from the two factors controlling the development of strength: (1) the ratio between water and binder; (2) the proportions of different binders (cement/slag) were changed experimentally in a mixture of samples to evaluate the strength of soil. The experimental results show an optimal combination of 30% cement and 70% slag with a binder content of 120 kg/m3 and a maximum water binder ratio (w/b) of 5. Such proportions of mixture demonstrated effective soil stabilization both on a pilot test scale and on full scale for industrial works. The correlation between the compressive strength and relative deformation of specimens revealed that strength has the highest values for w/b = 5 and the lowest for w/b = 7. In case of high water content in soil and wet samples, the condition of a w/b ≤ 5 will require a higher amount of binder.
A Prefeasibility Study for the Adaptive Reuse of Cultural Historical Landscapes as Drivers and Enablers of Sustainable Development
Authors: Della Spina Lucia
Published: August 2023

The international scientific debate on the growing concern over land consumption has gained prominence in recent years. The awareness of the link between cultural heritage and sustainable development has become increasingly evident, leading to a greater focus on the Adaptive Reuse (AR) of cultural assets as a conscious process of creating new values. This trend has prompted a reflection on urban planning practices and the promotion of AR and valorization policies for heritage, which can contribute to environmental sustainability, social cohesion, and cultural identity, thereby providing fertile ground for innovation and local economic development. However, decisions regarding AR interventions pose significant complexity due to the multiple interests at stake, as well as high costs that discourage investiments. For these reasons, this paper proposes a multi-methodological approach—applied to a project for the AR of a cluster of mountain huts located in the Sila National Park (SNP)—aimed at effectively supporting decisions related to the evaluation of feasibility and economic sustainability of cultural heritage landscapes that have not yet been adequately valorized. This approach was applied to a pilot project of AR, allowing for the discussion of the proposed evaluation methodological framework. The final step involved verifying the economic feasibility and financial sustainability of the methodology based on a Financial Feasibility Plan (FFP) of the proposed of the new destination to ‘Rifugio Diffuso’ (RD). The assessment aimed to evaluate the intervention’s ability to create value, generate a level of profitability that meets private investment expectations, and promote sustainable development of the local economy.