bluuebunny/crossref_metadata_2025 huggingface dataset:
Pathogenesis of Anemia in Canine Babesiosis: Possible Contribution of Pro-Inflammatory Cytokines and Chemokines—A Review
Authors: Zygner Wojciech , Gójska-Zygner Olga , Norbury Luke J.
Published: January 2023

Canine babesiosis is a tick-borne protozoan disease caused by intraerythrocytic parasites of the genus Babesia. The infection may lead to anemia in infected dogs. However, anemia is not directly caused by the pathogen. The parasite’s developmental stages only have a marginal role in contributing to a decreased red blood cell (RBC) count. The main cause of anemia in affected dogs is the immune response to the infection. This response includes antibody production, erythrophagocytosis, oxidative damage of RBCs, complement activation, and antibody-dependent cellular cytotoxicity. Moreover, both infected and uninfected erythrocytes are retained in the spleen and sequestered in micro-vessels. All these actions are driven by pro-inflammatory cytokines and chemokines, especially IFN-γ, TNF-α, IL-6, and IL-8. Additionally, imbalance between the actions of pro- and anti-inflammatory cytokines plays a role in patho-mechanisms leading to anemia in canine babesiosis. This article is a review of the studies on the pathogenesis of anemia in canine babesiosis and related diseases, such as bovine or murine babesiosis and human or murine malaria, and the role of pro-inflammatory cytokines and chemokines in the mechanisms leading to anemia in infected dogs.
Acknowledgment to the Reviewers of Pharmaceuticals in 2022
Authors:   Pharmaceuticals Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Fiber and Electrical Field Alignment Increases BDNF Expression in SH-SY5Y Cells following Electrical Stimulation
Authors: Huynh Quy-Susan , Holsinger R. M. Damian
Published: January 2023

The limited expression of neurotrophic factors that can be included in neural tissue engineering scaffolds is insufficient for sustained neural regeneration. A localized and sustained method of introducing neurotrophic factors is required. We describe our attempt at inducing neuroblastoma cells to express trophic factors following electrical stimulation. Human SH-SY5Y neuroblastoma cells, cultured on polycaprolactone electrospun nanofibers, were electrically stimulated using a 100 mV/mm electric field. Nuclear morphology and brain-derived neurotrophic factor (BDNF) expression were analyzed. Cells were classified based on the type of fiber orientation and the alignment of these fibers in relation to the electric field. Nuclear deformation was mainly influenced by fiber orientation rather than the electrical field. Similarly, fiber orientation also induced BDNF expression. Although electrical field alone had no significant effect on BDNF expression, combining fiber orientation with electrical field resulted in BDNF expression in cells that grew on electrospun fibers that were aligned perpendicular to the electrical field.
Efficacy of Single-Dose Albendazole for the Treatment of Soil-Transmitted Helminthic Infections among School Children in Rwanda—A Prospective Cohort Study
Authors: Kabatende Joseph , Barry Abbie , Mugisha Michael , Ntirenganya Lazare , Bergman Ulf , Bienvenu Emile , Aklillu Eleni
Published: January 2023

Mass drug administration (MDA) of single-dose albendazole to all at-risk populations as preventive chemotherapy (deworming) is recommended by WHO to halt transmission of soil-transmitted helminth (STH) in endemic countries. We assessed the effectiveness of single-dose albendazole against STH infection in the western province of Rwanda, where STH prevalence remains high despite the implementation of preventive chemotherapy for over a decade. Two weeks before the scheduled MDA, 4998 school children (5–15 years old) were screened for STH infections (Ascaris lumbricoides, Trichuris trichiura, and hookworm), and 1526 children who tested positive for at least one type of STH parasite were enrolled and received single-dose albendazole (400 mg) through MDA. A follow-up stool exam was performed at three weeks post-treatment using Kato–Katz. Efficacy was assessed by cure rate (CR), defined as the proportion of children who became egg-free, and egg reduction rates (ERRs) at three weeks post-treatment. The CR and ERR for hookworms (CR = 96.7%, ERR = 97.4%) was above, and for Ascaris lumbricoides (CR = 95.1%, ERR = 94.6%) was borderline compared with the WHO efficacy threshold (CR and ERR ≥ 95%). However, the CR and ERR for T. trichiura (CR = 17.6% ERR = 40.3%) were below the WHO threshold for efficacy (CR and ERR ≥ 50%). Having moderate-to-heavy infection intensity and coinfection with another type of STH parasites were independent risk factors for lower CR and ERR against Trichirus trichiura (p < 0.001). Single-dose albendazole used in the MDA program is efficacious for the treatment and control for hookworms and Ascaris lumbricoides infections but not effective for Trichirus trichiura. An alternative treatment regimen is urgently needed to prevent, control, and eliminate STH as a public health problem.
Plasmonic Generation of Spatiotemporal Optical Vortices
Authors: Kashapov Artem I. , Bezus Evgeni A. , Bykov Dmitry A. , Doskolovich Leonid L.
Published: January 2023

We investigate the transformation of spatiotemporal optical signals using the Kretschmann configuration with an additional dielectric layer, which can be referred to as the “generalized Kretschmann setup”. We demonstrate that in the considered structure, it is possible to achieve the condition of generating a reflected optical pulse containing a spatiotemporal optical vortex, which appears to be impossible in the conventional Kretschmann configuration. High-quality generation of spatiotemporal optical vortices using the investigated structure was confirmed by the results of rigorous numerical simulations. The obtained results are promising for applications in analog optical computing and optical information processing systems.
Biostimulation of Petroleum-Contaminated Soil Using Organic and Inorganic Amendments
Authors: Udume Ogochukwu A. , Abu Gideon O. , Stanley Herbert O. , Vincent-Akpu Ijeoma F. , Momoh Yusuf , Eze Michael O.
Published: January 2023

The most common approaches for the in-situ bioremediation of contaminated sites worldwide are bioaugmentation and biostimulation. Biostimulation has often proved more effective for chronically contaminated sites. This study examined the effectiveness of optimized water hyacinth compost in comparison with other organic and inorganic amendments for the remediation of crude oil-polluted soils. Water hyacinth was found to be rich in nutrients necessary to stimulate microbial growth and activity. An organic geochemical analysis revealed that all amendments in this study increased total petroleum hydrocarbon (TPH) biodegradation by ≥75% within 56 days, with the greatest biodegradation (93%) occurring in sterilized soil inoculated with optimized water hyacinth compost. This was followed by polluted soil amended with a combination of spent mushroom and water hyacinth composts (SMC + WH), which recorded a TPH biodegradation of 89%. Soil amendment using the inorganic fertilizer NPK (20:10:10) resulted in 86% TPH biodegradation. On the other hand, control samples (natural attenuation) recorded only 4% degradation. A molecular analysis of residual polycyclic aromatic hydrocarbons (PAHs) showed that the 16 PAHs designated by the US EPA as priority pollutants were either completely or highly degraded in the combined treatment (SMC + WH), indicating the potential of this amendment for the environmental remediation of soils contaminated with recalcitrant organic pollutants.
Identification, Characterization, and Control of Black Spot on Chinese Kale Caused by Sphaerobolus cuprophilus sp. nov.
Authors: Kalayanamitra Pancheewa , Kalayanamitra Kal , Nontajak Sutasinee , Taylor Paul W. J. , Jonglaekha Nuchnart , Bussaban Boonsom
Published: January 2023

Chinese kale (Brassica alboglabra) is commonly grown and consumed throughout Asia and is often treated with chemicals to control pests and diseases. In Thailand, public standards, Good Agricultural Practice (GAP), and International Federation of Organic Agriculture Movement (IFOAM) programs were introduced for the cultivation of Chinese kale with minimum input of chemical treatments. Black spot caused by the fungus Sphaerobolus has been affecting the plants grown under IFOAM standards in Chiang Mai, Thailand, for several years. Strongly adhering glebal spore masses of the fungus on leaf and stem surfaces have adversely affected postharvest management, especially in the quality classification of the product. Both morphological and phylogenetic (combined ITS, mtSSU, and EF 1-α dataset) studies confirmed a novel species, S. cuprophilus. Pathogenicity tests involving inoculation of Chinese kale by non-wound and mulch inoculation bioassays resulted in the production of symptoms of black spot and the re-isolation of S. cuprophilus, indicating that the new fungal species is the causal agent of black spots. Inhibitory effects of antagonistic bacteria and chemical fungicides, both allowed for use in plant cultivation under either IFOAM or GAP standards, indicated that Bacillus amyloliquefaciens strains (PBT2 and YMB7), chlorothalonil (20 and 500 ppm) and thiophanate-methyl (500 and 1500 ppm) were the most effective in controlling the growth of the causal fungus by 83 to 93%. However, copper oxychloride (5 to 20 ppm), a recommended chemical in control of downy mildew of Chinese kale, showed hormetic effects on S. cuprophilus by promoting the growth and sporulation of the fungus. The findings of this study provide vital information regarding the association of S. cuprophilus and Chinese kale and will support decisions to manage fungal diseases of this vegetable.
Acknowledgment to the Reviewers of Reproductive Medicine in 2022
Authors:   Reproductive Medicine Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Geometric Quality Assessment of Prefabricated Steel Box Girder Components Using 3D Laser Scanning and Building Information Model
Authors: Tan Yi , Chen Limei , Wang Qian , Li Shenghan , Deng Ting , Tang Dongdong
Published: January 2023

Prefabricated steel box girders (SBGs) are widely adopted in bridge engineering due to their light weight and low lifecycle cost. To smoothly assemble SBG components on a construction site, it is necessary to inspect their geometric quality and ensure that all the as-is SBG components have the correct dimensions. However, the traditional inspection method is time-consuming and error-prone. This study developed a non-contact geometric quality assessment technique based on 3D laser scanning to accurately assess the locations and dimensions of SBG components. First, a robust normal-based region-growing algorithm was developed to divide the SBG components into segments with different labels. The scanned data related to the T ribs were then extracted through the proposed subtraction algorithm after the identification of the steel cabin. Lastly, the required items for geometric quality inspection were calculated based on the extracted as-is SBG components. The feasibility of the proposed geometric quality assessment method was validated through a real SBG project. Field test results showed that the developed inspection technique could assess the geometric quality of prefabricated SBG components in a more accurate and efficient manner compared to traditional measurement approaches.
Reduction in Uncertainty in Forest Aboveground Biomass Estimation Using Sentinel-2 Images: A Case Study of Pinus densata Forests in Shangri-La City, China
Authors: Li Lu , Zhou Boqi , Liu Yanfeng , Wu Yong , Tang Jing , Xu Weiheng , Wang Leiguang , Ou Guanglong
Published: January 2023

The uncertainty from the under-estimation and over-estimation of forest aboveground biomass (AGB) is an urgent problem in optical remote sensing estimation. In order to more accurately estimate the AGB of Pinus densata forests in Shangri-La City, we mainly discuss three non-parametric models—the artificial neural network (ANN), random forests (RFs), and the quantile regression neural network (QRNN) based on 146 sample plots and Sentinel-2 images in Shangri-La City, China. Moreover, we selected the corresponding optical quartile models with the lowest mean error at each AGB segment to combine as the best QRNN (QRNNb). The results showed that: (1) for the whole biomass segment, the QRNNb has the best fitting performance compared with the ANN and RFs, the ANN has the lowest R2 (0.602) and the highest RMSE (48.180 Mg/ha), and the difference between the QRNNb and RFs is not apparent. (2) For the different biomass segments, the QRNNb has a better performance. Especially when AGB is lower than 40 Mg/ha, the QRNNb has the highest R2 of 0.961 and the lowest RMSE of 1.733 (Mg/ha). Meanwhile, when AGB is larger than 160 Mg/ha, the QRNNb has the highest R2 of 0.867 and the lowest RMSE of 18.203 Mg/ha. This indicates that the QRNNb is more robust and can improve the over-estimation and under-estimation in AGB estimation. This means that the QRNNb combined with the optimal quantile model of each biomass segment provides a method with more potential for reducing the uncertainties in AGB estimation using optical remote sensing images.
In-Orbit Performance of the GRACE Accelerometers and Microwave Ranging Instrument
Authors: Murböck Michael , Abrykosov Petro , Dahle Christoph , Hauk Markus , Pail Roland , Flechtner Frank
Published: January 2023

The Gravity Recovery and Climate Experiment (GRACE) satellite mission has provided global long-term observations of mass transport in the Earth system with applications in numerous geophysical fields. In this paper, we targeted the in-orbit performance of the GRACE key instruments, the ACCelerometers (ACC) and the MicroWave ranging Instrument (MWI). For the ACC data, we followed a transplant approach analyzing the residual accelerations from transplanted accelerations of one of the two satellites to the other. For the MWI data, we analyzed the post-fit residuals of the monthly GFZ GRACE RL06 solutions with a focus on stationarity. Based on the analyses for the two test years 2007 and 2014, we derived stochastic models for the two instruments and a combined ACC+MWI stochastic model. While all three ACC axes showed worse performance than their preflight specifications, in 2007, a better ACC performance than in 2014 was observed by a factor of 3.6 due to switched-off satellite thermal control. The GRACE MWI noise showed white noise behavior for frequencies above 10 mHz around the level of 1.5×10−6 m/Hz. In the combined ACC+MWI noise model, the ACC part dominated the frequencies below 10 mHz, while the MWI part dominated above 10 mHz. We applied the combined ACC+MWI stochastic models for 2007 and 2014 to the monthly GFZ GRACE RL06 processing. This improved the formal errors and resulted in a comparable noise level of the estimated gravity field parameters. Furthermore, the need for co-estimating empirical parameters was reduced.
Special Issue Review: Artificial Intelligence and Machine Learning Applications in Remote Sensing
Authors: Chen Ying-Nong , Fan Kuo-Chin , Chang Yang-Lang , Moriyama Toshifumi
Published: January 2023

Remote sensing is used in an increasingly wide range of applications. Models and methodologies based on artificial intelligence (AI) are commonly used to increase the performance of remote sensing technologies. Deep learning (DL) models are the most widely researched AI-based models because of their effectiveness and high performance. Therefore, we organized a Special Issue on remote sensing titled “Artificial Intelligence and Machine Learning Applications in Remote Sensing.” In this paper, we review nine articles included in this Special Issue, most of which report studies based on satellite data and DL, reflecting the most prevalent trends in remote sensing research, as well as how DL architecture and the functioning of DL models can be analyzed and explained is a hot topic in AI research. DL methods can outperform conventional machine learning methods in remote sensing; however, DL remains a black box and understanding the details of the mechanisms through which DL models make decisions is difficult. Therefore, researchers must continue to investigate how explainable DL methods for use in the field of remote sensing can be developed.
Suitability Analysis of Machine Learning Algorithms for Crack Growth Prediction Based on Dynamic Response Data
Authors: Omar Intisar , Khan Muhammad , Starr Andrew
Published: January 2023

Machine learning has the potential to enhance damage detection and prediction in materials science. Machine learning also has the ability to produce highly reliable and accurate representations, which can improve the detection and prediction of damage compared to the traditional knowledge-based approaches. These approaches can be used for a wide range of applications, including material design; predicting material properties; identifying hidden relationships; and classifying microstructures, defects, and damage. However, researchers must carefully consider the appropriateness of various machine learning algorithms, based on the available data, material being studied, and desired knowledge outcomes. In addition, the interpretability of certain machine learning models can be a limitation in materials science, as it may be difficult to understand the reasoning behind predictions. This paper aims to make novel contributions to the field of material engineering by analyzing the compatibility of dynamic response data from various material structures with prominent machine learning approaches. The purpose of this is to help researchers choose models that are both effective and understandable, while also enhancing their understanding of the model’s predictions. To achieve this, this paper analyzed the requirements and characteristics of commonly used machine learning algorithms for crack propagation in materials. This analysis assisted the authors in selecting machine learning algorithms (K nearest neighbor, Ridge, and Lasso regression) to evaluate the dynamic response of aluminum and ABS materials, using experimental data from previous studies to train the models. The results showed that natural frequency was the most significant predictor for ABS material, while temperature, natural frequency, and amplitude were the most important predictors for aluminum. Crack location along samples had no significant impact on either material. Future work could involve applying the discussed techniques to a wider range of materials under dynamic loading conditions.
A High-Temperature Superconducting Bandpass Dual-Mode Filter with Tunable Relative Bandwidth and Center Frequency
Authors: Shang Zhaojiang , Shen Liejun , Huang Jianyong , Zhang Huibin
Published: January 2023

This study proposes a high-temperature superconducting (HTS) bandpass filter with a continuously tunable bandwidth and center frequency. The proposed filter combines several gallium arsenide varactors and a dual-mode resonator (DMR). The even and odd modes of the DMR can be tuned simultaneously using a single bias voltage. The capacitive value of varactors in the circuit is tuned continuously under continuous voltage and frequency tunability. External couplings and the interstage can be realized using an interdigital coupling structure; a fixed capacitor is added to the feeder to improve its coupling strength. A low-insertion loss within the band is obtained using HTS technology. Additionally, the proposed filter is etched on a 0.5 mm-thick MgO substrate and combined with YBCO thin films for demonstration. For the as-fabricated device, the tuning frequency range of 1.22~1.34 GHz was 9.4%; the 3-dB fractional bandwidth was 12.95~17.39%, and the insertion loss was 2.28~3.59 dB. The simulation and experimental measurement results were highly consistent.
One-Step Crank-Nicolson Direct-Splitting Algorithm with Enhanced Absorption to Evaluate Low-Pressure Discharge for Satellite Sensors in Outer Space
Authors: Wang Yangjing , Xie Yongjun , Su Pu , Jiang Haolin , Wu Peiyu
Published: January 2023

Low-pressure discharge causes air ionization resulting in performance degeneration or failure for the satellite sensors in outer space. Here, a one-step Crank-Nicolson Direct-Splitting (CNDS) algorithm is proposed to evaluate the electrical behavior of satellite sensors under the low-pressure discharge circumstance. To be more specific, the CNDS algorithm is proposed in the Lorentz medium, which can accurately analyze the ionized air and generated plasma. Higher order perfectly matched layer (PML) is modified in the Lorentz medium to efficiently terminate the unbounded lattice. It can be concluded that the proposed algorithm shows entire considerable performance in the low-pressure discharge evaluation. The proposed PML formulation behaviors enhanced absorbing performance compared with the existing algorithm. Through the experiments, it can be observed that the low-pressure discharge phenomenon causes performance variation, which shows a significant influence on the satellite sensors. Meanwhile, results show considerable agreement between the simulation and experiment results which indicates the effectiveness of the algorithm.
Frequency-Domain-Based Structure Losses for CycleGAN-Based Cone-Beam Computed Tomography Translation
Authors: Pai Suraj , Hadzic Ibrahim , Rao Chinmay , Zhovannik Ivan , Dekker Andre , Traverso Alberto , Asteriadis Stylianos , Hortal Enrique
Published: January 2023

Research exploring CycleGAN-based synthetic image generation has recently accelerated in the medical community due to its ability to leverage unpaired images effectively. However, a commonly established drawback of the CycleGAN, the introduction of artifacts in generated images, makes it unreliable for medical imaging use cases. In an attempt to address this, we explore the effect of structure losses on the CycleGAN and propose a generalized frequency-based loss that aims at preserving the content in the frequency domain. We apply this loss to the use-case of cone-beam computed tomography (CBCT) translation to computed tomography (CT)-like quality. Synthetic CT (sCT) images generated from our methods are compared against baseline CycleGAN along with other existing structure losses proposed in the literature. Our methods (MAE: 85.5, MSE: 20433, NMSE: 0.026, PSNR: 30.02, SSIM: 0.935) quantitatively and qualitatively improve over the baseline CycleGAN (MAE: 88.8, MSE: 24244, NMSE: 0.03, PSNR: 29.37, SSIM: 0.935) across all investigated metrics and are more robust than existing methods. Furthermore, no observable artifacts or loss in image quality were observed. Finally, we demonstrated that sCTs generated using our methods have superior performance compared to the original CBCT images on selected downstream tasks.
k-Tournament Grasshopper Extreme Learner for FMG-Based Gesture Recognition
Authors: Barioul Rim , Kanoun Olfa
Published: January 2023

The recognition of hand signs is essential for several applications. Due to the variation of possible signals and the complexity of sensor-based systems for hand gesture recognition, a new artificial neural network algorithm providing high accuracy with a reduced architecture and automatic feature selection is needed. In this paper, a novel classification method based on an extreme learning machine (ELM), supported by an improved grasshopper optimization algorithm (GOA) as a core for a weight-pruning process, is proposed. The k-tournament grasshopper optimization algorithm was implemented to select and prune the ELM weights resulting in the proposed k-tournament grasshopper extreme learner (KTGEL) classifier. Myographic methods, such as force myography (FMG), deliver interesting signals that can build the basis for hand sign recognition. FMG was investigated to limit the number of sensors at suitable positions and provide adequate signal processing algorithms for perspective implementation in wearable embedded systems. Based on the proposed KTGEL, the number of sensors and the effect of the number of subjects was investigated in the first stage. It was shown that by increasing the number of subjects participating in the data collection, eight was the minimal number of sensors needed to result in acceptable sign recognition performance. Moreover, implemented with 3000 hidden nodes, after the feature selection wrapper, the ELM had both a microaverage precision and a microaverage sensitivity of 97% for the recognition of a set of gestures, including a middle ambiguity level. The KTGEL reduced the hidden nodes to only 1000, reaching the same total sensitivity with a reduced total precision of only 1% without needing an additional feature selection method.
Deficits of Sensory Integration and Balance as Well as Scoliotic Changes in Young Schoolgirls
Authors: Wilczyński Jacek , Habik Tatarowska Natalia , Mierzwa Molenda Marta
Published: January 2023

The aim of this study was to assess the relationship between sensory integration and balance deficits as well as scoliotic changes in young schoolgirls. The study comprised 54 girls aged 11 years with scoliotic changes. The Clinical Test of Sensory Integration and Balance of the Biodex Balance System platform were used to analyze the deficits in sensory integration and balance. Scoliotic changes were assessed using the Diers Formetric III 4D optoelectronic method. In the present study, there was a significant relationship between sensory integration and balance deficits as well as spine curvature angle (°) (p = 0.01), vertebral surface rotation (°) (p = 0.03), pelvic tilt (°) (p = 0.02), and lateral deviation (mm) (p = 0.04). The integration of the sensory systems has a positive effect on the structure of the intended and controlled movement as well as body posture and the development of the spine. In the treatment of scoliotic changes, one should also consider exercises that improve sensory integration as well as position and balance reactions.
Acknowledgment to the Reviewers of Sensors in 2022
Authors:   Sensors Editorial Office
Published: January 2023

High-quality academic publishing is built on rigorous peer review [...]
Lightweight and Energy-Efficient Deep Learning Accelerator for Real-Time Object Detection on Edge Devices
Authors: Kim Kyungho , Jang Sung-Joon , Park Jonghee , Lee Eunchong , Lee Sang-Seol
Published: January 2023

Tiny machine learning (TinyML) has become an emerging field according to the rapid growth in the area of the internet of things (IoT). However, most deep learning algorithms are too complex, require a lot of memory to store data, and consume an enormous amount of energy for calculation/data movement; therefore, the algorithms are not suitable for IoT devices such as various sensors and imaging systems. Furthermore, typical hardware accelerators cannot be embedded in these resource-constrained edge devices, and they are difficult to drive real-time inference processing as well. To perform the real-time processing on these battery-operated devices, deep learning models should be compact and hardware-optimized, and hardware accelerator designs also have to be lightweight and consume extremely low energy. Therefore, we present an optimized network model through model simplification and compression for the hardware to be implemented, and propose a hardware architecture for a lightweight and energy-efficient deep learning accelerator. The experimental results demonstrate that our optimized model successfully performs object detection, and the proposed hardware design achieves 1.25× and 4.27× smaller logic and BRAM size, respectively, and its energy consumption is approximately 10.37× lower than previous similar works with 43.95 fps as a real-time process under an operating frequency of 100 MHz on a Xilinx ZC702 FPGA.
A Simultaneous Pipe-Attribute and PIG-Pose Estimation (SPPE) Using 3-D Point Cloud in Compressible Gas Pipelines
Authors: Nguyen Hoa-Hung , Park Jae-Hyun , Jeong Han-You
Published: January 2023

An accurate estimation of pipe attributes, pose of pipeline inspection gauge (PIG), and downstream pipeline topology is essential for successful in-line inspection (ILI) of underground compressible gas pipelines. Taking a 3D point cloud of light detection and ranging (LiDAR) or time-of-flight (ToF) camera as the input, in this paper, we present the simultaneous pipe-attribute and PIG-pose estimation (SPPE) approach that estimates the optimal pipe-attribute and PIG-pose parameters to transform a 3D point cloud onto the inner pipe wall surface: major- and minor-axis lengths, roll, pitch, and yaw angles, and 2D deviation from the center of the pipe. Since the 3D point cloud has all spatial information of the inner pipe wall measurements, this estimation problem can be modeled by an optimal transformation matrix estimation problem from a PIG sensor frame to the global pipe frame. The basic idea of our SPPE approach is to decompose this transformation into two sub-transformations: The first transformation is formulated as a non-linear optimization problem whose solution is iteratively updated by the Levenberg–Marquardt algorithm (LMA). The second transformation utilizes the gravity vector to calculate the ovality angle between the geometric and navigation pipe frames. The extensive simulation results from our PIG simulator based on the robot operating system (ROS) platform demonstrate that the proposed SPPE can estimate the pipe attributes and PIG pose with excellent accuracy and is also applicable to real-time and post-processing non-destructive testing (NDT) applications thanks to its high computational efficiency.
A Novel Scalable Reconfiguration Model for the Postdisaster Network Connectivity of Resilient Power Distribution Systems
Authors: Imteaj Ahmed , Akbari Vahid , Amini Mohammad Hadi
Published: January 2023

The resilient operation of power distribution networks requires efficient optimization models to enable situational awareness. One of the pivotal tools to enhance resilience is a network reconfiguration to ensure secure and reliable energy delivery while minimizing the number of disconnected loads in outage conditions. Power outages are caused by natural hazards, e.g., hurricanes, or system malfunction, e.g., line failure due to aging. In this paper, we first propose a distribution-network optimal power flow formulation (DOPF) and define a new resilience evaluation indicator, the demand satisfaction rate (DSR). DSR is the rate of satisfied load demand in the reconfigured network over the load demand satisfied in the DOPF. Then, we propose a novel model to efficiently find the optimal network reconfiguration by deploying sectionalizing switches during line outages that maximize resilience indicators. Moreover, we analyze a multiobjective scenario to maximize the DSR and minimize the number of utilized sectionalizing switches, which provides an efficient reconfiguration model preventing additional costs associated with closing unutilized sectionalizing switches. We tested our model on a virtually generated 33-bus distribution network and a real 234-bus power distribution network, demonstrating how using the sectionalizing switches can increase power accessibility in outage conditions.
Sparse Depth-Guided Image Enhancement Using Incremental GP with Informative Point Selection
Authors: Yang Geonmo , Lee Juhui , Kim Ayoung , Cho Younggun
Published: January 2023

We propose an online dehazing method with sparse depth priors using an incremental Gaussian Process (iGP). Conventional approaches focus on achieving single image dehazing by using multiple channels. In many robotics platforms, range measurements are directly available, except in a sparse form. This paper exploits direct and possibly sparse depth data in order to achieve efficient and effective dehazing that works for both color and grayscale images. The proposed algorithm is not limited to the channel information and works equally well for both color and gray images. However, efficient depth map estimations (from sparse depth priors) are additionally required. This paper focuses on a highly sparse depth prior for online dehazing. For efficient dehazing, we adopted iGP for incremental depth map estimation and dehazing. Incremental selection of the depth prior was conducted in an information-theoretic way by evaluating mutual information (MI) and other information-based metrics. As per updates, only the most informative depth prior was added, and haze-free images were reconstructed from the atmospheric scattering model with incrementally estimated depth. The proposed method was validated using different scenarios, color images under synthetic fog, real color, and grayscale haze indoors, outdoors, and underwater scenes.
Dependence of Sensitivity, Derivative of Transfer Curve and Current on Bias Voltage Magnitude and Polarity in Tunneling Magnetoresistance Sensors
Authors: Fuśnik Łukasz , Szafraniak Bartłomiej , Wrona Jerzy , Cardoso Susana , Freitas Paulo. P. , Wiśniowski Piotr
Published: January 2023

The sensitivity of tunneling magnetoresistance sensors is an important performance parameter. It depends on the derivative of resistance versus magnetic field (transfer curve) and the current and is expressed as the product of the two factors. Previous research has demonstrated that the bias voltage has a significant impact on the sensitivity. However, no research has been conducted into the dependence of current and the derivative on bias voltage magnitude and polarity, and their contribution to the sensitivity. Thus, this paper investigates the dependence of sensitivity, derivative of resistance versus magnetic field curve and current on bias voltage magnitude and polarity in CoFeB/MgO/CoFeB-based tunneling magnetoresistance sensors with weak, strong and no voltage-controlled perpendicular magnetic anisotropy modification. It demonstrates that the sensitivity dependence on bias voltage for sensors with voltage controlled magnetic anisotropy modification showed no saturation up to 1 V. Moreover, the sensitivity asymmetry with respect to bias polarity changed significantly with bias, reaching a ratio of 6.7. Importantly, the contribution of current and the derivative of resistance versus magnetic field curve to the sensitivity showed a crossover. The current dominated the bias dependence of sensitivity below the crossover voltage and the derivative above the voltage. Furthermore, the crossover voltage in sensors without voltage controlled magnetic anisotropy modification did not depend on polarity, whereas in sensors with voltage controlled magnetic anisotropy modification, it appeared at significantly higher voltage under positive than negative polarity.
A Lightweight Algorithm for Insulator Target Detection and Defect Identification
Authors: Han Gujing , Zhao Liu , Li Qiang , Li Saidian , Wang Ruijie , Yuan Qiwei , He Min , Yang Shiqi , Qin Liang
Published: January 2023

The accuracy of insulators and their defect identification by UAVs (unmanned aerial vehicles) in transmission-line inspection needs to be further improved, and the model size of the detection algorithm is significantly reduced to make it more suitable for edge-end deployment. In this paper, the algorithm uses a lightweight GhostNet module to reconstruct the backbone feature extraction network of the YOLOv4 model and employs depthwise separable convolution in the feature fusion layer. The model is lighter on the premise of ensuring the effect of image information extraction. Meanwhile, the ECA-Net channel attention mechanism is embedded into the feature extraction layer and PANet (Path Aggregation Network) to improve the recognition accuracy of the model for small targets. The experimental results show that the size of the improved model is reduced from 244 MB to 42 MB, which is only 17.3% of the original model. At the same time, the mAp of the improved model is 0.77% higher than that of the original model, reaching 95.4%. Moreover, the mAP compared with YOLOv5-s and YOLOX-s, respectively, is improved by 1.98% and 1.29%. Finally, the improved model is deployed into Jetson Xavier NX and run at a speed of 8.8 FPS, which is 4.3 FPS faster than the original model.
Fruit Phantoms for Robotic Harvesting Trials—Mango Example
Authors: Goulart Rafael , Jarvis Dennis , Walsh Kerry B.
Published: January 2023

Experimental trials on the performance of end-effectors for the automated harvest of soft fruit are constrained by seasonal limitations on fruit availability and fruit perishability, necessitating the use of different sets of fruit across time. Consequently, the use of fruit and stalk phantoms, rather than real fruit, is an attractive proposition. In this paper, a process for the cost-effective production of stable fruit phantoms using silicone (polydimethylsiloxane, PDMS) and starch was presented. A preliminary consideration was also presented for the creation of a phantom fruit stalk, involving a wooden dowel or a magnetic latching. Mango fruit phantoms were benchmarked to mango fruit in terms of density, firmness, brittleness, etc.
Gender Diversity and Business Performance Nexus: A Synoptic Panorama Based on Bibliometric Network Analysis
Authors: Bogdan Victoria , Popa Dorina-Nicoleta , Beleneşi Mărioara , Rus Luminița , Scorțe Carmen-Mihaela
Published: January 2023

Board gender diversity literature has demonstrated the positive influence of female leaders’ traits, such as openness to negotiation and collaboration, greater flexibility in approaching professional tasks, and improved quality of life, on the business development strategy and consequently on the performance of the companies. This study aims to investigate gender diversity’s link to business performance through bibliometric network analysis, highlighting the scientific literature dynamics through a scientific mapping of the existing knowledge. The temporal evolution of the gender diversity and business performance nexus in the scientific publications selected from Web of Science was examined for 1996–2021, investigating more than 2000 documents and focusing on author, keyword, paper, journal, and topic analysis. The statistical processing of publications was performed in R using Bibliometrix and Biblioshiny software, revealing the most influential and prolific authors and also the most representative journals. Results of the panoramic synopsis analysis revealed outstanding research trends, topics related to women and business performance including women directors, firm performance, issues of board diversity including size, structure, board gender diversity, and independence of members, and issues connected to leadership and gender with items such as gender equality, diversity management, women on boards, and culture.
Techno-Economic and Environmental Study of Optimum Hybrid Renewable Systems, including PV/Wind/Gen/Battery, with Various Components to Find the Best Renewable Combination for Ponorogo Regency, East Java, Indonesia
Authors: Xu Aoqi , Awalin Lilik Jamilatul , Al-Khaykan Ameer , Fard Habib Forootan , Alhamrouni Ibrahim , Salem Mohamed
Published: January 2023

Nowadays, hybrid renewable systems can be the best solution for meeting electricity demand, especially where grid extension and environmental issues are important. This study aimed to find the best combination of the typical components used in East Java, Indonesia. In this regard, four types of photovoltaic (PV) panels, four types of wind turbines, and two types of batteries were selected, and the HOMER software simulated all possible combinations of the systems, including 32 scenarios (Sen). Then, considering the most important 15 parameters, such as pollutant emissions and economic values, the results were analyzed and sorted by the multicriteria decision-making (MCDM) method to find the best scenario for the case-study region. The results showed that SunPower E20-327 as PV, Eocycle EO10 10 kW as wind turbine, and Generic 1 kWh Li-Ion as the battery could be the best selection to design a hybrid renewable system for the case-study region since it can fulfill both economic and environmental needs. The cost of energy (COE) of the best-designed system and net present cost (NPC) are 0.24 ($/kWh) and 1.64 million $, respectively, where the renewable fraction (RF) is 55.1% and the scaled annual average load is 1126 kWh/day. The results of the sensitivity analysis on the best scenario’s parameters (where the capital cost of PV, battery, and wind turbine changes from 0.6 to 1.2, from 0.7 to 1.2, and from 0.7 to 1.4 of the current price, and diesel price from 0.5 to 1.1 ($/L)) showed that the RF, COE, and NPC values ranged between 51% to 93%, 0.2 to 0.3 ($/kWh), and 1.4 to 2.1 (million $), respectively.
Architecture Engineering and Construction Industrial Framework for Circular Economy: Development of a Circular Construction Site Methodology
Authors: Fagone Clelia , Santamicone Margherita , Villa Valentina
Published: January 2023

All sectors have been affected by digital, economic, and demographic transitions. These will also be completely changing the paradigms of the construction industry, which is increasingly being pressured to implement strategic solutions in order to mitigate its own current environmental impact. Existing environmental standards and voluntary protocols are useful tools for reducing the environmental impact of buildings and infrastructures above all during the operation phase, but there is no consistent methodology for creating a circular and sustainable construction stage at the moment, probably because this represents a small stage in the entire life cycle of a structure; nevertheless, the construction industry impacts a lot on the environment and should be managed in a circular way. A detailed analysis of the current problem is provided, as well as a potential solution involving a transition to a circular economic model for the entire construction lifecycle, supported by innovative models and methodologies. The purpose of this article is to create a “Circular Construction Sites” (CCS) flow using circular concepts and to develop a circular methodology for construction sustainability, considering practical solutions related to materials, water, and waste management. According to the circular economy model, the circular construction site can communicate with other industrial ecosystems to promote external circularity and zero waste production. A set of potential key performance indicators (KPIs) for monitoring circularity processes is presented. Finally, a method is proposed that circularizes not only the building or infrastructure design, but also the construction phase.
A Deep Learning-Based Approach to Generating Comprehensive Building Façades for Low-Rise Housing
Authors: Wan Da , Zhao Runqi , Zhang Sheng , Liu Hui , Guo Lian , Li Pengbo , Ding Lei
Published: January 2023

In recent years, as machine learning has been widely studied in the field of architecture, scholars have demonstrated that computers can be used to learn the graphical features of building façade generation. However, existing deep learning in façade generation has yet to generate only a single façade, without comprehensive generation of five façades including the roof. Moreover, most of the existing literature has utilized the Pix2Pix algorithm for façade generation experiments, failing to attempt to replace the original generator in Pix2Pix with a different generator for experiments. This study addresses the above issues by collecting and filtering entries from the international Solar Decathlon (SD competition) to obtain a data set. Subsequently, a low-rise residential building façade generation model based on the Pix2Pix neural network was constructed for training and testing. At the same time, the original U-net generator in Pix2Pix was replaced with three different generators, U-net++, HRNet and AttU-net, for training and test results were obtained. The results were evaluated from both subjective and objective aspects and it was found that the AttU-net generative network showed the best comprehensive generation performance for such façades. HRNet is acceptable if there is a need for fast training and generation
Civic Reporting Indicators and Biocultural Conservation: Opportunities and Challenges for Sustainable Tourism
Authors: Branstrator Julia R. , Cavaliere Christina T. , Day Jonathon , Bricker Kelly S.
Published: January 2023

Citizen science (CS) within sustainable tourism is an underutilized tool for biocultural conservation. The aims of this research integrate conceptual and applied approaches to situate post-positivist and interpretive paradigms within CS and sustainable tourism. The aims are fulfilled by the creation of the new Civic Reporting Indicators (CRIs), developed through analysis of the 174 Global Sustainable Tourism Council Destination (GSTC-D) criteria and indicators. It was determined that 114 indicators are perceivable audibly and/or visibly by untrained citizens. The rearticulation of GSTC-D criteria into the CRIs utilizes embodied perceptions and observations reportable by untrained visitors and residents. The CRIs are framed within ethical principles of research, CS, and sustainable tourism. The inclusion of interpretive paradigms within CS provides epistemological innovation that validates lived experiences and embodied knowledge, fostering agency and empowerment within sustainability narratives. The CRIs may harness end-user observations by utilizing information communication technologies (ICTs) to amass longitudinal and real-time data for smart, sustainable destination management and biocultural conservation. Engaging citizens through the CRIs has the potential to contribute valid observations that amass democratic, longitudinal, and cost-effective data. Designing accessible ICT platforms for destination management may enact civic agency and critical social reflection to democratize sustainability engagement.
Tuning the Cu/Ce Ratio for Improved Benzene Oxidation over Gold-Promoted Alumina-Supported CuO-CeO2
Authors: Tabakova Tatyana , Petrova Petya , Karakirova Yordanka , Avdeev Georgi , Kolentsova Elitsa , Ilieva Lyuba
Published: January 2023

Increased levels and detrimental effects of volatile organic compounds (VOCs) stimulate research efforts to develop catalysts with high efficiency in complete hydrocarbon oxidation. This work is focused on the complete oxidation of benzene as a probe reaction for VOCs elimination over alumina-supported CuO-CeO2 mixed oxide promoted by gold. The benzene molecule is the most stable among the aromatic hydrocarbons with toxic and often carcinogenic effects known as BTEX (benzene, toluene, ethylbenzene, and xylenes) owing to the symmetry and stability of the benzene ring. Use of low-cost materials as support is an appropriate strategy aimed at improving catalyst economic profitability. The effect of the Cu-Ce ratio, namely 2:1 and 1:5, and the role of supported gold in the catalyst performance were evaluated. Analysis of the impact of support composition in benzene oxidation was based on sample characterization by textural measurements, PXRD, EPR spectroscopy, and the TPR technique. Special attention was paid to the disturbed symmetry of the ceria crystallographic structure by defects formation and its implication for the catalytic activity. Gold on alumina-supported binary oxide catalysts exhibited a significantly higher activity than promoted supported monometallic oxides. The best performance of the Au/Cu-Ce 1:5 sample was related to the highest concentration of paramagnetic Cu2+ ions and the best copper species dispersion evidenced by PXRD, EPR, and TPR results. The catalyst achieved stable total oxidation to CO2 and water by 94% benzene conversion at 250 °C, thus implying the potential of this composition in developing efficient catalytic materials for atmospheric pollutant abatement.