bluuebunny/crossref_metadata_2025 huggingface dataset:
Coenzyme Q10 Supplementation in Athletes: A Systematic Review
Authors: Fernandes Matheus Santos de Sousa , Fidelis Débora Eduarda da Silvia , Aidar Felipe J. , Badicu Georgian , Greco Gianpiero , Cataldi Stefania , Santos Gabriela Carvalho Jurema , de Souza Raphael Frabrício , Ardigò Luca Paolo
Published: September 2023

Background: To summarize available evidence in the literature on the impacts of CoQ10 supplementation on metabolic, biochemical, and performance outcomes in athletes. Methods: Six databases, Cochrane Library (33 articles), PubMed (90 articles), Scopus (55 articles), Embase (60 articles), SPORTDiscus (1056 articles), and Science Direct (165 articles), were researched. After applying the eligibility criteria, articles were selected for peer review independently as they were identified by June 2022. The protocol for this systematic review was registered on PROSPERO (CRD42022357750). Results: Of the 1409 articles found, 16 were selected for this systematic review. After CoQ10 supplementation, a decrease in oxidative stress markers was observed, followed by higher antioxidant activity. On the other hand, lower levels of liver damage markers (ALT); Aspartate aminotransferase (AST); and Gamma-glutamyl transpeptidase (γGT) were identified. Finally, we found a reduction in fatigue indicators such as Creatine Kinase (CK) and an increase in anaerobic performance. Conclusions: This systematic review concludes that supplementation with orally administered CoQ10 (30–300 mg) was able to potentiate plasma antioxidant activity and anaerobic performance, reducing markers linked to oxidative stress and liver damage in athletes from different modalities aged 17 years old and older.
Correction: Dahill et al. Associations between Parents’ Body Weight/Shape Comments and Disordered Eating Amongst Adolescents over Time—A Longitudinal Study. Nutrients 2023, 15, 1419
Authors: Dahill Lucy , Hay Phillipa , Morrison Natalie , Touyz Stephen , Mitchison Deborah , Bussey Kay , Mannan Haider
Published: September 2023

There was an error in the original publication [...]
Anti-Aging Potential of Plants of the Anak Dalam Tribe, Jambi, Indonesia
Authors: Lestari Uce , Muhaimin Muhaimin , Chaerunisaa Anis Yohana , Sujarwo Wawan
Published: September 2023

The process of skin aging is a physiological phenomenon that can not be avoided. According to global population data, the rate of aging increases by approximately 13% every year. The impact of skin aging has become a significant concern and challenge for developed countries. Consequently, there has been a search for potential new anti-aging agents. This review aims to provide an overview of the current research status of plants of the Anak Dalam Tribe (Indonesian: Suku Anak Dalam [SAD]; referred to as SAD henceforth) in Jambi Province, Indonesia, for the development of potential new anti-aging agents. One such discovery is a product derived from natural ingredients with the ability to prevent premature aging. These new anti-aging plants have been used for centuries by the Anak Dalam tribe, for treating skin diseases and maintaining skin health through traditional remedies. Recent research on herbal formulations used by the SAD community in Indonesia for skin beauty treatments, reported by Research on Medicinal Plants and Herbs or RISTOJA, indicates that 64 plant species are used for skin care. Among these plants, Toona sinensis, Curcuma heyneana, Curcuma zedoaria, Curcuma longa, and Kaempferia rotunda are the most commonly used medicinal plants with anti-aging properties. T. sinensis is a tree, while the others are herbs. T. sinensis shows the highest potential for development as an anti-aging agent, with its extracts, active fractions, and bioactive quercetin isolates known to possess strong anti-aging activities both in vitro and in vivo. Furthermore, C. heyneana, C. longa, C. zedoaria, and K. rotunda also show potential for further research, and three of them have demonstrated good potential for in vivo anti-aging activities. Only K. rotunda demonstrates relatively weaker antioxidant activity compared to T. sinensis, C. heyneana, C. longa, and C. zedoaria. Nevertheless, K. rotunda can still be developed to search for potential opportunities as agents with other activities, while T. sinensis, C. heyneana, C. longa, and C. zedoaria in the findings could be an opportunity to explore the potential of new anti-aging agents. In conclusion, of the five medicinal plants traditionally used by the SAD in Jambi, Indonesia, C. longa has received the most extensive research and shows potential for the development of anti-aging solutions. C. zedoaria, C. heyneana and K. rotunda show good potential for in vivo anti-aging activity. T. sinensis is the least-studied medicinal plant. Nevertheless, it has potential for development, as it is widely used by the SAD community for both traditional medicine and skin care.
Energy and Exergy Analysis of Hydrogen-Based Fluidized Bed Direct Reduction towards Efficient Fossil-Free Ironmaking
Authors: Du Zhan , Liu Wanchao , Pan Feng , Zou Zheng
Published: September 2023

Hydrogen-based fluidized bed direct reduction (H-FBDR) is an important and promising route for fossil-free ironmaking. In this study, to achieve the optimal operation state of energy use and exergy efficiency, the influences of the metallization process and the ratios of H2 injected on the energy and exergy flows in the H-FBDR process are studied. The results show that the thermodynamically designed two-stage reduction process (first: Fe2O3→FeO; second: FeO→Fe) requires a smaller H2 quantity than other metallization processes. According to the mass, energy, and exergy balance analyses, variations in the H2 consumption, exergy destruction, and energy/exergy losses of the overall system, iron ore preheater (F1), fluidized bed reactor system (R), heat exchanger (E), and gas preheater (F2) with different ratios of H2 injected (η) are derived. The total H2 consumption, total exergy destruction, and energy/exergy losses rise with increasing η, and sharp increases are observed from η = 1.3 to η = 1.8. The exergy efficiencies (φ) can be ranked as φR > φE > φF1 ≈ φF2, and the exergy destruction in components F1 and F2 is mainly caused by the combustion reaction, whereas physical exergy destruction dominates for components R and E. The performances of components F1, E, and F2 degrade from η = 1.0 to η = 1.8, and significant degradation arises when η exceeds 1.3. Thus, considering the H2 consumption, thermodynamic efficiency, and energy/exergy losses, the ratio of H2 injected should be set below 1.3. Notably, although the energy loss in the H-FBDR system is 2 GJ/h at η = 1.3, the exergy loss is only 360 MJ/h, in which the recycled gases from component E occupy 320 MJ/h, whereas the total exergy destruction is 900 MJ/h. Therefore, improving the performance of operation units, particularly the components F1 and F2, is as important as recovering the heat loss from component E for optimizing the H-FBDR process.
Iron and Hydrogen Peroxidation-Induced Post-Treatment Improvement of Municipal Mesophilic Digestate in an Alkaline Environment and Its Impact on Biosolids Quality
Authors: Hyder Umme Sharmeen , AlSayed Ahmed , Elbeshbishy Elsayed , McPhee Joseph , Misir Reshmi
Published: September 2023

Challenges associated with mesophilic digestate (MD) involve volume, odor, and pathogens, which effective post-digestion treatments can address. The efficiency of MD post-treatment can be enhanced by conditioning with ferric chloride (FeCl3), hydrogen peroxide (H2O2), and polymer. This study aimed to observe the effect of combined chemical conditioning on volume reduction, phosphorus (P) release, odor, and pathogen reduction potential for MD. MD was conditioned with polymer only, polymer and FeCl3 at pH adjusted to 8.0 with lime (Ca(OH)2), and a blend of polymer, FeCl3, and hydrogen peroxide (H2O2) at pH 8.0. The results show that adding all three chemicals improved post-treatment efficiency at 2.1 kg/t DS FeCl3, 2.1 kg/t DS polymer, and 600 mg/L H2O2 at pH 8.0, compared with polymer or dual conditioning. At the combined dose, cake solid content, centrate P removal, and odor reduction capability improved compared with raw MD by 20%, 99%, and 66%, respectively. Combined chemical treatment reduced fecal coliform by 98% but does not fulfil class A requirements and showed 50% regrowth potential. The synergic effect of polymer, FeCl3, H2O2, and alkaline pH breakdown EPS, reduced water holding capacity and formed compacted flocs for better water removal and settling. This combination also precipitated P through FeCl3 while H2O2 oxidation curbs odor, enhancing further P removal from centrate.
The Challenge of Plastic Management for Waste Electrical and Electric Equipment Recycling in the Global South: A Case Comparison between Europe and Latin America
Authors: Chaine Cecilia , Hursthouse Andrew S. , McLellan Iain , Viza Evi , Miller Jan
Published: September 2023

Countries with emerging legislation on the waste electrical and electric equipment (WEEE), but limited infrastructure, may find in other, more robust, systems the tools to develop adaptable and socioeconomically viable management schemes. Additives found in the plastics in electronic goods, such as brominated flame retardants (BFRs), are components of a safety system, but introduce characteristics that result in their waste being hazardous. Established and emerging regulatory systems need to implement legislation that impacts the management of WEEE, to reduce risks to human health and the environment, while maximising opportunities for resource recovery from widely varying materials. To assess the context of developed and emerging regulatory systems, a baseline study was undertaken of WEEE plastics in Scotland and Uruguay. For the identification of BFRs in plastics, an internationally validated screening methodology using X-ray fluorescence was adopted at different processing operations. It was observed that, using a threshold of 830 mg/kg for Br as a BFR tracer, in Scotland, more than 70% of the plastics would be recyclable, while, in Uruguay, that fraction dropped to 50%. These results, and the wider literature discussion, highlight the impact that regulatory frameworks have on the quality and recyclability of recovered material. We identify future actions to be considered by policy-makers for a more sustainable regulatory approach.
Not Secular: Interrogating the Sacred-Secular Binary through Gospel-Pop Performance
Authors: Williams Matthew A.
Published: September 2023

Secularisation theory proposed that the modernisation of society would bring about a decline in religiosity across the West, leading to ‘entzauberung’ (disenchantment). Eventually, society would be devoid of belief in the transcendent. Some theorists have challenged this by suggesting (with some qualifying factors) that enchantment better describes the secular age we occupy. Charles Taylor suggests that we can perceive the enchantment of a secular age through the human relationship with art. In this article, I suggest that, when present in popular music, black gospel music (in particular) complicates notions of the sacred-secular binary. The sacred-secular distinction was not familiar to West Africans arriving in the New World during the transatlantic slave trade. Music had played a central role in the lives of pre-diaspora Africans, with no differentiation between sacred and secular musicking. Despite some of the historical opposition to secular music in many black-majority churches, gospel music owes its heritage to this West African worldview. In this article, I propose a four-quadrant model that troubles the accepted binaries of sacred and secular. I use the Kingdom Choir’s 2018 performance of ‘Stand by Me’ at the Royal Wedding of the Duke and Duchess of Sussex as a basis for discussing alternative ways of viewing holy-profane, sacred-secular dichotomies.
Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
Authors: Gao Wenning , Yue Fuzhan , Xia Zhenghuan , Liu Xin , Zhao Zhilong , Zhang Yao , Liu Zongqiang
Published: September 2023

The GNSS-S radar utilizes the signals of a global navigation satellite system (GNSS) to carry out target detection. Due to the very low power of GNSS signals, long-term accumulation is needed to improve the gain of the echo signals. However, when it is used for moving object detection, the random movement of the target will cause residual Doppler frequency after the echoes are correlated and compressed through the direct signal. The residual Doppler frequency will cause two problems: on the one hand, the signal coherence will deteriorate, affecting the coherent accumulation gain; on the other hand, the amplitude of the signal after compression will decrease due to the sensitivity of GNSS signals to Doppler frequency. Therefore, how to increase the signal amplitude and eliminate the phase fluctuation caused by the Doppler frequency shift in the GNSS echoes of moving targets is an important issue for GNSS-S radar to detect moving targets. This paper proposes a dual-frequency GNSS echo enhancement method that uses the dual-frequency signals transmitted by the GNSS satellites to enhance and regularize the target echo. First, the phase relationship model of the GNSS dual-frequency echo is constructed, and the phase difference is made to the compressed dual-frequency echo signal to obtain the differential phase without fluctuation; then, the amplitudes of the dual-frequency echo signals are added together; and finally, a new signal with enhanced amplitude and consistent phase is constructed by using the dual-frequency additive amplitude and differential phase, and the long-term coherent accumulation of the signal is carried out, which can improve the processing gain of the weak echo signal of the moving target. The simulation and field experiments show that this method makes full use of the energy of the GNSS dual-frequency signal and eliminates the phase fluctuation in the echo signal of the moving target so that the compressed signal energy remains consistent in the slow-time dimension. After long-term coherent accumulation, the echo SNR was greatly improved, which enabled the detection of two high-speed cars by GNSS-S radar in the experiment.
CD-MQANet: Enhancing Multi-Objective Semantic Segmentation of Remote Sensing Images through Channel Creation and Dual-Path Encoding
Authors: Zhang Jinglin , Li Yuxia , Zhang Bowei , He Lei , He Yuan , Deng Wantao , Si Yu , Tong Zhonggui , Gong Yushu , Liao Kunwei
Published: September 2023

As a crucial computer vision task, multi-objective semantic segmentation has attracted widespread attention and research in the field of remote sensing image analysis. This technology has important application value in fields such as land resource surveys, global change monitoring, urban planning, and environmental monitoring. However, multi-target semantic segmentation of remote sensing images faces challenges such as complex surface features, complex spectral features, and a wide spatial range, resulting in differences in spatial and spectral dimensions among target features. To fully exploit and utilize spectral feature information, focusing on the information contained in spatial and spectral dimensions of multi-spectral images, and integrating external information, this paper constructs the CD-MQANet network structure, where C represents the Channel Creator module and D represents the Dual-Path Encoder. The Channel Creator module (CCM) mainly includes two parts: a generator block and a spectral attention module. The generator block aims to generate spectral channels that can expand different ground target types, while the spectral attention module can enhance useful spectral information. Dual-Path Encoders include channel encoders and spatial encoders, intended to fully utilize spectrally enhanced images while maintaining the spatial information of the original feature map. The decoder of CD-MQANet is a multitasking decoder composed of four types of attention, enhancing decoding capabilities. The loss function used in the CD-MQANet consists of three parts, which are generated by the intermediate results of the CCM, the intermediate results of the decoder, and the final segmentation results and label calculation. We performed experiments on the Potsdam dataset and the Vaihingen dataset. Compared to the baseline MQANet model, the CD-MQANet network improved mean F1 and OA by 2.03% and 2.49%, respectively, on the Potsdam dataset, and improved mean F1 and OA by 1.42% and 1.25%, respectively, on the Vaihingen dataset. The effectiveness of CD-MQANet was also proven by comparative experiments with other studies. We also conducted a thermographic analysis of the attention mechanism used in CD-MQANet and analyzed the intermediate results generated by CCM and LAM. Both modules generated intermediate results that had a significant positive impact on segmentation.
Integrated Assessments of Land Degradation in the Three-Rivers Headwater Region of China from 2000 to 2020
Authors: Pan Yao , Yin Yunhe , Cao Wei
Published: September 2023

Since the 1970s, certain areas within the Three-Rivers Headwater Region (TRHR) of China have faced severe land degradation due to the combined effects of climate change and human activities, leading to restricted ecological service functions and hindering the achievement of sustainable development goals (SDGs). Land degradation in the TRHR has received widespread attention. However, the current research mainly focuses on single-dimensional degradation and lacks a comprehensive evaluation of patterns and structures, as well as above-ground and underground assessments. To address this gap, this study employed the SDG indicator 15.3.1 framework, comprehensively considering fragmentation and habitat quality index based on land cover changes, grassland degradation index, and soil water erosion index. These indexes represent the three land degradation pathways of landscape degradation, vegetation degradation, and soil erosion. This study assessed land degradation patterns in the TRHR from 2000 to 2020. Results show that approximately 44.67% of the TRHR experienced land degradation during this period, mainly in meadow-dominated regions. Additionally, 5.64% of the regions experienced the superimposition of two or more land degradation pathways, with the frequent coexistence of soil erosion and grassland degradation, accounting for 4.1% of the affected areas. Landscape degradation affected approximately 2.39% of the regions, characterized by increased grassland fragmentation or habitat quality degradation. In terms of grassland degradation, 22.26% of the regions showed medium degradation, while 7.21% and 5.63% experienced moderate and severe degradation, respectively. Moreover, approximately 13.36% of the region faced a worsening situation of soil erosion. Approximately 55.34% of the study area underwent land improvement, with significant enhancements mainly concentrated in the western and eastern regions. The regrowth of grassland in the western region and the enhancement and homogenization of grassland productivity in the eastern region played pivotal roles in promoting land improvement. This study provides critical insights into the land degradation pattern in the TRHR over the past 20 years, offering valuable references for formulating and implementing measures to protect and construct the ecological security barrier of the plateau.
Slope-Scale Remote Mapping of Rock Mass Fracturing by Modeling Cooling Trends Derived from Infrared Thermography
Authors: Franzosi Federico , Crippa Chiara , Derron Marc-Henri , Jaboyedoff Michel , Agliardi Federico
Published: September 2023

The reliable in situ quantification of rock mass fracturing and engineering quality is critical for slope stability, surface mining and rock engineering applications, yet it remains difficult due to the heterogeneous nature of fracture networks. We propose a method to quantify and map the slope-scale geomechanical quality of fractured rock masses using infrared thermography (IRT). We use the Mt. Gorsa quarry (Trentino, Italy) as a field laboratory to upscale a physics-based approach, which was developed in the laboratory, to in situ conditions, including the effects of fracture heterogeneity, environmental conditions and IRT limitations. We reconstructed the slope in 3D using UAV photogrammetry, characterized the rock mass quality in the field at selected outcrops in terms of the Geological Strength Index (GSI) and measured their cooling behavior through 18h time-lapse IRT surveys. With ad hoc field experiments, we developed a novel procedure to correct IRT data in outdoor environments with complex topography. This allowed for a spatially distributed quantification of the rock mass surface cooling behavior in terms of a Curve Shape Parameter (CSP). Using non-linear regression, we established a quantitative CSP-GSI relationship, which allowed for the CSP to be translated into GSI maps. Our results demonstrate the possibility of applying infrared thermography to the slope-scale mapping of rock mass fracturing based on a physics-based experimental methodology.
Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data
Authors: Ren Weiwei , Li Xin , Zheng Donghai , Zeng Ruijie , Su Jianbin , Mu Tinghua , Wang Yingzheng
Published: September 2023

Due to the scarcity of observational data and the intricate precipitation–runoff relationship, individually applying physically based hydrological models and machine learning (ML) techniques presents challenges in accurately predicting floods within data-scarce glacial river basins. To address this challenge, this study introduces an innovative hybrid model that synergistically harnesses the strengths of multi-source remote sensing data, a physically based hydrological model (i.e., Spatial Processes in Hydrology (SPHY)), and ML techniques. This novel approach employs MODIS snow cover data and remote sensing-derived glacier mass balance data to calibrate the SPHY model. The SPHY model primarily generates baseflow, rain runoff, snowmelt runoff, and glacier melt runoff. These outputs are then utilized as extra inputs for the ML models, which consist of Random Forest (RF), Gradient Boosting (GDBT), Long Short-Term Memory (LSTM), Deep Neural Network (DNN), Support Vector Machine (SVM) and Transformer (TF). These ML models reconstruct the intricate relationship between inputs and streamflow. The performance of these six hybrid models and SPHY model is comprehensively explored in the Manas River basin in Central Asia. The findings underscore that the SPHY-RF model performs better in simulating and predicting daily streamflow and flood events than the SPHY model and the other five hybrid models. Compared to the SPHY model, SPHY-RF significantly reduces RMSE (55.6%) and PBIAS (62.5%) for streamflow, as well as reduces RMSE (65.8%) and PBIAS (73.51%) for floods. By utilizing bootstrap sampling, the 95% uncertainty interval for SPHY-RF is established, effectively covering 87.65% of flood events. Significantly, the SPHY-RF model substantially improves the simulation of streamflow and flood events that the SPHY model struggles to capture, indicating its potential to enhance the accuracy of flood prediction within data-scarce glacial river basins. This study offers a framework for robust flood simulation and forecasting within glacial river basins, offering opportunities to explore extreme hydrological events in a warming climate.
LESS LiDAR: A Full-Waveform and Discrete-Return Multispectral LiDAR Simulator Based on Ray Tracing Algorithm
Authors: Luo Yaotao , Xie Donghui , Qi Jianbo , Zhou Kun , Yan Guangjian , Mu Xihan
Published: September 2023

Light detection and ranging (LiDAR) is a widely used technology for the acquisition of three-dimensional (3D) information about a wide variety of physical objects and environments. However, before conducting a campaign, a test is typically conducted to assess the potential of the utilized algorithm for information retrieval. It might not be a real campaign but rather a simulation to save time and costs. Here, a multi-platform LiDAR simulation model considering the location, direction, and wavelength of each emitted laser pulse was developed based on the large-scale remote sensing (RS) data and image simulation framework (LESS) model, which is a 3D radiative transfer model for simulating passive optical remote sensing signals using the ray tracing algorithm. The LESS LiDAR simulator took footprint size, returned energy, multiple scattering, and multispectrum LiDAR into account. The waveform and point similarity were assessed with the LiDAR module of the discrete anisotropic radiative transfer (DART) model. Abstract and realistic scenes were designed to assess the simulated LiDAR waveforms and point clouds. A waveform comparison in the abstract scene with the DART LiDAR module showed that the relative error was lower than 1%. In the realistic scene, airborne and terrestrial laser scanning were simulated by LESS and DART LiDAR modules. Their coefficients of determination ranged from 0.9108 to 0.9984. Their mean was 0.9698. The number of discrete returns fitted well and the coefficient of determination was 0.9986. A terrestrial point cloud comparison in the realistic scene showed that the coefficient of determination between the two sets of data could reach 0.9849. The performance of the LESS LiDAR simulator was also compared with the DART LiDAR module and HELIOS++. The results showed that the LESS LiDAR simulator is over three times faster than the DART LiDAR module and HELIOS++ when simulating terrestrial point clouds in a realistic scene. The proposed LiDAR simulator offers two modes for simulating point clouds: single-ray and multi-ray modes. The findings demonstrate that utilizing a single-ray simulation approach can significantly reduce the simulation time, by over 28 times, without substantially affecting the overall point number or ground pointswhen compared to employing multiple rays for simulations. This new LESS model integrating a LiDAR simulator has great potential in terms of simultaneously simulating LiDAR data and optical images based on the same 3D scene and parameters. As a proof of concept, the normalized difference vegetation index (NDVI) results from multispectral images and the vertical profiles from multispectral LiDAR waveforms were simulated and analyzed. The results showed that the proposed LESS LiDAR simulator can fulfill its design goals.
Synthesis of Valid Camera Poses for the Inspection of Triangular Facets in a 3D Mesh
Authors: Parrott Edward , Pickard Joshua K. , Dubay Rickey
Published: September 2023

Automation of visual quality inspection tasks in manufacturing with machine vision is beginning to be the de facto standard for quality inspection as manufacturers realize that machines produce more reliable, consistent and repeatable analyses much quicker than a human operator ever could. These methods generally rely on the installation of cameras to inspect and capture images of parts; however, there is yet to be a method proposed for the deployment of cameras which can rigorously quantify and certify the performance of the system when inspecting a given part. Furthermore, current methods in the field yield unrealizable exact solutions, making them impractical or impossible to actually install in a factory setting. This work proposes a set-based method of synthesizing continuous pose intervals for the deployment of cameras that certifiably satisfy constraint-based performance criteria within the continuous interval.
Reconstruction of a Car–Running Pedestrian Accident Based on a Humanoid Robot Method
Authors: Wang Qian , Wei Bo , Wei Zheng , Gao Shang , Jin Xianlong , Yang Peizhong
Published: September 2023

Due to the characteristics of multibody (MB) and finite element (FE) digital human body models (HBMs), the reconstruction of running pedestrians (RPs) remains a major challenge in traffic accidents (TAs) and new innovative methods are needed. This study presents a novel approach for reconstructing moving pedestrian TAs based on a humanoid robot method to improve the accuracy of analyzing dynamic vehicle–pedestrian collision accidents. Firstly, we applied the theory of humanoid robots to the corresponding joints and centroids of the TNO HBM and implemented the pedestrian running process. Secondly, we used rigid–flexible coupling HBMs to build pedestrians, which can not only simulate running but also analyze human injuries. Then, we validated the feasibility of the RP reconstruction method by comparing the simulated dynamics with the pedestrian in the accident. Next, we extracted the velocity and posture of the pedestrian at the moment of collision and further validated the modeling method through a comparison of human injuries and forensic autopsy results. Finally, by comparing two other cases, we can conclude that there are relative errors in both the pedestrian injury results and the rest position. This comparative analysis is helpful for understanding the differences in injury characteristics between the running pedestrian and the other two cases in TAs.
Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter
Authors: Li Yaqin , Wang Dandan , Yuan Cao , Li Hao , Hu Jing
Published: September 2023

The Segment Anything Model (SAM) is a versatile image segmentation model that enables zero-shot segmentation of various objects in any image using prompts, including bounding boxes, points, texts, and more. However, studies have shown that the SAM performs poorly in agricultural tasks like crop disease segmentation and pest segmentation. To address this issue, the agricultural SAM adapter (ASA) is proposed, which incorporates agricultural domain expertise into the segmentation model through a simple but effective adapter technique. By leveraging the distinctive characteristics of agricultural image segmentation and suitable user prompts, the model enables zero-shot segmentation, providing a new approach for zero-sample image segmentation in the agricultural domain. Comprehensive experiments are conducted to assess the efficacy of the ASA compared to the default SAM. The results show that the proposed model achieves significant improvements on all 12 agricultural segmentation tasks. Notably, the average Dice score improved by 41.48% on two coffee-leaf-disease segmentation tasks.
Developments of Recent Applications for Early Diagnosis of Diseases Using Electronic-Nose and Other VOC-Detection Devices
Authors: Wilson Alphus Dan
Published: September 2023

This Editorial provides summaries and an overview of research and review articles published in the Sensors journal, volumes 21 (2021), 22 (2022), and 23 (2023), within the biomedical Special Issue “Portable Electronic-Nose Devices for Noninvasive Early Disease Detection”, which focused on recent sensors, biosensors, and clinical instruments developed for noninvasive early detection and diagnosis of human and animal diseases. The ten articles published in this Special Issue provide new information associated with recent electronic-nose (e-nose) and related volatile organic compound (VOC)-detection technologies developed to improve the effectiveness and efficiency of diagnostic methodologies for early disease detection prior to symptom development. For review purposes, the summarized articles were placed into three broad groupings or topic areas, including veterinary-wildlife pathology, human clinical pathology, and the detection of dietary effects on VOC emissions. These specified categories were used to define sectional headings devoted to related research studies with a commonality based on a particular disease being investigated or type of analytical instrument used in analyses.
Generic and Model-Based Calibration Method for Spatial Frequency Domain Imaging with Parameterized Frequency and Intensity Correction
Authors: Lohner Stefan A. , Nothelfer Steffen , Kienle Alwin
Published: September 2023

Spatial frequency domain imaging (SFDI) is well established in biology and medicine for non-contact, wide-field imaging of optical properties and 3D topography. Especially for turbid media with displaced, tilted or irregularly shaped surfaces, the reliable quantitative measurement of diffuse reflectance requires efficient calibration and correction methods. In this work, we present the implementation of a generic and hardware independent calibration routine for SFDI setups based on the so-called pinhole camera model for both projection and detection. Using a two-step geometric and intensity calibration, we obtain an imaging model that efficiently and accurately determines 3D topography and diffuse reflectance for subsequently measured samples, taking into account their relative distance and orientation to the camera and projector, as well as the distortions of the optical system. Derived correction procedures for position- and orientation-dependent changes in spatial frequency and intensity allow the determination of the effective scattering coefficient μs′ and the absorption coefficient μa when measuring a spherical optical phantom at three different measurement positions and at nine wavelengths with an average error of 5% and 12%, respectively. Model-based calibration allows the characterization of the imaging properties of the entire SFDI system without prior knowledge, enabling the future development of a digital twin for synthetic data generation or more robust evaluation methods.
Environment-Aware Rate Adaptation Based on Occasional Request and Robust Adjustment in 802.11 Networks
Authors: Yu Weijie , Wang Li , Song Jin , He Lijun , Wang Yanting
Published: September 2023

The IEEE 802.11 standard provides multi-rate support for different versions. As there is no specification on the dynamic strategy to adjust the rate, different rate adaptation algorithms are applied according to different manufacturers. Therefore, it is often hard to interpret the performance discrepancy of various devices. Moreover, the ever-changing channels always challenge the rate adaptation, especially in the scenario with scarce spectrum and low SNR. As a result, it is important to sense the radio environment cognitively and reduce the unnecessary oscillation of the transmission rate. In this paper, we propose an environment-aware robust (EAR) algorithm. This algorithm employs an occasional small packet, designs a rate scheme adaptive to the environment, and enhances the robustness. We verify the throughput of EAR using network simulator NS-3 in terms of station number, motion speed and node distance. We also compare the proposed algorithm with three benchmark methods: AARF, RBAR and CHARM. Simulation results demonstrate that EAR outperforms other algorithms in several wireless environments, greatly improving the system robustness and throughput.
A Non-Intrusive Monitoring System on Train Pantographs for the Maintenance of Overhead Contact Lines
Authors: Rodríguez-Arana Borja , Ciáurriz Pablo , Gil-Negrete Nere , Alvarado Unai
Published: September 2023

The condition monitoring of an overhead contact line (OCL) is investigated by developing an innovative monitoring system for a pantograph on an electrical multiple unit of a regional line. Kinematic and dynamic modelling of the pantograph is conducted to support the designed monitoring system. The modelling is proved through rigorous test-rig experiments, while the proposed methodology is then validated through extensive field tests. The field tests serve a dual purpose: First, to validate the monitoring system using benchmark measurements of the tCat® trolley, and second, to assess the reproducibility of measurements in a realistic case. This paper presents the OCL monitoring system developed in the framework of the H2020 project SIA. The accuracy of our results is not far from that of other commercial systems, with just 12 mm of absolute error in the height measurement. Therefore, they provide reliable information about trends in various key performance indicators (KPIs) that facilitates the early detection of failures and the diagnosis of anomalies. The results highlight the importance of model calibration and validation in enabling novel health monitoring capabilities for the pantograph. By continuously monitoring the parameters and tracking their degradation trends, our approach allows for optimized scheduling of maintenance tasks for the OCL.
Investigating Imperfect Cloning for Extending Quantum Communication Capabilities
Authors: Iqbal Masab , Velasco Luis , Costa Nelson , Napoli Antonio , Pedro Joao , Ruiz Marc
Published: September 2023

Quantum computing allows the implementation of powerful algorithms with enormous computing capabilities and promises a secure quantum Internet. Despite the advantages brought by quantum communication, certain communication paradigms are impossible or cannot be completely implemented due to the no-cloning theorem. Qubit retransmission for reliable communications and point-to-multipoint quantum communication (QP2MP) are among them. In this paper, we investigate whether a Universal Quantum Copying Machine (UQCM) generating imperfect copies of qubits can help. Specifically, we propose the Quantum Automatic Repeat Request (QARQ) protocol, which is based on its classical variant, as well as to perform QP2MP communication using imperfect clones. Note that the availability of these protocols might foster the development of new distributed quantum computing applications. As current quantum devices are noisy and they decohere qubits, we analyze these two protocols under the presence of various sources of noise. Three major quantum technologies are studied for these protocols: direct transmission (DT), teleportation (TP), and telecloning (TC). The Nitrogen-Vacancy (NV) center platform is used to create simulation models. Results show that TC outperforms TP and DT in terms of fidelity in both QARQ and QP2MP, although it is the most complex one in terms of quantum cost. A numerical study shows that the QARQ protocol significantly improves qubit recovery and that creating more clones does not always improve qubit recovery.
A Graph-Based Hybrid Reconfiguration Deformation Planning for Modular Robots
Authors: Wei Ruopeng , Liu Yubin , Dong Huijuan , Zhu Yanhe , Zhao Jie
Published: September 2023

The self-reconfigurable modular robotic system is a class of robots that can alter its configuration by rearranging the connectivity of their component modular units. The reconfiguration deformation planning problem is to find a sequence of reconfiguration actions to transform one reconfiguration into another. In this paper, a hybrid reconfiguration deformation planning algorithm for modular robots is presented to enable reconfiguration between initial and goal configurations. A hybrid algorithm is developed to decompose the configuration into subconfigurations with maximum commonality and implement distributed dynamic mapping of free vertices. The module mapping relationship between the initial and target configurations is then utilized to generate reconfiguration actions. Simulation and experiment results verify the effectiveness of the proposed algorithm.
Assessment System for Child Head Injury from Falls Based on Neural Network Learning
Authors: Yang Ziqian , Tsui Baiyu , Wu Zhihui
Published: September 2023

Toddlers face serious health hazards if they fall from relatively high places at home during everyday activities and are not swiftly rescued. Still, few effective, precise, and exhaustive solutions exist for such a task. This research aims to create a real-time assessment system for head injury from falls. Two phases are involved in processing the framework: In phase I, the data of joints is obtained by processing surveillance video with Open Pose. The long short-term memory (LSTM) network and 3D transform model are then used to integrate key spots’ frame space and time information. In phase II, the head acceleration is derived and inserted into the HIC value calculation, and a classification model is developed to assess the injury. We collected 200 RGB-captured daily films of 13- to 30-month-old toddlers playing near furniture edges, guardrails, and upside-down falls. Five hundred video clips extracted from these are divided in an 8:2 ratio into a training and validation set. We prepared an additional collection of 300 video clips (test set) of toddlers’ daily falling at home from their parents to evaluate the framework’s performance. The experimental findings revealed a classification accuracy of 96.67%. The feasibility of a real-time AI technique for assessing head injuries in falls through monitoring was proven.
Automated Video-Based Capture of Crustacean Fisheries Data Using Low-Power Hardware
Authors: Gregory Dal Toé Sebastian , Neal Marie , Hold Natalie , Heney Charlotte , Turner Rebecca , McCoy Emer , Iftikhar Muhammad , Tiddeman Bernard
Published: September 2023

This work investigates the application of Computer Vision to the problem of the automated counting and measuring of crabs and lobsters onboard fishing boats. The aim is to provide catch count and measurement data for these key commercial crustacean species. This can provide vital input data for stock assessment models, to enable the sustainable management of these species. The hardware system is required to be low-cost, have low-power usage, be waterproof, available (given current chip shortages), and able to avoid over-heating. The selected hardware is based on a Raspberry Pi 3A+ contained in a custom waterproof housing. This hardware places challenging limitations on the options for processing the incoming video, with many popular deep learning frameworks (even light-weight versions) unable to load or run given the limited computational resources. The problem can be broken into several steps: (1) Identifying the portions of the video that contain each individual animal; (2) Selecting a set of representative frames for each animal, e.g, lobsters must be viewed from the top and underside; (3) Detecting the animal within the frame so that the image can be cropped to the region of interest; (4) Detecting keypoints on each animal; and (5) Inferring measurements from the keypoint data. In this work, we develop a pipeline that addresses these steps, including a key novel solution to frame selection in video streams that uses classification, temporal segmentation, smoothing techniques and frame quality estimation. The developed pipeline is able to operate on the target low-power hardware and the experiments show that, given sufficient training data, reasonable performance is achieved.
Cropland Mapping Using Sentinel-1 Data in the Southern Part of the Russian Far East
Authors: Dubrovin Konstantin , Stepanov Alexey , Verkhoturov Andrey
Published: September 2023

Crop identification is one of the most important tasks in digital farming. The use of remote sensing data makes it possible to clarify the boundaries of fields and identify fallow land. This study considered the possibility of using the seasonal variation in the Dual-polarization Radar Vegetation Index (DpRVI), which was calculated based on data acquired by the Sentinel-1B satellite between May and October 2021, as the main characteristic. Radar images of the Khabarovskiy District of the Khabarovsk Territory, as well as those of the Arkharinskiy, Ivanovskiy, and Oktyabrskiy districts in the Amur Region (Russian Far East), were obtained and processed. The identifiable classes were soybean and oat crops, as well as fallow land. Classification was carried out using the Support Vector Machines, Quadratic Discriminant Analysis (QDA), and Random Forest (RF) algorithms. The training (848 ha) and test (364 ha) samples were located in Khabarovskiy District. The best overall accuracy on the test set (82.0%) was achieved using RF. Classification accuracy at the field level was 79%. When using the QDA classifier on cropland in the Amur Region (2324 ha), the overall classification accuracy was 83.1% (F1 was 0.86 for soybean, 0.84 for fallow, and 0.79 for oat). Application of the Radar Vegetation Index (RVI) and VV/VH ratio enabled an overall classification accuracy in the Amur region of 74.9% and 74.6%, respectively. Thus, using DpRVI allowed us to achieve greater performance compared to other SAR data, and it can be used to identify crops in the south of the Far East and serve as the basis for the automatic classification of cropland.
Novel Bayesian Inference-Based Approach for the Uncertainty Characterization of Zhang’s Camera Calibration Method
Authors: Gutiérrez-Moizant Ramón , Boada María Jesús L. , Ramírez-Berasategui María , Al-Kaff Abdulla
Published: September 2023

Camera calibration is necessary for many machine vision applications. The calibration methods are based on linear or non-linear optimization techniques that aim to find the best estimate of the camera parameters. One of the most commonly used methods in computer vision for the calibration of intrinsic camera parameters and lens distortion (interior orientation) is Zhang’s method. Additionally, the uncertainty of the camera parameters is normally estimated by assuming that their variability can be explained by the images of the different poses of a checkerboard. However, the degree of reliability for both the best parameter values and their associated uncertainties has not yet been verified. Inaccurate estimates of intrinsic and extrinsic parameters during camera calibration may introduce additional biases in post-processing. This is why we propose a novel Bayesian inference-based approach that has allowed us to evaluate the degree of certainty of Zhang’s camera calibration procedure. For this purpose, the a prioriprobability was assumed to be the one estimated by Zhang, and the intrinsic parameters were recalibrated by Bayesian inversion. The uncertainty of the intrinsic parameters was found to differ from the ones estimated with Zhang’s method. However, the major source of inaccuracy is caused by the procedure for calculating the extrinsic parameters. The procedure used in the novel Bayesian inference-based approach significantly improves the reliability of the predictions of the image points, as it optimizes the extrinsic parameters.
Wearable Device for Cumulative Chlorobenzene Detection and Accessible Mitigation Strategies
Authors: Mago Aryan , Yang Yeon-Suk , Shim Jae-Hyuck , John Aijaz Ahmad
Published: September 2023

Chronic exposure to low concentrations of volatile organic compounds (VOCs), such as chlorobenzene, is not being monitored in industrializing countries, although VOC exposure is associated with carcinogenic, organ-toxic, and endocrine-disrupting effects. Current VOC-sensing technologies are inaccessible due to high cost, size, and maintenance or are ineffective due to poor sensitivity or reliability. In particular, marginalized individuals face barriers to traditional prescription VOC treatments due to cost, lack of transportation, and limited access to physicians; thus, alternative treatments are needed. Here, we created a novel cumulative wearable color-changing VOC sensor with a paper-based polydiacetylene sensor array for chlorobenzene. With a single smartphone picture, the sensor displays 14 days of logged chlorobenzene exposure data, interpreted by machine-learning (ML) techniques, including principal component analysis. Further, we explored the efficacy of affordable and accessible treatment options to mitigate a VOC’s toxic effects. Vitamin D and sulforaphane are naturally found in cruciferous vegetables, like broccoli, and can be used to treat chlorobenzene-mediated bone degradation. Our platform combines these components into a smartphone app that photographs the sensor’s colorimetric data, analyzes the data via ML techniques, and offers accessible treatments based on exposure data.