bluuebunny/crossref_metadata_2025 huggingface dataset:
Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole
Authors: Yin Bing , Xie Yun , Liu Bing , Liu Baoyuan
Published: November 2023

Low-density weather station and high topographic variance limited rainfall erosivity (RE) calculation for Tibetan Plateau (TP). The accuracy of RE prediction from three data sources (a High-resolution Precipitation dataset for the Third Pole (TPHiPr), IMERG Final Run (IMERG-F) and weather station daily precipitation data) were evaluated for the TP, and the variations were analyzed from 2001 to 2020. The results showed that TPHiPr can more accurately characterize spatial and temporal variations of the RE on the TP. TPHiPr can better represent the impact of topography on precipitation, effectively compensating the deficiencies in precipitation data from low-density stations. The R2 and NSE between the mean annual/monthly RE of TPHiPr and the station data were around 0.9. TPHiPr effectively revealed rain shadow areas on the northern slopes of the Himalayas and calculated RE more accurately in the broad-leaved evergreen forest zone on the southern flank of the Himalayas and the arid regions to the northwest. RE from 2001 to 2020 showed an overall increasing trend. However, TPHiPr produced underestimates in the southern valleys and the eastern Hengduan Mountains, while overestimates in the southeastern area at lower elevations. This research provided a new and more accurate RE data for the TP.
Using Multisource High-Resolution Remote Sensing Data (2 m) with a Habitat–Tide–Semantic Segmentation Approach for Mangrove Mapping
Authors: Sun Ziyu , Jiang Weiguo , Ling Ziyan , Zhong Shiquan , Zhang Ze , Song Jie , Xiao Zhijie
Published: November 2023

Mangrove wetlands are hotspots of global biodiversity and blue carbon reserves in coastal wetlands, with unique ecological functions and significant socioeconomic value. Annual fine-scale monitoring of mangroves is crucial for evaluating national conservation programs and implementing sustainable mangrove management strategies. However, annual fine-scale mapping of mangroves over large areas using remote sensing remains a challenge due to spectral similarities with coastal vegetation, tidal periodic fluctuations, and the need for consistent and dependable samples across different years. In previous research, there has been a lack of strategies that simultaneously consider spatial, temporal, and methodological aspects of mangrove extraction. Therefore, based on an approach that considers mangrove habitat, tides, and a semantic segmentation approach, we propose a method for fine-scale mangrove mapping suitable for long time-series data. This is an optimized hybrid model that integrates spatial, temporal, and methodological considerations. The model uses five sensors (GF-1, GF-2, GF-6, ZY-301, ZY-302) to combine deep learning U-Net models with mangrove habitat information and algorithms during low-tide periods. This method produces a mangrove map with a spatial resolution of 2 m. We applied this algorithm to three typical mangrove regions in the Beibu Gulf of Guangxi Province. The results showed the following: (1) The model scored above 0.9 in terms of its F1-score in all three study areas at the time of training, with an average accuracy of 92.54% for mangrove extraction. (2) The average overall accuracy (OA) for the extraction of mangrove distribution in three typical areas in the Beibu Gulf was 93.29%. When comparing the validation of different regions and years, the overall OA accuracy exceeded 89.84% and the Kappa coefficient exceeded 0.74. (3) The model results are reliable for extracting sparse and slow-growing young mangroves and narrow mangrove belts along roadsides. In some areas where tidal flooding occurs, the existing dataset underestimates mangrove extraction to a certain extent. The fine-scale mangrove extraction method provides a foundation for the implementation of fine-scale management of mangrove ecosystems, support for species diversity conservation, blue carbon recovery, and sustainable development goals related to coastal development.
Landsat-8 to Sentinel-2 Satellite Imagery Super-Resolution-Based Multiscale Dilated Transformer Generative Adversarial Networks
Authors: Wang Chunyang , Zhang Xian , Yang Wei , Wang Gaige , Zhao Zongze , Liu Xuan , Lu Bibo
Published: November 2023

Image super-resolution (SR) techniques can improve the spatial resolution of remote sensing images to provide more feature details and information, which is important for a wide range of remote sensing applications, including land use/cover classification (LUCC). Convolutional neural networks (CNNs) have achieved impressive results in the field of image SR, but the inherent localization of convolution limits the performance of CNN-based SR models. Therefore, we propose a new method, namely, the dilated Transformer generative adversarial network (DTGAN) for the SR of multispectral remote sensing images. DTGAN combines the local focus of CNNs with the global perspective of Transformers to better capture both local and global features in remote sensing images. We introduce dilated convolutions into the self-attention computation of Transformers to control the network’s focus on different scales of image features. This enhancement improves the network’s ability to reconstruct details at various scales in the images. SR imagery provides richer surface information and reduces ambiguity for the LUCC task, thereby enhancing the accuracy of LUCC. Our work comprises two main stages: remote sensing image SR and LUCC. In the SR stage, we conducted comprehensive experiments on Landsat-8 (L8) and Sentinel-2 (S2) remote sensing datasets. The results indicate that DTGAN generates super-resolution (SR) images with minimal computation. Additionally, it outperforms other methods in terms of the spectral angle mapper (SAM) and learned perceptual image patch similarity (LPIPS) metrics, as well as visual quality. In the LUCC stage, DTGAN was used to generate SR images of areas outside the training samples, and then the SR imagery was used in the LUCC task. The results indicated a significant improvement in the accuracy of LUCC based on SR imagery compared to low-resolution (LR) LUCC maps. Specifically, there were enhancements of 0.130 in precision, 0.178 in recall, and 0.157 in the F1-score.
Bridging the Gap: Comprehensive Boreal Forest Complexity Mapping through LVIS Full-Waveform LiDAR, Single-Year and Time Series Landsat Imagery
Authors: Diaz-Kloch Nicolas , Murray Dennis L.
Published: November 2023

The extrapolation of forest structural attributes from LiDAR has traditionally been restricted to local or regional scales, hindering a thorough assessment of single-year versus time series predictors across expansive spatial scales. We extrapolated the vertical complexity captured by the Land, Vegetation, and Ice Sensor (LVIS) full-wave form LiDAR of boreal forests in the Alaska–Yukon–Northwest Territories region, utilizing predictors from Landsat images from 1989 to 2019. This included both single-year and long-term estimates of vegetation indices, alongside constant factors like terrain slope and location. Random forest regression models comparing the single-year and 15-year and 30-year time series models were applied. Additionally, the potential of estimating horizontal forest complexity from vertical complexity was explored using a moving window approach in the Kluane Valley. While the extended time series marginally enhanced model accuracy, a fine-tuned single-year model proved superior (R2 = 0.84, relative RRMSE = 8.4%). In estimating the horizontal complexity, the variance in a 5 × 5 moving window displayed the most promising results, aligning with traditional horizontal structure measures. Single-year Landsat models could potentially surpass time series models in predicting forest vertical complexity, with the added capability to estimate horizontal complexity using variance in a moving window approach.
Altered Trends in Light Use Efficiency of Grassland Ecosystem in Northern China
Authors: Yuan Liuhuan , Zhang Tianyou , Yao Hongbin , Zheng Cheng , Wen Zhongming
Published: November 2023

Light use efficiency (LUE) is a crucial indicator used to reflect the ability of terrestrial ecosystems to transform light energy. Understanding the long-term trends in LUE and its influencing factors are essential for determining the future carbon sink and carbon sequestration potential of terrestrial ecosystems. However, the long-term interannual variability of LUE in grasslands in northern China at the ecosystem scale is poorly understood due to the limitations of the year length and the coverage of the site data. In this study, we assessed the long-term LUE trends in the grasslands of northern China from 1982 to 2018 and then revealed the relationships between interannual variability in LUE and climate factors. Our study showed a substantial rising trend for LUE from 1982 to 2018 in the grasslands of northern China (3.42 × 10−3 g C/MJ/yr). Regarding the different grassland types, alpine meadow had the highest growth rate (4.85 × 10−3 g C/MJ/yr), while temperate steppe had the lowest growth rate (1.58 × 10−3 g C/MJ/yr). The climate factors driving LUE dynamics were spatially heterogeneous in grasslands. Increasing precipitation accelerated the interannual growth rate of LUE in temperate steppe, and increasing temperature accelerated the interannual growth rate of LUE in other types. In addition, the temporal dynamic of LUE showed different trends in relation to time scales, and the growth trend slowed down after 1998. Our results should be considered in developing future grassland management measures and predicting carbon cycle–climate interactions.
An Efficient Pose Estimation Algorithm for Non-Cooperative Space Objects Based on Dual-Channel Transformer
Authors: Ye Ruida , Ren Yuan , Zhu Xiangyang , Wang Yujing , Liu Mingyue , Wang Lifen
Published: November 2023

Non-cooperative space object pose estimation is a key technique for spatial on-orbit servicing, where pose estimation algorithms based on low-quality, low-power monocular sensors provide a practical solution for spaceborne applications. The current pose estimation methods for non-cooperative space objects using monocular vision generally consist of three stages: object detection, landmark regression, and perspective-n-point (PnP) solver. However, there are drawbacks, such as low detection efficiency and the need for prior knowledge. To solve the above problems, an end-to-end non-cooperative space object pose estimation learning algorithm based on dual-channel transformer is proposed, a feature extraction backbone network based on EfficientNet is established, and two pose estimation subnetworks based on transformer are also established. A quaternion SoftMax-like activation function is designed to improve the precision of orientation error estimating. The method only uses RGB images, eliminating the need for a CAD model of the satellite, and simplifying the detection process by using an end-to-end network to directly detect satellite pose information. Experiments are carried out on the SPEED dataset provided by the European Space Agency (ESA). The results show that the proposed algorithm can successfully predict the satellite pose information and effectively decouple the spatial translation information and orientation information, which significantly improves the recognition efficiency compared with other methods.
The Wave Period Parameterization of Ocean Waves and Its Application to Ocean Wave Simulations
Authors: Lv Jialei , Zhang Wenjing , Shi Jian , Wu Jie , Wang Hanshi , Cao Xuhui , Wang Qianhui , Zhao Zeqi
Published: November 2023

The wave period is a wave parameter that is significantly influenced by factors such as wind speed and bottom topography. Previous research on wave period parameterization has primarily focused on wind-dominated sea areas and may not be applicable to certain regions, such as the equatorial calm or coastal areas dominated by swell waves. To address this limitation, this paper utilizes the third-generation wave numerical model SWAN to perform wave numerical simulations for a portion of the Northwest Pacific Ocean. The simulation incorporates observational data from nearshore stations, buoys, and satellite altimeters for error analysis. To develop a new wave parameterization scheme (WS-23), we employ extensive NDBC buoy data and incorporate the exponential rate and wave age characteristics that were previously established by predecessors. Our scheme introduces a judgement mechanism to distinguish between wind waves, swell waves, and mixed waves. The resulting ocean wave factor enhances the mean wave period values calculated using the model and other parameterization schemes. The experimental results demonstrate that our new parameterization scheme effectively improves the abnormal peak of the fitting data. Comparing the output values of the mean wave period element output of the SWAN model with our new parameterization scheme, we observe a reduction in the mean values of Ea, Ec, and RMSE by 0.231, 1.94%, and 0.162, respectively, while increasing the average r by 0.05.
A Novel, Efficient Algorithm for Subsurface Radar Imaging below a Non-Planar Surface
Authors: Ullmann Ingrid , Vossiek Martin
Published: November 2023

In classical radar imaging, such as in Earth remote sensing, electromagnetic waves are usually assumed to propagate in free space. However, in numerous applications, such as ground penetrating radar or non-destructive testing, this assumption no longer holds. When there is a multi-material background, the subsurface image reconstruction becomes considerably more complex. Imaging can be performed in the spatial domain or, equivalently, in the wavenumber domain (k-space). In subsurface imaging, to date, objects with a non-planar surface are commonly reconstructed in the spatial domain, by the Backprojection algorithm combined with ray tracing, which is computationally demanding. On the other hand, objects with a planar surface can be reconstructed more efficiently in k-space. However, many non-planar surfaces are partly planar. Therefore, in this paper, a novel concept is introduced that makes use of the efficient k-space-based reconstruction algorithms for partly planar scenarios, too. The proposed algorithm forms an image from superposing sub-images where as many image parts as possible are reconstructed in the wavenumber domain, and only as many as necessary are reconstructed in the spatial domain. For this, a segmentation scheme is developed to determine which parts of the image volume can be reconstructed in the wavenumber domain. The novel concept is verified by measurements, both from monostatic synthetic aperture radar data and multiple-input–multiple-output radar data. It is shown that the computational efficiency for imaging irregularly shaped geometries can be significantly augmented when applying the proposed concept.
Method Comparison for Simulating Non-Gaussian Beams and Diffraction for Precision Interferometry
Authors: Zhao Mengyuan , Tao Yazheng , Weber Kevin , Kaune Tim , Schuster Sönke , Hao Zhenxiang , Wanner Gudrun
Published: November 2023

In the context of simulating precision laser interferometers, we use several examples to compare two wavefront decomposition methods—the Mode Expansion Method (MEM) and the Gaussian Beam Decomposition (GBD) method—for their precision and applicability. To assess the performance of these methods, we define different types of errors and study their properties. We specify how the two methods can be fairly compared and based on that, compare the quality of the MEM and GBD through several examples. Here, we test cases for which analytic results are available, i.e., non-clipped circular and general astigmatic Gaussian beams, as well as clipped circular Gaussian beams, in the near, far, and extremely far fields of millions of kilometers occurring in space-gravitational wave detectors. Additionally, we compare the methods for aberrated wavefronts and their interaction with optical components by testing reflections from differently curved mirrors. We find that both methods can generally be used for decomposing non-Gaussian beams. However, which method is more accurate depends on the optical system and simulation settings. In the given examples, the MEM more accurately describes non-clipped Gaussian beams, whereas for clipped Gaussian beams and the interaction with surfaces, the GBD is more precise.
A Real-Time Vessel Detection and Tracking System Based on LiDAR
Authors: Qi Liangjian , Huang Lei , Zhang Yi , Chen Yue , Wang Jianhua , Zhang Xiaoqian
Published: November 2023

Vessel detection and tracking is of utmost importance to river traffic. Efficient detection and tracking technology offer an effective solution to address challenges related to river traffic safety and congestion. Traditional image-based object detection and tracking algorithms encounter issues such as target ID switching, difficulties in feature extraction, reduced robustness due to occlusion, target overlap, and changes in brightness and contrast. To detect and track vessels more accurately, a vessel detection and tracking algorithm based on the LiDAR point cloud was proposed. For vessel detection, statistical filtering algorithms were integrated into the Euclidean clustering algorithm to mitigate the effect of ripples on vessel detection. Our detection accuracy of vessels improved by 3.3% to 8.3% compared to three conventional algorithms. For vessel tracking, L-shape fitting of detected vessels can improve the efficiency of tracking, and a simple and efficient tracking algorithm is presented. By comparing three traditional tracking algorithms, an improvement in multiple object tracking accuracy (MOTA) and a reduction in ID switch times and number of missed detections were achieved. The results demonstrate that LiDAR point cloud-based vessel detection can significantly enhance the accuracy of vessel detection and tracking.
A Homogeneous Colorimetric Strategy Based on Rose-like CuS@Prussian Blue/Pt for Detection of Dopamine
Authors: Yang Di , Ran Jiao , Yi Huafei , Feng Pujin , Liu Bingqian
Published: November 2023

The development of effective methods for dopamine detection is critical. In this study, a homogeneous colorimetric strategy for the detection of dopamine based on a copper sulfide and Prussian blue/platinum (CuS@PB/Pt) composite was developed. A rose-like CuS@PB/Pt composite was synthesized for the first time, and it was discovered that when hydrogen peroxide was present, the 3,3′,5,5′-tetramethylbenzidine (TMB) changed from colorless into blue-oxidized TMB. The CuS@PB/Pt composite was characterized with a scanning electron microscope (SEM), an energy dispersive spectrometer (EDS), and an X-ray photoelectron spectrometer (XPS). Moreover, the catalytic activity of the CuS@PB/Pt composite was inhibited by the binding of dopamine to the composite. The color change of TMB can be evaluated by the UV spectrum and a portable smartphone detection device. The developed colorimetric sensor can be used to quantitatively analyze dopamine between 1 and 60 µM with a detection limit of 0.28 μM. Furthermore, the sensor showed good long-term stability and good performance in human serum samples. Compared with other reported methods, this strategy can be performed rapidly (16 min) and has the advantage of smartphone visual detection. The portable smartphone detection device is portable and user-friendly, providing convenient colorimetric analysis for serum. This colorimetric strategy also has considerable potential for the development of in vitro diagnosis methods in combination with other test strips.
Effects of External Abdominal Pressure Support on Dynamic Balance: A Randomized Crossover Study
Authors: Nakai Yuki , Kijimuta Takara , Takeshita Yasufumi , Kiyama Ryoji , Araki Sota , Miyazaki Takasuke , Kawada Masayuki
Published: November 2023

Abdominal pressure is vital in protecting the lumbar spine and controlling postural balance. Dynamic balance is associated with movement stability, adaptation to load, and reduced injury risk. Although trunk stability has been examined using belts and braces, the effects of external abdominal pressure support (APS) on balance control remain unknown. In this study, we aimed to determine the effects of external APS on dynamic balance. Overall, 31 young adults participated in this randomized crossover study. External APS was provided using a device that could be pressurized and decompressed by inflating a cuff belt wrapped around the trunk. The modified Star Excursion Balance Test was performed under external APS and non-APS conditions. The maximum anterior, posterolateral, and posteromedial values normalized to the spinal malleolar distance and their respective composite values were compared between the two conditions with and without APS. Posterolateral, posteromedial, and composite values were significantly higher in the APS condition than in the non-APS condition (p < 0.001). The external APS was effective in immediately improving dynamic balance. Furthermore, APS was effective in dynamic balance control as it improved stability during anterior trunk tilt, which displaces the center of gravity forward.
Experimental Investigation for Shear Wave Velocity and Dynamic Characteristics of Unsaturated Sand–Clay Liners
Authors: Alnuaim Ahmed , Al-Mahbashi Ahmed M.
Published: November 2023

This study aims to investigate the shear wave velocity and dynamic characteristics of unsaturated sand–expansive clay liners (SECLs) over a wide range of suctions. Liner layers have gained significant interest as environmentally friendly materials for several geotechnical and geoenvironmental applications. These materials are typically found in an unsaturated state as compacted layers and are exposed to dynamic loads from natural phenomena or manmade activities. In such circumstances, sustainable and stable performance should be ensured during the operation and lifetime of these layers by addressing the dynamic characteristics of these materials and possible degradation. Several specimens belonging to different liners of sand and expansive clay were prepared at different suction levels. The shear wave velocity was determined using the bender element technique (BEls). The specimens were then subjected to extensive cycles of dynamic loads up to 500 cycles in the triaxial dynamic loading system. The shear wave velocity and dynamic characteristics of both liners, such as shear modulus (G), damping ratio (D), and degradation index (δ), were determined on the basis of soil suction and loading cycles. Results indicated a descending trend of shear wave velocity with an increase in suction up to 130 MPa, and a significant reduction in shear modulus was detected. Meanwhile, the damping ratio demonstrated a significant increase with the increase in the suction levels of both liners. The reported results are of great significance for sustainable design and modeling of the unsaturated behavior of liner layers in several applications of geotechnical and geoenvironmental problems.
Determining Allometry and Carbon Sequestration Potential of Breadfruit (Artocarpus altilis) as a Climate-Smart Staple in Hawai‘i
Authors: Livingston Chad , Lincoln Noa Kekuewa
Published: November 2023

Breadfruit (Artocarpus altilis) is an underutilized Pacific tree crop that has been highlighted as having substantial potential to contribute to global food security and climate-smart agriculture, including adaptation to and mitigation of climate change. To explore the carbon sequestration potential of breadfruit production, we characterize tree volume, wood density, carbon density, foliar biomass, and growth rates of breadfruit in Hawai‘i. Strong relationships to trunk or branch diameter were displayed for wood density (r2 0.81), carbon density (r2 0.87), and foliar biomass (r2 0.91), which were combined to generate an allometric prediction of tree volume (r2 0.98) based on tree diameter at breast height. Growth rates, as measured by diameter at breast height, were well predicted over time when trees were classified by habitat suitability. We extrapolate potential breadfruit growth and carbon sequestration in above-ground biomass to the landscape scale over time. This study shows that breadfruit is on the low end of broadleaf tropical trees in moist and wet environments, but in an orchard can be expected to sequester ~69.1 tons of carbon per hectare in its above-ground biomass over a 20-year period.
The Influence of Atmospheric Pressure and Organic Loading on the Sustainability of Simultaneous Nitrification and Denitrification
Authors: Yu Wei-Jia , Sun Ji , Zhang Wei-Jia , Chen Yue , Yang Jun-Ling , Li Shu-Ping , Zhu Guang-Can , Lu Yong-Ze
Published: November 2023

In high-altitude regions, a diminished atmospheric oxygen content significantly impairs the aeration efficiency of municipal wastewater, posing a challenge to sustainable wastewater management. Consequently, conventional biological wastewater treatment methods necessitate elevated energy consumption in high-altitude areas, rendering them economically and environmentally unsustainable. The simultaneous nitrification and denitrification (SND) process, owing to its minimal oxygen requirements, emerges as a promising and sustainable solution in low-pressure environments. Additionally, owing to the unique lifestyle and natural conditions in plateau regions, the organic loading in municipal wastewater is often low. To comprehensively assess the impact of low pressure and organic loading on the SND process, three laboratory-scale reactors were implemented. This study revealed that low pressure and the introduction of organic matter enhanced both nitrogen removal performance and SND efficiency. The sludge volume index decreased by 93.5%, indicating a substantial improvement in the microbial aggregation ability and the formation of a more favorable SND sludge structure. 16S rRNA sequencing results demonstrated alterations in the microbial community structure due to low pressure and the addition of organic matter, leading to a substantial increase in the abundance of nitrifying and denitrifying bacteria. Furthermore, the prediction results of functional genes indicated the upregulation of genes related to the nitrification and denitrification processes with decreasing pressure and the addition of organic matter. This enhancement underlines the improved microbial nitrogen removal function. This study underscores the positive influence of low pressure and organic loading on the SND system, thereby substantially enhancing the economic and environmental sustainability of the SND process in plateau regions.
Measuring the Performance of a Strategic Asset Management Plan through a Balanced Scorecard
Authors: de-Almeida-e-Pais José Edmundo , Raposo Hugo D. N. , Farinha José Torres , Cardoso Antonio J. Marques , Lyubchyk Svitlana , Lyubchyk Sergiy
Published: November 2023

The purpose of this paper is to propose a tool to measure the performance of a Strategic Asset Management Plan (SAMP) based on a Balanced Scorecard (BSC). The SAMP converts organizational objectives into asset management objectives, as well as specifies the role of the asset management system, providing support to achieve asset management objectives. The SAMP becomes the heart of the organization and integrates the long-term, medium-term, and short-term plans. In the SAMP, the balance among performance, costs, and risks are taken into consideration in order to achieve the organization’s objectives. On the other hand, the SAMP is a guide to set the asset management objectives while describing the role of the Asset Management System (AMS) in meeting these objectives. Since the SAMP is the central figure of AMS, it is important to measure its performance and should be built and improved through an iterative process. This indicates that it is not just a document, it is “the document” that should be treated as a “living being”, which needs to adapt to internal and external changes quickly. The BSC is an excellent tool where, through the appropriate Key Performance Indicators (KPIs), the progress can be measured, and is supported by four perspectives: Financial, Customer, Internal Business Process, and Learning and Growth.
Research on Sustainable Cooperation Strategies for Cross-Regional Supply Chain Enterprises in Uncertain Environments
Authors: Li Cui , Wu Doudou , Shao Tengfei
Published: November 2023

Considering the fuzziness of cooperation and the sustainability of the redevelopment of cross-regional supply chain enterprises, the fuzzy participation degree and the generalized redistribution coefficient are introduced to describe the problem of cooperation and benefit distribution of cross-regional supply chain enterprises. A fuzzy average monotone game is constructed to study the strategies of cross-regional supply chain enterprises to increase the average benefit with the expansion of the alliance scale; a generalized fuzzy reduced game is discussed to ensure the partner selection decision of supply chain enterprises; the concepts of generalized fuzzy core, generalized fuzzy bargaining set, and generalized fuzzy proportional distribution are proposed; the equivalence between the generalized fuzzy core and the generalized fuzzy bargaining set of the fuzzy average monotone game of the cross-regional supply chain enterprises is proved; the nonempty generalized fuzzy core solution of the fuzzy average monotone game is characterized; and the example analysis shows the stability of the cross-regional supply chain enterprise alliance and the existence of the optimal generalized redistribution scheme. The research results not only satisfy the willingness of cross-regional supply chain enterprises to participate in cooperation with some resources from the “environmental” pillar of supply chain enterprise management sustainability but also achieve the strategy of retaining partial benefits for the redevelopment of supply chain alliances from the “economic” pillar of supply chain enterprise management sustainability, which provides a theoretical basis for the cooperation and benefit redistribution of cross-regional supply chain enterprises under uncertain environments.
Recent Technological Progress of Fiber-Optical Sensors for Bio-Mechatronics Applications
Authors: Abdhul Rahuman Mohomad Aqeel , Kahatapitiya Nipun Shantha , Amarakoon Viraj Niroshan , Wijenayake Udaya , Silva Bhagya Nathali , Jeon Mansik , Kim Jeehyun , Ravichandran Naresh Kumar , Wijesinghe Ruchire Eranga
Published: November 2023

Bio-mechatronics is an interdisciplinary scientific field that emphasizes the integration of biology and mechatronics to discover innovative solutions for numerous biomedical applications. The broad application spectrum of bio-mechatronics consists of minimally invasive surgeries, rehabilitation, development of prosthetics, and soft wearables to find engineering solutions for the human body. Fiber-optic-based sensors have recently become an indispensable part of bio-mechatronics systems, which are essential for position detection and control, monitoring measurements, compliance control, and various feedback applications. As a result, significant advancements have been introduced for designing and developing fiber-optic-based sensors in the past decade. This review discusses recent technological advancements in fiber-optical sensors, which have been potentially adapted for numerous bio-mechatronic applications. It also encompasses fundamental principles, different types of fiber-optical sensors based on recent development strategies, and characterizations of fiber Bragg gratings, optical fiber force myography, polymer optical fibers, optical tactile sensors, and Fabry–Perot interferometric applications. Hence, robust knowledge can be obtained regarding the technological enhancements in fiber-optical sensors for bio-mechatronics-based interdisciplinary developments. Therefore, this review offers a comprehensive exploration of recent technological advances in fiber-optical sensors for bio-mechatronics. It provides insights into their potential to revolutionize biomedical and bio-mechatronics applications, ultimately contributing to improved patient outcomes and healthcare innovation.
Trace Level Analysis of Per- and Polyfluorinated Substances in Fish from Various Regions in Switzerland
Authors: Jaus Alexandra , Rhyn Peter , Valentini Judit
Published: November 2023

Per- and polyfluorinated substances (PFASs) are persistent man-made chemicals which can end up in the food chain. In this study, the concentrations of 15 PFASs in various wild fish species from different regions in Switzerland were determined excluding hot spots of contamination. After clean-up with SPE, the samples were analyzed by HPLC-MS/MS. PFASs were detected in all but 1 of the 83 fish samples (0.07 to 40.7 µg/kg fish muscle meat). The most abundant compound in fish from subalpine lakes was perfluorooctane sulfonic acid (PFOS), comprising more than 80% of the total contamination while perfluorononanoic (PFNA), -decanoic (PFDA) and -undecanoic (PFUnDA) acid dominated in high alpine fish. PFAS levels were more elevated in subalpine lakes (median PFASs 11.1–19.0 µg/kg) than in the high alpine Lake Sils (median PFASs 0.66–2.67 µg/kg) or streams and canals in Valais (median PFASs 0.56 µg/kg). Our results indicate that wild fish may be one of the PFAS sources in human diet.
Lessons Learned from the COVID-19 Vaccination Campaigns in Veneto Region: Population Vaccination Centers as Support for the Traditional Outpatient Model
Authors: Cinquetti Sandro , De Polo Anna , Marcotrigiano Vincenzo , Battistin Marica , Bino Erica , De Mattia Giulia , Fagherazzi Jacopo , Fiorito Nahuel , Manzi Mattia , Voltolini Anna , Mognato Martina , Napoli Christian
Published: November 2023

The extraordinary vaccination campaigns of the COVID-19 pandemic era put organizational and operational systems to the test in numerous territorial contexts. In the Veneto region, the activation of population vaccination centers (CVPs) guaranteed the provision of vaccines to mountain areas. These centers, drive-in and building-based, improved the efficiency of dose administration in relation to similar conditions where healthcare workers (HCWs) were routinely involved in clinics. Overall, a comparison of the two models investigated, with the same numbers of HCWs involved and the same opening hours for the vaccination sites, has shown that the CVPs are able to guarantee three times as many vaccines administered, compared with the traditional outpatient model. This study aims to provide a detailed analysis of the adopted organizational model, highlighting the best practices and improvements required to guarantee a timely and effective public health response, and evaluating the opportunities to deploy these innovative methods actively in a standard context.
Impact of Vaccination on the Course and Outcome of COVID-19 in Patients with Multimorbidity
Authors: Lomonosov Kirill , Lomonosova Alyona , Mindlina Alla , Polibin Roman , Antipov Maksim , Grimm Gleb
Published: November 2023

Vaccination is the most cost-effective method of preventing COVID-19; however, data on its effect on patients with multimorbidity is limited. The aim was to evaluate the effect of vaccination against new coronavirus infection (NCI) in patients with multimorbid pathology in hospital treatment on the outcome of COVID-19 disease. An analysis was carried out of 1832 records of patients in one of the COVID-19 hospitals in Moscow for 2020–2022. Statistical analysis was carried out using the StatTech v. 3.1.3 software, and the binary logistic regression (BLR) method was used to obtain prognostic models. The median age of patients was 69 years, and 76% of them had received two vaccine doses. To assess the outcome of the disease, two prognostic models were obtained depending on the presence of a multimorbidity in patients: cardiovascular pathology and/or atherosclerosis and/or type 2 diabetes mellitus (Model 1) or atherosclerosis and/or type 2 diabetes mellitus and/or encephalopathy (Model 2), against the background of the presence or absence of vaccination against NCI. When assessing the outcome of NCI in Model 1, the odds of death decreased by 3.228 times with two doses of Sputnik V in patients with multimorbidity. According to Model 2, for patients with multimorbidity, the chances of death decreased by 3.281 times with two doses of Sputnik V. The presence of two doses of Sputnik V increased the likelihood of recovery in patients with multimorbidity by more than three times.
COVID-19 Vaccine Knowledge, Attitude, Acceptance and Hesitancy among Pregnancy and Breastfeeding: Systematic Review of Hospital-Based Studies
Authors: Gianfredi Vincenza , Berti Alessandro , Stefanizzi Pasquale , D’Amico Marilena , De Lorenzo Viola , Moscara Lorenza , Di Lorenzo Antonio , Venerito Vincenzo , Castaldi Silvana
Published: November 2023

The risk of unfavourable outcomes for SARS-CoV-2 infection is significant during pregnancy and breastfeeding. Vaccination is a safe and effective measure to lower this risk. This study aims at reviewing the literature concerning the anti-SARS-CoV-2 vaccine’s acceptance/hesitancy among pregnant and breastfeeding women attending hospital facilities. A systematic review of literature was carried out. Hospital-based observational studies related to vaccination acceptance, hesitancy, knowledge and attitude among pregnant and breastfeeding women were included. Determinants of acceptance and hesitancy were investigated in detail. Quality assessment was done via the Johann Briggs Institute quality assessment tools. After literature search, 43 studies were included, 30 of which only focused on pregnant women (total sample 25,862 subjects). Sample size ranged from 109 to 7017 people. Acceptance of the SARS-CoV-2 vaccine ranged from 16% to 78.52%; vaccine hesitancy ranged between 91.4% and 24.5%. Fear of adverse events for either the woman, the child, or both, was the main driver for hesitancy. Other determinants of hesitancy included religious concerns, socioeconomic factors, inadequate information regarding the vaccine and lack of trust towards institutions. SARS-CoV-2 vaccine hesitancy in hospitalized pregnant women appears to be significant, and efforts for a more effective communication to these subjects are required.
Unveiling Oxidative Stress-Induced Genotoxicity and Its Alleviation through Selenium and Vitamin E Therapy in Naturally Infected Cattle with Lumpy Skin Disease
Authors: Ahmad Waqas , Sattar Adeel , Ahmad Mehmood , Aziz Muhammad Waqar , Iqbal Asif , Tipu Muhammad Yasin , Mushtaq Rana Muhammad Zahid , Rasool Naeem , Ahmed Hafiz Saleet , Ahmad Muhammad
Published: November 2023

Lumpy skin disease (LSD) is a contagious infection of cattle caused by a virus of the Poxviridae family, genus Capripoxvirus. In Pakistan, recent outbreaks have resulted in significant nationwide mortality and economic losses. A 20-day prospective cohort study was performed on sixty infected cattle with the aim to evaluate LSD-induced oxidative stress’s genotoxic role and to determine the ameliorative effect of antioxidant therapy using principal component analysis (PCA) and a multivariable ordinal logistic regression model. LSDV was identified from scab samples and nodular lesions using RPO30-specific gene primers. The infected cattle were divided into control and treated groups. The animals were observed initially and finally on day 20 to evaluate the homeostatic, oxidative, and genotoxic changes. The animals in the treated group were administered a combination of selenium (Se) and vitamin E at the standard dose rate for five consecutive days. A substantial (p < 0.05) improvement in the hematological indices was observed in the treated group. The treated group also showed a significant (p < 0.05) reduction in levels of serum nitric oxide (NO) and malondialdehyde (MDA) post-therapy. The PCA at the final sampling data of the treated group showed that Principal Component (PC1 eigenvalue 1.429) was influenced by superoxide dismutase (SOD; 0.3632), catalase (CAT; 0.2906), and glutathione (GSH; 0.0816) and PC2 (eigenvalue 1.200) was influenced by CAT (0.4362), MDA (0.2056), and NO (0.0693). A significant correlation between serum NO (76%) and MDA levels (80%) was observed with genetic damage index (GDI) scores. The ordinal logistic regression model regarding the use of antioxidant therapy revealed 73.95-times (95%CI; 17.36–314.96) improvement in the GDI in treated animals. The multivariable ordinal logistic regression showed that each unit increase in NO and MDA resulted in a 13% increase in genotoxicity in infected individuals. In conclusion, our study revealed that LSD-induced oxidative stress and lipid peroxidation product causes genotoxicity in affected animals. Furthermore, the combined Se and vitamin E therapy significantly alleviated oxidative stress and genotoxicity in LSD-affected cattle.
Effects of Environmental Factors on the Distribution and Diversity of Aquatic Oligochaetes
Authors: Atanacković Ana , Popović Nataša , Marinković Nikola , Tomović Jelena , Đuknić Jelena , Stanković Jelena , Paunović Momir
Published: November 2023

The aim of our study was to detect the actual distribution of oligochaete species and to identify their ecological differentiation with respect to environmental factors: altitude, temperature, oxygen concentration, conductivity, total organic carbon, and waterbody type. Although widespread, differentiation of oligochaete communities in four waterbody types and altitudinal groups can be observed through alpha and beta diversity. Their differences were analyzed using MANOVA, while the ecological preferences of species were presented with logistic Gaussian regression analyses. The highest number of the species of Oligochaeta was recorded in oligochaete communities in medium and large rivers. Total beta diversity decreased with the decreasing of waterbody size, the increasing of size of the substrate particles, river flow velocity, as well as altitude. Communities from small mountain rivers and streams and large and medium rivers with coarser substrate differed from other oligochaete communities. When coarser substrate was prevalent in smaller and medium rivers, a domination of a certain family was observed: Lumbriculidae (>800 m a.s.l.), Propappidae and Enchytraeidae (500–800 m), and Naididae (<500 m a.s.l.). Common species of Oligochaeta, with significantly overlapping ranges in running waters in Serbia, still show a clear grouping with respect to preference for certain types of waterbodies.
Satellite-Based Monitoring of Growing Agricultural Water Consumption in Hyper-Arid Regions
Authors: Ebrahimivand Ashkan , Hooshyaripor Farhad , Rezaei-Gharehaghaj Salar , Razi Sahand , Salamttalab Mohammad Milad , Kolahi Mahdi , Noori Roohollah
Published: November 2023

Land-use change has a key role in hydrologic processes and biodiversity. Although many satellite-based studies have been conducted to reveal the interaction between land-use changes in hydrological processes worldwide, the land-use change impact on agricultural water consumption in hyper-arid regions is poorly understood. Here, we investigate increased agricultural water consumption in the Qom province, a hyper-arid region in Iran, using derived imageries from Landsat 5 Tm and Landsat 8 OLI during the last three decades. We used maximum likelihood classification (MLC) and decision tree classification (DTC) to analyze the satellite images. The MLC method showed that croplands have increased from 30,547 ha in 1989 to 39,255 ha in 2019 (i.e., a 29% increase). In this period, the total orchard area increased from 3904 ha to 6307 ha, revealing a growth of 61%. In the DTC method, the increases in the cropland and orchard areas were, respectively, 34% and 60%. Although both MLC and DTC satisfied the accuracy criteria, the former was more consistent than the latter concerning ground data and documented statistics. Satellite-based and MLC results showed an increase in agricultural water consumption from 152 million cubic meters (MCM) in 1989 to 209 MCM in 2019, showing a 38% increase (i.e., 1.27% annually). Our findings send an alarming message for policymakers to reduce the water demand through sustainable agricultural practices in the Qom province, where the agricultural sector uses approximately 90% of annual water demand.
Predicting Future Flood Risks in the Face of Climate Change: A Frequency Analysis Perspective
Authors: Anghel Cristian Gabriel , Ilinca Cornel
Published: November 2023

The frequency analysis of maximum flows represents a direct method to predict future flood risks in the face of climate change. Thus, the correct use of the tools (probability distributions and methods of estimating their parameters) necessary to carry out such analyzes is required to avoid possible negative consequences. This article presents four probability distributions from the generalized Beta families, using the L- and LH-moments method as parameter estimation. New elements are presented regarding the applicability of Dagum, Paralogistic, Inverse Paralogistic and the four-parameter Burr distributions in the flood frequency analysis. The article represents the continuation of the research carried out in the Faculty of Hydrotechnics, being part of larger and more complex research with the aim of developing a normative regarding flood frequency analysis using these methods. According to the results obtained, among the four analyzed distributions, the Burr distribution was found to be the best fit model because the theoretical values of the statistical indicators calibrated the corresponding values of the observed data. Considering the existence of more rigorous selection criteria, it is recommended to use these methods in the frequency analysis.
Modeling Morphodynamic Impacts and Optimization of Marine Hydrokinetic Arrays in Shallow Offshore Environments
Authors: Moghadam Hanieh , Ortiz Alejandra C.
Published: November 2023

Marine hydrokinetic (MHK) devices hold the promise of expanding renewable energy production by tapping into the power of waves and currents for electricity generation. However, these devices remain in the developmental stage, necessitating research to understand their environmental impacts, lower operational costs, and prevent equipment failures. In this study, we investigate various MHK array configurations to gain insights into their effects on wave patterns, water flow, and sediment conditions, considering both short-term and long-term morphodynamic changes under average and extreme conditions in shallow offshore environments. Our objectives encompass understanding the influence of mean and extreme environmental conditions on MHK devices, evaluating their impact on the bathymetry of the ocean floor, and exploring the role of different array configurations in morphodynamic evolution. Our findings, based on modeling these devices as static lumps, reveal that sediment erosion downstream of MHKs increases by 50% after one year of average conditions. When accounting for the absorption of 30% of the energy by MHK devices, downstream sediment deposition surges by almost 125%. Moreover, alterations in MHK arrays, such as spacing, size, and number, result in noticeable changes in sedimentation magnitudes during storm conditions. While long-term mean wave conditions have minimal effects on sedimentation, extreme wave conditions, akin to large storm events, bring about significant alterations in ocean floor bathymetry, potentially leading to costly maintenance of the MHK arrays. Our research provides a valuable framework for site analysis, enabling the estimation of maintenance needs and the optimization of array configurations to minimize sedimentation-related issues.