bluuebunny/crossref_metadata_2025 huggingface dataset:
Aluminum Melt Filtration with Carbon Bonded Alumina Filters
Authors: Voigt Claudia , Hubálková Jana , Zienert Tilo , Fankhänel Beate , Stelter Michael , Charitos Alexandros , Aneziris Christos G.
Published: September 2020

The wetting behavior was measured for Al2O3-C in contact with AlSi7Mg with a conventional sessile drop test (vacuum, 950 °C and 180 min) and a sessile drop test with a capillary purification unit (vacuum, 730 °C and 30 min). The conventional test yielded contact angles of around 92°, whereas the sessile drop measurement with capillary purification showed a strongly non-wetting behavior with a determined apparent contact angle of the rolling drop of 157°. Filtration tests, which were repeated twice, showed that the Al2O3-C filter possessed a better filtration behavior than the Al2O3 reference filter. For both filtration trials, the PoDFA (porous disc filtration analysis) index of the Al2O3-C filter sample was equal to half of the PoDFA index of the Al2O3 reference filter sample, indicating a significantly improved filtration performance when using Al2O3-C filter. Notable is the observation of a newly formed layer between the aluminum and the Al2O3-C coating. The layer possessed a thickness between 10 µm up to 50 µm and consisted of Al, C, and O, however, with different ratios than the original Al2O3-C coating. Thermodynamic calculations based on parameters of the wetting and filtration trials underline the possible formation of an Al4O4C-layer.
Two-Agent Preemptive Pareto-Scheduling to Minimize Late Work and Other Criteria
Authors: He Ruyan , Yuan Jinjiang
Published: September 2020

In this paper, we consider three preemptive Pareto-scheduling problems with two competing agents on a single machine. In each problem, the objective function of agent A is the total completion time, the maximum lateness, or the total late work while the objective function of agent B is the total late work. For each problem, we provide a polynomial-time algorithm to characterize the trade-off curve of all Pareto-optimal points.
Non-Parametric Analysis of Efficiency: An Application to the Pharmaceutical Industry
Authors: Díaz Ricardo F. , Sanchez-Robles Blanca
Published: September 2020

Increases in the cost of research, specialization and reductions in public expenditure in health are changing the economic environment for the pharmaceutical industry. Gains in productivity and efficiency are increasingly important in order for firms to succeed in this environment. We analyze empirically the performance of efficiency in the pharmaceutical industry over the period 2010–2018. We work with microdata from a large sample of European firms of different characteristics regarding size, main activity, country of origin and other idiosyncratic features. We compute efficiency scores for the firms in the sample on a yearly basis by means of non-parametric data envelopment analysis (DEA) techniques. Basic results show a moderate average level of efficiency for the firms which encompass the sample. Efficiency is higher for companies which engage in manufacturing and distribution than for firms focusing on research and development (R&D) activities. Large firms display higher levels of efficiency than medium-size and small firms. Our estimates point to a decreasing pattern of average efficiency over the years 2010–2018. Furthermore, we explore the potential correlation of efficiency with particular aspects of the firms’ performance. Profit margins and financial solvency are positively correlated with efficiency, whereas employee costs display a negative correlation. Institutional aspects of the countries of origin also influence efficiency levels.
A Comprehensive Review of Micro-Inertial Measurement Unit Based Intelligent PIG Multi-Sensor Fusion Technologies for Small-Diameter Pipeline Surveying
Authors: Guan Lianwu , Cong Xiaodan , Zhang Qing , Liu Fanming , Gao Yanbin , An Wendou , Noureldin Aboelmagd
Published: September 2020

It is of great importance for pipeline systems to be is efficient, cost-effective and safe during the transportation of the liquids and gases. However, underground pipelines often experience leaks due to corrosion, human destruction or theft, long-term Earth movement, natural disasters and so on. Leakage or explosion of the operating pipeline usually cause great economical loss, environmental pollution or even a threat to citizens, especially when these accidents occur in human-concentrated urban areas. Therefore, the surveying of the routed pipeline is of vital importance for the Pipeline Integrated Management (PIM). In this paper, a comprehensive review of the Micro-Inertial Measurement Unit (MIMU)-based intelligent Pipeline Inspection Gauge (PIG) multi-sensor fusion technologies for the transport of liquids and gases purposed for small-diameter pipeline (D < 30 cm) surveying is demonstrated. Firstly, four types of typical small-diameter intelligent PIGs and their corresponding pipeline-defects inspection technologies and defects-positioning technologies are investigated according to the various pipeline defects inspection and localization principles. Secondly, the multi-sensor fused pipeline surveying technologies are classified into two main categories, the non-inertial-based and the MIMU-based intelligent PIG surveying technology. Moreover, five schematic diagrams of the MIMU fused intelligent PIG fusion technology is also surveyed and analyzed with details. Thirdly, the potential research directions and challenges of the popular intelligent PIG surveying techniques by multi-sensor fusion system are further presented with details. Finally, the review is comprehensively concluded and demonstrated.
Functional Analysis of Two Novel Streptococcus iniae Virulence Factors Using a Zebrafish Infection Model
Authors: Soh Kar Yan , Loh Jacelyn Mei San , Hall Christopher , Proft Thomas
Published: September 2020

Streptococcus iniae is a major fish pathogen that contributes to large annual losses in the aquaculture industry, exceeding US$100 million. It is also reported to cause opportunistic infections in humans. We have recently identified two novel S. iniae virulence factors, an extracellular nuclease (SpnAi) and a secreted nucleotidase (S5nAi), and verified their predicted enzymatic activities using recombinant proteins. Here, we report the generation of green fluorescent S. iniae spnAi and s5nAi deletion mutants and their evaluation in a transgenic zebrafish infection model. Our results show nuclease and nucleotidase activities in S. iniae could be attributed to SpnAi and S5nAi, respectively. Consistent with this, larvae infected with the deletion mutants demonstrated enhanced survival and bacterial clearance, compared to those infected with wild-type (WT) S. iniae. Deletion of spnAi and s5nAi resulted in sustained recruitment of neutrophils and macrophages, respectively, to the site of infection. We also show that recombinant SpnAi is able to degrade neutrophil extracellular traps (NETs) isolated from zebrafish kidney tissue. Our results suggest that both enzymes play an important role in S. iniae immune evasion and might present potential targets for the development of therapeutic agents or vaccines.
Faecal Microbiota of Dogs Offered a Vegetarian Diet with or without the Supplementation of Feather Meal and either Cornmeal, Rye or Fermented Rye: A Preliminary Study
Authors: Hankel Julia , Abd El-Wahab Amr , Grone Richard , Keller Birgit , Galvez Eric , Strowig Till , Visscher Christian
Published: September 2020

Anthropomorphism of dogs has affected feeding and the choice of components present in diets for dogs. Conflicting trends are present: raw or vegetarian appear more prevalent. Animal-derived proteins seem to have unfavourable impacts on intestinal microflora by decreasing the presence of Bacteroidetes. This preliminary study evaluates whether effects of diets with animal proteins on intestinal microbiota can be compensated by the addition of certain carbohydrates to dog diet. Eight female beagles were included in a cross-over study and fed a vegetarian diet or the same diet supplemented with feather meal (2.7%) and either 20% of cornmeal, fermented or non-fermented rye (moisture content of the diets about 42%). A 16S rRNA gene amplification was performed within the hypervariable region V4 on faecal samples and sequenced with the Illumina MiSeq platform. The Firmicutes/Bacteroidetes ratio tended to shift to the advantage of Firmicutes when feather meal and cornmeal were added (Firmicutes/Bacteroidetes ratio of 5.12 compared to 2.47 when offered the vegetarian diet) and tended to switch back to the advantage of Bacteroidetes if rye: fermented (2.17) or not (1.03) was added. The addition of rye might have the potential to compensate possible unfavourable effects of diets with animal proteins on intestinal microbiota of dogs.
Mycobacteriosis and Infections with Non-tuberculous Mycobacteria in Aquatic Organisms: A Review
Authors: Delghandi Mohammad Reza , El-Matbouli Mansour , Menanteau-Ledouble Simon
Published: September 2020

The Mycobacteriaceae constitute a family of varied Gram-positive organisms that include a large number of pathogenic bacteria. Among these, non-tuberculous mycobacteria are endemic worldwide and have been associated with infections in a large number of organisms, including humans and other mammals and reptiles, as well as fish. In this review, we summarize the most recent findings regarding this group of pathogens in fish. There, four species are most commonly associated with disease outbreaks: Mycobacterium marinum, the most common of these fish mycobacterial pathogens, Mycobacterium fortuitum, Mycobacterium gordonae, and Mycobacterium chelonae. These bacteria have a broad host range: they are zoonotic, and infections have been reported in a large number of fish species. The main route of entry of the bacterium into the fish is through the gastrointestinal route, and the disease is associated with ulcerative dermatitis as well as organomegaly and the development of granulomatous lesions in the internal organs. Mycobacteriaceae are slow-growing and fastidious and isolation is difficult and time consuming and diagnostic is mostly performed using serological and molecular tools. Control of the disease is also difficult: there is currently no effective vaccine and infections react poorly to antibiotherapy. For this reason, more research is needed on the subject of these vexing pathogens.
Association of Vitamin D Status with Lower Limb Muscle Strength in Professional Basketball Players: A Cross-Sectional Study
Authors: Kim Do Kyung , Park Geon , Kuo Liang-Tseng , Park Won-Hah
Published: September 2020

Vitamin D deficiency in athletes may play a role in influencing fracture risk and athletic performance. This study aimed to examine the vitamin D status of basketball players and determine its correlation with muscle strength. We included 36 male professional basketball players (mean age, 22.6 ± 3.2 years) categorized by vitamin D status. We examined the muscle strength of knee extension/flexion and ankle dorsiflexion/plantarflexion using an isokinetic dynamometer. Eleven (30.5%), fifteen (41.7%), and ten (27.8%) players had deficient (<20 ng/mL), insufficient (20–32 ng/mL), and sufficient vitamin D levels (>32 ng/mL), respectively. In the dominant side, there were no significant correlations of vitamin D level with knee extension/flexion strength (r = 0.134, p = 0.436; r = −0.017, p = 0.922, respectively), or with plantarflexion/dorsiflexion ankle strength (r = −0.143, p = 0.404; r = 1.109, p = 0.527, respectively). Moreover, the isokinetic lower limb strengths were not significantly different between the three groups in all settings (all p > 0.05). In conclusion, professional basketball players had a high prevalence of vitamin D insufficiency. Though it may not be associated with muscle strength, maintaining adequate vitamin D levels by micronutrients monitoring, regular dietician consultation, and supplementation is still a critically considerable strategy to enhance young athletes’ health.
Preventive Role of Diet Interventions and Dietary Factors in Type 2 Diabetes Mellitus: An Umbrella Review
Authors: Toi Phung Lam , Anothaisintawee Thunyarat , Chaikledkaew Usa , Briones Jamaica Roanne , Reutrakul Sirimon , Thakkinstian Ammarin
Published: September 2020

Background: Although the body of evidence indicates clear benefits of dietary modifications for prevention of type-2 diabetes mellitus (T2DM), it may be difficult for healthcare providers to recommend which diet interventions or dietary factors are appropriate for patients as there are too many modalities available. Accordingly, we performed an umbrella review to synthesize evidence on diet interventions and dietary factors in prevention of T2DM. Methods: Medline and Scopus databases were searched for relevant studies. Systematic reviews with meta-analyses of randomized-controlled trial or observational studies were eligible if they measured effects of diet interventions and/or dietary factors including dietary patterns, food groups, and nutrients on risk of T2DM. The effect of each diet intervention/factor was summarized qualitatively. Results: Sixty systematic reviews and meta-analyses were eligible. Results of the review suggest that healthy dietary patterns such as Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets, and high consumption of whole grains, low-fat dairy products, yogurt, olive oil, chocolate, fiber, magnesium, and flavonoid significantly reduced the risk of T2DM. In contrast, high glycemic index and glycemic load diets, high consumption of red and processed meat, and sugar or artificial sugar-sweetened beverages significantly increased risk of T2DM. Prescribing diet interventions with or without physical activity interventions significantly decreased risk of T2DM in both high-risk and general population. Conclusion: High consumption of Mediterranean and DASH diet, and interventions that modified the quality of diet intake significantly reduced risk of T2DM especially in the high-risk population. These lifestyle modifications should be promoted in both individual and population levels to prevent and decrease burden from T2DM in the future.
A Cognitive Perspective on Knowledge How: Why Intellectualism Is Neuro-Psychologically Implausible
Authors: Stephens Andreas , Felix Cathrine V.
Published: September 2020

We defend two theses: (1) Knowledge how and knowledge that are two distinct forms of knowledge, and; (2) Stanley-style intellectualism is neuro-psychologically implausible. Our naturalistic argument for the distinction between knowledge how and knowledge that is based on a consideration of the nature of slips and basic activities. We further argue that Stanley’s brand of intellectualism has certain ontological consequences that go against modern cognitive neuroscience and psychology. We tie up our line of thought by showing that input from cognitive neuroscience and psychology, on multiple levels of analysis, cohere in supporting the distinction between two separate forms of knowledge. The upshot is a neuro-psychologically plausible understanding of knowledge.
Covalent Adaptable Network and Self-Healing Materials: Current Trends and Future Prospects in Sustainability
Authors: Khan Ajmir , Ahmed Naveed , Rabnawaz Muhammad
Published: September 2020

This work estimates that if the growth of polymer production continues at its current rate of 5% each year, the current annual production of 395 million tons of plastic will exceed 1000 million tons by 2039. Only 9% of the plastics that are currently produced are recycled while most of these materials end up in landfills or leak into oceans, thus creating severe environmental challenges. Covalent adaptable networks (CANs) materials can play a significant role in reducing the burden posed by plastics materials on the environment because CANs are reusable and recyclable. This review is focused on recent research related to CANs of polycarbonates, polyesters, polyamides, polyurethanes, and polyurea. In particular, trends in self-healing CANs systems, the market value of these materials, as well as mechanistic insights regarding polycarbonates, polyesters, polyamides, polyurethanes, and polyurea are highlighted in this review. Finally, the challenges and outlook for CANs are described herein.
Core–Shell Eudragit S100 Nanofibers Prepared via Triaxial Electrospinning to Provide a Colon-Targeted Extended Drug Release
Authors: Ding Yanfei , Dou Cheng , Chang Shuyue , Xie Zhengming , Yu Deng-Guang , Liu Yanan , Shao Jun
Published: September 2020

In this study, a new modified triaxial electrospinning is implemented to generate an Eudragit S100 (ES100)-based core–shell structural nanofiber (CSF), which is loaded with aspirin. The CSFs have a straight line morphology with a smooth surface, an estimated average diameter of 740 ± 110 nm, and a clear core–shell structure with a shell thickness of 65 nm, as disclosed by the scanning electron microscopy and transmission electron microscopy results. Compared to the monolithic composite nanofibers (MCFs) produced using traditional blended single-fluid electrospinning, aspirin presented in both of them amorously owing to their good compatibility. The CSFs showed considerable advantages over the MCFs in providing the desired drug-controlled-release profiles, although both of them released the drug in an erosion mechanism. The former furnished a longer time period of time-delayed-release and a smaller portion released during the first two-hour acid condition for protecting the stomach membranes, and also showed a longer time period of aspirin-extended-release for avoiding possible drug overdose. The present protocols provide a polymer-based process-nanostructure-performance relationship to optimize the reasonable delivery of aspirin.
Consistent Comparison of Remotely Sensed Sea Ice Concentration Products with ERA-Interim Reanalysis Data in Polar Regions
Authors: Liang Shuang , Zeng Jiangyuan , Li Zhen , Qiao Dejing , Zhang Ping , Bi Haiyun
Published: September 2020

Sea ice concentration (SIC) plays a significant role in climate change research and ship’s navigation in polar regions. Satellite-based SIC products have become increasingly abundant in recent years; however, the uncertainty of these products still exists and needs to be further investigated. To comprehensively evaluate the consistency of the SIC derived from different SIC algorithms in long time series and the whole polar regions, we compared four passive microwave (PM) satellite SIC products with the ERA-Interim sea ice fraction dataset during the period of 2015–2018. The PM SIC products include the SSMIS/ASI, AMSR2/BT, the Chinese FY3B/NT2, and FY3C/NT2. The results show that the remotely sensed SIC products derived from different SIC algorithms are generally in good consistency. The spatial and temporal distribution of discrepancy among satellite SIC products for both Arctic and Antarctic regions are also observed. The most noticeable difference for all the four SIC products mostly occurs in summer and at the marginal ice zone, indicating that large uncertainties exist in satellite SIC products in such period and areas. The SSMIS/ASI and AMSR2/BT show relatively better consistency with ERA-Interim in the Arctic and Antarctic, respectively, but they exhibit opposite bias (dry/wet) relative to the ERA-Interim data. The sea ice extent (SIE) and sea ice area (SIA) derived from PM and ERA-Interim SIC were also compared. It is found that the difference of PM SIE and SIA varies seasonally, which is in line with that of PM SIC, and the discrepancy between PM and ERA-Interim data is larger in Arctic than in Antarctic. We also noticed that different algorithms have different performances in different regions and periods; therefore, the hybrid of multiple algorithms is a promising way to improve the accuracy of SIC retrievals. It is expected that our findings can contribute to improving the satellite SIC algorithms and thus promote the application of these useful products in global climate change studies.
Extreme Climate Event and Its Impact on Landscape Resilience in Gobi Region of Mongolia
Authors: Vova Oyudari , Kappas Martin , Renchin Tsolmon , Fassnacht Steven R.
Published: September 2020

The dzud, a specific type of climate disaster in Mongolia, is responsible for serious environmental and economic damage. It is characterized by heavy snowfall and severe winter conditions, causing mass livestock deaths that occur through the following spring. These events substantially limit socioeconomic development in Mongolia. In this research, we conducted an analysis of several dzud events (2000, 2001, 2002, and 2010) to understand the spatial and temporal variability of vegetation conditions in the Gobi region of Mongolia. The present paper also establishes how these extreme climatic events affect vegetation cover and local grazing conditions using the seasonal aridity index (aAIZ), time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI), and livestock data. We also correlated aAIZ, NDVI, and seasonal precipitation in the varied ecosystems of the study area. The results illustrate that under certain dzud conditions, rapid regeneration of vegetation can occur. A thick snow layer acting as a water reservoir combined with high livestock losses can lead to an increase of the maximum August NDVI. The Gobi steppe areas showed the highest degree of vulnerability to climate, with a drastic decline of grassland in humid areas. Another result is that snowy winters can cause a 10 to 20-day early peak in NDVI and a following increase in vegetation growth. During a drought year with dry winter conditions, the vegetation growth phase begins later due to water deficiency, which leads to weaker vegetation growth. Livestock loss and the reduction of grazing pressure play a crucial role in vegetation recovery after extreme climatic events in Mongolia.
A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL
Authors: Khanal Nishanta , Matin Mir Abdul , Uddin Kabir , Poortinga Ate , Chishtie Farrukh , Tenneson Karis , Saah David
Published: September 2020

Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.
Tree Line Identification and Dynamics under Climate Change in Wuyishan National Park Based on Landsat Images
Authors: Xu Dandan , Geng Qinghong , Jin Changshan , Xu Zikun , Xu Xia
Published: September 2020

The alpine tree line ecotone, reflecting interactions between climate and ecology, is very sensitive to climate change. To identify tree line responses to climate change, including intensity and local variations in tree line advancement, the use of Landsat images with long-term data series and fine spatial resolution is an option. However, it is a challenge to extract tree line data from Landsat images due to classification issues with outliers and temporal inconsistency. More importantly, direct classification results in sharp boundaries between forest and non-forest pixels/segments instead of representing the tree line ecotone (three ecological regions—tree species line, tree line, and timber line—are closely related to the tree line ecotone and are all significant for ecological processes). Therefore, it is important to develop a method that is able to accurately extract the tree line from Landsat images with a high temporal consistency and to identify the appropriate ecological boundary. In this study, a new methodology was developed based on the concept of a local indicator of spatial autocorrelation (LISA) to extract the tree line automatically from Landsat images. Tree line responses to climate change from 1987 to 2018 in Wuyishan National Park, China, were evaluated, and topographic effects on local variations in tree line advancement were explored. The findings supported the methodology based on the LISA concept as a valuable classifier for assessing the local spatial clusters of alpine meadows from images acquired in nongrowing seasons. The results showed that the automatically extracted line from Landsat images was the timber line due to the restriction in spatial autocorrelation. The results also indicate that parts of the tree line in the study area shifted upward vertically by 50 m under a 1 °C temperature increase during the period from 1987 to 2018, with local variations influenced by slope, elevation, and interactions with aspect. Our study contributes a novel result regarding the response of the alpine tree line to global warming in a subtropical region. Our method for automatic tree line extraction can provide fundamental information for ecosystem managers.
Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
Authors: Hunt Samuel E. , Mittaz Jonathan P. D. , Smith David , Polehampton Edward , Yemelyanova Rose , Woolliams Emma R. , Donlon Craig
Published: September 2020

The Sentinel-3 mission is part of the Copernicus programme space segment and has the objective of making global operational observations of ocean, land and atmospheric parameters with its four on-board sensors. Two Sentinel-3 satellites are currently on orbit, providing near-daily global coverage. Sentinel-3A was launched on 16 February 2016 and Sentinel-3B on 25 April 2018. For the early part of its operation, Sentinel-3B flew in tandem with Sentinel-3A, flying 30 s ahead of its twin mission. This provided a unique opportunity to compare the instruments on the two satellites, and to test the per pixel uncertainty values in a metrologically-robust manner. In this work, we consider the tandem-phase data from the infrared channels of one of the on-board instruments: the Sea and Land Surface Temperature Radiometer, SLSTR. A direct comparison was made of both the Level 1 radiance values and the Level 2 sea surface temperature values derived from those radiances. At Level 1, the distribution of differences between the sensor values were compared to the declared uncertainties for data gridded on to a regular latitude-longitude grid with propagated pixel uncertainties. The results showed good overall radiometric agreement between the two sensors, with mean differences of ∼0.06 K, although there was a scene-temperature dependent difference for the oblique view that was consistent with what was expected from a stray light effect observed pre-flight. We propose a means to correct for this effect based on the tandem data. Level 1 uncertainties were found to be representative of the variance of the data, expect in those channels affected by the stray light effect. The sea surface temperature results show a very small difference between the sensors that could be in part due to the fact that the Sentinel-3A retrieval coefficients were also applied to the Sentinel-3B retrieval because the Sentinel-3B coefficients are not currently available. This will lead to small errors between the S3A and S3B retrievals. The comparison also suggests that the retrieval uncertainties may need updating for two of the retrieval processes that there are extra components of uncertainty related the quality level and the probability of cloud that should be included. Finally, a study of the quality flags assigned to sea surface temperature pixel values provided valuable insight into the origin of those quality levels and highlighted possible uncertainties in the defined quality level.
Detecting Montane Flowering Phenology with CubeSat Imagery
Authors: John Aji , Ong Justin , Theobald Elli J. , Olden Julian D. , Tan Amanda , HilleRisLambers Janneke
Published: September 2020

Shifts in wildflower phenology in response to climate change are well documented in the scientific literature. The majority of studies have revealed phenological shifts using in-situ observations, some aided by citizen science efforts (e.g., National Phenology Network). Such investigations have been instrumental in quantifying phenological shifts but are challenged by the fact that limited resources often make it difficult to gather observations over large spatial scales and long-time frames. However, recent advances in finer scale satellite imagery may provide new opportunities to detect changes in phenology. These approaches have documented plot level changes in vegetation characteristics and leafing phenology, but it remains unclear whether they can also detect flowering in natural environments. Here, we test whether fine-resolution imagery (<10 m) can detect flowering and whether combining multiple sources of imagery improves the detection process. Examining alpine wildflowers at Mt. Rainier National Park (MORA), we found that high-resolution Random Forest (RF) classification from 3-m resolution PlanetScope (from Planet Labs) imagery was able to delineate the flowering season captured by ground-based phenological surveys with an accuracy of 70% (Cohen’s kappa = 0.25). We then combined PlanetScope data with coarser resolution but higher quality imagery from Sentinel and Landsat satellites (10-m Sentinel and 30-m Landsat), resulting in higher accuracy predictions (accuracy = 77%, Cohen’s kappa = 0.39). The model was also able to identify the timing of peak flowering in a particularly warm year (2015), despite being calibrated on normal climate years. Our results suggest PlanetScope imagery holds utility in global change ecology where temporal frequency is important. Additionally, we suggest that combining imagery may provide a new approach to cross-calibrate sensors to account for radiometric irregularity inherent in fine resolution PlanetScope imagery. The development of this approach for wildflower phenology predictions provides new possibilities to monitor climate change effects on flowering communities at broader spatiotemporal scales. In protected and tourist areas where the flowering season draws large numbers of visitors, such as Mt. Rainier National Park, the modeling framework presented here could be a useful tool to manage and prioritize park resources.
Performances of Polarization-Retrieve Imaging in Stratified Dispersion Media
Authors: Wang Xinyang , Hu Tianwei , Li Dekui , Guo Kai , Gao Jun , Guo Zhongyi
Published: September 2020

We constructed an active imaging model within 10 km of the atmosphere from the satellite to the ground based on Monte Carlo (MC) algorithm, and, because of the inhomogeneous distributions of the scattering particles in atmosphere environment, 10 km atmosphere layer was divided into ten layers in our model. The MC algorithm was used to simulate the transmission process of photons through the atmosphere. By launching lasers of linear polarization states from satellites to ground, the intensity, degree of polarization (DoP), polarization difference (PD), and polarization retrieve (PR) images can be obtained. The contrast of the image, peak signal to noise ratio (PSNR), and structural similarity index (SSI) were used to evaluate the imaging quality. The simulated results demonstrate that the contrast of images is degraded as the atmosphere becomes worse. However, PR imaging have a better contrast and better visibility in different atmospheric conditions. Meanwhile, we found that Mueller matrix (MM) can retrieve the original images very well in a certain range of atmospheric conditions. Finally, the simulation also shows that different wavelengths of light sources have different penetration characteristics, and, in general, infrared light shows better performances than visible light for imaging.
Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping
Authors: Duan Puhong , Lai Jibao , Ghamisi Pedram , Kang Xudong , Jackisch Robert , Kang Jian , Gloaguen Richard
Published: September 2020

Combining both spectral and spatial information with enhanced resolution provides not only elaborated qualitative information on surfacing mineralogy but also mineral interactions of abundance, mixture, and structure. This enhancement in the resolutions helps geomineralogic features such as small intrusions and mineralization become detectable. In this paper, we investigate the potential of the resolution enhancement of hyperspectral images (HSIs) with the guidance of RGB images for mineral mapping. In more detail, a novel resolution enhancement method is proposed based on component decomposition. Inspired by the principle of the intrinsic image decomposition (IID) model, the HSI is viewed as the combination of a reflectance component and an illumination component. Based on this idea, the proposed method is comprised of several steps. First, the RGB image is transformed into the luminance component, blue-difference and red-difference chroma components (YCbCr), and the luminance channel is considered as the illumination component of the HSI with an ideal high spatial resolution. Then, the reflectance component of the ideal HSI is estimated with the downsampled HSI image and the downsampled luminance channel. Finally, the HSI with high resolution can be reconstructed by utilizing the obtained illumination and the reflectance components. Experimental results verify that the fused results can successfully achieve mineral mapping, producing better results qualitatively and quantitatively over single sensor data.
A Time Series Data Filling Method Based on LSTM—Taking the Stem Moisture as an Example
Authors: Song Wei , Gao Chao , Zhao Yue , Zhao Yandong
Published: September 2020

In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verify the validity and accuracy of the stem water filling data of the LSTM (Long Short-Term Memory) model. This paper compared the accuracy of missing stem water data for plants under different data filling methods to solve the problem of data loss in sensor data collection. Original stem moisture data was selected from Lagerstroemia Indica which was planted in the Haidian District of Beijing in June 2017. Part of the data which treated as missing data was manually deleted. Interpolation methods, time series statistical methods, the RNN (Recurrent Neural Network), and LSTM neural network were used to fill in the missing part and the filling results were compared with the original data. The result shows that the LSTM has more accurate performance than the RNN. The error values of the bidirectional LSTM model are the smallest among several models. The error values of the bidirectional LSTM are much lower than other methods. The MAPE (mean absolute percent error) of the bidirectional LSTM model is 1.813%. After increasing the length of the training data, the results further proved the effectiveness of the model. Further, in order to solve the problem of one-dimensional filling error accumulation, the LSTM model is used to conduct the multi-dimensional filling experiment with environmental data. After comparing the filling results of different environmental parameters, three environmental parameters of air humidity, photosynthetic active radiation, and soil temperature were selected as input. The results show that the multi-dimensional filling can greatly extend the sequence length while maintaining the accuracy, and make up for the defect that the one-dimensional filling accumulates errors with the increase of the sequence. The minimum MAPE of multidimensional filling is 1.499%. In conclusion, the data filling method based on LSTM neural network has a great advantage in filling the long-lost time series data which would provide a new idea for data filling.
Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment
Authors: Arshad Saba , Sualeh Muhammad , Kim Dohyeong , Nam Dinh Van , Kim Gon-Woo
Published: September 2020

In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.
Effect of a Concurrent Cognitive Task, with Stabilizing Visual Information and Withdrawal, on Body Sway Adaptation of Parkinsonian’s Patients in an Off-Medication State: A Controlled Study
Authors: Delafontaine Arnaud , Hansen Clint , Marolleau Iris , Kratzenstein Stefan , Gouelle Arnaud
Published: September 2020

Background: In persons with Parkinson’s disease (pwPD) any additional somatosensory or distractor interference can influence the posture. When deprivation of vision and dual-task are associated, the effect on biomechanical performance is less consistent. The aim of this study was to evaluate the role of the visual deprivation and a cognitive task on the static balance in earlier stage PD subjects. Methods: Fifteen off-medication state pwPD (9 women and 6 men), 67.7 ± 7.3 years old, diagnosed PD since 5.4 ± 3.4 years, only Hoehn and Yahr state 2 and fifteen young control adults (7 women and 8 men) aged 24.9 ± 4.9 years, performed semi-tandem task under four randomized experimental conditions: eyes opened single-task, eyes closed single-task, eyes opened dual-task and eyes closed dual-task. The center of pressure (COP) was measured using a force plate and electromyography signals (EMG) of the ankle/hip muscles were recorded. Traditional parameters, including COP pathway length, ellipse area, mediolateral/anteroposterior root-mean-square and non-linear measurements were computed. The effect of vision privation, cognitive task, and vision X cognitive was investigated by a 2 (eyes opened/eyes closed) × 2 (postural task alone/with cognitive task) repeated-measures ANOVA after application of a Bonferroni pairwise correction for multiple comparisons. Significant interactions were further analyzed using post-hoc tests. Results: In pwPD, both COP pathway length (p < 0.01), ellipse area (p < 0.01) and mediolateral/anteroposterior root-mean-square (p < 0.01) were increased with the eyes closed, while the dual-task had no significant effect when compared to the single-task condition. Comparable results were observed in the control group for who COP pathway was longer in all conditions compared to eyes opened single-task (p < 0.01) and longer in conditions with eyes closed compared to eyes opened dual-task (p < 0.01). Similarly, all differences in EMG activity of pwPD were exclusively observed between eyes opened vs. eyes closed conditions, and especially for the forward leg’s soleus (p < 0.01) and backward tibialis anterior (p < 0.01). Conclusions: These results in pwPD without noticeable impairment of static balance encourage the assessment of both visual occlusion and dual-task conditions when the appearance of significant alteration during the dual-task could reveal the subtle worsening onset of the balance control.
A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
Authors: Zhang Dejing , Yuan Yuan , Bi Yanqing
Published: September 2020

Time synchronization is a key technique in large-scale wireless sensor network applications. In order to tackle the problems of multi-hop synchronization error accumulation, clock frequency skew swinging, and network topology changes, a time synchronization protocol based on dynamic routing and forwarding certification (DRFC-TSP) is proposed in this paper. During the time synchronization process, a reference node with fewer synchronization hops and a more stable clock frequency is selected for every single hop, in order to obtain the best synchronization route. In this way, synchronization error accumulation can be restrained and the impact of clock frequency skew swinging on the time synchronization precision can be reduced. Furthermore, changes of the network topology can be well adapted by dynamic routing, in which the reference node is updated in every synchronization round. In the forwarding certification process, the status of nodes forwarding synchronous information outwards is authored by information exchange between neighboring nodes. Only synchronous information of the certificated nodes with a better performance can be forwarded. The network traffic can be decreased and the time synchronization precision can also be ensured, even with less energy consumption. Feasibility testing in large-scale wireless sensor networks is verified on NS2 simulation and more performances are evaluated on an embedded Linux platform.
Ultrawideband, Wide Scanning Stripline-Fed Tightly Coupled Array Antenna Based on Parallel-Dipole Elements
Authors: Liang Xiuye , Yin Weishuang , Chen Ang , Zhang Zhe , Zeng Jianping , Shi Lei , Guan Fang , Liu Xiaohan , Zi Jian
Published: September 2020

A stripline-fed tightly coupled array antenna with compact size, large scan volume and low cross-polarization characteristics is proposed for ultrawideband (UWB) applications. Simple impedance-matching process is realized by using parallel dual dipoles. Meanwhile, the parallel symmetrical radiating structures minimize the cross-polarization field components dramatically. The mitigation of various undesired resonances is studied in detail. An infinite array is designed to achieve 3:1 bandwidth (6−18 GHz) when scanning ±60∘ in the E-/D-planes (VSWR < 2.5) and H-plane (VSWR < 3.5). The cross-polarization levels remain below −29 dB at broadside. A 16 × 16 prototype is fabricated to demonstrate the design. The measured results are consistent well with the simulated ones. The overall size of the prototype at the lowest operating frequency is 3×3×0.4λ03 (15×15×2cm3). Due to its wide bandwidth, good electronic scan performance and compact size, the proposed antenna array is a good candidate for modern wireless platforms.
Sensors, Signal and Image Processing in Biomedicine and Assisted Living
Authors: Iakovidis Dimitris K.
Published: September 2020

Sensor technologies are crucial in biomedicine, as the biomedical systems and devices used for screening and diagnosis rely on their efficiency and effectiveness [...]
Detection of Potentially Compromised Computer Nodes and Clusters Connected on a Smart Grid, Using Power Consumption Data
Authors: Almshari Mohammed , Tsaramirsis Georgios , Khadidos Adil Omar , Buhari Seyed Mohammed , Khan Fazal Qudus , Khadidos Alaa Omar
Published: September 2020

Monitoring what application or type of applications running on a computer or a cluster without violating the privacy of the users can be challenging, especially when we may not have operator access to these devices, or specialized software. Smart grids and Internet of things (IoT) devices can provide power consumption data of connected individual devices or groups. This research will attempt to provide insides on what applications are running based on the power consumption of the machines and clusters. It is therefore assumed that there is a correlation between electric power and what software application is running. Additionally, it is believed that it is possible to create power consumption profiles for various software applications and even normal and abnormal behavior (e.g., a virus). In order to achieve this, an experiment was organized for the purpose of collecting 48 h of continuous real power consumption data from two PCs that were part of a university computer lab. That included collecting data with a one-second sample period, during class as well as idle time from each machine and their cluster. During the second half of the recording period, one of the machines was infected with a custom-made virus, allowing comparison between power consumption data before and after infection. The data were analyzed using different approaches: descriptive analysis, F-Test of two samples of variance, two-way analysis of variance (ANOVA) and autoregressive integrated moving average (ARIMA). The results show that it is possible to detect what type of application is running and if an individual machine or its cluster are infected. Additionally, we can conclude if the lab is used or not, making this research an ideal management tool for administrators.
Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data
Authors: Wang Peixiao , Gao Fei , Zhao Yuhui , Li Ming , Zhu Xinyan
Published: September 2020

Accurate detection of locations of indoor high-density crowds is crucial for early warning and emergency rescue during indoor safety accidents. The spatial structure of indoor environments is more complicated than outdoor environments. The locations of indoor high-density crowds are more likely to be the sites of security accidents. Existing detection methods for high-density crowd locations mostly focus on outdoor environments, and relatively few detection methods exist for indoor environments. This study proposes a novel detection framework for high-density indoor crowd locations termed IndoorSRC (Simplification–Reconstruction–Cluster). In this paper, a novel indoor spatiotemporal clustering algorithm called Indoor-STAGNES is proposed to detect the indoor trajectory stay points to simplify indoor movement trajectory. Then, we propose use of a Kalman filter algorithm to reconstruct the indoor trajectory and properly align and resample the data. Finally, an indoor spatiotemporal density clustering algorithm called Indoor-STOPTICS is proposed to detect the locations of high-density crowds in the indoor environment from the reconstructed trajectory. Extensive experiments were conducted using indoor Wi-Fi positioning datasets collected from a shopping mall. The results show that the IndoorSRC framework evidently outperforms the existing baseline method in terms of detection performance.