Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration, super-resolution, and light-field imaging, and discuss how the latest Deep Learning research can be applied to other image reconstruction tasks such as structured illumination, spectral deconvolution, and sample stabilisation. Despite its successes, Deep Learning also poses significant challenges, has often misunderstood capabilities, and overlooked limits. We will address key questions, such as: What are the challenges in obtaining training data? Can we discover structures not present in the training data? And, what is the danger of inferring unsubstantiated image details?

Objective: To recommend how physicians can best respond to families whose hopes for a miracle via divine intervention influence their medical decisions, like, for example, making them not want to withdraw ventilatory support in cases of poor neurologic prognosis because they are still hoping for God to intervene. Methods: Auto-ethnographic analysis of chaplaincy experience in this clinical context yields a nuanced 90-second, point-of-care spiritual intervention physicians can use to address the religious aspect of families who base medical decisions on their hopes for a miracle via divine intervention. Explanation of how spiritual intervention dovetails with existing physician communication protocol for responding to families hoping for a miracle. Results: Spiritual intervention for religious aspect of miracle-hoping families is integrated into existing physician communication protocol for responding to families hoping for a miracle with recommendations for utilization of existing communication technology when necessary. Conclusion: Properly addressing the religious dimension of families hoping for a miracle may be helpful for physicians interested in decreasing their own stress levels, improving outcomes for this clinical context, and ensuring that unintentional discrimination does not perpetuate racial disparities in end-of-life care.

Wetlands are recognized as one of the world’s most valuable natural resources. With the increasing world population, human demands on wetland resources for agricultural expansion and urban development continue to increase. In addition, global climate change has pronounced impacts on wetland ecosystems through alterations in hydrological regimes. To better manage and conserve wetland resources, we need to know the distribution and extent of wetlands and monitor their dynamic changes. Wetland maps and inventories can provide crucial information for wetland conservation, restoration, and management. Geographic Information System (GIS) and remote sensing technologies have proven to be useful for mapping and monitoring wetland resources. Recent advances in geospatial technologies have greatly increased the availability of remotely sensed imagery with better and finer spatial, temporal, and spectral resolution. This chapter presents an introduction to the uses of GIS and remote sensing technologies for wetland mapping and monitoring. A case study is presented to demonstrate the use of high-resolution light detection and ranging (LiDAR) data and aerial photographs for mapping prairie potholes and surface hydrologic flow pathways.

Philip Morris International (PMI) has developed the Population Health Impact Model (PHIM) to quantify, in the absence of epidemiological data, the effects of marketing a candidate modified risk tobacco product (cMRTP) on the public health of a whole population. Various simulations were performed to understand the harm reduction impact on the U.S. population over a 20-year period under various scenarios. The overall reduction in smoking attributable deaths (SAD) over the 20-year period was estimated as 934,947 if smoking completely went away and between 516,944 and 780,433 if cMRTP use completely replaces smoking. The reduction in SADs was estimated as 172,458 for the World Health Organization (WHO) 2025 Target and between 70,274 and 90,155 for the gradual cMRTP uptake. Combining the scenarios (WHO 2025 Target and cMRTP uptake), the reductions were between 256,453 and 268,796, depending on the cMRTP effective dose. These results show how a cMRTP can reduce overall population harm additionally to existing tobacco control efforts.

This paper is aimed at studying the temporal evolution of the surface displacements occurred over the past few years in the ocean-reclaimed platforms of the Shanghai megacity (China), which are mainly ascribable to consolidation processes of large dredger fills and alluvial deposits. With respect to previous analyses carried out over the same area, this work provides a joint multi-platform differential interferometry synthetic aperture radar (DInSAR) analysis, based on the application of the advanced Small BAseline Subset (SBAS) algorithm. This led us to retrieve long-term deformation time-series that are helpful for a better understanding of the on-going deformation phenomena. To this aim, we have exploited two sequences of SAR data collected by the ASAR/ENVISAT and by the COSMO-SkyMed (CSK) sensors, respectively, spanning the whole time period from 2007 to 2016. Unfortunately, the large time gap (of about three years) existing between the available ASAR/ENVISAT and CSK datasets gave rise to additional difficulties for their combination. Nevertheless, this problem has been faced by benefiting from the knowledge of a time-dependent model describing the temporal evolution of the expected deformations affecting the Shanghai ocean-reclaimed platforms.

Document classification is one of the most critical steps in the document analysis pipeline. There are two types of approaches for document classification, known as image-based and multimodal approaches. The image-based document classification approaches are solely based on the inherent visual cues of the document images. In contrast, the multimodal approach co-learns the visual and textual features, and it has proved to be more effective. Nonetheless, these approaches require a huge amount of data. This paper presents a novel approach for document classification that works with a small amount of data and outperforms other approaches. The proposed approach incorporates a hierarchical attention network(HAN) for the textual stream and the EfficientNet-B0 for the image stream. The hierarchical attention network in the textual stream uses the dynamic word embedding through fine-tuned BERT. HAN incorporates both the word level and sentence level features. While the earlier approaches rely on training on a large corpus (RVL-CDIP), we show that our approach works with a small amount of data (Tobacco-3482). To this end, we trained the neural network at Tobacco-3428 from scratch. Thereby, we outperform state-of-the-art by obtaining an accuracy of 90.3%. This results in a relative error reduction rate of 7.9%.

COVID-19 is causing major turmoil around the globe, and the clinical trial industry is likely to face unprecedented challenges to health and business sectors. In an effort to find a suitable treatment and prevention options for COVID-19, several COVID-19 clinical trials are being planned and initiated, while a large number of clinical trials for non- COVID-19 indications are suffering delays. With over more than 1000 trials being disrupted and more trials being added to this category daily, there is a direct impact on trial site activation and patient enrolment. This analysis deals with the specific impacts of the COVID-19 pandemic on the clinical trial and pharmaceutical industry. The objective of this study is to provide an updated information of the disrupted clinical trials and its impact on various therapeutic areas and different drugs. Among the severely affected clinical trials, oncology and CNS trials are the hardest hit therapy areas.This article will certainly emphasize the need for advanced and innovative approaches to maintain the health of the clinical trial ecosystem by continuing the existing trials and the start of the new studies. We have to take and follow necessary actions to guarantee that the initiatives will not be locked during the COVID-19 pandemic, both for the treatment of patients and for the researchers to conduct decision-relevant clinical trials.

Crop type and field boundary mapping enable cost-efficient crop management on the field scale and serve as the basis for yield forecasts. Our study uses a data set with crop types and corresponding field borders from the federal state of Bavaria, Germany, as documented by farmers from 2016 to 2018. The study classified corn, winter wheat, barley, sugar beet, potato, and rapeseed as the main crops grown in Upper Bavaria. Corresponding Sentinel-2 data sets include the normalised difference vegetation index (NDVI) and raw band data from 2016 to 2018 for each selected field. The influences of clouds, raw bands, and NDVI on crop type classification are analysed, and the classification algorithms, i.e., support vector machine (SVM) and random forest (RF), are compared. Field boundary detection and extraction are based on non-iterative clustering and a newly developed procedure based on Canny edge detection. The results emphasise the application of Sentinel’s raw bands (B1–B12) and RF, which outperforms SVM with an accuracy of up to 94%. Furthermore, we forecast data for an unknown year, which slightly reduces the classification accuracy. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

The latest methods based on deep learning have achieved amazing results regarding the complex work of inpainting large missing areas in an image. This type of method generally attempts to generate one single "optimal" inpainting result, ignoring many other plausible results. However, considering the uncertainty of the inpainting task, one sole result can hardly be regarded as a desired regeneration of the missing area. In view of this weakness, which is related to the design of the previous algorithms, we propose a novel deep generative model equipped with a brand new style extractor which can extract the style noise (a latent vector) from the ground truth image. Once obtained, the extracted style noise and the ground truth image are both input into the generator. We also craft a consistency loss that guides the generated image to approximate the ground truth. Meanwhile, the same extractor captures the style noise from the generated image, which is forced to approach the input noise according to the consistency loss. After iterations, our generator is able to learn the styles corresponding to multiple sets of noise. The proposed model can generate a (sufficiently large) number of inpainting results consistent with the context semantics of the image. Moreover, we check the effectiveness of our model on three databases, i.e., CelebA, Agricultural Disease, and MauFlex. Compared to state-of-the-art inpainting methods, this model is able to offer desirable inpainting results with both a better quality and higher diversity. The code and model will be made available on https://github.com/vivitsai/SEGAN.

This paper presents an exact solution to the Timoshenko beam theory (TBT) for first-order analysis, second-order analysis, and stability. The TBT covers cases associated with small deflections based on shear deformation considerations, whereas the Euler–Bernoulli beam theory (EBBT) neglects shear deformations. Thus, the Euler–Bernoulli beam is a special case of the Timoshenko beam. The moment-curvature relationship is one of the governing equations of the EBBT, and closed-form expressions of efforts and deformations are available in the literature. However, neither an equivalent to the moment-curvature relationship of EBBT nor closed-form expressions of efforts and deformations can be found in the TBT. In this paper, a moment-shear force-curvature relationship, the equivalent in TBT of the moment-curvature relationship of EBBT, was presented. Based on this relationship, first-order and second-order analyses were conducted, and closed-form expressions of efforts and deformations were derived for various load cases. Furthermore, beam stability was analyzed and buckling loads were calculated. Finally, first-order and second-order element stiffness matrices were determined.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Existing methods enhance mid-low resolution remote sensing ship detection by feeding super-resolved images to the detectors. Although these methods marginally improve the detection accuracy, the correlation between image super-resolution (SR) and ship detection is under-exploited. In this paper, we propose a simple but effective ship detection method called ShipSR-Det, in which both the output image and the intermediate features of the SR module are fed to the detection module. Using the super-resolved feature representation, the potential benefit introduced by image SR can be fully used for ship detection. We apply our method to the SSD and Faster-RCNN detectors and develop ShipSR-SSD and ShipSR-Faster-RCNN, respectively. Extensive ablation studies validate the effectiveness and generality of our method. Moreover, we compare ShipSR-Faster-RCNN with several state-of-the-art ship detection methods. Comparative results on the HRSC2016, DOTA and NWPU VHR-10 datasets demonstrate the superior performance of our proposed method.

Gwosch et al. (2020) and Balzarotti et al. (2017) purport MINFLUX as the next revolutionary fluorescence microscopy technique claiming a spatial resolution in the range of 1-3 nm in fixed and living cells. Though the claim of molecular resolution is attractive, I am concerned whether true 1 nm resolution has been attained. Here, I compare the performance with other super-resolution methods focussing particularly on spatial resolution claims, atypical image rendering, visualisation enhancement, subjective filtering of localizations, detection vs labelling efficiency and the possible limitations when imaging biological samples containing densely labelled structures. I hope the analysis and evaluation parameters presented here are not only useful for future research directions but also microscope users, developers and core facility managers when deciding on an investment for the next 'state-of-the-art' instrument.

Biological systems are highly regulated. They are also highly resistant to sudden perturbations enabling them to maintain the dynamic equilibrium essential for sustenance of life. This robustness is conferred by regulatory mechanisms that influence the activity of enzymes/proteins within their cellular context, to adapt to changing environmental conditions. However, the initial rules governing the study of enzyme kinetics were tested and implemented for mostly cytosolic enzyme systems that were easy to isolate and/or recombinantly express. Moreover, these enzymes lacked complex regulatory modalities. Now, with academic labs and pharmaceutical companies turning their attention to more complex systems (for instance, multi-protein complexes, oligomeric assemblies, membrane proteins and post-translationally modified proteins), the initial axioms defined by Michaelis-Menten (MM) kinetics are rendered inadequate and the development of a new kind of kinetic analysis to study these systems is required. The current review strives to present an overview of enzyme kinetic mechanisms that are atypical and, oftentimes, do not conform to the classical MM kinetics. Further, it presents initial ideas on the design and analysis of experiments in early drug-discovery for such systems, to enable effective screening and characterisation of small-molecule inhibitors with desirable physiological outcomes.

The European Space Agency (ESA) defines as Earth observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow. ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem never accomplished to date in operating mode by any EO data provider at the ground segment. Herein, it is considered: (I) necessary not sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes. (II) Synonym of EO Analysis Ready Data (ARD) format. (III) Equivalent to a horizontal policy for background developments in Space Economy 4.0. In compliance with the GEO-CEOS Quality Assurance Framework for EO Calibration/Validation guidelines, to contribute toward filling an analytic and pragmatic information gap from multi-sensor EO big data to timely, comprehensive and operational EO value-adding information products and services, this work presents an innovative AutoCloud+ CV software toolbox for cloud and cloud-shadow quality layer detection in ESA EO Level 2 product. In vision, spatial information dominates color information. Inspired by this true-fact, the inherently ill-posed AutoCloud+ CV software was conditioned, designed and implemented to be “universal”, meaning fully automated (no human-machine interaction is required), near real-time, robust to changes in input data and scalable to changes in MS imaging sensor’s spatial and spectral resolution specifications.

Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the sub-district level and aggregated to monthly, annual and seasonal rainfall totals and the number of rainy days. The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. Non-Parametric Mann-Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann-Kendall tests (Pre-Whitening, Trend Free Pre-Whitening, Bias Corrected Pre-Whitening and two variants of Variance Correction Approaches) were applied. A significant increasing summer rainfall trend is observed in 6 out of 27 stations. Significant decreasing trends are observed at two stations in the south-west monsoon season and at two stations in the north-east monsoon season. To identify the trend change-points in the time series, distribution-free Cumulative SUM test and sequential Mann-Kendall tests were applied. Two open-source library packages were developed in R language namely, ‘modifiedmk’ and ‘trendchange’ to implement the statistical tests mentioned in this paper. The study will benefit water resource management, drought mitigation, socio-economic development and sustainable agricultural planning in the region.

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

Efficiently targeted cancer therapy without causing detrimental side effects is necessary for alleviating patient care and improving survival rates. This paper presents observations of morphological changes in H1299 human lung cancer cells following W-band MMW irradiation (75 – 105 GHz) at a non-thermal power density of 0.2 mW/cm², investigated over 14 days of subsequent physiological incubation following exposure. Microscopic analyses of physical parameters measured indicate MMW irradiation induces significant morphological changes characteristic of apoptosis and senescence. The Immediate short-term responses translate into long-term effects, retained over the duration of the experiment(s); reminiscent of the phenomenon of Accelerated Cellular Senescence (ACS) achieving terminal tumorigenic cell growth. Further, results were observed to be treatment-specific in energy (dose) dependent manner and were achieved without the use of chemotherapeutic agents, ionizing radiation or thermal ablation employed in conventional methods; thereby overcoming associated side effects. Adaptation of the experimental parameters of this study for clinical oncology concomitant with current developmental trends of non-invasive medical endoscopy alleviates MMW therapy as an effective treatment procedure for human non-small cell lung cancer (NSCLC).

The evolution of integrable classical systems leads to conserved quantities and vanishing Poisson brackets. In contrast, such invariants do not exist in the dynamics of non-integrable systems, which include (but are not limited to) deterministic models with long-term chaotic behavior. The object of this review is to briefly survey the mathematical background of nonintegrability and its role in the physics unfolding well-above the Standard Model (SM) scale.

People often say that beer tastes better from a bottle than from a can. However, one can ask whether this perceived difference is reliable across consumers; And, if so, whether it is purely a psychological phenomenon (associated with the influence of packaging on taste perception), or whether instead it reflects some more mundane physico-chemical interaction between the packaging material (or packing procedure/process) and the contents. We conducted two experiments in order to address these important questions. In the main experiment, 151 participants at the 2016 Edinburgh Science Festival were served a beer in a plastic cup. The beer was either poured from a bottle or can (i.e., a between-participants experimental design was used) and the participants were encouraged to pick up the packaging in order to inspect the label before tasting the beer. The participants rated the perceived taste, quality, and freshness of the beer, as well as their likelihood of purchase, and their estimate of the price. All of the beer came from the same batch (from Barney’s Brewery in Edinburgh). Nevertheless, those who evaluated the bottled beer rated it as tasting better than those who rated the beer that had been served from a can. Having demonstrated such a perceptual difference in terms of taste, we then went on to investigate whether people would prefer one packaging format over the other when the beer from bottle and can was served to a new group of participants blind (i.e., when the participants did not know the packaging material). The participants in this control study (N = 29) were asked which beer they preferred or else could state that the two samples tasted the same. No sign of preference was obtained under such conditions. Explanations for the psychological impact of the packaging format, in terms of differences in packaging weight (between tin and glass), and/or prior associations of quality with specific packaging materials/formats (what some have chosen to call ‘image molds’) are discussed.

Laws and regulations make powerful contribution in addressing multitudes of public health concerns. We examined the Public Health Act (PHA) in Malawi to understand its relevance to the ever-growing and changing threats posed by infectious and non-infectious diseases. The current Public Health Act of Malawi came into effect in 1948 to protect and preserve public health. The Act has undergone several amendments, the last one being in 1975. It draws much of its inspiration and standards from the 19th century British laws on insanitary housing, poor ventilation, and drainage. Such laws are silent on emerging major public health concerns including non-communicable diseases (NCDs) such as cardiovascular diseases and diabetes as well as road traffic injuries. This makes the Act outdated and ill equipped to address the 21st century public health concerns. Although supplementary legislation such as the HIV/AIDS Act and Mental Treatment Act have recently been enacted, they are yet to be consolidated into the Public Health Act. Consequently, existing policies and strategic plans that are meant to address gaps in public health and ensure coordinated effort lack support of laws and regulations. The Act also places great emphasis on mandatory vaccinations, quarantine and isolation against smallpox, a disease that has long been eradicated. Furthermore, although the Public Health Act outlines powers, duties and penalties, it fails to reinforce acceptable behaviour due to the insignificant penalties for noncompliance. There is a need for immediate and prompt revision and restructuring of the Public Health Act based on scientific evidence. Such laws require adequate consultation and interaction with key experts and stakeholders from a wide range of disciplines.

Biological invasions have reached an unprecedented level and the number of introduced species is still increasing worldwide. Despite major advances in invasion science, the determinants of success of introduced species, the magnitude and dimensions of their impact, and the mechanisms sustaining successful invasions are still debated. Empirical studies show divergent impacts of non-native populations on ecosystems and contrasting effects of biotic and abiotic factors on the dynamics of non-native populations; this is hindering the emergence of a unified theory of biological invasions. We propose a synthesis that merges perspectives from population, community, and ecosystem levels. Along a timeline of ecosystem transformation driven by non-native species, from historical to human-modified ecosystems, we order invasion concepts and theories to clarify their chaining and relevance during each step of the invasion process. This temporal sorting of invasion concepts shows that each concept is relevant at a specific stage of the invasion. Concepts and empirical findings on non-native species may appear contradictory. However, we suggest that, when mapped onto an invasion timeline, they may be combined in a complementary way. An overall scheme is proposed to summarise the theoretical dynamics of ecosystems subjected to invasions. For any given case study, this framework provides a guide through the maze of theories and should help choose the appropriate concepts according to the stage of invasion.

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

The use of unmanned aerial vehicle (UAV) during emergency response of a disaster has been widespread in recent years and the terrain images captured by the cameras on board these vehicles are significant sources of information for such disaster monitoring operations. Thus, analyzing such images are important for assessing the terrain of interest during such emergency response operations. Further, these UAVs are mainly used in disaster monitoring systems for the automated deployment of sensor nodes in real time. Therefore, deploying and localizing the wireless sensor nodes optimally, only in the regions of interest that are identified by segmenting the images captured by UAVs, hold paramount significance thereby effecting their performance. In this paper, the highly effective nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied for the segmentation of such terrestrial images and also for the localization of the deployed sensor nodes. The problem is formulated as a multi-dimensional optimization problem and PGSA has been used to solve it. Furthermore, the proposed method has been compared to other existing evolutionary methods and simulation results show that PGSA gives better performance with respect to both speed and accuracy unlike other techniques in literature.

Pine nuts are non-wood forest products (NWFP) with constantly growing market notwithstanding a series of phytosanitary issues and related trade problems. The aim of paper is to review the literature on the relationship between phytosanitary problems and trade development. Production and trade of pine nuts in Mediterranean Europe have been negatively affected by the spreading of Sphaeropsis sapinea (a fungus) associated to an adventive insect Leptoglossus occidentalis (fungal vector), with impacts on forest management activities, production and profitability and thus in value chain organization. Reduced availability of domestic production in markets with growing demand has stimulated the import of pine nuts. China has become a leading exporter of pine nuts, but its export is affected by a symptom associated to the nuts of some pine species: the ‘pine nut syndrome’ (PNS). Most of the studies embraced during the review are associated to PNS occurrence associated to the nuts of Pinus armandii. In the literature review we highlight the need for a comprehensive and interdisciplinary approach to the analysis of the pine nuts value chain organisation, where research on food properties and clinical toxicology be connected to breeding and forest management, forest pathology and entomology and trade development studies.

The object of this paper is the bending analysis of isotropic rectangular Kirchhoff plates subjected to non-uniform heating (NUH) using the Fourier transform method. The bottom and top surfaces of the plate are assumed to have different changes in temperature, whereas the change in temperature of the mid-surface is zero. According to classical plate theory, the governing equation of the plate contains second derivatives of the NUH; these derivatives are zero by constant value of the NUH, which leads to its absence in the governing equation. This paper presented an approach by which Fourier sine transform was utilized to describe the NUH, while the double trigonometric series of Navier and the simple trigonometric series of Lévy were utilized to describe the deflection curve. Thus, the NUH appeared in the governing equation, which simplified the analysis. Rectangular plates simply supported along all edges were analyzed, bending moments, twisting moments, and deflections being determined. In addition, rectangular plates simply supported along two opposite edges were analyzed; the other edges having various support conditions (free, simply supported, and fixed).

Over the years, artificial intelligence (AI) is spreading its roots in different areas by utilizing the concept of making the computers learn and handle complex tasks that previously require substantial laborious tasks by human beings. With better accuracy and speed, AI is helping lawyers to streamline work processing. New legal AI software tools like Catalyst, Ross intelligence, and Matlab along with natural language processing provide effective quarrel resolution, better legal clearness, and superior admittance to justice and fresh challenges to conventional law firms providing legal services using leveraged cohort correlate model. This paper discusses current applications of legal AI and suggests deep learning and machine learning techniques that can be applied in future to simplify the cumbersome legal tasks.

Gut bacteria play an important role in several metabolic processes and human diseases, such as obesity and its co-morbidities, like fatty liver disease, insulin resistance/diabetes and cardiovascular events. Among several factors, dietary patterns, probiotics, prebiotics, synbiotics, antimicrobials and non-dietary factors, such as stress, age, exercise and climatic conditions, can dramatically impact the human gut microbiota diversity and equilibrium. However, the effect of minor food constituents, including food additives and trace contaminants, on human gut microbiota has received less attention. Consequently, the present review aimed to provide an objective perspective of the current knowledge regarding the impacts of minor food constituents on human gut microbiota and consequently, on human health.

QSO B0218+357 is a blazar located at a cosmological redshift of 0.944. The source is gravitationally lensed by a spiral galaxy located at the redshift of 0.68. Strong gravitational lensing splits the signals emitted by the source into two components separated by 10-12 days, as observed in radio and gamma-ray bands. In July 2014 a GeV flare was observed by Fermi-LAT, triggering follow-up observations with the MAGIC telescopes at energies above 100 GeV. The expected time delay between the components allowed us to plan broad band MWL observations before, during and after the trailing component of the emission. The MAGIC observations at the expected time of arrival of the trailing component resulted in the first detection of QSO B0218+357 in Very-High-Energy (VHE, >100 GeV) gamma rays. It is both the farthest known VHE object and one of only a few Flat Spectrum Radio Quasars detected in this energy range. We report here the observed multiwavelength spectral and temporal properties of the emission during the 2014 flare.

New class of biologically active and non-active compounds can be synthesized via transition metal mediated Suzuki cross coupling reaction that has a great impact on the advancement of organic chemistry. These resulted products can lend a helping hand in pharmaceutical and polymer chemistry for the betterment of mankind. Suzuki-Miyaura cross coupling reaction is one of the best tools through which many natural and non-natural compounds can be synthesized.

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

An effective on-board cloud detection method in small satellites would greatly improve the downlink data transmission efficiency and reduce the memory cost. In this paper, an ensemble method combining a lightweight U-Net with wavelet image compression is proposed and evaluated. The red, green, blue and infrared waveband images from Landsat-8 dataset are trained and tested to estimate the performance of proposed method. The LeGall-5/3 wavelet transform is applied on the dataset to accelerate the neural network and improve the feasibility of on-board implement. The experiment results illustrate that the overall accuracy of the proposed model achieves 97.45% by utilizing only four bands. Tests on low coefficients of compressed dataset have shown that the overall accuracy of the proposed method is still higher than 95%, while its inference speed is accelerated to 0.055 second per million pixels and maximum memory cost reduces to 2Mb. By taking advantage of mature image compression system in small satellites, the proposed method provides a good possibility of on-board cloud detection based on deep learning.

Originally developed to image the shadow region of the central black hole in Sagittarius A* and in the nearby galaxy M87, the Event Horizon Telescope (EHT) provides deep, very high angular resolution data on other AGN sources too. The challenges of working with EHT data have spurred the development of new image reconstruction algorithms. This work briefly reviews the status of the EHT and its utility for observing AGN sources, with emphasis on novel imaging techniques that offer the promise of better reconstructions at 1.3 mm and other wavelengths.

MicroRNAs (miRNAs) are crucial regulatory RNAs, originated from hairpin precursors. For the past decade, researchers are focusing extensively on miRNA profiles in various plants. However studies on precursor miRNAs (pre-miRNAs) global profiling stay static even in model plants. Here, for the first time in a non-model plant, Abelmoschus esculentus with negligible genome information, we are reporting the global profiling to characterize the miRNAs and their associated pre-miRNAs by applying next generation sequencing approach. Preliminarily we performed sRNA (small RNA) sequencing with five biological replicates of leaf samples to attain 207,285,863 reads and data analysis using miRPlant keyed out 128 known and 845 novel miRNA candidates. With the objective to seize their associated hairpin precursors, we accomplished pre-miRNA sequencing to attain 83,269,844 reads. The paired end reads are merged, adaptor trimmed and the resulting 40-241 nt (nucleotide) sequences were picked out for analysis by using perl scripts from miRGrep tool and in-house built shell script for Minimum Fold Energy Index (MFEI) calculation. Applying stringent criteria of dicer cleavage pattern and perfect stem loop structure revealed precursors for 57 known miRNAs of 15 families and 18 novel miRNAs. Quantitative Real Time (qRT) PCR was performed to determine the expression of selected miRNAs.

This paper presents an upcoming nonvolatile memories (NVM) overview. Non-volatile memory devices are electrically programmable and erasable to store charge in a location within the device and to retain that charge when voltage supply from the device is disconnected. The non-volatile memory is typically a semiconductor memory comprising thousands of individual transistors configured on a substrate to form a matrix of rows and columns of memory cells. Non-volatile memories are used in digital computing devices for the storage of data. In this paper we have given introduction including a brief survey on upcoming NVM's such as FeRAM, MRAM, CBRAM, PRAM, SONOS, RRAM, Racetrack memory and NRAM. In future Non-volatile memory may eliminate the need for comparatively slow forms of secondary storage systems, which include hard disks.

Abstract The aim of this research is to conduct a systematic review analysing the role of the physiotherapy interventions in palliative care. PRISMA as a critical appraisal tool was utilised for the selection of the research articles. The inclusion criteria were based on the year of publication, ease of availability, language, geographical location, and study type. To ensure the credibility, databases such as Elsevier, Proquest, and EBSCO Host were used to filter the grey content. Data published in the past ten years (2009-onwards) was only included to ensure the selection of the most recent interventions used by the physiotherapists. A total of 11 articles were selected which determined that physiotherapy interventions involving breathing exercises, aerobic exercises, manual therapies, and educational awareness were critical to promoting the functional capability and empower the patients.

Plant breeding primarily focuses on improving conventional agronomic traits, e.g. yield, quality, and resistance to biotic and abiotic stress; however, genetic improvement methods are being rapidly enhanced through genomics and phenomics. In the Genomics-Phenomics-Agronomics (GPA) paradigm, diverse research approaches have been conducted to bridge any two of these elements, and recently, all of them together. This review first highlights the progress to link i) genomics to agronomics; ii) genomics to phenomics; and iii) phenomics to agronomics. Secondly, the GPA domain is dissected into different layers, each addressing the three elements simultaneously. These dissected layers include genetic dissection through gene mapping using genome-wide association studies and genomic selection using Best Linear Unbiased Prediction, Bayesian approaches, and machine learning. The objective of the review is to help readers to grasp the core developments among the exponentially growing literature in each of these fields. Through this review, the connections among the three elements of the GPA paradigm are coherently integrated toward the prospect of sustainable development of agronomic traits through both genomics and phenomics.

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

This paper presents an approach to the vibration analysis of axially functionally graded non-prismatic Timoshenko beams (AFGNPTB) using the finite difference method (FDM). The characteristics (cross-sectional area, moment of inertia, elastic moduli, shear moduli, and mass density) of axially functionally graded beams vary along the longitudinal axis. The Timoshenko beam theory covers cases associated with small deflections based on shear deformation and rotary inertia considerations. The FDM is an approximate method for solving problems described with differential equations. It does not involve solving differential equations; equations are formulated with values at selected points of the structure. In addition, the boundary conditions and not the governing equations are applied at the beam’s ends. In this paper, differential equations were formulated with finite differences, and additional points were introduced at the beam’s ends and at positions of discontinuity (supports, hinges, springs, concentrated mass, spring-mass system, etc.). The introduction of additional points allowed us to apply the governing equations at the beam’s ends and to satisfy the boundary and continuity conditions. Moreover, grid points with variable spacing were also considered, the grid being uniform within beam segments. Vibration analysis of AFGNPTB was conducted with this model, and natural frequencies were determined. Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of AFGNPTB, considering the damping. The results obtained in this study showed good agreement with those of other studies, and the accuracy was always increased through a grid refinement.

Radio loud active galactic nuclei are composed of different spatial features, each one characterized by different spectral properties in the radio band. Among them, blazars are the most common class of sources detected at gamma-rays by \fermi, and their radio emission is dominated by the flat spectrum compact core. In this contribution, we explore the connection between emission at high energy revealed by \fermi\ and at radio frequency. Taking as a reference the strong and very highly significant correlation found between gamma rays and cm-$\lambda$ radio emission, we explore different behaviours found as we change the energy range in gamma rays and in radio, therefore changing the physical parameters of the zones involved in the radiation emission. We find that the correlation weakens when we consider (1) gamma rays of energy above 10 GeV (except for high synchrotron peaked blazars) or (2) low frequency radio data taken by the Murchison Widefield Array; on the other hand, the correlation strengthens when we consider mm-$\lambda$ data taken by ALMA.

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the comparison of two popular off-the-shelf sUAVs: 3DR Iris and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every second for two minutes. The position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. Results showed that both sUAV performed well in both the hovering test and the rectilinear motion test. This demonstrates that both sUAVs can be used for agricultural monitoring.

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

This study sought to investigate steel frames’ performance with dual lateral loader system (Frame bending + bracings of divergent and convergent) in near and far filed to fault. In order to this, four categories of steel frame with dual system with 8, 10 and 12 story are designed with average formation based on existing seismic regulation in 2800 standard of Iran and tenth chapter of national regulations of construction (planning and performing steel construction). Time history non-linear dynamic analysis under the effect of near and far field earthquakes has been done on plan’s models. Then the maximum of floors’ dislocation, floors’ drift, roof dislocation, base shear and energy curves of frames are shown and compared with each other. All non-linear time history analyses have been accomplished using PERFORM 3D software.

High redshift blazars are among the most powerful objects in the Universe. Although they represent a significant fraction of the extragalactic hard X-ray sky, they are not commonly detected in gamma-rays. High redshift (z>2) objects represent <10 per cent of the AGN population observed by Fermi so far, and gamma-ray flaring activity from these sources is even more uncommon. The characterization of the radio-to-gamma-ray properties of high redshift blazars represents a powerful tool for the study of the energetics of such extreme objects and the Extragalactic Background Light. This contribution will present results of multi-band campaigns, from radio to gamma-rays, on PKS 0836+710, PKS 2149-306, and TXS 0536+145. The latter is the highest redshift detection of a flaring gamma-ray blazar so far. At the peaks of their respective flares these sources reached an apparent isotropic gamma-ray luminosity of about 10^50 erg/s, which is comparable with the luminosity observed from the most powerful blazars. The physical properties derived from the multi-wavelength observations of these sources are then compared with those shown by the high redshift population.

There must exist a reformulation of quantum field theory which does not refer to classical time. We propose a pre-quantum, pre-spacetime theory, which is a matrix-valued Lagrangian dynamics for gravity, Yang-Mills fields, and fermions. The definition of spin in this theory leads us to an eight dimensional octonionic space-time. The algebra of the octonions reveals the standard model; model parameters are determined by roots of the cubic characteristic equation of the exceptional Jordan algebra. We derive the asymptotic low energy value 1/137 of the fine structure constant, and predict the existence of universally interacting spin one Lorentz bosons, which replace the hypothesised graviton. Gravity is not to be quantized, but is an emergent four-dimensional classical phenomenon, precipitated by the spontaneous localisation of highly entangled fermions.

The Directive 2014/95, in force in 2017, is the first European step that requires mandatory non-financial information to undertakings (all “public interest entities” with more than 500 employees). The regulation is concerning sustainability information as environmental, social and employee, human rights and anti-corruption and bribery matters and disclosure of diversity policy for board members. The study, in the strand of the regulation of accounting, part of the broader field of research into accounting regulation, contributes to the debate on the quality of regulation, in this specific case referred to sustainability disclosure. The regulation of sustainability matters is studied in literature broadly in a post-implementation phase and at national level. This research, instead, aims to analyse, through the causal chain of regulatory policy, in the ex-ante stage, the quality of the regulation and, at least, the usefulness of the normative pressure. The Oil & Gas sector is chosen as sample of the study, because it is one of the most advanced sectors in sustainability disclosure. The examination of the law, in terms of content requirements (what) and location of information (where), is the basis to apply the disclosure-scoring system, a partial form of content analysis, to the reports of the sample. The findings reveal a good level of completeness of non-financial information, however, there are some areas that have to be improved to reach the requests of the Directive. Results show also the presence of overlap between financial reports and sustainability ones. In conclusion, the regulation is useful to prompt undertakings to reflect on their reporting and so doing improve their sustainability approach.

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

The article focuses on mining of non-energy mineral resources with minimum environmental impacts. It issues from research results of a project Competence Centre for Effective and Ecological Mining of Mineral Resources implemented at the Faculty of Mining and Geology at VŠB-Technical University of Ostrava, Czech Geological Survey, a company Watrad ltd., a state enterprise Diamo, a company RPS Ostrava plc and a company Sedlecký kaolin plc. The paper starts with a partial analysis of the existing legal norms related to mining and processing of mineral resources. Next, it analyses mineral resource mining options free of negative environmental impacts. The fundamental tool to assess potential environmental impacts of mining is the implementation of Environmental Impact Assessment (EIA) process for a given mineral resource. In the Czech Republic environmental impact assessment is carried out by course of Act 100/2001 Coll. Its important amendment is Act 39/2015 Coll. claiming, inter alia, that the environmental impact assessment is rigidly connected with other permits and procedures, such as the zoning process and building construction permits. The article describes the environmental impacts of mining of non-energy mineral resources, including the following factors: appropriation of land, impacts on surface water, ground water and soil, noise, influence on the landscape character, and air pollution. The paper also includes a case study summarizing information on the environmental factors that may play a role in potential underground mining of graphite in the deposit Český Krumlov - Městský Vrch and the deposit Lazec - Křenov.

This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Finally, in the last section we present a concerning numerical example and the respective software.

A time-sequential working, spatially multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array is presented. The wavelength-selective filter barrier emits the light from a display over a larger active area than common autostereoscopic barrier displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. To find well working display designs, automated searches by simulation and computational evaluation has been proceeded. Wavelength-selective filter barrier arrangements exhibit characteristics different from common barrier displays with similar barrier pitch and ascent. In particular, these constructions show strong angular luminance dependency under barrier inclination specified by correspondent slant angle. In time-sequential implementation it is important to avoid that quick eye or eyelid movement lead to visible color artifacts. In the millisecond regime tunable liquid crystal Fabry-Pérot color filters for the colors red, green and blue are presented. They consist of a sub-micrometer thick nematic layer sandwiched between dielectric mirrors and ITO-electrodes. These cells shall switch narrow-banding light of red, green or blue. An array for a glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures. The newly introduced design is usable as a multi user display as well as a single user system with user adaptive control.

This article develops algorithms for the characterization and the visualization of micro-scale features by using a small number of sample points, and with a goal to mitigate for the measurement shortcomings which are often destructive or time consuming. We implement the algorithms to rapidly examine the microscopic features of a Microelectromechanical System (MEMS) surface. Such images are highly dense; therefore, traditional image processing techniques might be computationally expensive. The contribution of this research include first, we develop local and global algorithm based on modified Thin Plate Spline (TPS) model to reconstruct high resolution images of the micro-surface’s topography, and its derivatives by using low resolution images. Second, we obtain a bending energy algorithm from our modified TPS model, and use it to filter out image defects. Finally, we develop a computationally efficient Windowing technique, which combines TPS and Linear Sequential Estimation (LSE), to enhance the visualization of images. The Windowing technique allows rapid image reconstruction based on the reduction of inverse problem.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

This paper presents experimental identification and vibration suppression of a flexible manipulator with non-collocated piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model to compensate the non-collocation. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Besides, vibrations of the higher modes are also slightly suppressed. Accordingly, multi-mode vibrations of the manipulator are well attenuated, and the tip displacement converges quickly with the proposed method.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Prompt removal of misfolded membrane proteins and misassembled membrane protein complexes is essential for membrane homeostasis. However, the elimination of these toxic proteins from the hydrophobic membrane environment has high energetic barriers. Transmembrane FtsH is the only known ATP-dependent protease responsible for this task, unlike other well-studied soluble ATP-dependent proteases. The mechanisms by which FtsH recognizes, unfolds, translocates, and proteolyzes its substrates remain unclear. Here, we report the crystal structures of the Thermotoga maritima FtsH periplasmic domain (PD) in an associative trimeric state at a 1.5-1.95 Å resolution. We also describe the pH-dependent oligomerization states of the isolated PD using dynamic light scattering. These observations help us understand how FtsH recognizes membrane-anchored misfolded proteins.

Corona virus disease (COVID-19) has infected over more than 10 million people around the globe and killed at least 500K worldwide by the end of June 2020. As this disease continues to evolve and scientists and researchers around the world now trying to find out the way to combat this disease in most effective way. Chest X-rays are widely available modality for immediate care in diagnosing COVID-19. Precise detection and diagnosis of COVID-19 from these chest X-rays would be practical for the current situation. This paper proposes one shot cluster based approach for the accurate detection of COVID-19 chest x-rays. The main objective of one shot learning (OSL) is to mimic the way humans learn in order to make classification or prediction on a wide range of similar but novel problems. The core constraint of this type of task is that the algorithm should decide on the class of a test instance after seeing just one test example. For this purpose we have experimented with widely known Generalized Regression and Probabilistic Neural Networks. Experiments conducted with publicly available chest x-ray images demonstrate that the method can detect COVID-19 accurately with high precision. The obtained results have outperformed many of the convolutional neural network based existing methods proposed in the literature.

Non-melanoma skin cancers (NMSCs) are the leading cause of skin cancer-related morbidity and mortality. Effective strategies are needed to control NMSC occurrence and progression. Non-toxic, plant-derived extracts have been shown to exert multiple anti-cancer effects. Graviola (Annona muricata), a tropical fruit-bearing plant, has been used in traditional medicine against multiple human diseases including cancer. The current study investigated the effects of graviola leaf and stem extract (GLSE) and its solvent-extracted fractions on two human NMSC cell lines, UW-BCC1 and A431. GLSE was found to: i) dose-dependently suppress UW-BCC1 and A431 cell growth, motility, wound closure, and clonogenicity; ii) induce G₀/G₁ cell cycle arrest by downregulating cyclin/cdk factors while upregulating cdk inhibitors, and (iv) induce apoptosis as evidenced by cleavage of caspases-3, -8 and PARP. Further, GLSE suppressed levels of activated hedgehog (Hh) pathway components Smo, Gli 1/2, and Shh while inducing SuFu. GLSE also decreased the expression of pro-apoptotic protein Bax while decreasing the expression of the anti-apoptotic protein Bcl-2. We determined that these activities were concentrated in an acetogenin/alkaloid-rich dichloromethane subfraction of GLSE. Our data identify graviola extracts and their constituents as promising sources for new chemopreventive and therapeutic agent(s) to be further developed for the control of NMSCs

Neuroscience has overwhelmingly and understandably focused on the synaptic modality of signal transmission. There is evidence, however, that from an evolutionary perspective, non-synaptic transmission (NST) preceded synaptic signaling. Moreover, in modern nervous systems, NST coexists and extensively interacts with synaptic transmission modifying neuronal dynamics. In fact, NST remains widespread in complex animals, especially within the interoceptive system where the dearth of insulating barriers such as myelin sheaths and the blood-brain barrier enhances the communication between neural and non-neural tissues mediated by NST. We suggest that this physiological arrangement makes a fundamental contribution to interoception¾the process of sensing visceral states¾which is an essential underpinning of the capacity to feel and the foundation of affective processing.

This paper presents an approach to the vibration analysis of axially functionally graded (AFG) non-prismatic Euler-Bernoulli beams using the finite difference method (FDM). The characteristics (cross-sectional area, moment of inertia, elastic moduli, and mass density) of AFG beams vary along the longitudinal axis. The FDM is an approximate method for solving problems described with differential equations. It does not involve solving differential equations; equations are formulated with values at selected points of the structure. In addition, the boundary conditions and not the governing equations are applied at the beam’s ends. In this paper, differential equations were formulated with finite differences, and additional points were introduced at the beam’s ends and at positions of discontinuity (supports, hinges, springs, concentrated mass, spring-mass system, etc.). The introduction of additional points allowed us to apply the governing equations at the beam’s ends and to satisfy the boundary and continuity conditions. Moreover, grid points with variable spacing were also considered, the grid being uniform within beam segments. Vibration analysis of AFG non-prismatic Euler-Bernoulli beams was conducted with this model, and natural frequencies were determined. Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of AFG non-prismatic Euler-Bernoulli beams, considering the damping. The results obtained in this paper showed good agreement with those of other studies, and the accuracy was always increased through a grid refinement.

This work promotes new methods in Mathematical Modeling, consisting in the use of the methods of Non-standard Analysis in Topoi, having as its main purpose, the mathematical definitions of the pseudoparticles from the title, with arguments from Biology/Physiology (Mathematical Neuroscience), Physics (String Theory and Emergent Quantum Mechanics), and Cybernetics (Global Brain, including Natural and Artificial Intelligence). The connections between brain and mind will be scketched via the genetic/epigenetic interplay. The topoi model the intuitionistic logic (multi-valued) and have been used in Quantum Physics while Non-Standard Analysis in SET (= the Category of sets) has been applied in Mathematical Economics; topics from Theory of Categories were also used in the study of Consciousness; however, the combination topoi - non-standard analysis was never used until now in Applied Mathematics. Another important objective is to produce progress in this aria of Pure Mathematics also - the build of non-standard analysis in classes of topoi, already used in Physics. We propose the logic of non-standard extensions in topoi as a model of the human thinking (based on infons/receptons), these theories representing new top and very difficult results in Abstract Mathematics either.

This paper focuses on the problem of finite-time stabilization of homogeneous, non-Lipschitz systems with dilations. A key contribution of this paper is the design of a virtual recursive Hölder, non-Lipschitz state feedback which renders the non-Lipschitz systems in the special case dominated by a lower-triangular nonlinear system, finite-time stable. The proof is based on a recursive design algorithm developed recently, to construct the virtual Hölder continuous, finite-time stabilizer as well as a C¹ positive definite and proper Lyapunov function that guarantees finite-time stability of the non-Lipschitz non-linear systems.

One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.

This study aims to test a model linking destination image, perceived value, tourist satisfaction, and tourist loyalty. Based on a sample of 300 tourists travelling by car from the World Natural Heritage Site of Tianchi, China, a new model of destination image was explored and data were analysed using partial least squares structural equation modelling (PLS-SEM). The results show that perceived value and satisfaction are direct antecedents of destination loyalty. Above all, perceived value and tourist satisfaction mediate the relationship between destination image and loyalty. Finally, this study discusses the theoretical and management implications of the findings in order to boost the tourism industry in the context of car trips.

Two of the most common activation functions (AF) in deep neural networks (DNN) training are Sigmoid and ReLU. Sigmoid was tend to be more popular the previous decades, but it was suffering with the common vanishing gradient problems. ReLU has resolved these problems by using zero gradient and not tiny values for negative weights and the value “1” for all positives. Although it significant resolves the vanishing of the gradients, it poses new issues with dying neurons of the zero values. Recent approaches for improvements are in a similar direction by just proposing variations of the AF, such as Leaky ReLU (LReLU), while maintaining the solution within the same unresolved gradient problems. In this paper, the combining of the Sigmoid and ReLU in one single function is proposed, as a way to take the advantages of the two. The experimental results demonstrate that by using the ReLU’s gradient solution on positive weights, and Sigmoid’s gradient solution on negatives, has a significant improvement on performance of training Neural Networks on image classification of diseases such as COVID-19, text and tabular data classification tasks on five different datasets.

A supply chain management (SCM) system is a strategic cooperative that organically integrates all supply chains to improve the performance of a company. The greatest critical success factor of SCM is partnership. Without cooperation between companies, SCM performance is limited. Does this imply, therefore, that companies within the supply chain can achieve mutual transactions equally? If the power between companies is unequal, how does this affect their partnership? The focus of this study is to assess whether power types enhance SCM performance through partnerships. We categorize power types as mediated and non-mediated. Mediated power is categorized based on coercion, reward, and legitimate, while non-mediated power is categorized based on information, expert, and reference. Therefore, this study examines how power types form a causal partnership relationship within the supply chain, and performs an empirical investigation on how the partnerships influence SCM performance.

High pressure crystallization technique has been successfully used to prepare an elusive form II of a non-steroidal anti-inflammatory drug, Mefenamic acid. Single crystal of form II was grown at 0.3 GPa from an 4:1 methanol/ ethanol mixture as a solvent using Diamond Anvil Cell. Comparison of crystal structures show that the efficient packing of MA molecules in Form II results from the structural flexibility of MA acid molecules. Compression studies performed on the single crystal of form I resulted in 14 % decrease of unit cell volume up to 2.5 GPa. No phase transition was observed up to this pressure. A reconstructive phase transition is required to induce conformational changes in the structure, which is confirmed by crystallization at high pressure results.

A rigorous model for the electron is presented by generalizing the Coulomb's Law or Gauss's Law of electrostatics, using a unified theory of electricity and gravity. The permittivity of the free-space is allowed to be variable, dependent on the energy density associated with the electric field at a given location, employing generalized concepts of gravity and mass/energy density. The electric field becomes a non-linear function of the source charge, where concept of the energy density needs to be properly defined. Stable solutions are derived for a spherically symmetric, surface-charge distribution of an elementary charge. This is implemented by assuming that the gravitational field and its equivalent permittivity function is proportional to the energy density, as a simple first-order approximation, with the constant of proportionality referred to as the Unifield Electro-Gravity (UEG) constant. The stable solution with the lowest mass/energy is assumed to represent a ``static'' electron without any spin. Further, assuming that the mass/energy of a static electron is half of the total mass/energy of an electron including its spin contribution, the required UEG constant is estimated. More fundamentally, the lowest stable mass of a static elementary charged particle, its associated classical radius, and the UEG constant are related to each other by a dimensionless constant, independent of any specific value of the charge or mass of the particle. This dimensionless constant is numerologically found to be closely related to the the fine structure constant. This possible origin of the fine structure constant is further strengthened by applying the proposed theory to successfully model the Casimir effect, from which approximately the same above relationship between the UEG constant, electron's mass and classical radius, and the fine structure constant, emerges.

Semantic image segmentation has recently witnessed considerable progress by training deep convolutional neural networks (CNNs). The core issue of this technique is the limited capacity of CNNs to depict visual objects. Existing approaches tend to utilize approximate inference in a discrete domain or additional aides and do not have a global optimum guarantee. We propose the use of the multi-label manifold ranking (MR) method in solving the linear objective energy function in a continuous domain to delineate visual objects and solve these problems. We present a novel embedded single stream optimization method based on the manifold ranking (MR) model to avoid approximations without sacrificing expressive power. In addition, we propose a novel network, which we refer to as dual multi-scale manifold ranking (\textbf{DMSMR}) network, that combines the dilated, multi-scale strategies with the single stream MR optimization method in the deep learning architecture to further improve the performance. Experiments on high resolution images, including close-range and remote sensing datasets, demonstrate that the proposed approach can achieve competitive accuracy without additional aides in an end-to-end manner.

Today, the style of light construction materials used in building is one of the most important factors in reducing building's dead load and better performance of the structures in the earthquake. One of the ways to reduce the structure weight is to use lightweight blocks instead of using traditional materials. The main purpose of this research is to compare density, compressive strength and water absorption volume of non-load-bearing lightweight blocks made of natural and artificial lightweight aggregates. Scoria lightweight aggregates of Sanandaj, Ghorveh mine, pumice in Tabriz, Bostanabad mine, and Leca in Leca enterprise have been used to make the samples. Given the importance of the materials used, grading of coarse-grained materials has been conducted based on the standard of 7657 and grading of fine materials have been conducted based on the standard of 302.The experiments' results show that Scoria blocks due to hard texture, and high mechanical resistance of their lightweight aggregates, have had higher compressive strength, and density and lower water absorption volume compared to pumice and Lika lightweight aggregate blocks. Pumice blocks despite having desirable compressive strength and lower density compared to the two other blocks have higher water absorption volume, and do not meet the standard conditions. This same factor causes it faces with less interest. Among these Lika blocks with density of 1151.94 (kg per cubic meter) below 2000 kilograms per cubic meter of Iran density standard of 7782 (28-day compressive strength of 2.57 MPa), higher than 2.5 MPa of Iran compressive strength standard of 7782 (and water absorption volume of 282.92 kg per cubic meter) below 288 kilograms per cubic meter of Iran water absorption volume standard of 7782 (as a non-load-bearing lightweight block) have been diagnosed desirable.

Compared to conventional allothermal gasification of solid fuels (e.g. biomass, charcoal, lignite etc.), plasma-assisted gasification offers an efficient method to apply energy into the gasification process to increase the flexibility of operation conditions and to increase the reaction kinetics. In particular, non-thermal plasmas (NTP) are promising, in which thermal equilibrium is not reached and electrons have substantially higher mean energy than gas molecules. Thus it is generally assumed that in NTP the supplied energy is utilized more efficiently for generating free radicals initiating gasification reactions than thermal plasma processes. In order to investigate this hypothesis, we compared purely thermal to non-thermal plasma assisted gasification of biomass in steam in a drop tube reactor at atmospheric pressure. The NTP was provided by means of gliding arcs between two electrodes aligned in the inlet steam flow. Electric power of about 1 kW was supplied using a high voltage generator operating at frequencies between 70 and 150 kHz and voltage amplitudes up to 10 kV. A laser-assisted optical method (Raman spectroscopy) was applied for measuring the gas temperature both in the conventionally heated steam and flow-down of the visible plasma filaments of the gliding arcs. Reaction yields and rates were evaluated using these measured gas temperatures. The first experimental results have shown that the non-thermal plasma not only promotes the carbon conversion of the fuel particles, but also accelerates the reaction kinetics. The carbon conversion is increased by nearly 10% using wood powder as the fuel. With charcoal powder more than 3% are converted into syngas.

In order to improve the performance of the draft tube in hydraulic turbine, a multi–objective optimization method for the draft tube is developed by combining the design of experiment (DOE), the radial basis function (RBF) and the non–dominated sorting genetic algorithm (NSGA–II) in this paper. The geometrical design variables of the median section in the draft tube and the cross section in its exit diffuser are considered as design parameters in this optimization, which objective function is to maximize the pressure recovery factor (Cp) and minimize the energy loss coefficient (ζ). The limited numbers of design matrix required for the shape optimization of the draft tube is generated by optimal Latin hypercube (OLH) method of the DOE technique, of which performances are evaluated through computational fluid dynamic (CFD) numerical simulation. For reducing of the computational consumption, the approximate model is used based on the RBF. The Pareto optimal solutions are finally performed using the NSGA–II for obtaining the best geometrical parameters of the draft tube. The optimization result of the draft tube shows a marked performance improvement over the original, which verifies the theoretical validity and feasibility of the proposed method in this paper.

The objective of the prototype is to eliminate the polluting contamination of water sources, due to the leak of industrial waste without any kind of treatment, mainly generated by the industries and home sector. In this project, a prototype of water purification by plasma technology has been designed. The prototype will convert contaminated water into the plasma stream and eliminate the pathogens from the water by exposing it to ultraviolet radiation and plasma sterilisation. The polluted water will be accelerated at high speed using a water pump in order to convert it into a liquid-gas mixture for ease plasma generation. This process will be achieved when the electric supply from a source of alternating current (AC) is applied to the water by means of high voltage electrodes. After which, the mixture slows down to return into liquid form and the clean water is obtained. The whole process takes place without significantly raising the temperature also knows as non-thermal plasma. The device also has an automatic flow and pressure control system. Finally, a short feasibility study has been conducted on the water samples collected and report obtained from Chennai Metropolitan Water Supply and Sewage boards are reported. It has been concluded that this new plasma-based water treatment system will be more efficient and cheaper than the current wastewater treatment techniques and can be used in the future as the replacement of current secondary and tertiary treatments of industrial wastewater.

The performance of modified electrode of nanocomposite film consisting of polypyrrole-chitosan-titanium dioxide (Ppy-CS-TiO2) has been explored as non-enzymatic glucose biosensors. The synergy effect of TiO2 nanoparticles and conducting polymer on the current response of electrode resulted in higher sensitivity for nanocomposite modified electrode. The incorporation of TiO2 nanoparticles in the nanocomposite films were confirmed by XPS spectra. The FESEM and HR-TEM provided more evidences for the presence of TiO2 in Ppy-CS structure. Glucose biosensing properties were determined by amperommetry and cyclic voltammetry (CV) methods. The interfacial properties of nanocomposite electrodes were studied by electrochemical impedance spectroscopy (EIS). The developed biosensors showed a good sensitivity over the liner range of 1-14 mM with a detection limit of 614 μM for glucose. It also exhibited good selectivity and long term stability with no interference effect. The Ppy-CS-TiO2 nanocomposites film presented high electron transfer kinetics.

Numerical models widely used for hydrocarbon phase behavior and compositional flow simulations are based on assumption of thermodynamic equilibrium. However, it is not uncommon for oil and gas-condensate reservoirs to exhibit essentially non-equilibrium phase behavior, e.g., in the processes of secondary recovery after pressure depletion below saturation pressure, or during gas injection, or for condensate evaporation at low pressures. In many cases the ability to match field data with equilibrium model depends on simulation scale. The only method to account for non-equilibrium phase behavior adopted by the majority of flow simulators is the option of limited rate of gas dissolution (condensate evaporation) in black oil models. For compositional simulations no practical yet thermodynamically consistent method has been presented so far except for some upscaling techniques in gas injection problems. Previously reported academic non-equilibrium formulations have a common drawback of doubling the number of flow equations and unknowns compared to the equilibrium formulation. In the paper a unified thermodynamically-consistent formulation for compositional flow simulations with non-equilibrium phase behavior model is presented. Same formulation and a special scale-up technique can be used for upscaling of an equilibrium or non-equilibrium model to a coarse-scale non-equilibrium model. A number of test cases for real oil and gas-condensate mixtures are given. Model implementation specifics in a flow simulator are discussed and illustrated with test simulations. A non-equilibrium constant volume depletion algorithm is presented to simulate condensate recovery at low pressures in gas-condensate reservoirs. Results of satisfactory model matching to field data are reported and discussed.

The present paper aims to increase knowlodge of the methods of resistance estimating of concrete in situ by means of non-destructive tests used to integrate the quantitative results from cylindrical specimens (core). The results of experimental investigations carried out on concrete conglomerate samples of a school building are shown. The experimental campaign then will be presented like a case study, conducted on a series of concrete beams and pillars of an existing building. The distructive tests on cores were conducted at the Civil Structures Laboratory of the Engineering Department of the University of Campania "Luigi Vanvitelli". The expression obtained through the calibration procedure of the values of non-destructive tests with those provided by the core drills allowed to estimate the average values of the compressive strength of the concrete. It is highlighted how this result was achieved with a very limited core number provided that they are extracted in selected points and that there was a proportionality link with the resistances obtained from non distructive tests.

Olive trees have been of economic and cultural value since pre-Roman times, and continue to dominate landscapes and agriculture in many mediterranean regions. Recent mass losses of olive trees in Southern Italy due to an exotic plant pathogen highlight the need for methods that to monitor the olive trees in a landscape or region operationally. Here, we develop a method for counting olive trees from aerial photographs and test it in areas with a high diversity of olive tree ages, sizes, and shapes. This heterogeneity complicates tree counting as centennial trees often have crowns that are split into multiple segments, resembling multiple crowns, while nearby crowns often form a semi-closed canopy comprising multiple trees. Comparisons with reference counts in two 20 ha sites and over three different years indicate the automated counts tend to be reasonably accurate (median error 13%, n = 6), but heavily influenced by a few olive orchards with particularly high planting densities and a relatively closed canopy in which distinguishing individual trees is challenging. Overall, the algorithm estimated tree densities well (counting 82 to 109 trees/ha versus 87 to 104 trees/ha in the reference counts), indicating the method is suitable to track the number of olive trees over large areas.

This article develops duality principles and related convex dual formulations suitable for the local optimization of non-convex primal formulations for a large class of models in physics and engineering. The results are based on standard tools of functional analysis, calculus of variations and duality theory. In particular, we develop applications to a Ginzburg-Landau type equation.

Acquisition of real-time hydraulic data is an essential component for flood forecasting. However, we frequently face difficulties in obtaining discharge data using classical contact methods during high magnitude floods and for systems experiencing rapid hydro-geomorphological adjustment. Therefore, we developed low-cost, non-contact sensors and platforms that are designed to overcome these difficulties. These advances enable flood flow properties to be monitored at multiple locations across a river catchment, at low-cost, and communicated in near real-time by using an image velocimetry method. This is an optics-based approach for stream flow measurement using commercially available near-infrared digital cameras to acquire video footage in full HD (30fps). Video footage is then subjected to optical flow tracking techniques based on cross-correlation, and feature-based tracking, enabling the displacement rates of detected features (for example natural foam, seeds, woody debris, and turbulent structures) to be computed. This manual provides step by step guidance to install an image-based gauging station. It contains the list of necessary components, the calibration process of a new camera and the assembly procedure of the system.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

Splines and quasi-interpolation operators are important both in approximation theory and applications. In this paper, we construct a family of quasi-interpolation operators for the bivariate quintic spline spaces S₅³ (∆_mn⁽²⁾). Moreover, the properties of the proposed quasi-interpolation operators are studied, as well as its applications for solving two-dimensional Burgers’ equation and image reconstruction. Some numerical examples show that these methods, which are easy to implement, provide accurate results.

The early embryonic heart is a multi-layered tube consisting of (1) an outer myocardial tube; (2) an inner endocardial tube; and (3) an extracellular matrix layer interposed between myocardium and endocardium, called “cardiac jelly” (CJ). During the past decades, research on CJ has mainly focused on its molecular and cell biological aspects. This review focuses on the morphological and biomechanical aspects of CJ. Special attention is given to (1) the spatial distribution and fiber architecture of CJ; (2) the morphological dynamics of CJ during the cardiac cycle; and (3) the removal/remodeling of CJ during advanced heart looping stages, which leads to the formation of ventricular trabeculations and endocardial cushions. CJ acts as a hydraulic skeleton displaying striking structural and functional similarities with the mesoglea of jellyfish. CJ not only represents a filler substance, facilitating end-systolic occlusion of the embryonic heart lumen. Its elastic components antagonize the systolic deformations of the heart wall and thereby power the refilling phase of the ventricular tube. Non-uniform spatial distribution of CJ generates non-circular cross sections of the opened endocardial tube (initially elliptic, later deltoid), which seem to be advantageous for valveless pumping. Endocardial cushions arise from non-removed remnants of the original CJ.

Saliency detection in synthetic aperture radar (SAR) image is a difficult problem. This paper proposed a multitask saliency detection (MSD) model for the saliency detection task of SAR image. Firstly, we extract four features of SAR image as the input of the MSD model, which include the intensity, orientation, uniqueness and global contrast. Then, the saliency map is generated by the multitask sparsity pursuit (MTSP) which integrates the multiple features collaboratively. Subjective and objective evaluation of the MSD model verifies its effectiveness. Based on the saliency maps of the source images, an image fusion method is proposed for the SAR and color optical image fusion. The experimental results of real data show the proposed image fusion method is superior to the presenting methods in terms of several universal quality evaluation indexes, as well as in the visual quality. The salient areas in the SAR image can be highlighted and the spatial and spectral details of color optical image can also be preserved in the fusion result.

The quantization error (QE) from Self-Organizing Map (SOM) output after learning is exploited in this studies. SOM learning is applied on time series of spatial contrast images with variable relative amount of white and dark pixel contents, as in monochromatic medical images or satellite images. It is proven that the QE from the SOM output after learning provides a reliable indicator of potentially critical changes in images across time. The QE increases linearly with the variability in spatial contrast contents of images across time when contrast intensity is kept constant. The hitherto unsuspected capacity of this metric to capture even the smallest changes in large bodies of image time series after using ultra-fast SOM learning is illustrated on examples from SOM learning studies on computer generated images, MRI image time series, and satellite image time series. Linear trend analysis of the changes in QE as a function of the time an image of a given series was taken gives proof of the statistical reliability of this metric as an indicator of local change. It is shown that the QE is correlated with significant clinical, demographic, and environmental data from the same reference time period during which test image series were recorded. The findings show that the QE from SOM, which is easily implemented and requires computation times no longer than a few minutes for a given image series of 20 to 25, is useful for a fast analysis of whole series of image data when the goal is to provide an instant statistical decision relative to change/no change between images.

Fundamental science and applied research and technology development (RTD) are facing significant challenges that particularly compound to the notorious credibility, reproducibility, funding and sustainability crises. The underlying, serious shortcomings are substantially amplified by a metrics-obsessed publication culture, and a growing cohort of academics fishing for fairly stagnant (public) funding budgets. This work presents, for the first time, a groundbreaking strategy to successfully address these severe issues; the novel strategy proposed here leverages the distributed ledger technology (DLT) “blockchain” to capitalize on cryptoeconomic mechanisms, such as tokenization, consensus, crowdsourcing, smart contracts, reputation systems as well as staking, reward and slashing mechanisms. This powerful toolbox, which is so far widely unfamiliar to traditional scientific and RTD communities (“TradSci”), is synergistically combined with the exponentially growing computing capabilities for virtualizing experiments through digital twin methods in a future scientific “metaverse”. Project contributions, such as hypotheses, methods, experimental data, modelling, simulation, assessment, predictions and directions are crowdsourced using blockchain, and captured by so-called non-fungible tokens (“NFTs”). The so enabled, highly integrative approach, termed decentralized science (“DeSci”), is destined to move research out of its present silos, and to markedly enhance quality, credibility, efficiency, transparency, inclusiveness, sustainability, impact, and sustainability of a wide spectrum of academic and commercial research initiatives.

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

There must exist a reformulation of quantum field theory, even at low energies, which does not depend on classical time. The octonionic theory proposes such a reformulation, leading to a pre-quantum pre-spacetime theory. The ingredients for constructing such a theory, which is also a unification of the standard model with gravitation, are : (i) the pre-quantum theory of trace dynamics – a matrix-valued Lagrangian dynamics, (ii) the spectral action principle of non-commutative geometry, (iii) the number system known as the octonions, for constructing a non-commutative manifold and for defining elementary particles via Clifford algebras, (iv) a Lagrangian with $E_8 \times E_8$ symmetry. The split bioctonions define a sixteen dimensional space (with left-right symmetry) whose geometry (evolving in Connes time) relates to the four known fundamental forces, while predicting two new forces, $SU(3)_{grav}$ and $U(1)_{grav}$. This latter interaction is possibly the theoretical origin of MOND. Coupling constants of the standard model result from left-right symmetry breaking, and their values are theoretically determined by the characteristic equation of the exceptional Jordan algebra of the octonions. The quantum-to-classical transition, precipitated by the entanglement of a critical number of fermions, is responsible for the emergence of classical spacetime, and also for the familiar formulation of quantum theory on a spacetime background.

The present review focuses on recent clinical trials that analyze the efficacy of intravitreal therapeutic agents for the treatment of dry age-related macular degeneration (AMD), such as neuroprotective drugs, and complement inhibitors, also called immunomodulatory or anti-inflammatory. A systematic literature search was performed to identify randomized controlled trials published prior to January 2019. Patients affected by dry AMD treated with intravitreal therapeutic agents were included. The changes in the correct visual acuity and the reduction in geographic atrophy progression were evaluated. Several new drugs have shown some promising results, including those targeting the complement cascade and agents called neuroprotective. The action potential of the two groups of drugs is to block the complement cascade model for immunomodulating agents, and prevent the degeneration and apoptosis of ganglion cells for the neuroprotectors, respectively. To the best of knowledge, and after extensive studies on the matter, there are still many investigations to be carried out on dry AMD in collaboration between researchers. They will have to identify truly effective molecules, understand the practical potential of pluripotent stem cells, and refine gene therapies. Only in-depth clinical trials will be able to allow the most appropriate and personalized treatments for each dry AMD patient.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

Lambertianic acid (LA) is a biologically active compound from the leaves of Pinus koraiensis. In the present study, apoptotic mechanisms of LA plus TNF-related apoptosis-inducing ligand (TRAIL) were elucidated in non-small cell lung cancer cells (NSCLCs). Cytotoxicity assay, flow cytometry, immunoprecipitation and Western blotting were performed. Here, combined treatment of LA and TRAIL increased cytotoxicity, sub-G1 population and cleaved poly (ADP-ribose) polymerase (PARP) and caspase3/8/9 in A549 and H1299 cells compared to LA or TRAIL alone. Furthermore, combined treatment of LA and TRAIL significantly decreased anti-apoptotic proteins such as B-cell lymphoma 2 (Bcl-2), Fas-like inhibitor protein (FLIP) and X-linked inhibitor of apoptosis protein (XIAP) and enhanced the activation of pro-apoptotic proteins Bid compared to LA or TRAIL alone. In addition, combined treatment of LA and TRAIL upregulated the expression of Death receptor 4 (DR4) and downregulated phosphorylation of nuclear factor kappa-light-chain-enhancer of activated B cells (p-NF-B), inhibitory protein of kB family (p-IB) and FLIP in A549 and H1299 cells along with disrupted binding of XIAP with caspase3 or NF-B. Overall, these findings suggest that lambertianic acid enhances TRAIL-induced apoptosis via inhibition of XIAP/NF-B in TRAIL resistant NSCLCs.

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

This study examines the effect of market risk on the financial performance of 31 non-financial companies listed on the Casablanca Stock Exchange (CSE) over the period 2000-2016. We utilize three alternative variables to assess financial performance, namely return on assets, return on equity and profit margin. Next, we use the degree of financial leverage, the book-to-market ratio, and the gearing ratio as market risk variables. Besides, we employ the pooled OLS model, the fixed effects model, the random-effects model, the difference GMM and the system GMM models. The results show that market risk indicators have a negative and significant influence on the companies' financial performance. The elasticities are greater following the book-to-market ratio compared to the degree of financial leverage and the gearing ratio, respectively. In most cases, the firm size, the tangibility ratio, and the cash holdings ratio have a positive effect on financial performance, whereas the firms' age, the debt-to-income ratio, stock turnover, and leverage hurt the performance of these non-financial companies. Therefore, decision-makers and managers should mitigate market risk through appropriate strategies of risk management, such as derivatives and insurance techniques.

We are of the opinion that during the design of a binary classifier one ought to consider adding an “I don’t know” answer. We provide the case for the introduction of this third category when a human needs to make a decision based on the answer from a binary classifier. We discuss the costs and potential benefits of its introduction. Colloquially, we have used the term “I don’t know”, but formally we refer to it as NotAvailable. A procedure to define NotAvailable predictions in binary classifiers, called all leave-one-out models (ALOOM), is presented as proof of the concept. Furthermore, we discuss the potential benefits of applying ALOOM in real life applications.

Among all the processing technologies of heat-treatable aluminum alloys like 2219 aluminum alloy, using friction stir welding (FSW) as the joining technology and using electromagnetic forming (EMF) for plastic forming technology both have obvious advantages and successful applications. Therefore, there is a broad prospect for the compound technologies which can be used on the 2219 aluminum alloy to manufacture the large-scale thin-wall parts in the astronautic industry. The microstructure and mechanical properties of 2219 aluminum alloy under the process compounded of FSW, heat treatment, and EMF were investigated by means of tensile test, optical microscope (OM), and scanning electron microscope (SEM). The results show that the reduction of strength, which was caused during the FSW process, can be recovered effectively by the post-welding heat treatment composed of solid solution and aging, while the ductility was still reduced after heat treatment. Under the compound technology of FSW, heat treatment, and EMF, the forming limit of 2219 aluminum alloy decreased distinctly due to the poor ductility of the welding joint. A ribbon-pattern, which was formed due to the banded structure caused by FSW process, was found on the fracture surface of welded 2219 aluminum alloy after EMF treatment. During the EMF process, because of the effects of induced eddy current, a unique structure, which was manifested as a molted-surface appearance under the SEM observation, was formed as the material fractured.

With a large number of annotated non-coding RNAs (ncRNAs), repetitive sequences are found to constitute functional components (termed as repetitive elements) in ncRNAs that perform specific biological functions. Bioinformatics analysis is a powerful tool for improving our understanding of the role of repetitive elements in ncRNAs. This chapter summarizes recent findings that reveal the role of repetitive elements in ncRNAs. Furthermore, relevant bioinformatics approaches are systematically reviewed, which promises to provide valuable resources for studying the functional impact of repetitive elements on ncRNAs.

The basis of the work of electroencephalography (EEG) is the registration of electrical impulses from the brain or some of its individual areas using a special sensor/electrode. This method is used for the treatment and diagnosis of various diseases. The use of wet electrodes in this case does not seem viable, for several well-known reasons. As a result of this, a detailed analysis of modern EEG sensors developed over the past few years is carried out, which will allow researchers to choose this type of sensor more carefully and, as a result, conduct their research more competently. Due to the absence of any standards in the production and testing of dry EEG sensors, the main moment of this manuscript is a detailed description of the necessary steps for testing a dry electrode, which will allow researchers to maximize the potential of the sensor in the various type of research.

Aiming at the problems that are poor generalization performance, low retrieval accuracy and large time consumption of existing content-based image retrieval system, the hierarchical image retrieval method based on multi feature fusion is proposed in this paper. The retrieval accuracy rates on Corel5K, UKbeach and Holidays are 68.23(Top 1), 3.73(N-S) and 88.20(mAp), respectively. The experimental results show that the method proposed in this paper can effectively improve the deficiency of single feature retrieval and save time significantly in the premise of a small amount of loss of accuracy.

The outbreak of novel Coronavirus (SARS-COV-2 ) disease (COVID-19) in Wuhan has attracted worldwide attention. SARS-COV-2 known to share a similar clinical manifestation that includes various symptoms such as pneumonia, fever, breathing difficulty, and in particular, SARS-COV-2 also causes a severe in ammation state that leads to death. Consequently, massive and rapid research growth has been observed across the globe to elucidate the mechanisms of infections and disease progression in genotype and phenotype scale. Data Science is playing a pivotal role in in-silico analysis to draw hidden and novel insights about the SARS-COV-2 origin, pathogenesis, COVID-19 outbreak forecasting, medical diagnosis, and drug discovery. With the availability of multi-omics, radiological, biomolecular, and medical data urges to develop novel exploratory and predictive models or customise exiting learning models to t the current problem domain. The presence of many approaches generates the need for the systematic surveys to guide both data scientists and medical practitioners. We perform an elaborate study on the state-of-the-art data science method ologies in action to tackle the current pandemic scenario. We consider various active COVID-19 data analytics domains such as phylogeny analysis, SARS-COV-2 genome identication, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological analysis, and most importantly (existing) drug discovery. We highlight types of data, their generation pipeline, and the data science models in use. We believe that the current study will give a detailed sketch of the road map towards handling COVID-19 like situation by leveraging data science in the future. We summarise our review focusing on prime challenges and possible future research directions .

A lensless camera is an ultra-thin computational-imaging system. Existing lensless cameras are based on the axial arrangement of an image sensor and a coding mask, and therefore, the back side of the image sensor cannot be captured. In this paper, we propose a lensless camera with a novel design that can capture the front and back sides simultaneously. The proposed camera is composed of multiple coded image sensors, which are complementary-metal-oxide-semiconductor~(CMOS) image sensors in which air holes are randomly made at some pixels by drilling processing. When the sensors are placed facing each other, the object-side sensor works as a coding mask and the other works as a sparsified image sensor. The captured image is a sparse coded image, which can be decoded computationally by using compressive-sensing-based image reconstruction. We verified the feasibility of the proposed lensless camera by simulations and experiments. The proposed thin lensless camera realizes super field-of-view imaging without lenses or coding masks, and therefore can be used for rich information sensing in confined spaces. This work also suggests a new direction in the design of CMOS image sensors in the era of computational imaging.

Sports video analysis is an interesting and emerging field in the domain of computer vision. It has become a very popular subject among scholars all over the world in the last decade due to the need of automated classification of events such as offsides, in/out situations, goals and so on. The most important problem in sports video analysis is detection and tracking of ball and players. Extraction of court lines is also another important problem since it is necessary in the classification of aforementioned events. In this study, considering some of the previous work on the subject, we develop a method for tracking the ball and players in tennis videos where the camera position and orientation is supposed fixed.