Attractive Field Research Articles

Image declipping: Saturation correction in single images

High dynamic range (HDR) images present fine details in a scene and are visually more appealing than low dynamic range (LDR) images, since they contain a greater dynamic range of color gamut. HDR compatible displays are currently high-cost, therefore tone-mapping algorithms have widely been used to obtain high quality images for LDR screens with a lower cost. However, tone-mapped images may contain clipped pixel regions, which should be corrected to retrieve the lost information, to acquire visually pleasing LDR images. In a single image, the recovery of color and texture information in clipped regions is challenging, yet an attractive research field in image processing. Although there are several algorithms present in literature, developing a general framework for different types of image content is hard to achieve. This study proposes a single image declipping method based on linear embeddings, difference of pixels and block-search. Experimental results carried out on a tone-mapped HDR image dataset and LDR images demonstrate that the proposed algorithm is able to successfully recover saturated pixels in various types of images. Detailed statistical and visual comparisons show that this approach produces superior results on average for both tone-mapped and LDR images when compared to existing techniques.

Biomolecular engineering of drugs loading in Riboflavin-targeted polymeric devices: simulation and experimental

The synthesis of polymeric nanoparticles (NPs) with efficient drug loading content and targeting moieties is an attractive field and remains a challenge in drug delivery systems. Atomistic investigations can provide an in-depth understanding of delivery devices and reduce the number of expensive experiments. In this paper, we studied the self-assembly of poly (lactic-co-glycolic acid)-b-poly (ethylene glycol) with different molecular weights and surface compositions. The innovation of this molecular study is the loading of an antitumor drug (docetaxel) on a targeting ligand (riboflavin). According to this work, a novel, biocompatible and targeted system for cancer treatment has been developed. The obtained results revealed a correlation between polymer molecular weight and the stability of particles. In this line, samples including 20 and 10 w/w% moiety NPs formed from polymers with 3 and 4.5 kDa backbone sizes, respectively, are the stable models with the highest drug loading and entrapment efficiencies. Next, we evaluated NP morphology and found that NPs have a core/shell structure consisting of a hydrophobic core with a shell of poly (ethylene glycol) and riboflavin. Interestingly, morphology assessments confirmed that the targeting moiety located on the surface can improve drug delivery to receptors and cancerous cells. The developed models provided significant insight into the structure and morphology of NPs before the synthesis and further analysis of NPs in biological environments. However, in the best cases of this system, Dynamic Light Scattering (DLS) tests were also taken and the results were consistent with the results obtained from All Atom and Coarse Grained simulations.

Modeling and Dynamic Analysis for a Motion of Mounted-Based Axisymmetric Rigid Body under Self-Excited Vibrations in an Attractive Newtonian Field

In this article, the mathematical modeling and dynamic analysis for a motion of a rigid body mounted on a vibrating base affected by a strongly nonlinear damping and an attractive Newtonian field are investigated. The governing equations are derived, and specific conditions for asymptotic stability of the motion are stated. Results of analytical analysis are used to identify some distinct types of motion, including the cases that are apparently regular or chaotic. A reduction technique via averaging is used to obtain the amplitude equation and the response diagrams to identify regions where the jump phenomenon may occur. Moreover, a simple analytical relationship between the parameters of the system, describing whether the jump phenomenon will be possible, is obtained. Homoclinic bifurcation diagrams are used to argue apparently that the chaotic behavior is slowly approaching a strange attractor at specific values of system parameters. Results of numerical solutions using the fourth-order Runge-Kutta method are closely coincided with the analytical ones to verify all types of motion.

Educational Plans of Applicants of the Russian Province

The article is based on the materials of a field study conducted in the framework of the research project “Admission to a university in 2020: factors of choice, trends and prospects of the admission campaign (on the example of the Volgograd region).” The purpose of the article is to determine the trends in the professional plans of school graduates in the Volgograd region. The article discusses the results of a questionnaire survey of school graduates of the regional center and municipal districts of the Volgograd region. The study showed that school graduates in the Volgograd region are focused on getting education in Volgograd, and graduates of Volgograd schools – at the universities in major cities. Educational migration is carried out in stages: first from rural areas and small towns to large cities, and then from large cities to major cities and capital cities. The region’s school graduates are characterized by the first stage of migration, and the Volgograd school graduates – the second. The choice of the city of study depends on the area of residence. However, the geographical factor is not dominant. Information technology is one of the most attractive fields of professional occupation for school graduates in Volgograd whilst school graduates in the Volgograd region are much less focused on choosing IT educational programs. The main internal parameters that influence the choice of an educational institution by applicants in Volgograd and Volgograd region, are equal. They are the number of budget places, USE passing score and the quality of education at university. At the same time, regional applicants pay relatively more attention to the cost of education, which is associated with a lower level of material prosperity among families in municipal districts, in comparison with families in the regional center.

A novel color image encryption scheme based on a new dynamic compound chaotic map and S-box.

In modern technological era image encryption has become an attractive and interesting field for researchers. They work for improving the security of image data from unauthorized sources. Chaos theory, due to its randomness and unpredictable behaviors, is considered favorite for the purpose of image encryption. This paper proposes a diffusion based image encryption algorithm by using chaotic maps. Firstly a chaotic map (piecewise linear chaotic map) is used for the generation of S-box, then it is used for the pixel values modification to generate element of non-linearity. After this these modified values are further diffused with another random sequence, generated by tent logistic chaotic map. Finally the color components of pre-encrypted image are mixed with each other so that the developed randomness uniformly distributed in them. For image data we develop non-linearity and diffusion by using S-box and then more randomness is added in the pre-encrypted image with the help of Boolean operation XOR. The use of this combination of chaotic maps along with S-box and Boolean operation XOR is a different technique, that provides satisfactory results for security aspects and also works efficiently.

Advances in Self-Powered Ultraviolet Photodetectors Based on P-N Heterojunction Low-Dimensional Nanostructures.

Self-powered ultraviolet (UV) photodetectors have attracted considerable attention in recent years because of their vast applications in the military and civil fields. Among them, self-powered UV photodetectors based on p-n heterojunction low-dimensional nanostructures are a very attractive research field due to combining the advantages of low-dimensional semiconductor nanostructures (such as large specific surface area, excellent carrier transmission channel, and larger photoconductive gain) with the feature of working independently without an external power source. In this review, a selection of recent developments focused on improving the performance of self-powered UV photodetectors based on p-n heterojunction low-dimensional nanostructures from different aspects are summarized. It is expected that more novel, dexterous, and intelligent photodetectors will be developed as soon as possible on the basis of these works.

Enhancement on physical, chemical, and biological properties of HNT-PVA-ALG-HAp biocomposite coating on implant substrate for biomedical application

Bone tissue engineering (BTE) is an attractive field of research because there has been significant growth in the number of orthopedic diseases and injuries over the past several years. Biomaterial device-related infections are considered as one of the most important concerns in total dental and orthopedic prostheses. In this situation, the successful instant enactment of titanium (Ti6Al4V) and its alloys served as prosthetic devices have been a motivation for the improvement of long-time implant biomedical applications. However, successful adaptation of these implant substrates remains a significant challenge on the safety issue. The objective of this study is to observe the bone formation and osseointegration in different composite-coated implant surfaces. In this study, the electrophoretic technique was used to coat different composites on Ti–6Al–4V alloy implants. The physicochemical properties and structural-relationships of the biocomposite coatings were investigated by using various characterization techniques, such as FTIR, XRD, SEM with EDAX, HR-TEM with EDAX and biological assays (Apoptotic assay by Acridine orange staining). From the results, the developed HNT-PVA-ALG-HAp biocomposite coating has enhanced the mechanical as well as biological properties.

AI-enabled digital forgery analysis and crucial interactions monitoring in smart communities

Digital forgery has become one of the attractive research fields in today’s technology. There are several types of forgery in digital media transmission, especially digital image transmission. A common type of forgery is copy-move forgery (CMF). The CMF may be encountered in streets, railway stations, underground stations, or festivals. This type of forgery may lead to hugger-mugger in some cases. Therefore, there is a need to find a sufficient countermeasure mechanism to detect image forgeries. This paper presents a new CMFD approach that depends on deep learning for IoT based smart cities. Two well-known deep learning models, namely CNN and ConvLSTM, are adopted for CMFD. The proposed models are tested on MICC-220, MICC-600 and MICC 2000 datasets for validation. Several tests are performed to verify the effectiveness of the proposed models. The simulation results reveal that the testing accuracy reaches 95%, 73%, and 94% for MICC-F220, MICC-F600 and MICC-F2000 datasets. In addition, the proposed approach achieves an accuracy of 85% for a combined set of all datasets.

Progress in Topological Mechanics

Topological mechanics is rapidly emerging as an attractive field of research where mechanical waveguides can be designed and controlled via topological methods. With the development of topological phases of matter, recent advances have shown that topological states have been realized in the elastic media exploiting analogue quantum Hall effect, analogue quantum spin Hall effect, analogue quantum valley Hall effect, higher-order topological physics, topological pump, topological lattice defects and so on. This review aims to introduce the experimental and theoretical achievements with defect-immune protected elastic waves in mechanical systems based on the abovementioned methods, respectively. From these discussions, we predict the possible perspective of topological mechanics.

Mobile health for detection of atrial fibrillation-Status quo and perspectives

Mobile Health (mHealth) zur Detektion von Vorhofflimmern ist ein innovatives häusliches Monitoring des Herzrhythmus. mHealth im Kontext von Vorhofflimmern macht diagnostische Technologien nicht nur breit verfügbar, sie gestattet auch die Integration in telemedizinische Behandlungskonzepte und ermöglicht die aktive Teilhabe der Patient*innen am Behandlungsprozess. Die Erkennung von Vorhofflimmern mittels mHealth-Anwendungen erfolgt meist EKG-basiert oder durch die Detektion der Pulswelle mittels Photoplethysmographie (PPG). Einige Anwendungen benötigen zusätzliche Sensoren, andere bedienen sich integrierter Sensorik von Smartphones oder -watches. Unabhängig von der zugrunde liegenden Methode konnte für die meisten mHealth-Anwendungen eine hohe diagnostische Treffsicherheit zur Detektion von Vorhofflimmern nachgewiesen werden (analytische Validierung). Die Evidenzlage zu positiven Versorgungseffekten und der Verbesserung medizinischer Endpunkte (klinische Validierung) ist allerdings bisher gering. Das Screening symptomatischer oder asymptomatischer Patient*innen und die Nachsorge nach antiarrhythmischer Maßnahme sind Möglichkeiten der Integration in die Versorgungsrealität. Die präventive Detektion des Vorhofflimmerns ist ein attraktives Anwendungsfeld von mHealth mit viel Potenzial für die Zukunft. Zurzeit ist die Integration in die Versorgungsrealität allerdings nur eingeschränkt gegeben. Die Kostenerstattung und die ärztliche Vergütung sind neben adäquaten Informations- und Qualifizierungsangeboten Voraussetzung, um eine flächendeckende Anwendung zukünftig sicherstellen zu können. Das im Jahr 2019 verabschiedete Digitale-Versorgung-Gesetz regelt zwar unter anderem die Erstattung digitaler Gesundheitsanwendungen, doch klammert es primärpräventive Anwendungen bisher aus.

Automatic Identification of Emotional Information in Spanish TV Debates and Human–Machine Interactions

Automatic emotion detection is a very attractive field of research that can help build more natural human–machine interaction systems. However, several issues arise when real scenarios are considered, such as the tendency toward neutrality, which makes it difficult to obtain balanced datasets, or the lack of standards for the annotation of emotional categories. Moreover, the intrinsic subjectivity of emotional information increases the difficulty of obtaining valuable data to train machine learning-based algorithms. In this work, two different real scenarios were tackled: human–human interactions in TV debates and human–machine interactions with a virtual agent. For comparison purposes, an analysis of the emotional information was conducted in both. Thus, a profiling of the speakers associated with each task was carried out. Furthermore, different classification experiments show that deep learning approaches can be useful for detecting speakers’ emotional information, mainly for arousal, valence, and dominance levels, reaching a 0.7F1-score.

A Novel Path Voting Algorithm for Surface Crack Detection

Path voting is a widely used technique for line structure detection in images. Traditional path voting, based on minimal-path, is performed to track paths based on how seeds grow. The former requires to set a starting point and an end point. Thus, the performance of minimal-path path voting depends on the initialization. However, high-quality initialization often requires human interaction, which limits its applications in practice. In this paper, a fully automatic path voting method has been proposed and applied for crack detection. The proposed path voting is performed to segment images, which partitions an image patch along the potential crack path and integrates the path to form a crack probability map. After path voting, crack seeds are sampled and modeled into a graph, and the edge weights are assigned using an attraction field algorithm. Finally, cracks are extracted by using spanning tree and tree pruning algorithms. Experimental results demonstrate that the proposed path voting approach can effectively infer the cracks from 2D optic images and 3D depth images.

Social Order as a Financial and Economical Instrument for Regulating Employment and Labour Migration in the Russian Federation

The improvement of the financial and economic mechanism and instruments for implementing state projects and programs is one of the most pressing issues for the Russian Federation in the regulation of economic processes. In this area, the most important thing for the state is the issue of providing citizens equally with socially significant services in demand and creating opportunities for broad access to the market of competitive services for all interested state and non-governmental organisations of various organisational and legal forms, and the possibility of creating new jobs as well. The state solving these problems is consistently developing and adopting legislative acts that regulate the process of practical implementation at all levels of government’s hierarchy, creating an opportunity for an independent regional and municipal policy in the field of employment, orderly attraction and regulation of migration processes. The article discusses new, most relevant approaches and considers the mechanism and tools, which make it possible to solve the tasks set by the authorities until 2024. The conclusions the author has drawn reflect the possibility of achieving concrete results, considering the tasks to be solved in the area of implementing the social order, regulating the employment of the population and labour migration at the regional and municipal levels.

Efficient ammonia electrosynthesis by coupling to concurrent methanol oxidation

The electrosynthesis of ammonia by nitrogen reduction reaction is one of the most attractive research fields; however, it suffers from both extremely low NH3 yield, poor faradic efficiency and the sluggish kinetics of anodic oxygen evolution reaction. Herein, a novel nitrogen-methanol co-electrolysis system has been established to reduce the overall energy consumption and produce value-added formate at anode by employing anodic methanol oxidation reaction substituting for oxygen evolution reaction, for which noble-metal-free Bi-based electrode catalysts were designed and used. Significantly, the Bi/Bi2O2CO3 nanosheets exhibits a rather high NH3 yield of 3.18 ± 0.26 μg h−1 cm−2 and a faradic efficiency of 14.59% ± 1.41% at −1.3 V (vs. Ag/AgCl). Excitingly, the overall required potential of established nitrogen reduction reaction electrolyzer is decreased by 251 mV at 10 mA cm−2 of the novel designed nitrogen-methanol co-electrolysis system compared with traditional nitrogen-water co-electrolysis, manifesting much decreased energy consumption of the coupled system.

Real Economy Effects on Consumption-Based CAPM

The consumption-based capital asset pricing model (CCAPM) is an attractive research field in finance, and extant studies have examined the impacts of different factors towards traditional CCAPM, intending to improve the model from the practical perspective. In this paper, we comprehensively scrutinize the real economy effects on the CCAPM by comprising expenditure on durable, expenditure on non-durable goods, services, and real estate four factors. Our study pays great attention to the real economy effect on the CCAPM based on two types of portfolios. By employing both time-series and cross-sectional analysis, our empirical results suggest that the real economy factors can help traditional CCAPM to produce better asset pricing results. Particularly, incorporating the real estate component into the CCAPM model can improve its explanation power on the stock market risk. Our results are potentially useful for investors, portfolios managers and policy makers towards the CCAPM.

A solid-in-oil-in-water emulsion: An adjuvant-based immune-carrier enhances vaccine effect

The biomaterial-based immunoengineering has become one of the most attractive research fields in the last decade. In the present study, a solid-in-oil-in-water (S/O/W) emulsion encapsulating antigen in the oil phase of an oil-in-water (O/W) emulsion was prepared as a novel vaccine carrier consisting of similar materials to the emulsion adjuvant of which the safety, immunogenicity and vaccination efficacy have been already confirmed in human. Direct observation by high-resolution confocal laser scanning microscopy and small angle X-ray scattering analysis showed that the antigens were dispersed inside of the oil phase of the S/O/W emulsion as solid-state particles. The S/O/W emulsion robustly produced antigen-specific antibodies and enhanced the antitumor effects in a therapeutic cancer vaccination compared with free antigens or the O/W emulsion in vivo. This result is in good agreement with the activation effect of antigen-specific cytotoxic T lymphocytes and antigen presentation by the S/O/W emulsion, indicating that the S/O/W emulsion consisting of already approved materials is a promising vaccine carrier to produce both humoral and cellular immunity.

Foresight: a new approach based on the Z-number cognitive map

Foresight has recently emerged as one of the most attractive and practical fields of study, while being used to draw up a preferable future and formulate appropriate strategies for achieving predetermined goals. The present research aimed at providing a framework for foresight with a primary focus on the role of a cognitive approach and its combination with the concept of fuzzy cognitive map in the environments of uncertainty and ambiguity. The proposed framework consisted of the 3 phases: pre-foresight, foresight, and post-foresight. The main stage (foresight) focused on the role of imagination and intuition in drawing the future in the experts’ minds and depicting their perceptions above perceptions in the form of a fuzzy cognitive map influenced by variables related to the subject under study in order to determine a preferable future. The use of a Z-number concept and integrating it with fuzzy cognitive maps in the foresight-oriented decision-making space, which was mainly saturated with uncertainty and ambiguity, was one of the main strengths of the proposed framework in the current investigation. The present paper focused primarily on the evolution of expert’s knowledge with regard to the topic of foresight. The role of Z-number in various processes, from data collection to illustration, analysis, and aggregation of cognitive maps, was considered for gaining knowledge and understanding into the nature of future. Moreover, an ultimate objective was realized through identifying, aggregating, and selecting the variables from each expert’s perspective and then the relationship between each variable was determined in the main stage of foresight. Finally, the proposed framework was presented and explicated in the form of a case study, which revealed satisfactory results.

Phenotypic characterization of bacterial isolates from marine waters and plastisphere communities of the Ross Sea (Antarctica)

Background: In aquatic environments, microbial biofilms are hot spots of microbial diversity, as well as a substrate for larval settlement of many invertebrate species. Examining the functional diversity of microorganisms in polar regions is a new and still unknown field of aquatic microbiology, that is attracting increasing interest for its significance in both scientific research and resources exploitation. The context and purpose of the study: In the framework of research aimed at studying microbial colonization occurring at Terra Nova Bay, culturable heterotrophic bacteria were quantified and characterized phenotypically at two study areas (Road Bay and Tethys Bay) with different environmental characteristics: the first one was close to Mario Zucchelli research station and impacted by anthropogenic pressure due to sewage wastes, while the second was exposed to salinity gradients due to glacier melting. In this context, artificial structures hosting plastic (polyvinyl chloride, PVC and polyethylene, PE) panels were deployed at -5 and -20m depths at each of the stations (one Impact and one Control) chosen per each study area. Water samples were contextually collected at the time of recovery of the plastic panels, namely after 12 months of deployment; at Road Bay, at the -5 m depth, a short-term experiment was also performed, with the recovery of the panels after 2.5 and 9 months of deployment. Bacterial strains were isolated in axenic culture both from water and biofilm matrices, and examined for their main phenotypical traits and functional diversity, through Gram staining, oxidase production, glucose fermentation and screened for enzymatic activity profiles (proteolytic, glycolytic and phosphatasic activities) using specific fluorogenic substrates. Results: Most of the bacterial isolates were Gram-negative, oxidase-positive and glucose-fermenting strains. Higher enzyme diversification was found in Road Bay at the site impacted by the sewage wastes from the research station, compared to the control site. A higher abundance of heterotrophic bacteria was detected on PVC compared to PE. Several pigmented strains, ascribable to Flavobacterium sp., were isolated from biofilm with respect to the ones isolated from the pelagic environment. Main findings: The screening of bacterial isolates for the production of hydrolytic enzymes revealed that proteolytic and phosphatase activities were a common metabolic trait of both water and biofilm-derived isolated. Microbes inhabiting Antarctica represent a very important and not completely known source of bioactive molecules with possibly multiple applications, from biomedicine to pharmaceutical, cosmetics, biodegradation, and so on. Conclusions: This is the first contribution to characterize phenotypically the bacteria colonizing the Ross Sea seabed in comparison with pelagic heterotrophic bacteria and to explore their metabolic abilities and the biotechnological potential of these microorganisms. The preliminary data regarding the metabolic profiles and composition of the microbial community have highlighted the plasticity of the microbial community, whose metabolism is modulated by the organic matter supplies found near the Mario Zucchelli Station. Brief summary: Heterotrophic bacteria in the waters and in the biofilm covering the surface of settlement panels in plastics deployed at sea for different time periods were studied in two bays of the Ross Sea, Road Bay and Tethys Bay. The main phenotypical characteristics of the bacterial isolates were examined. The production of hydrolytic enzymes makes microbial biofilms interesting sources of molecules with a potential applicative interest. Any potential implications: Bioprospecting research on polar microorganisms represents an attractive field of microbiology that is supported by increasing attention towards the discovery of hydrolytic enzymes as new active compounds having unexplored properties and applications.

Building Footprint Generation Through Convolutional Neural Networks With Attraction Field Representation

Building footprint generation is a vital task in a wide range of applications, including, to name a few, land use management, urban planning and monitoring, and geographical database updating. Most existing approaches addressing this problem fall back on convolutional neural networks (CNNs) to learn semantic masks of buildings. However, one limitation of their results is blurred building boundaries. To address this, we propose to learn attraction field representation for building boundaries, which is capable of providing an enhanced representation power. Our method comprises two elemental modules: an Img2AFM module and an AFM2Mask module. More specifically, the former aims at learning an attraction field representation conditioned on an input image, which is capable of enhancing building boundaries and suppressing the background. The latter module predicts segmentation masks of buildings using the learned attraction field map. The proposed method is evaluated on three datasets with different spatial resolutions: the ISPRS dataset, the INRIA dataset, and the Planet dataset. From experimental results, we find that the proposed framework can well preserve geometric shapes and sharp boundaries of buildings, which brings significant improvements over other competitors. The trained model and code are available at https://github.com/lqycrystal/AFM_building.

Spacecraft with Periodic Mass Redistribution: Regular and Chaotic Behaviour

The motion of a spacecraft containing a moving massive point in the central field of Newtonian attraction is considered. Within the framework of the so-called “satellite approximation”, the center of mass of the system is assumed to move in an unperturbed elliptical Keplerian orbit. The spacecraft’s dynamics about its center of mass is studied. Conditions under which the spacecraft rotates about a perpendicular to the plane of the orbit uniformly with respect to the true anomaly are found. Such uniform rotations are achieved using a specially selected rule for changing the position of a massive point with respect to the spacecraft. Necessary conditions for these uniform rotations are studied numerically. An analysis of the nonintegrability of a special class of spacecraft’s rotation is carried out using the method of separatrix splitting. Poincaré sections are constructed for certain parameter values. Several linearly stable periodic motions are pointed out and investigated.