To make prognosis one needs to build a model based on historical data. In the paper we propose a framework for modelling of long-term non-homogeneous data with non-Gaussian properties. These specific properties have been identified in real datasets describing the degradation process of the machine. The framework covers deterministic and random components separation, modelling of heavy-tailed, time-varying properties of random part as well as identification of possible autodependence hidden in the random sequence and identification of distribution for a random part. Due to non-linear trends, time-dependent scale (equivalent to the variance for Gaussian distributed data) and non-Gaussian characteristics present in the data, the final formula of the model is complex, its identification is challenging and requires specific, suitable to heavy-tailed processes, statistical methods. The paper provides two kind of novelties — first of all, it uses real data from condition monitoring systems and our findings may be novel and surprising to predictive maintenance community, secondly — processing such specific data opens new areas for general data modelling and highlight novel research directions.
The aim of this review is to show the possibilities of food production during space travel and to demonstrate the potential of technological solutions that can play a significant role in achieving the goal of colonizing other planets. The paper briefly outlines the conditions of space flight and the associated possible threats that may occur. It is assumed that the basic problem is cosmic radiation, which not only can significantly affect the health of astronauts, but also prevent potential cultivation of plants or animal breeding on board a spacecraft. The solution to this problem proposed here is a shield which provides protection against collisions with high kinetic energy particles, while reducing the speed of corpuscular radiation. Particular attention is given to various biotechnological and bioengineering methods that could be used for food production on board a spacecraft. Technological development in the field of bioprinting or genetic modification of organisms may play a key role in the success of long-distance technological missions. Moreover, organisms such as algae, fungi and insects are indicated as a potential source of energy for future colonizers. In sum, the review covers both the field of engineering and biotechnology, as well as the possibility of checking these technological methods in the test flights.
Salivary gland (SG) dysfunction impairs the life quality of many patients, such as patients with radiation therapy for head and neck cancer and patients with Sjögren’s syndrome. Multiple SG engineering strategies have been considered for SG regeneration, repair, or whole organ replacement. An in-depth understanding of the development and differentiation of epithelial stem and progenitor cells niche during SG branching morphogenesis and signaling pathways involved in cell–cell communication constitute a prerequisite to the development of suitable bioengineering solutions. This review summarizes the essential bioengineering features to be considered to fabricate an engineered functional SG model using various cell types, biomaterials, active agents, and matrix fabrication methods. Furthermore, recent innovative and promising approaches to engineering SG models are described. Finally, this review discusses the different challenges and future perspectives in SG bioengineering.
The specific physico-chemical properties of chitosan (Ch), a biopolymer isolated from chitin, and its impact on the stability of colloidal dispersity have focused the interest of science and industry. However, in some cases chitosan alone is not enough to provide high stability to dispersions, making it necessary to add surfactant to the chitosan/oxide system, leading to superior stabilizing properties due to the association of polymer and surfactant molecules to form complexes that can modify the ability of bare chitosan for adsorbing on colloidal materials. This study explores the interactions between chitosan and alumina in the presence of three different anionic surfactants: the hydrocarbon SDS (sodium dodecyl sulfate), the fluorocarbon FS-91 (Capstone® FS-91), and the silicone A-Si (Silphos A-100). Different analytical methods evidenced chitosan adsorption on the alumina surface, forming hybrid organic-inorganic materials. This process can be enhanced by adding surfactant, with SDS leading to a strong increase of chitosan adsorption. Elemental mapping and scanning electron microscope imaging have provided a confirmation of the co-adsorption of polymer and surfactant on the alumina surface. The latter emerges as a very important finding because the results have shown that small quantities of surfactant (as low as 0.002% v/v) can strongly influence the adsorption and stability of multicomponent colloidal systems. This allows decreasing the chitosan amount required for the enhancement of the colloidal stability in relation to dispersions without added surfactants, providing the basis for reducing the production costs of colloidal dispersion, which opens new opportunities to chemical industry.
Proteases are enzymes that hydrolyze peptide bonds in proteins and peptides; thus, they control virtually all biological processes. Our understanding of protease function has advanced considerably from nonselective digestive enzymes to highly specialized molecular scissors that orchestrate complex signaling networks through a limited proteolysis. The catalytic activity of proteases is tightly regulated at several levels, ranging from gene expression through trafficking and maturation to posttranslational modifications. However, when this delicate balance is disturbed, many diseases develop, including cancer, inflammatory disorders, diabetes, and neurodegenerative diseases. This new understanding of the role of proteases in pathologic physiology indicates that these enzymes represent excellent molecular targets for the development of therapeutic inhibitors, as well as for the design of chemical probes to visualize their redundant activity. Recently, numerous platform technologies have been developed to identify and optimize protease substrates and inhibitors, which were further used as lead structures for the development of chemical probes and therapeutic drugs. Due to this considerable success, the clinical potential of proteases in therapeutics and diagnostics is rapidly growing and is still not completely explored. Therefore, small molecules that can selectively target aberrant protease activity are emerging in diseases cells. In this review, we describe modern trends in the design of protease drugs as well as small molecule activity-based probes to visualize selected proteases in clinical settings.
In order to enhance caproic acid concentration from wheat straw fermentation and elucidate the microbial community inside the system. This study investigated ethanol addition with different mode for wheat straw co-cultured with rumen fluid. The results showed that segmentation addition of ethanol resulted in a higher caproic acid of 1473 mg/L. The structural characteristics of the microorganism community were further analyzed to reveal the functional of microorganisms in the acid fermentation from straw. Among them, Prevotella had a strong hemicellulose degradation capacity; Corynebacterium, Ureibacillus, and Clostridium_sensu_stricto_12, were correlated with the increasing trend of caproic acid. Control the proper ethanol concentration and optimum fermentation conditions would avoid the biotoxicity for the microorganisms and enhance the product yield.
The energy transition is an essential effort from a variety of sectors and levels to achieve a carbon-neutral, larger-renewable integrated civilization. The transportation industry, which is largely concentrated in urban areas, emits more than 20% of total greenhouse gas emissions. Various technological difficulties are confronted and resolved as a result of this focus. Consequently, pursuit and research focusing on the integration of electric vehicles (EVs) powered by renewable energy sources are currently a viable option for combating climate change and advancing energy transition. According to current trends, this type of service will diminish the use of internal combustion engines in the future months. A study of the global market scenario for EVs and their future prospects is conducted. Whether energy storage devices and power electronics converters are properly interfaced determines the efficiency of EVs. Moreover, we provide our thoughts on what to expect in the near future in this domain and even the research areas that are still accessible to both industrial and academics.
The aim of the research work was to present a multilayer hydrogel capsule with controlled nutrient release properties as an innovative fertilizer designed for sustainable agriculture. Preparation of the capsules included the following steps: sorption of micronutrients (Cu, Mn, Zn) on eggshells (1) and their immobilization in sodium alginate, with the crosslinking agent being the NPK solution (2). The capsules were coated with an additional layer of a mixture of biopolymers (0.79% alginate, 0.24% carboxymethylcellulose and 8.07% starch)by means of dipping and spraying techniques. The biocomposites were characterized by limited (<10% within 100 h for the structures encapsulated by the dipping method) release of fertilizer ions (except for small K⁺ ions). The hydrogel fertilizer formulations were analyzed for physicochemical properties such as macro- and micronutrient content, surface morphology analysis, coating structure evaluation, mechanical properties, swelling and drying kinetics. High nutrient bioavailability was confirmed in vitro (extraction in water and neutral ammonium citrate). Germination and pot tests have revealed that the application of multicomponent hydrogel fertilizers increases the length of cucumber roots by 20%, compared to the commercial product.
The rising share of renewable energy sources (RES) in the energy mix and the desire to reduce the emission of greenhouse gases and harmful substances have driven broad-based technological advances on a global scale. In recent years, Millennium and Sustainable Development Goals have appeared, seeking to improve and level up standards of living while protecting the environment. Coupled with this, regulations are regularly issued that set increasingly stringent limits on emissions. This review examines the opportunities and challenges facing the maritime sector in light of the game-changing goals set by the International Maritime Organization (IMO). First, the review gives a short introduction to RES and considers the feasibility of producing green hydrogen and using ammonia as a carrier. It goes on to discuss the use of ammonia as a marine fuel from a technological, scientific, economic, and legal regulations point of view. The impact of emission regulations on the maritime sector and the challenges of bringing ammonia on board ships are factored in. Finally, the paper presents a technical justification for using solid oxide fuel cells to boost the efficiency of energy conversion.
This paper presents a new algorithm for detecting turn-to-turn faults in power transformers. Turn-to-turn faults between two parallel conductors have been taken into account, since they are the most difficult to detect due to the smallest short-circuit current level, especially when they are located close to the middle of the coil. Generally, standard differential protection relays fail in such a case, when low number of turns are shorted. Therefore, a new method was proposed here that is based on the combination of the negative-sequence current integral criterion and the unique differential quantities. The sensitivity and security of the presented method are analyzed and discussed. The performance of the proposed algorithm and three other commonly used methods have been tested with the signals generated using the MATLAB/Simulink program. The results confirm that the proposed algorithm is very sensitive to turn-to-turn faults and gives sufficient stabilization under external faults with CT saturation.
The goal of this work was to study the corrosion resistance of boiler steel and protective alloy coatings. Four ashes obtained from wood biomass, straw, refused-derived fuel and coal were analysed. The aggressive components of ash, such as potassium, sodium, sulphur and chlorine, can accelerate the operating problems of heat exchanging surfaces in power boilers, leading to enhanced slagging, fouling, agglomeration of ash, and corrosion. Four boiler steels were chosen for detailed investigation: 16Mo3, P265GH, Inconel 625 and 686. They were covered with ashes, placed in a furnace and heated through 4 months at 750 °C under oxidizing conditions. Multifaceted ash analysis (chemical composition, thermal behaviour, phase composition, and characteristic melting temperatures) was performed. Additionally, both slagging and fouling indices were calculated to predict the sintering properties of the studied ashes. The surfaces of studied steels after the exposure of ash presence were analysed using SEM-EDS technique. The results showed that the corrosive impact of the ashes depended on the aggressive components present in the ash. Furthermore, FactSage thermochemical equilibrium calculations were used to predict the amount of liquid slag and solid phases under the studied conditions and to determine the transformation of the mineral phases of the ash.
This article proposes a scheme for multifactor authentication based on electroencephalography (EEG) signal analysis. A solution for EEG signal acquisition and recording of acquisition results has been implemented, a machine learning model has been developed and trained, then a classifier that determines the user's login procedure familiarity has been built, and a solution to carry out the described experiments has been implemented, in the form of a mobile application. Besides, a multifactor authentication system, based on the EEG signal combined with user image verification, using the brain–computer interface system with a single EEG electrode based on the NeuroSky MindWave device was proposed. Based on the defined scenarios, experiments were conducted, followed by a survey on the research group, and the obtained results were analyzed. In the case of an experiment related to login simulation, a high classification accuracy rate was obtained, both for the classifier itself (83.33%) and the proposed user authentication system (77.78%). Analyzing the results of the EEG signal recording used in the classification, it seems that the proposed solution is promising not only due to the high accuracy and a low false rejections rate but also through confirmed associations in the analysis of brain wave signal, corresponding to the results of research in the literature. A proposed multi-factor authentication system based on image selection and EEG analysis can be implemented in many areas as a modern solution in securing IT systems.
A novel process to produce a H2-rich syngas from a high moisture-containing agricultural waste digestate is proposed. This process combines the use of hydrothermal carbonization (HTC), dewatering, pyrolysis, and catalytic reforming. Due to the feature of the high moisture content in the digestate, the effect of the HTC and dewatering on the process performance is of interest, and four scenarios were considered. Furthermore, three pyrolytic temperatures were chosen to understand the effect of pyrolysis conditions on the produced H2-rich syngas. A life cycle assessment was conducted to investigate the environmental impact of the proposed process. Results show that the application of HTC technology, increases the process efficiency, produces less syngas from one ton of digestate, lowers the cumulative energy demand and the negative carbon emissions. When the dewatering technology is used, the syngas yield is promoted but the H2 concentration in the syngas is reduced. The H2 to CO molar ratio reaches the maximum value of 9.2 when using a 450 ˚C pyrolysis temperature, by only using HTC. When the combining process of HTC and dewatering is used, it results in the highest process efficiency, but the smallest relative negative CO2 equivalent emissions by treating one ton of dry digestate.
Computer Vision (CV) has been employed in several different industries, with remarkable success in image classification applications, such as medicine, production quality control, transportation systems, etc. CV models rely on excessive images to train prospective models. Usually, the process of acquiring images is expensive and time-consuming. In this study, we propose a method that consists of multiple steps to increase image classification accuracy with a small amount of data. In the initial step, we set up multiple datasets from an existing dataset. Because an image carries pixel values between 0 and 255, we divided the images into pixel intervals depending on dataset type. If the dataset is grayscale, the pixel interval is divided into two parts, whereas it is divided into five intervals when the dataset consists of RGB images. In the next step, we trained the model using the original dataset and each created datasets separately. In the training process, each image illustrates a non-identical prediction space where we propose a top-three prediction probability ensemble method. Top-three predictions of newly generated images are ensemble to the corresponding probabilities of the original image. Results demonstrate that learning patterns from each pixel interval and ensemble the top three prediction vastly improves the performance and accuracy and the method can be applied to any model.KeywordsDeep learning ensemble methodClassification taskImage pixel interval
High demands of medical and husbandry sectors led to a massive annual production of 100,000 tonnes of various antibiotics on a global scale. Considering that an access to sublethal doses of these pharmaceuticals causes subsequent increase in resistance of virulent human bacterial pathogens, there is a substantial need for development of novel methods and techniques for degradation of antibiotics from aqueous solutions. Here, a high-throughput continuous flow plasma pencil and plasma brush methodologies were developed and verified for its applicability in removal of antibiotics from solutions, as estimated by high-performance liquid chromatography coupled with diode-array detection (HPLC-DAD) or ultraperformance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). The removal efficiency of drugs treated by the plasma pencil was in the range from 3 to 24%, and was associated with 1.39% to 10.76% losses in the antibacterial properties of the plasma-treated solutions. However, application of a unique plasma brush increased the antibiotics degradation rates significantly, now falling in the range from 29 to 67% that led to 12.06-81.59% decrease in the antimicrobial properties of the plasma-exposed antibiotics solutions. The deviations in the effectiveness of the plasma pencil and the plasma brush methods were associated with the types and amounts of generated species. Various intermediate degradation products were also detected in the post-plasma solutions, which derived from the oxygen attack to nucleophilic sides of the antibiotic molecules. We assume that such novel high-throughput, continuous flow methodological setups may find application in hospitals as an end-of-pipe technology for routine elimination of pollutants before reaching municipal sewage treatment plants.
Ontology integration is the task of combining a set of ontologies into a single ontology. Such ontology should contain all the knowledge expressed in the partial ontologies, without all the potential conflicts between them being resolved. In the literature, one may find several approaches to this problem, however, to the best of our knowledge most of them assume that the input ontologies remain static in time, therefore there is no risk of the outcome of integration becoming stale. If one of the partial ontologies changes over time (evolve) then the outcome of its integration may become obsolete. Therefore, a necessity of performing ontology integration once again appears. However, such a procedure may be very time and resource consuming. In this paper, we propose a solution for this issue. We claim that it is possible to update the integrated ontology based solely on a description of changes applied to the input ontologies, acquiring a similar quality as if the ontology integration be conducted from scratch. KeywordsOntology integrationOntology evolutionKnowledge management
In this paper, we derive the Mean Value for nodes in Fuzzy Communication Structure (FCS). The novel solution is proposed for a class of cooperative games over FCS with a sub-additive characteristic function. We propose two cooperative game concepts over communication multigraph with fuzzy parameters. We also introduce how the fuzzy characteristic function is calculated via maximal flow. In the first model, parameters (arc capacities) of FCS are given by fuzzy numbers. In the second model, parameters (arc capacities) of FCS are given by fuzzy numbers, too, and there is also given fuzzy goal determining the degree of satisfaction with the flow value that can be transmitted in the network between each pair of network vertices. In order to compare nodes’ Mean Values, we use the possibility measure and expected fuzzy number value. Obtained results may be useful for managing of development of a network of communication nature. Illustrating examples are presented. KeywordsFuzzy communication structureMaximal flowFuzzy cooperative gameSub-additive characteristic function
This paper addresses the problem of classifying the proficiency of second language learners using multilingual models. Such models can be extremely useful in applications supporting the learning of multiple, even rare languages. Experiments based on Czech, German and Italian languages have been reported in the literature. This dataset was extended with texts in English. SVM, random forest, and logistic regression methods were used to train the model with different sets of language features. For the monolingual models – which served as benchmarks – the best results were observed for the random forest and SVM methods. For multilingual models, in contrast to other studies, the best results were obtained using the SVM algorithm. Models trained on a feature set containing n-grams of POS, n-grams of dependencies, and POS distribution performed better than models trained only on n-grams of POS, used in other works on multilingual models. The experiments confirmed the feasibility of using multilingual models in place of monolingual ones. Multilingual models were also able to classify texts in a language that was not involved in model learning. KeywordsClassificationLanguage proficiencyCEFR
Pulsed electric fields (PEFs) are commonly used to facilitate the delivery of various molecules, including pharmaceuticals, into living cells. However, the applied protocols still require optimization regarding the conditions of the permeabilization process, i.e., pulse waveform, voltage, duration, and the number of pulses in a burst. This study highlights the importance of electrochemical processes involved in the electropermeabilization process, known as electroporation. This research investigated the effects of electroporation on human non-small cell lung cancer cells (A549) in potassium (SKM) and HEPES-based buffers (SHM) using sub-microsecond and microsecond range pulses. The experiments were performed using 100 ns – 100 μs (0.6–15 kV/cm) bursts with 8 pulses in a sequence. It was shown that depending on the buffer composition, the susceptibility of cells to PEF varies, while calcium enhances the cytotoxic effects of PEF, if high cell membrane permeabilization is triggered. It was also determined that electroporation with calcium ions induces oxidative stress in cells, including lipid peroxidation (LPO), generation of reactive oxygen species (ROS), and neutral lipid droplets. Here, we demonstrated that calcium ions and optimized pulse parameters could potentiate PEF efficacy and oxidative alternations in lung cancer cells. Thus, the anticancer efficacy of PEF in lung cancers in combination with standard cytostatic drugs or calcium ions should be considered, but this issue still requires in-depth detailed studies with in vivo models.
Improper collection and processing of waste electrical and electronic equipment (WEEE) pose a serious threat to the environment and prevent the recovery of valuable materials. Due to the decreasing availability of resources and production materials, the relevance of WEEE recycling has increased. WEEE should be seen as an important source of raw materials for European economies. Moreover, e-waste recycling has a positive impact on the environment by limiting energy use and CO2 emissions during ore processing. The objective of this study was the analysis of the product use times, the reasons for purchasing new devices, and the consumers’ behavior, knowledge, and awareness concerning WEEE collection and treatment. This study discusses these issues based on a survey on electronic waste management in Wroclaw (Poland). The results from 495 questionnaire respondents indicated a shortening use time of many types of electronic and electric equipment (EEE). The “failure of the device” was the primary reason respondents replaced their products. It was indicated by 90% of the respondents. In many cases, the repair of devices was unprofitable. The most important factor determining the purchase of new devices was their price. The environmental factors, mainly those giving economic benefits, were also considered. Despite the implementation of the extended producer responsibility, the functioning model of WEEE collection has proven to be ineffective. A significant amount of small WEEE ended up in waste bins. For this reason, the willingness of residents to pay an additional fee for WEEE collection was also estimated. The results indicated that campaigns to increase residents’ awareness of WEEE management should continue.
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