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The CCT and two DCCT diagrams were constructed for the HSLA steel containing Cr, V, Nb, and N microadditions, taking into account industrial processing parameters of this material in a seamless tubes rolling mill. The typical finish hot rolling temperature of 1173 K was used for the construction of the standard CCT diagram and the effect of previous austenite deformation at this temperature was evaluated in the DCCT diagram. Another DCCT diagram was developed after heating at 1553 K, followed by plastic deformation at 1173 K. The prior austenite grain size in the hot rolled material after heating at 1173 K was approx. 10 μm, the heating of the as-cast material at 1553 K resulted in the prior austenite grain size over 200 μm. The effect of the previous austenite deformation after low-temperature heating on the CCT diagram was negligible. The high-temperature heating showed a great influence on the austenite decomposition processes. The Ferrite-start temperature was significantly reduced at high cooling rates and the preferred decomposition of coarse grained austenite to acicular ferrite suppressed the bainite transformation at medium cooling rates. The developed DCCT diagrams can be used for the prediction of austenite decomposition products during the cooling of the seamless tubes from the finish rolling temperature. The CCT diagram can be utilized for the quality heat treatment of tubes.

Use of the protective steel capsules enabled to manage the laboratory hot flat rolling of the extremely brittle as-cast aluminide Fe-40at.%Al-Zr-B with the total height reduction of almost 70 %. The hot rolling parameters were optimized to obtain the best combination of deformation temperature (from 1160°C up to 1240°C) and rolling speed (from 0.14 m·s−1 to 0.53 m·s−1). The resistance against cracking and refinement of the highly heterogeneous cast microstructure were the main criteria. Both experiments and mathematical simulations based on FEM demonstrated that it is not possible to exploit enhanced plasticity of the investigated alloy at low strain rates in the hot rolling process. The heat flux from the sample to the working rolls is so intensive at low rolling speed that even the protective capsule does not prevent massive appearance of the surface transverse cracking. The homogeneity and size of product’s grain was influenced significantly by temperature of deformation, whereas the effect of rolling speed was relatively negligible. The optimal forming parameters were found as rolling temperature 1200°C and the rolling speed 0.35 m·s−1. The effective technology of the iron aluminide Fe-40at.% Al-Zr-B preparation by simple processes of melting, casting and hot rolling was thus established and optimized.

Influence of the methodology of mathematical processing of experimental data on the calculated value of the activation energy in hot forming was evaluated for three very different alloys: iron aluminide, low-alloyed steel and magnesium alloy. Application of the mean strain rates is appropriate but calculations with use of nominal strain rates of the plastometric tests do not give significantly different results. Calculations based on the processing of the simplified power dependence between strain rate and peak stress can give the results differing very substantially from those that were gained by the solution of the complex hyperbolic relationship. This has negative impacts, particularly at prediction of the maximum flow stress in the broad range of forming conditions, represented by the Zener-Hollomon parameter.

Compression tests of magnesium alloy Mg-3Al-1Zn (AZ31) at different temperatures and strain rate were made on plastometer Gleeble 3800. Deformation behaviour and particularly shape of stress-strain curves of the alloy AZ31 differ significantly at low and high values of Zener–Hollomon parameter Z. The border between these areas was determined mathematically as Z = 2.9E+13 s–1. While the calculated activation energy Q was for both these areas practically identical (157 or 155 kJ mol–1), mathematical description of coordinates of the peak stress differs considerably. Regression and statistical analysis of experimental data have confirmed unequivocally, that it was impossible to describe by a uniform equation the whole set of data (i.e. traditional stress-strain curves, as well as those with atypical initial stage, given by the massive twinning). That's why two mathematical models were developed enabling prediction of the flow stress of investigated magnesium alloy in dependence on temperature, strain and strain rate, with inclusion of the influence of dynamic recrystallisation.

The need for enhancing image spatial resolution has motivated the researchers to propose numerous super-resolution (SR) techniques, including those developed specifically for hyperspectral data. Despite significant advancements in this field attributed to deep learning, little attention has been given to evaluating the practical value of super-resolved images in specific applications. Most methods are validated in application-independent scenarios, often using simulated low-resolution images, resulting in overly optimistic conclusions. In this article, we propose task-based evaluation strategies for hyperspectral image SR and we present results obtained with various approaches that include pansharpening, multispectral–hyperspectral data fusion, and single-image SR. We demonstrate that the proposed framework allows us to highlight both benefits and limitations of each method and can, therefore, guide the development of SR techniques suitable for real-world applications.

In this work, the synthesis, structural analysis and anticancer properties of 5-methyl-9-trifluoromethyl-12H-quino[3,4-b][1,4]benzothiazinium chloride (3) are described. Compound 3 was synthesized by reacting 1-methyl-4-butylthio-3-(benzoylthio)quinolinium chloride with 4-(trifluoromethyl)aniline, respectively. The structure of the resulting product was determined using 1H-NMR and 13C-NMR spectroscopy as well as HR-MS spectrometry. The spatial geometry of agent 3 and the arrangement of molecules in the crystal (unit cell) were also confirmed using X-ray diffraction. The tetracyclic quinobenzothiazinium system is fairly planar because the dihedral angle between the planes formed by the benzene ring and the quinoline system is 173.47°. In order to obtain insight into the electronic charge distribution of the investigated molecule, electronic structure calculations employing the Density Functional Theory (DFT) were performed. Moreover, antiproliferative activity against a set of pancreatic cancer cell lines was tested, with compound 3 showing IC50 values against human primary pancreatic adenocarcinoma BxPC-3 and human epithelioid pancreatic carcinoma Panc-1 of 0.051 µM and 0.066 µM, respectively. The IC50 value of cytotoxicity/cell viability of the investigated compound assessed on normal human lung fibroblasts WI38 was 0.36 µM.

A simple methodology was used for calculating the equivalent strain values during forming the sample alternately in two mutually perpendicular directions. This method reflects an unexpected material flow out of the nominal deformation zone when forming on the MAXStrain II device. Thus it was possible to perform two temperature variants of the simulation of continuous rolling and cooling of a long product made of steel containing 0.17% C and 0.80% Mn. Increasing the finishing temperature from 900°C to 950°C and decreasing the cooling rate from 10°C/s to 5°C/s led to a decrease in the content of acicular ferrite and bainite and an increase in the mean grain size of proeutectoid ferrite from about 8 µm to 14 µm. The result was a change in the hardness of the material by 15%.

Abstract Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs. This is inherent to remote sensing, including Sentinel-2 satellite images that are available free of charge at a high revisit frequency, but whose spatial resolution is limited to 10m ground sampling distance. The resolution can be increased with super-resolution algorithms, in particular when performed from multiple images captured at subsequent revisits of a satellite, taking advantage of information fusion that leads to enhanced reconstruction accuracy. One of the obstacles in multi-image super-resolution consists in the scarcity of real-world benchmarks—commonly, simulated data are exploited which do not fully reflect the operating conditions. In this paper, we introduce a new benchmark (named MuS2) for super-resolving multiple Sentinel-2 images, with WorldView-2 imagery used as the high-resolution reference. Within MuS2, we publish the first end-to-end evaluation procedure for this problem which we expect to help the researchers in advancing the state of the art in multi-image super-resolution.

UWB has been in existence for several years, but it was only a few years ago that it transitioned from a specialized niche to more mainstream applications. Recent market data indicate a rapid increase in the popularity of UWB in consumer products, such as smartphones and smart home devices, as well as automotive and industrial real-time location systems. The challenge of achieving accurate positioning in indoor environments arises from various factors such as distance, location, beacon density, dynamic surroundings, and the density and type of obstacles. This research used MFi-certified UWB beacon chipsets and integrated them with a mobile application dedicated to iOS by implementing the near interaction accessory protocol. The analysis covers both static and dynamic cases. Thanks to the acquisition of measurements, two main candidates for indoor localization infrastructure were analyzed and compared in terms of accuracy, namely UWB and LIDAR, with the latter used as a reference system. The problem of achieving accurate positioning in various applications and environments was analyzed, and future solutions were proposed. The results show that the achieved accuracy is sufficient for tracking individuals and may serve as guidelines for achievable accuracy or may provide a basis for further research into a complex sensor fusion-based navigation system. This research provides several findings. Firstly, in dynamic conditions, LIDAR measurements showed higher accuracy than UWB beacons. Secondly, integrating data from multiple sensors could enhance localization accuracy in non-line-of-sight scenarios. Lastly, advancements in UWB technology may expand the availability of competitive hardware, facilitating a thorough evaluation of its accuracy and effectiveness in practical systems. These insights may be particularly useful in designing navigation systems for blind individuals in buildings.

Apples are the most important species in Polish fruit production, and their fresh fruit and processing products occupy a very important place in the European and world food economies. A natural process on the apple market is the replacement of older cultivars with new ones. Consequently, breeding work is being carried out all over the world to obtain new, attractive apple cultivars. In this work, four new apple hybrids obtained in the Department of Pomology and Horticulture Economics at the University of Life Sciences in Warsaw (WULS—SGGW), i.e., two red-skinned hybrids (‘I’P’ and ‘TL’) and two green-skinned hybrids (‘GL’ and ‘LG’), were tested for their main physico-chemical characteristics after harvest and after storage, as well as for their consumer acceptability in a comparison with three popular cultivars in Poland (‘Šampion’, ‘Golden Delicious’, and ‘Ligol’). The hybrids evaluated were differentiated in terms of the tested features. It was found that the fruits of the ‘GL’ hybrid had the highest firmness—both after harvest and post storage; it was also one of the highest-rated overall impressions by consumers among the cultivars tested (only ‘Ligol’ apples had higher scores). The hybrid with low fruit quality and low health-promoting properties at this stage of the study was ‘TL’, whose overall impression was also rated lowest by consumers. The study also showed that the content of bioactive compounds and antioxidant activity of apples can be higher in green-skinned cultivars compared to red-skinned cultivars.

Alternative methods of presenting the information contained in mathematical images, which are adapted to the needs of blind people, are significant challenges in modern education. This article presents an alternative multimodal method that substitutes the sense of sight with the sense of touch and hearing to convey graphical information. The developed method was evaluated at a center specializing in the education of the blind in Poland, on a group of 46 students aged 15–19. They solved a set of 60 high school-level problems on geometry, mathematical analysis, and various types of graphs. We assessed the mechanisms introduced for the sense of touch and hearing, as well as the overall impression of the users. The system usability scale and the NASA task load index tests were used in the evaluation. The results obtained indicate an overall increase in user satisfaction and usefulness of the proposed approach and a reduction in the workload during exercise solving. The results also show a significant impact of the proposed navigation modes on the average time to reach objects in the drawing. Therefore, the presented method could significantly contribute to the development of systems supporting multimodal education for people with blindness.

Teaching mathematics to blind people is a challenge of modern educational methods. This article presents a method of preparing the adapted material and its usage in the learning process of mathematics by blind people, as well as the results of evaluating the proposed approach. The presented results were obtained based on a mathematical analysis course conducted in two classes—with and without using the developed method. The developed method uses the conceptualization of knowledge as a graph. The learning process is supported by feedback processes that consider the mechanisms of knowledge and error vectors, on which a personalized adaptation of the learning path is made for each particular student. The evaluation process has shown a statistically significant improvement in learning results achieved by blind students. The average final test score in the group working with the platform during learning increased by 14%. In addition, there was an increase in cooperation between blind students who had the opportunity to take on the role of a teacher, which was observed in 27% of the participants. Our results indicate the effectiveness of the developed approach and motivate us to evaluate the method in a broader group of students. The engagement of students indirectly indicates overcoming the barriers known from the state of the art: uncertainty, poor motivation, and difficulties in consolidating the acquired skills.

It is not realistic to optimize the roll pass design of profile rolling mills, which typically roll hundreds of profiles, using physical modelling or operational rolling. The use of reliable models of microstructure evolution is preferable here. Based on the mathematical equations describing the microstructure evolution during hot rolling, a modified microstructure evolution model was presented that better accounts for the influence of strain-induced precipitation (SIP) on the kinetics of static recrystallization. The time required for half of the structure to soften, t0.5, by static recrystallization was calculated separately for both situations in which strain-induced precipitation occurred or did not occur. On this basis, the resulting model was more sensitive to the description of grain coarsening in the high-rolling-temperature region, which is a consequence of the rapid progress of static recrystallization and the larger interpass times during rolling on cross-country and continuous mills. The modified model was verified using a plain strain compression test (PSCT) simulation of rolling a 100-mm-diameter round bar performed on the Hydrawedge II hot deformation simulator (HDS-20). Four variants of simulations were performed, differing in the rolling temperature in the last four passes. For comparison with the outputs of the modified model, an analysis of the austenite grain size after rolling was performed using optical metallography. For indirect comparison with the model outputs, the SIP initiation time was determined based on the NbX precipitate size distribution obtained by TEM. Using the PSCT and the outputs from the modified microstructure evolution model, it was found that during conventional rolling, strain-induced precipitation occurs after the last pass and thus does not affect the austenite grain size. By lowering the rolling temperature, it was possible to reduce the grain size by up to 56 μm, while increasing the mean flow stress by a maximum of 74%. The resulting grain size for all four modes was consistent with the operating results.

Tensile membrane structures combine a prestressed roofing envelope material and supporting elements. To design these structures, there is a set of recommendations in the European Design Guide for Tensile Surface Structures and some other national standards. However, currently, there is no official standard related to the design process of tensile structures in the European Union. The structure studied in this project is considered as permanent roofing of an external testing device in the shape of a simple hyperbolic paraboloid without enclosing walls. Snow and wind loads were analyzed as the most critical types of loading in the location. Determining the value of the snow load is relatively simple according to the European standard. However, in the case of the wind load, this shape is not considered in the European standard and needs to be solved experimentally or by numerical simulation in a wind tunnel. The present contribution focuses on numerical analysis of the wind flow in RFEM software and simulation of the wind tunnel in RWIND software.

Ni-Al-Mo based alloys can be used as materials for high temperature applications. They can be prepared by various techniques such as an electron beam zone melting, which allows us to obtain the alloys with a directionally solidified structure and unique properties. A plasma-melted Ni-Mo master alloy was used for the preparation of the experimental alloys. Ni-Al-Mo alloys were melted in an induction furnace and then cast centrifugally in the form of bars. These bars were then re-melted in the electron beam zone furnace. The structure of these alloys was multi-phase. The structure was formed by the phases Ni3(Al,Mo) and (Ni) with variable content of molybdenum. The structure also contained particles rich in molybdenum (Mo, MoNi). The alloys were submitted to the compression tests at a temperature of 800 °C. The yield strength of alloys achieved the value of approx. 800 MPa. The different molybdenum content affected the values of the maximal flow stress. The alloys with higher molybdenum content showed higher maximal flow stress, namely approx. 1300 MPa. The results show that these alloys are very promising for the production of structural components operating at elevated temperatures.

Based on dilatometric tests, the effect of various values of previous deformation on the kinetics of austenite transformations during the cooling of 100Cr6 steel has been studied. Dilatometric tests have been performed with the use of the optical dilatometric module of the plastometer Gleeble 3800. The obtained results were compared to metallographic analyses and hardness measurements HV30. Uniaxial compression deformations were chosen as follows: 0, 0.35, and 1; note that these are true (logarithmic) deformations. The highly important finding was the absence of bainite. In addition, it has been verified that with the increasing amount of deformation, there is a further shift in the pearlitic region to higher cooling rates. The previous deformation also affected the temperature martensite start, which decreased due to deformation. The deformation value of 1 also shifted the critical cooling rate required for martensite formation from the 12 °C/s to 25 °C/s.

Stress-strain curves of the EN AW 6082 aluminium alloy with 1.2 Si-0.51 Mg-0.75 Mn (wt.%) were determined by the uniaxial compression tests at temperatures of 450−550 °C with a strain rate of 0.5−10 s−1. The initial structure state corresponded to three processing types: as-cast structure non-homogenized or homogenized at 500 °C, and the structure after homogenization and hot extrusion. Significantly higher flow stress appeared as a result of low temperature forming of the non-homogenized material. Hot deformation activation energy Q-values varied between 99 and 122 kJ·mol−1 for both homogenized materials and from 200 to 216 kJ·mol−1 for the as-cast state, while the Q-values calculated from the measured steady-state stress were always higher than those calculated from the peak stress values. For the extruded state of the 6082 alloy, the physically-based model was developed to reliably predict the flow stress influenced by dynamic softening, temperature, strain rate, and true strain up to 0.6.

In recent years, the study of the hot deformation behavior of various materials is significantly marked by an increasing utilization of artificial neural networks, which are frequently employed for a hot flow curve description. This specific kind of description is commonly solved via a Feed-Forward Multi-Layer Perceptron architecture and rarely via a Radial Basis architecture. Both network architectures are compared to assess their suitability in the process of a hot flow curve description under a wide range of thermomechanical conditions. The performed survey is also aimed on the eventual utilization of corresponding modifications of both studied networks, namely on a Cascade-Forward Multi-Layer Perceptron and Generalized Regression network. The main results have shown that the Feed-Forward Multi-Layer Perceptron architecture represents a good choice if very high accuracy is a crucial goal. However, in the case of this architecture, finding the proper parameters can be time-consuming and the hardware burdensome. On the contrary, for the flow curve description the almost unused Radial Basis network offers a very easy training procedure and significantly shorter computing time under acceptable accuracy. The results of the submitted research should then serve as a background for the selection and following application of a suitable network architecture in the process of solving future flow curve description tasks.

In recent years, the utilization of artificial neural networks (ANNs) as regression models to solve the issue of hot flow stress forecasting has become a standard approach. In a connection with this kind of regression issue, employed ANNs are usually learned via a shallow learning technique while only limited attention has been paid to a deep learning method. In the frame of the submitted research, the shallow learning approach is thoroughly compared to the deep learning techniques which are based on the use of a Restricted Boltzmann Machine (RBM) and an Auto-Encoder (AE). To do so, these learning techniques are applied on a feed-forward multi-layer ANN describing the experimental hot flow curve dataset of micro-alloyed medium carbon steel. In comparison with the shallow learning method, both deep learning approaches provided higher accuracy in the network response – especially in the case of a higher number of hidden layers. The results have also shown that neither the RBM-based deep learning method nor the AE-based method had a significant effect on the duration of the necessary calculations. However, it turned out that the RBM-based method can, under certain conditions, lead to a more reliable network performance.

Introduction Proper nutrition has the task of providing the right amount of energy and nutrients. A balanced diet should include vegetables and fruit at every meal. These products are rich in vitamin C, which is involved in many metabolic pathways in the body. A well-balanced diet will generally provide you with enough ascorbic acid. The content of vitamins in products depends on many factors, including the place of origin of the product, season, species, processing methods, or the conditions and time of storage. Vitamin C is easily destroyed by contact with oxygen, light radiation or metal ions. Aim of the study The aim of the study is to assess the content of vitamin C in juices squeezed from different types of oranges using various processing methods. Material and methods Three species of oranges were used for the study: Valencia, Saluciana and Novelina. Each type of orange was squeezed with 3 processing methods, using: a juicer, a slow-speed squeezer and a manual squeezer. The determination of vitamin C content in the obtained juices was performed by the Tillmans titration method with the use of standard, blue, 2,6-dichlorophenolindophenol dye. The vitamin C content in juices was determined: immediately after squeezing the juice, one hour after squeezing and 24 hours after squeezing. The juices were stored in the refrigerator between determinations. A pH meter was used to measure the pH, and a portable digital sucrose refractometer was used for the% sucrose measurement. Results The highest content of vitamin C immediately after pressing was observed in the juice of hand-squeezed oranges from the Salustiana variety. The decrease in vitamin C after 24 hours averaged 37.8%. Statistically significant differences were found between the method of squeezing and the time after squeezing the juice. Valencia variety was characterized by the highest pH, Navelina - the lowest. Significant differences were found in the mean level of the pH indicator and the type of orange.

Abstract The synthesis of phase‐pure ternary solutions of tetradymite‐type materials (BixSb1−x)2Te3 (x=0.25; 0.50; 0.75) in an ionic liquid approach has been carried out. The nanoparticles are characterized by means of energy‐dispersive X‐ray spectroscopy (EDX), powder X‐ray diffraction (PXRD), scanning electron microscopy (SEM), and transmission electron microscopy. In addition, the role of different processing approaches on the thermoelectric properties ‐ Seebeck coefficient as well as electrical and thermal conductivity ‐ is demonstrated.

Suitable and complete sets of stress-strain curves significantly affected by dynamic recrystallization were analyzed for 11 different iron, copper, magnesium, titanium or nickel based alloys. Using the same methodology, apparent hot deformation activation energy Qp and Qss values were calculated for each alloy based on peak stress and steady-state stress values. Linear dependence between quantities Qp and Qss was found, while Qp values are on average only about 6% higher. This should not be essential in predicting true stress of a specific material depending on the temperature-compensated strain rate and strain.

High-temperature plastic properties of heat-resistant stainless steel X15CrNiSi 20-12 were assessed on the basis of hot tensile tests and nil strength tests. The results were supported by metallographic analyses using SEM and EDX analysis. The formability of the investigated steel can be divided into roughly three temperature areas. In the temperature range of 900°C to about 1050°C, formability was negatively affected by precipitation of carbide particles at grain boundaries. As the temperature rose to 1200°C, these particles dissolved, resulting in an increase in formability. Further temperature increases resulted in a relatively steep drop in formability caused by overheating of the material. The nil ductility temperature of 1280°C and the nil-strength temperature of 1362°C were determined. The Plastic and strength properties of the investigated material were compared with the deformation behavior of the reference steel X5CrNi 18-10, which shows a significantly wider range of suitable forming temperatures.

Abstract Background Nowadays, not only are single genomes commonly analyzed, but also metagenomes, which are sets of, DNA fragments (reads) derived from microbes living in a given environment. Metagenome analysis is aimed at extracting crucial information on the organisms that have left their traces in an investigated environmental sample.In this study we focus on the MetaSUB Forensics Challenge (organized within the CAMDA 2018 conference) which consists in predicting the geographical origin of metagenomic samples. Contrary to the existing methods for environmental classification that are based on taxonomic or functional classification, we rely on the similarity between a sample and the reference database computed at a reads level. Results We report the results of our extensive experimental study to investigate the behavior of our method and its sensitivity to different parameters. In our tests, we have followed the protocol of the MetaSUB Challenge, which allowed us to compare the obtained results with the solutions based on taxonomic and functional classification. Conclusions The results reported in the paper indicate that our method is competitive with those based on taxonomic classification. Importantly, by measuring the similarity at the reads level, we avoid the necessity of using large databases with annotated gene sequences. Hence our main finding is that environmental classification of metagenomic data can be proceeded without using large databases required for taxonomic or functional classification. Reviewers This article was reviewed by Eran Elhaik, Alexandra Bettina Graf, Chengsheng Zhu, and Andre Kahles.

Influence of the initial grain size on hot deformation behavior of the low-alloy Mn-Ti-B steel was investigated. The uniaxial compression tests were performed in range of the deformation temperatures of 900-1200°C and strain rates of 0.1-10 s–1. One set of samples was heated directly to the deformation temperature, which corresponded to the initial austenitic grain size of 19-56 μm; the other set of samples was uniformly preheated at the temperature of 1200°C. Whereas the values of activation energy, peak stress and steady-state stress values practically did not depend on the initial austenitic grain size, the peak strain values of coarser-grained structure significantly increase mainly at high values of the Zener-Hollomon parameter. This confirms the negative effect of the large size of the initial grain on the dynamic recrystallization kinetics, which can be explained by the reduction in nucleation density.

The aim of the performed experiments was to determine the influence of deformation and of austenitization temperature on the kinetics of phase transformations during cooling of high-carbon steel (0.728 wt. % C). The CCT and DCCT diagrams for austenitization temperature 940°C and DCCT diagram for austenitization temperature 1000°C were constructed with the use of dilatometric tests. On the basis of obtained results, a featureless effect of austenitization temperature and deformation on the kinetics of phase transformations during cooling of investigated steel was observed. Critical cooling rates for the transformation of martensite in microstructure fluctuated from 5 to 7°C · s–1 (depending on the parameters of austenitization and deformation), but only at cooling rates higher than 8°C · s–1 a dominant share of martensite was observed in the investigated steel, which resulted in the significant increase of hardness.

The work deal with an assembling and comparing of transformation diagrams of two low-alloy steels, specifically 16MnCrS5 and 20MnCrS5. In this work, diagrams of the type of CCT and DCCT of both steels were assembled. Transformation diagrams were assembled on the basis of dilatometric tests realized on the plastometer Gleeble 3800, of metallographic analyses and of hardness measurements. In addition, for comparison, the transformation diagrams were assembled even with use of the QTSteel 3.2 software. Uniform austenitization temperature of 850°C was chosen in case of both steels and even both types of diagrams. In case of both steels, an influence of deformation led to expected acceleration of phase transformations controlled by diffusion and also of bainite transformation. In both cases, the kinetics of martensitic transformation was not significantly affected by deformation.

Dynamic changes have taken place in the production of sweet cherry (Prunus avium L.) in Poland over the last 20 years. New cultivars with both early- and late-ripening dates have appeared, and modern high-intensity cultivation techniques are being implemented. The main attribute of the fruit, in addition to its unique taste, is its health-promoting properties. In this research, which was conducted at the Warsaw University of Life Sciences in 2021, our main objectives were to evaluate seven selected Czech cherry cultivars (‘Jacinta’; ‘Horka’; ‘Tamara’; ‘Helga’; ‘Fabiola’; ‘Kasandra’; ‘Kordia’) with regard to their dessert quality and contents of biologically active compounds, and to indicate the most valuable cultivar under the conditions of central Poland. The cultivars tested differed in both the fruit quality and health-promoting properties. ‘Jacinta’, ‘Horka’, Tamara’ and ‘Fabiola’ had the largest fruits. The ‘Fabiola’ and ‘Kordia’ fruits had the highest firmness, while the ‘Jacinta’ and ‘Horka’ fruits had the highest soluble solid contents (SSCs) and titratable acidities (TAs). We found the highest SSC-to-TA ratio in the ‘Fabiola’ cultivar. ‘Jacinta’ proved to be the most valuable cultivar in terms of bioactive compounds, and it had the highest antioxidant activity (DPPH). Some of the traits were closely correlated with each other, mainly in relation to the biologically active compounds. Darker fruits contained more bioactive compounds and had a higher antioxidant activity. It was also proved that size of fruits as well as SSC and TA are also correlated with fruit color. Intensively colored fruit are larger and have higher SSC and TA. In sweet cherry fruit, the contents of polyphenols and flavonoids, as well as the high DPPH, are strongly determined by the high content of cyanidin-3-galactoside.

Applying computer vision techniques to distinguish between spontaneous and posed smiles is an active research topic of affective computing. Although there have been many works published addressing this problem and a couple of excellent benchmark databases created, the existing state-of-the-art approaches do not exploit the action units defined within the Facial Action Coding System that has become a standard in facial expression analysis. In this work, we explore the possibilities of extracting discriminative features directly from the dynamics of facial action units to differentiate between genuine and posed smiles. We report the results of our experimental study which shows that the proposed features offer competitive performance to those based on facial landmark analysis and on textural descriptors extracted from spatial-temporal blocks. We make these features publicly available for the UvA-NEMO and BBC databases, which will allow other researchers to further improve the classification scores, while preserving the interpretation capabilities attributed to the use of facial action units. Moreover, we have developed a new technique for identifying the smile phases, which is robust against the noise and allows for continuous analysis of facial videos.

Nil strength temperature of 1062°C and nil ductility temperature of 1040°C were experimentally set for CuFe2 alloy. The highest formability at approx. 1020°C is unusable due to massive grain coarsening. The local minimum of ductility around the temperature 910°C is probably due to minor formation of γ-iron. In the forming temperatures interval 650-950°C and strain rate 0.1-10 s–1 the flow stress curves were obtained and after their analysis hot deformation activation energy of 380 kJ·mol–1 was achieved. Peak stress and corresponding peak strain values were mathematically described with good accuracy by equations depending on Zener-Hollomon parameter.

The hot deformation behavior of selected non-alloyed carbon steels was investigated by isothermal continuous uniaxial compression tests. Based on the analysis of experimentally determined flow stress curves, material constants suitable for predicting peak flow stress σp, peak strain εp and critical strain εcrDRX necessary to induce dynamic recrystallization and the corresponding critical flow stresses σcrDRX were determined. The validity of the predicted critical strains εcrDRX was then experimentally verified. Fine dynamically recrystallized grains, which formed at the boundaries of the original austenitic grains, were detected in the microstructure of additionally deformed specimens from low-carbon investigated steels. Furthermore, equations describing with perfect accuracy a simple linear dependence of the critical strain εcrDRX on peak strain εp were derived for all investigated steels. The determined hot deformation activation energy Q decreased with increasing carbon content (also with increasing carbon equivalent value) in all investigated steels. A logarithmic equation described this dependency with reasonable accuracy. Individual flow stress curves of the investigated steels were mathematically described using the Cingara and McQueen model, while the predicted flow stresses showed excellent accuracy, especially in the strains ranging from 0 to εp.

Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.

Hyperspectral images capture very detailed information about scanned objects and, hence, can be used to uncover various characteristics of the materials present in the analyzed scene. However, such image data are difficult to transfer due to their large volume, and generating new ground-truth datasets that could be utilized to train supervised learners is costly, time-consuming, very user-dependent, and often infeasible in practice. The research efforts have been focusing on developing algorithms for hyperspectral data classification and unmixing, which are two main tasks in the analysis chain of such imagery. Although in both of them, the deep learning techniques have bloomed as an extremely effective tool, designing the deep models that generalize well over the unseen data is a serious practical challenge in emerging applications. In this paper, we introduce the deep ensembles benefiting from different architectural advances of convolutional base models and suggest a new approach towards aggregating the outputs of base learners using a supervised fuser. Furthermore, we propose a model augmentation technique that allows us to synthesize new deep networks based on the original one by injecting Gaussian noise into the model’s weights. The experiments, performed for both hyperspectral data classification and unmixing, show that our deep ensembles outperform base spectral and spectral-spatial deep models and classical ensembles employing voting and averaging as a fusing scheme in both hyperspectral image analysis tasks.

Rods from mild steel S235JR were intensively rolled in the laboratory continuous mill. Specifically defined temperature of phase transformation Ar was determined from the free cooling curves measured by the temperature scanner. The Ar value increased from 763 to 786 °C with rolling temperature descending from 1 200 to 800 °C. The value of Ar = 730 °C was obtained at free cooling of the non-deformed rod of the same diameter 9,8 mm from heating temperature 1 000 °C. The obtained results were compared with continuous cooling transformation (CCT) and deformation continuous cooling transformation (DCCT) diagrams based on the dilatometric tests.

Formability of hypereutectoid C-Mn-Cr-V steel in hot condition was investigated with use of plastometric methods. A wide range of deformation temperatures 1 300 - 640 °C for hot tensile tests was proposed with use of nil-strength temperature (NST), determined by special plastometric method, and as well as with use of the calculated temperatures of phase transformations during heating of the investigated steel. Ultimate tensile strength of the investigated steel was increasing exponentially with the decreasing deformation temperature. Ductility of the investigated steel in hot condition increased with the increasing deformation temperature up to the temperatures ranging from 1 150 to 1 250 °C, after which a sharp decline of formability took place in investigated material.

The aim of this article was to assess the impact of previous plastic deformation on the kinetics of transformations of four selected steels. The research was conducted with use of the universal plastometer GLEEBLE 3800, when Continuous Cooling Transformation (CCT) and Deformation Continuous Cooling Transformation (DCCT) diagrams of selected steels were constructed on the basis of dilatometric tests. The research confirmed that the strain accelerates the particularly the transformations controlled by diffusion. Bainitic transformation was accelerated in three of the four steels. In the case of martensitic transformation the effect of the previous deformation was relatively small, but with clearly discernible trend.

Although hyperspectral images capture very detailed information about the scanned objects, their efficient analysis, transfer, and storage are still important practical challenges due to their large volume. Classifying and segmenting such imagery are the pivotal steps in virtually all applications, hence developing new techniques for these tasks is a vital research area. Here, deep learning has established the current state of the art. However, deploying large-capacity deep models on-board an Earth observation satellite poses additional technological challenges concerned with their memory footprints, energy consumption requirements, and robustness against varying-quality image data, with the last problem being under-researched. In this paper, we tackle this issue, and propose a set of simulation scenarios that reflect a range of atmospheric conditions and noise contamination that may ultimately happen on-board an imaging satellite. We verify their impact on the generalization capabilities of spectral and spectral-spatial convolutional neural networks for hyperspectral image segmentation. Our experimental analysis, coupled with various visualizations, sheds more light on the robustness of the deep models and indicate that specific noise distributions can significantly deteriorate their performance. Additionally, we show that simulating atmospheric conditions is key to obtaining the learners that generalize well over image data acquired in different imaging settings.

The combined effect of deformation temperature and strain value on the continuous cooling transformation (CCT) diagram of low-alloy steel with 0.23% C, 1.17% Mn, 0.79% Ni, 0.44% Cr, and 0.22% Mo was studied. The deformation temperature (identical to the austenitization temperature) was in the range suitable for the wire rolling mill. The applied compressive deformation corresponded to the true strain values in an unusually wide range. Based on the dilatometric tests and metallographic analyses, a total of five different CCT diagrams were constructed. Pre-deformation corresponding to the true strain of 0.35 or even 1.0 had no clear effect on the austenite decomposition kinetics at the austenitization temperature of 880 °C. During the long-lasting cooling, recrystallization and probably coarsening of the new austenitic grains occurred, which almost eliminated the influence of pre-deformation on the temperatures of the diffusion-controlled phase transformations. Decreasing the deformation temperature to 830 °C led to the significant acceleration of the austenite → ferrite and austenite → pearlite transformations due to the applied strain of 1.0 only in the region of the cooling rate between 3 and 35 °C·s−1. The kinetics of the bainitic or martensitic transformation remained practically unaffected by the pre-deformation. The acceleration of the diffusion-controlled phase transformations resulted from the formation of an austenitic microstructure with a mean grain size of about 4 µm. As the analysis of the stress–strain curves showed, the grain refinement was carried out by dynamic and metadynamic recrystallization. At low cooling rates, the effect of plastic deformation on the kinetics of phase transformations was indistinct.

Description of flow stress evolution, specifically an approximation of a set of flow curves acquired under a wide range of thermomechanical conditions, of various materials is often solved via so-called flow stress models. Some of these models are associated with a description of significant flow-curve coordinates. It is clear, the more accurate the coordinates description, the more accurate the assembled model. In the presented research, Zener–Hollomon-based relations, multi-layer perceptron networks and multivariate polynomials are employed to describe the peak and steady-state coordinates of an Invar 36 flow curve dataset. Comparison of the utilized methods in the case of the studied alloy has showed that the suitable description is given by the multivariate polynomials although the Zener–Hollomon and perceptron networks also offer valuable results.

The deformation behaviour of a coarse-grained as-cast medium-carbon steel, alloyed with 1.2% Mn, 0.8% Cr and 0.2% Mo, was studied by uniaxial compression tests for the strain rates of 0.02 s−1–20 s−1 in the unusually wide range of temperatures (650–1280 °C), i.e., in various phase regions including the region with predominant bainite content (up to the temperature of 757 °C). At temperatures above 820 °C, the structure was fully austenitic. The hot deformation activation energies of 648 kJ·mol−1 and 364 kJ·mol−1 have been calculated for the temperatures ≤770 °C and ≥770 °C, respectively. This corresponds to the significant increase of flow stress in the low-temperature bainitic region. Unique information on the hot deformation behaviour of bainite was obtained. The shape of the stress-strain curves was influenced by the dynamic recrystallization of ferrite or austenite. Dynamically recrystallized austenitic grains were strongly coarsened with decreasing strain rate and growing temperature. For the austenitic region, the relationship between the peak strain and the Zener–Hollomon parameter has been derived, and the phenomenological constitutive model describing the flow stress depending on temperature, true strain rate and true strain was developed. The model can be used to predict the forming forces in the seamless tubes production of the given steel.

In the presented research, conventional hot processing maps superimposed over the flow stress maps or activation energy maps are utilized to study a correlation among the efficiency of power dissipation, flow stress, and activation energy evolution in the case of Cr-Mo low-alloyed steel. All maps have been assembled on the basis of two flow curve datasets. The experimental one is the result of series of uniaxial hot compression tests. The predicted one has been calculated on the basis of the subsequent approximation procedure via a well-adapted artificial neural network. It was found that both flow stress and activation energy evolution are capable of expressing changes in the studied steel caused by the hot compression deformation. A direct association with the course of power dissipation efficiency is then evident in the case of both. The connection of the presence of instability districts to the activation energy evolution, flow stress course, and power dissipation efficiency was discussed further. Based on the obtained findings it can be stated that the activation energy processing maps represent another tool for the finding of appropriate forming conditions and can be utilized as a support feature for the conventionally-used processing maps to extend their informative ability.

The nil-strength temperature, zero strength temperature and solidus temperature are significant parameters with respect to the processes of melting, casting and welding steels. With the use of physical tests performed on the universal plastometer Gleeble 3800 and calculations in the IDS software, the nil-strength temperatures, zero strength temperatures and solidus temperatures of nine non-alloy carbon steels have been determined. Apart from that, solidus temperatures were also calculated by the use of four equations expressing a mathematical relation of this temperature to the chemical composition of the investigated steels. The nil-strength and zero strength temperatures and the solidus temperatures decreased with increasing carbon content in the investigated steels. Much higher content of sulfur in free-cutting steel resulted in a decrease of all the temperatures investigated. The zero strength temperatures determined by calculation in the IDS software during solidification were approximately 43−85 °C higher than the nil-strength temperatures determined experimentally during heating of the investigated steels. The linear dependence of experimentally measured nil-strength temperature on the calculated zero strength temperature for the investigated steels was determined. Based on regression analyses, there were determined mathematical relations which described with high accuracy a linear dependence of the nil-strength and zero strength temperatures on the solidus temperature of the investigated steels.

The aim of the performed experiments was to determine the influence of a cooling rate on the evolution of microstructure and hardness of the steel 27MnCrB5. By using dilatometric tests performed on the plastometer Gleeble 3800 and by using mathematical modelling in the software QTSteel a continuous cooling transformation diagram for a heating temperature of 850°C was constructed. Conformity of diagrams constructed for both methods is relatively good, except for the position and shape of the ferrite nose. The values of hardness, temperatures of phase transformations and the volume fractions of structural phases upon cooling from the temperature of 850°C at the rate from 0.16°C · s–1 to 37.2°C · s–1 were determined. Mathematically predicted proportion of martensite with real data was of relatively solid conformity, but the hardness values evaluated by mathematical modelling was always higher.

Flows stress of the steel C45 were predicted on the basis of experimentally obtained flow stress curves, using uniaxial hot compression tests on the plastometer HDS-20, by two completely different types of mathematical models, moreover with comparison to a model comprised in the FEM database of the FORGE software. The tests were carried out within the temperature range from 900 to 1 280 °C, at the strain rate from 0,1 to 100 s-1 and deformations up to 1,0. It follows from the results of flow stress prediction that models designed on the basis of experimental measurements have much better information capability than the generated model implemented into the database of the FORGE software, however, their extrapolation for larger deformations is limited.

CCT and DCCT steel diagrams of the steel 32CrB4 were determined by the universal plastometer GLEEBLE 3 800 on the basis of dilatometric tests. Dilatometric analysis showed that compared to the diagram provided by the software QTSteel th e noses of individual curves are in fact shifted towards shorter times. Preceding deformation significantly affected the decay diagram of the investigated steel. Shorter times, which were available for recovery of the deformed structure during more rapid cooling, resulted in a significant shift of the curves in the DCCT diagram towards shorter times. At low cooling rates the effect of deformation was practically negligible, since recrystallization took place between the deformation and beginning of the phase transformation.

Rolling of round bar made of steel 32CrB4 was simulated physically. The interrupted torsion test led to the final microstructure with coarser grains, however, thanks to comparatively large dimensions of the sample this type of experiment was not so sensitive to the cooling rate of the final cooling as compression test. Too high temperature of the end of controlled cooling of the samples after compression test led to partial self-quenching of the investigated material. Decelerated cooling below the temperature of the start of martensitic transformation led in both applied types of compression tests to the parameters of the resulting microstructure and hardness closest to the parameters obtained after laboratory rolling.

This article describes the method of strengthening a church building in Ruda Śląska against predicted effects of mining exploitation. The building had been already exposed to effects of 22 mining incidents which significantly damaged walls and vaults. Therefore, it was decided to strengthen the building before new exploitation works. Strengthening works included longitudinal walls support with the tendon system built over the vault support. Additionally, ribs were strengthened by suspending them to new reinforced concrete ribs placed over the existing ones. And precast concrete cube elements were used in new ribs.

The paper concerns a church building, where as a result of mining exploitation a number of damages have been observed, including cracking of the nave. The method of repairing the damage was presented. Taking into consideration the further planned mining exploitation, a concept was put forward to reinforce the church with steel tie rods at the level of the vaults over the nave, which will provide for its protection against adverse impact of the tilt of the land surface.

Various physical methods for the determination of phase transformation temperatures were compared using C-Mn and C-Mn-V-Nb steels. The measurement using temperature scanner, variously located thermocouples, dilatometer, different thermal analysis (DTA) and anisothermal plastometric test were completed. The specimens were heated to 1 323 K and 1 473 K in the case of the C-Mn-V-Nb microalloyed steel. The aim of the different heat treatment were to obtain different levels of precipitates’ dissolution. It was found that the better particles’ distribution and precipitation due to the cooling lead to the enlargement of the two-phase region in the material. The good agreement of result gained by used methods was achieved. We found that all used methods can be used for common steels, but the temperature scanner seems not to be precious enough for microalloyed steels.

The development analysed in this article was under the effects of mining activity in 2012-2016, most importantly in the area of influence of the mining basin. As a result of the mining activity, the terrain became considerably deformed and sloped. Effects on the buildings' condition consisted in tilt of the segments, measuring between 10-25 mm/m. The units also moved in relation to each other at the movement joints, and their structure suffered heavy damage, mostly in the form of cracks and chips on the walls. For ribbon buildings, i.e. buildings which may be divided with movement joints and which may form long rows, specific locations on top of a mining basin may lead to endangering the safety of structures and their users, even in newly constructed buildings.