Bydgoszcz University of Science and Technology

The importance of permanent magnet (PM) motor technology and its impact on electromechanical drives has grown exponentially since the publication of the bestselling second edition. The PM brushless motor market has grown considerably faster than the overall motion control market. This rapid growth makes it essential for electrical and electromechanical engineers and students to stay up-to-date on developments in modern electrical motors and drives, including their control, simulation, and CAD. Reflecting innovations in the development of PM motors for electromechanical drives, Permanent Magnet Motor Technology: Design and Applications, Third Edition demonstrates the construction of PM motor drives and supplies ready-to-implement solutions to common roadblocks along the way. This edition supplies fundamental equations and calculations for determining and evaluating system performance, efficiency, reliability, and cost. It explores modern computer-aided design of PM motors, including the finite element approach, and explains how to select PM motors to meet the specific requirements of electrical drives. The numerous examples, models, and diagrams provided in each chapter facilitate a lucid understanding of motor operations and characteristics. This 3rd edition of a bestselling reference has been thoroughly revised to include: Chapters on high speed motors and micromotors Advances in permanent magnet motor technology Additional numerical examples and illustrations An increased effort to bridge the gap between theory and industrial applications Modified research results The growing global trend toward energy conservation makes it quite possible that the era of the PM brushless motor drive is just around the corner. This reference book will give engineers, researchers, and graduate-level students the comprehensive understanding required to develop the breakthroughs that will push this exciting technology to the forefront.

The skin is the largest organ of the human body and it protects the body from the external environment. It has become the topic of interest of researchers from various scientific fields. Microorganisms ensure the proper functioning of the skin. Of great importance, are the mutual relations between such microorganisms and their responses to environmental impacts, as dysbiosis may contribute to serious skin diseases. Molecular methods, used for microorganism identification, allow us to gain a better understanding of the skin microbiome. The presented article contains the latest reports on the skin microbiota in health and disease. The review discusses the relationship between a properly functioning microbiome and the body's immune system, as well as the impact of internal and external factors on the human skin microbiome.

Insect feeding on plants causes a complex series of coordinated defence responses. Little is known, however, about the time-dependent aspect of induced changes. Here we present a time series-based investigation of Arabidopsis thaliana Ler subjected to attack by a specialist pest of Brassicaceae species, Brevicoryne brassicae. Transcriptome and metabolome changes were studied at 6, 12, 24 and 48 h after infestation to monitor the progress of early induced responses. The use of full-genome oligonucleotide microarrays revealed the initiation of extensive gene expression changes already during the first 6 h of infestation. Data indicated the involvement of reactive oxygen species (ROS) and calcium in early signalling, and salicylic acid (SA) and jasmonic acid (JA) in the regulation of defence responses. Transcripts related to senescence, biosynthesis of anti-insect proteins, indolyl glucosinolates (GS) and camalexin, as well as several uncharacterized to date WRKY transcription factors, were induced. Follow-up studies of defence-involved secondary metabolites revealed depositions of callose at the insects' feeding sites, a decrease in the total level of aliphatic GS, particularly 3-hydroxypropyl glucosinolate, and accumulation of 4-methoxyindol-3-ylmethyl glucosinolate 48 h after the attack. The novel role of camalexin, induced as a part of defence against aphids, was verified in fitness experiments. Fecundity of B. brassicae was reduced on camalexin-accumulating wild-type (WT) plants as compared with camalexin-deficient pad3-1 mutants. Based on experimental data, a model of plant-aphid interactions at the early phase of infestation was proposed.

The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

The effect of Zn-doping in CoFe2O4 nanoparticles (NPs) through chemical co-precipitation route was investigated in term of structural, optical, and magnetic properties. Both XRD and FTIR analyses confirm the formation of cubic spinel phase, where the crystallite size changes with Zn content from 46 to 77 nm. The Scherrer method, Williamson-Hall (W-H) analysis, and size-strain plot method (SSPM) were used to study of crystallite sizes. The TEM results were in good agreement with the results of the SSP method. SEM observations reveal agglomeration of fine spherical-like particles. The optical band gap energy determined from diffuse reflectance spectroscopy (DRS) varies increases from 1.17 to 1.3 eV. Magnetization field loops reveal a ferromagnetic behavior with lower hysteresis loop for higher Zn content. The magnetic properties are remarkably influenced with Zn doping; saturation magnetization (Ms) increases then decreases while both coercivity (HC) and remanent magnetization (Mr) decrease continuously, which was associated with preferential site occupancy and the change in particle size.

The relationship between germination and melatonin applied during osmo- and hydropriming was studied in cucumber seeds. The proportion of nuclei with different DNA contents, the mean ploidy and the (2C + 4C = 8C)/2C ratio in unprimed and primed, dry and imbibed at 10 degrees C seeds were established by flow cytometry. Thiobarbituric acid reactive substances and protein oxidation were also estimated. Melatonin and indole-3-acetic acid (IAA) concentrations in the seeds were determined using high-performance liquid chromatography with electrochemical detection. Being sensitive to chilling stress, seeds that germinated well (99%) at 25 degrees C showed only 30% germination at 15 degrees C, and almost no germination (4%) at 10 degrees C. Hydropriming in water improved seed germination to 50-60% at 15 degrees C and the addition of melatonin (25-100 M) also increased the rate of germination. Osmopriming in polyethylene glycol increased germination at 15 degrees C to 78%, and 98% when combined with 50 M melatonin. Osmoprimed seeds germinated even at 10 degrees C and reached 43%, and 83% when 50 M melatonin was applied. None of the treatments induced DNA synthesis, although during the first 24 hr of imbibition at 10 degrees C the mean ploidy and the (2C + 4C = 8C)/2C ratio increased, which is indicative of the advanced Phase II of germination. Hydro- and osmopriming slightly decreased IAA content in the seeds in most of the cases; only hydropriming with 100 and 500 M melatonin increased it. Melatonin protected membrane structure against peroxidation during chilling, but excessive melatonin levels in cucumber seeds (approximately 4 microg/g fresh weight) provoked oxidative changes in proteins. There is still lack of information explained clearly the role of melatonin in plant physiology. This molecule acts multidirectionally and usually is alliged to other compounds.

In this work, a chromatographic method for identification of volatile organic compounds was compared with canine recognition. Gas chromatography and mass spectrometry (GC-TOF MS) were used for determination of concentrations of trace gases present in human breath. The technique enables rapid determination of compounds in human breath, at the parts per billion level. Linear correlations were from 0.83-234.05 ppb, the limit of detection was the range 0.31-0.75 ppb, and precision, expressed as relative standard deviation (RSD), was less than 10.00 %. Moreover, trained dogs are able to discriminate breath samples of patients with diagnosed cancer. We found a positive correlation between dog indications and the ethyl acetate and 2-pentanone content of breath (r = 0.85 and r = 0.97, respectively). The methods presented for detection of lung cancer markers in exhaled air could be used as a potential non-invasive tool for screening. In addition, the canine method is relatively simple and inexpensive in comparison with chromatography.

Products made of titanium and its alloys are widely used in modern areas like the mechanical engineering, instrument making, aerospace and medical sector. High strength and low thermal conductivity are the causes of difficulties with the machinability of these alloys. It is important to find ways to increase machinability by cutting titanium alloys. One way to implement this is to apply various methods of cooling on workpieces of titanium alloys and on cutting tools during machining. In this review article, an extensive analysis of the literature on such cooling techniques as dry, conventional cooling system, minimum quantity of lubricant (MQL), minimum quantity cooling lubrication (MQCL), cryogenic lubrication, and high-pressure cooling (HPC) is performed. The following groups of Ti alloys are considered: high-strength structural and high-temperature Ti alloys, intermetallic compounds, pure titanium, as well as composites CFRPs/Ti alloys. For the processes of turning, milling, drilling, and grinding, etc. it is shown how the type of cooling affects the surface integrity include surface roughness, tool wear, tool life, temperature, cutting forces, environmental aspects, etc. The main advantages, disadvantages and prospects of different cooling methods are also shown. The problems and future trends of these methods for the machining of Ti and its alloys are indicated.

Bearing in mind the aspiration of the world economy to create as complete a closed loop of raw materials and energy as possible, it is important to know the individual links in such a system and to systematise the knowledge. Polymer materials, especially poly(vinyl chloride) (PVC), are considered harmful to the environment by a large part of society. The work presents a literature review on mechanical and feedstock recycling. The advantages and disadvantages of various recycling methods and their development perspectives are presented. The general characteristics of PVC are also described. In conclusion, it is stated that there are currently high recycling possibilities for PVC material and that intensive work is underway on the development of feedstock recycling. Based on the literature review, it was found that PVC certainly meets the requirements for materials involved in the circular economy.

Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, $$V_{B}$$ into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N, in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power- that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements.

A new method is proposed for fractionation of soil organic carbon, based on its susceptibility to oxidation with KMnO4 solutions of various concentrations. This method can be used for: characterizing the organic matter of various soils, evaluation of qualitative changes of organic matter under the influence of fertilization and crop rotation, and for evaluation of susceptibility to oxidation of various substrates of humification

Abstract Hematopoietic-cell transplantation (HCT) is widely used for acquired and congenital disorders of the hematopoietic system. Number of transplants performed in Europe and associated countries continues to rise with 47,468 HCT in 42,901 patients [19,630 allogeneic (41%) and 27,838 autologous (59%)] reported by 701 centers in 50 countries in 2018. Main indications were myeloid malignancies 10,679 (25%; 97% allogeneic), lymphoid malignancies 27,318 (64%; 20% allogeneic), solid tumors 1625 (4%; 2.9% allogeneic), and nonmalignant disorders 3063 (7%; 81% allogeneic). This year’s analysis focuses on cellular therapies with the marked growth in CAR T-cell therapies from 151 in 2017 to 301 patients reported in 2018. Other cellular therapy numbers show less significant changes. Important trends in HCT include a 49% increase in allogeneic HCT for chronic phase CML (although transplant numbers remain low) and a 24% increase in aplastic anemia. In autologous HCT, there is an ongoing increase in autoimmune diseases (by 19%), predominantly due to activity in multiple sclerosis. This annual report reflects current activity and highlights important trends, useful for health care planning.

Currently, expert systems and applied machine learning algorithms are widely used to automate network intrusion detection. In critical infrastructure applications of communication technologies, the interaction among various industrial control systems and the Internet environment intrinsic to the IoT technology makes them susceptible to cyber-attacks. Given the existence of the enormous network traffic in critical Cyber-Physical Systems (CPSs), traditional methods of machine learning implemented in network anomaly detection are inefficient. Therefore, recently developed machine learning techniques, with the emphasis on deep learning, are finding their successful implementations in the detection and classification of anomalies at both the network and host levels. This paper presents an ensemble method that leverages deep models such as the Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and a meta-classifier (i.e., logistic regression) following the principle of stacked generalization. To enhance the capabilities of the proposed approach, the method utilizes a two-step process for the apprehension of network anomalies. In the first stage, data pre-processing, a Deep Sparse AutoEncoder (DSAE) is employed for the feature engineering problem. In the second phase, a stacking ensemble learning approach is utilized for classification. The efficiency of the method disclosed in this work is tested on heterogeneous datasets, including data gathered in the IoT environment, namely IoT-23, LITNET-2020, and NetML-2020. The results of the evaluation of the proposed approach are discussed. Statistical significance is tested and compared to the state-of-the-art approaches in network anomaly detection.

The construction industry is among the sectors that need closer attention due to their environmental impact. The Circular Economy (CE) model promotes the transition to more sustainable production models, which are based on careful management of resources and the reduction of negative externalities generated by such businesses. Its application in this industry can foster significant improvements in sustainability. However, the measurement of the degree of implementation of CE is difficult, owing to an absence of psychometrically sound measures. In this paper, the development of the CE scale for the building industry was described, treated as an instrument that allows for a direct measurement of the importance of CE for companies. The processes used to generate items by applying the e-Delphi research technique were explained in the article, and the developed scale was tested and validated through confirmatory factor analysis (CFA). The final construction is composed of seven different weighted dimensions: four related to Resource Management: 3Rs (Reduce, Reuse, and Recycle), Efficient Management of Energy, Water, and Materials; two dimensions regarding environmental impact: Emissions and Wastes generated; and, one providing indicators of transition to the CE.

The completion of germination of seeds of Arabidopsis thaliana is marked by the appearance of the radicle through the surrounding endosperm and testa. Using confocal microscopy and green fluorescent protein (GFP)-transformed embryos to highlight the epidermal cell walls it has been possible to conduct time-lapse photography of individual embryos during their germination. This reveals that the elongation of embryo cells to effect completion of germination does not occur within the radicle itself, but rather within a discrete region that is immediately proximal to the radicle. This region, identifiable as the lower hypocotyl and hypocotyl-radicle transition zone, is also definable by accumulation of carbohydrate-containing bodies during germination, and distinct GFP expression of GAL4-GFP in enhancer trap lines. Flow cytometric studies show that there is an increase in the proportion of 4C nuclei in the axis which coincides with a considerable increase in length of the hypocotyl, and the occurrence of endopolyploid (8C and 16C) nuclei accompanies the 2-fold increase in mean cell size in the region of elongation, the lower hypocotyl, and hypocotyl-radicle transition zone. Thus the observed cell elongation during germination is accompanied by an increase in nuclear DNA content, and the resultant elongation of the axis to effect radicle emergence is due to cell expansion, not to cell division. When studying the molecular events involved in the completion of germination, therefore, it may be prudent to focus on this region of elongation.

The analysis and processing of ECG signals are a key approach in the diagnosis of cardiovascular diseases. The main field of work in this area is classification, which is increasingly supported by machine learning-based algorithms. In this work, a deep neural network was developed for the automatic classification of primary ECG signals. The research was carried out on the data contained in a PTB-XL database. Three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet, and the third on the convolutional network, but with additional entropy-based features. The dataset was divided into training, validation, and test sets in proportions of 70%, 15%, and 15%, respectively. The studies were conducted for 2, 5, and 20 classes of disease entities. The convolutional network with entropy features obtained the best classification result. The convolutional network without entropy-based features obtained a slightly less successful result, but had the highest computational efficiency, due to the significantly lower number of neurons.

Nod factors (NFs) are lipochitooligosaccharidic signal molecules produced by rhizobia, which play a key role in the rhizobium-legume symbiotic interaction. In this study, we analyzed the gene expression reprogramming induced by purified NF (4 and 24 h of treatment) in the root epidermis of the model legume Medicago truncatula Tissue-specific transcriptome analysis was achieved by laser-capture microdissection coupled to high-depth RNA sequencing. The expression of 17,191 genes was detected in the epidermis, among which 1,070 were found to be regulated by NF addition, including previously characterized NF-induced marker genes. Many genes exhibited strong levels of transcriptional activation, sometimes only transiently at 4 h, indicating highly dynamic regulation. Expression reprogramming affected a variety of cellular processes, including perception, signaling, regulation of gene expression, as well as cell wall, cytoskeleton, transport, metabolism, and defense, with numerous NF-induced genes never identified before. Strikingly, early epidermal activation of cytokinin (CK) pathways was indicated, based on the induction of CK metabolic and signaling genes, including the CRE1 receptor essential to promote nodulation. These transcriptional activations were independently validated using promoter:β-glucuronidase fusions with the MtCRE1 CK receptor gene and a CK response reporter (TWO COMPONENT SIGNALING SENSOR NEW). A CK pretreatment reduced the NF induction of the EARLY NODULIN11 (ENOD11) symbiotic marker, while a CK-degrading enzyme (CYTOKININ OXIDASE/DEHYDROGENASE3) ectopically expressed in the root epidermis led to increased NF induction of ENOD11 and nodulation. Therefore, CK may play both positive and negative roles in M. truncatula nodulation.

Soils are vital for supporting food security and other ecosystem services. Climate change can affect soil functions both directly and indirectly. Direct effects include temperature, precipitation, and moisture regime changes. Indirect effects include those that are induced by adaptations such as irrigation, crop rotation changes, and tillage practices. Although extensive knowledge is available on the direct effects, an understanding of the indirect effects of agricultural adaptation options is less complete. A review of 20 agricultural adaptation case-studies across Europe was conducted to assess implications to soil threats and soil functions and the link to the Sustainable Development Goals (SDGs). The major findings are as follows: (a) adaptation options reflect local conditions; (b) reduced soil erosion threats and increased soil organic carbon are expected, although compaction may increase in some areas; (c) most adaptation options are anticipated to improve the soil functions of food and biomass production, soil organic carbon storage, and storing, filtering, transforming, and recycling capacities, whereas possible implications for soil biodiversity are largely unknown; and (d) the linkage between soil functions and the SDGs implies improvements to SDG 2 (achieving food security and promoting sustainable agriculture) and SDG 13 (taking action on climate change), whereas the relationship to SDG 15 (using terrestrial ecosystems sustainably) is largely unknown. The conclusion is drawn that agricultural adaptation options, even when focused on increasing yields, have the potential to outweigh the negative direct effects of climate change on soil degradation in many European regions.

Plants produce cytokinin (CK) hormones for controlling key developmental processes like source/sink distribution, cell division or programmed cell-death. Some plant pathogens have been shown to produce CKs but the function of this mimicry production by non-tumor inducing pathogens, has yet to be established. Here we identify a gene required for CK biosynthesis, CKS1, in the rice blast fungus Magnaporthe oryzae. The fungal-secreted CKs are likely perceived by the plant during infection since the transcriptional regulation of rice CK-responsive genes is altered in plants infected by the mutants in which CKS1 gene was deleted. Although cks1 mutants showed normal in vitro growth and development, they were severely affected for in planta growth and virulence. Moreover, we showed that the cks1 mutant triggered enhanced induction of plant defenses as manifested by an elevated oxidative burst and expression of defense-related markers. In addition, the contents of sugars and key amino acids for fungal growth were altered in and around the infection site by the cks1 mutant in a different manner than by the control strain. These results suggest that fungal-derived CKs are key effectors required for dampening host defenses and affecting sugar and amino acid distribution in and around the infection site.

Cyber attacks are currently blooming, as the attackers reap significant profits from them and face a limited risk when compared to committing the “classical” crimes. One of the major components that leads to the successful compromising of the targeted system is malicious software. It allows using the victim’s machine for various nefarious purposes, e.g., making it a part of the botnet, mining cryptocurrencies, or holding hostage the data stored there. At present, the complexity, proliferation, and variety of malware pose a real challenge for the existing countermeasures and require their constant improvements. That is why, in this paper we first perform a detailed meta-review of the existing surveys related to malware and its detection techniques, showing an arms race between these two sides of a barricade. On this basis, we review the evolution of modern threats in the communication networks, with a particular focus on the techniques employing information hiding. Next, we present the bird’s eye view portraying the main development trends in detection methods with a special emphasis on the machine learning techniques. The survey is concluded with the description of potential future research directions in the field of malware detection.