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Waste recycling is a solution that reduces the environmental impact of waste landfilling or incineration. The aim of this paper is to investigate both the effect of incorporating recycled fibers obtained by defibrating 50/50 hemp/rPP nonwoven waste and the effect of the compatibilizer on the properties of composite materials. Composites incorporating 50% and 100% recycled fibers were treated with 2.5% and 5% maleated polypropylene (MAPP), respectively, and compared to both the untreated composites and the composite obtained by thermoforming from the nonwovens that generated the waste. The incorporation of 50% and 100% recycled fibers into composites decreased the tensile strength by 17.1–22.6%, the elongation at break by 12.4–20.1%, the flexural strength by 6.6–9%, and flexural modulus by 10.3–37%. The addition of 5% MAPP showed the greatest improvements in mechanical properties of composites containing 100% recycled fibers, as follows: 19.2% increase in tensile strength, 3.8% increase in flexural strength, and 14.8% increase in flexural modulus. Thermal analysis established that at temperatures ranging between 20 °C and 120 °C, the composites were thermally stable. SEM analysis revealed good coverage of the reinforcing fibers, and EDX analysis confirmed the presence of the compatibilizing agent in the structure of the composite material.

MotivationHere, we performed a benchmarking analysis of five tools for microbe sequence detection using transcriptomics data (Kraken2, MetaPhlAn2, PathSeq, DRAC and Pandora). We built a synthetic database mimicking real-world structure with tuned conditions accounting for microbe species prevalence, base calling quality and sequence length. Sensitivity and positive predictive value (PPV) parameters, as well as computational requirements, were used for tool ranking.ResultsGATK PathSeq showed the highest sensitivity on average and across all scenarios considered. However, the main drawback of this tool was its slowness. Kraken2 was the fastest tool and displayed the second-best sensitivity, though with large variance depending on the species to be classified. There was no significant difference for the other three algorithms sensitivity. The sensitivity of MetaPhlAn2 and Pandora was affected by sequence number and DRAC by sequence quality and length. Results from this study support the use of Kraken2 for routine microbiome profiling based on its competitive sensitivity and runtime performance. Nonetheless, we strongly endorse to complement it by combining with MetaPhlAn2 for thorough taxonomic analyses.Availability and implementationhttps://github.com/fjuradorueda/MIME/ and https://github.com/lola4/DRAC/.Supplementary informationSupplementary data are available at Bioinformatics Advances online.

Viral recombination is a major evolutionary mechanism driving adaptation processes, such as the ability of host-switching. Understanding global patterns of recombination could help to identify underlying mechanisms and to evaluate the potential risks of rapid adaptation. Conventional approaches (e.g., those based on linkage disequilibrium) are computationally demanding or even intractable when sequence alignments include hundreds of sequences, common in viral data sets. We present a comprehensive analysis of recombination across 30 genomic alignments from viruses infecting humans. In order to scale the analysis and avoid the computational limitations of conventional approaches, we apply newly developed topological data analysis methods able to infer recombination rates for large data sets. We show that viruses, such as ZEBOV and MARV, consistently displayed low levels of recombination, whereas high levels of recombination were observed in Sarbecoviruses, HBV, HEV, Rhinovirus A, and HIV. We observe that recombination is more common in positive single-stranded RNA viruses than in negatively single-stranded RNA ones. Interestingly, the comparison across multiple viruses suggests an inverse correlation between genome length and recombination rate. Positional analyses of recombination breakpoints along viral genomes, combined with our approach, detected at least 39 nonuniform patterns of recombination (i.e., cold or hotspots) in 18 viral groups. Among these, noteworthy hotspots are found in MERS-CoV and Sarbecoviruses (at spike, Nucleocapsid and ORF8). In summary, we have developed a fast pipeline to measure recombination that, combined with other approaches, has allowed us to find both common and lineage-specific patterns of recombination among viruses with potential relevance in viral adaptation.

International audience; This work presents two novel methods that simultaneously optimize both the design of a finite impulse response (FIR) filter and its multiplierless hardware implementation. We use integer linear programming (ILP) to minimize the number of adders used to implement a direct/transposed FIR filter adhering to a given frequency specification. The proposed algorithms work by either fixing the number of adders used to implement the products (multiplier block adders) or by bounding the adder depth (AD) used for these products. The latter can be used to design filters with minimal AD for low power applications. In contrast to previous multiplierless FIR filter approaches, the methods introduced here ensure adder count optimality. We perform extensive numerical experiments which demonstrate that our simultaneous filter design approach yields results which are in many cases on par or better than those in the literature.

Motivation The human leukocyte antigen (HLA) locus plays a critical role in tissue compatibility and regulates the host response to many diseases, including cancers and autoimmune di3orders. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional modifications and bias due to amplification. Results Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA-sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two-field resolution for Class I genes, and over 99.7% for Class II. Furthermore, we evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. Availability and implementation arcasHLA is available at https://github.com/RabadanLab/arcasHLA. Supplementary information Supplementary data are available at Bioinformatics online.

The paper presents an original self-tuning strategy for a minimum variance controller used to control highly disturbed processes. The role of the proposed algorithm is to implement a self-protection mechanism against major disturbances produced by measurable perturbations acting on a controlled plant. By a variable setting of the control penalty factor (ρ), which weights the influence of the control variance in a cost criterion function, the main objective is to maintain the stability of the control system, even under action of high perturbations. This new self-tuning strategy is based on the assumption that the disturbances acting on the controlled plant are measurable, being implemented and tested for the case of an induction generator integrated into a wind energy conversion system. The power control system's main goal is to reject the disturbance generated by a mechanical torque variation, due to variable wind speed, acting as an external measurable perturbation. Depending on the variation range of this mechanical torque (small variations corresponding to a relatively stationary regime or high variations due to wind gusts), the proposed algorithm sets the control penalty factor to different values. As a result, the overall effect of the self-tuning strategy will be to extend the operating range of the wind aggregate, by maintaining the stability of the control system (strongly disturbed by high wind gusts) and also by an efficient disturbance rejection. As a novelty, the base rule of the self-tuning strategy proposes a rectangular pulse variation of the control penalty factor when a high disturbance occurs. By setting a high amplitude for this pulse variation of ρ, the control is strongly limited, thus ensuring the system stability. At the end of this pulse with a suitable width, the effect of disturbance is fast rejected by returning to a small stationary value of ρ, which allows high enough control. Although designed and validated for a particular case, this self-tuning strategy is generally valid for the minimum variance control of any other process disturbed by measurable perturbations.

Background Respiratory viral infections are a major cause of morbidity and mortality worldwide. However, their characterization is incomplete because prevalence estimates are based on syndromic surveillance data. Here, we address this shortcoming through the analysis of infection rates among individuals tested regularly for respiratory viral infections, irrespective of their symptoms. Methods We carried out longitudinal sampling and analysis among 214 individuals enrolled at multiple New York City locations from fall 2016 to spring 2018. We combined personal information with weekly nasal swab collection to investigate the prevalence of 18 respiratory viruses among different age groups and to assess risk factors associated with infection susceptibility. Results 17.5% of samples were positive for respiratory viruses. Some viruses circulated predominantly during winter, whereas others were found year round. Rhinovirus and coronavirus were most frequently detected. Children registered the highest positivity rates, and adults with daily contacts with children experienced significantly more infections than their counterparts without children. Conclusion Respiratory viral infections are widespread among the general population with the majority of individuals presenting multiple infections per year. The observations identify children as the principal source of respiratory infections. These findings motivate further active surveillance and analysis of differences in pathogenicity among respiratory viruses.

Significance Fifty percent of diffuse large B cell lymphoma (DLBCL) evade immune-surveillance via somatic genetic lesions abrogating the expression of the class I major histocompatibility complex (MHC-I) complex on the cell surface, thus preventing the presentation of tumor neoantigens to the immune system. The results herein significantly extend these findings by showing that an additional 40% of DLBCL cases, despite expressing MHC-I, carry monoallelic HLA-I genetic alterations that limit the repertoire of neoantigens for presentation to immune cells. Both MHC-INEG and MHC-IPOS/monoallelically disrupted cases have significantly higher mutational load. Notably, homozygosis of HLA-I loci is significantly and preferentially enriched in the germline of DLBCL patients, suggesting a stepwise process by which limited neoantigen presentation is selected during DLBCL development., Fifty percent of diffuse large B cell lymphoma (DLBCL) cases lack cell-surface expression of the class I major histocompatibility complex (MHC-I), thus escaping recognition by cytotoxic T cells. Here we show that, across B cell lymphomas, loss of MHC-I, but not MHC-II, is preferentially restricted to DLBCL. To identify the involved mechanisms, we performed whole exome and targeted HLA deep-sequencing in 74 DLBCL samples, and found somatic inactivation of B2M and the HLA-I loci in 80% (34 of 42) of MHC-INEG tumors. Furthermore, 70% (22 of 32) of MHC-IPOS DLBCLs harbored monoallelic HLA-I genetic alterations (MHC-IPOS/mono), indicating allele-specific inactivation. MHC-INEG and MHC-IPOS/mono cases harbored significantly higher mutational burden and inferred neoantigen load, suggesting potential coselection of HLA-I loss and sustained neoantigen production. Notably, the analysis of >500,000 individuals across different cancer types revealed common germline HLA-I homozygosity, preferentially in DLBCL. In mice, germinal-center B cells lacking HLA-I expression did not progress to lymphoma and were counterselected in the context of oncogene-driven lymphomagenesis, suggesting that additional events are needed to license immune evasion. These results suggest a multistep process of HLA-I loss in DLBCL development including both germline and somatic events, and have direct implications for the pathogenesis and immunotherapeutic targeting of this disease.

Self-balancing vehicles represent an interesting topic to be treated in future means of transport. They can have the advantages of being small, practical, easy to carry and to use. The main goal of the article is the study and analyze of a PID classic controller on two-wheel experimental robot. The main objective is to maintain a vertical position with low angle deviations. The proposed system uses an accelerometer and a gyroscope module in order to obtain position and angle velocity, respectively stepper motors for robot driving.

This paper presents a study on the efficiency of using convolutional neural networks for biometric security systems. Fingerprint identification in images is mainly treated and a VisualGeometryGroup-16 architecture integrated in a Siamese network is used. The Siamese network uses two metrics to determine the similarity between two input images, each metric having a certain experimentally determined threshold. The datasets for training, validation and testing were taken from three sources to increase diversity and contain synthetic changes with different levels of alteration. They are processed in such a manner that they can be received as input for the network. This study aims to establish whether using neural networks is reliable for a biometric security system.

The emergence of SARS-CoV-2 underscores the need to better understand the evolutionary processes that drive the emergence and adaptation of zoonotic viruses in humans. In the betacoronavirus genus, which also includes SARS-CoV and MERS-CoV, recombination frequently encompasses the Receptor Binding Domain (RBD) of the Spike protein, which, in turn, is responsible for viral binding to host cell receptors. Here, we find evidence of a recombination event in the RBD involving ancestral linages to both SARS-CoV and SARS-CoV-2. Although we cannot specify the recombinant nor the parental strains, likely due to the ancestry of the event and potential undersampling, our statistical analyses in the space of phylogenetic trees support such an ancestral recombination. Consequently, SARS-CoV and SARS-CoV-2 share an RBD sequence that includes two insertions (positions 432-436 and 460-472), as well as the variants 427N and 436Y. Both 427N and 436Y belong to a helix that interacts directly with the human ACE2 (hACE2) receptor. Reconstruction of ancestral states, combined with protein-binding affinity analyses using the physics-based trRosetta algorithm, reveal that the recombination event involving ancestral strains of SARS-CoV and SARS-CoV-2 led to an increased affinity for hACE2 binding, and that alleles 427N and 436Y significantly enhanced affinity as well. Structural modeling indicates that ancestors of SARS-CoV-2 may have acquired the ability to infect humans decades ago. The binding affinity with the human receptor was subsequently boosted in SARS-CoV and SARS-CoV-2 through further mutations in RBD. In sum, we report an ancestral recombination event affecting the RBD of both SARS-CoV and SARS-CoV-2 that was associated with an increased binding affinity to hACE2.ImportanceThis paper addresses critical questions about the origin of the SARS-CoV-2 virus: what are the evolutionary mechanisms that led to the emergence of the virus, and how can we leverage such knowledge to assess the potential of SARS-like viruses to become pandemic strains? In this work, we demonstrate common mechanisms involved in the emergence of human-infecting SARS-like viruses: first, by acquiring a common haplotype in the RBD through recombination, and further, through increased specificity to the human ACE2 receptor through lineage specific mutations. We also show that the ancestors of SARS-CoV-2 already had the potential to infect humans at least a decade ago, suggesting that SARS-like viruses currently circulating in wild animal species constitute a source of potential pandemic re-emergence.

Peripheral T cell lymphoma not otherwise specified (PTCL-NOS) comprises heterogeneous lymphoid malignancies characterized by pleomorphic lymphocytes and variable inflammatory cell-rich tumor microenvironment. Genetic drivers in PTCL-NOS include genomic alterations affecting the VAV1 oncogene; however, their specific role and mechanisms in PTCL-NOS remain incompletely understood. Here we show that expression of Vav1-Myo1f, a recurrent PTCL-associated VAV1 fusion, induces oncogenic transformation of CD4

Plasmablastic lymphoma (PBL) is an aggressive B-cell non-Hodgkin lymphoma associated with immunodeficiency in the context of human immunodeficiency virus (HIV) infection or iatrogenic immunosuppression. While a rare disease in general, the incidence is dramatically increased in regions of the world with high HIV prevalence. The molecular pathogenesis of this disease is poorly characterized. Here, we defined the genomic features of PBL in a cohort of 110 patients from South Africa (15 by whole-exome sequencing and 95 by deep targeted sequencing). We identified recurrent mutations in genes of the JAK–STAT signaling pathway, including STAT3 (42%), JAK1 (14%), and SOCS1 (10%), leading to its constitutive activation. Moreover, 24% of cases harbored gain-of-function mutations in RAS family members (NRAS and KRAS). Comparative analysis with other B-cell malignancies uncovered PBL-specific somatic mutations and transcriptional programs. We also found recurrent copy number gains encompassing the CD44 gene (37%), which encodes for a cell surface receptor involved in lymphocyte activation and homing, and was found expressed at high levels in all tested cases, independent of genetic alterations. These findings have implications for the understanding of the pathogenesis of this disease and the development of personalized medicine approaches. Significance: Plasmablastic lymphoma is a poorly studied and extremely aggressive tumor. Here we define the genomic landscape of this lymphoma in HIV-positive individuals from South Africa and identify pervasive mutations in JAK–STAT3 and RAS–MAPK signaling pathways. These data offer a genomic framework for the design of improved treatment strategies targeting these circuits. See related commentary by Küppers, p. 23. This article is highlighted in the In This Issue feature, p. 5

Efficient presentation of aberrant peptide fragments by the human leukocyte antigen class I (HLA-I) genes is necessary for immune detection and killing of cancer cells. Patient HLA-I genotypes are known to impact the efficacy of cancer immunotherapy, and the somatic loss of HLA-I heterozygosity has been established as a factor in immune evasion. While global deregulated expression of HLA-I has been reported in different tumor types, the role of HLA-I allele-specific expression loss – that is, the preferential RNA expression loss of specific HLA-I alleles – has not been fully characterized in cancer. In the present study, we quantified HLA-I allele-specific expression (ASE) across eleven TCGA tumor types using a novel method from input RNA and whole-exome sequencing data. Allele-specific loss in at least one of the three HLA-I genes (ASE loss) was pervasive and associated to worse overall survival across tumor types, including pancreatic adenocarcinomas, prostate carcinomas and glioblastomas, among others. In particular, our analysis shows that detection of neoantigens with binding affinity to the specific HLA-I genes subject to ASE loss was a top prognostic indicator of overall survival. Additionally, we found that ASE loss hindered immunotherapy in retrospective analyses. Together, these results highlight the prevalence of HLA-I ASE loss – a previously uncharacterized phenomenon in cancer – and provide initial evidence of its clinical significance in cancer prognosis and immunotherapy treatment.

In the paper there is presented an extended strategy for a self-tuning minimum variance control system, by using an additional external integral component. In order to estimate the process parameters, a parameter estimator based on the Givens orthogonal transformation was used. The strategy was tested and validated for the case of a synchronous generator connected to a power energy system, considered as controlled process. The proposed solution tries to achieve two main goals: the control system stability, respectively a zero steady-state error. For certain plants, the second objective can be achieved by supplementing with an external integral component, operating in parallel with the minimum variance controller. The paper proves the necessity of this external integral component, under the conditions when the minimum variance control law cannot contain an integral term, which would destabilize the control system.

This paper describes a SCADA system for a microgrid laboratory that simulates a grid-connected wind turbine. The SCADA application monitors the entire system, ensuring reliable and stable operation. It gathers information from all the elements of the system, including the embedded controller of the emulator and the grid-connected inverter. It can monitor real-time parameters such as voltages, currents, or powers, and can store measurements in a MySQL database. This system also contains an interface for remote viewing designed using LabVIEW Remote Panels embedded into a web page. In order to tighten the security features on the website, the default web server provided by NI is replaced with WAMP server, making it possible to control the content of the website using server-side technology. Only authorized users can view the SCADA application or access the database for historical data.

This paper presents a stability and performance analysis of a self-tuning minimum variance control system. Designed through a cost function minimization, the control law is described by a linear difference model with time varying parameters. Based on a linearized process model around an operating point and by using a parameter estimator, the control system automatically adapts itself when process parameters change (as effect of a disturbance). However, the performance of the control system is strongly conditioned by an a priori setting of a factor that weights the control variance term of the cost function. The goal of this stability analysis is to provide a strategy regarding how to tune this control penalty factor, which significantly influences the stability and performance of the control system. Two approaches were considered: one based on the control system response in relation to the disturbance, validated by a second one based on frequency response analysis.

Autonomous vehicles (AVs) are the alternative solution for mobility in the future. The first major benefit of the self-driving cars would be the safety. Human errors cause most of the car incidents. Self-driving cars could improve public transportation services and decrease auto ownership because personal car won’t be necessary anymore. Self-driving car can be a solution to reduce the emissions of carbon dioxide (CO2). There are also some challenges to overcome: self-driving cars cannot operate very good in bad weather conditions and the legislative structure needs to be defined in case of any incident. This article presents the current status of this technology and what are the challenges of implementing intelligent systems that allow the handling of a vehicle without human intervention.

Background Respiratory viral infections are a leading cause of disease worldwide. However, the overall community prevalence of infections has not been properly assessed, as standard surveillance is typically acquired passively among individuals seeking clinical care. Methods We conducted a prospective cohort study in which participants provided daily diaries and weekly nasopharyngeal specimens that were tested for respiratory viruses. These data were used to analyze healthcare seeking behavior, compared with cross‐sectional ED data and NYC surveillance reports, and used to evaluate biases of medically attended ILI as signal for population respiratory disease and infection. Results The likelihood of seeking medical attention was virus‐dependent: higher for influenza and metapneumovirus (19%‐20%), lower for coronavirus and RSV (4%), and 71% of individuals with self‐reported ILI did not seek care and half of medically attended symptomatic manifestations did not meet the criteria for ILI. Only 5% of cohort respiratory virus infections and 21% of influenza infections were medically attended and classifiable as ILI. We estimated 1 ILI event per person/year but multiple respiratory infections per year. Conclusion Standard, healthcare‐based respiratory surveillance has multiple limitations. Specifically, ILI is an incomplete metric for quantifying respiratory disease, viral respiratory infection, and influenza infection. The prevalence of respiratory viruses, as reported by standard, healthcare‐based surveillance, is skewed toward viruses producing more severe symptoms. Active, longitudinal studies are a helpful supplement to standard surveillance, can improve understanding of the overall circulation and burden of respiratory viruses, and can aid development of more robust measures for controlling the spread of these pathogens.

BackgroundViral infection of the respiratory tract is one of the major causes of hospital visits for young children. In this study, we report the occurrence and co-occurrence of different virus types and subtypes among the patients arriving at the pediatric emergency room of New York Presbyterian Hospital, a major urban hospital.MethodsWe collected nasal swabs from the patients and their accompanying persons. We also recorded the levels (None, Mild, High, and Severe) of their symptoms (Fever, Chill, Muscle Pain, Watery Eye, Runny Nose, Sneezing, Sore throat and Cough). The collected swabs were tested for the presence of common viruses infecting the respiratory tract.ResultsHuman Rhinovirus was the most common virus among the patients, followed Influenza and Respiratory Syncytial Virus. Human Rhinovirus was most common in summer, autumn and spring. In contrast, influenza was more common in winter. Further, Influenza A virus was more likely to co-occur with Corona Virus 229E. In comparison, Influenza was less likely to co-occur with Human Rhinovirus. Moreover, Influenza, Parainfluenza and Corona virus were associated with more severe symptoms, while Human Rhinovirus was associated with less severe symptoms. In addition, we observed that Influenza and Respiratory Syncytial Virus were more likely to infect a patient when these viruses also infected the accompanying person. We also found that it was difficult to distinguish among viruses based on the symptoms. The inability to distinguish among different virus types and subtypes is explained by the fact that multiple viruses lead to similar symptoms.ConclusionsThe findings of this study provide a better understanding of respiratory viral infections in small children presenting at a pediatric emergency room in New York.

The human leukocyte antigen (HLA) complex is necessary for antigen presentation and regulates both innate and adaptive immune responses. In the context of cancer and treatment therapies, the HLA locus plays a critical role in tumor recognition and tolerance mechanisms. In silico HLA class I and class II typing, as well as expression quantification from next-generation RNA sequencing, can therefore have great potential clinical applications. However, HLA typing from short-read data is a challenging task given the high polymorphism and homology at the HLA locus. In this chapter, we present our highly accurate HLA typing solution, arcasHLA. We provide a detailed outline for practitioners using our protocol to perform HLA typing and demonstrate the applicability of arcasHLA in several clinical samples from tumors.

The present paper proposes (as the main contribution) an additional self-tuning mechanism for an adaptive minimum-variance control system, whose main goal is to extend its functionality for a large value range of unmeasurable perturbations which disturb the controlled process. Through the standard design procedure, a minimum variance controller uses by default an internal self-tuning mechanism based on the process parameter estimates. However, the main parameter which overwhelmingly influences the control performance is the control penalty factor ( . This parameter weights the term that describes the control variance in a criterion function whose minimization is the starting point of the control law design. The classical minimum-variance control involves an off-line tuning of this parameter, its value being set as constant throughout the entire operating regime. Based on the measurement of the process output error, the contribution of the proposed strategy consists in a real-time tuning of the control penalty factor, to ensure the stability of the control system, even under conditions of high disturbances. The proposed tuning mechanism adjusts this parameter by implementing a bipositional switching strategy based on a sharp hysteresis loop. Therefore, instead of the standard solution that involves a constant value of the control penalty factor &nbsp, (a priori computed and set), this paper proposes a dual value for this controller parameter. The main objective is to allow the controlled process to operate in a stable fashion even in more strongly disturbed regimes (regimes where the control system becomes unstable and is usually switched off for safety reasons). To validate the proposed strategy, an induction generator integrated into a wind energy conversion system was considered as controlled plant. Operating under the action of strong disturbances (wind gusts, electrical load variations), the extension of safe operating range (thus avoiding the system disengagement) is an important goal of such a control system.

The human leukocyte antigen (HLA) complex is necessary for antigen presentation and regulates both innate and adaptive immune responses. In the context of cancer and treatment therapies, the HLA locus plays a critical role in tumor recognition and tolerance mechanisms. In silico HLA class I and class II typing, as well as expression quantification from next-generation RNA sequencing, can therefore have great potential clinical applications. However, HLA typing from short-read data is a challenging task given the high polymorphism and homology at the HLA locus. In this chapter, we present our highly accurate HLA typing solution, arcasHLA. We provide a detailed outline for practitioners using our protocol to perform HLA typing and demonstrate the applicability of arcasHLA in several clinical samples from tumors.

Changing technologies, where development time for a new work product must be decreased with each year due to a higher customer competition, also leads to a change in mindset in the way in which product development is currently done. Gamification models are one of this “game-changing” strategies that was considered suitable to also be implemented in the automotive software product development topic, but not only in this area. This paper describes the current situation in adopting on large scale the product development phases as gamified scenario, together with the presentation of the results of several questionnaires applied on automotive project team members. The aim of the paper is to present a procedure for obtaining a gamification application on an automotive project describing in detail each step which needs to be fulfilled in an iterative way. As a starting point for the model, the DMA (Design – Mechanics – Aesthetic) procedure was used, supported with elements from the PC/game console development, but also the existing tools/frameworks were taken into consideration. The obtained method can be applied on each automotive project, but there are the steps/iterations which still need to be performed, while also the positive and negative aspects are presented.

Living in the modern society, where the Internet of Things (IoT) network is constantly evolving and Things belonging to the network are expanding new functions in a fast pace, there is inevitably a need of having a new vision over the software project management that should keep up the pace with these changes. The impact of the new smart devices is major and can be seen at all levels of software project management. By creating a project management solution across the IoT network, we aim to improve the efficiency and to optimize parameters such as: teamwork, analysis of a large amount of data, project managers and stakeholders input, resource planning, project development and feedback speed. A great contribution in achieving optimization and efficiency improvement for the software project management solution have all the smart devices using high computing power, high data transmission speed and efficient data processing. Therefore, this paper proposes an Agile project management solution that adapts to the IoT network, embedding Things with their features correspondingly into the project management process. Putting the project management process across the IoT network, the Agile project management solution slightly changes the steps of software project management as to increase different project parameters as like: team collaboration, team efficiency, project technique level, project technological level, project automation level.

Computing rational minimax approximations can be very challenging when there are singularities on or near the interval of approximation - precisely the case where rational functions outperform polynomials by a landslide. We show that far more robust algorithms than previously available can be developed by making use of rational barycentric representations whose support points are chosen in an adaptive fashion as the approximant is computed. Three variants of this barycentric strategy are all shown to be powerful: (1) a classical Remez algorithm, (2) a "AAA-Lawson" method of iteratively reweighted least-squares, and (3) a differential correction algorithm. Our preferred combination, implemented in the Chebfun MINIMAX code, is to use (2) in an initial phase and then switch to (1) for generically quadratic convergence. By such methods we can calculate approximations up to type (80, 80) of $|x|$ on $[-1, 1]$ in standard 16-digit floating point arithmetic, a problem for which Varga, Ruttan, and Carpenter required 200-digit extended precision., 29 pages, 11 figures