Robert Bosch (Brazil)

AIM: The aim of this study was to investigate the accuracy of the self-reported measure of adherence and the relation between adherence to warfarin use, demographic and clinical variables, and the satisfaction with the treatment in patients affected by stroke. METHODS: This is a correlational, quantitative, and cross-sectional study, carried out in the outpatient clinics of a public university hospital from October 2017 to April 2018. Sociodemographic and clinical data were collected through interviews and hospital charts, as well as by applying the Measurement of Treatment Adherence (MTA) and the Duke Anticoagulation Satisfaction Scale, in their Brazilian versions. Results of the international normalized ratio (INR) were collected. Measurements of accuracy of the MTA scale were calculated in relation to the INR classification. RESULTS: Of 99 patients (55.6% male with a mean age of 58.6 years), 57.6% presented with therapeutic INR values and 75.8% of the patients were adherent to the oral anticoagulant therapy according to the MTA. The accuracy analysis of the measurement provided by the MTA scale in relation to the INR classification showed a sensitivity of 77.2% and a specificity of 26.2%. The patients' satisfaction with the treatment was high. The Duke Anticoagulation Satisfaction Scale had an average total score of 46.4, with the dimension impact in the field having the highest score (20.3). CONCLUSION: Stroke patients were adherent and satisfied with the oral anticoagulant therapy. The MTA had good sensitivity and poor specificity. Sociodemographic and clinical characteristics identified were not associated with adherence and satisfaction with treatment.

Engine fault diagnosis is a critical task in automotive aftermarket management. Developing appropriate fault-labeled datasets can be challenging due to nonlinearity variations and divergence in feature distribution among different engine kinds or operating scenarios. To solve this task, this study experimentally measures audio emission signals from compression ignition engines in different vehicles, simulating injector failures, intake hose failures, and absence of failures. Based on these faults, a hybrid approach is applied to classify different conditions that help the planning and decision-making of the automobile industry. The proposed hybrid approach combines the wavelet packet transform (WPT), Markov blanket feature selection, random convolutional kernel transform (ROCKET), tree-structured Parzen estimator (TPE) for hyperparameters tuning, and ten machine learning (ML) classifiers, such as ridge regression, quadratic discriminant analysis (QDA), naive Bayes, k-nearest neighbors (k-NN), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extra trees (ET), gradient boosting machine (GBM), and LightGBM. The audio data are broken down into sub-time series with various frequencies and resolutions using the WPT. These data are subsequently utilized as input for obtaining an informative feature subset using a Markov blanket-based selection method. This feature subset is then fed into the ROCKET method, which is paired with ML classifiers, and tuned using Optuna using the TPE approach. The generalization performance applying the proposed hybrid approach outperforms other standard ML classifiers.

In Brazilian zoos wild felids in captivity are generally fed meat. However, considering the basic nutritional needs of the domestic cat Felis catus , beef may be deficient in calcium, phosphorus, copper, manganese, vitamins A, D and E, thiamin, folic acid and biotin. The aim of this study was to determine the influence of vitamin (A, D, E, B 1 , B 2 , folic acid and biotin) and mineral (calcium, phosphorus, iron, copper, iodine and manganese) supplementation on the quality of semen in Jaguars Panthera onca. Between August 1998 and August 1999 eight adult ♂ Jaguars were offered red meat (beef) and bovine heart once a day. The vitamin/mineral supplementation used was based on the National Research Council recommendations for the domestic cat F. catus. Semen was collected by electroejaculation at the beginning of the trial and every 2 months thereafter for 1 year ( n = 7 collection dates). Statistical analyses were performed using the Wilcoxon test (for motility, vigour and sperm concentration), the Friedman test (for sperm and testicular volume) and analysis of variance (for sperm morphology). There were no significant differences between collections for semen volume, concentration, motility and vigour ( P >0·05) during the trial period. There was also no difference in morphology between samples with respect to secondary defects but the percentage of primary defects decreased ( P <0·05) between the means of the third and the seventh collection series. The conclusion is that dietary supplementation with vitamins and minerals appears to decrease sperm abnormalities, improving seminal characteristics in the animals studied.

The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations.

Electrochemical machining (ECM) is widely applied to manufacture parts with complex geometries, used in electronic components and the automotive, military, and aeronautics industries. These parts have a surface shaped by controlled anodic dissolution at high current density levels, using a neutral solution of inorganic salts (i.e., NaCl or NaNO3) as the electrolyte. Such conditions generate a high amount of sludge that deposits onto the surfaces of equipment, devices, cathodes, and working pieces, requiring daily and complicated sludge management during the series production in the industry. Thus, the main goal of the present work is to propose a simple way to reduce sludge generation in the ECM industrial process. To do so, complexing (EDTA) or reducing (ascorbic acid) agents were added to the electrolyte composition, creating parallel reactions to keep the metallic ions from precipitating. The complexing agent EDTA resulted in a 30% reduction in sludge mass, using an alkaline solution (pH > 10.0). The reducing agent, ascorbic acid, resulted in a 90% reduction in sludge mass, using an acidic solution (pH < 5.0). This sludge reduction has the potential to contribute significantly to increasing equipment, devices, and cathode lifetime, as well as reducing costs associated with centrifuge or filter maintenance (sludge removal from electrolyte) and increasing the productivity of industrial ECM processes.

Multi-Robot Systems (MRS) are transforming diverse domains like logistics, cargo management, and agriculture. However, ensuring that the behavior is correct under various network conditions within such complex environments is challenging, and meeting the desired automation goals is difficult. We propose the CORNET 2.0 co-simulation framework to jointly and accurately simulate multi-agent robotic systems within physical environments and the communication network models within such environments. Our modular framework allows diverse robot and network models to seamlessly integrate to simulate the robot's autonomy, physical space, and network features, such as latency, throughput, and loss intrinsic to the network topology and communication technology. A key novelty of CORNET 2.0 is its accurate synchronizing of mobility and time, which ensures that the physical location of a robot at a point in time, and the network properties and packets that flow from that location, are aligned. This is vital to model and validate MRS coordination algorithms that rely on network interactions. We provide a detailed evaluation of CORNET 2.0 in modeling real-world MRS use cases, such as leader-follower and warehouse environments, that help highlights the benefits.

Plasma Transferred Arc (PTA) hardfacing technique is recognized for the high quality welded surfaces produced. Processing features make this technique an excellent tool for surface alloying and in‐situ processing. This can be of particular interest for the development of high temperature resistant coatings usually associated with low room temperature toughness materials.

Model Predictive Control (MPC) is a popular approach used for motion planning in dynamical systems. Given a finite horizon cost, we seek an optimal control law subject to safety constraints. However, in the presence of obstacles, existing MPC formulations are often slow and may lead to infeasibility. We propose a real-time implementable MPC formulation using control barrier functions (CBF) and successive convexification. We represent the non-convex obstacle avoidance constraints using CBFs that ensure that a feasible solution always exists. We then reformulate the non-convex optimal control problem using successive convexification to enable the use of computationally-efficient conic solvers. Our approach enables controller synthesis at real-time, which is difficult with existing approaches that rely on nonlinear solvers. We demonstrate the method in simulation, where we navigate a UAV to a target while avoiding dynamic obstacles in the environment.

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs’ development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development.

<div class="section abstract"><div class="htmlview paragraph">For lifetime verification in automotive components it is necessary to simulate their operation in test benches that reproduce the conditions of vehicular application, according to the boundary conditions defined in the project.</div><div class="htmlview paragraph">In durability tests of electric fuel pumps for Otto cycle engines, test benches are often used comprised of fuel tanks made of stainless steel provided with a fuel temperature control system. They also have a hydraulic system that allows simulating the fuel circulation loop of the vehicle consisting of fuel filter and fuel pressure regulator. Moreover, they are also equipped with flow meters and fuel pressure for monitoring the test.</div><div class="htmlview paragraph">It has been observed in test bench durabilities that fuel pumps of Flex Fuel type are likely to present high levels of corrosion on their outer housing, even higher than expected after this kind of test. However this behavior has not been found in automotive vehicles under real conditions of use and after a long period of running, suggesting that the problem lies in the operational conditions of the test bench itself.</div><div class="htmlview paragraph">This paper aims to study the cause of this phenomenon, associated with the ground difference between the bench and the tank of the vehicle, describe it from a theoretical perspective and propose modifications to the settings that best simulate the test vehicle conditions, leading to more reliable results at the end of the test.</div></div>

Opacity and attack detectability are important properties for any system as they allow the states to remain private and malicious attacks to be detected, respectively. In this paper, we show that a fundamental trade-off exists between these properties for a linear dynamical system, in the sense that one cannot have an opaque system without making it vulnerable to undetectable attacks. We first characterize the opacity conditions for the system in terms of its weakly unobservable subspace (WUS) and show that the number of opaque states is proportional to the size of the WUS. Further, we establish conditions under which increasing the opaque sets also increases the set of undetectable attacks. This highlights a fundamental trade-off between security and privacy. We demonstrate our results on a team of delivery UAVs.

Autonomous exploration in an unknown environment is widely studied, and many exploration strategies exist. However, in most of the works, safety is not usually given top priority. The reason behind the violation of safety by most of the exploration algorithms in real-world applications is the ignorance of some or all of the following factors (i) the mathematical model of the robot, (ii) practical constraints on states (like constrained steering angle, speed, etc.) and inputs (like actuator saturation), and (iii) hardware constraints such as sampling time, sensor noise, modelling uncertainties, etc. In this work, we propose an autonomous exploration framework for the 2-D exploration problem that considers the factors above to provide safety guarantees for the robot and the environment. The effectiveness of this method is shown in a high-fidelity simulation of different robots with onboard sensors in different simulation environments and real-world implementation.

Security is a big concern in the adaptation of edge computing for smart grid technologies. As the processing power and capabilities of Intelligent Electronic Devices (IEDs) in the power grid increases, they also become prime targets for attackers to exploit for their own gain. Utilities are especially concerned with distribution systems, as the IEDs may not use dedicated fibre communications. It is no longer sufficient to use a single method to secure an IED; using multiple methods can increase the difficulty for an attacker to be covert and may detect malicious behaviour faster. This paper describes some techniques that are currently being implemented on a custom built multifunctional IED using a credit card sized supercomputer called Parallella. An implementation of a remote attestation scheme based on process memory is discussed. An approach to detect malicious device behaviour using osquery, a free and open source tool is also discussed. Finally, network booting and how it is used it to ease updating or resetting devices in the field is elaborated.

<div class="htmlview paragraph">Since in 1978 the Robert Bosch GmbH first started with series production of anti-lock braking systems over 150 million of their units have been installed world-wide. The current ABS generation 8 is a modular system which permits ABS, TCS and ESP<sup>®</sup> braking control systems to be implemented as an integrated product family.</div> <div class="htmlview paragraph">Although the benefits of ABS in road traffic safety by significantly improving steerability and car control while braking are well known in Europe, USA, and Japan, there are still weak installation rates in emerging markets like Brazil: For vehicles sold in 2006 in the Brazilian market, the installation rate is about 14%. Major reason for this situation is the cost of ABS unit influenced by development and application work, which usually has to be carried out for each car individually.</div> <div class="htmlview paragraph">To address this Bosch is considering a cost optimized system in between today's full performance ABS and a standard braking system without modulation. Currently 2 approaches are in evaluation:</div> <div class="htmlview paragraph"> <ul class="list disc"> <li class="list-item"><div class="htmlview paragraph">To downsize the ABS hardware with respect to the limitations given by the vehicle itself. For mini cars and small vehicles the top speed and consequently the necessary braking performance is usually limited.</div></li> <li class="list-item"><div class="htmlview paragraph">To decrease the development expenses and the testing time. Here computational simulation and quick application are necessary to reach feasibility.</div></li> </ul> </div>

Yield maps from combine harvesters are essential in precision agriculture for capturing within-field variability and guiding variable-rate input management. However, in large-scale systems such as those in the Brazilian Cerrado, these maps are often inconsistent due to calibration errors, use of multiple harvesters, and complex post-processing. Orbital remote sensing offers an alternative by providing consistent vegetation index (VI) data for crop monitoring and yield estimation. This study developed regression models relating Sentinel-2 VIs (EVI, TVI, NDVI, and NDRE) to cotton yield data obtained from combine harvesters across 30 commercial plots in Mato Grosso, Brazil, over six cropping seasons (2019–2024), totaling 76 plot-season datasets. Vegetation indices were grouped into 15-day intervals based on days after sowing, and a logistic growth function was applied in the regression modeling. Model performance evaluated using 15 independent plot-seasons showed good pixel-level accuracy, with RMSE of 0.695 t ha−1 and R2 of 0.78, with EVI performing slightly better. At the plot scale, mean yield predictions across all datasets achieved an RMSE of 0.41 t ha−1, reflecting the higher reliability of module-based yield measurements. These results demonstrate the potential of Sentinel-2 VIs combined with logistic regression to predict cotton yields in the Cerrado, complementing or replacing harvester-based monitoring.

<div class="section abstract"><div class="htmlview paragraph">The growing demand for lower prices, higher quality and responsiveness is defining the current competitive scenario for all the organizations. Global market is inserted in a context of volatility, uncertainty, complexity and ambiguity. Based on that need, the new management directions must be flexible on the product development in order to precisely achieve its target under constant market changes.</div><div class="htmlview paragraph">It is in the daily work processes and routine that the relations between people take place and it happens in all of the hierarchical levels (similar ones and distinguished ones). Organizations are responsible to promote an organizational environment favorable to the sharing relationships, aiming to motivate each employee individually to exchange information, knowledge and experience.</div><div class="htmlview paragraph">The Agile Methodologies appear as an alternative to the traditional methods of development. Keeping essential practices of the traditional methodologies, the agile methodologies focus on other dimensions of the project: the collaboration with users during all stages of the development and the motivation of the team involved in the project. The key factor that defines these new methodologies is to have a project scope that can be changed during the project development according to the market changes as well as the project processes according to the needs of the team. In terms of team, agile methodologies differentiate itself for having a multifunctional and self-organized team.</div><div class="htmlview paragraph">This paper will present a case study using Scrum Methodology applied in two development projects of power tools with two different teams and the same Scrum Master. It will be shown the advantages and gains of such a methodology and the barriers found during the project execution.</div></div>

In this paper, we consider the notion of resilience of a dynamical system, defined by the maximum disturbance a controlled dynamical system can withstand while satisfying given temporal logic specifications. Given a dynamical system and a specification, the objective is to synthesize the controller such that the closed-loop system satisfies this specification while maximizing its resilience. The problem is formulated as a robust optimization program where the objective is to compute the maximum resilience while simultaneously synthesizing the corresponding controller parameters. For linear systems and linear controllers, exact solutions are provided for the class of time-varying polytopic specifications. For the case of nonlinear systems, nonlinear controllers and more general specifications, we leverage tools from the scenario optimization approach, offering a probabilistic guarantee of the solution as well as computational feasibility. Different case studies are presented to illustrate the theoretical results.

Digital Twin Shop Floor (DTS) é uma ferramenta recente que tem chamado a atenção tanto da indústria quanto dos estudiosos, oferecendo possíveis benefícios para os sistemas de produção e logística ao construir uma réplica virtual do chão de fábrica e estabelecer uma interação entre o modelo digital e sua contraparte física. No entanto, há uma falta de artigos científicos na literatura que revisem a aplicação do DTS nas indústrias. Nesse contexto, este artigo revisa a literatura científica e analisa as otimizações propostas para encontrar melhorias aplicáveis a um gêmeo digital já existente de um grande fabricante de autopeças no Brasil. O presente trabalho também traz uma análise das tecnologias utilizadas nos artigos revisados, bem como dos métodos de modelagem e simulação. Além disso, cada artigo é discutido individualmente, focando em suas contribuições alcançáveis para o fabricante. Por fim, são delineados os trabalhos futuros relacionados à otimização do DTS pelo fabricante.

The overall cost of structural corrosion is estimated to be in the order of trillions of dollars throughout the world. This is a fault that can bring numerous harms to industries through water infiltrations, roof breaking and collapsing of structures, and even create work-related accidents. In this research, an artificial intelligence was developed to evaluate aerial images of factory roofs, aiming towards a more accurate and efficient maintenance. Previous studies were conducted to evaluate optimal flight conditions of the unmanned aerial vehicle such as ambient illuminance, cruise speed and flight height. Afterwards a Taguchi analysis was made. Based on the knowledge of building maintenance, an artificial neural network was elaborated containing three great levels in its architecture. In the first step, an extractor model was applied to obtain only the information from roofs in the captured images. Next, the second stage included corrosion detection and its segmentation. In the third stage, the network performed the criticality classification of the detected failures. The proposed system was trained and evaluated in a total of 249,000 m2 of constructed area and the results presented potential of this application for a diagnosis of corrosions in metal structures.

<div class="htmlview paragraph">Self-lubricating P/M bearings were manufactured with different compacting force values. Installed on electric motors, and submitted to endurance tests, those with lower density (5.75 g/cm<sup>3</sup>) presented a bad behaviour, due to an insufficient lubrication caused by oil loss. Bearings with a density equal or superior to 6.15 g/cm<sup>3</sup> presented a better behaviour. A research on quality evaluation tests for P/M materials was conducted, in order to define which were the best suited to characterize the behaviour of bearings with highest sensitivity, quickness and at lowest costs. Radial rupture force and oil permeability tests revealed the most adequate for the purpose.</div>