Improving reconfigurable systems reliability by combining periodical test and redundancy techniques: a case study

This paper revises and introduces to the field of reconfigurable computer systems, some traditional techniques used in the fields of fault-tolerance and testing of digital circuits. The target area is that of on-board spacecraft electronics, as this class of application is a good candidate for the use of reconfigurable computing technology. Fault tolerant strategies are used in order for the system to adapt itself to the severe conditions found in space. In addition, the paper describes some problems and possible solutions for the use of reconfigurable components, based on programmable logic, in space applications

A Graph Neural Network Approach to Nanosatellite Task Scheduling: Insights into Learning Mixed-Integer Models

This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNN). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbit while taking into account Quality-of-Service (QoS) considerations such as priority, minimum and maximum activation events, execution time-frames, periods, and execution windows, as well as constraints on the satellite's power resources and the complexity of energy harvesting and management. The ONTS problem has been approached using conventional mathematical formulations and precise methods, but their applicability to challenging cases of the problem is limited. This study examines the use of GNNs in this context, which has been effectively applied to many optimization problems, including traveling salesman problems, scheduling problems, and facility placement problems. Here, we fully represent MILP instances of the ONTS problem in bipartite graphs. We apply a feature aggregation and message-passing methodology allied to a ReLU activation function to learn using a classic deep learning model, obtaining an optimal set of parameters. Furthermore, we apply Explainable AI (XAI), another emerging field of research, to determine which features -- nodes, constraints -- had the most significant impact on learning performance, shedding light on the inner workings and decision process of such models. We also explored an early fixing approach by obtaining an accuracy above 80\% both in predicting the feasibility of a solution and the probability of a decision variable value being in the optimal solution. Our results point to GNNs as a potentially effective method for scheduling nanosatellite tasks and shed light on the advantages of explainable machine learning models for challenging combinatorial optimization problems

Uterus torsion in Nellore cow : case report

A torção de útero em bovinos é um caso de emergência obstétrica que, na maioria das vezes, leva à morte do feto e da mãe. O tratamento clínico é pouco eficaz e em grande parte dos casos a operação cesariana é indicada. O trabalho descreve o caso de uma vaca Nelore de 36 meses encaminhada ao Hospital Escola de Grandes Animais da Universidade de Brasília com torção uterina e sinais de toxemia. Após tentativas improdutivas de rolamento, o animal foi submetido à cesariana e veio a morrer durante o procedimento cirúrgico. Vários casos de torção de útero em bovinos são descritos em raças europeias, porém, pouco se conhece desse tipo de patologia em animais da raça Nelore. _______________________________________________________________________________ ABSTRACTUterus torsion in cows is an emergency case that usually leads to foetus and cow death. Conservative management has low efficacy and cesarian surgery is indicated in the majority of the cases. The report describes the case of a 36-year-old Nellore cow taken to Hospital Escola de Grandes Animais of Universidade de Brasíla with uterus torsion and toxemia signs. After improductive rolling procedures, the animal was lead to a cesarian surgery and died during the procedure. Uterus torsion is described in european breeds, but this patology is not well-known in Nellore cattle

Spatio-temporal variability of erosivity in Mato Grosso, Brazil

The impact of rainfall on surfaces lacking vegetal cover can dissociate soil particles, thereby initiating the erosion process. This is known as rainfall erosivity and is expressed by the R factor in the Universal Soil Loss Equation. Agricultural areas often show seasonally erosion susceptibility throughout the year due to oscillations of the soil exposure rate and the vegetation change. Considering that approximately 30 million ha of the Mato Grosso State in Brazil is used for agriculture, this study aimed to predict and map the spatial and temporal variability of its territory. We evaluated the monthly (EI30) and annual (R) erosivity for 158 rain gauge stations and spatialized the values of EI30 and R by the Kriging method. It was observed that R values ranked as very high in the north, and high and medium-high in the south of Mato Grosso state. The mean value is 8835 MJ mm ha-1 h-1 year-1, considered high. Ninety-one percent of the annual erosivity was concentrated in the period between October and April, corresponding to the rainy season. The highest R factor values were found in the macro-regions of the northwest, north, west and medium-north of Mato Grosso State

METHOD FOR LANDSLIDES IDENTIFICATION AT THE SAO PAULO STATE COAST, BRAZIL

Satellite images are an important tool to map natural disaster, mainly debris flow. The Support Vector Machines (SVM) algorithm has been used to classify the natural disaster, obtaining good results, although some images present shadows and mists which difficult the classification. Some enhancements minimize those problems facilitating the classification process. This paper aims to present a method to classify debris flow areas near to an important road of the Sao Paulo State coast, Brazil, using LANDSAT images. Maximum Likelihood Classification (MLC) and SVM algorithms were applied. Due to the shadows the classification points huge debris flow areas. To neutralize the influence of shadows, Normalized Difference Vegetation Index (NDVI) was employed which turns easier to sample the training areas and perform the classification. MLC algorithm cannot be applied in case of a unique band, SVM can. So SVM is performed for the enhancement of classification and better results are observed with the combined methods SVM/NDVI. The overlay of this classification and Digital Terrain Model confirms the coincidence of debris flow event and classification. This method was very effective to the area now studied and may be useful to debris flow mapping

Synthesis, Dna Binding, And Antiproliferative Activity Of Novel Acridine-thiosemicarbazone Derivatives.

In this work, the acridine nucleus was used as a lead-compound for structural modification by adding different substituted thiosemicarbazide moieties. Eight new (Z)-2-(acridin-9-ylmethylene)-N-phenylhydrazinecarbothioamide derivatives (3a-h) were synthesized, their antiproliferative activities were evaluated, and DNA binding properties were performed with calf thymus DNA (ctDNA) by electronic absorption and fluorescence spectroscopies. Both hyperchromic and hypochromic effects, as well as red or blue shifts were demonstrated by addition of ctDNA to the derivatives. The calculated binding constants ranged from 1.74 × 10(4) to 1.0 × 10(6) M(-1) and quenching constants from -0.2 × 10(4) to 2.18 × 10(4) M(-1) indicating high affinity to ctDNA base pairs. The most efficient compound in binding to ctDNA in vitro was (Z)-2-(acridin-9-ylmethylene)-N- (4-chlorophenyl) hydrazinecarbothioamide (3f), while the most active compound in antiproliferative assay was (Z)-2-(acridin-9-ylmethylene)-N-phenylhydrazinecarbothioamide (3a). There was no correlation between DNA-binding and in vitro antiproliferative activity, but the results suggest that DNA binding can be involved in the biological activity mechanism. This study may guide the choice of the size and shape of the intercalating part of the ligand and the strategic selection of substituents that increase DNA-binding or antiproliferative properties.1613023-1304

Universal Verification Platform and Star Simulator for Fast Star Tracker Design

Developing star trackers quickly is non-trivial. Achieving reproducible results and comparing different algorithms are also open problems. In this sense, this work proposes the use of synthetic star images (a simulated sky), allied with the standardized structure of the Universal Verification Methodology as the base of a design approach. The aim is to organize the project, speed up the development time by providing a standard verification methodology. Future rework is reduced through two methods: a verification platform that us shared under a free software licence; and the layout of Universal Verification Methodology enforces reusability of code through an object-oriented approach. We propose a black-box structure for the verification platform with standard interfaces, and provide examples showing how this approach can be applied to the development of a star tracker for small satellites, targeting a system-on-a-chip design. The same test benches were applied to both early conceptual software-only implementations, and later optimized software-hardware hybrid systems, in a hardware-in-the-loop configuration. This test bench reuse strategy was interesting also to show the regression test capability of the developed platform. Furthermore, the simulator was used to inject specific noise, in order to evaluate the system under some real-world conditions