Shape memory performance of asymmetrically reinforced epoxy/carbon fibre fabric composites in flexure

In this study asymmetrically reinforced epoxy (EP)/carbon fibre (CF) fabric composites were prepared and their shape memory properties were quantified in both unconstrained and fully constrained flexural tests performed in a dynamic mechanical analyser (DMA). Asymmetric layering was achieved by incorporating two and four CF fabric layers whereby setting a resin- and reinforcement-rich layer ratio of 1/4 and 1/2, respectively. The recovery stress was markedly increased with increasing CF content. The related stress was always higher when the CF-rich layer experienced tension load locally. Specimens with CF-rich layers on the tension side yielded better shape fixity ratio, than those with reinforcement layering on the compression side. Cyclic unconstrained shape memory tests were also run up to five cycles on specimens having the CF-rich layer under local tension. This resulted in marginal changes in the shape fixity and recovery ratios

BIOMECHANICAL MODELS AND MEASURING TECHNIQUES FOR ULTRASOUND-BASED MEASURING SYSTEM DURING GAIT

Nowadays clinical motion analysis is a usual method. More and more laboratories offer their facilities and use this investigation for supporting doctors in their decisions. During the past two years a new and modern on-line motion analysis system was established at the Department of Applied Mechanics which is capable for a complex analysis of the upper limb, the gait, the run, other cyclic movements, and the spine. This paper focuses on the presentation of a new 3D motion analysis technique for treadmill walking. An ultrasound-based 3D measurement system and a measuring arrangement developed were used to measure and determine gait parameters during treadmill walking. The model considers each limb segment to be a rigid body, linked to each other by a joint. This paper also presents a new 3D motion analysis software package for treadmill walking and introduce the DataManager developed. We studied knee kinematics and temporal-distance gait measurement parameters (step length, stride length, stride width, etc.) to be obtained from treadmill walking. Treadmill walking allows the analysis of several cycles of each subject. On the basis of the analysis the standard deviation of temporal-gait parameters and the knee kinematics data of each subject can be established. The 3D movement analysis system presented is a suitable and standardized procedure for quick gait analysis

Uncovering High-Level Corruption: Cross-National Objective Corruption Risk Indicators Using Public Procurement Data

Measuring high-level corruption is subject to extensive scholarly and policy interest, which has achieved moderate progress in the last decade. This article develops two objective proxy measures of high-level corruption in public procurement: single bidding in competitive markets and a composite score of tendering ‘red flags’. Using official government data on 2.8 million contracts in twenty-eight European countries in 2009–14, we directly operationalize a common definition of corruption: unjustified restriction of access to public contracts to favour a selected bidder. Corruption indicators are calculated at the contract level, but produce aggregate indices consistent with well-established country-level indicators, and are also validated by micro-level tests. Data are published at http://digiwhist.eu/resources/data/

Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic

We consider the problem of using a heuristic policy to improve the value approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in non-adversarial settings such as planning with large-state space Markov Decision Processes. Current improvements to UCT focus on either changing the action selection formula at the internal nodes or the rollout policy at the leaf nodes of the search tree. In this work, we propose to add an auxiliary arm to each of the internal nodes, and always use the heuristic policy to roll out simulations at the auxiliary arms. The method aims to get fast convergence to optimal values at states where the heuristic policy is optimal, while retaining similar approximation as the original UCT in other states. We show that bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs better compared to the original UCT algorithm and its variants in two benchmark experiment settings. We also examine conditions under which UCT-Aux works well.Comment: 16 pages, accepted for presentation at ECML'1

Verification of simplified gait analysis

In this study we describe a simplified gait analysis system for the estimation of spatial-temporal parameters and kinetic parameters during walking on an instrumented treadmill. This method proposes to compute the values of temporal (length of double support phase, length of stance phase, time of step, time of stride) and few spatial parameters (step length, stride length, cadence) from the graph of time vs. ground reaction force at a constant gait speed. The method was verified by a clinical gait analysis method using a ZEBRIS ultrasound-based measuring system with ARMMODEL software, and the measurement error rate of the method was determined by statistical calculations. The maximum relative value of the average difference of temporal parameters is 0.79 % in cycle time and that of spatial parameters is 0.66%. On the basis of statistical analysis, it can be established that the measurement method is reproducible. This system is simple, inexpensive and does not provoke any discomfort to subjects. It can be car ried on for long periods of time during a treadmill walk, thus providing new longitudinal information such as the stride-to-stride variability of gait. Several clinical applications can be proposed such as outcome evaluation after different orthopaedical, rheumatological, neurological diseases, and monitoring of the rehabilitation process

Hydrogenation of CO on supported Rh catalysts

In contrast to classical Field-Programmable Gate Arrays (FPGAs), partial and run-time reconfigurable (RTR) FPGAs can selectively reconfigure partitions of its hardware almost immediately while it is still powered and operative. In this way, RTR FPGAs combine the flexibility of software with the high efficiency of hardware. However, their potential cannot be fully exploited due to the increased complexity of the design process, and the intricacy to generate partial reconfigurations. FPGAs are often seen as a single auxiliary area to accelerate algorithms for specific problems. However, when several RTR partitions are implemented and combined with a processor system, new opportunities and challenges appear due to the creation of a heterogeneous RTR embedded system-on-chip (SoC). The aim of this thesis is to investigate how the flexibility, reusability, and productivity in the design process of partial and RTR embedded SoCs can be improved to enable research and development of novel applications in areas such as hardware acceleration, dynamic fault-tolerance, self-healing, self-awareness, and self-adaptation. To address this question, this thesis proposes a solution based on modular reconfigurable IP-cores and design-and-reuse principles to reduce the design complexity and maximize the productivity of such FPGA-based SoCs. The research presented in this thesis found inspiration in several related topics and sciences such as reconfigurable computing, dependability and fault-tolerance, complex adaptive systems, bio-inspired hardware, organic and autonomic computing, psychology, and machine learning. The outcome of this thesis demonstrates that the proposed solution addressed the research question and enabled investigation in initially unexpected fields. The particular contributions of this thesis are: (1) the RecoBlock SoC concept and platform with its flexible and reusable array of RTR IP-cores, (2) a simplified method to transform complex algorithms modeled in Matlab into relocatable partial reconfigurations adapted to an improved RecoBlock IP-core architecture, (3) the self-healing RTR fault-tolerant (FT) schemes, especially the Upset-Fault-Observer (UFO) that reuse available RTR IP-cores to self-assemble hardware redundancy during runtime, (4) the concept of Cognitive Reconfigurable Hardware (CRH) that defines a development path to achieve self-adaptation and cognitive development, (5) an adaptive self-aware and fault-tolerant RTR SoC that learns to adapt the RTR FT schemes to performance goals under uncertainty using rule-based decision making, (6) a method based on online and model-free reinforcement learning that uses a Q-algorithm to self-optimize the activation of dynamic FT schemes in performance-aware RecoBlock SoCs. The vision of this thesis proposes a new class of self-adaptive and cognitive hardware systems consisting of arrays of modular RTR IP-cores. Such a system becomes self-aware of its internal performance and learns to self-optimize the decisions that trigger the adequate self-organization of these RTR cores, i.e., to create dynamic hardware redundancy and self-healing, particularly while working in uncertain environments.Partiell och run-time rekonfigurering (RTR) betyder att en del av en integrerad krets kan konfigureras om, medan den resterande delens operation kan fortlöpa. Moderna Field Programmable Gate Array (FPGA) kretsar är ofta partiell och run-time rekonfigurerbara och kombinerar därmed mjukvarans flexibilitet med hårdvarans effektivitet. Tyvärr hindrar dock den ökade designkomplexiteten att utnyttja dess fulla potential. Idag ses FPGAer mest som hårdvaruacceleratorer, men helt nya möjligheter uppstår genom att kombinera ett multiprocessorsystem med flera rekonfigurerbara partitioner som oberoende av varandra kan omkonfigureras under systemoperation. Målet med avhandlingen är att undersöka hur utvecklingsprocessen för partiella och run-time rekonfigurerbara FPGAer kan förbättras för att möjliggöra forskning och utveckling av nya tillämpningar i områden som hårdvaruacceleration, själv-läkande och själv-adaptiva system. I avhandlingen föreslås att en lösning baserad på modulära rekonfigurerbara hårdvarukärnor kombinerad med principer för återanvändbarhet kan förenkla komplexiteten av utvecklingsprocessen och leda till en högre produktivitet vid utvecklingen av inbyggda run-time rekonfigurerbara system. Forskningen i avhandlingen inspirerades av flera relaterade områden, så som rekonfigurerbarhet, tillförlitlighet och feltolerans, komplexa adaptiva system, bio-inspirerad hårdvara, organiska och autonoma system, psykologi och maskininlärning. Avhandlingens resultat visar att den föreslagna lösningen har potential inom olika tillämpningsområden. Avhandlingen har följande bidrag: (1) RecoBlock system-på-kisel plattformen bestående av flera rekonfigurerbara hårdvarukärnor, (2) en förenklad metod för att implementera Matlab modeller i rekonfigurerbara partitioner, (3) metoder för själv-läkande RTR feltoleranta system, t. ex. Upset-Fault-Observer, som själv-skapar hårdvaruredundans under operation, (4) utvecklandet av konceptet för kognitiv rekonfigurerbar hårdvara, (5) användningen av konceptet och plattformen för att implementera kretsar som kan användas i en okänd omgivning på grund av förmågan att fatta regel-baserade beslut, och (6) en förstärkande inlärnings-metod som använder en Q-algoritm för dynamisk feltolerans i prestanda-medvetna RecoBlock SoCs. Avhandlingens vision är en ny klass av själv-adaptiva och kognitiva hårdvarusystem bestående av modulära run-time rekonfigurerbara hårdvarukärnor. Dessa system blir själv-medvetna om sin interna prestanda och kan genom inlärning optimera sina beslut för själv-organisation av de rekonfigurerbara kärnorna. Därmed skapas dynamisk hårdvaruredundans och självläkande system som har bättre förutsättningar att kunna operera i en okänd omgivning.QC 20151201</p

Effect of postharvest on the economic viability of walnut production

In this study we were studying the question whether walnut production under domestic natural and economic circumstances shall be considered a profitable activity or not. Our partial objective is to determine, what level of natural inputs and production costs are required for walnut production, what yield level, selling price and production value can be attained, what level of profitability, rentability and efficiency may production have, is the establishment of a walnut orchard profitable on the entire lifespan of the plantation, and the production of which is more efficient: the dry shelled walnut production requiring postharvest activity or the raw, shelled walnut without postharvest activities. In this study, comparison of two systems is conducted. First version: producer establishes a walnut plantation and sells walnut raw and shelled. Second version: producer also invests into a drying facility, and in this case the end product is the dry, shelled walnut. If the producer sells walnut right after harvest in a raw bulk, total production costs in productive years reaches 974,011 HUF/ha. Attainable yield is 2.63 t/ha with 396.3 HUF/kg selling price, therefore the profit is 138,258 HUF/ha with 14.19% cost-related profitability. In the case when the producer sells dried, shelled walnut, production costs are 25% higher compared to that of raw walnut due to the cost of drying. By calculating with the postharvest loss, average yield is 1.84 t/ha, however, its selling price is way higher (882.84 HUF/kg), therefore the profit per hectare reaches 475,496 HUF with 39.01% cost-related profitability. Thus it can be stated that walnut production in an average year may be profitable even without postharvest, but efficiency is improved significantly when the producer sells the products dried. Investment profitability analysis revealed that production of raw, shelled walnut is not economically viable, since the plantation does not pay off on its entire lifespan (30 years), while walnut production with postharvest is efficient and rentable, since both net present value (NPV) and internal rate of return (IRR) showed more favourable values than in the previous case, and the orchard pays off in the 21th year after establishment