Kinematic measurements conducted during bike set-ups utilise either static or dynamic measures. There is currently limited data on reliability of static and dynamic measures nor consensus on which is the optimal method. The aim of the study was to assess the difference between static and dynamic measures of the ankle, knee, hip, shoulder and elbow. Nineteen subjects performed three separate trials of a 10min duration at a fixed workload (70% of peak power output). Static measures were taken with a standard goniometer (GM), an inclinometer (IM) and dynamic three dimensional motion capture (3DMC) using an eight camera motion capture system. Static and dynamic joint angles were compared over the three trials to assess repeatability of the measurements and differences between static and dynamic values. There was a positive correlation between GM and IM measures for all joints. Only the knee, shoulder and elbow were positively correlated between GM and 3DMC, and IM and 3DMC. Although all three instruments were reliable, 3D motion analysis utilised different landmarks for most joints and produced different means. Changes in knee flexion angle from static to dynamic are attributable to changes in the positioning of the foot. Controlling for this factor, the differences are negated. It was demonstrated that 3DMC is not interchangeable with GM and IM, and it is recommended that 3DMC develop independent reference values for bicycle configuration.

The ability to stand still in one place is important in a variety of activities of daily living. For persons with motion disorders, orthopaedic treatment, which changes geometric or biomechanical properties, can improve the individual'sposture and walking ability. Decisions on such treatment require insight in how posture and walking ability are aected, however, despite expectations based on experience, it is never a-priori known how a patient will react to a treatment. As this is very challenging to observe by the naked eye, engineering tools are increasingly employed to support clinical diagnostics and treatment planning. The development of predictive simulations allows for the evaluation of the eect of changed biomechanical parameters on the human biological system behavior and could become a valuable tool in future clinical decision making. In the first paper, we evaluated the use of the Zero Moment Point as a computationally inexpensive tool to obtain the ground reaction forces (GRFs) for normal human gait. The method was applied on ten healthy subjects walking in a motion analysis laboratory and predicted GRFs are evaluated against the simultaneously measured force plate data. Apart from the antero-posterior forces, GRFs are well-predicted and errors fall within the error ranges from other published methods. The computationally inexpensive method evaluated in this study can reasonably well predict the GRFs for normal human gait without using prior knowledge of common gait kinetics. The second manuscript addresses the complications in the creation and analysis of a posture prediction framework. The fmincon optimization function in MATLAB was used in conjunction with a musculoskeletal model in OpenSim. One clear local minimum was found in the form of a symmetric standing posture but perturbation analyses revealed the presence of many other postural congurations, each representing its own unique local minimum in the feasible parameter space. For human postural stance, this can translate to there being many different ways of standing without actually noticing a difference in the efforts required for these poses. / <p>This work was financially supported by the Swedish Scientic Council(Vetenskapsrådet) grant no. 2010-9401-79187-68, the ProMobilia handicapfoundation (ref. 13093), Sunnerdahls Handicap foundation (ansökan nr 11/14),and Norrbacka-Eugenia foundation (ansökan nr 218/15).</p><p></p><p></p>

Virtual machines are often considered more secure than regular machines due to the abstraction from the hardware layer. Abstraction does provide some extra security benefits, but many vulnerabilities that exist on a regular machine still exist on virtual machines. Moreover, the sheer amount of virtual machines that are running on many systems makes it difficult to analyse potential vulnerabilities. Vagrant is a management tool for virtual machines packaged in what is called boxes. There are currently no way to automatically scan these Vagrant boxes for vulnerabilities or insecure configurations to determine whether or not they are secure. Therefore we want to establish a method to detect the vulnerabilities of these boxes automatically without launching the box or executing code. There are two main parts in the method used to investigate the boxes. First there is the base box scanning. A base box is an image of which the final box is built upon. This base box is launched, a list of packages is extracted, and the information is then sent to a vulnerability scanner. There is also the analysis of the Vagrantfile. The Vagrantfile is the file that is used to ready the base box with needed software and configurations. The configuration file is written in Ruby and in order to extract information from this file a static code analysis is performed. The result for each box scanned is a list of all the vulnerabilities present on the base box as well as security configurations like SSH settings and shared folders that is retrieved from the Vagrantfile. The results are not completely accurate because the base box is used for the scan, rather than the box itself. Some of the configurations in the Vagrantfiles could not be retrieved because it required code execution or support for configurations done in by other means, like bash. The method does however provide a good indication of how many vulnerabilities a given box possesses.

Background. Malware has been a major issue for years and old signature scanning methods for detecting malware are outdated and can be bypassed by most advanced malware. With the help of machine learning, patterns of malware behavior and structure can be learned to detect the more advanced threats that are active today. Objectives. In this thesis, research to find state-of-the-art machine learning methods to detect malware is proposed. A dataset collection method will be found in research to be used in an experiment. Three selected methods will be re-implemented for an experiment to compare which has the best performance. All three algorithms will be trained and tested on the same dataset. Methods. A literature review with the snowballing technique was proposed to find the state-of-the-art detection methods. The malware was collected through the malware database VirusShare and the total number of samples was 14924. The algorithms were re-implemented, trained, tested, and compared by accuracy, true positive, true negative, false positive, and false negative. Results. The results showed that the best performing research available are image detection, N-Gram combined with meta-data and Function Call Graphs. However, a new method was proposed called Running Window Entropy which does not have a lot of research about it and still can achieve decent accuracy. The selected methods for comparison were image detection, N-Gram, and Running Window Entropy where the results show they had an accuracy of 94.64%, 96.45%, and 93.71% respectively. Conclusions. On this dataset, it showed that the N-Gram had the best performance of all three methods. The other two methods showed that, depending on the use case, either can be applicable.

Heavy rains and El Nio phenomenon are recurring natural phenomena at a national level. These can cause floods due to the overflowing of rivers, which, when close to cities, can cause both human and material losses. The district of Catacaos, located in the city of Piura, was the one with the highest number of injuries due to the flood caused by El Nio phenomenon in 2017. This phenomenon causes a large amounts of rainfalls due to the presence of abnormally warm waters along the northern coast of Peru [1]. It is for this reason that the need arose to carry out an analysis of the physical vulnerability due to instability of people through static equilibrium, in said district, in order to present maps of unsafe areas in the face of this phenomenon. In this investigation, flood hazard maps are generated simulating the one presented in 2017, using 2D hydraulic modeling. For the generation of vulnerability curves, the instability analysis is performed by moment and drag force. Finally, maps with unsafe areas are made using ArcGis software. Where the results obtained indicate that 29.37% of the city was flooded. Likewise, the vulnerability maps generated show us that women and men over 18 years of age in the city of Catacaos would be vulnerable to dragging and overturning in the face of floods in 16.54% and 13.21%, respectively, of the total studied area. This information will be useful for the development of future evacuation plans during floods, carried out by national entities. © Published under licence by IOP Publishing Ltd.

Philosophiae Doctor - PhD / This study examined the effects of using Dialogical Argumentation and Assessment for Learning as an Instructional Method (DAAFLIM) in teaching static electricity focussing on lightning as an example of static electricity to Grade 10 learners. Three groups of learners from two township schools were used as a sample for the study. The Solomon three-group design was employed in collecting data. One class was used as the experimental group and the other two were the control groups: control 1 group and control 2 group. The study drew on theoretical frameworks associated with prior knowledge of learners such as the constructivist viewpoint. The frameworks that were applied in the analysis of the data were Toulmin’s Argumentation Pattern (TAP) and Ogunniyi’s Contiguity Argumentation Theory (CAT). The experimental group and control group 2 were exposed to DAAFLIM as a teaching method and AFL as the assessment strategy. The control 1 group was taught in the traditional chalk- talk method and assessment was mostly summative. The experimental and control 1 groups received pre-tests and also wrote a post-test whereas the control 2 group only wrote the post test.

Indiana University-Purdue University Indianapolis (IUPUI) / Gopinath, Anoop. M.S.E.C.E., Purdue University, May 2018. Low-Power Hybrid TFET-CMOS Memory. Major Professor: Maher E. Rizkalla. The power consumption and the switching speed of the current CMOS technology have reached their limits. In contrast, architecture design within computer systems are continuously seeking more performance and e ciency. Advanced technologies that optimize the power consumption and switching speed may help deliver this e ciency. Indeed, beyond CMOS technology may be a viable approach to meeting the ever increasing need for low-power design. These technology includes devices such as Tunnel Field E ect Transistor (TFET), Graphene based devices such as GFET and GRNFET and FinFET. However, the low cross-sectional area of the channel asso- ciated with smaller technology nodes brings with it the challenges associated with leakage current below the threshold. Mitigating these challenges with devices such as TFETs may allow higher levels of integration, faster switching speed and lower power consumption. This thesis investigates the use of Gallium Nitride (GaN) TFET devices at 20nm for memory cells. These cells can be used in the L1 data cache of the Graphic Processing Units (GPU) thereby minimizing the static power and the dynamic power within these memory systems. The TFET technology was chosen since it has a low subthreshold slope of nearly 30mV/decade. This enables the TFET-based cells to function with a 0.6V supply voltage leading to reduced dynamic power consumption and leakage current when compared to the current CMOS technology. The results suggest that there are bene ts in pursuing an integrated TFET-based technology for Very Large Scale Integrated Circuit (VLSI) design. These bene ts are demonstrated using simulation at the schematic-level using Cadence Virtuoso.

In this investigation, the elasto-plastic behavior and the seismic performance of concrete reinforced frame structures reinforced are evaluated by applying the Pushover method. This evaluation is done on several cases: with high ductility steel (Grade 40), conventional steel (Grade 60) and high strength steel (Grade 75). For the previous, the capacity curve graph obtained from the displacement coefficient method was used to measure the capacity of the structure. In addition, the performance of the structure for different levels of seismic design are evaluated with the resulting values of ductility and rigidity of each case. The results showed that reinforcing a structure with a Grade 40 reinforcing steel increases the energy dissipation capacity, and if reinforced with a Grade 75 reinforcing steel increases the strength capacity in the structure. Finally, the comparative result of the various cases are presented to demonstrate the influence of reinforcing steel on the plastic behavior of concrete reinforced frame structures.

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