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In recent years, physically unclonable functions (PUFs) have gained significant attraction in IoT security applications, such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of different devices to generate unique fingerprints for security applications. When generating PUF-based secret keys, the reliability and entropy of the keys are vital factors. This study proposes a novel method for generating PUF-based keys from a set of measurements. Firstly, it formulates the group-based key generation problem as an optimization problem and solves it using integer linear programming (ILP), which guarantees finding the optimum solution. Then, a novel scheme for the extraction of keys from groups is proposed, which we call positioning syndrome coding (PSC). The use of ILP as well as the introduction of PSC facilitates the generation of high-entropy keys with low error correction costs. These new methods have been tested by applying them on the output of a capacitor network PUF. The results confirm the application of ILP and PSC in generating high-quality keys.
Steroid hormones (SHs) are a rising concern due to their high bioactivity, ubiquitous nature, and prolonged existence as a micropollutants in water, they pose a potential risk to both human health and the environment, even at low concentrations. Estrogens, progesterone, and testosterone are the three important types of steroids essential for human development and maintaining multiorgan balance, are focus to this concern. These steroid hormones originate
from various sources, including human and livestock excretions, veterinary medications, agricultural runoff, and pharmaceuticals, contributing to their presence in the environment. According to the recommendation of WHO, the guidance value for estradiol (E2) is 1 ng/L. There are several methods been attempted to remove the SH micropollutant by conventional water and wastewater technologies which are still under research. Among the various methods, electrochemical membrane reactor (EMR) is one of the emerging technologies that can address the challenge of insufficient SHs removal from the aquatic environment by conventional treatment. The degradation of SHs can be significantly influenced by various factors when treated with EMR.
In this project, the removal of SH and the important mechanism for the removal using carbon nanotube CNT-EMR is studied and the efficiency of CNT-EMR in treating the SH micropollutant is identified. By varying different parameters this experiment is carried out with the (PES-CNTs) ultrafiltration membrane. The study is carried out depending upon the SH removal based on the limiting factor such as cell voltage, flux, temperature, concentration, and type of the SH.
This thesis focuses on the development and implementation of a Datagram Transport Layer Security (DTLS) communication framework within the ns-3 network simulator, specifically targeting the LoRaWAN model network. The primary aim is to analyse the behaviour and performance of DTLS protocols across different network conditions within a LoRaWAN context. The key aspects of this work include the following.
Utilization of ns-3: This thesis leverages ns-3’s capabilities as a powerful discrete event network simulator. This platform enables the emulation of diverse network environments, characterized by varying levels of latency, packet loss, and bandwidth constraints.
Emulation of Network Challenges: The framework specifically addresses unique challenges posed by certain network configurations, such as duty cycle limitations. These constraints, which limit the time allocated for data transmission by each device, are crucial in understanding the real-world performance of DTLS protocols.
Testing in Multi-client-server Scenarios: A significant feature of this framework is its ability to test DTLS performance in complex scenarios involving multiple clients and servers. This is vital for assessing the behaviour of a protocol under realistic network conditions.
Realistic Environment Simulation: By simulating challenging network conditions, such as congestion, limited bandwidth, and resource constraints, the framework provides a realistic environment for thorough evaluation. This allows for a comprehensive analysis of DTLS in terms of security, performance, and scalability.
Overall, this thesis contributes to a deeper understanding of DTLS protocols by providing a robust tool for their evaluation under various and challenging network conditions.
Global energy demand is still on an increase during the last decade, with a lot of impact on the climate change due to the intensive use of conventional fossil-based fuels power plants to cover this demand. Most recently, leaders of the globe met in 2015 to come out with the Paris Agreement, stating that the countries will start to take a more responsible and effective behaviour toward the global warming and climate change issues. Many studies have discussed how the future energy system will look like with respecting the countries’ targets and limits of greenhouse gases and their CO2 emissions. However, these studies rarely discussed the industry sector in detail even though it is one of the major role players in the energy sector. Moreover, many studies have simulated and modelled the energy system with huge jumps of intervals in terms of years and environmental goals. In the first part of this study, a model will be developed for the German electrical grid with high spatial and temporal resolutions and different scenarios of it will be analysed meticulously on shorter periods (annual optimization), with different flexibilities and used technologies and degrees of innovations within each scenario. Moreover, the challenge in this research is to adequately map the diverse and different characteristics of the medium-sized industrial sector. In order to be able to take a first step in assessing the relevance of the industrial sector in Germany for climate protection goals, the industrial sector will be mapped in PyPSA-Eur (an open-source model data set of the European energy system at the level of the transmission network) by detailing the demand for different types of industry and assigning flexibilities to the industrial types. Synthetically generated load profiles of various industrial types are available. Flexibilities in the industrial sector are described by the project partner Fraunhofer IPA in the GaIN project and can be used. Using a scenario analysis, the development of the industrial sector and the use of flexibilities are then to be assessed quantitatively.
This paper will introduce the open-source model MyPyPSA-Ger, a myopic optimization model developed to represent the German energy system with a detailed mapping of the electricity sector, on a highly disaggregated level, spatially and temporally, with regional differences and investment limitations. Furthermore, this paper will give new outlooks on the German federal government 2050 emissions goals of the electricity sector to become greenhouse gas neutral by proposing new CO2 allowance strategies. Moreover, the regional differences in Germany will be discussed, their role and impact on the energy transition, and which regions and states will drive the renewable energy utilization forward.
Following a scenario-based analysis, the results point out the major keystones of the energy transition path from 2020 to 2050. Solar, onshore wind, and gas-fired power plants will play a fundamental role in the future electricity systems. Biomass, run of river, and offshore wind technologies will be utilized in the system as base-load generation technologies. Solar and onshore wind will be installed almost everywhere in Germany. However, due to the nature of Germany’s weather and geographical features, the southern and northern regions will play a more important role in the energy transition.
Higher CO2 allowance costs will help achieve the 1.5-degree-target of the electricity system and will allow for a rapid transition. Moreover, the more expensive, and the earlier the CO2 tax is applied to the system, the less it will cost for the energy transition, and the more emissions will be saved throughout the transition period. An earlier phase-out of coal power plants is not necessary with high CO2 taxes, due to the change in power plant’s unit commitment, as they prioritize gas before coal power plants. Having moderate to low CO2 allowance cost or no clear transition policy will be more expensive and the CO2 budget will be exceeded. Nonetheless, even with no policy, renewables still dominate the energy mix of the future.
However, maintaining the maximum historical installation rates of both national and regional levels, with the current emissions reduction strategy, will not be enough to reach the level of climate-neutral electricity system. Therefore, national and regional installation requirements to achieve the federal government emission reduction goals are determined. Energy strategies and decision makers will have to resolve great challenges in order to stay in line with the 1.5-degree-target.
Most recently, the federal government in Germany published new climate goals in order reach climate neutrality by 2045. This paper demonstrates a path to a cost optimal energy supply system for the German power grid until the year 2050. With special regard to regionality, the system is based on yearly myopic optimization with the required energy system transformation measures and the associated system costs. The results point out, that energy storage systems (ESS) are fundamental for renewables integration in order to have a feasible energy transition. Moreover, the investment in storage technologies increased the usage of the solar and wind technologies. Solar energy investments were highly accompanied with the installation of short-term battery storage. Longer-term storage technologies, such as H2, were accompanied with high installations of wind technologies. The results pointed out that hydrogen investments are expected to overrule short-term batteries if their cost continues to decrease sharply. Moreover, with a strong presence of ESS in the energy system, biomass energy is expected to be completely ruled out from the energy mix. With the current emission reduction strategy and without a strong presence of large scale ESS into the system, it is unlikely that the Paris agreement 2° C target by 2050 will be achieved, let alone the 1.5° C.
An import ban of Russian energy sources to Germany is currently being increasingly discussed. We want to support the discussion by showing a way how the electricity system in Germany can manage low energy imports in the short term and which measures are necessary to still meet the climate protection targets. In this paper, we examine the impact of a complete stop of Russian fossil fuel imports on the electricity sector in Germany, and how this will affect the climate coals of an earlier coal phase-out and climate neutrality by 2045.
Following a scenario-based analysis, the results gave a point of view on how much would be needed to completely rely on the scarce non-renewable energy resources in Germany. Huge amounts of investments would be needed in order to ensure a secure supply of electricity, in both generation energy sources (RES) and energy storage systems (ESS). The key findings are that a rapid expansion of renewables and storage technologies will significantly reduce the dependence of the German electricity system on energy imports. The huge integration of renewable energy does not entail any significant imports of the energy sources natural gas, hard coal, and mineral oil, even in the long term. The results showed that a ban on fossil fuel imports from Russia outlines huge opportunities to go beyond the German government's climate targets, where the 1.5-degree-target is achieved in the electricity system.
Method and system for extractin metal and oxygen from powdered metal oxides (EP000004170066A2)
(2023)
A method for extracting metal and oxygen from powdered metal oxides in electrolytic cell is proposed, the electrolytic cell comprising a container, a cathode, an anode and an oxygen-ion-conducting membrane, the method comprising providing a solid oxygen ion conducting electrolyte powder into a container, providing a feedstock comprising at least one metal oxide in powdered form into the container, applying an electric potential across the cathode and the anode, the cathode being in communication with the electrolyte powder and the anode being in communication with the membrane in communication with the electrolyte powder, such that at least one respective metallic species of the at least one metal oxide is reduced at the cathode and oxygen is oxidized at the anode to form molecular oxygen, wherein the potential across the cathode and the anode is greater than the dissociation potential of the at least one metal oxide and less than the dissociation potential of the solid electrolyte powder and the membrane.
A novel peptidyl-lys metalloendopeptidase (Tc-LysN) from Tramates coccinea was recombinantly expressed in Komagataella phaffii using the native pro-protein sequence. The peptidase was secreted into the culture broth as zymogen (~38 kDa) and mature enzyme (~19.8 kDa) simultaneously. The mature Tc-LysN was purified to homogeneity with a single step anion-exchange chromatography at pH 7.2. N-terminal sequencing using TMTpro Zero and mass spectrometry of the mature Tc-LysN indicated that the pro-peptide was cleaved between the amino acid positions 184 and 185 at the Kex2 cleavage site present in the native pro-protein sequence. The pH optimum of Tc-LysN was determined to be 5.0 while it maintained ≥60% activity between pH values 4.5—7.5 and ≥30% activity between pH values 8.5—10.0, indicating its broad applicability. The temperature maximum of Tc-LysN was determined to be 60 °C. After 18 h of incubation at 80 °C, Tc-LysN still retained ~20% activity. Organic solvents such as methanol and acetonitrile, at concentrations as high as 40% (v/v), were found to enhance Tc-LysN’s activity up to ~100% and ~50%, respectively. Tc-LysN’s thermostability, ability to withstand up to 8 M urea, tolerance to high concentrations of organic solvents, and an acidic pH optimum make it a viable candidate to be employed in proteomics workflows in which alkaline conditions might pose a challenge. The nano-LC-MS/MS analysis revealed bovine serum albumin (BSA)’s sequence coverage of 84% using Tc-LysN which was comparable to the sequence coverage of 90% by trypsin peptides.
Blockchain-IIoT integration into industrial processes promises greater security, transparency, and traceability. However, this advancement faces significant storage and scalability issues with existing blockchain technologies. Each peer in the blockchain network maintains a full copy of the ledger which is updated through consensus. This full replication approach places a burden on the storage space of the peers and would quickly outstrip the storage capacity of resource-constrained IIoT devices. Various solutions utilizing compression, summarization or different storage schemes have been proposed in literature. The use of cloud resources for blockchain storage has been extensively studied in recent years. Nonetheless, block selection remains a substantial challenge associated with cloud resources and blockchain integration. This paper proposes a deep reinforcement learning (DRL) approach as an alternative to solving the block selection problem, which involves identifying the blocks to be transferred to the cloud. We propose a DRL approach to solve our problem by converting the multi-objective optimization of block selection into a Markov decision process (MDP). We design a simulated blockchain environment for training and testing our proposed DRL approach. We utilize two DRL algorithms, Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) to solve the block selection problem and analyze their performance gains. PPO and A2C achieve 47.8% and 42.9% storage reduction on the blockchain peer compared to the full replication approach of conventional blockchain systems. The slowest DRL algorithm, A2C, achieves a run-time 7.2 times shorter than the benchmark evolutionary algorithms used in earlier works, which validates the gains introduced by the DRL algorithms. The simulation results further show that our DRL algorithms provide an adaptive and dynamic solution to the time-sensitive blockchain-IIoT environment.
An Overview of Technologies for Improving Storage Efficiency in Blockchain-Based IIoT Applications
(2022)
Since the inception of blockchain-based cryptocurrencies, researchers have been fascinated with the idea of integrating blockchain technology into other fields, such as health and manufacturing. Despite the benefits of blockchain, which include immutability, transparency, and traceability, certain issues that limit its integration with IIoT still linger. One of these prominent problems is the storage inefficiency of the blockchain. Due to the append-only nature of the blockchain, the growth of the blockchain ledger inevitably leads to high storage requirements for blockchain peers. This poses a challenge for its integration with the IIoT, where high volumes of data are generated at a relatively faster rate than in applications such as financial systems. Therefore, there is a need for blockchain architectures that deal effectively with the rapid growth of the blockchain ledger. This paper discusses the problem of storage inefficiency in existing blockchain systems, how this affects their scalability, and the challenges that this poses to their integration with IIoT. This paper explores existing solutions for improving the storage efficiency of blockchain–IIoT systems, classifying these proposed solutions according to their approaches and providing insight into their effectiveness through a detailed comparative analysis and examination of their long-term sustainability. Potential directions for future research on the enhancement of storage efficiency in blockchain–IIoT systems are also discussed.
Linux and Linux-based operating systems have been gaining more popularity among the general users and among developers. Many big enterprises and large companies are using Linux for servers that host their websites, some even require their developers to have knowledge about Linux OS. Even in embedded systems one can find many Linux-based OS that run them. With its increasing popularity, one can deduce the need to secure such a system that many personnel rely on, be it to protect the data that it stores or to protect the integrity of the system itself, or even to protect the availability of the services it offers. Many researchers and Linux enthusiasts have been coming up with various ways to secure Linux OS, however new vulnerabilities and new bugs are always found, by malicious attackers, with every update or change, which calls for the need of more ways to secure these systems.
This Thesis explores the possibility and feasibility of another way to secure Linux OS, specifically securing the terminal of such OS, by altering the commands of the terminal, getting in the way of attackers that have gained terminal access and delaying, giving more time for the response teams and for forensics to stop the attack, minimize the damage, restore operations, and to identify collect and store evidence of the cyber-attack. This research will discuss the advantages and disadvantages of various security measures and compare and contrast with the method suggested in this research.
This research is significant because it paints a better picture of what the state of the art of Linux and Linux-based operating systems security looks like, and it addresses the concerns of security enthusiasts, while exploring new uncharted area of security that have been looked at as a not so significant part of protecting the OSes out of concern of the various limitations and problems it entails. This research will address these concerns while exploring few ways to solve them, as well as addressing the ideal areas and situations in which the proposed method can be used, and when would such method be more of a burden than help if used.
Germany was considered the world's export champion for a long time, until it was overtaken by China in 2009. Both nations provide officially supported export credits to national exporting organizations, but the two systems operate differently. German export credit guarantees serve as a substitute when the private market is unable to assume the risks of exporting companies. The German Export Credit Agency Euler Hermes is responsible for processing applications on behalf of the Federal Government. China belongs to the largest providers of export finance with the institutions China EXIM and Sinosure. While Germany is bound by the OECD consensus, which defines the level playing field, Chinese export credit agencies have greater flexibility not being bound by international rules or agreements.
AI-based Ground Penetrating Radar Signal Processing for Thickness Estimation of Subsurface Layers
(2023)
This thesis focuses on the estimation of subsurface layer thickness using Ground Penetrating Radar (GPR) A-scan and B-scan data through the application of neural networks. The objective is to develop accurate models capable of estimating the thickness of up to two subsurface layers.
Two different approaches are explored for processing the A-scan data. In the first approach, A-scans are compressed using Principal Component Analysis (PCA), and a regression feedforward neural network is employed to estimate the layers’ thicknesses. The second approach utilizes a regression one-dimensional Convolutional Neural Network (1-D CNN) for the same purpose. Comparative analysis reveals that the second approach yields superior results in terms of accuracy.
Subsequently, the proposed 1-D CNN architecture is adapted and evaluated for Step Frequency Continuous Wave (SFCW) radar, expanding its applicability to this type of radar system. The effectiveness of the proposed network in estimating subsurface layer thickness for SFCW radar is demonstrated.
Furthermore, the thesis investigates the utilization of GPR B-scan images as input data for subsurface layer thickness estimation. A regression CNN is employed for this purpose, although the results achieved are not as promising as those obtained with the 1-D CNN using A-scan data. This disparity is attributed to the limited availability of B-scan data, as B-scan generation is a resource-intensive process.
In this paper, a concept for an anthropomorphic replacement hand cast with silicone with an integrated sensory feedback system is presented. In order to construct the personalized replacement hand, a 3D scan of a healthy hand was used to create a 3D-printed mold using computer-aided design (CAD). To allow for movement of the index and middle fingers, a motorized orthosis was used. Information about the applied force for grasping and the degree of flexion of the fingers is registered using two pressure sensors and one bending sensor in each movable finger. To integrate the sensors and additional cavities for increased flexibility, the fingers were cast in three parts, separately from the rest of the hand. A silicone adhesive (Silpuran 4200) was examined to combine the individual parts afterwards. For this, tests with different geometries were carried out. Furthermore, different test series for the secure integration of the sensors were performed, including measurements of the registered information of the sensors. Based on these findings, skin-toned individual fingers and a replacement hand with integrated sensors were created. Using Silpuran 4200, it was possible to integrate the needed cavities and to place the sensors securely into the hand while retaining full flexion using a motorized orthosis. The measurements during different loadings and while grasping various objects proved that it is possible to realize such a sensory feedback system in a replacement hand. As a result, it can be stated that the cost-effective realization of a personalized, anthropomorphic replacement hand with an integrated sensory feedback system is possible using 3D scanning and 3D printing. By integrating smaller sensors, the risk of damaging the sensors through movement could be decreased.
One of the main problematics of the seals tests is the time and money consuming they are. Up to now, there are few tries to do a digitalisation of a test where the seals behaviour can be known.
This work aims to digitally reproduce a seal test to extract their behaviour when working under different operation conditions to see their impact on the pimp’s efficiency. In this thesis, due to the Lomaking effect, the leakage and the forces applied on the stator will be the base of analysis.
First of all, among all the literature available for very different kind of seals and inner patterns, it has been chosen the most appropriate and precise data. The data chosen is “Test results for liquid Damper Seals using a Round-Hole Roughness Pattern for the Stator” from Fayolle, P. and “Static and Rotordynamic Characteristics of Liquid Annular Seals with Circumferentially/Grooved Stator and Smooth Rotor using three levels of circumferential Inlet-Fluid” from Torres J.M.
From the literature, dimensions of the test rig and the seals will be extracted to model them into a 3D CAD software. With the 3D CAD digitalisation, the fluid volumes for a rotor-centred position, meaning without eccentricity, will be extracted, and used. The following components have been modelled:
- Smooth Annular Liquid Seal (Grooved Rotor)
- Grooved Annular Liquid Seal (Smooth Rotor)
- Round-Hole Pattern Annular Liquid Seal (𝐻𝑑=2 𝑚𝑚) (Smooth Rotor)
- Straight Honeycomb Annular Liquid Seal (Smooth Rotor)
- Convergent Honeycomb Annular Liquid Seal (Smooth Rotor)
- Smooth Rotor / Smooth Annular Liquid Seal (Smooth Rotor)
As there is just one test rig, all the components have been adapted to the different dimensions of the seals by referencing some measures. This allows to test any seal with the same test rig.
Afterwards a CFD simulation that will be used to obtain leakage and stator forces. The parameters that will be changed are the rotational velocity of the fluid (2000 rpm, 4000 rpm, and 6000 rpm) and the pressure drop (2,068 bar, 4,137 bar, 6,205 bar, and 8,274 bar).
Those results will be compared to the literature ones, and they will determine if digitalisation can be validated or not. Even though the relative error is higher than 5% but the tendency is the same and it is thought that by changing some parameters the test results can be even closer to the literature ones.
The evolution of cellular networks from its first generation (1G) to its fourth generation (4G) was driven by the demand of user-centric downlink capacity also technically called Mobile Broad-Band (MBB). With its fifth generation (5G), Machine Type Communication (MTC) has been added into the target use cases and the upcoming generation of cellular networks is expected to support them. However, such support requires improvements in the existing technologies in terms of latency, reliability, energy efficiency, data rate, scalability, and capacity.
Originally, MTC was designed for low-bandwidth high-latency applications such as, environmental sensing, smart dustbin, etc. Nowadays there is an additional demand around applications with low-latency requirements. Among other well-known challenges for recent cellular networks such as data rate energy efficiency, reliability etc., latency is also not suitable for mission-critical applications such as real-time control of machines, autonomous driving, tactile Internet etc. Therefore, in the currently deployed cellular networks, there is a necessity to reduce the latency and increase the reliability offered by the networks to support use cases such as, cooperative autonomous driving or factory automation, that are grouped under the denomination Ultra-Reliable Low-Latency Communication (URLLC).
This thesis is primarily concerned with the latency into the Universal Terrestrial Radio Access Network (UTRAN) of cellular networks. The overall work is divided into five parts. The first part presents the state of the art for cellular networks. The second part contains a detailed overview of URLLC use cases and the requirements that must be fulfilled by the cellular networks to support them. The work in this thesis is done as part of a collaboration project between IRIMAS lab in Université de Haute-Alsace, France and Institute for Reliable Embedded Systems and Communication Electronics (ivESK) in Offenburg University of Applied Sciences, Germany. The selected use cases of URLLC are part of the research interests of both partner institutes. The third part presents a detailed study and evaluation of user- and control-plane latency mechanisms in current generation of cellular networks. The evaluation and analysis of these latencies, performed with the open-source ns-3 simulator, were conducted by exploring a broad range of parameters that include among others, traffic models, channel access parameters, realistic propagation models, and a broad set of cellular network protocol stack parameters. These simulations were performed with low-power, low-cost, and wide-range devices, commonly called IoT devices, and standardized for cellular networks. These devices use either LTE-M or Narrowband-IoT (NB-IoT) technologies that are designed for connected things. They differ mainly by the provided bandwidth and other additional characteristics such as coding scheme, device complexity, and so on.
The fourth part of this thesis shows a study, an implementation, and an evaluation of latency reduction techniques that target the different layers of the currently used Long Term Evolution (LTE) network protocol stack. These techniques based on Transmission Time Interval (TTI) reduction and Semi-Persistent Scheduling (SPS) methods are implemented into the ns-3 simulator and are evaluated through realistic simulations performed for a variety of low-latency use cases focused on industry automation and vehicular networking. For testing the proposed latency reduction techniques in cellular networks, since ns-3 does not support NB-IoT in its current release, an NB-IoT extension for LTE module was developed. This makes it possible to explore deployment limitations and issues.
In the last part of this thesis, a flexible deployment framework called Hybrid Scheduling and Flexible TTI for the proposed latency reduction techniques is presented, implemented and evaluated through realistic simulations. With help of the simulation evaluation, it is shown that the improved LTE network proposed and implemented in the simulator can support low-latency applications with low cost, higher range, and narrow bandwidth devices. The work in this thesis points out the potential improvement techniques, their deployment issues and paves the way towards the support for URLLC applications with upcoming cellular networks.
Fifth-generation (5G) cellular mobile networks are expected to support mission-critical low latency applications in addition to mobile broadband services, where fourth-generation (4G) cellular networks are unable to support Ultra-Reliable Low Latency Communication (URLLC). However, it might be interesting to understand which latency requirements can be met with both 4G and 5G networks. In this paper, we discuss (1) the components contributing to the latency of cellular networks and (2) evaluate control-plane and user-plane latencies for current-generation narrowband cellular networks and point out the potential improvements to reduce the latency of these networks, (3) present, implement and evaluate latency reduction techniques for latency-critical applications. The two elements we detected, namely the short transmission time interval and the semi-persistent scheduling are very promising as they allow to shorten the delay to processing received information both into the control and data planes. We then analyze the potential of latency reduction techniques for URLLC applications. To this end, we develop these techniques into the long term evolution (LTE) module of ns-3 simulator and then evaluate the performance of the proposed techniques into two different application fields: industrial automation and intelligent transportation systems. Our detailed evaluation results from simulations indicate that LTE can satisfy the low-latency requirements for a large choice of use cases in each field.
On a regular basis, we hear of well-known online services that have been abused or compromised as a result of data theft. Because insecure applications jeopardize users' privacy as well as the reputation of corporations and organizations, they must be effectively secured from the outset of the development process. The limited expertise and experience of involved parties, such as web developers, is frequently cited as a cause of risky programs. Consequently, they rarely have a full picture of the security-related decisions that must be made, nor do they understand how these decisions affect implementation accurately.
The selection of tools and procedures that can best assist a certain situation in order to protect an application against vulnerabilities is a critical decision. Regardless of the level of security that results from adhering to security standards, these factors inadvertently result in web applications that are insufficiently secured. JavaScript is a language that is heavily relied on as a mainstream programming language for web applications with several new JavaScript frameworks being released every year.
JavaScript is used on both the server-side in web applications development and the client-side in web browsers as well.
However, JavaScript web programming is based on a programming style in which the application developer can, and frequently must, automatically integrate various bits of code from third parties. This potent combination has resulted in a situation today where security issues are frequently exploited. These vulnerabilities can compromise an entire server if left unchecked. Even though there are numerous ad hoc security solutions for web browsers, client-side attacks are also popular. The issue is significantly worse on the server side because the security technologies available for server-side JavaScript application frameworks are nearly non-existent.
Consequently, this thesis focuses on the server-side aspect of JavaScript; the development and evaluation of robust server-side security technologies for JavaScript web applications. There is a clear need for robust security technologies and security best practices in server-side JavaScript that allow fine-grained security.
However, more than ever, there is this requirement of reducing the associated risks without hindering the web application in its functionality.
This is the problem that will be tackled in this thesis: the development of secure security practices and robust security technologies for JavaScript web applications, specifically, on the server-side, that offer adequate security guarantees without putting too many constraints on their functionality.
When people with hearing loss are provided with different devices in each ear, these devices usually have different processing latencies. This leads to static temporal offsets between both ears in the order of several milliseconds. This thesis measured effects of such offsets in stimulation timing on mechanisms of binaural hearing, such as sound localization and speech understanding in noise in hearing-impaired and normal-hearing listeners.
Subjects utilizing a cochlear implant (CI) in one ear and a hearing aid (HA) on the contralateral ear suffer from mismatches in stimulation timing due to different processing latencies of both devices. This device delay mismatch leads to a temporal mismatch in auditory nerve stimulation. Compensating for this auditory nerve stimulation mismatch by compensating for the device delay mismatch can significantly improve sound source localization accuracy. One CI manufacturer has already implemented the possibility of mismatch compensation in its current fitting software. This study investigated if this fitting parameter can be readily used in clinical settings and determined the effects of familiarization to a compensated device delay mismatch over a period of 3–4 weeks. Sound localization accuracy and speech understanding in noise were measured in eleven bimodal CI/HA users, with and without a compensation of the device delay mismatch. The results showed that sound localization bias improved to 0°, implying that the localization bias towards the CI was eliminated when the device delay mismatch was compensated. The RMS error was improved by 18% with this improvement not reaching statistical significance. The effects were acute and did not further improve after 3 weeks of familiarization. For the speech tests, spatial release from masking did not improve with a compensated mismatch. The results show that this fitting parameter can be readily used by clinicians to improve sound localization ability in bimodal users. Further, our findings suggest that subjects with poor sound localization ability benefit the most from the device delay mismatch compensation.
Users of a cochlear implant (CI) in one ear, who are provided with a hearing aid (HA) in the contralateral ear, so-called bimodal listeners, are typically affected by a constant and relatively large interaural time delay offset due to differences in signal processing and differences in stimulation. For HA stimulation, the cochlear travelling wave delay is added to the processing delay, while for CI stimulation, the auditory nerve fibers are stimulated directly. In case of MED-EL CI systems in combination with different HA types, the CI stimulation precedes the acoustic HA stimulation by 3 to 10 ms. A self-designed, battery-powered, portable, and programmable delay line was applied to the CI to reduce the device delay mismatch in nine bimodal listeners. We used an A-B-B-A test design and determined if sound source localization improves when the device delay mismatch is reduced by delaying the CI stimulation by the HA processing delay (τ HA ). Results revealed that every subject in our group of nine bimodal listeners benefited from the approach. The root-mean-square error of sound localization improved significantly from 52.6° to 37.9°. The signed bias also improved significantly from 25.2° to 10.5°, with positive values indicating a bias toward the CI. Furthermore, two other delay values (τ HA –1 ms and τ HA +1 ms) were applied, and with the latter value, the signed bias was further reduced in some test subjects. We conclude that sound source localization accuracy in bimodal listeners improves instantaneously and sustainably when the device delay mismatch is reduced.
In asymmetric treatment of hearing loss, processing latencies of the modalities typically differ. This often alters the reference interaural time difference (ITD) (i.e., the ITD at 0° azimuth) by several milliseconds. Such changes in reference ITD have shown to influence sound source localization in bimodal listeners provided with a hearing aid (HA) in one and a cochlear implant (CI) in the contralateral ear. In this study, the effect of changes in reference ITD on speech understanding, especially spatial release from masking (SRM) in normal-hearing subjects was explored. Speech reception thresholds (SRT) were measured in ten normal-hearing subjects for reference ITDs of 0, 1.75, 3.5, 5.25 and 7 ms with spatially collocated (S0N0) and spatially separated (S0N90) sound sources. Further, the cues for separation of target and masker were manipulated to measure the effect of a reference ITD on unmasking by A) ITDs and interaural level differences (ILDs), B) ITDs only and C) ILDs only. A blind equalization-cancellation (EC) model was applied to simulate all measured conditions. SRM decreased significantly in conditions A) and B) when the reference ITD was increased: In condition A) from 8.8 dB SNR on average at 0 ms reference ITD to 4.6 dB at 7 ms, in condition B) from 5.5 dB to 1.1 dB. In condition C) no significant effect was found. These results were accurately predicted by the applied EC-model. The outcomes show that interaural processing latency differences should be considered in asymmetric treatment of hearing loss.
Though the basic concept of a ledger that anyone can view and verify has been around for quite some time, today’s blockchains bring much more to the table including a way to incentivize users. The coins given to the miner or validator were the first source of such incentive to make sure they fulfilled their duties. This thesis draws inspiration from other peer efforts and uses this same incentive to achieve certain goals. Primarily one where users are incentivised to discuss their opinions and find scientific or logical backing for their standpoint. While traditional chains form a consensus on a version of financial "truth", the same can be applied to ideological truths too. To achieve this, creating a modified or scaled proof of stake consensus mechanism is explored in this work. This new consensus mechanism is a Reputation Scaled - Proof of Stake. This reputation can be built over time by voting for the winning side consistently or by sticking to one’s beliefs strongly. The thesis hopes to bridge the gap in current consensus algorithms and incentivize critical reasoning.
The increase of the Internet of Things (IoT) calls for secure solutions for industrial applications. The security of IoT can be potentially improved by blockchain. However, blockchain technology suffers scalability issues which hinders integration with IoT. Solutions to blockchain’s scalability issues, such as minimizing the computational complexity of consensus algorithms or blockchain storage requirements, have received attention. However, to realize the full potential of blockchain in IoT, the inefficiencies of its inter-peer communication must also be addressed. For example, blockchain uses a flooding technique to share blocks, resulting in duplicates and inefficient bandwidth usage. Moreover, blockchain peers use a random neighbor selection (RNS) technique to decide on other peers with whom to exchange blockchain data. As a result, the peer-to-peer (P2P) topology formation limits the effective achievable throughput. This paper provides a survey on the state-of-the-art network structures and communication mechanisms used in blockchain and establishes the need for network-based optimization. Additionally, it discusses the blockchain architecture and its layers categorizes existing literature into the layers and provides a survey on the state-of-the-art optimization frameworks, analyzing their effectiveness and ability to scale. Finally, this paper presents recommendations for future work.
The present document is aimed to propose a suitable thermal model for the cooling down process of a one piston air cooled reciprocating compressor. In order to achieve this, a thermographic camera is used to record the temperature of different measuring points throughout different operating conditions. This data is later analyzed, with statistical tools and graphical visualization. The thermal phenomena present in the thermal process is characterized according to the compressors' geometry. Finally, using the analysis and taking into consideration the thermal phenomena the optimal thermal model is selected. This paper belongs to a bigger project and the last step is to simulate the compressor and the accuracy of the proposed model.
The objective of this project is to enhance the operations of a micro-enterprise that deals with food ingredients. The emphasis is on streamlining procedures and executing effective tactics. By utilizing tools like SWOT analysis, evaluations, and strategy development, the company's strengths, weaknesses, opportunities, and threats were assessed. The company developed business-level and functional-level strategies to expedite growth and attain objectives based on the findings. Moreover, precise suggestions were given to minimize the quantity of SKUs and optimize operations. The work highlighted the significance of developing a process map for streamlining operations, boosting efficiency, and elevating customer contentment. Through the implementation of said recommendations and strategies, the company can strategically position itself for success within the highly competitive food ingredients industry.
The progress in machine learning has led to advanced deep neural networks. These networks are widely used in computer vision tasks and safety-critical applications. The automotive industry, in particular, has experienced a significant transformation with the integration of deep learning techniques and neural networks. This integration contributes to the realization of autonomous driving systems. Object detection is a crucial element in autonomous driving. It contributes to vehicular safety and operational efficiency. This technology allows vehicles to perceive and identify their surroundings. It detects objects like pedestrians, vehicles, road signs, and obstacles. Object detection has evolved from being a conceptual necessity to an integral part of advanced driver assistance systems (ADAS) and the foundation of autonomous driving technologies. These advancements enable vehicles to make real-time decisions based on their understanding of the environment, improving safety and driving experiences. However, the increasing reliance on deep neural networks for object detection and autonomous driving has brought attention to potential vulnerabilities within these systems. Recent research has highlighted the susceptibility of these systems to adversarial attacks. Adversarial attacks are well-designed inputs that exploit weaknesses in the deep learning models underlying object detection. Successful attacks can cause misclassifications and critical errors, posing a significant threat to the functionality and safety of autonomous vehicles. With the rapid development of object detection systems, the vulnerability to adversarial attacks has become a major concern. These attacks manipulate inputs to deceive the target system, significantly compromising the reliability and safety of autonomous vehicles. In this study, we focus on analyzing adversarial attacks on state-of-the-art object detection models. We create adversarial examples to test the models’ robustness. We also check if the attacks work on a different object detection model meant for similar tasks. Additionally, we extensively evaluate recent defense mechanisms to see how effective they are in protecting deep neural networks (DNNs) from adversarial attacks and provide a comprehensive overview of the most commonly used defense strategies against adversarial attacks, highlighting how they can be implemented practically in real-world situations.
Supporting the COVID-19 response in Asia and the Pacific—The role of the Asian Development Bank.
(2020)
The COVID-19 pandemic has affected all countries of the Asia Pacific region over the last few months with far reaching economic, health and social consequences. To counter the impact, governments have accelerated their health spending and announced large macroeconomic stabilization and stimulus policy packages. As with past disasters and crises in the region, the Asian Development Bank has reacted with a number of targeted support interventions since the very early stages of the outbreak. In mid- April 2020, the Bank then put forward a comprehensive COVID-19 Response Package totalling $20 billion to support its member countries which rests on four pillars.
The last few months have proven that multilateral development banks like the Asian Development Bank have the ability to respond quickly and to mobilize significant resources for a global emergency like COVID-19. Whilst this financial supported is urgently needed at this point, attention will need to be paid on how debt sustainability for low- and middle-income countries can be ensured in the coming years. Given the unprecedented scale of and uncertainty around the COVID-19 pandemic, it may offer a window of opportunity to redesign the way developmental finance is coordinated and the way it is delivered. This also includes a chance to “build back better” and to focus on a sustainable, resilient and green recovery.
Much of the research in the field of audio-based machine learning has focused on recreating human speech via feature extraction and imitation, known as deepfakes. The current state of affairs has prompted a look into other areas, such as the recognition of recording devices, and potentially speakers, by only analysing sound files. Segregation and feature extraction are at the core of this approach.
This research focuses on determining whether a recorded sound can reveal the recording device with which it was captured. Each specific microphone manufacturer and model, among other characteristics and imperfections, can have subtle but compounding effects on the results, whether it be differences in noise, or the recording tempo and sensitivity of the microphone while recording. By studying these slight perturbations, it was found to be possible to distinguish between microphones based on the sounds they recorded.
After the recording, pre-processing, and feature extraction phases we completed, the prepared data was fed into several different machine learning algorithms, with results ranging from 70% to 100% accuracy, showing Multi-Layer Perceptron and Logistic Regression to be the most effective for this type of task.
This was further extended to be able to tell the difference between two microphones of the same make and model. Achieving the identification of identical models of a microphone suggests that the small deviations in their manufacturing process are enough of a factor to uniquely distinguish them and potentially target individuals using them. This however does not take into account any form of compression applied to the sound files, as that may alter or degrade some or most of the distinguishing features that are necessary for this experiment.
Building on top of prior research in the area, such as by Das et al. in in which different acoustic features were explored and assessed on their ability to be used to uniquely fingerprint smartphones, more concrete results along with the methodology by which they were achieved are published in this project’s publicly accessible code repository.
The three lines of defense model (TLoD) aims to provide a simple and effective way to improve coordination and enhance communications on risk management and control by clarifying the essential roles and duties of different governance functions. Without effective coordination of these governance functions, work can be duplicated or key risks may be missed or misjudged. To address these challenges, professional standards recommend that the chief audit executive (CAE) coordinates activities with other internal and external governance stakeholders (assurance providers). We consider survey responses from 415 CAEs from Austria, Germany, and Switzerland to analyze determinants that help to implement the TLoD without any challenges and to explore the extent of (coordination) challenges between the internal audit function and the respective governance stakeholders. Our results show a great variance in the extent of coordination challenges dependent on different determinants and the respective governance stakeholder.
Photovoltaic-heat pump (PV-HP) combinations with battery and energy management systems are becoming increasingly popular due to their ability to increase the autarchy and utilization of self-generated PV electricity. This trend is driven by the ongoing electrification of the heating sector and the growing disparity between growing electricity costs and reducing feed-in tariffs in Germany. Smart control strategies can be employed to control and optimize the heat pump operation to achieve higher self-consumption of PV electricity. This work presents the evaluation results of a smart-grid ready controlled PV-HP-battery system in a single-family household in Germany, using 1-minute-high-resolution field measurement data. Within 12 months evaluation period, a self-consumption of 43% was determined. The solar fraction of the HP amounts to 36%, enabled also due to higher set temperatures for space heating and domestic hot water production. Accordingly, the SPF decreases by 4.0% the space heating and by 5.7% in the domestic hot water mode. The combined seasonal performance factor for the heat pump system increases from 4.2 to 6.7, when only considering the electricity taken from the grid and disregarding the locally generated electricity supplied from photovoltaic and battery units.
Background: This paper presents a novel approach for a hand prosthesis consisting of a flexible, anthropomorphic, 3D-printed replacement hand combined with a commercially available motorized orthosis that allows gripping.
Methods: A 3D light scanner was used to produce a personalized replacement hand. The wrist of the replacement hand was printed of rigid material; the rest of the hand was printed of flexible material. A standard arm liner was used to enable the user’s arm stump to be connected to the replacement hand. With computer-aided design, two different concepts were developed for the scanned hand model: In the first concept, the replacement hand was attached to the arm liner with a screw. The second concept involved attaching with a commercially available fastening system; furthermore, a skeleton was designed that was located within the flexible part of the replacement hand.
Results: 3D-multi-material printing of the two different hands was unproblematic and inexpensive. The printed hands had approximately the weight of the real hand. When testing the replacement hands with the orthosis it was possible to prove a convincing everyday functionality. For example, it was possible to grip and lift a 1-L water bottle. In addition, a pen could be held, making writing possible.
Conclusions: This first proof-of-concept study encourages further testing with users.
Total Cost of Ownership (TCO) is a key tool to have a complete understanding of the costs associated with an investment, as it allows to analyze not only the initial acquisition costs, but also the long-term costs related to operation, maintenance, depreciation, and other factors. In the context of the cement industry, TCO is especially important due to the complexity of the production processes and the wide variety of components and machinery involved in the process.
For this reason, a TCO analysis for the cement industry has been conducted in this study, with the objective of showing the different components of the cost of production. This analysis will allow the reader to gain knowledge about these costs, in the industrial model will be to make informed decisions on the adoption of technologies and practices that will allow them to reduce costs in the long run and improve their operational efficiency.
In particular, this study pursues to give visibility to technologies and practices that enable the reduction of carbon emissions in cement production, thus contributing to the sustainability of industry and the protection of the environment. By being at the forefront of sustainability issues, the cement industry can contribute to the achievement of environmentally friendly technologies and enable the development of people and industry.
The Oxyfuel technology has been selected as a carbon capture solution for the cement industry due to its practical application, low costs, and practical adaptation to non-capture processes. The adoption of this technology allows for a significant reduction in CO2 emissions, which is a crucial factor in achieving sustainability in the cement manufacturing process.
Carbon capture storage technologies represent a high investment, although these technologies increase the cost of production, the application of Oxyfuel technology is one of the most economically viable as the cheapest technology per capture according to the comparison. However, this price increase is a technical advantage as the carbon capture efficiency of this technology reaches 90%. This level of efficiency leads to a decrease in taxes for the generation of CO2 emissions, making the cement manufacturing process sustainable.
Schluckspecht project
(2022)
Im Rahmen einer Master Thesis wurde ausgehend von einem vorhandenen System On Chip Design, welches eingehende EKG-Datensignale verarbeitet, das bestehende System so erweitert dass es komplett über den standardisierten SPI-Bus steuerbar und auslesbar ist.
The aim of this paper is to identify indicators at country level that could prove useful in improving the effectiveness of fraud detection in European Structural and Investment Funds. We analyse data for 454 funds, belonging to the period 2014-2020, from the 28 countries that were members of the European Union in 2014. Explanatory results suggest the convenience of tracking funds, especially in countries with higher GDP and higher transparency levels, and the lesser relevance of the number of irregularities for countries with higher GDP and those receiving larger funds. Fraud and fraud detection rates in individual funds vary significantly across states. Federal states, such as the Federal Republic of Germany, are comparatively successful in detecting fraud in EU funds.
The objective of this thesis is the conceptual design of a battery management system for the first prototype of the UWC (University of the Western Cape) Modular Battery System. The battery system is a lithium-ion battery that aims to be used in renewable energy systems and for niche electric vehicles such as golf carts.
The concept that is introduced in this thesis comprises the parameter monitoring, the safety management and has its main focus on an accurate state of charge estimation.
Another battery system that was already implemented is used as base for the parameter monitoring and the safety management for the new battery management system. In contrast to that, the concept for the state of charge estimation must be developed completely.
Different methods for the state of charge estimation which are based on the measured voltage, current and temperature are discussed, evaluated and the chosen method is conceived in this thesis. The method used for the state of charge estimation is different for the time when the battery is active than when it is inactive. During charge and discharge Coulomb counting is used and when the cell is inactive voltage versus state of charge lookup tables are used to update the estimation.
To have an accurate estimation when the cell is inactive only for a short time, a model of the voltage relaxation is used to predict the voltage when the cells are in equilibrium. This allows the algorithm to reset the state of charge that is estimated by Coulomb counting – which tends to have a growing error over time – frequently.
To evaluate the accuracy of the voltage prediction, cell tests were executed where the voltage relaxation was sampled. The recursive least square method to predict the end voltage was tested with a MATLAB programme. With the help of voltage versus state of charge lookup tables it was possible to determine the state of charge accuracy with the accuracy of the voltage prediction.
Simulation based studies for operational energy system analysis play a significant role in evaluation of various new age technologies and concepts in the energy grid. Various modelling approaches already exist and in this original paper, four models representing these approaches are compared in two real-world hybrid energy system scenarios. The models, namely TransiEnt, µGRiDS, and OpSim (including pandaprosumer and mosaic) are classified into component-oriented or system-oriented approaches as deduced from the literature research. The methodology section describes their differences under standard conditions and the necessary parameterization for the purpose of creating a framework facilitating a closest possible comparison. A novel methodology for scenario generation is also explained. The results help to quantify primary differences in these approaches that are also identified in literature and qualify the influence of the accuracy of the models for application in a system-wide analysis. It is shown that a simplified model may be sufficient for the system-oriented approach especially when the objective is an optimization-based control or planning. However, from a field level operational point of view, the differences in the time series signify the importance of the component-oriented approaches.
A balcony photovoltaic (PV) system, also known as a micro-PV system, is a small PV system consisting of one or two solar modules with an output of 100–600 Wp and a corresponding inverter that uses standard plugs to feed the renewable energy into the house grid. In the present study we demonstrate the integration of a commercial lithium-ion battery into a commercial micro-PV system. We firstly show simulations over one year with one second time resolution which we use to assess the influence of battery and PV size on self-consumption, self-sufficiency and the annual cost savings. We then develop and operate experimental setups using two different architectures for integrating the battery into the micro-PV system. In the passive hybrid architecture, the battery is in parallel electrical connection to the PV module. In the active hybrid architecture, an additional DC-DC converter is used. Both architectures include measures to avoid maximum power point tracking of the battery by the module inverter. Resulting PV/battery/inverter systems with 300 Wp PV and 555 Wh battery were tested in continuous operation over three days under real solar irradiance conditions. Both architectures were able to maintain stable operation and demonstrate the shift of PV energy from the day into the night. System efficiencies were observed comparable to a reference system without battery. This study therefore demonstrates the feasibility of both active and passive coupling architectures.
To date, many experiments have been performed to study how the internal geometrical shapes of the annular liquid seal can reduce internal leakage and increase pump efficiency. These can be time-consuming and expensive as all rotordynamic coefficients must be determined in each case.
Nowadays, accurate simulation methods to calculate rotordynamic coefficients of annular seals are still rare. Therefore, new numerical methods must be designed and validated for annular seals.
The present study aims to contribute to this labour by providing a summary of the available test rig and seals dimensions and experimental results obtained in the following experiments:
− Kaneko, S et al., Experimental Study on Static and Dynamic Characteristics of Liquid Annular Convergent-Tapered Seals with Honeycomb Roughness Pattern (2003) [1] − J. Alex Moreland, Influence of pre-swirl and eccentricity in smooth stator/grooved rotor liquid annular seals, static and rotordynamic characteristics (2016) [2]
A 3D CAD simulation with Siemens NX Software of the test rig used in J. Alex Moreland’s experiment has been made. The following annular liquid seals have also been 3D modelled, as well as their fluid volume:
− Smooth Annular Liquid Seal (SS/GR) (J. Alex Moreland experiment)
− Grooved Annular Liquid Seal (GS/SR)
− Round-Hole Pattern Annular Liquid Seal (𝐻𝑑=2 mm) (GS/SR)
− Straight Honeycomb Annular Liquid Seal (GS/SR)
− Convergent Honeycomb Annular Liquid Seal (No. 3) (GS/SR)
− Smooth Annular Liquid Seal (SS/SR) (S. Kaneko experiment)
In the case of the seals used in S. Kaneko’s experiments, the test rig has been adapted to each seal, defining interpart expressions which can be easily modified.
Afterwards, it has been done a CFD simulation of the Smooth Annular Liquid Seal using Ansys CFX Software. To do so, the fluid volume geometry has been simplified to do a first approximation. Results have been compared for an eccentricity 𝜀0=0.00 for the following ranges of rotor speeds and differential of pressure:
− Δ𝑃= 2.07, 4.14, 6.21, and 8.27 bar,
− 𝜔= 2, 4, 6 and 8 krpm.
Even results obtained have the same trend as the one proportionated by the literature, they cannot be validated as the error is above 5%. It is also observed that as the pressure drop increases, the relative error decreases considerably.
The COVID-19 pandemic has led to an economic downturn in the Slovak Republic. To bridge corporate liquidity problems the Slovakian Government has introduced several support measures. The investigation discusses the effectiveness of the measures imposed. Based on theoretical foundations, the research question is empirically examined by using a qualitative expert survey. As the automotive industry plays a leading role in Slovakia, the research conducted is oriented towards the financing phases, a typical automotive exporter is undergoing. As a result of the research, bridging loans and government grants were identified as the most important measures. Additionally, tendencies towards political recommendations for action were identified. The research explored, that the Slovakian Government should focus on meeting the short-term liquidity needs, boosting exports and promoting innovation as well as considering a support package for the automotive industry.
For the treatment of bone defects, biodegradable, compressive biomaterials are needed as replacements that degrade as the bone regenerates. The problem with existing materials has either been their insufficient mechanical strength or the excessive differences in their elastic modulus, leading to stress shielding and eventual failure. In this study, the compressive strength of CPC ceramics (with a layer thickness of more than 12 layers) was compared with sintered β-TCP ceramics. It was assumed that as the number of layers increased, the mechanical strength of 3D-printed scaffolds would increase toward the value of sintered ceramics. In addition, the influence of the needle inner diameter on the mechanical strength was investigated. Circular scaffolds with 20, 25, 30, and 45 layers were 3D printed using a 3D bioplotter, solidified in a water-saturated atmosphere for 3 days, and then tested for compressive strength together with a β-TCP sintered ceramic using a Zwick universal testing machine. The 3D-printed scaffolds had a compressive strength of 41.56 ± 7.12 MPa, which was significantly higher than that of the sintered ceramic (24.16 ± 4.44 MPa). The 3D-printed scaffolds with round geometry reached or exceeded the upper limit of the compressive strength of cancellous bone toward substantia compacta. In addition, CPC scaffolds exhibited more bone-like compressibility than the comparable β-TCP sintered ceramic, demonstrating that the mechanical properties of CPC scaffolds are more similar to bone than sintered β-TCP ceramics.
Fast charging of lithium-ion batteries remains one of the most delicate challenges for the automotive industry, being seriously affected by the formation of lithium metal in the negative electrode. Here we present a physicochemical pseudo-3D model that explicitly includes the plating reaction as side reaction running in parallel to the main intercalation reaction. The thermodynamics of the plating reaction are modeled depending on temperature and ion concentration, which differs from the often-used assumption of a constant plating condition of 0 V anode potential. The reaction kinetics are described with an Arrhenius-type rate law parameterized from an extensive literature research. Re-intercalation of plated lithium was modeled to take place either via reverse plating (solution-mediated) or via an explicit interfacial reaction (surface-mediated). At low temperatures not only the main processes (intercalation and solid-state diffusion) become slow, but also the plating reaction itself becomes slower. Using this model, we are able to predict typical macroscopic experimental observables that are indicative of plating, that is, a voltage plateau during discharge and a voltage drop upon temperature increase. A spatiotemporal analysis of the internal cell states allows a quantitative insight into the competition between intercalation and plating. Finally, we calculate operation maps over a wide range of C-rates and temperatures that allow to assess plating propensity as function of operating condition.
In this paper an RFID/NFC (ISO 15693 standard) based inductively powered passive SoC (system on chip) for biomedical applications is presented. A brief overview of the system design, layout techniques and verification method is dis-cussed here. The SoC includes an integrated 32 bit microcontroller, sensor interface circuit, analog to digital converter, integrated RAM, ROM and some other peripherals required for the complete passive operation. The entire chip is realized in CMOS 0.18 μm technology with a chip area of 1.52mm x 3.24 mm.
An Ultra-Low-Power RFID/NFC Frontend IC Using 0.18 μm CMOS Technology for Passive Tag Applications
(2018)
Battery-less passive sensor tags based on RFID or NFC technology have achieved much popularity in recent times. Passive tags are widely used for various applications like inventory control or in biotelemetry. In this paper, we present a new RFID/NFC frontend IC (integrated circuit) for 13.56 MHz passive tag applications. The design of the frontend IC is compatible with the standard ISO 15693/NFC 5. The paper discusses the analog design part in details with a brief overview of the digital interface and some of the critical measured parameters. A novel approach is adopted for the demodulator design, to demodulate the 10% ASK (amplitude shift keying) signal. The demodulator circuit consists of a comparator designed with a preset offset voltage. The comparator circuit design is discussed in detail. The power consumption of the bandgap reference circuit is used as the load for the envelope detection of the ASK modulated signal. The sub-threshold operation and low-supply-voltage are used extensively in the analog design—to keep the power consumption low. The IC was fabricated using 0.18 μm CMOS technology in a die area of 1.5 mm × 1.5 mm and an effective area of 0.7 mm2. The minimum supply voltage desired is 1.2 V, for which the total power consumption is 107 μW. The analog part of the design consumes only 36 μW, which is low in comparison to other contemporary passive tags ICs. Eventually, a passive tag is developed using the frontend IC, a microcontroller, a temperature and a pressure sensor. A smart NFC device is used to readout the sensor data from the tag employing an Android-based application software. The measurement results demonstrate the full passive operational capability. The IC is suitable for low-power and low-cost industrial or biomedical battery-less sensor applications. A figure-of-merit (FOM) is proposed in this paper which is taken as a reference for comparison with other related state-of-the-art researches.
Design of a Programmable Passive SoC for Biomedical Applications Using RFID ISO 15693/NFC5 Interface
(2018)
Low power, low cost inductively powered passive biotelemetry system involving fully customized RFID/NFC interface base SoC has gained popularity in the last decades. However, most of the SoCs developed are application specific and lacks either on-chip computational or sensor readout capability. In this paper, we present design details of a programmable passive SoC in compliance with ISO 15693/NFC5 standard for biomedical applications. The integrated system consists of a 32-bit microcontroller, a sensor readout circuit, a 12-bit SAR type ADC, 16 kB RAM, 16 kB ROM and other digital peripherals. The design is implemented in a 0.18 µm CMOS technology and used a die area of 1.52 mm × 3.24 mm. The simulated maximum power consumption of the analog block is 592 µW. The number of external components required by the SoC is limited to an external memory device, sensors, antenna and some passive components. The external memory device contains the application specific firmware. Based on the application, the firmware can be modified accordingly. The SoC design is suitable for medical implants to measure physiological parameters like temperature, pressure or ECG. As an application example, the authors have proposed a bioimplant to measure arterial blood pressure for patients suffering from Peripheral Artery Disease (PAD).
How can manufacturers or service companies provide better services with connected products, without having acquired a powerful IT infrastructure nor the competences for software development?
Today companies can appeal to a relocated-IT-infrastructure provider, which is called Cloud.
Consequently, they do not have to manage and take care of the safety/security aspect, the updates and the breakdown of the infrastructure internally, as those are all managed by the provider.
It is possible to outsource the development of the software of the connected product to an external company. However, the question now is how fast this company can juggle from one Cloud to another in order to fulfil their clients wishes?
neverMind offers a solution based on a multi-protocols-platform linking the different connected products to a multitude of Clouds without having to redesign the whole communication stack/building block for each change in the Cloud-solution. This is the object of my thesis.
The development follows the V-Model, the first steps to understand the complexity of the project were the realisation of the product technical and architectural specifications. The last step before the Implementation was to design in details the progress and the process of every parts of the platform.
The outcome of the requirements analysis led me to divide the project in two parts:
• a “General Interface” acting as a gateway between the Client-application and “Cloud-modules”
• the “Cloud-modules” themselves.
So far, the specifications are drown up; the General Interface and a client example are coded, as well as a first Cloud-module template.
Young female handball players represent a high-risk population for anterior cruciate ligament (ACL) injuries. While the external knee abduction moment (KAM) is known to be a risk factor, it is unclear how cutting technique affects KAMs in sport-specific cutting maneuvers. Further, the effect of added game specificity (e.g., catching a ball or faking defenders) on KAMs and cutting technique remains unknown. Therefore, this study aimed: (i) to test if athletes grouped into different clusters of peak KAMs produced during three sport-specific fake-and-cut tasks of different complexities differ in cutting technique, and (ii) to test whether technique variables change with task complexity. Fifty-one female handball players (67.0 ± 7.7 kg, 1.70 ± 0.06 m, 19.2 ± 3.4 years) were recruited. Athletes performed at least five successful handball-specific sidestep cuts of three different complexities ranging from simple pre-planned fake-and-cut maneuvers to catching a ball and performing an unanticipated fake-and-cut maneuver with dynamic defenders. A k-means cluster algorithm with squared Euclidean distance metric was applied to the KAMs of all three tasks. The optimal cluster number of koptimal = 2 was calculated using the average silhouette width. Statistical differences in technique variables between the two clusters and the tasks were analyzed using repeated-measures ANOVAs (task complexity) with nested groupings (clusters). KAMs differed by 64.5%, on average, between clusters. When pooling all tasks, athletes with high KAMs showed 3.4° more knee valgus, 16.9% higher downward and 8.4% higher resultant velocity at initial ground contact, and 20.5% higher vertical ground reaction forces at peak KAM. Unlike most other variables, knee valgus angle was not affected by task complexity, likely due to it being part of inherent movement strategies and partly determined by anatomy. Since the high KAM cluster showed higher vertical center of mass excursions and knee valgus angles in all tasks, it is likely that this is part of an automated motor program developed over the players' careers. Based on these results, reducing knee valgus and downward velocity bears the potential to mitigate knee joint loading and therefore ACL injury risk.
The objective of this thesis is the quantification and qualification of neonicotinoid insecticides using thin-layer chromatography (TLC). Neonicotinoids are a relatively new form of pesticides, which have been proven to be extremely lethal to the honey bee, Apis mellifera. In this paper six forms of neonicotinoid insecticides (i.e. Acetamiprid, Thiacloprid, Imidacloprid, Clothianidin, Thaimethoxam, and Nitenpyram) are analysed. The initial steps are to first find a suitable mobile phase eluent, followed by the search for a reagent causing a luminescence effect of the neonicotinoids on a TLC plate. Subsequently, a calibration method is then used to find the detection limit of this TLC experiment. The aim is, therefore, to achieve a standard method of quantifying and qualifying neonicotinoids via TLC. Whilst a suitable mobile phase has been established, an optimal fluorescent reagent has yet to be found and more research on the subject must be carried out.