VHDL IMPLEMENTATION OF GENETIC ALGORITHM FOR 2-BIT ADDER

Future planetary and deep space exploration demands that the space vehicles should have robust system architectures and be reconfigurable in unpredictable environment. The Evolutionary design of electronic circuits, or Evolvable hardware (EHW), is a discipline that allows the user to automatically obtain the desired circuit design. The circuit configuration is under control of Evolutionary algorithms. The most commonly used evolutionary algorithm is Genetic Algorithm. The paper discusses on Cartesian Genetic Programming for evolving gate level designs and proposes Evolvable unit for 2-bit adder based on Genetic Algorithm

Intrinsically Evolvable Artificial Neural Networks

Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented

On robustness in biology: from sensing to functioning

Living systems are subject to various types of spatial and temporal noise at all scales and stages. Nevertheless, evolving under the pressure of natural selection, biology has mastered the ability of dealing with stochasticity. This is particularly crucial because these systems encounter numerous situations which require taking robust and proper actions in the presence of noise. Due to the complexity and variability of these situations, it is impossible to have a prescribed plan for an organism that keeps it alive and fully functional. Therefore, they have to be active, rather than passive, by following three essential steps: I) gathering information about their fluctuating environment, II) processing the information and making decisions via circuits that are inevitably noisy, and finally, III) taking the appropriate action robustly with organizations crossing multiple scales. Although various aspects of this general scheme have been subject of many studies, there are still many questions that remain unanswered: How can a dynamic environmental signal be sensed collectively by cell populations? and how does the topology of interactions affect the quality of this sensing? When processing information via the regulatory network, what are the drawbacks of multifunctional circuits? and how does the reliability of the decisions decrease as the multifunctionality increases? Finally, when the right decision is made and a tissue is growing with feedbacks crossing different scales, what are the crucial features that remain preserved from one subject to another? How can one use these features to understand the mechanisms behind these processes? This thesis addresses the main challenges for answering these questions and many more using methods from dynamical systems, network science, and stochastic processes. Using stochastic models, we investigate the fundamental limits arising from temporal noise on collective signal sensing and context-dependent information processing. Furthermore, by combining stochastic models and cross-scale data analyses, we study pattern formation during complex tissue growth.Lebende Systeme sind in allen Größenordnungen und Stadien verschiedenen Arten von räumlichem und zeitlichem Rauschen ausgesetzt. Dennoch hat die Biologie, die sich unter dem Druck der natürlichen Selektion entwickelt hat, die Fähigkeit gemeistert, mit stochastischen Fluktuationen umzugehen. Dies ist besonders wichtig, da Organismen auf zahlreiche Situationen stoßen, die es erfordern, in Gegenwart von Rauschen robuste und angemessene Maßnahmen zu ergreifen. Aufgrund der Komplexität und Variabilität dieser Situationen ist es unmöglich, einen vorgeschriebenen Plan für einen Organismus zu haben, der ihn überlebens- und funktionsfähig hält. Daher können Organismen sich nicht passiv verhalten, sondern befolgen aktiv drei wesentliche Schritte: I) Das Sammeln von Informationen über ihre dynamische Umgebung, II) Das Verarbeiten von Informationen und das Treffen von Entscheidungen über Regelnetzwerke, die unvermeidlich mit Rauschen behaftet sind, und schließlich, III) das robuste Funktionieren durch organisierte Maßnahmen, welche mehrere Größenordnungen überbrücken. Obwohl verschiedene Aspekte dieses allgemeinen Schemas Gegenstand vieler Studien waren, bleiben noch viele Fragen unbeantwortet: Wie kann ein dynamisches externes Signal kollektiv von Zellpopulationen wahrgenommen werden? Wie beeinflusst die Topologie der Interaktionen die Qualität dieser Wahrnehmung? Was sind die Nachteile multifunktionaler Schaltkreise bei der Verarbeitung von Informationen über das Regelnetzwerk? Wie nimmt die Zuverlässigkeit der Entscheidungen mit zunehmender Multifunktionalität ab? Und abschließend, wenn die richtige Entscheidung getroffen wurde und ein Gewebe wächst und dabei Rückkopplungen auf verschiedenen Größenordnungen erfährt, was sind die entscheidenden Merkmale, die von einem Versuchsobjekt zum anderen erhalten bleiben? Wie kann man diese Merkmale nutzen, um die Prozesse zu verstehen? Diese Arbeit befasst sich mit den wichtigsten Herausforderungen zur Beantwortung dieser und vieler weiterer Fragen mit Methoden aus dynamischen Systemen, Netzwerkforschung und stochastischen Prozessen

Cancer hyperthermia using gold and magnetic nanoparticles

An estimated 12 million people worldwide are diagnosed with cancer every year, with around 17 million cancer-related deaths per year predicted by 2030 (Thun et al. 2010). Contemporary clinical treatments include surgery, chemotherapy and radiotherapy, however all vary in success and exhibit unpleasant side effects. Localised tumour hyperthermia is a moderately new cancer treatment envisaged by researchers, which exploits exclusive tumour vulnerabilities to specific temperature profiles (42-45°C) leading to cancer cell apoptosis, whilst normal tissue cells are relatively unaffected. Hyperthermia is therefore proposed as an alternative potential therapy for cancer, by delivering localised treatment to cancer cells, without the severe side effects associated with traditional therapies. This project aimed to investigate potential hyperthermic treatment of cancer cells in vitro by adopting nanomedicine principles. Inorganic nanoparticles, such as gold or iron oxide, are both capable of generating heat when appropriately stimulated, therefore both have been suggested as candidates for inducing localised tumour heating following their internalisation into cells. In this project, both gold (GNPs) and magnetic (mNPs) were individually assessed for their potential to deliver toxic thermal energy to bone cancer cells (MG63) and breast cancer cells (MCF-7). Studies were carried out both in standard 2D monolayer and in 3D tumour spheroids. When considering use in vivo, it is essential that both GNPs and mNPs are biocompatible, therefore initial studies characterised the cell viability and metabolic activity following incubation with the NPs. The NP internalisation was subsequently verified, prior to hyperthermic studies. Following hyperthermic treatment, both GNPs and mNPs were confirmed as inducing cancer cell death. Further studies were carried out using the GNPs, to identify the cell death pathways activated, where mitochondrial stress was evident following 2D culture tests. Gene and protein expression analysis indicated that cell death occurred predominantly via several apoptotic pathways, through increased fold expression changes in apoptotic markers. Interestingly, cell protective mechanisms were simultaneously switched on, as cells were also observed to exhibit thermotolerance with a number of heat shock proteins (Hsps) being substantially increased during hyperthermic treatments

Ginseng

Ginseng is the most well-known Chinese medicine as well as one of the most used herbal medicines. It has a wide range of medical and pharmacological uses. This book provides an up-to-date critical view of the botanical description and complexity of ginseng, including its phytochemistry, traditional and biotechnological production systems, traditional usage, current applications, and future directions for the development of ginseng compounds as effective medicinal agents. It is a useful resource for academicians, scientists, students, and industry professionals interested in traditional medicine and ginseng

Reliability Enhancement of Perovskite Solar Cells: Role of Low-Dimensional Materials for Interfacial Modifications

239 p.Perovskite solar cells (PSCs) have drawn a great deal of attention in the photovoltaic community owing to their excellent power conversion efficiency and low-cost production. Interfaces remain the weakest part of the complete device, holding their further improvement towards commercialization. This thesis focuses on a comprehensive understanding of the photo-induced charge transfer dynamics and the reliability enhancement of PSCs based on interfacial modification through low-dimensional semiconductor materials. It is found that the two-dimensional (2D) transition metal dichalcogenides (TMDs)-based interfacial layer minimizes the energy barrier and charge accumulation at the interface of the perovskite/charge-transport-layer while prompting extraction of photo-induced charges in the device. The reduction of interface recombination and the enhancement of charge transfer dynamics at the NiOx nanocrystal/perovskite interface were further constructed by the application of a molecularly engineered dithieno thiophene-based thin organic semiconductor layer on NiOx. The developed strategy is further extended with the implementation of a 2D-C3N4 polymeric network, which enables greater PCE byreducing non-radiative losses and faulty charge build-up at the NiOx/perovskite interface. The greater stability of perovskite is established owing to the strong coordination of MA+ cations with unbound nitrogen electron pairs in the C3N4. A thorough grasp of the perovskite/electron transport layer interface was further studied, and it is found that 2D-TiS2 had a greater effect on PV performance when paired with PC60BM under ideal addition. The results presented in this thesis offer unique insight into the interfacial modification using low-dimensional materials to achieve simultaneous high efficiency and stability in PSCs.BCMaterials: Basque Center for Materials Applications & Nanostructure