Synthetic biology aims to engineer biological systems to meet new challenges and teach us more about natural biological systems. These pursuits range from the building of relatively simple transcriptional circuits, to engineering the metabolism of an organism, to reconstructing entire genomes. While we are still emerging from the foundational stages of this new field, we are already using engineered cells to discover underlying biological mechanisms, develop new therapeutics, and produce natural products. In this dissertation, we discuss the application of synthetic biology principles to the development of memory and pulse-detecting genetic circuits. In Chapter 2, we use novel transcriptional positive-feedback based memory devices integrated in human cells to study heterogeneous responses to cellular stresses. We built doxycycline, hypoxia, and DNA damage sensing versions of the device, demonstrating its modularity. In Chapter 3, we discuss further applications of the memory device in the study of long-term responses to hypoxia, gamma radiation, and inflammation. Finally, in Chapter 4 we describe work leading to the future construction of a pulse-detecting genetic circuit integrated in the E. coli genome. The work presented here illustrates the general applicability of synthetic biology in the study of biological phenomena and brings us one step closer to achieving a more exquisite understanding and control of natural systems.

Τα τελευταία χρόνια υπάρχει μια αυξημένη χρήση των κλειστών συστημάτων παρακολούθησης, είτε αυτά αφορούν την επιτήρηση κλειστών χώρων για λόγους ασφάλειας, είτε για παρακολούθηση της κυκλοφορίας σε δρόμους αυξημένης κυκλοφορίας ή ακόμα και για στατιστικούς λόγους για τον υπολογισμό επισκεπτών σε εμπορικά κέντρα ή διερχόμενων οχημάτων ή επιβατών σε μέσα μαζικής μεταφοράς, περίπτωση την οποία εξετάζουμε στην παρούσα εργασία. Η μεθοδολογία που αναπτύχτηκε περιλαμβάνει τρία κυρίως στάδια, την αφαίρεση του στατικού φόντου, την ανίχνευση των κινούμενων ατόμων και τον εντοπισμό τους σε όλη διαδρομή τους. / In recent years there has been an increased use of monitoring systems, whether they concern the monitoring of closed areas for security reasons or to monitor traffic on roads with increased traffic or even for statistical purposes to calculate visitors to shopping centers or transit vehicles or passenger to public transportation, which we are examining in this thesis. The methodology was developed in three main stages, removing the static background, the detection of moving people and track them throughout their journey.

Prostate cancer is the most common type of cancer in men in Europe and the USA. Some prostate tumours are regarded as stiffer than the surrounding normal tissue, and therefore it is of interest to be able to reliably measure prostate tissue stiffness. The methods presently used to detect prostate cancer are inexact, and new techniques are needed. In this licentiate thesis resonance sensor technology, with its ability to measure tissue stiffness, was applied to normal and cancerous prostate tissue. A piezoelectric transducer element in a feedback system can be set to vibrate at its resonance frequency. When the sensor element contacts an object a change in the resonance frequency is observed, and this feature has been utilized in sensor systems to describe physical properties of different objects. For medical applications it has been used to measure stiffness variations due to various pathophysiological conditions. An impression-controlled resonance sensor system was used to quantify stiffness in human prostate tissue in vitro using a combination of frequency change and force measurements. Measurements on prostate tissue showed statistically significant (p < 0.001) and reproducible differences between normal healthy tissue and tumour tissue when using a multivariate parameter analysis. Measured stiffness varied in both the normal tissue and tumour tissue group. One source of variation was assumed to be related to differences in tissue composition. Other sources of error could be uneven surfaces, different levels of dehydration of the prostates, and actual differences between patients. The prostate specimens were also subjected to morphometric measurements, and the sensor parameter was compared with the morphology of the tissue with linear regression. In the probe impression interval 0.5–1.7 mm, the maximum coefficient of determination was R2 ≥ 0.60 (p < 0.05, n = 75). An increase in the proportion of prostate stones (corpora amylacea), stroma, or cancer in relation to healthy glandular tissue increased the measured stiffness. Cancer and stroma had the greatest effect on the measured stiffness. The deeper the sensor was pressed, the greater, i.e., deeper, volume it sensed. It is concluded that prostate cancer increases the measured stiffness as compared with healthy glandular tissue, but areas with predominantly stroma or many stones could be more difficult to differentiate from cancer. Furthermore, the results of this study indicated that the resonance sensor could be used to detect stiffness variations in human prostate tissue in vitro, and especially due to prostate cancer. This is promising for the development of a future diagnostic tool for prostate cancer.

To test if a host contains any known security flaws over a network connection a Vulnerability Assessment (VA) could be made. This thesis describes different techniques used by VA tools over a network connection to detect known security flaws. To decrease the risk of flaws not being detected, several VA tools could be used. There is no common way of merging information from different VA tools. Therefore the Vulnerability Assessment Information Handler (VAIH) has been developed. The VAIH system consists of three parts. First, a intermediate language format defined in XML. Second, modules that converts the output of VA tools to the intermediate language format. Third, a program for reading and displaying the intermediate language format. The VAIH system makes it possible to merge the results from vulnerability assessment tools into one file that can be displayed and edited through a GUI.

The purpose of this paper is to find out whether Hough transform if it is helpful to building extraction or not. This paper is written with the intention to come up with a building extraction algorithm that captures building areas in images as accurately as possible and eliminates background interference information, allowing the extracted contour area to be slightly larger than the building area itself. The core algorithm in this paper is based on the linear feature of the building edge and it removes interference information from the background. Through the test with ZuBuD database in Matlab, we can detect images successfully.  So according to this study, the Hough transform works for extracting building in 2D images.

Delirium är ett allvarligt psykiskt syndrom som är vanligt förekommande hos äldre patienter. Förändrad uppfattning av tid och rum, hallucinationer, störningar i medvetandegrad, känsloliv samt minne är några symptom en deliriös person kan uppleva. Utöver obehaget dessa symptom orsakar hos en person kan delirium på sikt utgöra en risk utveckling av bland annat permanent kognitiv nedsättning, och i värsta fall kan syndromet leda till döden. Syftet med studien var att sammanställa kunskaper kring sjuksköterskors identifiering av delirium hos äldre personer. Tre forskningsfrågor skapades för att kunna besvara syftet: Vilka riskfaktorer beskrivs för att utveckla delirium? Vilka skattningsinstrument kan användas för att identifiera delirium? Vilka hinder och förutsättningar finns det för att identifiera delirium? Studien har genomförts som en integrerad litteraturöversikt. Sökning efter litteratur genomfördes i två databaser och kompletterades med manuell sökning, detta resulterade i sexton artiklar vilka analyserades och detta resulterade i tre grupper samt fem undergrupper. Grupperna består av frågeställningarna och undergrupper består av: RADAR; CAM; DOSS; Nu-Desc; Comprehensive nursing assessment. Fynden i studien visade på att riskfaktorerna för att utveckla delirium är många, och att hög ålder är den största riskfaktorn. Bedömningsinstrument finns i ett antal olika former och kan vara till stor hjälp för sjuksköterskor när det gäller identifiering av delirium. Hindren och utmaningarna är många, ett stort hinder är deliriums förmåga att maskera sig som andra sjukdomar såsom demens. Slutligen finns det förutsättningar för att delirium ska kunna identifieras, en av de viktigaste förutsättningarna är tillräckliga resurser i form av personal och tid, för att sjuksköterskor ska kunna få spendera tid med patienterna och på så vis lättare kunna identifiera förändringar som kan tyda på delirium. Sjuksköterskor är i behov av de verktyg som krävs för att kunna förbättra identifikationen och ge de äldre patienterna möjlighet till ökat välbefinnande och en god hälsa.

(in English) This diploma thesis deals with coin finds and their contexts in church interiors in Bohemia. It evaluates this issue and methodology and puts emphasis on usage of metal detectors in archeology. Coin finds are divided according to their relevant locations to two categories - mass findings or single findings. The knowledge is then evaluated in terms of church interiors and also in wider context. For easier orientation, in the total context, a history of every mentioned church is drawn in connection with coin finds. Integral part of diploma thesis is a well arranged catalogue of coin finds in churches interiors in Bohemia. At the end, coin finds are evaluated from several different points of view.

Question and answering sites are useful in sharing the knowledge by answering questions. It is a medium of sharing knowledge. Quora is the fastest emerging effective Q&A site, which is the best source of knowledge. Here you can ask a question, and get help in getting answers from people with firsthand experience, and blog about what you know. In this paper, we are investigating and identifying potential experts who are providing the best solutions to the questioner needs. We have considered several techniques in identifying user as an expert or non-expert. We have targeted the most followed topics in Quora and finally came up with five topics: Mathematics, Politics, Technology, Sports and Business. We then crawled the user profiles who are following these topics. Each topic dataset has many special features. Our research indicates that experts are quite different from normal users and tend to produce high quality answers to as many questions as possible to gain their reputation. After evaluation, we got a limited number of experts who have potential expertise in specific fields, achieving up to 97% accuracy and 0.987 AUC.

During natural disasters or crises, users on social media tend to easily believe contents of postings related to the events, and retweet the postings, hoping that the postings will be reached by many other users. Unfortunately, there are malicious users who understand the tendency and post misinformation such as spam and fake messages with expecting wider propagation. To resolve the problem, in this paper we conduct a case study of the 2013 Moore Tornado and Hurricane Sandy. Concretely, we (i) understand behaviors of these malicious users; (ii) analyze properties of spam, fake and legitimate messages; (iii) propose at and hierarchical classification approaches; and (iv) detect both fake and spam messages with even distinguishing between them. Our experimental results show that our proposed approaches identify spam and fake messages with 96.43% accuracy and 0.961 F-measure.

Smart grid architecture and Software-defined Networking (SDN) have evolved into a centrally controlled infrastructure that captures and extracts data in real-time through sensors, smart-meters, and virtual machines. These advances pose a risk and increase the vulnerabilities of these infrastructures to sophisticated cyberattacks like distributed denial of service (DDoS), false data injection attack (FDIA), and Data replay. Integrating machine learning with a network intrusion detection system (NIDS) can improve the system's accuracy and precision when detecting suspicious signatures and network anomalies. Analyzing data in real-time using trained and tested hyperparameters on a network traffic dataset applies to most network infrastructures. The NSL-KDD dataset implemented holds various classes, attack types, protocol suites like TCP, HTTP, and POP, which are critical to packet transmission on a smart grid network. In this paper, we leveraged existing machine learning (ML) algorithms, Support vector machine (SVM), K-nearest neighbor (KNN), Random Forest (RF), Naïve Bayes (NB), and Bagging; to perform a detailed performance comparison of selected classifiers. We propose a multi-level hybrid model of SVM integrated with RF for improved accuracy and precision during network filtering. The hybrid model SVM-RF returned an average accuracy of 94% in 10-fold cross-validation and 92.75%in an 80-20% split during class classification.