Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1063 Promoter Engineering for Cyanobacteria An Essential Step

Huang, H. -H. 2013. Promoter Engineering for Cyanobacteria. An Essential Step. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1063. 58 pp. Uppsala. ISBN 978-91-554-8724-9. Synthetic biology views a complex biological system as an ensemble in the hierarchy of parts, devices, systems, and networks. The practice of using engineering rules such as decoupling and standardization to understand, predict, and re-build novel biological functions from modeldriven designed genetic circuits is emphasized. It is one of the top ten technologies that could help solving the current and potential risks in human society. Cyanobacteria have been considered as a promising biological system in conducting oxygenic photosynthesis to convert solar energy into reducing power, which drives biochemical reactions to assimilate and generate chemicals for a specific purpose such as CO2 fixation, N2 fixation, bioremediation, or fuels production. The promoter is a key biological part to construct feedback loops in genetic circuits for a desired biological function. In this thesis, promoters that don't work in the cyanobacterium Synechocystis PCC 6803 in terms of promoter strength, and dynamic range of gene regulation are identified. Biological parts, such as ribosome binding sites, and reporter genes with and without protease tags were also characterized with the home-built broad-host-range BioBrick shuttle vector pPMQAK1. The strong L03 promoter, which can be tightly regulated in a wide dynamic range by the foreign Tet repressor, was created through an iterative promoter engineering cycle. The iteration cycle of DNA breathing dynamic simulations and quantification of a reporting signal at a single-cell level should guide through the engineering process of making promoters with intended regulatory properties. This thesis is an essential step in creating functional promoters and it could be applied to create more diverse promoters to realize the emphasized practices of synthetic biology to build synthetic cyanobacteria for direct fuel production and CO2 assimilation.
Generation for Human-Robot Teaming

As Artificial Intelligence (AI) becomes an integral part of our life, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to explain its behavior is one of the key requirements of an explainable agency. Prior work on explanation generation focuses on supporting the reasoning behind the robot’s behavior. These approaches, however, fail to consider the cognitive effort needed to understand the received explanation. In particular, the human teammate is expected to understand any explanation provided before the task execution, no matter how much information is presented in the explanation. In this work, we argue that an explanation, especially complex ones, should be made in an online fashion during the execution, which helps to spread out the information to be explained and thus reducing the cognitive load of humans. However, a challenge here is that the different parts of an explanation are dependent on each other, which must be taken into account when generating online explanations. To this end, a general formulation of online explanation generation is presented. We base our explanation generation method in a model reconciliation setting introduced in our prior work. Our approach is evaluated both with human subjects in a standard planning competition (IPC) domain, using NASA Task Load Index, as well as in simulation with ten different problems.
A Deep Learning Approach for Intrusion Detection System in Industry Network

Network has brought convenience to the world by allowing flexible transformation of data, but it also exposes a high number of vulnerabilities. A Network Intrusion Detection System (NIDS) helps system and network administrators to detect network security breaches in their organizations. Identifying anonymous and new attacks is one of the main challenges in IDSs researches. Deep learning (2010’s), which is a subfield of machine learning (1980’s), is concerned with algorithms that are based on the structure and function of brain called artificial neural networks. The progression on such learning algorithms may improve the functionality of IDS especially in Industrial Control Systems to increase its detection rate on unknown attacks. In this work, we propose a deep learning approach to implement an effective and enhanced IDS for securing industrial network. Keywords—Intrusion Detection System, Deep Learning, SCADA, Modbus, Industrial Control Systems, Artificial Neural Networks.
Psycholinguistic Representation of Individual Traumatic Memory in the Context of Social and Political Ambiguity

The paper focuses on a psycholinguistic study of individual traumatic memory. Among psycholinguistic tools of the research are psychographological analysis, propositional analysis, and frame analysis. The results of psychographological analysis revealed differences between psycholinguistic representations of traumatic events as compared to psycholinguistic representation of neutral events in norm for written speech. Higher psychographological indices in representation of traumatic events show the emotional lability (high sentence length), low awareness and meaningfulness of the event (high quotient of logical coherence), and low motivation (small number of words in the narratives). Frame analysis allowed revealing distribution of traumatic events in the context of political and social ambiguity. Propositional analysis showed distribution of propositional elements in the narration of traumatic events, which express the meaning of traumatic events in a person's life (attitude); significance of external circumstances in the awareness of the traumatic event (external object); the main participants of the traumatic event (external argument); the narrator and his/her role in the traumatic event (internal argument); low importance of localization and chronological order of the traumatic event (context); the secondary (passive) role of narrator in the traumatic event and emphasizing the importance of the external circumstances (internal objects). The results of T-test show the differences in psycholinguistic representation of traumatic memory between two samples: respondents without PTSD (95 people) and respondents with PTSD and tendencies to PTDS (111 people). The narrators of the second sample (with PTSD) have higher indices of norm deviations in psychographological analysis, a higher percent of the attitude and internal argument in the predicate in propositional analysis, lower variety of frame clusters of traumatic events than narrators of the first sample (without PTSD).
2019-03 Measuring Physiciansʼ Response to Incentives : Labour Supply , Multitasking , and Earnings

We measure the response of physicians to monetary incentives using matched administrative and time-use data on specialists from Québec (Canada). These physicians were paid fee-for-service contracts and supplied a number of different services. We model physician behaviour and derive a conditional earnings function that returns the maximum earnings a physician can generate in the labour market, conditional on total hours worked. The earnings function is estimated using both limitedinformation methods and full-information methods. Limited-information methods impose fewer restrictions on the data, but are less informative over incentive effects. Le Chatelier effects imply that they identify lower bounds to the own-price substitution effects. Full-information methods explain earnings and hours simultaneously. They identify the full response to incentives, including income effects. Our results confirm that physicians respond to financial incentives. The own-price substitution effects of a relative price change are both economically and statistically significant. Income effects are present, but are overridden when prices are increased for individual services. They are more prominent in the presence of broad-based fee increases. In such cases, the income effect empirically dominates the substitution effect, which leads physicians to reduce their supply of services. JEL Classification: I10, J22, J33, J44.
The first impression in a fine-dining restaurant. A study of C Restaurant in Tampere, Finland

Abstract Food business has become a popular trend for entrepreneurs these days. However, it seems that many entrepreneurs do not know how important is the first impression and the services offered to their guests. Consequently, many restaurants have a generally poor service, and therefore, many are closed as soon as they are opened. This study concerns more on how the first impression works in a fine-dining restaurant; hence, restaurateurs will able to know how to build on a customer’s good first impression. Previous studies, experts’ opinions, and the theories related to the topic of this study were referred and used in this study. The study was carried out in restaurant ‘C’, located in Tampere, Finland. Samples were collected from the guests who visited the restaurant ‘C’. This study contributes new knowledge regarding the first impression, and how it affects the customers’ decision behaviour as well as three different types of service clues (mechanic, humanic, and functional clues) and how they influence the customers’ perception.
Is Alcohol Affect Higher Education Students Performance: Searching and Predicting Pattern Using Data Mining Algorithms

Chronic heavy drinking and alcoholism can have serious repercussions for the learning, and memory. Excessive drinking among college students is associated with a variety of negative consequences that include alcohol poisoning; fatal and nonfatal injuries; blackouts; violence, academic failure; including sexually transmitted diseases, rape and assault; unintended pregnancy; property damage; including HIV/AIDS; criminal consequences and vocational that could jeopardize future job prospects. The present research intends to the student achievement in Higher Education using Data Mining techniques. This real-world data was collected by using performance reports and questionnaires which was collected and analyzed by MCA department, VBS Purvanchal University, India. In this experimental dataset used data set about MCA student on their courses which holds 450 instances. Four Decisions Tree algorithms (BFTree, J48, RepTree and Simple Cart) are applied in this work. The results showed that BFTree algorithm mostly proper to classify and predict student’s whose performance is excellent and who’s poor during studying the subjects.
CERN – EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH A NEW SPHERICAL PULSE COMPRESSOR WORKING WITH DEGENERATED “WHISPERING GALLERY” MODE

This paper introduces a new design concept of spherical pulse compressor. The main characteristics of this new design can be described as: spherical storage cavity, degenerated “Whispering Gallery” mode and a compact mode launcher. Presented at the 29th International Linear Accelerator Conference, Beijing, China, 16 21 Sep 2018 Geneva, Switzerland March 2019 CLIC – Note – 1153
Before It Gets Better: The Short-Term Employment Costs of Regulatory Reforms

The article exploits long time series of industry-level data in a group of OECD countries to analyze the short-term labor market effects of reforms that lower barriers to entry and dismissal costs. Estimates show that both policies induce non-negligible transitory employment losses. The strength of these effects varies depending on the underlying industry and labor market structure, and on cyclical conditions: The employment cost of deregulation is higher in economic downturns and negligible in good times. These findings prove robust to a set of specification and sensitivity checks and are confirmed after standard reverse causality and falsification tests.
Tilburg University Men deny and women cry , but who dies ? Do the wages of " denial " include early ischemic coronary heart disease ?

Objectives: In this study patients with documented ischemic coronary heart disease (ICHD; prior MI or CAD per catheterization) were tested for the association of various measures of emotional distress with Age at Initial Diagnosis. Methods: The measures were chosen because of a published track record at predicting mortality in this population. Females were oversampled to achieve equivalent numbers of each sex (n = 50), and thus equivalent statistical power. In a subset of patients (38 males and 32 females), Spouse/Friend Ketterer Stress Symptom Frequency Checklists (KSSFCs) were received. Results: Females reported more depression and anxiety than males. However, spouses or friends reported more anger for males. 0022-3999/04/$ – see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/S0022-3999(03)00501-4 * Corresponding author. Heart and Vascular Institute, Henry Ford Health Sciences Center, Henry Ford Hospital/CFP6, 2799 West Grand Boulevard, Detroit, MI 48202, USA. Tel. : +1-313-916-2523; fax: +1-313916-8846. E-mail address: markwketterer@cs.com (M.W. Ketterer). Denial (spouse/friend minus self-ratings) was greater in males for all three scales of the KSSFC (Anger, P= .005; Depression, P= .024; Anxiety, P= .001). Although females showed the same trend, self and spouse or friend ratings of distress were significantly associated with Age at Initial Diagnosis only in males. When split at the sample mean on the Spouse/Friend KSSFC AIAI (Anger) scale, Age at Initial Diagnosis occurred 14.2 years earlier in males. Conclusions: Use of a significant other in assessing psychosocial/emotional distress in males may confer greater accuracy, and therefore predictive power for clinical endpoints. D 2004 Elsevier Inc. All rights reserved.
Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction

This paper considers a multi-agent foraging problem, where multiple autonomous agents find resources (called pucks) in a bounded workspace and carry the found resources to a designated location, called the base. This article considers the case where autonomous agents move in unknown 3-D workspace with many obstacles. This article describes 3-D multi-agent foraging based on local interaction, which does not rely on global localization of an agent. This paper proposes a 3-D foraging strategy which has the following two steps. The first step is to detect all pucks inside the 3-D cluttered unknown workspace, such that every puck in the workspace is detected in a provably complete manner. The next step is to generate a path from the base to every puck, followed by collecting every puck to the base. Since an agent cannot use global localization, each agent depends on local interaction to bring every puck to the base. In this article, every agent on a path to a puck is used for guiding an agent to reach the puck and to bring the puck to the base. To the best of our knowledge, this article is novel in letting multiple agents perform foraging and puck carrying in 3-D cluttered unknown workspace, while not relying on global localization of an agent. In addition, the proposed search strategy is provably complete in detecting all pucks in the 3-D cluttered bounded workspace. MATLAB simulations demonstrate the outperformance of the proposed multi-agent foraging strategy in 3-D cluttered workspace.
Platform Users Task and Contract Publication Contract Item Selection & Task Data Submission Data Quality Validation & Payment Transaction

Crowdsensing is a sensing method which involves participants from general public to collect sensed data from their mobile devices, and also contribute and utilize a common database. To ensure a crowdsensing system to operate properly, there must be certain effective and efficient incentive mechanism to attract users and stimulate them to submit sensing data with high quality. Intuitively, the agreement on the qualities and payments in crowdsensing systems can be best modeled as a contract. However, none of existing incentive mechanisms consider data quality through effective contract design. In this paper, we design two quality-aware contract-based incentive mechanisms for crowdsensing, named QUAC-F and QUAC-I, under full information model and incomplete information model, respectively, which differ in the level of users’ information known to the system. Both QUAC-F and QUAC-I are guaranteed to maximize the platform utility while satisfying individual rationality and incentive compatibility. We evaluate the performance of our designed mechanisms based on a real dataset.
Job-training of Hungarian higher-education graduates

Considerable amounts of time and money are spent on job-training of school-leavers graduated from higher-education institutions. More than a half of the employees in our sample participated in job-training between graduation date (1999) and September 2000. The work in this paper considers two aspects of the problem. First, the relationship between training probability/training length and the initial human capital (proxied by level of education and in-school labour market experience) is concerned with, and, second, some elements of the training-costsharing decision is analysed. There are some signs that university education reduces the probability of training as compared to college education, whereas in-school labour market experience increases it. University education reduces training length, as well. In-school labour market experience has no effect on the length of job-training. Another important result is that school-leavers holding diplomas with "narrower" types of education are more likely to obtain training, and also to have longer training programmes. This implies a more severe matching problem in the case of "narrower" types of education, possibly due to prohibitive searching costs for finding a good-quality match. Results for the cost-sharing decision are in line with Becker's idea, since the firm is less likely to entirely cover the costs of general training and more likely to finance job-specific training programmes.
RGB-D-Based Pose Estimation of Workpieces with Semantic Segmentation and Point Cloud Registration

As an important part of a factory’s automated production line, industrial robots can perform a variety of tasks by integrating external sensors. Among these tasks, grasping scattered workpieces on the industrial assembly line has always been a prominent and difficult point in robot manipulation research. By using RGB-D (color and depth) information, we propose an efficient and practical solution that fuses the approaches of semantic segmentation and point cloud registration to perform object recognition and pose estimation. Different from objects in an indoor environment, the characteristics of the workpiece are relatively simple; thus, we create and label an RGB image dataset from a variety of industrial scenarios and train the modified FCN (Fully Convolutional Network) on a homemade dataset to infer the semantic segmentation results of the input images. Then, we determine the point cloud of the workpieces by incorporating the depth information to estimate the real-time pose of the workpieces. To evaluate the accuracy of the solution, we propose a novel pose error evaluation method based on the robot vision system. This method does not rely on expensive measuring equipment and can also obtain accurate evaluation results. In an industrial scenario, our solution has a rotation error less than two degrees and a translation error < 10 mm.
Automatic Segmentation of Rima Glottidis in 4 D Laryngeal CT Scans for Early Detection of Parkinson ’ s Disease

Parkinson’s disease (PD) is a neuro-degenerative disease which currently has neither cure nor any pathological tests. PD is a slowly progressive disease and symptoms appear when approximately 70% of the neuronal cells have affected. The temporal analysis of the calculated area of the rima glottidis gives indication of vocal impairment. In this paper, an automatic segmentation algorithm to segment the rima glottidis from 4D CT images using texture features and support vector machines (SVM) is presented. Automatic two dimensional region growing is then applied as a post processing step to segment the area accurately. The proposed segmentation algorithm resulted in accurate segmentation and a high correlation has been demonstrated between manually segmented area with automatic segmentation.
Person-by-Situation Perspectives on Close Relationships

This chapter reviews theories and research that have adopted interactional (person-by-situation) approaches to studying close relationships. Interactional thinking in social and personality psychology is discussed from historical and contemporary perspectives, emphasizing ways in which individuals and situations intersect. Three theoretical models that adopt person-by-situation frameworks applied to important interpersonal processes are reviewed: the cognitive–affective personality system (CAPS) model, interdependence theory, and attachment theory. The chapter explains how and why person-by-situation approaches have increased our understanding of individuals within relationships. Specific research programs are highlighted. This research has revealed that certain types of situations elicit unique reactions in people with specific dispositional strengths or vulnerabilities. Collectively, these research programs indicate that one can neither predict nor understand how individuals think, feel, or behave in relationships without knowing the relational context in which they are embedded. The chapter concludes by discussing some new directions in which interactional-based thinking might head.
PATTERN DISCOVERY: A PROGRESSIVE VISUAL ANALYTIC SYSTEM DESIGN TO SUPPORT CATEGORICAL DATA ANALYSIS

When using data-mining tools to analyze big data, users often need tools to support the understanding of individual data attributes and control the analysis progress. This requires the integration of data-mining algorithms with interactive tools to manipulate data and analytical process. This is where visual analytics can help. More than simple visualization of a dataset or some computation results, visual analytics provides users an environment to iteratively explore different inputs or parameters and see the corresponding results. In this research, we explore a design of progressive visual analytics to support the analysis of categorical data with a data-mining algorithm, Apriori. Our study focuses on executing data mining techniques step-by-step and showing intermediate result at every stage to facilitate sense-making. Our design, called Pattern Discovery Tool, targets for a medical dataset. Starting with visualization of data properties and immediate feedback of users’ inputs or adjustments, Pattern Discovery Tool could help users detect interesting patterns and factors effectively and efficiently. Afterward, further analyses such as statistical methods could be conducted to test those possible theories.