178 research outputs found
Beam Dynamics Design of a Multi-Ion RFQ for Medical Application
No full textParticle radiotherapy based on the medical accelerator is emerging as a major treatment for cancer. To enhance the clinical flexibility of particle radiotherapy and further promote the use of medical accelerators, the Shanghai Institute of Applied Physics (SINAP) has presented a new linear accelerator plan for medical application. The new plan utilizes a 200 MHz Radio Frequency Quadrupole (RFQ) as the injector. The RFQ is designed to accelerate ions with charge-to-mass ratios of 1/3 to 1/2 from 8 keV/u to 750 keV/u. For the beam dynamics design, a new design strategy is presented to enhance the suppression of space charge effects and improve beam capture efficiency by optimizing the modulation, synchronous phase, and focusing strength. The simulation results demonstrate that the multi-ion RFQ can operate at a maximum beam current of 3.2 mA while maintaining a transmission efficiency above 95% with a compact length of 2.5 m. Multi-particle simulations confirm the high reliability of the design. Additionally, input and mechanical error analyses evaluate the RFQ’s tolerance and stability. The research results demonstrate the feasibility of a compact, high-efficiency RFQ for multi-ion acceleration in medical applications, contributing to the advancement of particle therapy
Proxy-Based Sensor Deployment for Mobile Sensor Networks
No full textTo provide satisfactory coverage is very important in many sensor network applications such as military surveillance. In order to obtain the required coverage in harsh environments, mobile sensors are helpful since they can move to cover the area not reachable by static sensors. Previous work on mobile sensor deployment is based on a round by round process, where sensors move iteratively until the maximum coverage is reached. Although these solutions can deploy mobile sensors in a distributed way, the mobile sensors may move in a zig-zag way and waste a lot of energy compared to moving directly to the final location. To address this problem, we propose a proxy-based sensor deployment protocol. Instead of moving iteratively, sensors calculate their target locations based on a distributed iterative algorithm, move logically, and exchange new logical locations with their new logical neighbors. Actual movement only occurs when sensors determine their final locations. Simulation results show that the proposed protocol can significantly reduce the energy consumption compared to previous work, while maintaining similar coverage
Exploring the relation between gender politics and representative government in the Maghreb: analytical and empirical observations
No full textThis thesis uses analytical and empirical methods to explore the relation between gender standards and democratic standards in the Maghreb, which includes Algeria, Morocco, and Tunisia. The analytical approach consists of considering theories that link gender standards and democratic standards, and analyzing whether and to what extent such theories would apply or not apply to the Maghreb. The empirical approach consists of taking measurements that reflect gender standards and democratic standards across the three countries and four different milestones of their recent history (1970, 1980, 1990, 2000), and applying statistical methods to compute correlations and regressions. Because the empirical approach yields no significant correlation between gender standards and democratic standards in the Maghreb, I analyze this statistical correlation for other sets of countries that are part of Maghrebian identity: Arab countries, Muslim countries, African countries, and Mediterranean countries. The combined results of these analyses give us some insight into possible explanations of the empirical observations.Ph.D.Includes bibliographical references (p. 261-275)by Amel Mil
On Supporting Distributed Collaboration In Sensor Networks
No full textIn sensor networks, nodes may malfunction due to the hostile environment. Therefore, dealing with node failure is a very important research issue. In this paper, we study distributed cooperative failure detection techniques. In the proposed techniques, the nodes around a suspected node collaborate with each other to reach an agreement on whether the suspect is faulty or malicious. We first formalize the problem as how to construct a dominating tree to cover all the neighbors of the suspect and give the lower bound of the message complexity. Two tree-based propagation collection protocols are proposed to construct dominating trees and collect information via the tree structure. Instead of using the traditional flooding technique, we propose a coverage-based heuristic to improve the system performance. Theoretical analysis and simulation results show that the heuristic can help achieve a higher tree coverage with lower message complexity, lower delay and lower energy consumption
Mobile intelligence analytics for urban smart living
No full textToday, as the sensing technology and mobile computing have been popularized, a variety of mobile data related to human mobility and urban geography have been accumulated in a large amount. This type of data comprehensively records the fine-grain events of our cities through “4W” aspects of information: What happened? Where it happened? When it happened? And who did it? By proper analysis, this data can be a rich source of mobile intelligence to support various location-based and real-time decision-making solutions for a broad range of urban smart living applications. Indeed, mobile intelligence analytics plays an important role in urban life because city residents often make choices under more uncertainty and can benefit more from personalized advice based on their preferences and contexts. Therefore, it is especially meaningful to develop data-driven methodologies which can effectively and efficiently guide users to make optimal decisions to achieve the goal of urban smart living. In this dissertation, we aim to address the unique challenges of urban smart living in mobile and pervasive business environments from both theoretical and practical perspectives. Specifically, we first develop a safety-aware house ranking system by considering the impact of neighborhood criminal offenses on house values. The proposed framework extracts features regarding community safety conditions of different houses, and utilizes multiply safety features to rank houses by unit value. To enhance safety-aware ranking, we introduce major characteristics of house profile to control the similarity between houses during pair-wise ranker learning. The experimental results show that the proposed method substantially outperforms the baseline learn-to-rank methods for safety-aware house ranking. Moreover, in the second study, we introduce an effective point-of-interest (POI) recommender system to consider the temporal compatibility between POI popularity and user regularity. We propose to use the massive human mobility data to profile the temporal pattern of POI popularity, and infer the regularity pattern of users based on the POI they visited through a modeling intuition ``you are where you go". We demonstrate the effectiveness of the proposed model through the extensive experiments on the real-world datasets of New York City. Finally, we introduce a zone embedding framework to identify the urban functions of city zones by studying massive origin-destination transportation data. We focus on exploiting the idea of word embedding in natural language processing domain to learn zone functions in urban computing domain by developing a novel analog from word co-occurrence to zone co-occurrence using human mobility patterns. To incorporate the contexts of human mobility in our framework, we develop the directed and temporal co-occurrence for considering mobility direction and time, and the different importance of co-occurrence for considering travel distance and zone attractiveness. The evaluation validates the proposed method and shows that the learned embeddings can comprehensively capture the urban functions of city zones. From the three studies, we conclude that mobile intelligence analytics can be powerful at disclosing patterns, relations and hidden knowledge, and it is promising to explore the power of mobile intelligence to provide location-based insights, and ultimately, to improve business performance.Ph.D.Includes bibliographical referencesby Zijun Ya
From data to dynamism: the role of data and learning models in startup potential analysis
No full textIn the contemporary era, the landscape of innovation and entrepreneurship is dynamically evolving, fueled by a substantial surge in venture capital investments and the rapid expansion of the global startup ecosystem. This burgeoning growth not only highlights the vibrant nature of modern economies but also brings to the forefront the critical importance of identifying startups with high potential for success. As venture capital firms and investors seek to maximize their returns on investment, the ability to accurately assess and predict the future performance of these nascent companies becomes paramount. This dissertation delves into the heart of this challenge, aiming to refine and enhance the methodologies used in evaluating startup potential, thereby contributing valuable insights and tools to both academic scholars and industry practitioners.
Existing methods for assessing startup potential have predominantly relied on static variables such as financial performance indicators, market size estimates, and competitive positioning. While these factors offer valuable insights, they fall short in capturing the dynamic and often unpredictable nature of startup growth and success. This raises several pertinent questions: How can we move beyond these traditional metrics to more accurately predict startup success? Furthermore, is it possible to develop more advanced tools that not only provide predictions but also facilitate a more interactive, dynamic evaluation process? These questions highlight the limitations of current approaches and pave the way for the innovative research presented in this dissertation, which seeks to explore these opportunities through the application of advanced data analytics and learning models.
The dissertation is structured around three main chapters, each contributing to the overarching aim of developing a comprehensive framework for startup evaluation. The first chapter emphasizes the importance of mapping the interactions between various entities within the startup ecosystem, including companies, venture capital firms, and individuals. This interaction-centric view provides a foundational understanding of the complex interdependencies that influence startup success.
Building on this foundation, the second chapter introduces an expanded interaction network and integrates company demographic features to improve the identification of high-potential startups. Additionally, this chapter explores the entrepreneurial homophily principle, which posits that startups with similar characteristics tend to cluster together, further supporting the theoretical underpinnings of the proposed methodologies.
The third chapter represents a pioneering effort to leverage large language models (LLMs) for building an interactive, domain-centric tool aiming at dynamically evaluating startup potential. This novel application of LLMs opens up exciting possibilities for creating an interactive agent that can continuously update its assessments based on evolving data, offering a more fluid and responsive tool for venture capital decision-making.
In summary, this dissertation marks a significant advancement in the field of startup evaluation by utilizing a diverse array of entrepreneurial data, combined with cutting-edge learning models. The research not only advances our theoretical understanding of startup dynamics but also offers practical tools for identifying startups with the highest potential for success. Through its comprehensive analysis and innovative methodologies, this work stands as a seminal contribution to the ongoing efforts to enhance the precision and relevance of startup potential assessment.Ph.D.Includes bibliographical reference
Heterogeneous mobile data analytics for smart living
No full textWith the development of mobile, sensing, and positioning technologies, large-scale urban geographic data and human mobility data have been accumulated recently. The availability of heterogeneous mobile data and the emergence of big data technology provide unparalleled opportunities on understanding user behaviors and enabling smart living, e.g., developing livable and vibrant communities, improving energy efficiency in transportation, and enhancing urban planning. To this end, the objective of this dissertation is to exploit heterogeneous mobile data for developing data-driven solutions to enable smart living.
Along this line, we first provide a data driven solution to recommend Points-of- Interest (POIs) for the purpose of improving people’s experiences for urban living. Existing approaches for POI recommendation have been mainly focused on exploiting the information about user preferences, social influence, and geographical influence. However, these approaches cannot handle the scenario where users are expecting to have POI recommendation for a specific time period. To this end, we propose a unified recommender system to integrate the user interests and their evolving sequential preferences with temporal interval assessment. As a result, the proposed system can make recommendations dynamically for a specific time period and the traditional POI recommender system can be treated as the special case of the proposed system by setting this time period long enough.
In addition, we study the Point-of-Interest (POI) demand modeling issue in urban regions for urban planning. While some efforts have been made for the demand analysis of some specific POI categories, such as restaurants, it lacks systematic means to support POI demand modeling. To this end, we develop a systematic POI demand modeling framework, named Region POI Demand Identification (RPDI), to model POI demands by exploiting the daily needs of people identified from their large-scale mobility data.
Finally, we investigate intelligent bus routing to facilitate urban traveling. Optimal planning for public transportation is one of the keys helping to bring a sustain- able development and a better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. However, in many cities people prefer to take private transportation other than public transportation due to the inconvenience of public transportation services. We focus on the identification and optimization of flawed region pairs with problematic bus routing to improve utilization efficiency of public transportation services, according to people’s real demand for public transportation.Ph.D.Includes bibliographical reference
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