近年來國內便利商店越開越大，也紛紛以品牌延伸策略推出包羅萬象的產品/服務，與過去將坪效奉為至上準則，且以民生用品販售為主的便利商店發展模式大相逕庭。然而，回顧國內外關於品牌延伸策略的理論研究卻少有針對零售業界的理論模型，更不用說以便利商店產業為研究對象的深入實務探討。因此，本研究希望透過深入訪談與個案分析，歸納出消費者用以評價便利商店品牌延伸活動的評價因子，與探究便利商店業者品牌延伸活動的發展與限制。 透過文獻回顧，本研究提出零售商的品牌延伸策略類型與消費者評價因素為研究架構，配合國內兩大便利商店業者的6個品牌延伸案例為分析對象，以質化研究的個案分析法為主，深度訪談為輔，解構業者在發展不同品牌延伸的背後動機與相關策略活動，並深入了解消費者對於便利商店業者不同品牌延伸策略的看法。 根據研究結果，本研究認為便利商店業者在品牌延伸策略的運用上，應以聚焦核心能力所及範圍為原則，若欲跨足新專業領域，則宜強打配合廠商的品牌，採專業分工方式進行。而消費者在面對便利商店品牌延伸時，則會先經過一涉入程度判定過程，高涉入度消費者在購買決策前會積極蒐集相關資訊，並會和現有產品/服務提供商進行比對，因而面對此類消費者，應以強化品牌概念一致性、透過體驗行銷提高使用轉換率、加強資訊與促銷策略曝光度提高使用誘因，並以強化與配合廠商間的連結來降低認知風險為主要推廣策略；面對以嚐鮮為主要購買決策驅動引擎，並容易隨著口碑起舞的低涉入度消費者，應朝善用數位媒體來增加品牌黏著度，及管理數位口碑來強化口碑影響力，兩大方向著手發展推廣策略。

A significant challenge in understanding the evolution of a software system is coping with the huge amounts of data left behind during the evolution. One strategy for summarizing this data is to visualize its effect on the system’s architecture. Existing tools that implement this strategy often provide mechanisms to filter the data under consideration. However, this filtering is generally limited to showing the evolution over some unbroken sequence of time, for example the changes over the last six months. In this work we present an alternative approach designed to provide a method for examining the net effect of any set of changes on a systems architecture. We also present Motive, a prototype tool that implements this approach, and demonstrate how it can be used to answer questions about software evolution by describing case studies we conducted on two Java systems.

Text describes some of the most common tools used for project management and bug/issue tracking. Describes advantages and possibilities of connecting them to more complex systems of administration and version control. It also describe analysis and design of new FITkit specific system, and ways to catch errors in QDevKit application. The rest of text describes whole implementation including examples of applied constructions and logical layout.

Asset managers continue to prepare physical infrastructure investments needed to accommodate the emerging technologies, namely vehicle connectivity, electrification, and automation. The provision of new infrastructure and modification of existing infrastructure is expected to incur a significant amount of capital investment. Secondly, with increasing EV and CAV operations, the revenues typically earned from vehicle registrations and fuel tax are expected to change due to changing demand for vehicle ownership and amount of travel, respectively. This research estimated (i) the changes in highway expenditures in an era of ECAV operations, (ii) the net change in highway revenues that can be expected to arise from ECAV operations, and (iii) the changes in user equity across the highway user groups (vehicle classes). In assessing the changes in highway expenditures, the research developed a model to predict the cost of highway infrastructure stewardship based on current and/ or future system usage. <div><br></div><div>The results of the research reveal that CAVs are expected to significantly change the travel patterns, leading to increased system usage which in turn results in increased wear and tear on highway infrastructure. This, with the need for new infrastructure to support and accommodate the new technologies is expected to result in increased highway expenditure. At the same time, CAVs are expected to have significantly improved fuel economy as compared to their human driven counterparts, leading to a decrease in fuel consumption per vehicle, resulting in reduced fuel revenues. Furthermore, the prominence of EVs is expected to exacerbate this problem. This thesis proposed a revision to the current user fee structure to address these impacts. This revision contains two major parts designed to address the system efficiency and equity in the near and long term. For the near term, this thesis recommended a variable tax scheme under which each vehicle class pays a different fuel tax rate. This ensures that both equity and system efficiency are improved during the transition to ECAV. In the long term, this thesis recommended supplementing the fuel tax with a distance based VMT tax, applicable to electric vehicles.<br></div>

<p dir="ltr">Cellular-Vehicle to Everything or C-V2X refers to vehicles connected to their surroundings using cellular based networks. With the rise of connected vehicles, C-V2X is emerging as one of the major standards for message transmission in automotive scenarios. The project aims to study the feasibility of C-V2X-based message transmission by building a prototype system, named <b>RampCast</b>, for transmitting traffic information from roadside message boards to vehicles. The RampCast framework would also implement geocasting-based algorithms to deliver messages to targeted vehicles. These algorithms focus on improving location-based message delivery using retransmission and prioritization strategies. The messages used for transmission are selected from the 511 web application built by INDOT, which contains the live traffic information for the state of Indiana which includes Travel Time information, Crash Alerts, Construction Alerts etc.</p><p dir="ltr">The major objectives of this project consist of building the RampCast prototype, a system implementing C-V2X networks using a Software Defined Radio(SDR). The RampCast system implements a Publisher-subscriber messaging architecture with the primary actors being a Road Side Unit(RSU) and a Vehicle Onboard Unit(OBU). A data store containing traffic messages sourced from the 511 API is set up to be the input to the RampCast system. An end-to-end message transmission pipeline is built that would implement message transmission algorithms on the RSU and OBU side. Finally, the performance of message transmission on the RampCast system is evaluated using a metrics-capturing module. The system was evaluated on a test track in Columbus, Indiana. The performance metrics of the system were captured and analyzed, and the system met the key performance indicators for Latency, Packet Delivery Rate, and Packet Inter-reception Rate. The results indicate the satisfactory performance of the C-V2X standard for message transmission in the RampCast traffic guidance scenarios.</p>

便利商店自1979年由統一企業引進台灣，經過三十餘年的發展，目前為一寡占市場，市占率由連鎖超商體系的統一超商(7-ELEVEN)、全家便利商店、萊爾富超商及來來(OK)便利商店占據了將近九成九的市占率，而台灣的便利商店有其特有的產業生態，即現今便利商店中較具規模的品牌，都與上游的品牌商具有相當緊密的連結。而這樣的產業現況，讓台灣食品品牌商與便利商店之間略有體系對抗的意味存在，若將品牌商與通路之間視為一個整體，假設通路商為品牌商的點，其競爭行為即為「多點競爭」。無論對上游的品牌商或是下游的通路商，這種形式的緊密連結使其競爭合作關係的可能性與複雜度也比在單一產業中競爭來的複雜且多元許多。 　　本研究將品牌商與通路商視為一體，觀察其：(1)上下游相互協助的狀況是否存在；(2)上下游規模是否影響其競爭策略的擬定；(3)相互容忍的現況是否存在，採用田野調查法收集資料後，透過內容分析法中質性研究的方式，並且以動態競爭的觀點，對台灣便利商店與品牌商之間的特殊結構做分析，並將資料利用不同的觀點解讀，試圖探究其兩者之間的競爭行為是否具有相互影響的現象，並配合現有的客觀資料，嘗試說明便利商店與品牌商之間的互動關係與成因，也嘗試用品牌與通路規模當作分析的變項，驗證其是否為影響便利商店與品牌商競爭行為間的因子。 　　而結果也與預期相似，(1)品牌商及通路商有相互協助的現象存在。(2)上下游的規模也確實影響其策略的制定，品牌規模越大，越容易作為協助促銷的對象；通路規模越大，也越容易作為推廣品牌的點。(3)多點競爭的相互容忍現象存在於此產業相當多的品類當中，尤其是寡占的品類市場。

本論文主要探討中國上海便利店的現況及發展趨勢、競爭態勢，統一超商在台灣便利店市場之關鍵成功因素，以及統一超商進入中國上海便利店市場策略之探討，並試圖爲統一超商進入中國上海便利店市場提出未來發展策略之建議。 中國上海便利店市場之發展趨勢包括：(1) 密集開店，貼身競爭，以致大多數便利店不是虧損多年，便是薄利經營；(2) 上海便利店85%為本國國營企業，在大多數地區，便利店與超市只有面積大小、品種多少上的區別，並沒有明顯的功能差異；(3) 上海國民所得已將近一萬美元，新一代的中產階級崛起，他們要求好品質、好服務、有安全衛生保障的產品；(4)上海人口多元化顯著，有可能各種便利店包括土便利及洋便利同時存在，便利店可以“一業多態”的存在;(5) 在地法規限制、高成本、發展特許加盟上的困難、及物流系統不成熟皆為進入上海便利店市場的挑戰。 統一超商在台灣便利店市場之關鍵成功因素包括：(1)先佔優勢；(2) 成功的訂定目標客戶及自我定位；(3) 高密度展店及高市占率；(4) 完善的加盟制度；(5) 卡式管理及單店行銷結合；(6) 強大的後台系統; (7) 高效能的物流系統; (8) 靈活的行銷策略; (9) 完整的教育訓練。 從本文的分析中，我們得到: 統一超商進入上海便利店市場最適合採行「差異化集中策略」做為競爭策略。

In the last three decades, the electronic industry has registered a tremendous progress. The continuous and aggressive downsizing of the transistor feature sizes (CMOS scaling) has been the main driver of the astonishing growth and advancement of microelectronic industry. Currently, the CMOS scaling is almost reaching its limits. The gate oxide is now only a few atomic layers thick, and this extremely thin oxide causes a huge leakage current through the oxide. Therefore, a further reduction of the gate oxide thickness is extremely difficult and new materials with higher dielectric constant are being explored. However, the phenomena of oxide breakdown and reliability are still serious issues in these thin oxides. Oxide breakdown exhibits a soft breakdown behavior at low voltages, and this is posing as one of the most crucial reliability issues for scaling of the ultra-thin oxides. In addition, the stress-induced leakage current (SILC) due to oxide has emerged as a scaling problem for the non-volatile memory technologies. In this dissertation, a percolation modeling approach is introduced to study and understand the dramatic changes in the conductivity of a disordered medium. Two different simulation methods of percolative conduction, the site and bond percolation, are studied here. These are used in simulating the post-breakdown conduction inside the oxide. Adopting a Monte-Carlo method, oxide breakdown is modeled using a 2-D percolation theory. The breakdown statistics and post-breakdown characteristics of the oxide are computed using this model. In this work, the effects of different physical parameters, such as dimension and the applied stress are studied. The simulation results show that a thinning of oxide layer and increasing the oxide area result in softening of breakdown. It is observed that the breakdown statistics appear to follow Weibull characteristics. As revealed by simulations, the Weibull slope changes linearly with oxide thickness, while not having a significant change when the area is varied and when the amount of the applied stress is varied. It is shown that the simulation results are well correlated with the experimental data reported in the literature. In this thesis, studying the conduction through the oxide using percolation model, it was discovered that a critical or a quasi-critical phenomenon occurs depending on the oxide dimensions. The criticality of the phase-transition results in a hard breakdown while the soft breakdown occurs due to a quasi-critical nature of percolation for ultra-thin oxides. In the later part of the thesis, a quantum percolation model is studied in order to explain and model the stress induced leakage current. It is explained that due to the wave nature of electrons, the SILC can be modeled as a tunneling path through the stressed oxide with the smaller tunneling threshold compared to the virgin oxide. In addition to the percolation model, a Markov chain theory is introduced to simulate the movement of electron as a random walk inside the oxide, and the breakdown is simulated using this random-walk of electron through the accumulated traps inside the oxide. It is shown that the trapping-detrapping of electrons results in an electrical noise in the post-breakdown current having 1/f noise characteristics. Using simulation of a resistor network with Markov theory, the conductance of the oxide is computed. An analytical study of a 2-D site percolation system is conducted using recursive methods and useful closed-form expressions are derived for specialized networks.

In the last three decades, the electronic industry has registered a tremendous progress. The continuous and aggressive downsizing of the transistor feature sizes (CMOS scaling) has been the main driver of the astonishing growth and advancement of microelectronic industry. Currently, the CMOS scaling is almost reaching its limits. The gate oxide is now only a few atomic layers thick, and this extremely thin oxide causes a huge leakage current through the oxide. Therefore, a further reduction of the gate oxide thickness is extremely difficult and new materials with higher dielectric constant are being explored. However, the phenomena of oxide breakdown and reliability are still serious issues in these thin oxides. Oxide breakdown exhibits a soft breakdown behavior at low voltages, and this is posing as one of the most crucial reliability issues for scaling of the ultra-thin oxides. In addition, the stress-induced leakage current (SILC) due to oxide has emerged as a scaling problem for the non-volatile memory technologies. In this dissertation, a percolation modeling approach is introduced to study and understand the dramatic changes in the conductivity of a disordered medium. Two different simulation methods of percolative conduction, the site and bond percolation, are studied here. These are used in simulating the post-breakdown conduction inside the oxide. Adopting a Monte-Carlo method, oxide breakdown is modeled using a 2-D percolation theory. The breakdown statistics and post-breakdown characteristics of the oxide are computed using this model. In this work, the effects of different physical parameters, such as dimension and the applied stress are studied. The simulation results show that a thinning of oxide layer and increasing the oxide area result in softening of breakdown. It is observed that the breakdown statistics appear to follow Weibull characteristics. As revealed by simulations, the Weibull slope changes linearly with oxide thickness, while not having a significant change when the area is varied and when the amount of the applied stress is varied. It is shown that the simulation results are well correlated with the experimental data reported in the literature. In this thesis, studying the conduction through the oxide using percolation model, it was discovered that a critical or a quasi-critical phenomenon occurs depending on the oxide dimensions. The criticality of the phase-transition results in a hard breakdown while the soft breakdown occurs due to a quasi-critical nature of percolation for ultra-thin oxides. In the later part of the thesis, a quantum percolation model is studied in order to explain and model the stress induced leakage current. It is explained that due to the wave nature of electrons, the SILC can be modeled as a tunneling path through the stressed oxide with the smaller tunneling threshold compared to the virgin oxide. In addition to the percolation model, a Markov chain theory is introduced to simulate the movement of electron as a random walk inside the oxide, and the breakdown is simulated using this random-walk of electron through the accumulated traps inside the oxide. It is shown that the trapping-detrapping of electrons results in an electrical noise in the post-breakdown current having 1/f noise characteristics. Using simulation of a resistor network with Markov theory, the conductance of the oxide is computed. An analytical study of a 2-D site percolation system is conducted using recursive methods and useful closed-form expressions are derived for specialized networks.

<p>  </p> <p>Advancements in technology have propelled the availability of enriched and more frequent information about traffic conditions as well as the external factors that impact traffic such as weather, emergency response etc. Most newer vehicles are equipped with sensors that transmit their data back to the original equipment manufacturer (OEM) at near real-time fidelity. A growing number of such connected vehicles (CV) and the advent of third-party data collectors from various OEMs have made big data for traffic commercially available for use. Agencies maintaining and managing surface transportation are presented with opportunities to leverage such big data for efficiency gains. The focus of this dissertation is enhancing the use of CV data and applications derived from fusing it with other datasets to extract meaningful information that will aid agencies in data driven efficient decision making to improve network wide mobility and safety performance.   </p> <p>One of the primary concerns of CV data for agencies is data sampling, particularly during low-volume overnight hours. An evaluation of over 3 billion CV records in May 2022 in Indiana has shown an overall CV penetration rate of 6.3% on interstates and 5.3% on non-interstate roadways. Fusion of CV traffic speeds with precipitation intensity from NOAA’s High-Resolution Rapid-Refresh (HRRR) data over 42 unique rainy days has shown reduction in the average traffic speed by approximately 8.4% during conditions classified as very heavy rain compared to no rain. </p> <p>Both aggregate analysis and disaggregate analysis performed during this study enables agencies and automobile manufacturers to effectively answer the often-asked question of what rain intensity it takes to begin impacting traffic speeds. Proactive measures such as providing advance warnings that improve the situational awareness of motorists and enhance roadway safety should be considered during very heavy rain periods, wind events, and low daylight conditions.</p> <p>Scalable methodologies that can be used to systematically analyze hard braking and speed data were also developed. This study demonstrated both quantitatively and qualitatively how CV data provides an opportunity for near real-time assessment of work zone operations using metrics such as congestion, location-based speed profiles and hard braking. The availability of data across different states and ease of scalability makes the methodology implementable on a state or national basis for tracking any highway work zone with little to no infrastructure investment. These techniques can provide a nationwide opportunity in assessing the current guidelines and giving feedback in updating the design procedures to improve the consistency and safety of construction work zones on a national level.  </p> <p>CV data was also used to evaluate the impact of queue warning trucks sending digital alerts. Hard-braking events were found to decrease by approximately 80% when queue warning trucks were used to alert motorists of impending queues analyzed from 370 hours of queueing with queue trucks present and 58 hours of queueing without the queue trucks present, thus improving work zone safety. </p> <p>Emerging opportunities to identify and measure traffic shock waves and their forming or recovery speed anywhere across a roadway network are provided due to the ubiquity of the CV data providers. A methodology for identifying different shock waves was presented, and among the various case studies found typical backward forming shock wave speeds ranged from 1.75 to 11.76 mph whereas the backward recovery shock wave speeds were between 5.78 to 16.54 mph. The significance of this is illustrated with a case study of  a secondary crash that suggested  accelerating the clearance by 9 minutes could have prevented the secondary crash incident occurring at the back of the queue. Such capability of identifying and measuring shock wave speeds can be utilized by various stakeholders for traffic management decision-making that provide a holistic perspective on the importance of both on scene risk as well as the risk at the back of the queue. Near real-time estimation of shock waves using CV data can recommend travel time prediction models and serve as input variables to navigation systems to identify alternate route choice opportunities ahead of a driver’s time of arrival.   </p> <p>The overall contribution of this thesis is developing scalable methodologies and evaluation techniques to extract valuable information from CV data that aids agencies in operational decision making.</p>