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Here we report two highly emissive perylene diimide (PDI)-based metallacages and explore their complexation with polycyclic aromatic hydrocarbons, such as pyrene, triphenylene, and perylene. The fluorescence quantum yields of metallacages exceed 90% and their binding constants with perylene can reach as high as 2.41 × 104 M-1 in acetonitrile. These features enable further tuning of the emission of the host-guest complexes to obtain white-light emission based on the complementary orange emission of the metallacages and the blue emission of perylene. Moreover, owing to the huge differences of their quantum yields in solution and in the solid state, the host-guest complexes are successfully employed for information encryption. This study offers a general approach for the construction of emissive metallacages and explores their application for information encryption.

Ferroptosis is a mode of regulated cell death that depends on iron and plays pivotal roles in regulating various biological processes in human cancers. However, the role of ferroptosis in gastric cancer (GC) remains unclear. In our study, a total of 2721 differentially expressed genes (DEGs) were filtered based on The Cancer Genome Atlas (TCGA) ( n = 375 ) dataset. Weighted gene coexpression network (WGCNA) analysis was then used and identified 7 modules, of which the blue module with the most significant enrichment result was selected. By taking the intersections of the blue module and ferroptosis-related genes (FRGs), we obtained 23 common genes. Functional analysis was performed to explore the biological function of the genes of interest, and with univariate Cox regression (UCR) analysis, survival genes were screened to construct a prognostic model based on 3 genes (SLC1A5, ANGPTL4, and CGAS), which could play a role in predicting the survival of GC patients. UCR and multivariate Cox regression (MCR) analysis revealed that the prognostic index could be used as an independent prognostic indicator and validated using another GSE84437 dataset. Notably, patients in the high-risk group had higher mutation frequencies, such as TTN and TP53. TIMER analysis demonstrated that the risk score strongly correlated with macrophage and CD4+ T cell infiltration. In addition, the high- and low-risk groups illustrated different distributions of different immune statuses. Furthermore, the low-risk group had a higher immunophenoscore (IPS), which meant a better response to immune checkpoint inhibitors (ICIs). Finally, gene set enrichment analysis (GSEA) revealed several significant pathways involved in GC. In this study, a novel FRG signature was built that could predict GC prognosis and reflect the status of the tumor immune microenvironment.

Fiber-reinforced polymer (FRP) wrapping has become an attractive strengthening technique for concrete columns. Within this strengthening technique, FRP jackets are wrapped around the concrete column with the fibers in the jacket being oriented in the hoop direction. In practice, the FRP jackets can be either continuous or discontinuous along the column height and thus the resulting column is referred to as FRP fully or partially wrapped concrete columns. Existing research has demonstrated that the FRP partial wrapping strengthening technique by discrete FRP strips (rings) is a promising and economic alternative to the FRP full wrapping strengthening technique. Although a number of experimental investigations have been conducted on FRP partially wrapped concrete columns, the stress-strain behavior of FRP-confined concrete in partially wrapped concrete columns is not yet completely understood. This paper presents an experimental program to investigate the axial compressive behavior of circular concrete columns partially wrapped with FRP strips. The test results are presented and compared with five existing representative stress-strain models to examine the reliability and accuracy of each model. It has been demonstrated that the Teng et al.’s (2007) model is superior to the other four representative models and it provides reasonably accurate predictions of the ultimate axial stress of FRP partially wrapped concrete while it usually underestimates the ultimate axial strain .

Self-healing hydrogels have a great potential application in 3D printing, soft robotics, and tissue engineering. There have been a large number of successful strategies for developing hydrogels that exhibit rapid and autonomous recovery. However, developing a gel with an excellent self-healing performance within several seconds is still an enormous challenge. Here, an ultrafast self-healing hydrogel based on an agarose/PVA double network (DN) is presented. The gel utilizing a dynamic borate bond exhibits 100% cure in strength and elongation after healing for 10 s in air, and this hydrogel shows an excellent self-healing property underwater as well. In addition, the agarose/PVA DN hydrogel exhibits a smart self-healing property for an in situ priority recovery, ensuring that the shape and the function are the same as those of the original one. With the combination of self-healing properties, such a hydrogel could be applied to a board range of areas.

Fiber-reinforced polymer (FRP) jacketing/wrapping has become an attractive strengthening technique for concrete columns. Wrapping an existing concrete column with continuous FRP jackets with the fiber in the jacket being oriented in the hoop direction is referred to as FRP full wrapping strengthening technique. In practice, however, strengthening concrete columns with vertically discontinuous FRP strips is also favored and this technique is referred to as FRP partial wrapping strengthening technique. Existing research has demonstrated that FRP partial wrapping strengthening technique is a promising and economical alternative to the FRP full wrapping strengthening technique. Although extensive experimental investigations have hitherto been conducted on partially FRP-confined concrete columns, the confinement mechanics of confined concrete in partially FRP-confined circular columns remains unclear. In this paper, an experimental program consisting of fifteen column specimens was conducted and the test results were presented. A reliable three-dimensional (3D) finite element (FE) approach for modeling of partially FRP-confined circular columns was established. In the proposed FE approach, an accurate plastic-damage model for concrete under multiaxial compression is employed. The accuracy of the proposed FE approach was verified by comparisons between the numerical results and the test results. Numerical results from the verified FE approach were then presented to gain an improved understanding of the behavior of confined concrete in partially FRP-confined concrete columns.

This article estimates the impact of Taiwan’s Entry into the WTO on the movie box office. A sample of 2392 box office movies for the period 2002–2010 was examined by using difference-in-differences (DiD) models. The impact of Taiwan’s entry into the WTO on the movie box office was found to be negative by at least 1.83 million USD per year. Contrary to the normal perception of a preference for foreign (Hollywood) movies, the evidence based on a short-run analysis shows that Taiwanese preferred local movies. Compared to foreign movies, a local movie’s box office receipts were on average 2.03–2.41 % higher, ceteris paribus. The evidence corresponds to the discussion of the cultural premium.

In this paper, we present a method for resource allocation for small cells that integrate licensed and unlicensed RF operations motivated by the widespread WiFi hotspots and the common inclusion of WiFi interface in most cellular terminals. Small cells have proven popular for cell coverage enhancement and traffic offloading from macrocells. We formulate an optimization problem that jointly allocates resources over both licensed and unlicensed bands with the goal of maximizing sum small cell user equipment (SUE) rate while achieving fairness among these user equipments and controlling inter-cell interference to neighboring macrocell users. The proposed solution further considers the quality of service (QoS) requirement of SUE traffics to be distributed over both licensed and unlicensed bands. We show the formulation of the proposed optimization problem as an efficient and low complexity linear programming. We further show that our problem formulation can be modified to maximize the revenue of mobile network operators. Our proposed solution achieves better performance than several existing solutions.

We consider a femto cellular network consisting of multiple neighboring femtocells, e.g., in an enterprise deployment such as shopping malls, stadiums, or corporate premises. We present a practical but suboptimal channel assignment and interference management algorithm for fractional frequency reuse (FFR) wireless networks. More specifically, we propose an adaptive graph coloring approach for resource allocation with the goal of interference management among femtocells as well as achieving fairness among users. While the global-optimum solution has exponential complexity, our proposed scheme has a linear complexity in the number of femtocells. Although suboptimal, we have evaluated our algorithm in small scenarios, where direct evaluation is possible, and found that the achieved minimum user rate using the proposed algorithm is 85% of the optimal minimum rate. Additionally, we have analyzed several practical design considerations of our proposal such as channel feedback, latency, and computational complexity. We demonstrate the performance of our proposed solution against various alternatives and show that it provides better performance under various environment parameters. For example, in a dense femtocell deployment, the performance was improved by 47% over a full frequency reuse scheme.

This paper aims to investigate whether banks exploit their information advantage over bank-dependent borrowers, analyzing the impact of capital level on banking credit risk-taking under syndication loans. By using a unique data composed of 4828 syndicated loan of publicity banks facilities from the U.S. for the period 1987-2010, we propose theoretical issues of the impact and effectiveness of banks’ credit risk-taking from the perspectives of borrowers’ bank dependent. The results show that there is positive correlation between the ratios of bank’s capital over its total assets and banks’ credit risk-taking. It implies that banks with lower capital level charge higher lending spread for borrowers with fewer cash flows; hence the banks would bear a lower probability of default.

The future power grid is expected to provide unprecedented flexibility in how energy is generated, distributed, and managed, which increasingly necessitates an ability to perform accurate short-term small-scale electricity load and generation forecasting, e.g., at the level of individual buildings or sites. In this paper, we present a novel building-level neural network-based ensemble model for day-ahead electricity load forecasting and show that it outperforms the previously established best performing model, SARIMA, by up to 50%, in the context of load data from half a dozen operational commercial and industrial sites. In addition, we show a straightforward, automated way to select model parameters, making our model practical for use in real deployments.

This study constructs a flexible range-based volatility model by considering extreme-value information to explore the volatility and dependence structures between the oil price and the US dollar exchange rate. An asset-allocation strategy is implemented to evaluate the economic value and confirm the efficiency of this model. The empirical results indicate that the use of extreme-value information can not only enhance the explanatory power of volatility structures, but also sort out the asymmetric volatility effect in the oil market. Besides, investors can obtain extra benefits of between 72 and 713 annualized basis points by incorporating extreme-value information into their asset-allocation strategies; less risk-averse investors can generate higher benefits. The empirical results have potentially important implications for asset allocation and risk management.

The number of small-and mid-cap companies increases in recent decades due to the change on technologies. A considerable number of information systems are thus built for efficient production and management. As a result, a large number of operations and data are generated. The internal IT unit must have the ability to process the large amount of data, provide different services for each kinds of users, and make proper data backup. Because of the limited human resources and budget, a self-owned cloud storage system, that can effectively unify the existing and future storage system, may be a good choice to fulfill the needs. Functions that should be considered in the cloud storage system include: storage resource virtualization, construction of storage pools, supply of online data access, and backup of web services. In this study, we compare three common file systems, namely GlusterFS, Lustre, and Hadoop, on the time cost and efficiency as well as build up a cloud storage system combining these three file systems. Experimental results are as follows. First, the Hadoop has the best performance when the storage is of larger than 200MB. Second, the Lustre performs the best when the storage is of less than 200MB and more than 4MB file. Last, the GlusterFS is the best when the storage is of less than 4MB.

The recent advancements of IoT technologies, including broad penetration of Internet-connected smart appliances such as remotely controllable LED lights, thermostats, have changed the way we interact with the appliances in our homes and perform our daily activities. However, the significant heterogeneity in the emerging IoT devices has led to fragmented smart-home systems in which each single appliance vendor provides proprietary solution for appliance specific connectivity and user experience. To address this challenge, we present SPOT, a smartphone-based platform for multi-vendor smart-home appliances. SPOT consists of several novel mechanisms, including open device driver models using XML, which allow vendor-independent, user-driven device driver implementation and unified appliance control. To validate the flexibility and generality of our approach, we have built a SPOT prototype on Android platform that supports 8 real IoT devices in the market from different vendors.

While large-scale residential demand response (DR) has been considered promising because of the significant flexibility for controlling electricity demand, customer acquisition and engagement are the major barriers hindering its penetration. To overcome the challenges, we propose a comprehensive DR system framework both for utilities to support multiple DR programs and a wide range of controllable resources, i.e., a variety of Internet of Things (IoT) devices, in residential households and for customers to enjoy two-way, timely communication with utility companies and centralized control over heterogeneous IoT devices in their households (i.e., smart appliances), which facilitates their DR participation. As the key enabler of these advantages, we propose the use of mobile app DR client. Pilot-ready prototypes of the mobile app and DR server are demonstrated and evaluated.

Residential demand response (DR) services have the great potential of growth, yet they are not adopted extensively. A comprehensive residential DR framework proposed in [2] connects utilities with DR participants via mobile apps, supports multiple DR programs, and enables fine-granulated control. In this paper, we focus on mathematical modeling and solutions for economically optimal operation for popular DR programs so that utilities can appropriately select and combine those programs to meet their needs. Performances of multiple DR programs are evaluated via simulations based on real-world data.

Manuscript Type: Empirical Research Question/Issue: We set out in this study to explore the overall impact of external financing needs on corporate governance and firm value, arguing that external financing needs have extremely important impacts on corporate governance, essentially because external financing can prove to be very costly, largely as a result of asymmetric information. Thus, we suggest that improvements in corporate governance of firms with external financing needs could help to reduce costs of outside equity financing. Research Findings/Insights: Our results reveal that it is in fact firm valuation that has an effect on governance practices, as opposed to the reverse, and that external financing needs appear to strengthen the influence of the quality of corporate governance practices on firm value. Theoretical/Academic Implications: The external forces considered in this study are product market competition, investment opportunities, and external financing needs, with particular emphasis being placed upon the impact of external financing needs, since this relates directly to outside shareholders. Given that poor corporate governance practices signal higher asymmetric information costs, and thus, lead to an increase in the costs of raising external capital, it is important to gain a comprehensive understanding of the impact of external financing needs on firm value and corporate governance. Practitioner/Policy Implications: Our results demonstrate the important implications that corporate governance practices have for those firms with a particularly strong need for external equity, and the fact that external financing needs provide incentives for firms to seek out ways of making improvements to the overall quality of their corporate governance practices.

This paper sets out to investigate the effects of disclosure, and other corporate governance mechanisms, on equity liquidity, arguing that those companies adopting poor information transparency and disclosure practices will experience serious information asymmetry. Since poor corporate governance leads to greater information asymmetry, liquidity providers will incur relatively higher adverse information risks and will therefore offer higher information asymmetry components in their effective bid-ask spreads. The Transparency and Disclosure (T&D) rankings of the individual stocks on the S&P 500 index are employed to examine whether firms with greater T&D rankings have lower information asymmetry components and lower stock spreads. Our results reveal that the economic costs of equity liquidity, i.e. the effective spread and the quoted half-spread, are greater for those companies with poor information transparency and disclosure practices.

Heating, Ventilation and Air Conditioning (HVAC) system is largest energy consumer in buildings. Worldwide, buildings consume 20% of the total energy production. Therefore, increasing efficiency of the HVAC system will result in significant financial savings. As one solution, Thermal Energy Storage (TES) tanks are being utilized with buildings to store excess energy to be reused later. An optimal control strategy is crucial for optimal usage. Therefore, this paper presents a novel control framework based on Artificial Neural Networks (ANN) for optimally controlling a TES for achieving increased savings. The presented ANN controller utilizes 3 main inputs: 1) current TES energy availability, 2) predicted building power requirement, and 3) predicted utility load/price. In addition to the design details of the control framework, this paper presents implementation details of the ANN controller. Further, experiments on several test cases were carried out and the paper presents the experimental setup and obtained results for each test case. Performance of the presented ANN control framework was compared against a classical proportional derivative (PD) controller. It was observed that the presented framework resulted in better cost savings than the classical controller consistently for all the experimental test cases.

Wireless sensor networks have gained considerable attention in the past few years. They have found application domains in battlefield communication, homeland security, pollution sensing, and traffic monitoring. As such, there has been an increasing need to define and develop simulation frameworks for carrying out high-fidelity WSN simulation. In this article we present a modeling, simulation, and emulation framework for WSNs in J-Sim - an open source, component-based compositional network simulation environment developed entirely in Java. This framework is built on the autonomous component architecture and extensible internetworking framework of J-Sim, and provides an object-oriented definition of target, sensor, and sink nodes, sensor and wireless communication channels, and physical media such as seismic channels, mobility models, and power models (both energy-producing and energy-consuming components). Application-specific models can be defined by subclassing classes in the simulation framework and customizing their behaviors. We also include in J-Sim a set of classes and mechanisms to realize network emulation. We demonstrate the use of the proposed WSN simulation framework by implementing several well-known localization, geographic routing, and directed diffusion protocols, and perform performance comparisons (in terms of the execution time incurred and memory used) in simulating WSN scenarios in J-Sim and ns-2. The simulation study indicates the WSN framework in J-Sim is much more scalable than ns-2 (especially in memory usage). We also demonstrate the use of the WSN framework in carrying out real-life full-fledged Future Combat System (FCS) simulation and emulation

In the paper, we devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of (a) a static backbone of sparsely placed high-capability sensors which assume the role of a cluster head (CH) upon triggered by certain signal events; and (b) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a pre-determined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs cooperate with one another to ensure that for the most of time only one CH (preferably the CH that is closest to the target) is active; (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with non-redundant, essential information to determine the target location; and (I3) both packets with which sensors respond to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we show with the use of Voronoi diagram, the CH that is usually closest to the target is (implicitly) selected as the leader and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred.

It is well known that the performance of TCP deteriorates in a mobile wireless environment. This is due to the fact that although the majority of packet losses are results of transmission errors over the wireless links, TCP senders still take packet loss as an indication of congestion, and adjust their congestion windows according to the additive increase and multiplicative decrease (AIMD) algorithm. As a result, the throughput attained by TCP connections in the wireless environment is much less than it should be. The key problem that leads to the performance degradation is that TCP senders are unable to distinguish whether packet loss is a result of congestion in the wireline network or transmission errors on the wireless links. In this paper, we propose a light-weight approach, called syndrome, to improving TCP performance in mobile wireless environments. In syndrome, the BS simply counts, for each TCP connection, the number of packets that it relays to the destination host so far, and attaches this number in the TCP header. Based on the combination of the TCP sequence number and the BS-attached number and a solid theoretical base, the destination host will be able to tell where (on the wireline or wireless networks) packet loss (if any) occurs, and notify TCP senders (via explicit loss notification, ELN) to take appropriate actions. If packet loss is a result of transmission errors on the wireless link, the sender does not have to reduce its congestion window. Syndrome is grounded on a rigorous, analytic foundation, does not require the base station to buffer packets or keep an enormous amount of states, and can be easily incorporated into the current protocol stack as a software patch. Through simulation studies in ns-2 (UCB, LBNL, VINT network simulator, http://www-mash.cs.berkeley.edu/ns/), we also show that syndrome significantly improves the TCP performance in wireless environments and the performance gain is comparable to the heavy-weight SNOOP approach (either with local retransmission or with ELN) that requires the base station to buffer, in the worst case, a window worth of packets or states. Copyright © 2001 John Wiley & Sons, Ltd.

Due to the real time changes in energy prices, demand response for residential customers who pay for fixed energy rates helps the utility to reduce its costs. However, residential demand response market is largely untapped, mainly due to the lack of customers' participation. This paper discusses two demand response programs for residential customers. These programs are not restricted to peak hours and they are completely voluntary. Moreover, they provide a tradeoff between the incentive that the customers receive and the discomfort caused for the customers by participating in the DR program. This trade-off can potentially result in high customer participation. Using the publicly available data, we evaluate the savings that the customers and utility can expect from performing each of these programs.

Demand Response (DR) is a promising technology for meeting the world's ever increasing energy demands without corresponding increase in energy generation, and for providing a sustainable alternative for integrating renewables into the power grid. As a result, interest in automated DR is increasing globally and has led to the development of OpenADR, an internationally recognized standard. In this paper, we propose security-enhancement mechanisms to provide DR participants with verifiable information that they can use to make informed decisions about the validity of received DR event information.

This correspondence presents the first known results of complete recognition of continuous Mandarin speech for the Chinese language with very large vocabulary but very limited training data. Various acoustic and linguistic processing techniques were developed, and a prototype system of a continuous speech Mandarin dictation machine has been successfully implemented. The best recognition accuracy achieved is 92.2% for finally decoded Chinese characters.

This work studies dual-access-technology femtocells equipped with both cellular and non-cellular air interfaces with the goal of increasing per-user throughput as well as achieving user fairness. We target enterprise environments characterized by high data rates and/or dense user terminals such as corporate premises, shopping malls, or stadiums. To meet this high data demand, multiple femtocells are deployed for which the major challenge is interference management. We propose an architecture for interference management and resource allocation over both cellular and non-cellular bands. Numerical evaluation shows that our proposed solution outperforms other existing solutions.

WiFi interfaces have been recently incorporated in most cellular user equipments (UEs). In current practice, the UE selects either the licensed band for cellular technologies or the unlicensed WiFi band depending on the signal quality of both bands. At the same time, small (pico or femto) cells have also become popular means to offload traffic from traditional macro-cell networks and to improve cell coverage. This work presents a method for dynamic switching and aggregation of licensed and unlicensed bands in small cells for traffic offloading and per-user throughput enhancement. Our proposed method allows small cells to jointly control transmission in both licensed and unlicensed bands in order to maximize the sum of small cell user throughputs over both bands while constraining the interference effect to maintain the Quality of Service (QoS) requirements for macrocell user equipments. Performance evaluation shows that our proposed scheme outperforms other existing solutions.

The electricity grid is evolving from a monolithic centralized system to a smart distributed system, composed of distributed and renewable generation resources, where power supply and demand balancing is needed at a microgrid scale. In this paper, we explore model selection criteria for short-term microgrid-level load predictions. To this end, we experiment with five different models in the context of usage traces from six diverse sites collected over a period of eight months. We find that model selection is heavily influenced by the variability in the data and that models which do not use weather forecast information but rely only on historical usage data perform better on sites with highly variable loads.

The growth in Plug-In Electric Vehicle (EV) poses a great challenge to utilities due to the potential impact to power distribution systems. We propose an intelligent charging method to minimize the operation cost as well as consider both constraints of power grid and customers' schedule. To address the highly non-linear behavior in the power system, a novel Node Sensitivity Approach (NSA) is proposed to approximate the sensitivity of each EV load in the grid. The simulation results show the proposed method not only achieves the minimum operation cost but also avoids violating the system constraints.

Distributed antenna systems (DAS) have the advantages of achieving low system costs in deployment, operation, and management. While a simple blanket transmission scheme in DAS shows poor performance, several practical limitations prevent the beamforming scheme in DAS from being adopted in the existing systems. By taking advantages of spatial proximity and diversity between different radio equipments (REs) and mobile stations in DAS, we propose a novel approach to enhance the system capacity of orthogonal frequency division multiplexing (OFDM) DAS through power coordination between REs. In comparison with standalone base station deployment, blanket transmission scheme, and beamforming scheme, our proposal achieves higher system capacity by 35.9%, 82.2% and 14.2%, respectively.

As the demand for mobile broadband services are growing, cellular networks are heading towards their fourth generation (4G). Long Term Evolution (LTE) is emerging as a major candidate for 4G cellular networks. 4G networks are to support a myriad of applications which have different error tolerance and delay requirements. MIMO (Multiple Input Multiple Output) technology combined with OFDMA and more efficient modulation/coding schemes (MCS) are key physical layer technologies in LTE networks. However, in order to fully utilize the benefits of the advances in physical layer technologies MIMO configuration and MCS need to be dynamically adjusted to derive the promised gains of 4G at the application level. This paper proposes an application-profile based link adaptation architecture that adapts the physical layer transmission parameters based not only on channel conditions but also on application-specific requirements to transfer physical layer gains to the application layer.

The main purpose of this chapter is to discuss important quantitative methods used to do the research in quantitative finance and risk management. We first discuss statistics theory and methods. Second, we discuss econometric methods. Third, we discuss mathematics. Finally, we discuss other methods such as operation research, stochastic process, computer science and technology, entropy, and fuzzy set theory.

The main purpose of this chapter is to explore important finance theories. First, we discuss discounted cash-flow valuation theory (classical financial theory). Second, we discuss the Modigliani and Miller (M and M) valuation theory. Third, we examine Markowitz portfolio theory. We then move on to the capital asset pricing model (CAPM), followed by the arbitrage pricing theory. Finally, we will look at the option pricing theory and futures valuation and hedging.

This research sets out to investigate the relationship between credit risk and equity liquidity. We posit that as the firm’s default risk increases, informed trading increases in the firm’s stock and uninformed traders exit the market. Market-makers widen spreads in response to the increased probability of trades against informed investors. Using the default likelihood measure calculated by Merton’s method, this paper investigates whether financially ailing firms do indeed have higher bid-ask spreads. The panel threshold regression model is employed to examine the possible non-linear relationship between credit risk and equity liquidity. Since high default probability and worse economic prospects lead to greater expropriation by managers, and thus greater asymmetric information costs, liquidity providers will incur relatively higher costs and will therefore offer higher bid-ask spreads. This issue is further analyzed by investigating whether there is any evidence of increased vulnerability in the equity liquidity of firms with high credit risk. Our results show that the effects caused by increased default likelihood might precipitate investors’ loss of confidence in equities trading and thus a decrease in liquidity as evident during the Enron crisis period.

The purpose of this chapter is to discuss the interaction between investment, financing, and dividends policy of the firm. A brief introduction of the policy framework of finance is provided in Sect. 2.1. Section 2.2 discusses the interaction between investment and dividends policy. Section 2.3 discusses the interaction between dividends and financing policy. Section 2.4 discusses the interaction between investment and financing policy. Section 2.5 discusses the implications of financing and investment interactions for capital budgeting. Section 2.6 discusses the implications of different policies on the beta coefficients. The conclusion is presented in Sect. 2.7.

P2P applications are among the most popular Internet applications and constitute a majority of Internet traffic today. From the perspective of network management, a thorough understanding of the stochastic properties of P2P traffic is thus crucial to designing better traffic/resource control algorithms. However, previous work on measuring and analyzing P2P traffic focuses either on a specific type of P2P applications or report only qualitative or quantitative findings in the measured traffic, without systematically analyzing and modeling P2P traffic. In this paper, we aim to bridge the gap and perform a detailed analysis and modeling of P2P traffic of different types. We first gather and identify P2P traffic on a trans-pacific link between Japan and US. Then based on the flow level (transport layer) information, we analyze and model the marginal distributions of the traffic volume, connection duration, and connection interarrival times for P2P connections of Napster, BitTorrent (BT), eDonkey, Gnutella, and Fasttrack, respectively. We also study the burstiness property of P2P traffic generated by these five different applications and the implication/impact of connections with zero traffic volumes. A major finding of our study is that due to the different parameters/strategies used in the file sharing mechanism, P2P traffic of different applications exhibits different traffic characteristics. As a result, the traffic metrics of interest (i.e., the per-connection traffic volume, the connection duration, and the connection interarrival times) cannot be characterized uniformly with a single model among the different types of applications, but instead have to be modeled with different probability distributions.

Modeling and analysis of wireless traffic is fundamental to traffic engineering and resource management. The majority of existing work in this arena has focused on modeling/analyzing aggregate traffic, and little has been done in modeling/analyzing wireless traffic at the per-user level. In this paper, we bridge the gap and perform a detailed analysis and modeling on the traffic generated by individual wireless users, leveraging the data traces collected in a period of 4 months (November 2003 - February 2004) on the Dartmouth campus-wide 802.11 WLANs.Our study indicates that several parameters that characterize the wireless traffic generated by individual users are predictable, such as the traffic volume originating from a user and destined for a specific IP address, the set of destination IP addresses (for which connections initiated by a user are destined), and the patterns by which a wireless user connects to applications. We also model the per-connection traffic volume, the duration, and the interarrival time, of connections issued by a user using Weibull or Pareto distributions, and show that the burstiness of aggregate wireless traffic (which has been reported in the literature) is a direct consequence of the burstiness of the burstiness of traffic generated by individual users. These findings can be used to optimize traffic and resource management and provide better QoS (in terms of availability, resiliency, and performance).

Push-to-talk over cellular (PoC) is considered as one of important applications in next generation networks (NGN). The main objective of this study is to investigate the performance of our PoC system evaluated over three operational 3G networks in the US. Measurements are conducted in two stages. First, several generic measurements such as round trip time (RTT), UDP, TCP, and dormant mode are made to understand the fundamental characteristics of 3G packet switched (PS) networks for the voice and data transmissions of the PoC service. Next, various parameters and features in our PoC software are tested to evaluate the performance of startup latency, voice latency, and voice quality. In conclusion, through empirical study we have shown that PoC applications can achieve high quality in current 3G PS networks. Furthermore from these results we study the network requirements to be solved in NGN for the voice and data communications.

This study investigates the impact of decimalization (penny pricing) on the arbitrage relationship between index exchange-traded funds (ETFs) and E-mini index futures. Our empirical results reveal that subsequent to penny pricing, there is a significant fall in the mean ex-ante arbitrage profit, especially in the cases with higher transaction costs. Using OLS and quantile regressions to control for the influences of changes in market characteristics, we find that the overall pricing efficiency has deteriorated in the post-decimalization period. From the quantile regression analyses, it is found that the pricing efficiency is improved only when an extreme large mispricing signal is observed, implying that due to increased execution risk after decimalization, arbitragers will only execute trades when the expected profit is large enough. These results are consistent with the hypothesis that, due to the lowered market depth and increased execution risks, the introduction of decimalization has in general resulted in weakening the ability and willingness of arbitrageurs to initiate arbitrage trades, which subsequently leads to a reduction in the general efficiency of the cash/futures pricing system.

Distinct from wireless ad hoc networks, wireless sensor networks are data-centric, application-oriented, collaborative, and energy-constrained in nature. In this paper, we formulate the problem of data transport in sensor networks as an optimization problem, with the objective of maximizing the amount of information (utility) collected at sinks, subject to both the channel bandwidth and energy constraints. We then devise a distributed solution of the convex optimization problem, and explore in three directions. First, we devise a simple node capacity estimation method to on-line measure the node capacity. Second, we linearize the energy constraint by properly setting the value of the system lifetime in advance and controlling the data rate of a node so as to sustain its battery lifetime longer than the specified lifetime. Finally, we incorporate the optimization results into routing so as to provide sensors with opportunities to select better routes. The simulation results show that the utility-based approach balances between system utility and system lifetime

Wireless sensor networks (WSNs) have gained considerable attention in the past few years. As such, there has been an increasing need for defining and developing simulation frameworks for carrying out high-fidelity WSN simulation. In this paper, the authors presented a modeling and simulation framework for WSNs in J-Sim - an open-source, component-based compositional network simulation environment that is developed entirely in Java. This framework is built upon the autonomous component architecture (ACA) and the extensible internetworking framework (INET) of J-Sim, and provides an object-oriented definition of (i) target, sensor and sink nodes, (ii) sensor and wireless communication channels, and (iii) physical media such as seismic channels, mobility model and power model (both energy-producing and energy-consuming components). Application-specific models can be defined by sub-classing classes in the simulation framework and customizing their behaviors. The use of the proposed WSN simulation framework was demonstrated by implementing several well-known localization, geographic routing, and directed diffusion protocols. In addition, performance comparisons were performed (in terms of execution time incurred, and the memory used) in simulating several typical WSN scenarios in J-Sim and ns-2. The simulation study indicates that the proposed WSN simulation framework in J-Sim is much more scalable than ns-2 (especially in memory usage).

We propose an enhancement to DSR, called DSR with Connection-Aware Link-state Exchange aNd DiffERentiation (DSR-CALENDER). Specifically, to effectively collect and disseminate neighbor link states to nodes which may potentially use them, we devise a neighbor link-state information exchange mechanism: once a connection has been established, the neighbor link-state information is exchanged among nodes along the route from the source to the destination. As the information of the neighbor lists is piggybacked in data packets, the nodes on the source route are able to learn the partial topology around the neighborhood of the connection. In this manner, DSR-CALENDER balances the gain obtained from additional neighbor link states and the overhead incurred to distribute them. In the case that link failure occurs, an alternate route can be more effectively located in the route cache of the source or the intermediate nodes, and the number of times the route discovery phase is invoked can be reduced. To improve the quality of route caches, we investigate all possible sources from which a node may learn of its link states, and associate each of the sources with a different level of fidelity. In the case that a new/alternate route has to be computed, the level of fidelity is figured into the link cost so as to locate a route that likely exists. The simulation results show that with limited overhead incurred in neighbor list dissemination, DSR-CALENDER outperforms DSR with either path or link caches in terms of packet delivery ratio and route discovery overhead.

Bluetooth is a promising wireless technology that enables portable devices to form short-range wireless ad hoc networks. However, several issues must be properly addressed before its wide deployment, one of which is intra-piconet and inter-piconet scheduling. Several scheduling algorithms have been proposed, with the objective of maximizing throughput while maintaining fairness. None of them consider the issue of providing temporal quality of service (QoS). In this paper, we build upon the distance-constrained task model and its scheduling algorithm, in compliance with the Bluetooth specification, a scheduling algorithm to handle dynamic establishment and termination of connections with temporal QoS requirements. The simulation results show that the proposed algorithm outperforms others in scheduling QoS sensitive connections while maintaining high throughput and low delay for non-QoS-sensitive connection.

With the advancement of MEMS technologies, wireless networks consist of tiny sensor devices hold the promise of revolutionizing sensing in a wide range of application domains because of their flexibility, low cost and ease of deployment. However, the constrained computation power, battery power, storage capacity and communication bandwidth of the tiny devices pose challenging problems in the design and deployment of such systems. Target localization using acoustic signal with tiny wireless devices is a particularly difficult task due to the amount of signal processing and computation involved. In this paper, we provide an in-depth study of designing such wireless sensor networks for real-world acoustic tracking applications. We layout a cluster-based architecture to address the limitations of the tiny sensing devices. To achieve effective utilization of the scarce wireless bandwidth, a quality-driven paradigm to suppress redundant information and resolve contention is proposed. One instance of the quality-driven approach is implemented in the acoustic tracking system, where the quality of the tracking reports can be quantified numerically. We demonstrate the effectiveness of our proposed architecture and protocols using a sensor network testbed based on UCBerkeley mica motes. Considering the performance limitations of tiny sensor devices, the achieved acoustic target tracking accuracy is extraordinarily good. Our experimental study also shows that the acoustic target tracking quality can be indeed measured and used to assist resource allocation decisions. This application-driven design and implementation exercises also serve to identify important areas of further work in in-network processing and communications.

In this paper, we document our design of a MAC protocol, called OSU-MAC, subject to the physical layer characteristics and constraints of a narrow-band wireless modem testbed currently being built at the Ohio State University. The narrow-band wireless modem testbed is expected to support both real-time (bus location tracking) and non-real-time (regular) data applications. A number of techniques are proposed to support QoS imposed by the real-time applications, to deal with the asymmetry on the forward and reverse channels and the half-duplex transmission constraint imposed by the physical layer, and to enhance the error control capability of OSU-MAC. We also present simulation results to demonstrate the key functional characteristics of OSU-MAC.