The role of death qualification in venirepersons' evaluations of aggravating and mitigating circumstances in capital trials

Death qualification is a part of voir dire that is unique to capital trials. Unlike all other litigation, capital jurors must affirm their willingness to impose both legal standards (either life in prison or the death penalty). Jurors who assert they are able to do so are deemed “death-qualified” and are eligible for capital jury service: jurors who assert that they are unable to do so are deemed “excludable” or “scrupled” and are barred from hearing a death penalty case. During the penalty phase in capital trials, death-qualified jurors weigh the aggravators (i.e., arguments for death) against the mitigators (i.e., arguments for life) in order to determine the sentence. If the aggravating circumstances outweigh the mitigating circumstances, then the jury is to recommend death; if the mitigating circumstances outweigh the aggravating circumstances, then the jury is to recommend life. The jury is free to weigh each aggravating and mitigating circumstance in any matter they see fit. Previous research has found that death qualification impacts jurors' receptiveness to aggravating and mitigating circumstances (e.g., Luginbuhl & Middendorf, 1988). However, these studies utilized the now-defunct Witherspoon rule and did not include a case scenario for participants to reference. The purpose of this study was to investigate whether death qualification affects jurors' endorsements of aggravating and mitigating circumstances when Witt, rather than Witherspoon, is the legal standard for death qualification. Four hundred and fifty venirepersons from the 11 th Judicial Circuit in Miami, Florida completed a booklet of stimulus materials that contained the following: two death qualification questions; a case scenario that included a summary of the guilt and penalty phases of a capital case; a 26-item measure that required participants to endorse aggravators, nonstatutory mitigators, and statutory mitigators on a 6-point Likert scale; and standard demographic questions. Results indicated that death-qualified venirepersons, when compared to excludables, were more likely to endorse aggravating circumstances. Excludable participants, when compared to death-qualified venirepersons, were more likely to endorse nonstatutory mitigators. There was no significant difference between death-qualified and excludable venirepersons with respect to their endorsement of 6 out of 7 statutory mitigators. It would appear that the Furman v. Georgia (1972) decision to declare the death penalty unconstitutional is frustrated by the Lockhart v. McCree (1986) affirmation of death qualification. ^

Space time coding for polynomial phase modulated signals

Polynomial phase modulated (PPM) signals have been shown to provide improved error rate performance with respect to conventional modulation formats under additive white Gaussian noise and fading channels in single-input single-output (SISO) communication systems. In this dissertation, systems with two and four transmit antennas using PPM signals were presented. In both cases we employed full-rate space-time block codes in order to take advantage of the multipath channel. For two transmit antennas, we used the orthogonal space-time block code (OSTBC) proposed by Alamouti and performed symbol-wise decoding by estimating the phase coefficients of the PPM signal using three different methods: maximum-likelihood (ML), sub-optimal ML (S-ML) and the high-order ambiguity function (HAF). In the case of four transmit antennas, we used the full-rate quasi-OSTBC (QOSTBC) proposed by Jafarkhani. However, in order to ensure the best error rate performance, PPM signals were selected such as to maximize the QOSTBC’s minimum coding gain distance (CGD). Since this method does not always provide a unique solution, an additional criterion known as maximum channel interference coefficient (CIC) was proposed. Through Monte Carlo simulations it was shown that by using QOSTBCs along with the properly selected PPM constellations based on the CGD and CIC criteria, full diversity in flat fading channels and thus, low BER at high signal-to-noise ratios (SNR) can be ensured. Lastly, the performance of symbol-wise decoding for QOSTBCs was evaluated. In this case a quasi zero-forcing method was used to decouple the received signal and it was shown that although this technique reduces the decoding complexity of the system, there is a penalty to be paid in terms of error rate performance at high SNRs.

Planning Buy-Sell Agreements In The Hospitality Industry

In the article - Planning Buy-Sell Agreements In The Hospitality Industry - by John M. Tarras, Assistant Professor, School of Hotel, Restaurant and Institutional Management at Michigan State University, the author initially observes: “The vast majority of hospitality firms (restaurants, hotels, etc.) would be considered closely-held corporations. As such, they have unique planning problems compared to large, publicly-traded hospitality firms. One area of special concern to the closely-held hospitality firm is the planning and adoption of a buy-sell agreement.” The above thesis statement outlines the heart of the article; the buy-sell agreement in regard to smaller [closely held, as Tarras calls them] corporations. The theory is narrow and pro-active, spanning the gap between personal-to-corporate stock manipulations. “The primary purpose of a buy-sell agreement is to contribute to the orderly transfer of a shareholder's stock in a hospitality firm upon some future incident [typically retirement, withdrawal of a shareholder, disability, or death], as Tarras defines the concept. “The hospitality firm or the other shareholders would be committed to purchase the departing shareholder's stock at an agreed upon price and method, and to ensure that ample cash will be obtainable for such an impending sale. The buy-sell agreement provides a market for the shareholder or the shareholder's estate for the sale of otherwise illiquid stock,” the author further provides as canons of buy-sell agreements. In defining the buy-sell agreement with restrictive clauses, Tarras demonstrates, “…many closely-held hospitality firms desire to limit ownership to those individuals, either family or principal corporate employees, who are essential to the well-being of the firm.” Tarras says, another element of the buy-sell agreement is to furnish the departing shareholder with liquidity. “…there typically is some form of cash down payment with the remainder denoted by an interest-bearing promissory note [usually 5 to 15 years],” he informs. “The departing shareholders may require that the hospitality firm pledge the assets of the firm and that the remaining shareholders personally guarantee the promissory note.” “…the most frequent reason for establishing buy-sell agreements is for estate planning purposes,” Tarras says. There are tax advantages and liabilities for both the seller and buyer of stock via the buy-sell agreement, and the author enumerates many of these. One, big advantage of the buy-sell agreement is that it provides for the running of the company with a minimum of disruption through the stock-cash transition process, Tarras offers.

Branch and price solution approach for order acceptance and capacity planning in make -to -order operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. ^ For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver.^ The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. ^ The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.^

On-Line Real-Time Service-Oriented Task Scheduling Using TUF

We present our approach to real-time service-oriented scheduling problems with the objective of maximizing the total system utility. Different from the traditional utility accrual scheduling problems that each task is associated with only a single time utility function (TUF), we associate two different TUFs—a profit TUF and a penalty TUF—with each task, to model the real-time services that not only need to reward the early completions but also need to penalize the abortions or deadline misses. The scheduling heuristics we proposed in this paper judiciously accept, schedule, and abort real-time services when necessary to maximize the accrued utility. Our extensive experimental results show that our proposed algorithms can significantly outperform the traditional scheduling algorithms such as the Earliest Deadline First (EDF), the traditional utility accrual (UA) scheduling algorithms, and an earlier scheduling approach based on a similar model.

Intelligent retrieval system for conditions of contract documents in construction

The outcome of this research is an Intelligent Retrieval System for Conditions of Contract Documents. The objective of the research is to improve the method of retrieving data from a computer version of a construction Conditions of Contract document. SmartDoc, a prototype computer system has been developed for this purpose. The system provides recommendations to aid the user in the process of retrieving clauses from the construction Conditions of Contract document. The prototype system integrates two computer technologies: hypermedia and expert systems. Hypermedia is utilized to provide a dynamic way for retrieving data from the document. Expert systems technology is utilized to build a set of rules that activate the recommendations to aid the user during the process of retrieval of clauses. The rules are based on experts knowledge. The prototype system helps the user retrieve related clauses that are not explicitly cross-referenced but, according to expert experience, are relevant to the topic that the user is interested in.

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.

Delay-Sensitive Service Request Scheduling for Cloud Computing

Cloud computing realizes the long-held dream of converting computing capability into a type of utility. It has the potential to fundamentally change the landscape of the IT industry and our way of life. However, as cloud computing expanding substantially in both scale and scope, ensuring its sustainable growth is a critical problem. Service providers have long been suffering from high operational costs. Especially the costs associated with the skyrocketing power consumption of large data centers. In the meantime, while efficient power/energy utilization is indispensable for the sustainable growth of cloud computing, service providers must also satisfy a user's quality of service (QoS) requirements. This problem becomes even more challenging considering the increasingly stringent power/energy and QoS constraints, as well as other factors such as the highly dynamic, heterogeneous, and distributed nature of the computing infrastructures, etc. In this dissertation, we study the problem of delay-sensitive cloud service scheduling for the sustainable development of cloud computing. We first focus our research on the development of scheduling methods for delay-sensitive cloud services on a single server with the goal of maximizing a service provider's profit. We then extend our study to scheduling cloud services in distributed environments. In particular, we develop a queue-based model and derive efficient request dispatching and processing decisions in a multi-electricity-market environment to improve the profits for service providers. We next study a problem of multi-tier service scheduling. By carefully assigning sub deadlines to the service tiers, our approach can significantly improve resource usage efficiencies with statistically guaranteed QoS. Finally, we study the power conscious resource provision problem for service requests with different QoS requirements. By properly sharing computing resources among different requests, our method statistically guarantees all QoS requirements with a minimized number of powered-on servers and thus the power consumptions. The significance of our research is that it is one part of the integrated effort from both industry and academia to ensure the sustainable growth of cloud computing as it continues to evolve and change our society profoundly.