A resilience optimization approach for workforce-inventory control dynamics under uncertainty

The presence of uncertainties in manufacturing systems and supply chains can cause undesirable behavior. Failure to account for these in the design phase can further impair the capability of systems to respond to changes effectively. In this work, we consider a dynamic workforce-inventory control problem wherein inventory planning, production releases, and workforce hiring decisions need to be made. The objective is to develop planning rules to achieve important requirements related to dynamic transient behavior when system parameters are imprecisely known. To this end, we propose a resilience optimization model for the problem and develop a novel local search procedure that combines the strengths of recent developments in robust optimization technology and small signal stability analysis of dynamic systems. A numerical case study of the problem demonstrates significant improvements of the proposed solution in controlling fluctuations and high variability found in the system’s inventory, work-in-process, and workforce levels. Overall, the proposed model is shown to be computationally efficient and effective in hedging against model uncertainties.     

Profiling selected paths with loops

Path profiling records the frequency of each path in an executed program.To accomplish profiling,probes are instrumented in the program and executed as the program runs.So,the number of probes has a significant impact on the efficiency of a profiling technique.By profiling only the interesting paths,existing techniques try to improve the profiling efficiency by reducing the number of probes and optimize path encodings for efficient storage.However,they lack accuracy,waste time on running uninteresting paths,and can mainly deal with acyclic paths.In this article,a novel technique called Profiling Selected Paths(PSP)is introduced to profile selected paths,which enables custom selection for both acyclic and cyclic paths and increases the execution efficiency by early termination on uninteresting paths.Theoretical analysis and experimental evaluation indicate that PSP performs better than existing techniques.     

Metalloorganic solution deposition of ferroelectric PZT films

The metallo-organic solution deposition (MOSD or sol-gel) technique can be used for preparing lead zirconate titanate (PZT) ceramics with a wide range of compositions and crystal structures for electrooptical applications. Film morphology is affected by the stoichiometry of the film, hydrolysis and polymerization of the sol-gel solution, and thermal treatment of the deposited material. The lattice parameter decreases with the amount of titanium in the PZT, in agreement with ceramic data. A slight initial excess of lead in the coating solution is found to improve film morphology. Unlike traditional powder ceramic processing techniques, MOSD permits the growth of small uniform grains that are conducive to achieving consistent electronic and optical properties. Results show that the properties of films prepared by the MOSD process can be tailored to meet the needs of device applications.     

An underwater acoustic MAC protocol using reverse opportunistic packet appending

Underwater communication primarily utilizes propagation of acoustic waves in water. Its unique characteristics, including slow propagation speed and low data rates, pose many challenges to Media Access Control (MAC) protocol design. In most existing handshaking-based underwater MAC protocols, only an initiating sender can transmit data packets to its intended receiver after a channel reservation through a Request-to-Send (RTS)/Clear-to-Send (CTS) handshake. This conventional single-node transmission approach is particularly inefficient in underwater environments, as it does not account for long propagation delays. To improve channel utilization in high latency environments, we propose a novel approach that exploits the idle waiting time during a 2-way handshake to set up concurrent transmissions from multiple nodes. The sender can coordinate multiple first-hop neighbors (appenders) to use the current handshake opportunity to transmit (append) their data packets with partially overlapping transmission times. After the sender finishes transmitting its packets to its own receiver, it starts to receive incoming appended packets that arrive in a collision-free packet train. This not only reduces the amount of time spent on control signaling, but it also greatly improves packet exchange efficiency. Based on this idea, we propose an asynchronous, single-channel handshaking-based MAC protocol based on reverse opportunistic packet appending (ROPA). From extensive simulations (single- and multi-hop networks) and comparisons with several existing MAC protocols, including MACA-U, MACA-UPT, BiC-MAC, Slotted-FAMA, DACAP, unslotted Aloha, we show that ROPA significantly increases channel utilization and offers performance gains in throughput and delay while attaining a stable saturation throughput.     

“Area Without Numbers”: Using Touchscreen Dynamic Geometry to Reason About Shape

Abstract

In this article, we report on two lessons aimed at introducing junior high school students to the idea of shearing in a touchscreen dynamic geometry environment. By using shearing, we hoped to shift students’ attention away from a formula-driven, computational conception of area toward a more geometric one. We found that the students were able to solve several problems involving the comparison of polygons based on verbal and diagrammatic explanations. We describe the features of the touchscreen dynamic geometry technology that supported their learning, as well as the specific role that the teacher played in modeling students’ reasoning about area.     

A survey on algorithms for mining frequent itemsets over data streams

The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend learning has led to the study of online mining of frequent itemsets (FIs). Unlike mining static databases, mining data streams poses many new challenges. In addition to the one-scan nature, the unbounded memory requirement and the high data arrival rate of data streams, the combinatorial explosion of itemsets exacerbates the mining task. The high complexity of the FI mining problem hinders the application of the stream mining techniques. We recognize that a critical review of existing techniques is needed in order to design and develop efficient mining algorithms and data structures that are able to match the processing rate of the mining with the high arrival rate of data streams. Within a unifying set of notations and terminologies, we describe in this paper the efforts and main techniques for mining data streams and present a comprehensive survey of a number of the state-of-the-art algorithms on mining frequent itemsets over data streams. We classify the stream-mining techniques into two categories based on the window model that they adopt in order to provide insights into how and why the techniques are useful. Then, we further analyze the algorithms according to whether they are exact or approximate and, for approximate approaches, whether they are false-positive or false-negative. We also discuss various interesting issues, including the merits and limitations in existing research and substantive areas for future research.     

On tests of rate ratio under standard inverse sampling

Inverse sampling suggests one continues to sample subjects until a pre-specified number of rare events of interest is observed. It is generally considered to be more appropriate than the usual binomial sampling when the subjects come sequentially, when the response probability is rare, and when maximum likelihood estimators of some epidemiological measures are undefined under binomial sampling. Reliable but conservative exact conditional procedure for the ratio of the response probabilities of subject without the attribute of interest has been studied. However, such a procedure is inapplicable to the risk ratio (i.e., ratio of the response probabilities of subject with the attribute of interest). In this paper, we investigate various test statistics (namely Wald-type, score and likelihood ratio test statistics) for testing non-unity risk ratio under standard inverse sampling scheme, which suggests one continue to sample until the predetermined number of index subjects with the attributes of interest is observed. Both asymptotic and numerical approximate unconditional methods are considered for P-value calculation. Performance of these test procedures are evaluated under different settings by means of Monte Carlo simulation. In general, the Wald-type test statistic is preferable for its satisfactory and stable performance with approximate unconditional procedures. The methodologies are illustrated with a real example from a heart disease study.