A high-speed optical star network using TDMA and all-optical demultiplexing techniques

The authors demonstrate the use of time-division multiplexing (TDM) to realize a high capacity optical star network. The fundamental element of the demonstration network is a 10 ps, wavelength tunable, low jitter, pulse source. Electrical data is encoded onto three optical pulse trains, and the resultant low duty cycle optical data channels are multiplexed together using 25 ps fiber delay lines. This gives an overall network capacity of 40 Gb/s. A nonlinear optical loop mirror (NOLM) is used to carry out the demultiplexing at the station receiver. The channel to be switched out can be selected by adjusting the phase of the electrical signal used to generate the control pulses for the NOLM. By using external injection into a gain-switched distributed feedback (DFB) laser we are able to obtain very low jitter control pulses of 4-ps duration (RMS jitter <1 ps) after compression of the highly chirped gain switched pulses in a normal dispersive fiber. This enables us to achieve excellent eye openings for the three demultiplexed channels. The difficulty in obtaining complete switching of the signal pulses is presented. This is shown to be due to the deformation of the control pulse in the NOLM (caused by the soliton effect compression). The use of optical time-division multiplexing (OTDM) with all-optical switching devices is shown to be an excellent method to allow us to exploit as efficiently as possible the available fiber bandwidth, and to achieve very high bit-rate optical networks

Genetic Polymorphisms Associated with Perioperative Joint Infection following Total Joint Arthroplasty: A Systematic Review and Meta-Analysis

The number of orthopedic procedures, especially prosthesis implantation, continues to increase annually, making it imperative to understand the risks of perioperative complications. These risks include a variety of patient-specific factors, including genetic profiles. This review assessed the current literature for associations between patient-specific genetic risk factors and perioperative infection. The PRISMA guidelines were used to conduct a literature review using the PubMed and Cochrane databases. Following title and abstract review and full-text screening, eight articles remained to be reviewed—all of which compared single nucleotide polymorphisms (SNPs) to periprosthetic joint infection (PJI) in total joint arthroplasty (TJA). The following cytokine-related genes were found to have polymorphisms associated with PJI: TNFα (p p p p = 0.002), and IL-1B (p = 0.037). Protein- and enzyme-related genes that were found to be associated with PJI included: MBL (p p p p p p < 0.028). This review compiled a variety of genetic polymorphisms that were associated with periprosthetic joint infections. However, the power of these studies is low. More research must be conducted to further understand the genetic risk factors for this serious outcome

Operational Strategies for Increasing Secondary Materials in Metals Production Under Uncertainty

Increased use of secondary raw materials in metal production offers several benefits including reduced cost and lowered energy burden. The lower cost of secondary or scrap materials is accompanied by an increased uncertainty in elemental composition. This increased uncertainty for different scraps, if not managed well, results in an increased risk that the elemental concentrations in the final products fall outside customer specifications. Previous results show that incorporating this uncertainty explicitly into batch planning can modify the potential use of scrap materials while managing risk. Chance-constrained formulations provide one approach to uncertainty-aware batch planning; however, typical formulations assume normal distributions to represent the compositional uncertainty of the materials. Compositional variation in scrap materials has been shown to have a skewed distribution, and therefore, the performance of these models, in terms of their ability to provide effective planning, it may then be heavily influenced by the structure of the compositional data used. To address this issue, this work developed several approximations for skewed distributional forms within chance-constrained formulations. We explored a lognormal approximation based on Fenton’s method; a convex approximation based on Bernstein inequalities; and a linear approximation using fuzzy set theory. Each of these methods was formulated and case studies executed using compositional data from an aluminum remelter. Results indicate that the relationship between the underlying structure/distribution of the compositional data and how these distributions are formulated in batch planning can modify the use of secondary raw materials.National Science Foundation (U.S.) (Award 1133422