Design of few-mode fibers with M-modes and low differential mode delay

In this paper, we investigate the design of few-mode fibers (FMFs) guiding 2 to 12 linearly polarized (LP) modes with low differential mode delay (DMD) over the C-band, suitable for long-haul transmission. Two different types of refractive index profile have been considered: a graded-core with a cladding trench (GCCT) profile and a multi-step-index (MSI) profile. The profiles parameters are optimized in order to achieve: the lowest possible DMD and macro-bend losses (MBL) lower than the ITU-T standard recommendation. The optimization results show that the MSI profiles present lower DMD than the minimum achieved with a GCCT profile. Moreover, it is shown that the optimum DMD and the MBL scale with the number of modes for both profiles. The optimum DMD obtained for 12 LP modes is lower than 3 ps/km using a GCCT profile and lower than 2.5 ps/km using a MSI profile. The optimization results reveal that the most preponderant parameter of the GCCT profile is the refractive index relative difference at the core center, Δnco. Reducing Δn co, the DMD is reduced at the expense of increasing the MBL. Regarding the MSI profiles, it is shown that 64 steps are required to obtain a DMD improvement considering 12 LP modes. Finally, the impact of the fabrication margins on the optimum DMD is analyzed. The probability of having a manufactured FMF with 12 LP modes and DMD lower than 12 ps/km is approximately 68% using a GCCT profile and 16% using a MSI profile. © 2013 IEEE.

Design of few-mode fibers with arbitrary and flattened differential mode delay

This letter proposes the use of a refractive index profile with a graded core and a cladding trench for the design of few-mode fibers, aiming an arbitrary differential mode delay (DMD) flattened over the C+ L band. By optimizing the core grading exponent and the dimensioning of the trench, a deviation lower than 0.01 ps/km from a target DMD is observed over the investigated wavelength range. Additionally, it is found that the dimensioning of the trench is almost independent of the target DMD, thereby enabling the use of a simple design rule that guarantees a maximum DMD deviation of 1.8 ps/km for a DMD target between-200 and 200 ps/km. © 2012 IEEE.

A comparison of spatial patterns of TDP-43 cellular inclusions in familial and sporadic frontotemporal lobar degeneration with TDP-43 proteinopathy

Abnormal protein aggregates of transactive response (TAR) DNA-binding protein (TDP-43) in the form of neuronal cytoplasmic inclusions (NCI), oligodendroglial inclusions (GI), neuronal internuclear inclusions (NII), and dystrophic neurites (DN) are the pathological hallmark of frontotemporal lobar degeneration with TDP-43 proteinopathy (FTLD-TDP). To investigate the role of phosphorylated TDP-43 (pTDP-43) in neurodegeneration in FTLD-TDP, the spatial patterns of the pTDP-43-immunoreactive NCI, GI, NII, and DN were studied in frontal and temporal cortex in three groups of cases: (1) familial FTLD-TDP caused by progranulin (GRN) mutation, (2) a miscellaneous group of familial cases containing cases caused by valosin-containing protein (VCP) mutation, ubiquitin associated protein 1 (UBAP1) mutation, and cases not associated with currently known genes, and (3) sporadic FTLD-TDP. In a significant number of brain regions, the pTDP-43-immunoreactive inclusions developed in clusters and the clusters were distributed regularly parallel to the tissue boundary. The spatial patterns of the inclusions were similar to those revealed by a phosphorylation-independent anti-TDP-43 antibody. The spatial patterns and cluster sizes of the pTDP-43-immunoreactive inclusions were similar in GRN mutation cases, remaining familial cases, and in sporadic FTLD-TDP. Hence, pathological changes initiated by different genetic factors in familial cases and by unknown causes in sporadic FTLD-TDP appear to follow a parallel course resulting in very similar patterns of degeneration of frontal and temporal lobes.

Non-stationary stochastic inventory lot-sizing with emission and service level constraints in a carbon cap-and-trade system

Firms worldwide are taking major initiatives to reduce the carbon footprint of their supply chains in response to the growing governmental and consumer pressures. In real life, these supply chains face stochastic and non-stationary demand but most of the studies on inventory lot-sizing problem with emission concerns consider deterministic demand. In this paper, we study the inventory lot-sizing problem under non-stationary stochastic demand condition with emission and cycle service level constraints considering carbon cap-and-trade regulatory mechanism. Using a mixed integer linear programming model, this paper aims to investigate the effects of emission parameters, product- and system-related features on the supply chain performance through extensive computational experiments to cover general type business settings and not a specific scenario. Results show that cycle service level and demand coefficient of variation have significant impacts on total cost and emission irrespective of level of demand variability while the impact of product's demand pattern is significant only at lower level of demand variability. Finally, results also show that increasing value of carbon price reduces total cost, total emission and total inventory and the scope of emission reduction by increasing carbon price is greater at higher levels of cycle service level and demand coefficient of variation. The analysis of results helps supply chain managers to take right decision in different demand and service level situations.

Semi-supervised Gaussian process latent variable model with pairwise constraints

In machine learning, Gaussian process latent variable model (GP-LVM) has been extensively applied in the field of unsupervised dimensionality reduction. When some supervised information, e.g., pairwise constraints or labels of the data, is available, the traditional GP-LVM cannot directly utilize such supervised information to improve the performance of dimensionality reduction. In this case, it is necessary to modify the traditional GP-LVM to make it capable of handing the supervised or semi-supervised learning tasks. For this purpose, we propose a new semi-supervised GP-LVM framework under the pairwise constraints. Through transferring the pairwise constraints in the observed space to the latent space, the constrained priori information on the latent variables can be obtained. Under this constrained priori, the latent variables are optimized by the maximum a posteriori (MAP) algorithm. The effectiveness of the proposed algorithm is demonstrated with experiments on a variety of data sets. © 2010 Elsevier B.V.