Endoresection of the iris and ciliary body in epithelial downgrowth

We present a case of epithelial downgrowth with intractable glaucoma after multiple intraocular surgeries. The eye was successfully managed with a pars plana approach. The iris and epithelial tissue over the ciliary body were removed with intraocular scissors and a vitrector combined with unipolar diathermy and endophotocoagulation. The use of moderate hypotensive anesthesia may have helped prevent intraoperative hemorrhage. During more than 3 years of follow-up, intraocular pressure ranged from 13 to 19 mm Hg, visual acuity was 20/80 with aphakic correction with a rigid gas-permeable lens, and there was no recurrence of epithelial downgrowth.     

A simple token-based algorithm for the mutual exclusion problem in distributed systems

Solving the problem of mutually exclusive access to a critical resource is a major challenge in distributed systems. In some solutions, there is a unique token in the whole system which acts as a privilege to access a critical resource. Practical and easily implemented, the token-ring algorithm is one of the most popular token-based mutual exclusion algorithms known in this field’s literature. However, it suffers from low scalability and a high average waiting time for resource seekers. The present paper proposes a new algorithm which employs a two-dimensional torus logical structure of N processes and the token-ring algorithm concept. It performs in a way that increasingly raises scalability and reduces the average waiting time of the token-ring algorithm. The token makes a circular movement along the columns of the two-dimensional torus (vertical ring), while the requests for the critical resource make a circular movement along the rows of the torus (horizontal ring). In this algorithm, the number of messages exchanged is between \(2\sqrt{{N}}+1\) and 3\(\sqrt{{N}}+1\) under light load situations and, under heavy load situations, is at the most three messages per critical section invocation. Thus, in contrast with the leading algorithms, the proposed algorithm has gained significant improvements, in addition to having been proved to operate correctly.     

Agreement with Ethical Standards.

The Department of Psychology, University of Alabama wishes to notify their colleagues that, despite the considerable turmoil (over court-ordered desegregation) on their campus, the undersigned members of the Department continue in total agreement with the Ethical Standards of Psychologists, particularly with reference to Principle 1.13-1, as adopted by the APA in 1952. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Preparation of a new molecularly imprinted polymer based on self‐crosslinkable cellulose acrylate in aqueous solution: A drug delivery system for furosemide

In the present study, a new, green, and biodegradable molecularly imprinted polymer (MIP) based on modified cellulose is introduced for the drug furosemide. First, microcrystalline cellulose was converted into cellulose acrylate (CA). Then the prepared CA underwent self‐crosslinking in the drug preassembly solution. The preassembly solution of the drug was also prepared using furosemide as the template molecule and acrylamide as the functional monomer in an aqueous medium. The results obtained from the binding and selectivity studies indicate the successful preparation of the CA‐based MIP (CA‐MIP) in aqueous solution. In the final step, an in vitro release study of furosemide from the synthesized polymers was carried out in a pH = 7.41 phosphate‐buffered saline solution at 37 °C. The drug release profiles showed that the release rate of the CA‐MIP is more controlled than that of the corresponding nonimprinted polymer, especially at the early stages of release. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45581.     

Clay-hyperbranched epoxy/polyphenylsulfone nanocomposite membranes

Nanocomposite membranes containing polyphenylsulfone (PPSU) and a clay modified with a hyperbranched epoxy (HBE) were prepared by blending of modified montmorillonite (m-MMT) with a polymer solution using phase inversion method. The hyperbranched epoxy synthesized by polycondensation reaction of bisphenol A and triethanolamine with epichlorohydrin was grafted to amine-functionalized MMT by reaction between the epoxy groups of hyperbranched epoxy and the amine groups on the MMT surface. In this way, the m-MMT was exfoliated into single layers of nanoparticles in a solvent medium and the polymer chains were intercalated into m-MMT layers. The aim was to study the effect of this additive on the membrane separation efficiency. For this purpose, pure water flux, fouling, and pigment and heavy metal rejection were measured by a home-made dead end filtration cell and the performance of the prepared membranes was investigated. Hydrophilicity of the nanocomposite membranes was specified by water contact angle measurements. Degree of dispersion of additive into the polymer matrix and membrane morphology were studied by FESEM. Membrane surface area, pore size, and volume were evaluated by BET. The results indicated that the surface hydrophilicity increased after incorporation of m-MMT. Furthermore, the water permeability, salt rejection, and antifouling resistance of PPSU membranes were improved significantly. Membrane with 3 wt% m-MMT showed the best performance compared to other membranes.     

Defect Detection of Industrial Radiography Images of Ammonia Pipes by a Sparse Coding Model

Pipeline transportation systems for liquid anhydrous ammonia require periodic inspections for pipe defects. Defects such as crack-like flaws, including those due to stress corrosion and fatigue crack growth and fatigue of welded joints, can be tested by in situ radiography testing (RT). It is required that the detection technique should reveal the defect region without any significant distortion. Imaging noise such as that due to radiation scattering reduces contrast over the whole or part of the image, causing a reduction in image quality. Various image processing methods can be utilized to improve the outcome of RT of pipes. The sparse coding model is a powerful contrast improvement algorithm, but it tends to eliminate imaging details, passing them off as noise. We present a novel implementation of the sparse coding model based on a probabilistic interpretation to improve defect detection in ammonia pipelines radiography images. The performance of this procedure was evaluated in radiographic images of defect and corrosion regions for different pipe types. For ammonia pipes, the technique was found to offer a significant improvement in defect detection while preserving imaging details.     

Distributed unequal clustering algorithm in large-scale wireless sensor networks using fuzzy logic

Clustering is a promising and popular approach to organize sensor nodes into a hierarchical structure, reduce transmitting data to the base station by aggregation methods, and prolong the network lifetime. However, a heavy traffic load may cause the sudden death of nodes due to energy resource depletion in some network regions, i.e., hot spots that lead to network service disruption. This problem is very critical, especially for data-gathering scenarios in which Cluster Heads (CHs) are responsible for collecting and forwarding sensed data to the base station. To avoid hot spot problem, the network workload must be uniformly distributed among nodes. This is achieved by rotating the CH role among all network nodes and tuning cluster size according to CH conditions. In this paper, a clustering algorithm is proposed that selects nodes with the highest remaining energy in each region as candidate CHs, among which the best nodes shall be picked as the final CHs. In addition, to mitigate the hot spot problem, this clustering algorithm employs fuzzy logic to adjust the cluster radius of CH nodes; this is based on some local information, including distance to the base station and local density. Simulation results demonstrate that, by mitigating the hot spot problem, the proposed approach achieves an improvement in terms of both network lifetime and energy conservation.     

Information Maximization in a Linear Manifold Topographic Map

This article addresses the problem of unsupervised learning of multiple linear manifolds in a topology preserving neural map. The model finds simple linear estimations of the regions of the unknown data manifold. Each neuron of the map corresponds to a linear manifold whose basis and mean vectors and on- and off-manifold standard deviations must be learnt. The learning rules are derived based on competition between neurons and maximizing an approximation of the mutual information between the input and the output of each neuron. Neighborhood functions are also considered in the learning rules in order to develop the topology preserving property for the map. Considering two special density models for the input data, the optimal nonlinear input/output mappings of the neurons are found. Experimental results show a good performance for the proposed method on synthesized and practical problems compared with other relevant techniques.