A semi-parametric generalization of the Cox proportional hazards regression model: Inference and Applications

The assumption of proportional hazards (PH) fundamental to the Cox PH model sometimes may not hold in practice. In this paper, we propose a generalization of the Cox PH model in terms of the cumulative hazard function taking a form similar to the Cox PH model, with the extension that the baseline cumulative hazard function is raised to a power function. Our model allows for interaction between covariates and the baseline hazard and it also includes, for the two sample problem, the case of two Weibull distributions and two extreme value distributions differing in both scale and shape parameters. The partial likelihood approach can not be applied here to estimate the model parameters. We use the full likelihood approach via a cubic B-spline approximation for the baseline hazard to estimate the model parameters. A semi-automatic procedure for knot selection based on Akaike’s information criterion is developed. We illustrate the applicability of our approach using real-life data.     

TEXEMS: texture exemplars for defect detection on random textured surfaces

We present an approach to detecting and localizing defects in random color textures which requires only a few defect free samples for unsupervised training. It is assumed that each image is generated by a superposition of various-size image patches with added variations at each pixel position. These image patches and their corresponding variances are referred to here as textural exemplars or texems. Mixture models are applied to obtain the texems using multiscale analysis to reduce the computational costs. Novelty detection on color texture surfaces is performed by examining the same-source similarity based on the data likelihood in multiscale, followed by logical processes to combine the defect candidates to localize defects. The proposed method is compared against a Gabor filter bank-based novelty detection method. Also, we compare different texem generalization schemes for defect detection in terms of accuracy and efficiency.     

Modeling industrial thickener using computational fluid dynamics (CFD), a case study: Tailing thickener in the Sarcheshmeh copper mine

Separation of particles from liquid in the large gravitational tanks is widely used in mining and industrial wastewater treatment process. Thickener is key unit in the operational processes of hydrometallurgy and is used to separate solid from liquid. In this study, population balance models were combined with computational fluid dynamics (CFD) for modeling the tailing thickener. Parameters such as feed flow rate, flocculant dosage, inlet solid percent and feedwell were investigated. CFD was used to simulate the industrial tailing thickener with settled bed of 120 m diameter which is located in the Sarcheshmeh copper mine. Important factor of drag force that defines the rake torque of rotating paddles on the bed was also determined. Two phases turbulence model of Eulerian/Eulerian in accordance with turbulence model of k-ε was used in the steady-state. Also population balance model consists of 15 groups of particle sizes with Luo and Lehr kernel was used for aggregation/breakage kernel. The simulation results showed good agreement with the operational data.     

Efficient biodegradation of naphthalene by a newly characterized indigenous Achromobacter sp. FBHYA2 isolated from Tehran Oil Refinery Complex

A bacterial strain, FBHYA2, capable of degrading naphthalene, was isolated from the American Petroleum Institute (API) separator of the Tehran Oil Refinery Complex (TORC). Strain FBHYA2 was identified as Achromobacter sp. based on physiological and biochemical characteristics and also phylogenetic similarity of 16S rRNA gene sequence. The optimal growth conditions for strain FBHYA2 were pH 6.0, 30 °C and 1.0% NaCl. Strain FBHYA2 can utilize naphthalene as the sole source of carbon and energy and was able to degrade naphthalene aerobically very fast, 48 h for 96% removal at 500 mg/L concentration. The physiological response of Achromobacter sp., FBHYA2 to several hydrophobic chemicals (aliphatic and aromatic hydrocarbons) was also investigated. No biosurfactant was detected during bacterial growth on any aliphatic/aromatic hydrocarbons. The results of hydrophobicity measurements showed no significant difference between naphthalene- and LB-grown cells. The capability of the strain FBHYA2 to degrade naphthalene completely and rapidly without the need to secrete biosurfactant may make it an ideal candidate to remediate polycyclic aromatic hydrocarbon (PAH)-contaminated sites.     

An Immune-based Genetic Algorithm with Reduced Search Space Coding for Multiprocessor Task Scheduling Problem

Multiprocessor task scheduling is an important problem in parallel applications and distributed systems. In this way, solving the multiprocessor task scheduling problem (MTSP) by heuristic, meta-heuristic, and hybrid algorithms have been proposed in literature. Although the problem has been addressed by many researchers, challenges to improve the convergence speed and the reliability of methods for solving the problem are still continued especially in the case that the communication cost is added to the problem frame work. In this paper, an Immune-based Genetic algorithm (IGA), a meta-heuristic approach, with a new coding scheme is proposed to solve MTSP. It is shown that the proposed coding reduces the search space of MTSP in many practical problems, which effectively influences the convergence speed of the optimization process. In addition to the reduced search space offered by the proposed coding that eventuate in exploring better solutions at a shorter time frame, it guarantees the validity of solutions by using any crossover and mutation operators. Furthermore, to overcome the regeneration phenomena in the proposed GA (generating similar chromosomes) which leads to premature convergence, an affinity based approach inspired from Artificial Immune system is employed which results in better exploration in the searching process. Experimental results showed that the proposed IGA surpasses related works in terms of found makespan (20% improvement in average) while it needs less iterations to find the solutions (90% improvement in average) when it is applied to standard test benches.     

Dynamic hierarchical bandwidth allocation using Russian Doll Model in EPON

This paper demonstrates a new hierarchical Dynamic Bandwidth Allocation algorithm using the Russian Doll Model (RDM) to allocate bandwidth for intra-Optical Network Unit (ONU) in an Ethernet Passive Optical Network (EPON). The allocation of bandwidth is based on the classification and prioritization of service. The algorithm addresses the requests of ONUs and provides differentiated services by balancing priority and fairness. The simulation results show that the proposed algorithm performs well in supporting the triple-play services, i.e. video, voice, and data, as well as making effective adjustment in balancing bandwidth sharing between the ONUs compared with two other existing Dynamic Bandwidth Allocation (DBA) algorithm. The proposed algorithms shows significant performance improvements in terms of bandwidth utilization, packet delay and the fairness.     

Cluster Size Optimization in Sensor Networks with Decentralized Cluster-Based Protocols

Network lifetime and energy-efficiency are viewed as the dominating considerations in designing cluster-based communication protocols for wireless sensor networks. This paper analytically provides the optimal cluster size that minimizes the total energy expenditure in such networks, where all sensors communicate data through their elected cluster heads to the base station in a decentralized fashion. LEACH, LEACH-Coverage, and DBS comprise three cluster-based protocols investigated in this paper that do not require any centralized support from a certain node. The analytical outcomes are given in the form of closed-form expressions for various widely-used network configurations. Extensive simulations on different networks are used to confirm the expectations based on the analytical results. To obtain a thorough understanding of the results, cluster number variability problem is identified and inspected from the energy consumption point of view.     

Musculoskeletal disorders among female carpet weavers in Iran

The aim of this study was to determine factors associated with self-reported upper extremity musculoskeletal disorders (UEMSDs) among female carpet weavers. Data on demographic characteristics and workplace factors were collected from 626 female carpet weavers. Type of carpet weaving looms, weaving style (Persian vs. Turkish), daily working hours and work experience as well as personal factors including age and marital status were significantly and independently associated with self-reported UEMSDs (p < 0.05), while education, handedness and weight of weaving comb were not. A major finding of this study was the significant association between weaving style and UEMSDs. Better measures of the biomechanical requirements of carpet weaving tasks in Persian and Turkish weaving types are required to understand better their influences on the health of weavers and on UEMSDs in particular. Poor workstation design, in particular, design inappropriate for the anthropometric dimensions of weavers, was a major risk factor for musculoskeletal symptoms in carpet weaving. Therefore, this industry should develop equipment adapted to women's sizes and shapes. PRACTITIONER SUMMARY: This article provides an insight into the risk factors associated with self-reported UEMSDs among female carpet weavers, identifying major factors associated with upper extremity musculoskeletal symptoms and exploring guidelines for designing weaving workstations.     

A state-of the-art survey of TOPSIS applications

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors’ nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.