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Air cargo represents about one-third of the total value of global trade carried and is therefore an essential player in the world economy. Monitoring the financial stability of air cargo carriers is vital to the industry, governments, and stakeholders. To detect financial difficulties within the aviation industry numerous prediction models have been developed, but most focus on the passenger airlines. In addition, these prediction models typically do not take into account essential non-statistical factors. This paper identified qualitative factors that could influence the financial health of all-cargo airlines and attempted to understand the interaction among them. The factors were identified based on previous research and validated by expert opinions. Interpretive structural modelling (ISM) was used to determine any mutual influences among the factors. Further, the ISM methodology was able to establish the level of the factors and determine their driving power and dependence. The results are very useful for air cargo management in that they can use the findings of this study to overcome dominant factors.

Aluminium (Al) suffers from poor wear and scratch resistance and also low surface hardness. To overcome this, the authors fabricated a ~200 µm coating of fine tungsten (W) powder on Al using laser surface alloying (LSA) with laser power and scanning speed varying from 400-800 W and 800-1,200 mm/min respectively. Material characterisation in the form of SEM and XRD, undertaken on the LSA samples, revealed novel microstructures including formation of intermetallic phases Al4W, Al12W that exhibit substantially higher hardness. Scratch hardness measurements further consolidated that surface hardness of coated samples was more than three times as compared to bulk aluminium. Scratch testing at normal loads of 50-150 N, scratch speed of 30 mm/min, demonstrated a stable friction coefficient of 0.6 and higher coating adhesive strength. When applied to automobile components and aerospace products, the micro-coated LSA samples can provide far better alternatives to the current industrial grade of aluminium from the tribology point of view.

DNA microarrays help measure the expression levels of thousands of genes concurrently. A major challenge is to extract biologically relevant information and knowledge from massive amounts of microarray data. In this paper, we explore learning a compact representation of gene expression profiles by using a multi-task neural network model, so that further analyses can be carried out more efficiently on the data. The proposed network is trained with prediction tasks for Protein-Protein Interactions (PPIs), predicting Gene Ontology (GO) similarities as well as geometrical constrains, while simultaneously learning a high-level representation of gene expression data. We argue that deep networks can extract more information from expression data as compared to standard statistical models. We tested the utility of our method by comparing its performance with famous feature extraction and dimensionality reduction methods on the task of PPI prediction, and found the results to be promising.

The purpose of the study is to implement kaizen principles to assist small scale manufacturing organisation to become more efficient and more productive. The paper systematically categorises the published literature, analyses and reviews it methodically. In the frames of case study it has been analysed and implemented kaizen rules in the organisation. On the basis of the case study it can be stated, that introducing the kaizen concept bring the great changes in the organisation by eliminating wastes, for example: increasing of effectiveness and efficiency in the processes, improved visibility of the process, reduced delays, searching time and dangerous conditions. Kaizen is powerful tool and can be implemented in any industry whether it is micro, small, medium or large. The publications and case study presented in this paper will be useful to researchers, professionals and others concerned with this subject to understand the significance of kaizen.

Third-party logistics (3PL) providers offer logistics outsourcing to firms so that companies can focus on their core competencies. Because the supply chain has such a large impact on a firm's cost and customer service, it is important to ensure that the selected 3PL understands and is capable of providing the services needed by a firm. This paper explores the use of interpretive structural modelling (ISM) in evaluating the selection of a 3PL. Based on expert opinions, the participants rated the relationships between 12 main criteria for 3PL selection and then ISM was applied. The results indicated that cost has the most dependence and least driving power, whereas financial stability has the most driving power and least dependence. Cultural alignment showed weak driving power and weak dependence. Dependent attributes which had weak driving power and strong dependence were cost, reputation, and quality. Locations, collaboration, range of services, and experience had attributes which have strong driving power and strong dependence. And finally, financial stability, flexibility, information technology, and metrics were found to have strong driving power and weak dependence.

Closed queuing networks (CQNs) are generally used to model CONWIP systems. An implicit assumption in the CQN model is that whenever the work-in-process (WIP) level goes below the set value, there is always a job waiting for the released kanban. In other words, there are an infinite number of jobs in the external queue and as a result the kanban never waits in the resource pool. Modelling the CONWIP under this unrealistic assumption tends to underestimate the true cycle time of the kanban (and the job). In this technical note, we represent a CONWIP as a semi-open queuing network (SOQN) in which the job waits in the external queue until a kanban is available or vice-versa. Using published data and numerical experiments, we point out a modelling flaw of CONWIP systems in the literature and also show that the SOQN approach is a more appropriate model for representing CONWIP systems.