A multi-start tabu search method for a single-machine scheduling problem with periodic maintenance and sequence-dependent set-up times

In this paper we study a problem of sequencing jobs in a machine with programmed preventive maintenance and sequence-dependent set-up times. The problem combines two NP-hard problems, so we propose a heuristic method for solving it, which hybridizes multi-start strategies with Tabu Search. We compare our method with the only published metaheuristic algorithm for this problem on a set of 420 instances. The comparison favors the method developed in this work, showing that is able to find high quality solutions in very short computational times.     

Interannual vineyard crop variability in the Castilla–La Mancha region during the period 1991–1996 with Landsat Thematic Mapper images

The vineyard crop is considered an indicator of vegetation cover processes in the Castilla–La Mancha region, as the crop has undergone far-reaching changes in the last ten years: abandonment, removal of vineyards and replacement with other crops such as cereal. The so-called ‘Change detection’ is a process that allows identification of differences in the state of the vineyard by observing it at different times. Essentially, it involves the ability to quantify temporal effects using multi-temporal datasets.

The aim of this study is to analyse the vineyard variability during the period 1991–1996 using different Landsat-5 Thematic Mapper (TM) images belonging to an identified period that highlights differences in the vegetation phenology changes. The images were firstly georeferenced and radiometrically normalized for the change detection purpose. The study area is located in central Spain and involves five councils belonging to the Castilla–La Mancha region: Mota del Cuervo, Las Mesas, Tomelloso, Socuellamos and Villarrobledo, with a surface area of 174?040?ha. This area is especially cultivated by the vineyard crop. Moreover, the study area was classified with the images acquired in 1991, using the multi-temporal masking techniques for the vineyard inventory.

The changes experimented by the vineyard have been grouped in three categories: cereal (CE), abandoned vineyard (AB) and bare soil (BS). Therefore, two methods of Normalized Difference Vegetation Index (NDVI) image difference and NDVI Change Vector Analysis (CVA) were used to map these categories of change. The results indicate that 28.4% of vineyard area undergoes changes during the period 1991–1996. The results have been checked with the ground data and all information is highly conformed.     

Associative memory with dynamic synapses

We have examined a role of dynamic synapses in the stochastic Hopfield-like network behavior. Our results demonstrate an appearance of a novel phase characterized by quick transitions from one memory state to another. The network is able to retrieve memorized patterns corresponding to classical ferromagnetic states but switches between memorized patterns with an intermittent type of behavior. This phenomenon might reflect the flexibility of real neural systems and their readiness to receive and respond to novel and changing external stimuli.     

Analysis of the HLA class I associated peptide repertoire in a hepatocellular carcinoma cell line reveals tumor-specific peptides as putative targets for immunotherapy

HLA class I molecules present peptides on the cell surface to CD8(+) T cells. The repertoire of peptides that associate to class I molecules represents the cellular proteome. Therefore, cells expressing different proteomes could generate different class I-associated peptide repertoires. A large number of peptides have been sequenced from HLA class I alleles, mostly from lymphoid cells. On the other hand, T cell immunotherapy is a goal in the fight against cancer, but the identification of T cell epitopes is a laborious task. Proteomic techniques allow the definition of putative T cell epitopes by the identification of HLA natural ligands in tumor cells. In this study, we have compared the HLA class I-associated peptide repertoire from the hepatocellular carcinoma (HCC) cell line SK-Hep-1 with that previously described from lymphoid cells. The analysis of the peptide pool confirmed that, as expected, the peptides from SK-Hep-1 derive from proteins localized in the same compartments as in lymphoid cells. Within this pool, we have identified 12 HLA class I peptides derived from HCC-related proteins. This confirms that tumor cell lines could be a good source of tumor associated antigens to be used, together with MS, to define putative epitopes for cytotoxic T cells from cancer patients.     

Factorization of multipliers in passivity and IQC analysis

Multipliers are often used to find conditions for the absolute stability of Lur’e systems. They can be used either in conjunction with passivity theory or within the more recent framework of integral quadratic constraints (IQCs). We compare the use of multipliers in both approaches. Passivity theory requires that the multipliers have a canonical factorization and it has been suggested in the literature that this represents an advantage of the IQC theory. We consider sufficient conditions on the nonlinearity class for the associated multipliers to have a canonical factorization.     

Comparisons of Urban Travel Forecasts Prepared with the Sequential Procedure and a Combined Model

Detailed analyses and comparisons of urban travel forecasts prepared by applying the state-of-practice sequential procedure and the solution of a combined network equilibrium model are presented. The sequential procedure for solving the trip distribution, mode choice and assignment problems with feedback is the current practice in most transportation planning agencies, although its important limitations are well known. The solution of a combined model, in contrast, results from a single mathematical formulation, which ensures a well-converged and consistent result. Using a real network, several methods for solving the sequential procedure with feedback are compared to the solution of the combined model ESTRAUS. The results of these methods are shown to have various levels of instability. The paper concludes with a call for a new paradigm of travel forecasting practice based on an internally consistent model formulation that can be solved to a level of precision suitable for comparing alternative scenarios.     

Requirements Reuse for Improving Information Systems Security: A Practitioner’s Approach

Information systems security issues have usually been considered only after the system has been developed completely, and rarely during its design, coding, testing or deployment. However, the advisability of considering security from the very beginning of the system development has recently begun to be appreciated, and in particular in the system requirements specification phase. We present a practical method to elicit and specify the system and software requirements, including a repository containing reusable requirements, a spiral process model, and a set of requirements documents templates. In this paper, this method is focused on the security of information systems and, thus, the reusable requirements repository contains all the requirements taken from MAGERIT, the Spanish public administration risk analysis and management method, which conforms to ISO 15408, Common Criteria Framework. Any information system including these security requirements must therefore pass a risk analysis and management study performed with MAGERIT. The requirements specification templates are hierarchically structured and are based on IEEE standards. Finally, we show a case study in a system of our regional administration aimed at managing state subsidies.     

Ensembles of decision trees based on imprecise probabilities and uncertainty measures

In this paper, we present an experimental comparison among different strategies for combining decision trees built by means of imprecise probabilities and uncertainty measures. It has been proven that the combination or fusion of the information obtained from several classifiers can improve the final process of the classification. We use previously developed schemes, known as Bagging and Boosting, along with a new one based on the variation of the root node via the information rank of each feature of the class variable. To this end, we applied two different approaches to deal with missing data and continuous variables. We use a set of tests on the performance of the methods analyzed here, to show that, with the appropriate approach, the Boosting scheme constitutes an excellent way to combine this type of decision tree. It should be noted that it provides good results, even compared with a standard Random Forest classifier, a successful procedure very commonly used in the literature.