Non emergency patients transport - a mixed integer linear programming

This work presents a model and a heuristic to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving problems with one vehicle was presented, and this heuristic provides good results in terms of accuracy and computation time.

The non-emergency patient transport modelled as a team orienteering problem

This work presents an improved model to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving Orienteering Problems is presented, and this heuristic provides good results in terms of accuracy and computation time. Euclidean instances as well as asymmetric real data gathered from Google maps were used, and the model has a promising performance mainly with asymmetric cost matrices.

Genetic algorithms for the non emergency patients transport service

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the paper. The authors would like to thank Dr. Elaine DeBock for reviewing the manuscript.

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

PTX3-based genetic testing for risk of aspergillosis after lung transplant

PTX3-based genetic testing for risk of aspergillosis after lung transplant

Towards the design and implementation of aspect-oriented programming for spreadsheets

A spreadsheet usually starts as a simple and singleuser software artifact, but, as frequent as in other software systems, quickly evolves into a complex system developed by many actors. Often, different users work on different aspects of the same spreadsheet: while a secretary may be only involved in adding plain data to the spreadsheet, an accountant may define new business rules, while an engineer may need to adapt the spreadsheet content so it can be used by other software systems.Unfortunately,spreadsheetsystemsdonotoffermodular mechanisms, and as a consequence, some of the previous tasks may be defined by adding intrusive “code” to the spreadsheet. In this paper we go through the design and implementation of an aspect-oriented language for spreadsheets so that users can work on different aspects of a spreadsheet in a modular way. For example, aspects can be defined in order to introduce new business rules to an existing spreadsheet, or to manipulate the spreadsheet data to be ported to another system. Aspects are defined as aspect-oriented program specifications that are dynamically woven into the underlying spreadsheet by an aspect weaver. In this aspect-oriented style of spreadsheet development, differentusers develop,orreuse,aspects withoutaddingintrusive code to the original spreadsheet. Such code is added/executed by the spreadsheet weaving mechanism proposed in this paper.

Metaphorisms in programming

This paper introduces the metaphorism pattern of relational specification and addresses how specification following this pattern can be refined into recursive programs. Metaphorisms express input-output relationships which preserve relevant information while at the same time some intended optimization takes place. Text processing, sorting, representation changers, etc., are examples of metaphorisms. The kind of metaphorism refinement proposed in this paper is a strategy known as change of virtual data structure. It gives sufficient conditions for such implementations to be calculated using relation algebra and illustrates the strategy with the derivation of quicksort as example.

A genetic perspective on African prehistory

The various genetic systems (mitochondrial DNA, the Y-chromosome and the genome-wide autosomes) indicate that Africa is the most genetically diverse continent in the world and the most likely place of origin for anatomically modern humans. However, where in Africa modern humans arose and how the current genetic makeup within the continent was shaped is still open to debate. Here, we summarize the debate and focus especially on the maternally inherited mitochondrial DNA (mtDNA) and a recently revised chronology for the African mtDNA tree. We discuss the possible origin of modern humans in southern, eastern or Central Africa; the possibility of a migration from southern to eastern Africa more than 100 ka, carrying lineages within mtDNA haplogroup L0; the evidence for a climate-change-mediated population expansion in eastern Africa involving mtDNA haplogroup L3, leading to the “out-of-Africa” migration around 70–60 ka; the re-population of North Africa from the Near East around 40–30 ka suggested by mtDNA haplogroups U6 and M1; the evidence for population expansions and dispersals across the continent at the onset of the Holocene ; and the impact of the Bantu dispersals in Central, eastern and southern Africa within the last few millennia.

Genetic regulatory mechanisms by means of extended interactive Petri nets

In our work we have chosen to integrate formalism for knowledge representation with formalism for process representation as a way to specify and regulate the overall activity of a multi-cellular agent. The result of this approach is XP,N, another formalism, wherein a distributed system can be modeled as a collection of interrelated sub-nets sharing a common explicit control structure. Each sub-net represents a system of asynchronous concurrent threads modeled by a set of transitions. XP,N combines local state and control with interaction and hierarchy to achieve a high-level abstraction and to model the complex relationships between all the components of a distributed system. Viewed as a tool XP,N provides a carefully devised conflict resolution strategy that intentionally mimics the genetic regulatory mechanism used in an organic cell to select the next genes to process.

Quantifying the legacy of the Chinese Neolithic on the maternal genetic heritage of Taiwan and Island Southeast Asia

There has been a long-standing debate concerning the extent to which the spread of Neolithic ceramics and Malay-Polynesian languages in Island Southeast Asia (ISEA) were coupled to an agriculturally driven demic dispersal out of Taiwan 4000 years ago (4 ka). We previously addressed this question using founder analysis of mitochondrial DNA (mtDNA) control-region sequences to identify major lineage clusters most likely to have dispersed from Taiwan into ISEA, proposing that the dispersal had a relatively minor impact on the extant genetic structure of ISEA, and that the role of agriculture in the expansion of the Austronesian languages was therefore likely to have been correspondingly minor. Here we test these conclusions by sequencing whole mtDNAs from across Taiwan and ISEA, using their higher chronological precision to resolve the overall proportion that participated in the "out-of-Taiwan" mid-Holocene dispersal as opposed to earlier, postglacial expansions in the Early Holocene. We show that, in total, about 20 % of mtDNA lineages in the modern ISEA pool result from the "out-of-Taiwan" dispersal, with most of the remainder signifying earlier processes, mainly due to sea-level rises after the Last Glacial Maximum. Notably, we show that every one of these founder clusters previously entered Taiwan from China, 6-7 ka, where rice-farming originated, and remained distinct from the indigenous Taiwanese population until after the subsequent dispersal into ISEA.