Gestión ambiental en micro, pequeñas y medianas empresas de hospedaje

Las micro, pequeñas y medianas empresas (MIPyMES) mantienen por su propia naturaleza una posición que generalmente las pone en desventaja frente a los grandes corporativos; en el ámbito turístico, éstas son concebidas como entidades ausentes de gestión ambiental e innovación tecnológica. Las MIPYMES hoteleras de Ixtapan de la Sal, un destino turístico tradicional ubicado en el centro de México, afrontan una reconfiguración organizacional para conseguir el mejoramiento del sector; por ello, requieren establecer estrategias que les permita ser más rentables y responsables con el ambiente. El propósito del artículo es evaluar las prácticas ambientales que realizan las MIPyMES de hospedaje, identificando limitaciones y oportunidades en torno a la capacitación, comunicación, control, monitoreo e incorporación de tecnologías. Los resultados demuestran que aun cuando son empresas con escasos recursos económicos y humanos sí llevan a cabo prácticas ambientales, no obstante, el nivel de implementación es todavía elemental y requieren de apoyo para su mejora

A Hybrid MPI-OpenMP Strategy to Speedup the Compression of Big Next-Generation Sequencing Datasets

DNA sequencing has moved into the realm of Big Data due to the rapid development of high-throughput, low cost Next-Generation Sequencing (NGS) technologies. Sequential data compression solutions that once were sufficient to efficiently store and distribute this information are now falling behind. In this paper we introduce phyNGSC, a hybrid MPI-OpenMP strategy to speedup the compression of big NGS data by combining the features of both distributed and shared memory architectures. Our algorithm balances work-load among processes and threads, alleviates memory latency by exploiting locality, and accelerates I/O by reducing excessive read/write operations and inter-node message exchange. To make the algorithm scalable, we introduce a novel timestamp-based file structure that allows us to write the compressed data in a distributed and non-deterministic fashion while retaining the capability of reconstructing the dataset with its original order. Our experimental results show that phyNGSC achieved compression times for big NGS datasets that were 45% to 98% faster than NGS-specific sequential compressors with throughputs of up to 3GB/s. Our theoretical analysis and experimental results suggest strong scalability with some datasets yielding super-linear speedups and constant efficiency. We were able to compress 1 terabyte of data in under 8 minutes compared to more than 5 hours taken by NGS-specific compression algorithms running sequentially. Compared to other parallel solutions, phyNGSC achieved up to 6x speedups while maintaining a higher compression ratio. The code for this implementation is available at https://github.com/pcdslab/PHYNGS

Scalable Data Structure to Compress Next-Generation Sequencing Files and its Application to Compressive Genomics

It is now possible to compress and decompress large-scale Next-Generation Sequencing files taking advantage of high-performance computing techniques. To this end, we have recently introduced a scalable hybrid parallel algorithm, called phyNGSC, which allows fast compression as well as decompression of big FASTQ datasets using distributed and shared memory programming models via MPI and OpenMP. In this paper we present the design and implementation of a novel parallel data structure which lessens the dependency on decompression and facilitates the handling of DNA sequences in their compressed state using fine-grained decompression in a technique that is identified as in compresso data processing. Using our data structure compression and decompression throughputs of up to 8.71 GB/s and 10.12 GB/s were observed. Our proposed structure and methodology brings us one step closer to compressive genomics and sublinear analysis of big NGS datasets. The code for this implementation is available at https://github.com/pcdslab/PHYNGS

Aportes de la Calidad del Servicio en clínicas dentales

Debido a la necesidad de gestionar los nuevos modelos de negocios de las clínicas dentales y la demanda de nuevos servicios requeridos por el paciente, el conocimiento sobre la “calidad del servicio” del sector dental necesita ser ampliado. En la evaluación de la calidad del servicio dental y sanitario, al paciente le es difícil valorar los aspectos técnicos (calidad técnica). El paciente (cliente) de estos servicios se diferencia de otros clientes por su renuencia a recibir el servicio (tratamiento). En la investigación sobre calidad del servicio dental, desde la perspectiva del marketing, existe una tendencia a centrarse en la relación entre las expectativas del paciente y la calidad del servicio, así como también, en investigar el servicio al cliente y el pronto servicio (responsiveness), siendo el SERVQUAL el instrumento más utilizado. También, desde la perspectiva del dentista, se ha investigado sobre la satisfacción del cliente, incluso como sinónimo de calidad. En este artículo, proponemos la configuración del sector dental español y la tipología de las clínicas dentales españolas para luego identificar una serie de líneas de investigación relevantes: enseñar al paciente a evaluar la calidad técnica de la clínica dental, enseñar al dentista a demostrar su calidad técnica, determinar diferencias -en la evaluación de la calidad del servicio del paciente- cuando su enfermedad es grave y cuando no lo es, determinar cuánto afecta la renuencia del paciente a la percepción de la calidad del servicio, entre otra

A Parallel Algorithm for Compression of Big Next-Generation Sequencing Datasets

With the advent of high-throughput next-generation sequencing (NGS) techniques, the amount of data being generated represents challenges including storage, analysis and transport of huge datasets. One solution to storage and transmission of data is compression using specialized compression algorithms. However, these specialized algorithms suffer from poor scalability with increasing size of the datasets and best available solutions can take hours to compress gigabytes of data. In this paper we introduce paraDSRC, a parallel implementation of DSRC algorithm using a message passing model that presents reduction of the compression time complexity by a factor of O(1/p ). Our experimental results show that paraDSRC achieves compression times that are 43% to 99% faster than DSRC and compression throughputs of up to 8.4GB/s on a moderate size cluster. For many of the datasets used in our experiments super-linear speedups have been registered, making the implementation strongly scalable. We also show that paraDSRC is more than 25.6x faster than comparable parallel compression algorithms. The code will be available in author’s website if paper is accepted

Solitary electromechanical pulses in Lobster neurons

Investigations of nerve activity have focused predominantly on electrical phenomena. Nerves, however, are thermodynamic systems, and changes in temperature and in the dimensions of the nerve can also be observed during the action potential. Measurements of heat changes during the action potential suggest that the nerve pulse shares many characteristics with an adiabatic pulse. First experiments in the 1980s suggested small changes in nerve thickness and length during the action potential. Such findings have led to the suggestion that the action potential may be related to electromechanical solitons traveling without dissipation. However, they have been no modern attempts to study mechanical phenomena in nerves. Here, we present ultrasensitive AFM recordings of mechanical changes on the order of 2 - 12 {\AA} in the giant axons of the lobster. We show that the nerve thickness changes in phase with voltage change. When stimulated at opposite ends of the same axon, colliding action potentials pass through one another and do not annihilate. These observations are consistent with a mechanical interpretation of the nervous impulse.Comment: 9 pages, 4 figure