Comportamento ambiental de diferentes modelos de edificações para suínos, durante a estação quente.

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Características das construções para suínos utilizadas no sul do Brasil.

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A multisystem climate change adaptation approach for water sustainability in regional Australia

Climate change represents the possibility of losing quality and quantity of the existing ecosystems around the world. While dealing with the risks caused by climate change we need be aware about the various vulnerabilities across the different ecosystems or biodiversities on planet Earth. In order to mitigate these impacts, the process of adaptation across each ecosystem is important. But the process of adaptation and mitigation of the effects of climate change has to be done on a regional or local level. The challenge at this level is not only to assess vulnerabilities and risks to each ecosystem but to develop policies to adapt so as to achieve sustainability. In this paper we focus on the ecosystem of water and study the different impacts to it from the perspective of regional Australia. We then propose a digital ecosystem based architecture in a multi system approach which can be utilized for water sustainability adaptation process in Regional Australia

A digital information ecosystem structure for regional climate change assessment and adaptation

Global warming is one of the most important and challenging problems being faced in the current century. It triggers along with it a series of events that will have serious consequences on the different ecosystems on planet earth. In order to avoid and manage such consequences, we need dedicatedactions for resilience. In such a scenario the process of adaptation to climate change plays an important part. This is achieved by first identifying the various events of risks, assessing the level of their impacts and then developing policies by which they can be mitigated. For effective adaptation, theevents of global warming should be identified and assessed on a regional scale. In this paper, we propose a digital information ecosystem architecture for climate change adaptability. The proposed architecture will capture the different regional factors of global warming and then link them together formaking effective macro adaptation policies

Reconocimiento de suelos del Aljarafe alto (Sevilla - España)

36 páginas.-- fotos.-- perfiles de suelos.-- Un mapa (Se incorporará en breve).-- Memoria presentada en el XXXIII Curso Internacional de Edafología y Biología VegetalDirector: Clemente Salas, LuisEl presente trabajo se realizó en el área central del Aljarafe Alto; entre las coordenadas 06° 05' 59" a 06° 10' 45" longitud norte y 37" 09' 40" a 37° 21' 45" longitud oeste, sobre una superficie de 30 km2, a 24 km al suroeste de Sevilla, próximo al término Municipal de Bollullos de la Mitación. Geomorfológicamente se encuentra en una meseta o penillanura. Siendo el objetivo del presente estudio es el reconocimiento y clasificación taxonómica de los suelos, reconociéndose para una escala 1: 20,000 las siguientes unidades taxonómicas a nivel de subgrupo: Rhodoxeralfs Cálcico, Haploxeralfs Cálcico, Haploxeralfs Aquico y Xerochrepts Calciceróllico; así mismo, se han distinguido las unidades cartográficas: Asociación Rhodoxeralfs Cálcico-Haploxeralfs Cálcico en un 40% la de superficie; Asociación Haploxeralfs Cálcico-Rhodoxeralfs Cálcico en un 30%; evidenciando estas dos asociaciones alta sinuosidad en su distribución morfológica y diferenciándose como tales por el predominio de un taxón sobre el otro. y la asociación Xerochrepts Calciceróllico - Haploxera1fs Cálcico en un 10%; así como las Consociaciones Haploxeralfs Acnico en un 15% y la Consociación Xerochrepts Calciceróllico en un 5%.Patrocinado por la UNESCO, el Instituto de Cooperación Iberoamericana (ICI); el Consejo Superior de Investigaciones Científicas (CSIC) y la Universidad de Sevilla - España.Peer reviewe

On the construction of model Hamiltonians for adiabatic quantum computation and its application to finding low energy conformations of lattice protein models

In this report, we explore the use of a quantum optimization algorithm for obtaining low energy conformations of protein models. We discuss mappings between protein models and optimization variables, which are in turn mapped to a system of coupled quantum bits. General strategies are given for constructing Hamiltonians to be used to solve optimization problems of physical/chemical/biological interest via quantum computation by adiabatic evolution. As an example, we implement the Hamiltonian corresponding to the Hydrophobic-Polar (HP) model for protein folding. Furthermore, we present an approach to reduce the resulting Hamiltonian to two-body terms gearing towards an experimental realization.Comment: 35 pages, 8 figure

Efficient Enzymatic Preparation of Flavor Esters in Water

A straightforward biocatalytic method for the enzymatic preparation of different flavor esters starting from primary alcohols (e.g., isoamyl, n-hexyl, geranyl, cinnamyl, 2-phenethyl, and benzyl alcohols) and naturally available ethyl esters (e.g., formate, acetate, propionate, and butyrate) was developed. The biotransformations are catalyzed by an acyltransferase from Mycobacterium smegmatis (MsAcT) and proceeded with excellent yields (80-97%) and short reaction times (30-120 min), even when high substrate concentrations (up to 0.5 M) were used. This enzymatic strategy represents an efficient alternative to the application of lipases in organic solvents and a significant improvement compared with already known methods in terms of reduced use of organic solvents, paving the way to sustainable and efficient preparation of natural flavoring agents

Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage

While recent breakthroughs have proven the ability of noisy intermediate-scale quantum (NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the use of these devices for solving more practically relevant computational problems remains a challenge. Proposals for attaining practical quantum advantage typically involve parametrized quantum circuits (PQCs), whose parameters can be optimized to find solutions to diverse problems throughout quantum simulation and machine learning. However, training PQCs for real-world problems remains a significant practical challenge, largely due to the phenomenon of barren plateaus in the optimization landscapes of randomly-initialized quantum circuits. In this work, we introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for PQCs, which we show significantly improves the trainability and performance of PQCs on a variety of problems. Given a specific optimization task, this method first utilizes tensor network (TN) simulations to identify a promising quantum state, which is then converted into gate parameters of a PQC by means of a high-performance decomposition procedure. We show that this learned initialization avoids barren plateaus, and effectively translates increases in classical resources to enhanced performance and speed in training quantum circuits. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing, and opens up new avenues to harness the power of modern quantum hardware for realizing practical quantum advantage.Comment: 15 pages, 7 figure