ALBERT

Description: This work presents an energetic, exergoeconomic, environmental, and toxicity analysis of the simple gas turbine M501F3 based on a parametric analysis of energetic (thermal efficiency, fuel and air flow rates, and specific work output), exergoeconomic (exergetic efficiency and exergoeconomic operation costs), environmental (global warming, smog formation, acid rain indexes), and human toxicity indexes, by taking the compressor pressure ratio and the turbine inlet temperature as the operating parameters. The aim of this paper is to provide an integral, systematic, and powerful diagnostic tool to establish possible operation and maintenance actions to improve the gas turbine’s exergoeconomic, environmental, and human toxicity indexes. Despite the continuous changes in the price of natural gas, the compressor, combustion chamber, and turbine always contribute 18.96%, 53.02%, and 28%, respectively, to the gas turbine’s exergoeconomic operation costs. The application of this methodology can be extended to other simple gas turbines using the pressure drops and isentropic efficiencies, among others, as the degradation parameters, as well as to other energetic systems, without loss of generality.

Description: In this work, we propose a Bayesian methodology to make inferences for the memory parameter and other characteristics under non-standard assumptions for a class of stochastic processes. This class generalizes the Gamma-modulated process, with trajectories that exhibit long memory behavior, as well as decreasing variability as time increases. Different values of the memory parameter influence the speed of this decrease, making this heteroscedastic model very flexible. Its properties are used to implement an approximate Bayesian computation and MCMC scheme to obtain posterior estimates. We test and validate our method through simulations and real data from the big earthquake that occurred in 2010 in Chile.

Description: Energies, Vol. 11, Pages 949: Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting Energies doi: 10.3390/en11040949 Authors: Federico Divina Aude Gilson Francisco Goméz-Vela Miguel García Torres José Torres The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.

Description: The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.

Description: Materials, Vol. 11, Pages 1465: Biological Compatibility of a Polylactic Acid Composite Reinforced with Natural Chitosan Obtained from Shrimp Waste Materials doi: 10.3390/ma11081465 Authors: Yaret Gabriela Torres-Hernández Gloria Michel Ortega-Díaz Lucía Téllez-Jurado Nayeli Shantal Castrejón-Jiménez Alejandro Altamirano-Torres Blanca Estela García-Pérez Heberto Balmori-Ramírez The aim of this work is to evaluate the effect of chitosan content (1, 3 and 5 wt %) dispersed in polylactic acid (PLA) on the structure and properties of composites. Also, the hydrolytic degradation, and the cell viability and adhesion of human MG-63 osteoblasts are analyzed to determine the composites’ suitability for use in tissue engineering. For the manufacture of the materials, natural chitosan was extracted chemically from shrimp exoskeleton. The composites were fabricated by extrusion, because it is a low-cost process, it is reproducible, and it does not compromise the biocompatibility of the materials. FT-IR and XRD show that the chitosan does not change the polymer structure, and interactions between the composite components are discarded. In vitro degradation tests show that the composites do not induce significant pH changes in phosphate buffer solution due to their low susceptibility to hydrolytic degradation. The adhesion and morphological characteristics of the osteoblasts are evaluated using confocal microscopy and scanning electron microscopy. The cell viability is determined by the MTT assay. Osteoblasts adhesion is observed on the surface of PLA and composites. A higher amount of chitosan, higher number of cells with osteoblastic morphology, and mineralized nodules are observed on the composite surface. The highest metabolic activity is evidenced at 21 days. The results suggest that the Polylactic acid/chitosan composites are potentially suitable for use as a biomaterial.

Description: We present sea ice temperature and salinity data from first-year ice (FYI) and second-year ice (SYI) relevant to the temporal development of sea ice permeability and brine drainage efficiency from the early growth phase in October 2019 to the onset of spring warming in May 2020. Our dataset was collected in the central Arctic Ocean during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in 2019 to 2020. MOSAiC was an international transpolar drift expedition in which the German icebreaker RV Polarstern anchored into an ice floe to gain new insights into Arctic climate over a full annual cycle. In October 2019, RV Polarstern moored to an ice floe in the Siberian sector of the Arctic at 85 degrees north and 137 degrees east to begin the drift towards the North Pole and the Fram Strait via the Transpolar Drift Stream. The data presented here were collected during the first three legs of the expedition, so all the coring activities took place on the same floe. The end dates of legs 1, 2, and 3 were 13 December, 24 February, and 4 June, respectively. The dataset contributed to a baseline study entitled, Deciphering the properties of different Arctic ice types during the growth phase of the MOSAiC floes: Implications for future studies. The study highlights downward directed gas pathways in FYI and SYI by inferring sea ice permeability and potential brine release from several time series of temperature and salinity measurements. The physical properties presented in this paper lay the foundation for subsequent analyses on actual gas contents measured in the ice cores, as well as air-ice and ice-ocean gas fluxes. Sea ice cores were collected with a Kovacs Mark II 9 cm diameter corer. To measure ice temperatures, about 4.5 cm deep holes were drilled into the core (intervals varied by site and leg) . The temperatures were measured by a digital thermometer within minutes after the cores were retrieved. The ice cores were placed into pre-labelled plastic sleeves sealed at the bottom end. The ice cores were transported to RV Polarstern and stored in a -20 degrees Celsius freezer. Each of the cores was sub-sampled, melted at room temperature, and processed for salinity within one or two days. The practical salinity was estimated by measuring the electrical conductivity and temperature of the melted samples using a WTW Cond 3151 salinometer equipped with a Tetra-Con 325 four-electrode conductivity cell. The practical salinity represents the the salinity estimated from the electrical conductivity of the solution. The dataset also contains derived variables, including sea ice density, brine volume fraction, and the Rayleigh number.

Description: We present sea ice temperature and salinity data from first-year ice (FYI) and second-year ice (SYI) relevant to the temporal development of sea ice permeability and brine drainage efficiency from the early growth phase in October 2019 to the onset of spring warming in May 2020. Our dataset was collected in the central Arctic Ocean during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in 2019 to 2020. MOSAiC was an international transpolar drift expedition in which the German icebreaker RV Polarstern anchored into an ice floe to gain new insights into Arctic climate over a full annual cycle. In October 2019, RV Polarstern moored to an ice floe in the Siberian sector of the Arctic at 85 degrees north and 137 degrees east to begin the drift towards the North Pole and the Fram Strait via the Transpolar Drift Stream. The data presented here were collected during the first three legs of the expedition, so all the coring activities took place on the same floe. The end dates of legs 1, 2, and 3 were 13 December, 24 February, and 4 June, respectively. The dataset contributed to a baseline study entitled, Deciphering the properties of different Arctic ice types during the growth phase of the MOSAiC floes: Implications for future studies. The study highlights downward directed gas pathways in FYI and SYI by inferring sea ice permeability and potential brine release from several time series of temperature and salinity measurements. The physical properties presented in this paper lay the foundation for subsequent analyses on actual gas contents measured in the ice cores, as well as air-ice and ice-ocean gas fluxes. Sea ice cores were collected with a Kovacs Mark II 9 cm diameter corer. To measure ice temperatures, about 4.5 cm deep holes were drilled into the core (intervals varied by site and leg) . The temperatures were measured by a digital thermometer within minutes after the cores were retrieved. The ice cores were placed into pre-labelled plastic sleeves sealed at the bottom end. The ice cores were transported to RV Polarstern and stored in a -20 degrees Celsius freezer. Each of the cores was sub-sampled, melted at room temperature, and processed for salinity within one or two days. The practical salinity was estimated by measuring the electrical conductivity and temperature of the melted samples using a WTW Cond 3151 salinometer equipped with a Tetra-Con 325 four-electrode conductivity cell. The practical salinity represents the the salinity estimated from the electrical conductivity of the solution. The dataset also contains derived variables, including sea ice density, brine volume fraction, and the Rayleigh number.

Description: We present sea ice temperature and salinity data from first-year ice (FYI) and second-year ice (SYI) relevant to the temporal development of sea ice permeability and brine drainage efficiency from the early growth phase in October 2019 to the onset of spring warming in May 2020. Our dataset was collected in the central Arctic Ocean during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in 2019 to 2020. MOSAiC was an international transpolar drift expedition in which the German icebreaker RV Polarstern anchored into an ice floe to gain new insights into Arctic climate over a full annual cycle. In October 2019, RV Polarstern moored to an ice floe in the Siberian sector of the Arctic at 85 degrees north and 137 degrees east to begin the drift towards the North Pole and the Fram Strait via the Transpolar Drift Stream. The data presented here were collected during the first three legs of the expedition, so all the coring activities took place on the same floe. The end dates of legs 1, 2, and 3 were 13 December, 24 February, and 4 June, respectively. The dataset contributed to a baseline study entitled, Deciphering the properties of different Arctic ice types during the growth phase of the MOSAiC floes: Implications for future studies. The study highlights downward directed gas pathways in FYI and SYI by inferring sea ice permeability and potential brine release from several time series of temperature and salinity measurements. The physical properties presented in this paper lay the foundation for subsequent analyses on actual gas contents measured in the ice cores, as well as air-ice and ice-ocean gas fluxes. Sea ice cores were collected with a Kovacs Mark II 9 cm diameter corer. To measure ice temperatures, about 4.5 cm deep holes were drilled into the core (intervals varied by site and leg) . The temperatures were measured by a digital thermometer within minutes after the cores were retrieved. The ice cores were placed into pre-labelled plastic sleeves sealed at the bottom end. The ice cores were transported to RV Polarstern and stored in a -20 degrees Celsius freezer. Each of the cores was sub-sampled, melted at room temperature, and processed for salinity within one or two days. The practical salinity was estimated by measuring the electrical conductivity and temperature of the melted samples using a WTW Cond 3151 salinometer equipped with a Tetra-Con 325 four-electrode conductivity cell. The practical salinity represents the the salinity estimated from the electrical conductivity of the solution. The dataset also contains derived variables, including sea ice density, brine volume fraction, and the Rayleigh number.