Comparing hotels’ employer brand effectiveness through social media and websites

This research explores hotel companies’ employer branding (EB) through the internet. Many countries in the world will face a gap between the demand for talent in the hospitality and tourism industry and the available talent pool. Previous research found that, in an industry with high labour turnover and rather negative public image as an employer, EB approaches can be used to attract potential employees in a more targeted manner. The impact of on-line tools for companies to present themselves as a good employer brand is now crucial. For this study the online employer brand presence and effectiveness of 4-star and 5-star hotels are compared. Using content analysis, the presence of companies’ employer brand and the appreciation for their employer brand message was assessed. The brands involved are: Hilton, Doubletree, Radisson Blu, Park Plaza, Marriott, Crowne Plaza, Novotel, Holiday Inn and NH. The online presence of each brand was evaluated through their corporate career website, brand Facebook page and brand LinkedIn page. Results show that all brands have the possibility to improve their employer brand online profiles, while for all of them specific attention needs to be given to the consistent use of the channels. From a maximum score of 115 for a perfect online employer brand profile, the highest score was 64 and the lowest 37.Keywords: employer branding, employer brand message, Millennials, Facebook, LinkedIn, hospitality industry, talen

Binary Search Algorithm for Mixed Integer Optimization: Application to energy management in a microgrid

This paper presents a binary search algorithm to deal with binary variables in mixed integer optimization problems. One example of this kind of problem is the optimal operation of hydrogen storage and energy sale and purchase into a microgrids context. In this work was studied a system composed by a microgrid that has a connection with the external electrical network and a charging station for electric cars. The system modeling was carried out by the Energy Hubs methodology. The proposed algorithm transforms the MIQP (Mixed Integer Quadratic Program) problem into a QP (Quadratic Program) that is easier to solve. In this way the overall control task is carried out the electricity purchase and sale to the power grid, maximizes the use of renewable energy sources, manages the use of energy storages and supplies the charge of the parked vehicles.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-RUniversidad de Sevilla CNPq401126/2014-5Universidad de Sevilla CNPq303702/2011-

Advanced Control for Energy Management of Grid-Connected Hybrid Power Systems in the Sugar Cane Industry

This work presents a process supervision and advanced control structure, based on Model Predictive Control (MPC) coupled with disturbance estimation techniques and a finite-state machine decision system, responsible for setting energy productions set-points. This control scheme is applied to energy generation optimization in a sugar cane power plant, with non-dispatchable renewable sources, such as photovoltaic and wind power generation, as well as dispatchable sources, as biomass. The energy plant is bound to produce steam in different pressures, cold water and, imperiously, has to produce and maintain an amount of electric power throughout each month, defined by contract rules with a local distribution network operator (DNO). The proposed predictive control structure uses feedforward compensation of estimated future disturbances, obtained by the Double Exponential Smoothing (DES) method. The control algorithm has the task of performing the management of which energy system to use, maximize the use of the renewable energy sources, manage the use of energy storage units and optimize energy generation due to contract rules, while aiming to maximize economic profits. Through simulation, the proposed system is compared to a MPC structure, with standard techniques, and shows improved behavior.Ministerio de Economía y Competitividad CNPq401126/2014-5Ministerio de Economía y Competitividad CNPq303702/2011-7Ministerio de Economía y Competitividad DPI2016-78338-

The management model of european foundation for quality management (EFQM) for tourist destinations: a theoretical discussion

The competitiveness of a destination is related to the tourist experience. Destination management as a whole it is essential to manage cohesively resources and processes of a particular locality. In order to ensure efficient management, different quality models have been developed for companies such as: ISO 9000 family; European Foundation for Quality Management (EFQM); Malcolm Baldridge National Quality Award; and Balanced Scorecard. The purpose of this research is to discuss the adaptation of the EFQM model for tourist destinations. The EFQM model considers the means and the results as the two major groups of categories, as follows: the means (leadership, people, strategies, partners and resources and processes, products and services); and the results (results people, customer results, results and key findings societies). The adaptation of Mendes (2004) is a breakthrough as the specifics of the EFQM for the tourist destination, however, this model needs to be applied empirically so you can identify areas that require further adjustments and improvements. Keywords: Tourism. Management destinations. Models of excellence. European Foundation for Quality Management (EFQM)

Norito e Charnoenderbitos da Borda do Maciço Intrusivo de Venda Nova (Espírito Santo)

An irregular and narrow ring of charnoenderbites and norite envelopes gabbros and syenomonzonites in the Venda Nova inversely zoned pluton, Espírito Santo. The former have an almost massive structure, with foliation only locally well marked. The norite is a fine-grained cumulatic rock with granular hypidiomorphic to intergranular texture. The medium-grained charnoenderbites comprise enderbites, Opx-quartzdiorites and Opx-granodiorites. They are leucocratic to mesocratic with granular hypidiomorphic to porphyritic texture. Ortho and clinopyroxene are present in the both lithotypes. Subsolidus textures occur in the norite and charnoenderbites. The whole rock chemistry separates two different sequences: a basic one, with tholeiitic affinities, correspond to the noritic cumulate, and an intermediary one, medium-K calc-alkaline, comprising the charnoenderbites. They show clearly different chemical signature when compared to the alkalic affinity rocks of the inner domain of the pluton. Both the sequences have similar geochemical characteristics: they are metaluminous, Ca, Fe and Al enriched, and have low to moderate incompatible elements contents. Normal to depleted mantle protolith is inferred for both sequences, and garnet probably was a residual phase during the mantle partial melting

Geologia e petrologia da intrusão do Rio Novo do Sul - ES

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Petrografia e Geoquímica de Rochas Metamáficas e Metaultramáficas da região de Cambuci, Rio de Janeiro

No norte do estado do Rio de Janeiro são encontradas ocorrências localizadas de corpos de rochas máficas e ultramáficas variavelmente deformadas e metamorfisadas. Tratam-se de (meta)clinopiroxenitos, (meta)piroxenitos, (meta)dioritos a quartzodioritos e principalmente (meta)gabros, (meta)noritos e (meta)gabronoritos, com estrutura isotrópica a ligeiramente foliada, revelando aspecto tectonizado marcado por orientação de piroxênio/anfibólio e feldspato ou clots máficos. Textura primária granular, cumulática e de fluxo é observada em amostras desses vários litotipos, em contraponto com aquelas que apresentam fortes evidências de recristalização. Quimicamente possuem composição basáltica, assinatura toleítica e associação com ambiente de fundo oceânico, particularmente MORB transicional a enriquecido. O conjunto de dados permite interpretar essas rochas como remanescentes de paleo-oceano posicionados tectonicamente junto aos paragnaisses regionais

The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation