Abstract
The optimized dispatch of different distributed generations (DGs) in stand-alone microgrid (MG) is of great significance to the operation's reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL) and combined cooling-heating-power (CCHP) model of micro-gas turbine (MT), a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV), wind turbine (WT), fuel cell (FC), diesel engine (DE), MT and energy storage (ES). Four typical scenarios were designed according to different day types (work day or weekend) and weather conditions (sunny or rainy) in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers' comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO) to propose modified chaos particle swarm optimization (MCPSO) whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

Abstract
This paper proposes a mathematical model and two heuristic procedures to solve the cutting stock problem with usable leftovers, relating the implications of the model with aspects considering sustainability in terms of environmental, economic and social issues. The possibility of generating leftovers that can be used or sold, reduces raw material waste during the cutting process and, consequently, increases companies’ profits. By reducing waste and increasing profits, companies can become more competitive in the market. They can also integrate environmental aspects into their operational strategies and, therefore, create a better self-image and profitability, generating more jobs and contributing to a stronger local economy. We believe that the model is more likely to be adopted by smaller companies, which generally face numerous barriers but at the same time have a significant social impact, generating income and jobs. Based on the knowledge of the authors, this is the first study that relates a cutting problem with its implications for sustainability. Computational tests were performed, and the obtained results are discussed considering the win-win approach to sustainability.

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Abstract
Purpose: The purpose of this paper is to highlight possible hidden risks when allocating multi-skilled human resources to teams working in a multi-project environment. Are allocation strategies maximizing the use of skills for each project, the only way to improve the chances of all projects being successful? What are the risks in this strategy? What are the available alternatives? Design/methodology/approach: Simulation was used for different allocation strategies to evaluate, using two different metrics, the staffing of human resources in different projects. Three categories of companies were studied, and for each typology, virtual companies were created and several scenarios of collaborators, projects and tasks were simulated to evaluate the staffing process. Findings: It is shown that for different simulations, different allocation strategies and metrics are possible for evaluation and that there is no golden rule of staffing in organizations with multiple projects and with multiple skills collaborators. The staffing is very much dependent on the context of the company. Practical implications: The numerical method provides general managers with a useful tool to enable a better distribution of staff collaborators in teams handling multiple projects that require multi-skilled human resources. This method can also be used to evaluate training needs and hiring strategies, as it presents an overview of all human resources skills and motivations. Originality/value: For academics, the methodology developed enables the study of characteristics of human resources, skills and motivations, which are interesting for team formation. To practitioners, the numerical method is a practical tool for staffing in multiple skills and multiple projects. This tool can also diagnose each company situation regarding current collaborators’ skills and motivations, serving as a tool for training and for hiring.

Abstract
ABSTRACT - The objective of this study was to evaluate some crop management strategies to improve silage production by family farmers in the micro-region of Pelotas, southern State of Rio Grande do Sul (RS), Brazil. The seasonal analysis tool of de CSM-CERES-Maize model was used to assess aboveground dry biomass production under rainfed conditions. The simulations comprised scenarios involving four cultivars (Amarelão, AL 30, AG 5011 and AG 122), six nitrogen (N) fertilization strategies, 52 sowing dates, and 21 years of daily weather data. Silage productivity and quality were assessed, and a sowing window was established for each one of the cultivars based on this information. Regardless of the N rate and cultivar, sowings performed between June 26 and December 19 were not at risk of exceeding the deadline for silage harvesting in the region. For sowing occurred on December 19, regardless of the N rate, the average productivity of silage and the average amount of energy per unit area were lower for the creole variety Amarelão. For the same sowing date the average values of energy per unit of biomass weight indicated good silage quality, for all cultivars regardless of the N rate.Keywords: family farming, DSSAT, aboveground biomass, Zea mays L. ABORDAGEM DE MODELAGEM PARA ESTABELECER ESTRATÉGIAS DE PRODUÇÃO DE SILAGEM NA MICRORREGIÃO DE PELOTAS, BRASIL  RESUMO - O objetivo deste estudo foi avaliar estratégias de manejo para melhorar a produção de silagem pelos agricultores familiares na microrregião de Pelotas, sul do Rio Grande do Sul (RS), Brasil. A ferramenta de análise sazonal do modelo CSM-CERES-Maize foi usada para avaliar a produção de fitomassa seca em condições de sequeiro. Foi considerado nas simulações um cenário com quatro cultivares (Amarelão, AL 30, AG 5011 e AG 122); seis estratégias de adubação nitrogenada; 52 épocas de semeadura; e 21 anos de dados meteorológicos diários. A produtividade e a qualidade da silagem foram avaliadas e, com base nessas informações, uma janela de semeadura foi estabelecida para cada uma das cultivares. Independentemente da dose de nitrogênio e cultivar, as semeaduras realizadas entre 26 de junho e 19 de dezembro não correm risco de exceder o prazo para a colheita de silagem na região. Para semeadura realizada em 19 de dezembro, independentemente da taxa de N, a produtividade média de silagem e a quantidade média de energia por unidade de área foram menores para a variedade crioula Amarelão. Para esta mesma época de semeadura, os valores médios de energia por unidade de peso de fitomassa seca indicaram boa qualidade de silagem, para todas as cultivares, independentemente da dose de N.Palavras-chave: agricultura familiar, DSSAT, fitomassa seca da parte aérea, Zea mays L.

Abstract
AbstractThe International Cancer Genome Consortium (ICGC)’s Pan-Cancer Analysis of Whole Genomes (PCAWG) project aimed to categorize somatic and germline variations in both coding and non-coding regions in over 2,800 cancer patients. To provide this dataset to the research working groups for downstream analysis, the PCAWG Technical Working Group marshalled ~800TB of sequencing data from distributed geographical locations; developed portable software for uniform alignment, variant calling, artifact filtering and variant merging; performed the analysis in a geographically and technologically disparate collection of compute environments; and disseminated high-quality validated consensus variants to the working groups. The PCAWG dataset has been mirrored to multiple repositories and can be located using the ICGC Data Portal. The PCAWG workflows are also available as Docker images through Dockstore enabling researchers to replicate our analysis on their own data.