Automatic identification of charcoal origin based on deep learning

The differentiation between the charcoal produced from (Eucalyptus) plantations and native forests is essential to control, commercialization, and supervision of its production in Brazil. The main contribution of this study is to identify the charcoal origin using macroscopic images and Deep Learning Algorithm. We applied a Convolutional Neural Network (CNN) using VGG-16 architecture, with preprocessing based on contrast enhancement and data augmentation with rotation over the training set images. on the performance of the CNN with fine-tuning using 360 macroscopic charcoal images from the plantation and native forests. The results pointed out that our method provides new perspectives to identify the charcoal origin, achieving results upper 95 % of mean accuracy to classify charcoal from native forests for all compared preprocessing strategies

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Elective cancer surgery in COVID-19-free surgical pathways during the SARS-CoV-2 pandemic: An international, multicenter, comparative cohort study

PURPOSE As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19–free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19–free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19–free surgical pathways. Patients who underwent surgery within COVID-19–free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19–free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score–matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19–free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION Within available resources, dedicated COVID-19–free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Elective Cancer Surgery in COVID-19-Free Surgical Pathways During the SARS-CoV-2 Pandemic: An International, Multicenter, Comparative Cohort Study.

PURPOSE: As cancer surgery restarts after the first COVID-19 wave, health care providers urgently require data to determine where elective surgery is best performed. This study aimed to determine whether COVID-19-free surgical pathways were associated with lower postoperative pulmonary complication rates compared with hospitals with no defined pathway. PATIENTS AND METHODS: This international, multicenter cohort study included patients who underwent elective surgery for 10 solid cancer types without preoperative suspicion of SARS-CoV-2. Participating hospitals included patients from local emergence of SARS-CoV-2 until April 19, 2020. At the time of surgery, hospitals were defined as having a COVID-19-free surgical pathway (complete segregation of the operating theater, critical care, and inpatient ward areas) or no defined pathway (incomplete or no segregation, areas shared with patients with COVID-19). The primary outcome was 30-day postoperative pulmonary complications (pneumonia, acute respiratory distress syndrome, unexpected ventilation). RESULTS: Of 9,171 patients from 447 hospitals in 55 countries, 2,481 were operated on in COVID-19-free surgical pathways. Patients who underwent surgery within COVID-19-free surgical pathways were younger with fewer comorbidities than those in hospitals with no defined pathway but with similar proportions of major surgery. After adjustment, pulmonary complication rates were lower with COVID-19-free surgical pathways (2.2% v 4.9%; adjusted odds ratio [aOR], 0.62; 95% CI, 0.44 to 0.86). This was consistent in sensitivity analyses for low-risk patients (American Society of Anesthesiologists grade 1/2), propensity score-matched models, and patients with negative SARS-CoV-2 preoperative tests. The postoperative SARS-CoV-2 infection rate was also lower in COVID-19-free surgical pathways (2.1% v 3.6%; aOR, 0.53; 95% CI, 0.36 to 0.76). CONCLUSION: Within available resources, dedicated COVID-19-free surgical pathways should be established to provide safe elective cancer surgery during current and before future SARS-CoV-2 outbreaks

Cellular automaton in forestry planning

A inteligência artificial oferece tomadas de decisão automatizadas ao fazer uso de informações tabuladas na forma de variáveis capazes de explicar padrões de comportamento a um determinado nível de assertividade. Essa tese apresenta o desenvolvimento de um algoritmo de inteligência artificial que utiliza o conhecimento em autômatos celulares, grafos, regressão, busca heurística e simulação com objetivo de gerar cenários para o agendamento da colheita florestal otimizado e de forma autônoma, em função de variáveis, como custos, demanda de madeira, relevo, produtividade de máquinas, características da floresta, distância entre unidades de manejo, distância até o pátio de estocagem, crescimento e produção florestal, taxa de desconto, dentre outros. A discussão dos resultados foi realizada ao analisar o atendimento da demanda de madeira e o valor presente líquido (VPL) para um horizonte de 365 dias. O estudo conclui que a aplicação de critérios e regras simples em autômatos celulares para o planejamento florestal de curto prazo é capaz de otimizar o agendamento da colheita e maximizar o lucro para períodos anuais, mensais e diários. O sistema desenvolvido, Harvest Scheduling System (HSS) permite simular cenários para atendimento da demanda de madeira e produz resultados otimizados para o agendamento da colheita em tempo hábil. Cada um dos 30 cenários foi submetido a 1.000 iterações, variando critérios de vizinhança e regras de sorteio. Como produto deste trabalho, obteve-se um sistema capaz de otimizar o planejamento operacional da colheita florestal denominado Harvest Scheduling System (HSS), que utiliza a linguagem de programação Java, IDE (Integrated Development Environment) Netbeans 8.2, e JDK 8 (Java Development Kit). A aplicação de critérios e regras simples aos autômatos celulares para o planejamento florestal de curto prazo é capaz de otimizar o agendamento da colheita e maximizar o lucro para períodos anuais, mensais e diários. O sistema desenvolvido, Harvest Scheduling System (HSS) permite simular cenários para atendimento da demanda de madeira e produz resultados otimizados para o agendamento da colheita em tempo hábil.Artificial intelligence offers automated decision by using structured database with variables capable of explaining behavior patterns at a certain level of assertiveness. This study presents the development of an artificial intelligence algorithm that uses knowledge in cellular automata, graph theory, regression, metaheuristics and simulation in order to generate scenarios for optimized and autonomous forest harvesting scheduling by using variables such as costs, wood demand, terrain slope, machinery productivity, forest characteristics, distance between management units, distance to the stockyard, growth and yield, discount rate, among others. The discussion of the research was made by analyzing the wood supply and the net present value (NPV) in a time horizon of 365 days. The study concludes that the application of simple criteria and rules in cellular automata for short-term forest planning can optimize harvest scheduling and maximizing profit for annual, monthly and daily periods. The developed system “Harvest Scheduling System – HSS”, allows simulating scenarios to perform wood supply and optimized harvest scheduling in a timely manner. Each of the thirty scenarios was submitted to 1000 (one thousand) iterations with distinct neighborhood criteria and drawing rules. The “HSS” was developed based on Java programming language, Netbeans 8.2 Integrated Development Environment (IDE) and JDK 8 (Java Development Kit). Thirty scenarios were analyzed by applying six different co-evolution rules and five harvesting displacement costs (R$/km) between management units. Each scenario was submitted to 1000 (one thousand) iterations generating the same number of viable solutions (Net Present Value - NPV). The most profitable solution for each of the neighborhood and draws rules is associated with a schedule that seeks to combine the harvesting displacement costs and other variables of influence such as: age; discount rate; yield for initial state; periodic daily increment; planting and maintenance cost; harvesting cost, transporting cost; restrictions; among others. The planting and maintenance cost and harvesting displacement costs are the variables that most influence the best solution choice. The current increment over the one-year harvest horizon represents 6 to 10% of revenue

A diameter distribution model for even-aged agroforest stand of eucalyptus clones

O presente estudo teve como objetivo desenvolver e avaliar um modelo de distribuição de diâmetro para clones de eucalipto em sistema agroflorestal (SAF). Os dados foram provenientes de sistemas agroflorestais pertencentes à empresa Aço-Florestal do grupo Votarantim, no município de Paracatu, na região Noroeste do Estado de Minas Gerais. A recuperação das distribuições de diâmetros foi feita a partir de equações de regressão que correlacionam os parâmetros da função Weibull truncada a direita, ajustada pelo método da máxima verossimilhança em uma idade futura ([Beta]2 e Y2) com parâmetros em uma idade atual ([Beta]1 e Y1) e com características do povoamento em idades atual e futura. Para avaliar a cons istência e capacidade de projetar do sistema foram elaborados gráficos da distribuição média de diâmetros: distribuição inicial observada com a projetada para o mesmo momento inicial; distribuição observada em idade futura com a projetada para a mesma idade futura a partir de uma distribuição inicial; e distribuições projetadas para idades futuras a partir de diferentes distribuições iniciais. Perante essas análises pôde-se concluir que a função de densidade probabilidade Weibull completa e truncada à direita descreve de forma precisa a distribuição dos diâmetros por classe diamétrica em sistema agroflorestal. O sistema de equações para modelagem do crescimento e produção em nível de distribuição diamétrica proposto neste estudo pode ser aplicado em povoamentos de clones de eucalipto sob SAF.The objective of this study was to develop and evaluate a diameter distribution model of even-aged agroforest stand of eucalyptus clones. The data comes from an agroforest stand, owned by the Aço-Florestal Ltda. company that belongs to the Votorantim group, located in the northwest region of the Minas Gerais State, Brazil. The recovery of the diameter distributions was done starting from regression equations that correlate the parameters of the right hand side truncated Weibull function fitted by the maximum likelihood method in a future age ([Beta]2 and Y2), with parameters in a current age ([Beta]1 and Y1) and with characteristics of the stand in current and future ages. Diameter distributions graphs were built to evaluate the consistence and predicting capacity of the system of equations: initial distribution observed and the distribution predicted for the same initial moment; distribution observed in a future age and the distribution predicted for the same future age starting from an initial distribution; and predicted distributions for future ages starting from different initial distribution. Thus, those analyses allow concluding that the complete truncated right hand side Weibull density probability function describes the diameter distribution per diameter class in a precise way for agroforests. The system of equations to predict diameter distribution model can be applied in agroforest stand of eucalyptus clones.Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

Growth and yield prediction using the modified Buckman model

A model to manage even-aged stands was developed using a modification of the Buckman model. Data from Eucalyptus urophylla and Eucalyptus cloeziana stands located in the Northern region of Minas Gerais State, Brazil were used in the formulation of the system. The proposed model generated precise and unbiased estimates in non-thinned stands

Automatic identification of charcoal origin based on deep learning

The differentiation between the charcoal produced from (Eucalyptus) plantations and native forests is essential to control, commercialization, and supervision of its production in Brazil. The main contribution of this study is to identify the charcoal origin using macroscopic images and Deep Learning Algorithm. We applied a Convolutional Neural Network (CNN) using VGG-16 architecture, with preprocessing based on contrast enhancement and data augmentation with rotation over the training set images. on the performance of the CNN with fine-tuning using 360 macroscopic charcoal images from the plantation and native forests. The results pointed out that our method provides new perspectives to identify the charcoal origin, achieving results upper 95 % of mean accuracy to classify charcoal from native forests for all compared preprocessing strategie

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

Background: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit