Avaliação de cultivares de soja em duas épocas de semeadura, no município de Bataiporã, MS, safra 1998/99.

bitstream/item/39756/1/COT-12-1999.pd

Época de semeadura: um importante fator que afeta a produtividade da cultura da soja.

Temperatura do ar; Umidade do solo; Fotoperíodo.bitstream/item/65720/1/DOC34.pd

Landslide mapping from multi-sensor data through improved change detection-based Markov random field

Abstract Accurate landslide inventory mapping is essential for quantitative hazard and risk assessment. Although multi-temporal change detection techniques have contributed greatly to landslide inventory preparation, it is still challenging to generate quality change detection images (CDIs) for accurate landslide mapping. The recently proposed change detection-based Markov random field (CDMRF) provides an effective approach for rapid mapping of landslides with minimum user interventions. However, when CDI is generated by change vector analysis (CVA) alone, the CDMRF method may suffer from noise especially when the pre- and post-event remote sensing images are acquired under different atmospheric, illumination, and phenological conditions. This paper improved such CDMRF approach by integrating normalized difference vegetation index (NDVI), principal component analysis (PCA), and independent component analysis (ICA) generated CDIs with MRF for landslide inventory mapping from multi-sensor data. To justify the effectiveness and applicability, the improved methods were applied to map rainfall-, typhoon-, and earthquake-triggered landslides from the pre- and post-event satellite images acquired by very high resolution QuickBird, high resolution FORMOSAT-2, and moderate resolution Sentinel-2. Moreover, they were tested on pre-event Landsat-8 and post-event Sentinel-2 datasets, indicating that they are operational for landslide inventory mapping from combined multi-temporal and multi-sensor data. The results demonstrate that the improved δNDVI-, PCA-, and ICA-based approaches perform much better than CVA-based CDMRF in terms of completeness, correctness, Kappa coefficient, and F-measures. To the best of our knowledge, it is the first time that NDVI, PCA, and ICA are integrated with MRF for landslide inventory mapping from multi-sensor data. It is anticipated that this research can be a starting point for developing new change detection techniques that can readily generate quality CDI and for applying advanced machine learning algorithms (e.g., deep learning) to automatic detection of natural hazards from multi-sensor time series data

Clinical features and pathophysiology of disorders of arousal in adults: A window into the sleeping brain

Introduction: Disorders of Arousal (DoA) are NREM parasomnias that have been typically regarded as self-limited childhood manifestations. It is now clear that DoA can persist in adults, often presenting with distinctive characteristics. So far, few studies have described the clinical course and characteristics of DoA in adulthood, therefore a large part of their semiology is ignored. The aim of this study is to describe the clinical manifestations of DoA in an adult population and to provide a pathophysiological interpretation of their features. Methods: We screened our database for all 1,600 adult ( 6515 years) patients with sleep-related motor behaviors between 1995 and 2016. We identified 45 patients with typical DoA episodes, of whom a complete history, neurological examination and diagnostic video-polysomnography (VPSG) were available. All patients provided a detailed description of their episodes (with particular regards to semiology, frequency, and association with stressful life events) in different life periods. VPSG recordings were reviewed and DoA episodes were identified and assigned to three different categories according to their complexity. Results: Our population was composed of 45 adult patients ranging between 15 and 76 years. Sleepwalking was reported by 86% of patients, possibly associated with complex interactions with the environment and violent behaviors in 53% of cases; distressing mental contents were reported by 64%. Recall of the episodes was reported in 77% of patients. Non-restorative sleep was reported in 46% of patients. Stress was a potential episode trigger in 80% of patients. VPSG recordings documented 334 DoA episodes. According to our classification of motor patterns, 282 episodes (84%) were Simple Arousal Movements (SAMs), 34 (10%) Rapid Arousal Movements (RAMs) and 18 (5%) Complex Arousal Movements (CAMs). Discussion: Our study confirms that DoA in adulthood present with distinctive characteristics, such as non-restorative sleep, violence and complex, or bizarre behaviors. Alternative classifications of DoA based on motor patterns could be useful to characterize DoA episodes in adults, as different motor patterns often coexist in the same individual and minor episodes are more common but generally underreported by patients. Prospective studies are needed for a definitive characterization of DoA in adulthood throughout the life course