A Band Model of Cambium Development: Opportunities and Prospects

More than 60% of tree phytomass is concentrated in stem wood, which is the result of periodic activity of the cambium. Nevertheless, there are few attempts to quantitatively describe cambium dynamics. In this study, we develop a state-of-the-art band model of cambium development, based on the kinetic heterogeneity of the cambial zone and the connectivity of the cell structure. The model describes seasonal cambium development based on an exponential function under climate forcing which can be effectively used to estimate the seasonal cell production for individual trees. It was shown that the model is able to simulate different cell production for fast-, middle- and slow-growing trees under the same climate forcing. Based on actual measurements of cell production for two contrasted trees, the model effectively reconstructed long-term cell production variability (up to 75% of explained variance) of both tree-ring characteristics over the period 1937−2012. The new model significantly simplifies the assessment of seasonal cell production for individual trees of a studied forest stand and allows the entire range of individual absolute variability in the ring formation of any tree in the stand to be quantified, which can lead to a better understanding of the anatomy of xylem formation, a key component of the carbon cycle.

Evaluation of Reservoir Quality and Forecasted Production Variability along a Multi-Fractured Horizontal Well. Part 2: Selected Stage Forecasting

The current practice for multi-fractured horizontal well development in low-permeability reservoirs is to complete the full length of the well with evenly spaced fracture stages. Given methods to evaluate along-well variability in reservoir quality and to predict stage-by-stage performance, it may be possible to reduce the number of stages completed in a well without a significant sacrifice in well performance. Provision and demonstration of these methods is the goal of the current two-part study. In Part 1 of this study, reservoir and completion quality were evaluated along the length of a horizontal well in the Montney Formation in western Canada. In the current (Part 2) study, the along-well reservoir property estimates are first used to forecast per-stage production variability, and then used to evaluate production performance of the well when fewer stages are completed in higher quality reservoir. A rigorous and fast semi-analytical model was used for forecasting, with constraints on fracture geometry obtained from numerical model history matching of the studied Montney well flowback data. It is concluded that a significant reduction in the number of stages from 50 (what was implemented) to less than 40 could have yielded most of the oil production obtained over the forecast period.

Thinking probabilistically in the study of intonational speech prosody

Speech prosody, the melodic and rhythmic properties of a language, plays a critical role in our everyday communication. Researchers have identified unique patterns of prosody that segment words and phrases, highlight focal elements in a sentence, and convey holistic meanings and speech acts that interact with the information shared in context. The mapping between the sound and meaning represented in prosody is suggested to be probabilistic – the same physical instance of sounds can support multiple meanings across talkers and contexts while the same meaning can be encoded in physically distinct sound patterns (e.g., pitch movements). The current overview presents an analysis framework for probing the nature of this probabilistic relationship. Illustrated by examples from the literature and a dataset of German focus marking, we discuss the production variability within and across talkers and consider challenges that this variability imposes on the comprehension system. A better understanding of these challenges, we argue, will illuminate how the human perceptual, cognitive, and computational mechanisms may navigate the variability to arrive at coherent understanding of speech prosody. The current paper is intended to be an introduction for those who are interested in thinking probabilistically about the sound-meaning mapping in prosody. Open questions for future research are discussed with proposals for examining prosodic production and comprehension within a comprehensive, mathematically-motivated framework of probabilistic inference under uncertainty.

Examining the Relationship Between Speech Perception, Production Distinctness, and Production Variability

Several studies have demonstrated that individuals’ ability to perceive a speech sound contrast is related to the production of that contrast in their native language. The theoretical account for this relationship is that speech perception and production have a shared multimodal representation in relevant sensory spaces (e.g., auditory and somatosensory domains). This gives rise to a prediction that individuals with more narrowly defined targets will produce greater separation between contrasting sounds, as well as lower variability in the production of each sound. However, empirical studies that tested this hypothesis, particularly with regard to variability, have reported mixed outcomes. The current study investigates the relationship between perceptual ability and production ability, focusing on the auditory domain. We examined whether individuals’ categorical labeling consistency for the American English /ε/–/æ/ contrast, measured using a perceptual identification task, is related to distance between the centroids of vowel categories in acoustic space (i.e., vowel contrast distance) and to two measures of production variability: the overall distribution of repeated tokens for the vowels (i.e., area of the ellipse) and the proportional within-trial decrease in variability as defined as the magnitude of self-correction to the initial acoustic variation of each token (i.e., centering ratio). No significant associations were found between categorical labeling consistency and vowel contrast distance, between categorical labeling consistency and area of the ellipse, or between categorical labeling consistency and centering ratio. These null results suggest that the perception-production relation may not be as robust as suggested by a widely adopted theoretical framing in terms of the size of auditory target regions. However, the present results may also be attributable to choices in implementation (e.g., the use of model talkers instead of continua derived from the participants’ own productions) that should be subject to further investigation.

Comparative models disentangle drivers of fruit production variability of an economically and ecologically important long-lived Amazonian tree

Trees in the upper canopy contribute disproportionately to forest ecosystem productivity. The large, canopy-emergent Bertholletia excelsa also supports a multimillion-dollar commodity crop (Brazil nut), harvested almost exclusively from Amazonian forests. B. excelsa fruit production, however is extremely variable within populations and years, destabilizing local harvester livelihoods and the extractive economy. To understand this variability, data were collected in Acre, Brazil over 10 years at two sites with similar climate and forest types, but different fruit production levels, despite their proximity (~ 30 km). One site consistently produced more fruit, showed less individual- and population-level variability, and had significantly higher soil P and K levels. The strongest predictor of fruit production was crown area. Elevation and sapwood area also significantly impacted fruit production, but effects differed by site. While number of wet days and dry season vapor pressure prior to flowering were significant production predictors, no climatic variables completely captured annual observed variation. Trees on the site with higher available P and K produced nearly three times more fruits, and appeared more resilient to prolonged drought and drier atmospheric conditions. Management activities, such as targeted fertilization, may shield income-dependent harvesters from expected climate changes and production swings, ultimately contributing to conservation of old growth forests where this species thrives.

THE APPLICATION OF SMED METHOD IN THE INDUSTRIAL ENTERPRISE

The production process fluency is often interrupted by idle time. A significant proportion of individual idle times is caused by changeover. Trends, such as the individualization of requirements, the constant effort to meet the customers’ requirements on time and the maintaining of the production process fluency at low costs, are aimed at eliminating idle time. In terms of contradictory goals such as individualization of customer requirements, which is reflected in the high variability of production / products and minimalization of the production time and its fluency, it is necessary to pay increased attention to the changeover process. The problem related to the changeover process can be solved in two ways: by reducing the number of changeovers (reducing production variability and achieving dissatisfaction with individual customer requirements) or by shortening the changeover time (while maintaining production variability and ability to satisfy a wide range of individual customer requirements). The Single-Minute Exchange of Die - SMED method is used to shorten the time duration of the changeover process and eliminate waste in the given process. The aim of the paper is to apply the SMED method in vibration welder changeover process in a selected industrial enterprise and thus achieve a shortening of the changeover process. The SMED method was applied in the enterprise which belongs to the group of small and medium-sized enterprises. The research method was indirect observation via video recording and time snap. Various types of waste were identified based on the analysis, and subsequently eliminated by proposed rationalization measures. Finally, the time duration of the changeover process before the analysis and after the implementation of rationalization measures was compared.

Comparative Study of Photovoltaic Power Forecasting Methods

Electricity consumption is growing rapidly worldwide. Renewable energy resources, such as solar energy, play a crucial role in this scenario, contributing to satisfy demand sustainability. Although the share of Photovoltaic (PV) power generation has increased in the past years, PV systems are quite sensitive to climatic and meteorological conditions, leading to undesirable power production variability. In order to improve energy grid stability, reliability, and management, accurate forecasting models that relate operational conditions to power output are needed. In this work we evaluate the performance of regression methods applied to forecast short term (next day) energy production of a PV Plant. Specifically, we consider five regression methods and different configurations of feature sets. Our results suggest that MLP and SVR provide the best forecasting results, in general. Also, although features based on different solar irradiance levels play a key role in predicting power generation, the use of additional features can improve prediction results.