Volume Estimation Research Articles

Unveiling the Genetic Architecture of Semen Traits in Thai Native Roosters: A Comprehensive Analysis Using Random Regression and Spline Function Models.

Improving reproductive traits, particularly semen quality and quantity, is crucial for optimizing poultry production and addressing the current limitations in native chicken reproduction. The aim of this study was to develop a genetic model to estimate genetic parameters guiding the selection of individual Thai native roosters. Using data collected from 3475 records of 242 Thai native grandparent roosters aged 1-4 years, we evaluated semen traits (mass movement, semen volume, and sperm concentration) over 54 weeks. A random regression test-day model incorporating five covariance functions, including a linear spline function with four, five, six, and eight knots (SP4, SP5, SP6, and SP8) and second-order Legendre polynomial function (LG2), was used to estimate genetic parameters. The results showed that the SP8 model consistently outperformed the other models across all traits, with the lowest mean square error, highest coefficient of determination, and superior predictive ability. Heritability estimates for mass movement, semen volume, and sperm concentration ranged from 0.10 to 0.25, 0.22 to 0.25, and 0.11 to 0.24, respectively, indicating moderate genetic influence on these traits. Genetic correlations between semen volume and sperm concentration were highest in the SP8 model, highlighting a strong genetic association between these traits. The SP8 model also revealed a high genetic correlation between mass movement and semen volume, supporting the potential for selecting mass movement as a predictor of semen volume. In conclusion, this study highlights the effectiveness of random regression models with linear spline functions to evaluate the genetic parameters of semen traits in native Thai roosters. The SP8 model is a robust tool for breeders to enhance the reproductive performance of native Thai chickens, contributing to sustainable poultry production systems.

Generic and Specific Models for Volume Estimation in Forest and Savanna Phytophysiognomies in Brazilian Cerrado.

The Cerrado has high plant and vertebrate diversity and is an important biome for conserving species and provisioning ecosystem services. Volume equations in this biome are scarce because of their size and physiognomic diversity. This study was conducted to develop specific volumetric models for the phytophysiognomies Gallery Forest, Dry Forest, Forest Savannah, and Savannah Woodland, a generic model and a model for Cerrado forest formation. Twelve 10 m × 10 m (100 m²) (National Forest Inventory) plots were used for each phytophysiognomy at different sites (regions) of the Federal District (FD) where trees had a diameter at breast height (DBH; 1.30 m) ≥5 cm in forest formations and a diameter at base height (Db; 0.30 m) ≥5 cm in savanna formations. Their diameters and heights were measured, they were cut and cubed, and the volume of each tree was obtained according to the Smalian methodology. Linear and nonlinear models were adjusted. Criteria for the selection of models were determined using correlation coefficients, the standard error of the estimates, and a graphical analysis of the residues. They were later validated by the chi-square test. The resultant models indicated that fit by specific phytophysiognomy was ideal; however, the generic and forest formation models exhibited similar performance to specific models and could be used in extensive areas of the Cerrado, where they represent a high potential for generalization. To further increase our understanding, similar research is recommended for the development of specific and generic models of the total volume in Cerrado areas.

Measuring exploration: evaluation of modelling to generate alternatives methods in capacity expansion models

Abstract As decarbonisation agendas mature, macro-energy systems modelling studies have increasingly focused on enhanced decision support methods that move beyond least-cost modelling to improve consideration of additional objectives and tradeoffs. One candidate is modelling to generate alternatives (MGA), which systematically explores new objectives without explicit stakeholder elicitation. This paper provides comparative testing of four existing MGA methodologies and proposes a new Combination vector selection approach. We examine each existing method’s runtime, parallelizability, new solution discovery efficiency, and spatial exploration in lower dimensional (N ⩽ 100) spaces, as well as spatial exploration for all methods in a three-zone, 8760 h capacity expansion model case. To measure convex hull volume expansion, this paper formalizes a computationally tractable high-dimensional volume estimation algorithm. We find random vector provides the broadest exploration of the near-optimal feasible region and variable Min/Max provides the most extreme results, while the two tie on computational speed. The new Combination method provides an advantageous mix of the two. Additional analysis is provided on MGA variable selection, in which we demonstrate MGA problems formulated over generation variables fail to retain cost-optimal dispatch and are thus not reflective of real operations of equivalent hypothetical capacity choices. As such, we recommend future studies utilize a parallelized combined vector approach over the set of capacity variables for best results in computational speed and spatial exploration while retaining optimal dispatch.

Automated multiple sclerosis progression rate computation of a patient from 2D FLAIR images with Rayleigh-Weibull-Fuzzy imaging and augmented morphing method

Multiple sclerosis (MS) is a neurological demyelinating disorder affecting brain and spinal cord by attacking the myelin sheaths of nerves. Estimation of the volumetric changes in MS lesions is a challenging and specialized task which is executed and judged by medical experts. The change in the volume of the lesions provides crucial information on MS progression or regression by comparing the magnetic resonance images (MRI) taken in successive scans. However, visual comparison of the images, even with an expert eye, would not always lead to a conclusive decision nor a consensus on progression or regression. Therefore, we present an automated expert system for estimating MS progression rate by automatic lesion segmentation and volume estimation using two-dimensional MRIs, which is also adaptable to various parameters, slice thickness and increment. A clinical dataset is specially formed for this research which contains three sets of 135 MR images of an MS patient generated within approximately 23- and 6-month periods consecutively with identical device parameters. The lesions are segmented by a novel Rayleigh-Weibull-Fuzzy (RWF) imaging method based on the Nakagami distribution and specialized fuzzy 2-means. Subsequently, the segmentation module is trained to fit the ground truths images created by experts to achieve the highest dice score possible for a total number of 56 images containing lesions, which is found as 93.76%. Afterwards, several imaginary image sequences are generated by augmented linear and nonlinear morphing for re-segmentation of imaginary lesions by RWF. Finally, we estimated the volumetric change between the first two MRI sequences to adjust the morphing module and to predict the progression rate of the lesions in time. The framework automatically selected the highest accuracy, which is 99.9% in the training session and estimated the progression rate in the testing phase with 99.69% accuracy, which are not achievable without augmented morphing methodology. For the first time in the literature, an automated framework could estimate the MS progression rate from the raw MR images, which is also the main innovation of this paper and the outputs would be beneficial for the experts working on this field.

Towards spatially disaggregated cocaine supply chain modeling

Despite the global reach and economic scale of cocaine trafficking, our best geographic understanding of the global trade remains coarse. A more spatially disaggregated understanding of how the cocaine supply chain embeds across multiple locations is necessary for informing security policies and anticipating the spread and intensity of social and environmental harms associated with the cocaine trade. In this research, modeling methods used for legal supply chains are adapted to spatially disaggregate illicit supply chain flows. Profit and supply maximization model versions were compared to elucidate key decision parameters cocaine traffickers might be facing. Cocaine flows to EU+3 (Norway, Turkey, and United Kingdom) markets were estimated based on the smuggling capacity of major Central American ports and bilateral trade volumes of selected commodities most often seized with cocaine shipments. The resulting estimates of cocaine volumes diverted to EU+3 countries from Central America ranged between 938 and 1526 metric tons (MT). Generally, easier concealment and storage in Central America led to less volume supplied to the United States (US) and increased shipments to EU+3 markets. Importantly, the value of this modeling approach is not in the quantitative estimates produced, but in the methodological approach that provides the ability to rigorously ground any quantitative estimates of clandestine phenomenon in the best available data.

Large-scale inventory in natural forests with mobile LiDAR point clouds

Large-scale forest inventory at the individual tree level is critical for natural resource management decision making. Terrestrial Laser Scanning (TLS) has been used for individual tree level inventory at plot scale However, due to the inflexibility of TLS and the complex scene of natural forests, it is still challenging to localize and measure every tree at large scale. In this paper, we present a framework to conduct large-scale natural forest inventory at the individual tree level by taking advantage of deep learning models and Mobile Laser Scanning (MLS) systems. First, a deep learning model, ForestSPG, was developed to perform large-scale semantic segmentation on MLS LiDAR data in natural forests. Then, the forest segmentation results were used for individual stem mapping. Finally, Diameter at Breast Height (DBH) was measured for each individual stem. Two natural forests mapped with backpack and Unmanned Aerial Vehicle (UAV) LiDAR systems were tested. The results showed that the proposed ForestSPG is able to segment large-scale forest LiDAR data into multiple ecologically meaningful classes. The proposed framework was able to localize and measure all 5838 stems at individual tree level in a 20 ha natural forest in less than 20 min using UAV LiDAR. DBH measurement results on trees’ DBH larger than 38.1 cm (15 in) showed that backpack LiDAR was able to achieve 1.82 cm of Root Mean Square Error (RMSE) and UAV LiDAR was able to achieve 3.13 cm of RMSE. The proposed framework can not only segment complex forest components with LiDAR data from different platforms but also demonstrate good performance on stem mapping and DBH measurement. Our research provides and automatic and scalable solution for large-scale natural forest inventory at individual tree level, which can be the basis for large-scale estimation of wood volume and biomass.

Data-Driven Analytics on Traffic Volume Calibration and Estimation for Town-Maintained Highways: A Case Study from Connecticut

Data-Driven Analytics on Traffic Volume Calibration and Estimation for Town-Maintained Highways: A Case Study from Connecticut

Reducing tree volume overestimation in quantitative structure models using modeled branch topology and direct twig measurements

Abstract Quantitative Structure Models (QSMs) are fit to tree point clouds to represent the topology of trees as a network of cylinders. QSMs allow for the calculation of metrics difficult to measure without destructive sampling, including total tree volume. Current limitations in terrestrial laser scanning technology make small branches difficult to accurately resolve, causing overestimation of small branch volume in QSMs, which can translate into overestimating tree biomass. We present a new method called Real Twig to correct overestimated small branch and twig cylinders in QSMs. Real Twig differs from current methods by using twig diameters measured directly from corresponding tree species to model a unique taper for every path in the QSM, using the QSM’s inherent branching topology, but without relying on predefined mathematical or allometric relationships. To test Real Twig, we generated QSMs for different sets of trees that had detailed dry mass and density measurements obtained via felling after scanning. QSM-based biomass estimates were obtained by multiplying the tree’s QSM-based volume estimate by the tree’s specific basic density value. We trained our method with high-quality data consisting of five northern red oak (Quercus rubra L.) and five red maple (Acer rubrum L.) trees, using two different versions of TreeQSM, a widely used algorithm for generating QSMs. We further tested our method on three publicly available datasets, including managed forests and large tropical trees, collected with both phase-shift or time-of-flight sensors. QSMs corrected with our Real Twig method showed a very large improvement in tree biomass estimation, with a relative mean error of −1.2%, a relative root mean square error of 10.5%, and a concordance correlation coefficient of 0.999, compared to a relative mean error 76.8%, a relative root mean square error of 48.7%, and a concordance correlation coefficient of 0.982, when using the standard outputs of TreeQSM.

Two methods based on spline quasi-interpolants to estimate volumes enclosed by parametric surfaces

In this study, we explore two methods based on spline quasi−interpolants (abbr. QI) that rely exclusively on point evaluations to estimate the volume of a closed parametric surface. The first method involves a division of this surface evenly into parallel slices in order to approximate the contours of each slice using quadratic QI. Subsequently, we calculate numerically the areas of the interior surfaces, which will be used in a quadrature formula to obtain the approximate volume. The second method uses a bivariate QI to calculate the entire area, and an integration formula is applied to estimate the desired volume. In both methods, we study the associated error estimates. Numerical examples are provided to illustrate our methods, as well as a real-world application, as well as a study of the effect of noise on the robustness of both volume estimation methods.

A vision-based dietary survey and assessment system for college students in China

Understanding the current dietary habits of college students is essential due to the pressing issues of unbalanced diets and insufficient nutrition. However, traditional approaches frequently depend on recollection, which can introduce unconscious bias and make them difficult to implement. Herein, we introduce a new computer vision system to evaluate the dietary habits of college students in China. A specialized food dataset comprising RGB-D images, recipes with ingredient masses, and nutrient composition was created using data collected from college canteens. First, object detection models were utilized to identify food categories and locations. Subsequently, we introduced a method for automatically estimating the food volume of nonstandard portions using position and depth information. The final nutrients were derived directly or indirectly through the database. Experimental results demonstrate the applicability of the YOLOv8 object detection model and volume estimation method to our tasks. To support the development of devices for detecting food intake and diet management, we have made our dataset publicly available. The College Canteen Food-100 dataset is publicly available at https://github.com/zichengzichengzi/CC-FOOD-100.

Scalability challenges of machine learning models for estimating walking and cycling volumes in large networks

This study explores the scalability of machine learning models for estimating walking and cycling volumes across the extensive New South Wales (NSW) Six Cities Region in Australia using mobile phone and crowdsourced data. Previous research has focused on localized applications, missing the complexities of larger networks. The research addresses this gap by identifying unique challenges such as the scarcity and representativeness of observed count data, gaps in the crowdsourced and mobile phone data, and inconsistencies in link-level volume estimates. We propose and demonstrate the application of strategies like enhancing geographical diversity of observed count data and employing an extensive cross-validation approach in model training and testing. By leveraging various auxiliary datasets, the study demonstrates the effectiveness of these strategies in improving model performance. These findings provide valuable insights for transportation modelers, policymakers, and urban planners, offering a robust framework for supporting sustainable transportation infrastructure and policies with advanced data-driven methodologies.

Urban Traffic Identification by Comparing Machine Learning Algorithms

The Internet of Things (IoT) applied to intelligent transport systems has become a key element for understanding the way traffic flow behaves in cities, which helps in decision-making to improve the management of the transport system by monitoring and analyzing network traffic in real time, all with the aim of daily benefiting users of the city’s road infrastructure. Traffic volume estimation in real time, with high effectiveness, may help mobility management and improve traffic flow. Moreover, machine-learning algorithms have shown effectiveness in various scientific fields and have provided a significant platform for achieving intelligent applications. Therefore, we applied various machine learning algorithms to classify the vehicular traffic status in the traffic network of two cities with more than 2 million inhabitants. It was first necessary to establish, from the attributes provided by the datasets, the object class from the LOS (Level of Services) thresholds proposed by the National Academies of Sciences, Engineering, and Medicine, for the basic segments of highways in an urban area. We then selected the attributes of interest using the Recursive Feature Elimination Method (RFE) to reduce the dimensionality of the data, and applied the DT, RF, ET, KNN, and MLP algorithms to train and classify the level of vehicular congestion, defining various volumes of training and validation data. The results show the high effectiveness of the algorithms, highlighting the MLP algorithm as the one that provides the highest effectiveness on average for the evaluated datasets, with a mean precision of 99.5%.

Muscle Fat and Volume Differences in People With Hip-Related Pain Compared With Controls: A Machine Learning Approach.

Hip-related pain (HRP) affects young to middle-aged active adults and impacts physical activity, finances and quality of life. HRP includes conditions like femoroacetabular impingement syndrome and labral tears. Lateral hip muscle dysfunction and atrophy in HRP are more pronounced in advanced hip pathology, with limited evidence in younger populations. While MRI use for assessing hip muscle morphology is increasing, with automated deep-learning techniques showing promise, studies assessing their accuracy are limited. Therefore, we aimed to compare hip intramuscular fat infiltrate (MFI) and muscle volume, in individuals with and without HRP as well as assess the reliability and accuracy of automated machine-learning segmentations compared with human-generated segmentation. This cross-sectional study included sub-elite/amateur football players (Australian football and soccer) with a greater than 6-month history of HRP [n = 180, average age 28.32, (standard deviation 5.88) years, 19% female] and a control group of sub-elite/amateur football players without pain [n = 48, 28.89 (6.22) years, 29% female]. Muscle volume and MFI of gluteus maximus, medius, minimis and tensor fascia latae were assessed using MRI. Associations between muscle volume and group were explored using linear regression models, controlling for body mass index, age, sport and sex. A convolutional neural network (CNN) machine-learning approach was compared with human-performed muscle segmentations in a subset of participants (n = 52) using intraclass correlation coefficients and Sorensen-Dice index. When considering adjusted estimates of muscle volume, there were significant differences observed between groups for gluteus medius (adjusted mean difference 23 858 mm3 [95% confidence interval 7563, 40 137]; p = 0.004) and tensor fascia latae (6660 mm3 [2440, 13 075]; p = 0.042). No differences were observed between groups for gluteus maximus (18 265 mm3 [-21 209, 50 782]; p = 0.419) or minimus (3893 mm3 [-2209, 9996]; p = 0.21). The CNN was trained for 30 000 iterations and assessed its accuracy and reliability on an independent testing dataset, achieving high segmentation accuracy (mean Sorenson-Dice index >0.900) and excellent muscle volume and MFI reliability (ICC2,1 > 0.900). The CNN outperformed manual raters, who had slightly lower interrater accuracy (Sorensen-Dice index >0.800) and reliability (ICC2,1 > 0.800). The increased muscle volumes in the symptomatic group compared with controls could be associated with increased myofibrillar size, sarcoplasmic hypertrophy or both. These changes may facilitate greater muscular efficiency for a given load, enabling the athlete to maintain their normal level of function. In addition, the CNNs for muscle segmentation was more efficient and demonstrated excellent reliability in comparison to manual segmentations.

КАЛЬДЕРА КРАШЕНИННИКОВА (ВОСТОЧНАЯ КАМЧАТКА): ВОЗРАСТ И МАГНИТУДА ИЗВЕРЖЕНИЯ

New data on the composition of the pyroclastic deposits of the Krasheninnikov caldera allowed us to correlate it to the previously studied tephra horizon Geys30, which until recently was erroneously attributed to an eruption within the Geysernaya caldera. Pyroclastic flow deposits from the eruption that led to the formation of the Krasheninnikov caldera were found on the southern shore of Kronotsky Lake, and its distal tephra was found in the sediments of the Central Kamchatka Depression (CKD) at a distance of up to 200 km from the source (from Milkovo to Kliuchi villages). The age of this tephra was previously estimated at ~30 ka, which now allows us to accept this estimate for the Krasheninnikov caldera. The identification of the tephra of the caldera-forming eruption in distal sections allows a minimum estimate of the erupted pyroclastic volume of ~13 km3 and the eruption magnitude of 6.1.

MGA-Net: A novel mask-guided attention neural network for precision neonatal brain imaging

In this study, we introduce MGA-Net, a novel mask-guided attention neural network, which extends the U-net model for precision neonatal brain imaging. MGA-Net is designed to extract the brain from other structures and reconstruct high-quality brain images. The network employs a common encoder and two decoders: one for brain mask extraction and the other for brain region reconstruction. A key feature of MGA-Net is its high-level mask-guided attention module, which leverages features from the brain mask decoder to enhance image reconstruction. To enable the same encoder and decoder to process both MRI and ultrasound (US) images, MGA-Net integrates sinusoidal positional encoding. This encoding assigns distinct positional values to MRI and US images, allowing the model to effectively learn from both modalities. Consequently, features learned from a single modality can aid in learning a modality with less available data, such as US. We extensively validated the proposed MGA-Net on diverse and independent datasets from varied clinical settings and neonatal age groups. The metrics used for assessment included the DICE similarity coefficient, recall, and accuracy for image segmentation; structural similarity for image reconstruction; and root mean squared error for total brain volume estimation from 3D ultrasound images. Our results demonstrate that MGA-Net significantly outperforms traditional methods, offering superior performance in brain extraction and segmentation while achieving high precision in image reconstruction and volumetric analysis. Thus, MGA-Net represents a robust and effective preprocessing tool for MRI and 3D ultrasound images, marking a significant advance in neuroimaging that enhances both research and clinical diagnostics in the neonatal period and beyond.

Health workforce needs in Malawi: analysis of the Thanzi La Onse integrated epidemiological model of care

BackgroundTo make the best use of health resources, it is crucial to understand the healthcare needs of a population—including how needs will evolve and respond to changing epidemiological context and patient behaviour—and how this compares to the capabilities to deliver healthcare with the existing workforce. Existing approaches to planning either rely on using observed healthcare demand from a fixed historical period or using models to estimate healthcare needs within a narrow domain (e.g., a specific disease area or health programme). A new data-grounded modelling method is proposed by which healthcare needs and the capabilities of the healthcare workforce can be compared and analysed under a range of scenarios: in particular, when there is much greater propensity for healthcare seeking.MethodsA model representation of the healthcare workforce, one that formalises how the time of the different cadres is drawn into the provision of units of healthcare, was integrated with an individual-based epidemiological model—the Thanzi La Onse model—that represents mechanistically the development of disease and ill-health and patients’ healthcare seeking behaviour. The model was applied in Malawi using routinely available data and the estimates of the volume of health service delivered were tested against officially recorded data. Model estimates of the “time needed” and “time available” for each cadre were compared under different assumptions for whether vacant (or established) posts are filled and healthcare seeking behaviour.ResultsThe model estimates of volume of each type of service delivered were in good agreement with the available data. The “time needed” for the healthcare workforce greatly exceeded the “time available” (overall by 1.82-fold), especially for pharmacists (6.37-fold) and clinicians (2.83-fold). This discrepancy would be largely mitigated if all vacant posts were filled, but the large discrepancy would remain for pharmacists (2.49-fold). However, if all of those becoming ill did seek care immediately, the “time needed” would increase dramatically and exceed “time supply” (2.11-fold for nurses and midwives, 5.60-fold for clinicians, 9.98-fold for pharmacists) even when there were no vacant positions.ConclusionsThe results suggest that services are being delivered in less time on average than they should be, or that healthcare workers are working more time than contracted, or a combination of the two. Moreover, the analysis shows that the healthcare system could become overwhelmed if patients were more likely to seek care. It is not yet known what the health consequences of such changes would be but this new model provides—for the first time—a means to examine such questions.

Glacier Volume Estimation Using the Glaptop Model in the Cordillera Blanca of Peru

The spatial retreat of glaciers located in the Peruvian Andes is closely linked to global climate change. The glaciers of the Checquiaraju micro-basin of the Cordillera Blanca are the main sources of water for the Andean zone; however, they have been suffering an accelerated space retreat. Remote sensing and geographic information systems (GIS) techniques have been applied to estimate the variation in glacier area and volume from LANDSAT 5 images. Glacier area and volume results are correlated with normalized difference snow index (NDSI) data to discriminate the snow cover from the rest of the elements in the image through the digital numbers (DN) of two bands: the TM blue band and shortwave infrared. The glacial zone was separated from the non-glacial zone for a better fit with the glacier limits seen in the composite color image. Subsequently, the delimitation of the glacier cover was refined through supervised classification, and finally, the determination of the volume of the glacier was estimated using the Glabtop model. The evolution of the glacier area of the Checquiaraju micro-basin tends to retreat. In the period 1989–2004, the glacier area of the study area decreased by 0.44 km2 (representing 11.3% of the glacier area in 1989), considering a rate of decrease of 0.0275 km2/year, which shows the retreat of glaciers in the area, which is a source of supply. Keywords: Checquiaraju, SIG, LANDSAT 5, NDSI, Glabtop. Resumen El retroceso espacial de los glaciares ubicados en los Andes peruanos está estrechamente vinculado al cambio climático global. Los glaciares de la microcuenca de Checquiaraju de la Cordillera Blanca son una fuente de agua muy importante para la zona andina; pero ellos vienen sufriendo un retroceso espacial acelerado. Se ha aplicado las técnicas de la Teledetección y Sistemas de Información Geográfica (SIG) para estimar la variación del área y volumen glaciar a partir de imágenes LANDSAT 5. Los resultados de área y volumen glaciar se correlacionan con datos de NDSI (Normalized Difference Snow Index) para discriminar la cobertura nival del resto de los elementos en la imagen, a través de los números digitales (DN) de dos bandas TM banda en azul e infrarroja de onda corta. Se separó la zona glaciar de la zona no – glaciar para un mejor ajuste con los límites de glaciar visto en la imagen de color compuesta. Posterior se refinó la delimitación de la cobertura glaciar a través de la clasificación supervisada y finalmente la determinación del volumen del glaciar se estimó mediante el modelo Glabtop. La evolución del área glaciar de la microcuenca de Checquiaraju tiene una tendencia al retroceso. En el periodo 1989 – 2004, el área glaciar de la zona de estudio se redujo 0,44 km2 (lo que representa el 11,3 % del área glaciar en 1989), considerando una tasa de disminución de 0,0275 km2/año, lo que evidencia el retroceso de los glaciares en la zona, la cual es fuente de abastecimiento. Palabras Clave: Checquiaraju, SIG, LANDSAT 5, NDSI, Glabtop.

Identification and Characterization of Reclaimed and Underclaimed Mine Features Using Lidar and Temporal Remote Sensing Methods within the Coastal Plain Uranium Mining Region of Texas

We developed a spatiotemporal mapping approach utilizing multiple techniques for distinguishing and mapping known reclaimed mine sites from “unreclaimed” mine sites in a historic uranium mining district along the South Texas Coastal Plains. Lidar laser scanning penetrates the vegetation canopy to expose anthropogenic modifications to the landscape. The Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features. Visual interpretation of Landsat imagery and time-series analysis augmented the Lidar analysis revealing the temporal life cycle of mining. The combination of bare earth texture with time-lapse and time-series analyses revealed areas of disturbance for reclaimed mines. The spatiotemporal mapping approach proved to be most useful in identifying and characterizing the known mine pit and pile features, reclamation status, and areas of disturbance due to mining. Two mine waste volume estimation methods resulted in a 21% difference indicating that although the approach helps to map mine features and areas of mining disturbance for the purposes of mine land inventory, additional information is needed to improve the estimation of buried mine waste at reclaimed mine sites.

Estimation of reservoir properties using pre-stack seismic inversion and neural network in mature oil field, Upper Assam basin, India

The mature oil fields require comprehensive characterization for enhanced hydrocarbon production, and subsequently demands estimation of reservoir properties. The key properties viz. volume of clay, effective-porosity, hydrocarbon-saturation has been evaluated for an aging Oligocene reservoir of Upper Assam basin, located in northeastern India from seismic and well log data. Elastic properties (acoustic and shear impedance) and density are derived from pre-stack inversion of 3D seismic data. These elastic properties are analyzed for their sensitivity for discrimination of lithology and fluid-content, and many derived attributes are computed from elastic properties. These attributes are assessed for their predictability to predict the target reservoir properties using multi-attribute analysis. For each of the target property neural network is trained with the most predictable attributes, and multi-dimensional, non-linear neural network models are created using multilayered feed forward neural network (MLFN), followed by Probabilistic neural network (PNN). The specific neural network models for each target property are employed for quantitative estimate of volume of clay, effective-porosity, hydrocarbon-saturation in inter-well regions. The estimated properties leverage the identification of untapped oil reserves and provide promising opportunity for enhanced production through drilling of infill wells.

The impact of age and renal function on the pharmacokinetics and protein binding characteristics of fludarabine in paediatric and adult patients undergoing allogeneic haematopoietic stem cell transplantation conditioning

Abstract Aim To evaluate the population pharmacokinetics of unbound F-Ara-A (the circulating metabolite of fludarabine) in 211 patients (age range, 0.1–63.4 years) undergoing allogeneic haematopoietic stem cell transplantation conditioning. Methods Total (n = 2480) and unbound (n = 1403) F-Ara-A concentrations were measured in blood samples collected at timed intervals after fludarabine doses ranging from 10 to 50 mg/m2 and infused over 0.42–1.5 h. A three-compartment population pharmacokinetic model was developed based on unbound plasma concentrations and used to estimate F-Ara-A unbound pharmacokinetic parameters and fraction unbound (fu). A number of covariates, including glomerular filtration rate (GFR) and post-menstrual age (PMA), were evaluated for inclusion in the model. Results The base population mean estimates ± relative standard error (%RSE) for unbound clearance from the central compartment (CLu) and inter-compartmental clearances (Q2u, Q3u) were 3.42 ± 3%, 6.54 ± 24% and 1.47 ± 16% L/h/70 kg, respectively. The population mean estimates (%RSE) for the unbound volume of distribution into the central (V1u) and peripheral compartments (V2u, V3u) were 9.65 ± 8%, 8.17 ± 9% and 16.4 ± 10% L/70 kg, respectively, and that for fu was 0.877 ± 1%. Covariate model development involved differentiating F-Ara-A CLu into non-renal (1.81 ± 9% L/h/70 kg) and renal components (1.02 ± 9%*GFR L/h/70 kg). A sigmoidal maturation factor was applied to renal CLu, with population mean estimates for the Hill exponent and PMA at 50% mature of 2.97 ± 4% and 69.1 ± 8% weeks, respectively. Conclusion Patient age and GFR are predictors of unbound F-Ara-A CLu. This has the potential to impact dose requirements. Dose individualisation by target concentration intervention will be facilitated by this model once it is externally validated.