Search Results (15)

Large ruminant production in developing countries is inefficient with low growth rates and likely high greenhouse gas emissions per unit of meat or milk produced. Trials conducted in Lao PDR from 2017 to 2020, studied ad libitum supplementation for 12 weeks with 20 kg high-quality molasses nutrient blocks (Four Seasons Pty Ltd., Brisbane, Australia), that were either non-medicated; fenbendazole-medicated (Panacur100^®, Coopers Australia, 5 g/kg); triclabendazole-medicated (Fasinex^®, Novartis Australia, 5 g/kg or 10 g/kg, respectively); or formulated with urea (8% or 10% urea, respectively). Average daily gains were determined for access to all molasses blocks and compared with access to control blocks, no supplementation, or previously determined free-grazing baseline average daily gains (55–84 g in cattle; 92–106 g in buffalo). Productivity was significantly improved following access to all molasses blocks. Average daily gains following access to 8% urea and control blocks were calculated for three age cohorts of cattle: young calves <8 m (238–298 g), growing cattle (143–214 g) and lactating cows (179–191 g). Modelling using IPCC Inventory software model V 2.69 of published data demonstrated a conservative net abatement of 350 kg CO₂e was achievable over a 200-day feeding period. An additional trial of Emissions control blocks (n = 200) distributed to farmers (n = 60) and two educational institutions were conducted. Consumption rates (156 g/day) and farmer and institutional acceptance of these blocks were similar to our published findings with other molasses blocks, confirming all formulations of blocks improved animal productivity and body condition score, with healthier animals that were easier to manage. Modelling of changes in greenhouse gas emissions intensity identified an abatement of 470 kg CO₂e per Emissions control blocks consumed, delivering a total project emissions abatement of 94 t CO₂e. Provision of high-quality molasses blocks significantly improved smallholder large ruminant productivity and addition of greenhouse gas reducing agents is likely to achieve impressive abatement of greenhouse gas emissions due to improved efficiency of rumen fermentation and productivity. Full article

In case of a release of hazardous radioactive matter to the atmosphere from e.g., a nuclear power plant accident, atmospheric dispersion models are used to predict the spatial distribution of radioactive particles and gasses. However, at the early stages of an accident, only limited information about the release may be available. Thus, there is a need for source term estimation methods suitable for operational use shortly after an accident. We have developed a Bayesian inverse method for estimating the multi-nuclide source term describing a radioactive release from a nuclear power plant. The method provides a probabilistic source term estimate based on the early available observations of air concentration and gamma dose rate by monitoring systems. The method is intended for operational use in case of a nuclear accident, where no reliable source term estimate exists. We demonstrate how the probabilistic formulation can be used to provide estimates of the released amounts of each radionuclide as well as estimates of future gamma dose rates. The method is applied to an artificial case of a radioactive release from the Loviisa nuclear power plant in southern Finland, considering the most important dose-contributing nuclides. The case demonstrates that only limited air concentration measurement data may be available shortly after the release, and that to a large degree one will have to rely on gamma dose rate observations from a frequently reporting denser monitoring network. Further, we demonstrate that information about the core inventory of the nuclear power plant can be used to constrain the release rates of certain radionuclides, thereby decreasing the number of free parameters of the source term. Full article

Following the International Maritime Organization (IMO), in order to safeguard the realization of the Paris Agreement on climate protection, greenhouse gas (GHG) emissions have to be reduced by 50% by the year 2050. This objective shall be reached by decarbonization of maritime traffic, which is why ship operators currently increasingly search for alternative fuels. Moreover, since the start of the Ukrainian war in February 2022, this issue of alternative fuels has gained central importance in political agendas. A promising candidate for clean shipping that meets the IMO goals is ammonia since it is a carbon-free fuel. Ammonia (NH₃) shows good advantages in handling and storage, and it ensures long sea voyages without any significant loss in cargo space for a reasonable price. Hence, ammonia has the potential to improve the environmental footprint of global shipping enormously. Induced by the introduction of stricter regulations in the so-called emission control areas (ECAs) in Northern Europe in 2015 as well as the renewed global sulfur cap, which entered into force in 2020, ship operators had to decide between different compliance methods, among which the most popular solutions are related to the use of expensive low-sulfur fuel oils, newbuilds and retrofits for the usage of liquefied natural gas (LNG) or the installation of scrubber technology. A change to ammonia as a marine alternative fuel represents an additional novel future option, but the successful implementation depends on the availability of NH₃ in the ports, i.e., on the installation of the maritime NH₃ infrastructure. Currently, the single German NH₃ terminal with maritime access is located in Brunsbüttel, the western entrance to Kiel Canal. The distribution of NH₃ from the existing NH₃ hub to other German ports can be analyzed by the mathematical model of an inventory routing problem (IRP) that is usually solved by combinatorial optimization methods. This paper investigates the interrelated research questions, how the distribution of marine NH₃ fuel can be modeled as an IRP, which distribution mode is the most economic one for the German ports and which modal mix for the NH₃ supply leads to the greenest distribution. The results of this paper are empirically validated by data that were collected in several EU projects on sustainable supply chain management and green logistics. The paper includes a special section that is dedicated to the discussion of the economic turbulences related to the Ukrainian war together with their implications on maritime shipping. Full article

This study extends the newsvendor model to address customer balking and its penalty under service-level constraints. The model designed in this study determines the optimal order quantity to derive the maximum expected profit when a customer is reluctant to buy a product and the available inventory falls below a certain threshold. In addition, the service level is introduced into the procedure to determine the optimal order quantity, thus facilitating the process. Under these circumstances, we also propose a corresponding distribution-free model to determine tight lower bounds on expected profits under the worst-case scenario. To quantitatively evaluate our model’s performance, we compared profits based on the presence or absence of demand distribution, and demonstrated the effect of varying balking penalty costs and probabilities. Introducing a practical service level that can be used as a trade-off tool to help determine reasonable estimates and profitable decisions is beneficial when determining order quantity by comparing goodwill and holding costs. Full article

The number of trees and their spatial distribution are key information for forest management. In recent years, deep learning-based approaches have been proposed and shown promising results in lowering the expensive labor cost of a forest inventory. In this paper, we propose a new efficient deep learning model called density transformer or DENT for automatic tree counting from aerial images. The architecture of DENT contains a multi-receptive field convolutional neural network to extract visual feature representation from local patches and their wide context, a transformer encoder to transfer contextual information across correlated positions, a density map generator to generate spatial distribution map of trees, and a fast tree counter to estimate the number of trees in each input image. We compare DENT with a variety of state-of-art methods, including one-stage and two-stage, anchor-based and anchor-free deep neural detectors, and different types of fully convolutional regressors for density estimation. The methods are evaluated on a new large dataset we built and an existing cross-site dataset. DENT achieves top accuracy on both datasets, significantly outperforming most of the other methods. We have released our new dataset, called Yosemite Tree Dataset, containing a 10 km² rectangular study area with around 100k trees annotated, as a benchmark for public access. Full article

The important variable of horizontal visibility within forest stands is gaining increasing attention in studies and applications involving terrestrial laser scanning (TLS), photographic measurements of forest structure, and autonomous mobility. We investigated distributions of visibility distance, open arc length, and shaded arc length in three mature forest stands. Our analysis was based (1) on tree position maps and TLS data collected in 2013 and 2019 with three different scanners, and (2) on simulated digital twins of the forest stands, constructed with two pattern-generation models incorporating commonly used indices of tree position clumping. The model simulations were found to yield values for visibility almost identical to those calculated from the corresponding tree location maps. The TLS measurements, however, were found to diverge notably from the simulations. Overall, the probability of free line of sight was found to decrease exponentially with distance to target, and the probabilities of open arc length and shaded arc length were found to decrease and increase, respectively, with distance from the observer. The TLS measurements, which are sensitive to forest understory vegetation, were found to indicate increased visibility after vegetation removal. Our chosen visibility prediction models support practical forest management, being based on common forest inventory parameters and on widely used forest structure indices. Full article

With the increase in the varieties products and the increasing uncertainty about product demand, the production preparation time is a significant factor in addressing these issues. The trade-off between the reduction of the production preparation time and the associated cost remains a critical decision. With this backdrop, this study presents a continuous review production-inventory model with a variable production preparation time and a time-dependent setup cost. The demand during the preparation time is captured through a min-max distribution-free approach. In a stochastic framework, the order quantity, reorder point, and setup time are optimized by minimizing the expected cost considering the time-value effect. Further, a fuzzy model is formulated to tackle the imprecise nature of the production setup time and demand. Two algorithms are developed using an analytical approach to obtain the optimal solution. A numerical illustration is given to present the key insights of the model for effective inventory management. It is observed that order quantity and total cost are more sensitive at the lower side of the optimal setup time rather than at the higher side. The discount rate is also found to be a sensitive factor while minimizing the total expected cost. Full article

Information on forest composition, specifically tree types and their distribution, aids in timber stock calculation and can help to better understand the biodiversity in a particular region. Automatic satellite imagery analysis can significantly accelerate the process of tree type classification, which is traditionally carried out by ground-based observation. Although computer vision methods have proven their efficiency in remote sensing tasks, specific challenges arise in forestry applications. In this paper, we aim to improve tree species classification based on a neural network approach. We consider four species commonly found in Russian boreal forests: birch, aspen, pine, and spruce. We use imagery from the Sentinel-2 satellite, which has multiple bands (in the visible and infrared spectra) and a spatial resolution of up to 10 meters. Additionally, the short revisit time and free access policy make Sentinel-2 imagery a valuable data source for the purpose of forest classification. In computer vision terms, we define the problem of tree type classification as one of semantic segmentation, assigning a particular tree type to each pixel of the image. The forest inventory data contain tree type composition, but do not describe their spatial distribution within each individual stand. Therefore, some pixels can be assigned a wrong label if we consider each stand to be homogeneously populated by its dominant species. This calls for the use of a weakly supervised learning approach. To solve this problem, we use a deep convolutional neural network with a tailored loss function. We test the proposed models by creating a dataset of images for Leningrad Oblast of Russia. In our study, we demonstrate how to modify the training strategy, such that it can outperform basic per pixel neural network approaches. Full article

Product deterioration is a common phenomenon and is overlooked in most contemporary research on the newsboy problem. In this study, we have considered product deterioration in a production–inventory newsboy model based on multiple just-in-time (JIT) deliveries. This model is solved by a classical optimization technique for the manufacturer production size, wholesale price, replenishment plan, and retailer order policy using a distribution-free approach. Moreover, in order to improve business and entice more customers, a return policy and a post-sale warranty policy is adopted in the model. Theoretical development and numerical examples are provided to demonstrate the validity of this approach. Full article

Inventory models must consider the probability of sub-optimal manufacturing and careless shipping to prevent the delivery of defective products to retailers. Retailers seeking to preserve a reputation of quality must also perform inspections of all items prior to sale. Inventory models that include sub-lot sampling inspections provide reasonable conditions by which to establish a lower bound and a pair of upper bounds in terms of order quantity. This should make it possible to determine the conditions of an optimal solution, which includes a unique interior solution to the problem of an order quantity satisfying the first partial derivative. The approach proposed in this paper can be used to solve the boundary. These study findings provide the analytical foundation for an inventory model that accounts for defective items and sub-lot sampling inspections. The numerical examples presented in a previous paper are used to demonstrate the derivation of an optimal solution. A counter-example is constructed to illustrate how existing iterative methods do not necessarily converge to the optimal solution. Full article

Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R²) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass. Full article

The faster growth of technology stipulates the rapid development of new products; with the spread of new technologies old ones are outdated and their market demand declines sharply. The combined impact of demand uncertainty and short life-cycles complicate ordering and pricing decision of retailers that leads to a decrease in the profit. This study deals with the joint inventory and dynamic pricing policy for such products considering stochastic price-dependent demand. The aim is to develop a discount policy that enables the retailer to order more at the start of the selling season thus increase the profit and market share of the retailer. A multi-period newsvendor model is developed under the distribution-free approach and the optimal stocking quantities, unit selling price, and the discount percentage are obtained. The results show that the proposed discount policy increases the expected profit of the system. Additionally, the stocking quantity and the unit selling price also increases in the proposed discount policy. The robustness of the proposed model is illustrated with numerical examples and sensitivity analysis. Managerial insights are given to extract significant insights for the newsvendor model with discount policy. Full article

For inventory models with unknown distribution demand, during shortages, researchers used the first and the second moments to derive an upper bound for the worst case, that is the min-max distribution-free procedure for inventory models. They applied an iterative method to generate a sequence to obtain the optimal order quantity. A researcher developed a three-sequence proof for the convergence of the order quantity sequence. We directly provide proof for the original order quantity sequence. Under our proof, we can construct an increasing sequence and a decreasing sequence that both converge to the optimal order quantity such that we can obtain the optimal solution within the predesigned threshold value. Full article

Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km² study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives. Full article

The chemical composition of the troposphere in the tropics and Southern Hemisphere (SH) is significantly influenced by gaseous emissions released from African biomass burning (BB). Here we investigate how various emission estimates given in bottom-up BB inventories (GFEDv2, GFEDv3, AMMABB) affect simulations of global tropospheric composition using the TM4 chemistry transport model. The application of various model parameterizations for introducing such emissions is also investigated. There are perturbations in near-surface ozone (O₃) and carbon monoxide (CO) of ~60–90% in the tropics and ~5–10% in the SH between different inventories. Increasing the update frequency of the temporal distribution to eight days generally results in decreases of between ~5 and 10% in near-surface mixing ratios throughout the tropics, which is larger than the influence of increasing the injection heights at which BB emissions are introduced. There are also associated differences in the long range transport of pollutants throughout the SH, where the composition of the free troposphere in the SH is sensitive to the chosen BB inventory. Analysis of the chemical budget terms reveals that the influence of increasing the tropospheric CO burden due to BB on oxidative capacity of the troposphere is mitigated by the associated increase in NO_x emissions (and thus O₃) with the variations in the CO/N ratio between inventories being low. For all inventories there is a decrease in the tropospheric chemical lifetime of methane of between 0.4 and 0.8% regardless of the CO emitted from African BB. This has implications for assessing the effect of inter-annual variability in BB on the annual growth rate of methane. Full article