Gotowa bibliografia na temat „Adaptive Vertical Farm (AVF)”

Our research is on the spatial allocation of possible wind energy usage. We would like to carry this out with a newly developed model (CMPAM = Complex Multifactoral Polygenetic Adaptive Model), which basically is a climateoriented system, but other kind of factors are also considered. With this model those areas and terrains can be located where construction of wind farms would be reasonable. The wind field modeling core of CMPAM is mainly based on sequential Gaussian simulation (sGs) otherwise known as geostatistics. But concepts from atmospheric physics and Geographical Information Systems (GIS) are used as well. For application for Hungary WAsP generated 10 m wind speed data was used as input data. The geocorrection (geometric correction) of this data was performed by us. Using optimized variography and sGs, our results were applied for Hungary in different heights. Simulation results for different heights are summarized furthermore, an exponential regressive function describing the vertical wind profile was also established. From the complex analyses of CMPAM, results derived to the 100 m height are also included and explained in a map in this paper. This produces a basis for certain several possible sites for the utilization of wind energy, under given conditions.

In large wind farms, the interaction of atmospheric turbulence and wind turbine wakes leads to complex vortex dynamics and energy dissipation, resulting in reduced wind velocity and subsequent loss of wind power. This study investigates the influence of vortex stretching on wind power fluctuations within wind turbine wakes using scale-adaptive large eddy simulation. The proper orthogonal decomposition method was employed to extract the most energetic contributions to the wind power spectra. Vertical profiles of mean wind speed, Reynolds stresses, and dispersive stresses were analyzed to assess energy dissipation rates. Our simulation results showed excellent agreement when compared with wind tunnel data and more advanced numerical models, such as the actuator line model and the actuator line model with hub and tower effects. This highlights the important role of coherent and energetic flow components in the spectral behavior of wind farms. The findings indicate a persistent energy cascading length scale in the wake of wind turbines, emphasizing the vertical transport of energy to turbine blades. These results complement existing literature and provide new insights into the dynamics of wind turbine wakes and their impact on wind farm performance.

The paper examines the impact of a group of principles of integrity and harmonization on the architectural design of vertical farms. These objects are an efficient and sustainable approach to an agricultural production. Moreover, vertical farms can be located in urban environments which leads to a need of developing appropriate space-volumetric, compositional, planning, functional, and structural solutions for the harmonious and aesthetic integration of such buildings into the urban structure while preserving their functional efficiency. To address this issue, the authors propose a series of architectural techniques based on the such principles as contextualism, the function-form symbiosis, the identity of structure and form, the harmonization of volumetric-spatial and facade solutions, and the modern technologies implementation. The contextualism principle ensures the context influences vertical farm design decisions regarding height, shape, and siting to either blend into the surroundings or be an architectural dominant. Additionally, this principle assesses impacts on local parameters of insolation and wind. Function-form symbiosis requires simple, rhythmic architectural forms, robust materials and design reflecting both farming itself and the general industrial nature of the building. The identity principle mutually reinforces architectural form and structure according to the vertical farming needs. Height flexibility through open-web or core-and-shell structures provides adaptable and open spaces to support the production. The harmonization of volumetric-spatial and facade solutions creates a relationship between a building's external appearance, internal organization, and functional diversity. It employs typical compositional techniques as well as architectural expressionism conveying functional zones via colour, texture and form. The last principle is represented by implementing adaptive smart facades, renewable energy systems, green walls and roofs.

The developed GIS is an information—mathematical model of the agricultural land space, designed for computational analysis of the natural ecological conditions of the hierarchy of landscape areas (ALR) of the region and adaptation of agricultural technologies to them. At the local level of each farm or agricultural company, information on actual land productivity is differentiated relative to the information-mathematical 3D geometric and structural model of the field relief surface. The relief is represented by a system of homomorphic elementary surfaces, each of which is quasi-uniform in the vertical and lateral structure of natural ecological conditions and is distinctive in exposure and morphometric parameters. Homomorphic surfaces correspond to elementary agro-landscape ranges. Original algorithms have been developed for calculating morphometric parameters and exposure within elementary surfaces, the location of current lines, depending on the type of surface, the presence of special points in them and the type of structural lines limiting them. A computational classification of homomorphic surfaces by natural ecological properties affecting the processes of soil formation is provided—position in relation to heat/moisture-bearing flows, gradation of slopes, location within the slope. Algorithms and program modules of information—mathematical automated visualization of homomorphic elementary surfaces, representative points of agrochemical sampling and current lines are designed for interpolation of agrochemical analysis data on each surface; modules for calculating morphometric parameters of elementary surfaces and exposure provide calculation of solar radiation coming within their limits. Within the database, a diagram of relationships between elementary surface objects in a field system was developed. Systemic monitoring and analysis of the law of matter and energy redistribution within elementary surfaces with their corresponding soils, microclimate and agricultural crop, create the possibility of programming spatially differentiated application of biologically active substances within the boundaries of the field, predicting their rates in accordance with climatic trends.

Mycoplasma sp. comprises cell wall-less bacteria with reduced genome size and can infect mammals, reptiles, birds, and plants. Avian mycoplasmosis, particularly in chickens, is primarily caused by Mycoplasma gallisepticum (MG) and Mycoplasma synoviae. It causes infection and pathology mainly in the respiratory, reproductive, and musculoskeletal systems. MG is the most widely distributed pathogenic avian mycoplasma with a wide range of host susceptibility and virulence. MG is transmitted both by horizontal and vertical routes. MG infection induces innate, cellular, mucosal, and adaptive immune responses in the host. Macrophages aid in phagocytosis and clearance, and B and T cells play critical roles in the clearance and prevention of MG. The virulent factors of MG are adhesion proteins, lipoproteins, heat shock proteins, and antigenic variation proteins, all of which play pivotal roles in host cell entry and pathogenesis. Prevention of MG relies on farm and flock biosecurity, management strategies, early diagnosis, use of antimicrobials, and vaccination. This review summarizes the vital pathogenic mechanisms underlying MG infection and recapitulates the virulence factors of MG–host cell adhesion, antigenic variation, nutrient transport, and immune evasion. The review also highlights the limitations of current vaccines and the development of innovative future vaccines against MG.

While many studies were done about green facades’ thermal performance, limited studies were done about green facades for productive farming. Most focused only on one facade or building. According to that, this research questioned what the potentials of farming on facades and roofs in an entire neighbourhood are and what could such a farming system looks like, and what it costs. To address these questions, a literature review about urban farming and possible crops was done. A neighbourhood of 22 multi-floor residential buildings in Nablus\Palestine, was chosen as a case study, and two parametric tools, one for analysis (AGRI|gen\Analysis) and another for design (AGRI|gen\design) were developed and implemented. The study found that in the chosen neighbourhood, existing facades can provide about 28,500 m2 of farming area, but only half of the facades and all of the roofs were suitable for daylight-based farming. Tomatoes and cucumbers can be farmed on 25% and 33% of the facades, respectively, to fulfil about 350% and 237% of tomatoes and cucumbers consumption by the same neighbourhood simultaneously. Roofs were found to be more suitable for high DLI-requiring plants like sweet peppers as they can produce more than 315 times the local consumption. In terms of design, a modular adaptive facade system was designed to fit the neighbourhood to enhance the farming possibilities. The facade system needed about 40,824 modular units of which 73.3%, 10.1%, 8.7%, and 8% of them were LED, PV, Sensor, and fan units respectively, with an average system cost of about $55.2\m2 and a total cost of $1.7M. Finally, a comparison between the system and a proposed vertical farm building in the same region was done, and then related recommendations by the researcher were suggested. This research highlights the potential for productive farming on facades and roofs, which could contribute to sustainable and resilient cities.

Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

AbstractDuring the 20th century, American agriculture underwent dramatic changes. At the beginning, farms were more diverse, dependent on animal traction, on-farm inputs and income and, after initial land grants, nearly independent of government policy. However, external issues, such as government policies, mechanization, fossil fuel costs, increased consolidation and vertical integration of markets and increased societal awareness of the environment and concern with farming practices, have substantially altered the structure of agriculture. These external issues are significant drivers of agriculture and we grouped them into social/political, economic, environmental and technological drivers. Previous papers have examined specific effects of these drivers. Our objective is to examine how these drivers interact and influence today's agricultural systems. We developed four categories: (1) Commodity Crop Production, (2) Supply Chain Livestock Production, (3) Organic Production and (4) Extensive Livestock Production, to describe major current agricultural systems. These categories were developed as major and contrasting systems but do not represent all of American agriculture. Although it is not possible to predict the future, interactions among the various drivers will affect these systems differently. By examining multiple scenarios, we conclude the highly specialized systems (Nos. 1 and 2) are highly vulnerable to future changes, and that developing adaptive capacity is critical for dealing with new uncertainty. Sustainable agricultural systems will need balance among various domains to be able to adapt and survive. We suggest that the concept of dynamic-integrated agricultural systems may be the best way to meet this goal because of its ability to consider multiple goals and flexible producer decision-making.

Abstract This study explores error sources of the National Weather Service operational radar-based quantitative precipitation estimation (QPE) during the cool season over the complex terrain of the western United States. A new, operationally geared radar QPE was developed and tested using data from the National Oceanic and Atmospheric Administration Hydrometeorology Testbed executed during the 2005/06 cool season in Northern California. The new radar QPE scheme includes multiple steps for removing nonprecipitation echoes, constructing a seamless hybrid scan reflectivity field, applying vertical profile of reflectivity (VPR) corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive Z–R relationships. Specific issues in radar rainfall accumulations were addressed, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. The new radar QPE was tested in a 6-month period of the 2005/06 cool season and showed significant improvements over the current operational radar QPE (43% reduction in bias and 30% reduction in root-mean-square error) when compared with gauges. In addition, the new technique minimizes various radar artifacts and produces a spatially continuous rainfall product. Such continuity is important for accurate hydrological model predictions. The new technique is computationally efficient and can be easily transitioned into operations. One of the largest remaining challenges is obtaining accurate radar QPE over the windward slopes of significant mountain ranges, where low-level orographic enhancement of precipitation is not resolved by the operational radars leading to underestimation. Additional high-resolution and near-surface radar observations are necessary for more accurate radar QPE over these regions.

La population mondiale devrait atteindre près de 10 milliards d'ici 2050, entraînant une hausse attendue de la demande alimentaire due à la croissance démographique, au développement économique et à l'urbanisation. Pour répondre à cette demande de manière durable, les systèmes de cultures en serre, notamment l'agriculture verticale, se sont imposés comme une solution prometteuse, offrant des rendements élevés par unité de surface cultivée. La Serre Verticale Adaptative (SVA) est une serre verticale industrielle innovante qui ajuste dynamiquement la distance entre ses étagères empilées, optimisant ainsi les conditions de croissance à mesure que les plantes progressent dans leurs stades de développement. Ce principe adaptatif permet de surmonter le conflit traditionnel entre le maintien de conditions optimales et la minimisation de la consommation d'énergie. Cette thèse présente deux axes de recherche principaux contribuant au développement de la SVA. Le premier axe consiste à développer un modèle de croissance des cultures basé sur des données et une approche boîte noire pour les plantes cultivées dans les SVA. Étant donnée l'environnement dynamique de la cultivation qui s'adapte à la croissance des plantes tout au long du cycle de la production, un modèle précis de croissance des cultures est essentiel pour optimiser le mouvement des étagères et le contrôle du système. Tandis que les modèles de croissance dynamique traditionnels dépendent souvent de nombreux paramètres et sont spécifiques à certains types de cultures, nous proposons une approche boîte noire utilisant des réseaux de neurones á action directe pour prédire la hauteur des plantes à chaque étape. Ce modèle est adaptable à divers types de cultures, efficace en termes de calcul après l'entraînement et particulièrement adapté aux systèmes d'agriculture verticale innovants tels que la SVA. L'efficacité du modèle est démontrée à travers des ensembles de données synthétiques et réelles, mettant en avant son potentiel dans l'optimisation de la production agricole. Le second axe de recherche se concentre sur l'automatisation de la SVA grâce à l'intégration de véhicules aériens sans pilote (UAV) dans le cadre de l'agriculture de précision. L'intégration des UAV permet de réaliser des applications telles que le suivi de la santé des cultures en complément des données recueillies par les capteurs stationnaires existants, la pollinisation automatique, la pulvérisation et l'irrigation, etc., optimisant ainsi les opérations agricoles à travers les étagères empilées et complétant les capteurs stationnaires existants. Pour soutenir cette automatisation, nous proposons différentes méthodes de synthèse d'observateurs pour des systèmes non linéaires utilisant des inégalités matricielles linéaires (LMI) fournissant ainsi une estimation précise de l'état des UAV et garantissant la convergence exponentielle de l'observateur. Deux contributions majeures sont présentées : premièrement, une nouvelle condition LMI moins conservatrice appliquée pour résoudre le critère circulaire H_infty, et deuxièmement, une méthode de synthèse d'observateur non linéaire basée sur une extension dynamique de la sortie. Cette méthode minimise l'impact du bruit de mesure et garantit la propriété de stabilité entrée-état (ISS) de l'erreur d'estimation via une nouvelle condition LMI
The world's population is projected to approach 10 billion by 2050, driving an expected rise in food demand due to population growth, economic development, and urbanization. To meet this demand sustainably, greenhouse systems, particularly vertical farming, have emerged as a promising solution, offering high crop yields per unit of cultivation area. The Adaptive Vertical Farm (AVF), is an innovative industrial vertical greenhouse that dynamically adjusts the distance between its stacked shelves, optimizing growing conditions as plants progress through their growth stages. This adaptive principle overcomes the traditional conflict between maintaining optimal conditions and minimizing energy consumption. This thesis presents two main research axes contributing to the development of the AVF. The first axis focuses on developing a data-driven, black-box growth model for crops cultivated in AVFs. Given the dynamic adaptation to plant growth, an accurate crop growth model is essential for optimizing shelf movement and system control. While traditional dynamic growth models often rely on numerous parameters and are specific to certain crop types, we propose a black-box approach using feedforward neural networks to predict plant height at each time step. This model is adaptable to various crop types, computationally efficient after training, and particularly suitable for innovative vertical farming systems like the AVF. The effectiveness of the model is illustrated through synthetic and real-world datasets, showcasing its potential in optimizing crop production. The second research axis focuses on automating the AVF using Unmanned Aerial Vehicles (UAVs) within the context of Precision Agriculture. UAVs support applications such as crop health monitoring, automatic pollination, spraying, and irrigation, optimizing farm operations across stacked shelves and complementing the existing stationary sensors. To support this automation, we introduce observer designs for nonlinear systems using Linear Matrix Inequalities (LMIs) to provide accurate state estimation for UAVs, ensuring exponential convergence of the observer. Two key contributions are presented: first, a new, less conservative LMI condition applied to solve the H_infty circle criterion design, and second, a nonlinear observer design based on output dynamic extension. This method minimizes the impact of measurement noise and guarantees the Input-to-State Stability (ISS) property of the estimation error via a novel LMI condition

The thesis addresses the proximity of contemporary global human issues to local human issues and presents an architectural solution. By identifying, exploring and drawing closer the proximities between these global and local issues, new solutions can be developed for local application. There are new fields created for architecture when we understand and connect the proximity of objects of both cultural and biophysical creation, and when we understand and build on our ever-narrowing proximities between what has been and what is to come. The narrowing global conditions have direct implications on us as individual human beings and our individual local societies. These proximities have been explored, developed, and resolved for local application. The resulting research field for urban agriculture ultimately guided an appropriate architectural response within the city of Pretoria, South Africa.
Dissertation (MArch(Prof))--University of Pretoria, 2010.
Architecture
unrestricted

The research was supported and funded by the Ministry of Education and Science of Ukraine under grant No. 0121U108589 «Development of a complex of energy-efficient and resource-saving equipment and promising technologies for feeding farm animals of the AIC of Ukraine». The introduction of energy-efficient machines and technologies in the system of feed preparation and animal feeding is an important prerequisite for the development of agriculture. One of the advanced types of grinding technology are vibrating mills, which provide high specific productivity at relatively low energy consumption, adjustable tone of grinding products. Vibration impact on the product significantly increases the shock-absorbing effect with the possibility of wide and separate variation of shock and abrasion factors. Significant speed of mechanical and heat and mass transfer processes, a high degree of homogeneity of the product, the ability to effectively implement fine grinding and dispersion of the product at relatively low energy consumption lead to the widespread use of vibratory grinding.The constructive scheme of the mill is developed, in which the flat vertical vibrating field provides lifting of a part of loading and by means of the transport-reloading device carries out its continuously regulated movement from one grinding chamber to another, thereby circulating-spatial movement of the environment in which grinding shock interaction of grinding bodies and material that is crushed. One of the most important rules for the construction of vibrating mills is the need to maximize the degree of their automation in order to increase productivity, improve the quality of grinding and reduce the cost of the technological process.A constructive model of a controlled vibration mill with spatial-circulating motion was also developed, which constantly changes to the resonant mode of operation at the set technologically optimal parameters (productivity) and minimum energy consumption for vibration when changing the mass of the working body in the process of separation and unloading of crushed material from the grinding chamber.The aim of the study is to establish the dependence of the parameters of the crushed mass along the grinding chambers and in places of overload on the parameters of vibration of the vibration mill of continuous motion.Development of a structural model of adaptive vibration mill with spatial-circulating loading movement which when changing the mass of the working body in the process of separation and unloading of crushed material from the grinding chamber could constantly adapt to resonant mode at given technologically optimal parameters (productivity) and minimum energy consumptionResearch methods. Theoretical and experimental research methods were used in the work. Experiment planning and regression analysis methods were used in conducting experiments and processing experimental data. Verification of the adequacy of the obtained dependences with experimental data was carried out by methods of mathematical statistics.Scientific novelty: the theory and practice of vibration mechanics were further developed, in particular, the conditions of vertical lifting of the loading part in vibrating mills with a U-shaped chamber were determined and the influence of the main factors on the lifting height was studied; for the first time the scheme of the vibrating mill with spatial-circulating loading movement is developed, in which the effect of lifting of loading is used and by means of the transport-technological device reloading in the interconnected chambers is carried out.Practical significance. The conditions and parameters of the vibration field that regulate the intensity and duration of grinding are determined. The dependence of the productivity Q of a vibrating mill on the velocity of transporting the loaded mass along the grinding chamber has been established. The structure and two-circuit principle of control of work of adaptive vibration mill with spatial-circulating movement of loading are offered.