Wykorzystanie głębokich sieci neuronowych w ograniczaniu zmian klimatycznych związanych z konfliktem farmerów i pasterzy w ramach inicjatywy na rzecz zrównoważonej integracji społecznej

Peaceful coexistence of farmers and pastoralists is becoming increasingly elusive and has adverse impact on agricultural revolution and global food security. The targets of Sustainable Development Goal 16 (SDG 16) include promoting peaceful and inclusive societies for sustainable development, providing access to justice for all and building effective, accountable and inclusive institutions at all levels. As a soft approach and long term solution to the perennial farmers-herdsmen clashes with attendant humanitarian crisis, this study proposes a social inclusion architecture using deep neural network (DNN). This is against the backdrop that formulating policies and implementing programmes based on unbiased information obtained from historical agricultural data using intelligent technology like deep neural network (DNN) can be handy in managing emotions. In this vision paper, a DNN-based Farmers-Herdsmen Expert System (FHES) is proposed based on data obtained from the Nigerian National Bureau of Statistics for tackling the incessant climate change-induced farmers-herdsmen clashes, with particular reference to Nigeria. So far, many lives have been lost. FHES is modelled as a deep neural network and trained using farmers-herdsmen historical data. Input variables used include land, water, vegetation, and implements while the output is farmers/herders disposition to peace. Regression analysis and pattern recognition performed by the DNN on the farmers-herdsmen data will enrich the inference engine of FHES with extracted rules (knowledge base). This knowledge base is then relied upon to classify future behaviours of herdsmen/farmers as well as predict their dispositions to violence. Critical stakeholders like governments, service providers and researchers can leverage on such advisory to initiate proactive and socially inclusive conflict prevention measures such as people-friendly policies, programmes and legislations. This way, conflicts can be averted, national security challenges tackled, and peaceful atmosphere guaranteed for sustainable development.   Pokojowe współistnienie rolników i pasterzy staje się coraz mnie realne, co ma negatywny wpływ na rewolucję rolniczą i globalne bezpieczeństwo żywnościowe. Cele zrównoważonego rozwoju (SDG 16) obejmują promowanie tworzenia pokojowych i zintegrowanych społeczeństw na rzecz zrównoważonego rozwoju, zapewnienie wszystkim dostępu do uczciwego wymiaru sprawiedliwości i tworzenie skutecznych, odpowiedzialnych i integrujących instytucji na wszystkich poziomach. W ramach łagodnego podejścia i długofalowego podejścia do problemu konfliktów rolników-pasterzy w kontekście kryzysu humanitarnego, w niniejszym artykule zaproponowano architekturę integracji społecznej wykorzystującą głęboką sieć neuronową (DNN). Formułowanie polityki i wdrażanie programów w oparciu o obiektywne informacje uzyskane z historycznych danych przy użyciu inteligentnej technologii, takiej jak głęboka sieć neuronowa (DNN), może być przydatne w zarządzaniu emocjami. W niniejszym artykule zaproponowano oparty na danych uzyskanych od Nigeryjskiego Narodowego Urzędu Statystycznego system ekspercki rolników-pasterzy (FHES) oparty na DNN w celu przeciwdziałaniu nieustannym starciom rolników-pasterzy wywołanych zmianami klimatu, ze szczególnym uwzględnieniem Nigerii. Do tej pory wiele było ofiar. System FHES jest modelowany jako głęboka sieć neuronowa, przy użyciu danych historycznych hodowców-pasterzy. Zastosowane zmienne wejściowe obejmują ziemię, wodę, roślinność i narzędzia, podczas gdy zmienne wyjściowe to rolnicy-pasterze skłonni do pokoju. Analiza regresji i rozpoznawanie wzorców przeprowadzone przez DNN na danych rolników-pasterzy wzbogaci mechanizm wnioskowania systemu FHES o wyodrębnione reguły (baza wiedzy). Podstawą tej wiedzy jest klasyfikacja przyszłych zachowań pasterzy/rolników, a także przewidywanie ich skłonności do przemocy. Krytyczni interesariusze, tacy jak rządy, dostawcy usług i naukowcy, mogą wykorzystać takie doradztwo do zainicjowania proaktywnych i społecznie włączających środków zapobiegania konfliktom, takich jak przyjazne dla ludzi polityki, programy i prawodawstwo. W ten sposób można uniknąć konfliktów, stawić czoła wyzwaniom bezpieczeństwa narodowego i zagwarantować pokojową atmosferę dla zrównoważonego rozwoju

Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

A Novel Approach to Reservoir Simulation Using Supervised Learning

Numerical reservoir simulation has been a fundamental tool in field development and planning. It has been used to replicate reservoir performance and study the effects of different field conditions in various reservoir management scenarios, and during field development and planning. Consequently, physics-based simulations have been heavily used during various reservoir studies such as history matching, uncertainty quantification and production optimisation; grid size and geological complexity also have a significant influence on the speed of the simulation. Furthermore, heterogeneities such as natural or hydraulic fractures can cause convergence problems and make the simulation even more time-consuming and computationally expensive. Due to being computationally demanding, such studies are also extremely time intensive. As a result of this downside, it is practically impossible to follow workflows such as the closed-loop reservoir management approach, which recommends updating the model every time a set of new data is available. Additionally, any management scenario must be approached from a business and economic standpoint. This means that, based on the predefined objectives within the study, the respective layers of precision must be chosen by the user. Therefore, if less expensive techniques can be implemented and provide adequate results, the use of more accurate and costly methods cannot be justified. One popular solution in overcoming this problem involves the creation of an approximate proxy model for the required features of the desired reservoir. This is achieved by either replacing or combining the physics-based model with this approximate model. However, by following this approach, the designed proxy model is only able to represent its corresponding reservoir, with a new proxy model needed to be rebuilt from scratch for any new reservoir. With consideration to the overall runtime, it can be observed that the time taken in iteratively running a numerical reservoir simulation may be faster than the time taken by the entire process of building, validating and using a proxy model. Therefore, this thesis focuses on the feasibility, advantages and contribution of a complete stand-alone AI-based simulator, Deep Net Simulator (DNS), in a wide range of different conventional and tight sand reservoir scenarios in 1D, 2D and 3D space. This thesis involves the use of deep learning to create a data-driven simulator, Deep Net Simulator (DNS), that enables the simulation of a wide range of reservoirs. Unlike conventional proxy approaches, a large amount of data is collected from multiple reservoirs with varying configurations and complexities. This results in the creation of a comprehensive database, including various possible reservoirs’ features and scenarios. The hypothesis is that such an approach will enable the data driven model to perceive and understand the principles that make up reservoir modelling and that the model will act as an excellent approximation to the equations that traditional physics-based numerical simulators solve. This objective is highly possible, since deep learning has been shown to be a great universal function estimator, which is capable of estimating the physics once given enough data and observations. Hence, this thesis aims to develop a series of data-driven models with the aforementioned features for various types of reservoirs. Initially, a workflow is designed to integrate a commercial simulator with a data extraction algorithm, enabling the generation of input-output simulation datasets. Next, the datasets are generated and reviewed. These datasets are then used in the training, validating and testing of the developed models. These developed data-driven models are able to learn and reproduce the physics governing fluid flow for a range of different scenarios: a single-phase oil reservoir in one-dimensional space, a single-phase gas reservoir in two-dimensional space, a single-phase gas reservoir in three-dimensional space, and hydraulically fractured tight gas reservoirs in two-dimensional space. The developed model was evaluated in terms of precision, speed, and reliability. For each scenario, the developed model was compared with a commercial reservoir simulator, and its performance was assessed using the following metrics: mean absolute error, mean absolute percentage error (MAPE), mean relative error, mean square error, root mean square error and r squared. The developed model was able to predict 45%, 70% and 90% of the cases with less than 5%, 10% and 15% MAPE, respectively. Furthermore, depending on the number of cells requiring outputs, the developed model was able to reduce runtime by 100% up to 1.04E+08%. This thesis takes the first steps towards establishing a new approach using AI and deep learning, for reservoir management procedure that is cheaper, less computationally demanding and more adaptable. This approach may result in a better value creation alongside a quicker decision-making process and, possibly, the advantage of integrating other attributes and data that are currently not used in physics-based models.Thesis (Ph.D.) -- University of Adelaide, Australian School of Petroleum and Energy Resources, 202

Operabilidade dinâmica aplicada a sistemas em batelada.

Como contribuição inicial, este trabalho propõe a substituição do MPT por um código desenvolvido em MATLAB®, uma vez que este sana a deficiência do primeiro em regiões não convexas. No âmbito da operabilidade estacionária, aborda dois estudos de caso: uma mistura de correntes com diferentes restrições em vazões e temperaturas e um CSTR encamisado para condução de uma reação exotérmica. Neste, a EDS foi tratada do ponto de vista estatístico, em que quanto maior o distúrbio menor a probabilidade de ocorrência. Informações relativas ao índice de operabilidade (OI) foram inseridas em um controlador proporcional-integral de modo que este conduzisse, de forma rápida e direta, a um estado permanente que garanta as especificações desejadas. Quanto à perspectiva de operabilidade dinâmica, a proposta é fundamentada na análise a cada período. O modelo utilizado, bem como suas condições e restrições foi delimitado por um reator batelada de bancada. Uma rotina programada em VBA é capaz de realizar experimentações numéricas de forma automática e foi desenvolvida com o propósito de simular um massivo planejamento experimental utilizado na metodologia dependente do tempo. Este código cria um banco de dados série-temporal, aplica uma técnica para reconciliação dos elementos quanto ao passo e pode ser utilizada conjuntamente a grande maioria dos simuladores dinâmicos comerciais. Foi sugerida uma nova abordagem para a região desejada (DOS) baseada na região de confiabilidade conjunta de modo a estabelecer este conjunto em cada intervalo e com caráter de controle estatístico de processos. A proposta para delimitação dos entornos necessários à operabilidade apresentou respostas satisfatórias diante comportamentos convexos ou não. Os resultados mostraram que a EDSp apresentou maior eficiência em problemas não lineares, já que leva em conta um histórico na determinação de perturbações e a consideração dos extremos não necessariamente abrange os intermediários. Mediante falhas em faixas esperadas, a conciliação de operabilidade a uma malha “feedback” sempre assegurou a DOS. Foi obtido um perfil de superfícies, para monitoramento e controle da operação ao longo do tempo, que conduz à DOS requerida ao final da batelada.As an initial contribution, this work proposes the MPT replacement by a code developed in MATLAB®, since it heals the deficit of the first one in non-convex regions. In the stationary operability scope, it deals with two case studies: a mixture of streams with different restrictions in flow and temperatures and a jacketed CSTR to conduct an exothermic reaction. In this, the method treated the EDS from a statistical point of view in which as larger is the disorder lower is the probability of occurrence. Information on the operability index (OI) was entered into a proportional-integral controller so that it would lead, quickly and directly, to a steady state that guarantees the desired specifications. In dynamic operability scenery, the proposal performs analysis in each period. A batch bench reactor delimited the model as well as its conditions and restrictions. A VBA routine capable of automatically performs numerical experiments was developed with the purpose of simulating one massive experimental planning used in the time-dependent methodology. This code creates a time-series database, applies a technique for reconciling the elements in step and can be used with the vast majority of dynamic commercial simulators. A new approach for the DOS based on joint confidence region was suggested to establish this set at each interval and with statistical process control character. The proposal for shape delimitations required along the procedure showed satisfactory responses to convex and non-convex behaviors. The results showed that the EDSp presented greater efficiency in nonlinear problems since it takes into account historical data in the disturbance determination. Also, the extreme perturbation values do not necessarily cover the intermediates. Through failures in expected ranges, the reconciliation of operability and a feedback loop has always ensured DOS. Surface profiles were obtained for monitoring and control the operation over time, leading to the required DOS at the end of the batch.Cape

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

The role of management accounting systems in enhancing organisational effectiveness in Jordanian commercial banks

The research looks at the nature of MAS in Jordan, and at its role in enhancing organizational effectiveness in Jordanian commercial banks. It looks into MAS, their design, the causes behind their design, the operation of MAS, and subsequently any effects on OE. The first part of the fieldwork is a case study on one of the nine Jordanian commercial banks that form the research population. Data was collected from sixteen personal semi-structured interviews on the different aspects of MAS adopted in the case study. The second part of the fieldwork is a survey that covered the remaining eight Jordanian commercial banks. Data was collected from the eight banks through personal interviews based on the findings from the case study. The literature reviewed included areas of management accounting, management accounting research, management accounting systems, and organizational effectiveness

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field