Application of Fuzzy Modelling to Predict Construction Projects Cash Flow

Construction project managers are always looking for methods for forecasting future projects and preventing of potential delays in the project. One of the most crucial requirements of construction project managers and financial planners is awareness of project cash flow and financial status. On the other hand, the unique properties of construction projects with uncertainties such as activity duration, the variability of resources, material costs and also ambiguity in the employer’s payments are factors that have an effect on the correct prediction of project cash flow. Hence, the project team should examine project cash flow under uncertainty environment. There are many approaches for considering uncertainty such as fuzzy sets, interval theory, rough and grey system. But the most well-known approach is fuzzy sets which has wide applications in engineering and management. Hence in this paper, we proposed a new method for forecasting project cash flow under fuzzy environment. Finally, the proposed method was applied on an “Engineering, Procurement and Construction” (EPC) project and it is demonstrated that the proposed model has a high performance in the prediction of project cash flow

Optimal sizing of grid-connected rooftop photovoltaic and battery energy storage for houses with electric vehicle

A practical optimal sizing model is developed for grid-connected rooftop solar photovoltaic (PV) and battery energy storage (BES) of homes with electric vehicle (EV) to minimise the net present cost of electricity. Two system configurations, (1) PV-EV and (2) PV-BES-EV, are investigated for optimal sizing of PV and BES by creating new rule-based home energy management systems. The uncertainties of EV availability (arrival and departure times) and its initial state of charge, when arrives home, are incorporated using stochastic functions. The effect of popular EV models in the market is investigated on the optimal sizing and electricity cost of the customers. Several sensitivity analyses are adopted based on variations in the grid constrains, retail price and feed in tariff. Uncertainty analysis is provided based on the variations of insolation, temperature, and load to approve the optimal results of the developed model. A practical guideline is presented for residential customers in a typical grid-connected household to select the optimal capacity of PV or PV-BES system considering the model of EV. While the proposed optimization model is general and can be used for various case studies, real annual data of solar insolation, temperature, household\u27s load, electricity prices, as well as PV and BES market data are used for an Australian case study. The developed optimal sizing model is also applied to residential households in different Australian States

Loss Function Modeling of Efficiency Maps of Electrical Machines

This paper presents a novel approach in the modeling of efficiency maps for electrical machines. It is based on using the sum of terms in the form of kmnTmωn to represent the variation of the stator and rotor copper, iron and magnet losses with torque and speed. The effect of each term on the shape of the efficiency map is explored. Analysis is performed on the calculated efficiency and loss maps of an induction, an interior permanent magnet and a surface permanent magnet machine to demonstrate the validity of the approach

SARS-CoV-2 and pancreas: a potential pathological interaction?

The widespread extrapulmonary complications of coronavirus disease 2019 (COVID-19) have gained momentum; the pancreas is another major target for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we take a closer look into potential pathological interactions. We provide an overview of the current knowledge and understanding of SARS-CoV-2 infection of the pancreas with a special focus on pancreatic islets and propose direct, indirect, and systemic mechanisms for pancreas injury as result of the COVID-19–diabetes fatal bidirectional relationship

The liver-derived exosomes stimulate insulin gene expression in pancreatic beta cells under condition of insulin resistance

Introduction: An insufficient functional beta cell mass is a core pathological hallmark of type 2 diabetes (T2D). Despite the availability of several effective pharmaceuticals for diabetes management, there is an urgent need for novel medications to protect pancreatic beta cells under diabetic conditions. Integrative organ cross-communication controls the energy balance and glucose homeostasis. The liver and pancreatic islets have dynamic cross-communications where the liver can trigger a compensatory beta cell mass expansion and enhanced hormonal secretion in insulin-resistant conditions. However, the indispensable element(s) that foster beta cell proliferation and insulin secretion have yet to be completely identified. Exosomes are important extracellular vehicles (EVs) released by most cell types that transfer biological signal(s), including metabolic messengers such as miRNA and peptides, between cells and organs. Methods: We investigated whether beta cells can take up liver-derived exosomes and examined their impact on beta cell functional genes and insulin expression. Exosomes isolated from human liver HepG2 cells were characterized using various methods, including Transmission Electron Microscopy (TEM), dynamic light scattering (DLS), and Western blot analysis of exosomal markers. Exosome labeling and cell uptake were assessed using CM-Dil dye. The effect of liver cell-derived exosomes on Min6 beta cells was determined through gene expression analyses of beta cell markers and insulin using qPCR, as well as Akt signaling using Western blotting. Results: Treatment of Min6 beta cells with exosomes isolated from human liver HepG2 cells treated with insulin receptor antagonist S961 significantly increased the expression of beta cell markers Pdx1, NeuroD1, and Ins1 compared to the exosomes isolated from untreated cells. In line with this, the activity of AKT kinase, an integral component of the insulin receptor pathway, is elevated in pancreatic beta cells, as represented by an increase in AKT’s downstream substrate, FoxO1 phosphorylation. Discussions: This study suggests that liver-derived exosomes may carry a specific molecular cargo that can affect insulin expression in pancreatic beta cells, ultimately affecting glucose homeostasis

EXPERIMENTAL STUDY OF MOTIONS OF TWO FLOATING OFFSHORE STRUCTURES IN WAVES

Drilling is carried out in deeper to deeper waters around the globe to meet growing demands for oil and natural gas, and a number of multi body structures are deployed in various oil fields in the world. Investigation of hydrodynamic interaction of offshore structures is therefore worthwhile. Hydrodynamic interaction between floating offshore structures affects motion and relative motion especially during loading and offloading operations. Hydrodynamic interactions may lead to large motions of floating bodies that would cause damage to moorings and offloading systems and may collide with each other. This research work discusses experimental results of hydrodynamic interaction in surge, heave and pitch motion, relative motion and relative distance between a Tension Leg Platform (TLP) and semi-submersible (Tender Assisted Drilling) in regular waves. The experiment is conducted without tendon because of the depth limitation of the Towing Tank. However, in order to consider the contribution of mooring in linear direction, appropriate stiffness of horizontal springs have been used. The experiment was conducted for a full scale wave height of 3.77 m to 12.49 m for a separation distance of 21.7 m. From the analyses of the experimental and numerical results, it can be concluded that nonlinearity of the wave has an important effect on increasing the motion especially in the natural frequency region. Finally, a number of recommendations have been made for further study

Analysing Price, Quality and Lead Time Decisions with the Hybrid Solution Method of Fuzzy Logic and Genetic Algorithm

In this paper, the problem of determining the quality level, lead time for order delivery and price of a product produced by a manufacturer is considered. In this problem the demand for the product is influenced by all three decision variables: price, lead time and quality level. To formulate the demand function, a fuzzy rule base that estimates the demand value based on the three decision variables is developed. To doso, the linguistic knowledge of experts in the form of if-then rules is used to establish the fuzzy system. Moreover, in order to solve the problem, a genetic algorithm integrating the fuzzy rule base is proposed. Finally, to support the validity of the proposed solution, a numerical study is provided

CLEAR CELL ODONTOGENIC CARCINOMA OF THE MANDIBLE WITH PERINEURAL INVASION: A REVIEW

Objectives: Clear cell odontogenic carcinoma (CCOC) is a rare aggressive cancer of the oral cavity. Diagnosis is mainly by excluding other pathologic lesions containing clear cells. Perineural invasion may be an important feature in this lesion. Methods: We performed a literature review. We paid attention to the CCOC and perineural invasion in the search of English language literatures in the Pubmed. Results: Analysis of previously reported cases of CCOC showed that up to2014 there were 74 reported CCOC cases in English articles, cited in PubMed. There was only one previous report of perineural invasion. Anew case is also presented in this article. Conclusion: Clear cell odontogenic carcinoma needs a special immunohistochemical protocol and complete workup to reach a correct diagnosis. Perineural invasion should be considered in central lesions of the mandible

Real time web-based toolbox for computer vision

The last few years have been strongly marked by the presence of multimedia data (images and videos) in our everyday lives. These data are characterized by a fast frequency of creation and sharing since images and videos can come from different devices such as cameras, smartphones or drones. The latter are generally used to illustrate objects in different situations (airports, hospitals, public areas, sport games, etc.). As result, image and video processing algorithms have got increasing importance for several computer vision applications such as motion tracking, event detection and recognition, multimedia indexation and medical computer-aided diagnosis methods. In this paper, we propose a real time cloud-based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can be run in real time and in a secure way. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different algorithms without the need to download, install and configure software or hardware. Moreover, the platform offers the access to the integrated applications from multiple users thanks to the use of Docker (Merkel, 2014) containers and images. Experimentations were conducted within three kinds of algorithms: 1. image processing toolbox. 2. Video processing toolbox. 3. 3D medical methods such as computer-aided diagnosis for scoliosis and osteoporosis.  These experimentations demonstrated the interest of our platform for sharing our scientific contributions related to computer vision domain. The scientific researchers could be able to develop and share easily their applications fastly and in a safe way

Intelligent Coordination of Traditional Power Plants and Inverters Air Conditioners Controlled With Feedback-Corrected MPC in LFC

Demand response programs have been receiving more serious attention as alternatives for participating in load frequency control. Inverter air conditioners (IAC) are acknowledged as suitable devices for demand response due to their increasing contribution to network consumption. Despite their potential, their use presents challenges, including delayed responses, variable interference, and the absence of coordination with traditional generation units, which may affect control performance. Also, existing control strategies fail to consider operational and physical constraints, resulting in possible model mismatches. In this paper, a model predictive control with feedback correction (MPCFC) is proposed to dispatch control signals to the IACs so they can effectively participate in the frequency control of an interconnected power system. The feedback correction method is presented to enhance prediction accuracy in the MPC and weaken the influence of model parameter mismatches and external disturbances. Furthermore, to minimize the impacts of communication delays on frequency overshoot/undershoot, this study introduces an intelligent supervisory coordinator based on an artificial neural network to coordinate the reaction of traditional generation units and IACs to correct significant frequency variations brought on by the time delays. The effectiveness of the developed control scheme is verified through numerical studies by comparing it with the IAC with PI and MPC controllers (without coordinator) and the system without IACs. Case studies are investigated on a two-area power system in MATLAB/Simulink environment, and the OPAL-RT real-time simulator is used to validate the results.</p