Search

The rig state plays a crucial role in recognizing the operations carried out by the drilling crew and quantifying Invisible Lost Time (ILT). This lost time, often challenging to assess and report manually in daily reports, results in delays to the scheduled timeline. In this paper, the Naive Bayes algorithm was used to establish a novel rig state. Training data, consisting of a large set of rules, was generated based on drilling experts' recommendations. This dataset was then employed to build a Naive Bayes classifier capable of emulating the cognitive processes of skilled drilling engineers and accurately recognizing the actual drilling operation from surface data. The developed model was used to process high-frequency drilling data collected from three wells, aiming to derive the Key Performance Indicators (KPIs) related to each drilling crew's efficiency and quantify the ILT during the drilling connections. The obtained results revealed that the established rig state excelled in automatically recognizing drilling operations, achieving a high success rate of 99.747%. The findings of this study offer valuable insights for drillers and rig supervisors, enabling real-time visual assessment of efficiency and prompt intervention to reduce ILT. [ABSTRACT FROM AUTHOR]

Load balancing plays a critical role in ensuring system stability and optimal performance, and as such, it has been a subject of extensive research across diverse computing domains, particularly in heterogeneous systems. Such systems integrate various computing devices with distinct architectures and computational power, each designed to execute a particular type of workload having diverse and immediate requirements. This research introduces a novel load balancing approach that utilizes machine learning to monitor the load distribution of heterogeneous systems in real-time. It estimates the load of the system's devices according to our proposed definition of the load. This definition provided us with an accurate measurement of the actual load, enabling our approach to detect imbalances and locate underloaded devices. Subsequently, our approach assigns applications to these underloaded devices to achieve load balance. We conducted a comparative analysis between our proposed approach and various established load balancing methods from diverse computing domains. This evaluation included three critical criteria: load balancing, workload execution time, and energy consumption. Additionally, we introduced a novel metric called "IMBALANCE" that quantifies the difference in load distribution among the system devices. We implemented each load balancing approach in a real-time heterogeneous system consisting of a master host and three worker servers each equipped with a Central Processing Unit and Graphical Processing Unit. To ensure a comprehensive validation of our approach, we conducted experiments in three distinct scenarios, involving different types of workloads having varying levels of computing intensity. The experimental results consistently demonstrate significant performance improvements of our approach across all scenarios. Our approach excels in minimizing system imbalance, which, in turn, enhances load balancing. This leads to reduced workload execution times and minimized device idle periods, ultimately improving energy efficiency. [ABSTRACT FROM AUTHOR]

Nowadays, smart buildings rely on Internet of things (IoT) technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected objects. Fog is characterized by low latency with a wider spread and geographically distributed nodes to support mobility, real-time interaction, and location-based services. To provide optimum quality of user life in modern buildings, we rely on a holistic Framework, designed in a way that decreases latency and improves energy saving and services efficiency with different capabilities. Discrete EVent system Specification (DEVS) is a formalism used to describe simulation models in a modular way. In this work, the sub-models of connected objects in the building are accurately and independently designed, and after installing them together, we easily get an integrated model which is subject to the fog computing Framework. Simulation results show that this new approach significantly, improves energy efficiency of buildings and reduces latency. Additionally, with DEVS, we can easily add or remove sub-models to or from the overall model, allowing us to continually improve our designs. [ABSTRACT FROM AUTHOR]

In this work, the thermal conductivity of refrigerants systems from three different hydrofluoroolefins including R1234yf, R1234ze(E), and R1233zd(E) were studied using artificial neural network. A total of 4395 data points of liquid and vapor thermal conductivity at several temperatures (241.92 to 344.46 K) and pressures (0.068 to 21.73 MPa) were used to train and test the model. Five neurons were used in the input layer, fifteen neurons at hidden layer and one was used in the output layer. Bayesian Regulation back propagation algorithm, logarithmic sigmoid transfer function, and linear transfer function were used at the hidden and output layer, respectively. Temperature, pressure, applied heating power; acentric factor, and dipole moment were considered as input variables of the networks. The optimal parameters were obtained through the weights searching method. The average absolute relative deviations and correlation coefficient were 1.48 and 0.9998, respectively. This study shows, therefore, that the artificial neural network model represents an excellent alternative to estimate the thermal conductivity of different refrigerant systems with a good accuracy. [ABSTRACT FROM AUTHOR]

Incidents of hydraulic or oil spills in the oceans/seas or ports occur with some regularity during the exploitation, production and transportation of petroleum products. Immediate, safe, effective and environmentally friendly measures must be adopted to reduce the impact of the oil spill on marine life. Due to the difficulty to detect and clean these areas, semi-autonomous vehicles can make a significant contribution by implementing a cooperative and coordinated response. The paper proposes a concept study of Hybrid Monitoring Detection and Cleaning System (HMDCS-UV) for a maritime region using semi-autonomous unmanned vehicles. This system is based on a cooperative decision architecture for an unmanned aerial vehicle to monitor and detect dirty zones (i.e., hydraulic spills), and clean them up using a swarm of unmanned surface vehicles. The proposed solutions were implemented in a real cloud and were evaluated using different simulation scenarios. Experimental results show that the proposed HMDCS-UV can detect and reduce the level of hydraulic pollution in maritime regions with a significant gain in terms of energy consumption. [ABSTRACT FROM AUTHOR]

We propose a dynamic replication strategy that satisfies simultaneously availability and performance tenant requirements while taking into account the tenant budget and the provider profit. The proposed strategy is based on a cost model that aims to calculate the minimum number of replicas required to maintain a high data availability. A replica creation is triggered only when this number of replicas is not reached or when the response time objective is not satisfied. Then, the replication must be profitable for the provider when creating a new replica. Furthermore, data replication and query scheduling are coupled in order to place these replicas in a load balancing way while dealing with the tenant budget. The experiment results prove that the proposed strategy can significantly improve availability and performance while the tenant budget is taken into account. [ABSTRACT FROM AUTHOR]

The determination of thermophysical properties of hydrofluorocarbons (HFCS) is very important, especially the thermal conductivity. The present work investigated the potential of an artificial neural network (ANN) model to correlate the thermal conductivity of (HFCS) at (169.87-533.02) K, (0.047-68.201) MPa, and (0.0089-0.1984) W/(m·K) temperature, pressure, and thermal conductivity ranges, respectively, of 11 systems from 3 different categories including five pure systems (R32, R125, R134a, R152a, R143a), four binary mixtures systems (R32 + R125, R32 + R134a, R125 + R134a, R125 + R143a), and two ternary mixtures systems (R32 + R125 + R134a, R125 + R134a + R143a). Each one received 1817, 794 and 616 data points, respectively. The application of this model for these 3227 data points of liquid and vapor at several temperatures and pressures allowed to train, validate and test the model. This study showed that ANN models represent a good alternative to estimate the thermal conductivity of different refrigerant systems with a good accuracy. The squared correlation coefficients of thermal conductivity predicted by ANN were R2 = 0.998 with an acceptable level of accuracy of RMSE = 0.0035 and AAD = 0.002 %. The results of applying the trained neural network model to the test data indicate that the method has a highly significant prediction capability. [ABSTRACT FROM AUTHOR]

In this study, three marine algae collected from western coast of algerian mediteranean sea (Ulva lactuca, Dictyota dichotoma, and Corallina elongata) were tested using the agar-well diffusion method for their production of antibacterial and antifungal agents on various organisms that cause diseases of humans and plants (Eschirichia coli ATCC 25922, Staphylococcus aureus ATCC 25923, Salmonella sp, Candida albicans, and Penicillium sp.). The total phenol content and antimicrobial activity were determined using different crude seaweeds extracts (methanol, diethylether, and chloroform). The results show that the chloroform extracts of (Ulva lactuca and Corallina elongata) had the highest activity against E. coli and Salmonella sp. The methanol extract obtained from (Ulva lactuca, Dictyota dichotoma, and Corallina elongata) showed antifungal activity for Candida albicans. The results of the study revealed that the seaweeds from Algeria appear to have immense potential as a source of antibacterial and antifungal compounds; they can be used in treating diseases caused by these organisms. [ABSTRACT FROM AUTHOR]

The growth and thermal stability of textured iridium thin films used as bottom electrode in electronic devices based on ferroelectric materials were investigated. The thin films were grown using the dc magnetron sputtering technique. The Ir layers directly deposited on SiO/Si substrates present a mixed (111) and (200) orientations, while the films grown on Ti seed layers exhibit a strong preferred (111) orientation favoured by good matching with the titanium lattice. The substrate temperature during the growth of iridium/titanium stack has a significant effect on the surface morphology of the iridium layer and its thermal stability. The as-grown surface of 20-nm-thick Ir films is smooth, having a root-mean-square (rms) roughness of 0.7 nm. After thermal annealing the Ir film shows an increased surface roughness due to the formation of agglomerations. The change in the surface morphology of the Ir layer is due to titanium diffusion and its oxidation. Thicker and better crystallised iridium thin films annealed in oxygen atmosphere at 700 °C show a good thermal stability with only a slight modification of the surface morphology. Within the limits of experimental error, there is no change in the electrical resistivity before and after thermal annealing. The rms roughness has not varied significantly and the XPS investigation shows no traces of titanium oxide on iridium surface. Ir/Ti stack deposited under the optimum conditions could be successfully used as electrode in devices based on oxide thin films. [ABSTRACT FROM AUTHOR]

Methyl 2-(bis((3,5-dimethyl-1 H-pyrazol-1-yl)methyl)amino) acetate, BT36, and methyl 2-(bis((3,5-dimethyl-1 H-pyrazol-1-yl)methyl)amino)-3-(1 H-indol-3-yl)propanoate, BT 43, have been synthesized. Investigation by weight-loss measurement and use of electrochemical techniques revealed the compounds are very effective inhibitors of corrosion of C38 steel in 1 M HCl solutions-percentage protection exceeded 95 % for BT43 at concentrations as low as 10 M. An impedance study in the absence and presence of these compounds revealed the mechanism of protection was cathodic inhibition by polarization and charge-transfer. The Langmuir adsorption isotherm was obeyed. Quantum chemical data calculated by use of DFT at the B3LYP/6-31G* level of theory revealed a good correlation between inhibition efficiency and the molecular structure of BT36 and BT43. The highest occupied molecular orbital, the lowest unoccupied molecular orbital, the separation energy (Δ E), and the dipole moment ( μ) from the inhibitor to the metal surface explain the experimental data well. [ABSTRACT FROM AUTHOR]

Our work concerns interaction spherical study of three toxic products, bromobenzene, tetrachloroethylene, and 4-hydroxy-chromene-2-one; using the Leap Frog algorithm, we calculated new values of cut-off of the box through Lennard-Jones potential parameters. This model was adapted to allow the determination of the characteristics for the SP, SP and SP state points and was applied to study the properties for the three products by molecular dynamics. This method provides an advantage to confirm the structure-activity relationship for these compounds. We calculated the thermodynamic and structural properties for both canonical NVT and isothermal-isobaric NPT ensembles of these products. Numerical system results have been compared with both experimental data and recent investigation theoretical. Our simulation model isobaric-isothermal system gives accurate results, and comparing with the canonical system, this model agrees very well with the experimental data. We aim to demonstrate that the classical approach with a low statistical uncertainty for liquid toxic leads to data in very good agreement with experiment or other types of calculations. We obtained a good prediction of the thermodynamic properties. We hope that this model with a lower threshold to 2.5σ could be an effective starting material for studying the properties of complex systems. [ABSTRACT FROM AUTHOR]

Corrosion inhibition of copper through six bipyrazolic compounds has been elucidated by means of density functional theory (DFT)-derived reactivity indexes. The DFT calculated parameters and experimental corrosion inhibition efficiency (IE%) indicate that their inhibition effect is closely related to the frontier orbital energies, polarizability, electronic chemical potential and global nucleophilicity. The quantum chemistry calculations were performed at the B3LYP/6-31G (d) level. The theoretical results, predicted using DFT-based reactivity indexes, are in good agreement with experimental outcomes. [ABSTRACT FROM AUTHOR]

Multimedia and real-time applications require bandwidth guarantees, which can be achieved by resource reservation. However, bandwidth reservation in ad-hoc networks is a very challenging task due to the instability of radio channels, node mobility and lack of coordination between mobile nodes. Most proposed reservation protocols focus on point-to-point reservations, and only a few work has been done to propose an efficient end-to-end reservation scheme. In this paper, we propose a reservation scheme called End-to-end reservation scheme for voice and data traffic support (EERV). The basic component of our protocol is a handshake mechanism which has the role to establish point-to-point reservations, and to ensure consistency of reservations between neighbor nodes. Both data and voice sources are allowed to establish reservations with priority given to voice sources. This reservation scheme is extended to support the reservation and release of resources along a path in cooperation with the routing layer. Our reservation scheme is based on DSR, a well-known reactive routing protocol. Mobility of nodes and node failure cause frequent reservation breakages resulting in significant performance degradation. In order to resolve this issue we propose a reservation loss detection and reservation recovery mechanisms. We achieved intensive simulations to evaluate the performance of our protocol. Simulation results show the effectiveness of this protocol. [ABSTRACT FROM AUTHOR]

In radiofrequency glow discharge emission spectrometry (RF-GDOES), the excitation voltage used to create the plasma is applied to the back or front end of the sample to be analyzed. In this paper we focus on back-applied voltage systems (a configuration that represents about half of the instruments available on the market), and on applied voltage problems (the power coupling efficiency and materials analysis are beyond the scope of this study). In the RF-GDOES of nonconductive samples, a voltage drop develops inside the material. The voltage transfer coefficient is defined as the ratio between the peak voltage in front of the sample (facing the plasma) and the peak voltage applied to the back of the sample. In this work, we show that it is possible to increase the voltage transfer coefficient by increasing the capacitance of the sample. The capacitance of a given nonconductive material depends on its surface, its thickness and its permittivity. Increasing the voltage transfer coefficient permits higher power deposition in the plasma. This study is based on an electrical equivalent circuit for the discharge device, which takes into account the sample and reactor capacitances as well as the voltage probes used for the measurements. This circuit, when modeled by a commercial electrical circuit simulator, gives the voltage transfer coefficient as a function of the sample capacitance. Different approaches to increasing the sample capacitance and their influence on the voltage transfer coefficient are presented and related to the 750.4 nm argon line intensity, which is correlated to the electron density. [ABSTRACT FROM AUTHOR]