Effect of organisational position and network centrality on project coordination

This study approaches project based coordination from a real-world dataset of complex multi-million dollar projects with hundreds of people working together. Using the Enron dataset provides this study with real-world interactions for exploring the effect of the employee’s organisational position and network centrality on project based coordination. With an understanding of centrality and coordination, we explore whether centrally ‘well-connected’ people are able to exercise greater project based coordination within the network structure. In this study, we explore questions such as–What is the relationship between network centrality and coordination? Which degrees of centrality (i.e., in-degree, out-degree, betweenness, closeness) have higher correlation in project based coordination? Is there a relationship between actors’ organisational position and their network position with the project group?     

Monitoring process variation using modified EWMA

A new control chart, namely, modified exponentially weighted moving average (EWMA) control chart, for monitoring the process variance is introduced in this work by following the recommendations of Khan et al.¹⁵ The proposed control chart deduces the existing charts to be its special cases. The necessary coefficients, which are required for the construction of modified EWMA chart, are determined for various choices of sample sizes and smoothing constants. The performance of the proposed modified EWMA is evaluated in terms of its run length (RL) characteristics such as average RL and standard deviation of RL. The efficiency of the modified EWMA chart is investigated and compared with some existing control charts. The comparison reveals the superiority of proposal as compared with other control charts in terms of early detection of shift in process variation. The application of the proposal is also demonstrated using a real-life dataset.     

Dual Branches of MHD Three-Dimensional Rotating Flow of Hybrid Nanofluid on Nonlinear Shrinking Sheet

In this study, magnetohydrodynamic (MHD) three-dimensional (3D) flow of alumina (Al₂O₃) and copper (Cu) nanoparticles of an electrically conducting incompressible fluid in a rotating frame has been investigated. The shrinking surface generates the flow that also has been examined. The single-phase (i.e., Tiwari and Das) model is implemented for the hybrid nanofluid transport phenomena. Results for alumina and copper nanomaterials in the water base fluid are achieved. Boundary layer approximations are used to reduce governing partial differential (PDEs) system into the system of the ordinary differential equations (ODEs). The three-stage Lobatto IIIa method in bvp4c solver is applied for solutions of the governing model. Graphical results have been shown to examine how velocity and temperature fields are influenced by various applied parameters. It has been found that there are two branches for certain values of the suction/injection parameter b: The rise in copper volumetric concentration improved the velocity of hybrid nanofluid in the upper branch. The heat transfer rate improved for the case of hybrid nanofluid as compared to the viscous fluid and simple nanofluid.     

Emission and performance characteristics of an indirect ignition diesel engine fuelled with waste cooking oil

Biofuel has so far been backed by government policies in the quest for low carbon fuel in the near future and promises to ensure energy security through partially replacing fossil fuels. At present biodiesel is mostly produced by transesterification reaction from oil-seed feedstock and has to conform to ASTM D6751 specifications. Biodiesel sustainability has sparked debate on the pros and cons of biodiesel and the question of food security. The use of waste cooking oil such as palm and coconut oil in diesel engines is more sustainable if they can perform similarly to ordinary diesel fuel (B0). This paper presents the experimental study carried out to evaluate emission and performance characteristics of a multi-cylinder diesel engine operating on waste cooking oil such as 5% palm oil with 95% ordinary diesel fuel (P5) and 5% coconut oil with 95% ordinary diesel fuel (C5). B0 was used for comparison purposes. The results show that there are reductions in brake power of 1.2% and 0.7% for P5 and C5 respectively compared with B0. In addition, reduction of exhaust emissions such as unburned hydrocarbon (HC), smoke, carbon mono-oxide (CO), and nitrogen oxides (NO_x) is offered by the blended fuels.     

Design of a t‐chart using generalized multiple dependent state sampling

In this article, a new t‐chart based on generalized multiple dependent state (GMDS) sampling is proposed for efficient monitoring of a process by assuming that the time between events follows the exponential distribution. The proposed t‐chart has two pair of control limits and utilizes the past sample information with the current sample information. The control chart coefficients are estimated by considering different values of the in‐control average run lengths. The proposed t‐chart is compared with the existing chart by using the out‐of‐control average run length and extra quadratic loss function. The comparison reveals that the proposed charting strategy has better shift detection ability in process mean. An illustrative example is given for the practical implementation of the proposed chart.     

Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

The COVID-19 virus exhibits pneumonia-like symptoms, including fever, cough, and shortness of breath, and may be fatal. Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities. Clinical studies help to make a correct diagnosis of COVID-19, where the disease has already spread to the organs in most cases. Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread. Therefore, clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold, which are worth investigating. Current supervised Machine Learning (ML) based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography (CT) for disease detection. This strategy relies on a larger clinical dataset and does not focus on early symptom identification. Towards this end, an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms, fever, and cough. Specifically, various metrics, including body temperature, breathing and cough patterns, and physical activity, were considered in this study. Finally, a lightweight ML algorithm based on the K-Means and Isolation Forest technique was implemented on relatively small data including 40 individuals. The proposed technique shows an outlier detection with an accuracy of 89%, on average.     

Dynamic Stability Improvement of Decentralized Wind Farms by Effective Distribution Static Compensator

Dynamic instability of decentralized wind energy farms is a major issue to deliver continuous green energy to electricity consumers. This instability is caused by variations of voltage and frequency parameters due to intermittencies in wind power. Previously, droop control and inverter-based schemes have been proposed to regulate the voltage by balancing reactive power, while inertial control, digital mapping technique of proportional-integral-differential (PID) controller and efficiency control strategy have been developed to regulate the frequency. In this paper, voltage stability is improved by a new joint strategy of distribution static compensator (DSTATCOM) six-pulse controller based reactive power management among decentralized wind turbines and controlled charging of capacitor bank. The frequency stability is ensured by a joint coordinated utilization of capacitor bank and distributed wind power turbines dispatching through a new DSTATCOM six-pulse controller scheme. In both strategies, power grid is contributed as a backup source with less priority. These new joint strategies for voltage and frequency stabilities will enhance the stable active power delivery to end users. A system test case is developed to verify the proposed joint strategies. The test results of the proposed new schemes are proved to be effective in terms of stability improvement of voltage, frequency and active power generation.     

Linear stability analysis of MHD flow of micropolar fluid with thermal radiation and convective boundary condition: Exact solution

Magnetohydrodynamic (MHD) flow of micropolar fluid by including the thermal radiation and convective condition on a shrinking surface in the presence of mass suction effects has been investigated. The momentum, angular momentum and energy equations, and the solutions of these equations are valid for whole Navier stokes, and microrotational and energy equations have been solved exactly. We obtain the solution in the form of an incomplete γ function for the energy equation. The results reveal that dual solutions exist for certain domains of different physical parameters. Furthermore, high suction produces the high effect of drag force, and as a result, coefficient of skin friction increases in the first solution. Stability analysis has been performed and determined that the first solution is more stable.     

Novel poly(thiourea‐ether‐imide)s derived from 4,4′‐oxydiphenyl‐bis(thiourea): probing the possibility for high‐temperature applications

Our interest in the fabrication of high‐performance polyimides has led to thiourea‐substituted poly(thiourea‐ether‐imide)s (PTEIs) with good retention of thermal properties along with flame retardancy. A new aromatic monomer, 4,4′‐oxydiphenyl‐bis(thiourea) (ODPBT), was efficiently synthesized and polymerized with various dianhydrides (pyromellitic dianhydride, 3,3′,4,4′‐benzophenonetetracarboxylic dianhydride and 4,4′‐(hexafluoroisopropylidene)diphthalic dianhydride) via two‐stage chemical imidization to fabricate a series of PTEIs. The structural characterization of ODPBT and the polymers was carried out using Fourier transform infrared, ¹H NMR and ¹³C NMR spectral techniques along with crystallinity, organosolubility, inherent viscosity and gel permeation chromatographic measurements. PTEIs bearing C?S and ? O? moieties in the backbone demonstrated an amorphous nature and were readily soluble in various amide solvents. The novel polymers had inherent viscosities of 1.16–1.23 dL g^?1 and molecular weights of ca 90 783–96 927 g mol^?1. Their thermal stability was substantiated via 10% weight loss in the temperature range 516–530 °C under inert atmosphere. The polyimides had glass transition temperatures of 260–265 °C. Incorporation of thiourea functionalities into polymer backbones is demonstrated to be an effective way to enhance their thermal properties and flame retardancy. Thus, ODPBT can be considered as an excellent candidate for use in the synthesis of high‐performance polymeric materials. Copyright © 2010 Society of Chemical Industry     

Evolutionary dynamics of scientific collaboration networks: multi-levels and cross-time analysis

Several studies exist which use scientific literature for comparing scientific activities (e.g., productivity, and collaboration). In this study, using co-authorship data over the last 40 years, we present the evolutionary dynamics of multi level (i.e., individual, institutional and national) collaboration networks for exploring the emergence of collaborations in the research field of “steel structures”. The collaboration network of scientists in the field has been analyzed using author affiliations extracted from Scopus between 1970 and 2009. We have studied collaboration distribution networks at the micro-, meso- and macro-levels for the 40 years. We compared and analyzed a number of properties of these networks (i.e., density, centrality measures, the giant component and clustering coefficient) for presenting a longitudinal analysis and statistical validation of the evolutionary dynamics of “steel structures” collaboration networks. At all levels, the scientific collaborations network structures were central considering the closeness centralization while betweenness and degree centralization were much lower. In general networks density, connectedness, centralization and clustering coefficient were highest in marco-level and decreasing as the network size grow to the lowest in micro-level. We also find that the average distance between countries about two and institutes five and for authors eight meaning that only about eight steps are necessary to get from one randomly chosen author to another.