Disciplined or Agile?

Software requirements changes become necessary due to changes in customer requirements and changes in business rules and operating environments; hence, requirements development, which includes requirements changes, is a part of a software process. Previous studies have shown that failing to manage software requirements changes well is a main contributor to project failure. Given the importance of the subject, there is a plethora of efforts in academia and industry that discuss the management of requirements change in various directions, ways, and means. This chapter provided information about the current state-of-the-art approaches (i.e., Disciplined or Agile) for RCM and the research gaps in existing work. Benefits, risks, and difficulties associated with RCM are also made available to software practitioners who will be in a position of making better decisions on activities related to RCM. Better decisions can lead to better planning, which will increase the chance of project success.

Wearable Bluetooth Triage Healthcare Monitoring System

Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients’ vitals, including respiratory rate, temperature, and pulse rate. Ideally, these checks should be performed continuously and remotely to reduce the workload on triage nurses; optimizing tools and monitoring systems can be introduced and include a wearable patient monitoring system that is not at the expense of the patient’s comfort and can be remotely monitored through wireless connectivity. In this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome that enhances contact with skin, to detect wirelessly both respiration and cardiac events at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. To complete Triage parameter collection, we also included a medical-grade contact thermometer. Performances of cardiac and respiratory events detection were assessed. The instantaneous heart and respiratory rates provided by the proposed sensor, the ECG and the respiratory belt were compared via statistical analyses. In all considered sensor positions, very high performances were achieved for the detection of both cardiac and respiratory events, except for the wrist, which provided lower performances for respiratory rates. These promising yet preliminary results suggest the proposed wireless sensor could be used as a wearable, hands-free monitoring device for triage assessment within emergency departments. Further tests are foreseen to assess sensor performances in real operating environments.

Multicomponent Stress-Strength Reliability Based on Progressive First Failure for Kumaraswamy Model: Bayesian and Non-Bayesian Estimation

It is highly common in many real-life settings for systems to fail to perform in their harsh operating environments. When systems reach their lower, upper, or both extreme operating conditions, they frequently fail to perform their intended duties, which receives little attention from researchers. The purpose of this article is to derive inference for multicomponent reliability where stress-strength variables follow unit Kumaraswamy distributions based on the progressive first failure. Therefore, this article deals with the problem of estimating the stress-strength function, R when X,Y, and Z come from three independent Kumaraswamy distributions. The classical methods namely maximum likelihood for point estimation and asymptotic, boot-p and boot-t methods are also discussed for interval estimation and Bayes methods are proposed based on progressive first-failure censored data. Lindly’s approximation form and MCMC technique are used to compute the Bayes estimate of R under symmetric and asymmetric loss functions. We derive standard Bayes estimators of reliability for multicomponent stress–strength Kumaraswamy distribution based on progressive first-failure censored samples by using balanced and unbalanced loss functions. Different confidence intervals are obtained. The performance of the different proposed estimators is evaluated and compared by Monte Carlo simulations and application examples of real data.

Field-Proven Well Surveillance Techniques Designed for an Early Well Scaling Diagnosis and Timely Well Performance Reinstatement: Case Study from the Kashagan Field

Some wells in the Kashagan field did not perform as well as expected. Despite producing virtually no water, calcite deposition was found to be the root cause of the problem. A comprehensive well surveillance program, which was proven to be very efficient for an early scaling diagnosis, was developed by the operator, North Caspian Operating Company (hereafter NCOC). As a result, well scaling is currently well managed and prevented from reoccurring. The objective of this paper is to share an early experience with well scaling in the Kashagan field, as well as to describe the developed set of well surveillance techniques. The aim of the various well surveillance techniques discussed in this paper is to improve an Operator's ability to identify the very first signs of scale accumulation. This, in its turn, enables to introduce timely adjustments to the well operating envelope and to schedule a scale remediation / inhibition treatment with the intention to prevent any potential scaling initiation from further development. The approach is quite extensive and incorporates continuous BHP/BHT monitoring, routine well testing, PTA analysis, and fluid/water sampling. Developed approach experienced multiple revisions and modifications. Further optimization continues, however, the described well surveillance techniques represent the latest Operator's vision on the most efficient way for well scaling monitoring and identification. In the Kashagan field, BHP/BHT readings have proved to be the most direct and instantaneous indication of any early signs of potential deterioration in well performance (qualitative analysis) while well testing and PTAs are considered as the most essential techniques in confirming and quantifying scaling severity (quantitative analysis). It is important to mention that BHT increase is explained by Joule-Thomson heating effect being specific for the Kashagan fluid (happening during increased pressure drawdown). This, in turns, enables to predict future well performance, design well operating envelop accordingly and, most importantly, develop a yearly schedule for proactive well treatments with SI. In conclusion, it shall be highlighted that discussed complex of well surveillance techniques has been concluded to be very efficient and reliable tool in identifying any scaling tendencies at its initial stage. Due to successful implementation of this approach in the Kashagan field, scale development is now well-managed and kept under control. To mention, that utilization of well surveillance techniques and methods outlined in this paper may reduce the time required to identify and ultimately mitigate well scale accumulation in any active assets with similar operating environments.

Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids

Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their corresponding actions. In this paper, a novel energy trading system based on a double deep Q-networks (DDQN) algorithm and a double Kelly strategy is proposed for improving profits while reducing dependence on the main grid in the microgrid systems. The DDQN algorithm is proposed in order to select optimized action for improving energy transactions. Additionally, the double Kelly strategy is employed to control the microgrid’s energy trading quantity for producing long-term profits. From the simulation results, it is confirmed that the proposed strategies can achieve a significant improvement in the total profits and independence from the main grid via optimized energy transactions.

Strategy and Business Survival during Disruptive Times: A Survey of Passenger Transporters in Harare, Zimbabwe during the COVID-19 Lockdown

Business leaders must be strategic about their operations to ensure success in different operating environments. The COVID-19, which emerged as a public health pandemic that affected businesses in different sectors differently. Passenger transport businesses were directly affected after the movement of people was banned for extended periods, during the lockdowns. The lockdowns represented a disruption in the operating environments. The study sought to assess the effect of strategic management on the survival of passenger transport operators during the COVID-19 induced lockdown disruptions in Harare, Zimbabwe. Data were collected using survey questionnaires distributed to representatives of 100 passenger transporters in Harare, from which questionnaires were returned. Results showed that 84 percent of the passenger transport operators have formal organizational structures of which about 73 confirmed the practice of strategic planning. Therefore, there is high adoption of strategic management by passenger transport operators in Harare. The most effective strategies for enhancing business survival in disruptions are diversification and competitive strategies. The logistic regression model results showed that the existence of a formal structure minimized the negative impact of the COVID-19 lockdown disruptions on the survival of passenger transport operators in Harare.

Estimation of Photovoltaic Cell Parameters Using Measurement Data of Photovoltaic Module String Currents and Voltages

Photovoltac (PV) models play an important role in the simulation analysis and fault diagnosis of PV systems. The<br>single diode model (SDM) is the most frequently used model in research and applications. There are numerous proposed methods to identify the SDM parameters. However, the characteristics of PV cells alter during the lifetime in normal operating environments; these variations may be due to degradation, faults, dust, weed, and so on. Therefore, it is crucial to estimate the actual parameters of the PV cells that represent those present state. The contribution of this study is to propose a method to estimate PV cell parameters on the basis of the measurement data regarding the currents and voltages of the PV module strings. A PV string model is described on the basis of the adaptive SDM for the PV cells in the system, and the parameters of each cell model are obtained by minimizing the difference between the measured string voltages and the string voltages computed by the model. The application of the proposed method to real data measured in a PV power plant is also presented to evaluate the proposed method.

Estimation of Photovoltaic Cell Parameters Using Measurement Data of Photovoltaic Module String Currents and Voltages

Photovoltac (PV) models play an important role in the simulation analysis and fault diagnosis of PV systems. The<br>single diode model (SDM) is the most frequently used model in research and applications. There are numerous proposed methods to identify the SDM parameters. However, the characteristics of PV cells alter during the lifetime in normal operating environments; these variations may be due to degradation, faults, dust, weed, and so on. Therefore, it is crucial to estimate the actual parameters of the PV cells that represent those present state. The contribution of this study is to propose a method to estimate PV cell parameters on the basis of the measurement data regarding the currents and voltages of the PV module strings. A PV string model is described on the basis of the adaptive SDM for the PV cells in the system, and the parameters of each cell model are obtained by minimizing the difference between the measured string voltages and the string voltages computed by the model. The application of the proposed method to real data measured in a PV power plant is also presented to evaluate the proposed method.