This research focuses on a decomposed-weighted-sum particle swarm optimization (DWS-PSO) approach that is proposed for optimal operations of price-driven demand response (PDDR) and PDDR-synergized with the renewable and energy storage dispatch (PDDR-RED) based home energy management systems (HEMSs). The algorithm for PDDR-RED-based HEMS is developed by combining a DWS-PSO-based PDDR scheme for load shifting with the dispatch strategy for the photovoltaic (PV), storage battery (SB), and power grid systems. Shiftable home appliances (SHAs) are modeled for mixed scheduling (MS). The MS includes advanced as well as delayed scheduling (AS/DS) of SHAs to maximize the reduction in the net cost of energy ( $CE$ ). A set of weighting vectors is deployed while implementing algorithms and a multi-objective-optimization (MOO) problem is decomposed into single-objective sub-problems that are optimized simultaneously in a single run. Furthermore, an innovative method to carry out the diversified performance analysis (DPA) of the proposed algorithms is also proposed. The method comprises the construction of a diversified set of test problems (TPs), defining of performance metrics, and computation of the metrics. The TPs are constructed for a set of standardized dynamic pricing signal and for scheduling models for MS and DS. The simulation results show the gradient of the tradeoff line for the reduction in $CE$ and related discomfort for DPA.

Career planning has become more important than standalone job decisions in the current environment, where individuals have a wider range of choices of occupations and jobs than ever before. This paper reviews the theoretical developments in career guidance theory and recommends a holistic approach towards ‘Career Path Guidance’, incorporating psychological, sociological and developmental perspectives. An approach that considers several future steps in a career cannot be without challenges. The contextual and environmental situations along with individual characteristics are subject to change along a career path. Additionally, different stages in a career path are another variable that needs consideration to achieve a sustainable future. It has, therefore, been proposed that the career course be mapped by considering external factors, internal improvements and path-related dynamism. A multidimensional understanding of career guidance and the amount, complexity and sensitivity of the data involved mean that this approach may be better executed using computer-assisted systems that can source and logically analyze a large amount of information from several sources to produce coherent, sustainable and practical career path decisions.

Telecommunication systems are heterogeneous networks with parts supplied by many vendors. Such complex systems face a number of faults that may deny services to the end users resulting in revenue losses to the telecommunication companies. Best case scenario is to avoid these faults completely, or failing that, correct the faults as soon as possible. Therefore there is a need for self-healing networks that can proactively predict and correct faults automatically. In this paper a fault prediction technique is presented that is useful in a self-healing network. The proposed technique first trains an artificial intelligence technique on the historical alarm data to find correlations and then uses these correlations to predict future alarms. The artificial intelligence techniques being used are, Artificial neural network, support vector machine, Kalman filter and hidden Markov model. In this paper we reported on artificial neural network. The proposed technique is applied on the alarm data from a real telecommunication company and prediction accuracies of the proposed technique are calculated. The details of the proposed fault prediction technique and results that suggest optimal parameters are presented. The proposed technique is effective in a proactive self-healing network.