A Review on Optimality Investigation Strategies for the Balanced Assignment Problem

Mathematical Selection is a method in which we select a particular choice from a set of such. It have always been an interesting field of study for mathematicians. Accordingly, Combinatorial Optimization is a sub field of this domain of Mathematical Selection, where we generally, deal with problems subjecting to Operation Research, Artificial Intelligence and many more promising domains. In a broader sense, an optimization problem entails maximising or minimising a real function by systematically selecting input values from within an allowed set and computing the function's value. A broad region of applied mathematics is the generalisation of metaheuristic theory and methods to other formulations. More broadly, optimization entails determining the finest virtues of some fitness function, offered a fixed space, which may include a variety of distinct types of decision variables and contexts. In this work, we will be working on the famous Balanced Assignment Problem, and will propose a comparative analysis on the Complexity Metrics of Computational Time for different Notions of solving the Balanced Assignment Problem

Dynamic p-cycles selection in optical WDM Mesh networks

P-cycles have been recognized as a useful protection scheme in WDM mesh networks. This is a type of shared link protection that not only retains the mesh-like capacity efficiency, but also achieves the ring-like protection switching speed. However, finding the optimal set of p-cycles for protecting traffic demands is not a simple task and is an NP-hard problem. A general approach is to determine a set of candidate p-cycles and then determine optimal or near-optimal solutions by using integer linear programming (ILP) models or heuristics. In a dense mesh network, however, the number of candidate cycles is huge, and increases exponentially if the node number is increased. Thus, searching for a suitable set of efficient candidate cycles is crucial and imperative to balancing the computational time and the optimality of solutions. In this paper, we propose a dynamic P-cycles selection (DPS) algorithm that improves the efficiency of enumerating candidate p-cycles. The dynamic approach for cycle selection is based on the network state. In the DPS algorithm, all cycles are found and stored, then an efficient and sufficient set of p-cycles is extracted to achieve 100% working protection, minimize the spare capacity, and reduce time complexity

Biomarker Discovery and Validation for Proteomics and Genomics: Modeling And Systematic Analysis

Discovery and validation of protein biomarkers with high specificity is the main challenge of current proteomics studies. Different mass spectrometry models are used as shotgun tools for discovery of biomarkers which is usually done on a small number of samples. In the discovery phase, feature selection plays a key role. The first part of this work focuses on the feature selection problem and proposes a new Branch and Bound algorithm based on U-curve assumption. The U-curve branch-and-bound algorithm (UBB) for optimization was introduced recently by Barrera and collaborators. In this work we introduce an improved algorithm (IUBB) for finding the optimal set of features based on the U-curve assumption. The results for a set of U-curve problems, generated from a cost model, show that the IUBB algorithm makes fewer evaluations and is more robust than the original UBB algorithm. The two algorithms are also compared in finding the optimal features of a real classification problem designed using the data model. The results show that IUBB outperforms UBB in finding the optimal feature sets. On the other hand, the result indicate that the performance of the error estimator is crucial to the success of the feature selection algorithm. To study the effect of error estimation methods, in the next section of the work, we study the effect of the complexity of the decision boundary on the performance of error estimation methods. First, a model is developed which quantifies the complexity of a classification problem purely in terms of the geometry of the decision boundary, without relying on the Bayes error. Then, this model is used in a simulation study to analyze the bias and root-mean-square error (RMS) of a few widely used error estimation methods relative to the complexity of the decision boundary. The results show that all the estimation methods lose accuracy as complexity increases. Validation of a set of selected biomarkers from a list of candidates is an important stage in the biomarker identification pipeline and is the focus of the the next section of this work. This section analyzes the Selected Reaction Monitoring (SRM) pipeline in a systematic fashion, by modelling the main stages of the biomarker validation process. The proposed models for SRM and protein mixture are then used to study the effect of different parameters on the final performance of biomarker validation. We focus on the sensitivity of the SRM pipeline to the working parameters, in order to identify the bottlenecks where time and energy should be spent in designing the experiment

Learning Near-optimal Decision Rules for Energy Efficient Building Control

Recent studies suggest that advanced optimization based control methods such as model predictive control (MPC) can increase energy efficiency of buildings. However, adoption of these methods by industry is still slow, as building operators are used to working with simple controllers based on intuitive decision rules that can be tuned easily on-site. In this paper, we suggest a synthesis procedure for rule based controllers that extracts prevalent information from simulation data with MPC controllers to construct a set of human readable rules while preserving much of the control performance. The method is based on the ADABOOST algorithm from the field of machine learning. We focus on learning binary decisions, considering also the ranking and selection of measurements on which the decision rules are based. We show that this feature selection is useful for both complexity reduction and decreasing investment costs by pruning unnecessary sensors. The proposed method is evaluated in simulation for six different case studies and is shown to maintain the high performance of MPC despite the tremendous reduction in complexity

Implications of Human Resource Practices and Other Structural Factors on Commitment of Public Medical Professionals in India

In this paper we focus on often neglected issue and inadequately studied area of commitment of public sector health professionals and some of the issues surrounding human resources as its determinants. The paper argues that success or failure of new initiatives in health sector critically hinges on the commitment of the staff. This paper is based on the questionnaire study and focused group discussion of 175 doctors working as district medical officers at district level and holding key administrative positions at state level in four states in India. These four Indian states account for nearly 22 per cent of India’s population. The findings provide some important insights that would be useful in drawing future agenda of strengthening health sector and involving all stakeholders in implementation process. The study finds critical linkage between human resource (HR) practices and commitment of doctors working in the government. Specifically, following HR practices are found critical in influencing organizational commitment: transparency in selection/postings, supportive training and capacity strengthening climate, recognition of performance and regular performance feedback. Further, results suggest that certain work environment and structural factors facilitate these practices. Health officials’ roles need to be redefined and given complexity of coordination at various levels, officials need to be allocated higher responsibilities. There is also a need to improve interpersonal relations within departments and coordination among agencies and officials at various levels. It is also observed that the structural rigidities in the system leading to obstruction in information sharing across various levels needs to be addressed to ensure effective healthcare delivery. This study highlights the criticality of administrative and structural issues for reforms of healthcare sector in India. Addressing human resources issues is critical for ensuring commitment from staff in implementing new initiatives or health reform agenda. National Rural Health Mission (NRHM) also identifies the human resources and capacities as an important challenge. Institutions that are critical vehicles to implement the NHRM would remain weak owing to low commitment of people. It would be important to focus on HR issues before any new initiative is proposed and implemented. The departments of health across states need to broaden and deepen the understanding of HR management and planning issues. For this purpose they may need to set-up HR division having appropriate competency and skill-mix to address the issues and work towards making the right changes. The papers discusses that these changes will be required at both strategic and operational levels.

Principles and philosophies for speech and language therapists working with people with primary progressive aphasia: An international expert consensus

Purpose: Primary progressive aphasia (PPA) is a language-led dementia associated with Alzheimer’s pathology and fronto-temporal lobar degeneration. Multiple tailored speech and language interventions have been developed for people with PPA. Speech and language therapists/speech-language pathologists (SLT/Ps) report lacking confidence in identifying the most pertinent interventions options relevant to their clients living with PPA during their illness trajectory. Materials and methods: The aim of this study was to establish a consensus amongst 15 clinical-academic SLT/Ps on best practice in selection and delivery of speech and language therapy interventions for people with PPA. An online nominal group technique (NGT) and consequent focus group session were held. NGT rankings were aggregated and focus groups video recorded, transcribed, and reflexive thematic analysis undertaken. Results: The results of the NGT identified 17 items. Two main themes and seven further subthemes were identified in the focus groups. The main themes comprised (1) philosophy of person-centredness and (2) complexity. The seven subthemes were knowing people deeply, preventing disasters, practical issues, professional development, connectedness, barriers and limitations, and peer support and mentoring towards a shared understanding. Conclusions: This study describes the philosophy of expert practice and outlines a set of best practice principles when working with people with PPA.Implications for rehabilitation Primary progressive aphasia (PPA) describes a group of language led dementias which deteriorate inexorably over time. Providing speech and language therapy for people with PPA is complex and must be person centred and bespoke. This study describes the philosophy of expert practice and outlines a set of best practice principles for speech and language therapists/pathologists working with people with people with PPA