Search

This study examines how genetic algorithms (GA) can be used to make protective structures for photovoltaic (PV) systems more efficient. We tried to increase the efficiency of solar cell systems in five different construction scenarios with regard to costs, energy savings, and payback time. To optimize this, we used construction parameters such as width, height, depth, insulation thickness, shading, and roof angle. An evolutionary algorithm generated the most efficient individual with parameters of 32.89 m width, 8.83 m height, 1.46 m depth, WWR = 32.52%, insulation = 8.96 cm, shading = 7.94 m², and roof angle = 72.08°. The solar panels had an efficiency of 31.27%. According to the cost analysis, Building 4, which had installation costs of $40,000 and annual maintenance costs of $1,500, provided the greatest energy savings of $7,000 per year and a payback period of five years. The features that distinguished Building 2 were installation cost, $ 25000; the pay back of installation was seven years; annual maintenance cost $ 800 per year. But it also saved less power which was around four thousand dollars for the year consumption. In Building 3 the greatest energy savings were found and pay back period was also five years In Buildings 3 and 5 average cost and performance was observed. According to the practical outcomes of the study, it has been perceived that applying of genetic algorithms can lead to enhancement of the economic efficiency of the solar power plants including energy saving aspects. It is therefore argued here that these skills can be applied to building design such that possible returns on solar panel systems are boosted.

Logic Encryption is a hardware security technique that protects integrated circuit designs that are fabricated at untrusted pure play foundries from being pirated or maliciously modified. In the technique, logic gates are added to the design that are driven by an added key input bus, such that the correct behavior of the circuit is recovered with only the exact correct key input pattern. However, the power, performance, and area (PPA) cost of implementing logic encryption has often been ignored in the literature in favor of increasing the level of security provided. This has proved to be a significant hurdle in transitioning the method to use in commercial-grade designs and a systematic methodology of constraining the cost of logic encryption is needed. In this paper, we propose a generalized Constraint-Directed Logic Encryption (CDLE) methodology. In CDLE, the potential design space of encrypted versions of a circuit is searched to apply logic encryption under PPA constraints. Two example CDLE methods are proposed. The first is a concurrent tree search method which uses commercial tools to sample designs for their PPA cost and determine the optimal encryption strategy. In this method, PPA cost is accurately analyzed at the cost of heavy runtime. The second is a machine learning approach which estimates the PPA cost to predict the optimal encryption strategy. The machine learning model developed in this work is limited, but the results are promising as a direction for study in logic encryption. Detailed experimental results evaluating both methods are presented.

It is very important to detect and correct faults for ensuring the validity and reliability of these circuits. In this regard, a comparative study with related existing techniques is undertaken. Two techniques to achieve the testability of reversible circuits are introduced that have been improved in terms of quantum cost and fault coverage rate. Considering this aspect, the main focus of these techniques is on the efficient detection and location of faults with 100% accuracy. These techniques for fault detection in reversible circuit design, in addition to being able to produce the correct outputs, can also provide information for fault location that has already been done at a higher cost. Proposed approaches have been successfully tested for all types of SMGF, MMGF, PMGF, RGF, and SBF. In order to verify the functional correctness of the proposed scheme, it also has executed the testing over a reversible full adder circuit, and findings are checked. In the following, the proposed approach of reversible sequential circuits is presented for the first time so far. The cost metrics are evaluated for all the proposed designs and compared the estimated results against some existing design approaches of reversible circuits for better understanding. [ABSTRACT FROM AUTHOR]

The hierarchical routing algorithm is categorized as a kind of routing method using node clustering to create a hierarchical structure in large-scale mobile ad hoc network (LMANET). In this paper, we proposed a new hierarchical clustering algorithm (HCAL) and a corresponded protocol for hierarchical routing in LMANET. The HCAL is designed based on a cost metric in the form of the link expiration time and node's relative degree. Correspondingly, the routing protocol for HCAL adopts a reactive protocol to control the existing cluster head (CH) nodes and handle proactive nodes to be considered as a cluster in LMANET. Hierarchical clustering algorithm jointly utilizes table-driven and on-demand routing by using a combined weight metric to search dominant set of nodes. This set is composed by link expiration time and node's relative degree to establish the intra/intercommunication paths in LMANET. The performance of the proposed algorithm and protocol is numerically evaluated in average end-to-end delay, number of CH per round, iteration count between the CHs, average CH keeping time, normalized routing overhead, and packet delivery ratio over a number of randomly generated benchmark scenarios. Furthermore, to corroborate the actual effectiveness of the HCAL algorithm, extensive performance comparisons are carried out with some state-of-the-art routing algorithms, namely, Dynamic Doppler Velocity Clustering, Signal Characteristic-Based Clustering, Dynamic Link Duration Clustering, and mobility-based clustering algorithms. [ABSTRACT FROM AUTHOR]

Background: The collaborative care model (CCM) has been consistently shown to achieve effectiveness in depression management compared to usual care. In the present study, we aimed to determine the impact of age on cost metrics in patients who were treated with CCM for 6 months after a diagnosis of depression. Methods: The upper quartile of age was 50 years and older (n = 56), and the comparison group was composed of patients in the three younger quartiles, aged 18–49 years (n = 163). Results: Patients in the older age group had an elevated median Current Procedure Terminology cost rank of 255.5 compared to 168.0 for the younger patients (P < 0.001). Multiple logistic regression analysis revealed that being in the upper quartile of age (≥50 years) (odds ratio = 2.272, 95% confidence interval: 1.064–4.851; P = 0.034) and having higher numbers of clinical visits 6 months prior to index (odds ratio = 1.209, 95% confidence interval: 1.118–1.307; P < 0.001) were significant predictor variables of being cost rank outliers (>80th percentile) in patients with CCM. Conclusion: Medical cost utilization in the 6 months after diagnosis of depression was significantly higher in patients in the upper age quartile (≥50 years) enrolled in CCM than those in the lower quartiles (age <50). [ABSTRACT FROM AUTHOR]

Achieving the goals of information security is a key factor in the decision to outsource information technology and, in particular, to decide on the migration of organizational data, applications, and other resources to the infrastructure, based on cloud computing. And the key issue in the selection of optimal architecture and the subsequent migration of business applications and data to the cloud organization information environment is the question of the total cost of ownership of IT infrastructure. This paper focuses on solving the problem of minimizing the total cost of ownership cloud.

Over the last decade Learning Objects (LOs) have gained a lot of attention as a common format for developing and sharing digital educational content in the field of technology-enhanced learning. The main advantage of LOs is considered to be their potential for component-based reuse in different learning settings supporting different learning activities. However, despite the importance of the concept of reuse and its potential benefits in digital educational content production and deployment, there are only sporadic efforts to study issues related to LOs reuse that would allow interested parties (such as people, organizations and initiatives) to assess the conditions for and eventually implement systematic LOs reuse within the context of learning activities design and development. This is a drawback in adopting the LOs paradigm towards reducing costs and effort. In this paper, we study existing efforts for the definition of the different steps involved during the LOs lifecycle, we identify the aspects of LOs reuse within the context of learning activities design and development, we propose a thorough workflow for LOs lifecycle that can support LOs reuse and enable us to define a set of metrics for cost effective LOs reuse, and discuss the cost effectiveness conditions in various use cases [ABSTRACT FROM AUTHOR]

Few sectors of the economy have been left unscathed in these economic times. Healthcare construction has been less affected than residential and nonresidential construction sectors, but driven by re-evaluation of healthcare system capital plans, projects are now being put on hold or canceled. The industry is searching for ways to improve the value proposition for project delivery and process controls. In other industries, benchmarking component costs has led to significant, sustainable reductions in costs and cost variations. Kaiser Permanente and the Construction Industry Institute (CII), a research component of the University of Texas at Austin, an industry leader in benchmarking, have joined with several other organizations to work on a national benchmarking and metrics program to gauge the performance of healthcare facility projects. This Initiative will capture cost, schedule, delivery method, change, functional, operational, and best practice metrics. This program is the only one of its kind. The CII Web-based interactive reporting system enables a company to view its information and mine industry data. Benchmarking is a tool for continuous improvement that is capable not only of grading outcomes; it can inform all aspects of the healthcare design and construction process and ultimately help moderate the increasing cost of delivering healthcare. [ABSTRACT FROM AUTHOR]

k-Anonymity is a method for providing privacy protection by ensuring that data cannot be traced to an individual. In a k-anonymous dataset, any identifying information occurs in at least k tuples. To achieve optimal and practical k-anonymity, recently, many different kinds of algorithms with various assumptions and restrictions have been proposed with different metrics to measure quality. This paper evaluates a family of clustering-based algorithms that are more flexible and even attempts to improve precision by ignoring the restrictions of user-defined Domain Generalization Hierarchies. The evaluation of the new approaches with respect to cost metrics shows that metrics may behave differently with different algorithms and may not correlate with some applications’ accuracy on output data. [Copyright &y& Elsevier]

• We develop improved guidelines for cost evaluation of CCS from industry. • We recommend understanding the impact of possible energy supply strategies on cost. • We provide basis on how to better account for retrofitting costs. • We recommend including case representative transport and storage costs. • We discuss how to better reflect technological maturity for industrial applications. This paper contributes to the development of improved guidelines for cost evaluation of Carbon Capture and Storage (CCS) from industrial applications building on previous work in the field. It discusses key challenges and factors that have a large impact on the results of cost evaluations, but are often overlooked or insufficiently addressed. These include cost metrics (especially in the context of industrial plants with multiple output products), energy supply aspects, retrofitting costs, CO 2 transport and storage, maturity of the capture technology. Where possible examples are given to demonstrate their quantitative impact and show how costs may vary widely on a case-by-case basis. Recommendations are given to consider different possible heat and power supply strategies, as well as future energy and carbon price scenarios, to better understand cost performances under various framework conditions. Since retrofitting CCS is very relevant for industrial facilities, further considerations are made on how to better account for the key elements that constitute retrofitting costs. Furthermore, instead of using a fixed unit cost for CO 2 transport and storage, cost estimates should at least consider the flowrate, transport mode, transport distance and type of storage, to make more realistic cost estimates. Recommendations are also given on factors to consider when assessing the technological maturity level of CCS in various industrial applications, which is important when assessing cost contingencies and cost uncertainties. Lastly, we urge techno-economic analysis practitioners to clearly report all major assumptions and methods, as well as ideally examine the impact of these on their estimates. [ABSTRACT FROM AUTHOR]