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Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

It is always of great importance for managers in organizations to evaluate their staff members and create incentive systems, using instruments such as Data Envelopment Analysis (DEA) and DEA-R (DEA models based on ratio analysis). The purpose of this paper is to propose a two-stage network incentives system for commercial banks.

Centralized Resource Allocation (CRA) models make it possible to project all decision-making units (DMUs) onto the efficient frontier by solving a single linear programming model. In this paper, we use our proposed DEA-R-based CRA models to evaluate commercial banks in a two-stage case when the only ratios available are the assets-to-costs and income-to-assets vectors.

Thirteen commercial banks modeled as two-stage networks were evaluated by the models proposed in two different cases of ratio data. Results suggest that the proposed methodology yields more accurate efficiency scores, thus allowing better discrimination among DMUs. Furthermore, evaluating the DMUs when they are structured as two-stage (or even three-stage) networks makes it possible to examine the incentives system in more detail. Therefore, the use of incentive systems by managers would allow a better focus on the priority activities of commercial banks and a faster movement toward the frontier of best practices.

The super-efficiency scores of a number of commercial banks are evaluated based on the CRA model, as a cornerstone criterion for the two-stage evaluation in DEA-R, thus allowing the rank of each commercial bank in terms of the incentives system rather on the performance of the productive process.

A Nash bargaining game data envelopment analysis (NBG-DEA) model is proposed to measure the efficiency of dynamic multi-period network structures. This paper aims to propose NBG-DEA model to measure the performance of decision-making units with complicated network structures.

As the proposed NBG-DEA model is a non-linear mathematical programming, finding its global optimum solution is hard. Therefore, meta-heuristic algorithms are used to solve non-linear optimization problems. Fortunately, the NBG-DEA model optimizes the well-formed problem, so that it can be solved by different non-linear methods including meta-heuristic algorithms. Hence, a meta-heuristic algorithm, called particle swarm optimization (PSO) is proposed to solve the NBG-DEA model in this paper. The case study is Industrial Management Institute (IMI), which is a leading organization in providing consulting management, publication and educational services in Iran. The sub-processes of IMI are considered as players where their pay-off is defined as the efficiency of sub-processes. The network structure of IMI is studied during multiple periods.

The proposed NBG-DEA model is applied to measure the efficiency scores in the IMI case study. The solution found by the PSO algorithm, which is implemented in MATLAB software, is compared with that generated by a classic non-linear method called gradient descent implemented in LINGO software.

The experiments proved that suitable and feasible solutions could be found by solving the NBG-DEA model and shows that PSO algorithm solves this model in reasonable central process unit time.

The purpose of this paper is to develop a simultaneous measurement of overall performance and its two dimensions of efficiency and effectiveness in the case of Chinese construction industry.

A relational two-stage data envelopment analysis (DEA) method, which builds a relationship between component stages and can effectively identify inefficient stages, is developed and applied in order to measure overall performance, efficiency and effectiveness.

The construction industry of the Eastern region in China demonstrated the best results for overall performance, efficiency and effectiveness. The gaps between regions were primarily reflected in differences of pure technical efficiency. Performance indicators in the whole construction industry improved steadily and but could be improved more effectively. The coefficients of variation became smaller and more well-balanced across the whole industry.

Improving overall performance should focus on promoting construction efficiency at the project level and increasing management effectiveness at the company level. Sustainable development policies, which may include large investment and preferential policies, can narrow performance differences among the regions’ construction industries, and ultimately promote overall performance for the whole industry.

The relational two-stage DEA model is further developed in a variable returns-to-scale condition. The developed approach is generic and can provide a pathway for simultaneously measuring performance, efficiency and effectiveness and to recognise competitive advantages for promoting sustainable development.

The design method of high-resolution capacitor arrays was proposed to improve the precision of successive approximation register (SAR) analog-to-digital converters (ADCs) without calibration and optimize the circuit area.

According to calculation of equivalent series capacitors and change of voltage at the comparator input node, two three-stage structures of capacitor arrays and a general design flow of the multi-stage capacitor arrays were presented. Non-ideal factors on the capacitor arrays were analyzed, and the applications of the two structures were explained based on the capacitor mismatch.

A multi-stage capacitor array for 16-bit SAR ADCs was implemented. The simulation result shows that its nonlinear error was less than 0.3LSB with no gain error and the sampling capacitance accounted for 92.42% of the total capacitance. Effects of capacitive parasitic and mismatch on capacitor arrays were confirmed.

The proposed method focused on capacitor arrays design of high-resolution SAR ADCs. It effectively reduced nonlinear errors, improved SNR and optimized the area of SAR ADCs. The design method was suitable for SAR ADCs with different resolutions to improve their precision.

The purpose of this paper is to examine the industrial production efficiency, pollution treatment efficiency, total factor energy efficiency and water efficiency in China with the consideration of technological innovation. This study also explores the distribution proportion of technological innovation between industrial production substage and pollution treatment substage.

A nonparametric method, data envelopment analysis (DEA), is used as the model foundation of this study. Specifically, a novel two-stage range-adjusted measure (RAM-DEA) with shared inputs is constructed to analyze the China’s industrial system. In this study, the panel data of 30 provinces from 2008 to 2015 are used.

This study found that although the current environmental regulation reduced the efficiency of industrial production, it could significantly improve the pollution treatment level. However, the lack of pollution treatment capacity was still an obstacle for development of China's industrial system. Compared with the total factor energy efficiency, the total factor water efficiency had more room for improvement. The optimal distribution of technological innovation in the two substages performed little change and the distribution roughly followed the “three-seven principle”.

More attention should be paid to improve the pollution treatment level and total factor water efficiency. And more R&D expenditure should be used in the industrial production substage in the eastern coastal areas, while in the inland areas, more R&D expenditure should be used in the pollution treatment substage.

This study proposed a model to environmental efficiency score with considering interval data under two-stage evaluation structure, which could strengthen the theory and expand the application scope of DEA approach.

The banking industry, which is one of the most significant industries when taking into account both deposit sizes and employment statistics in Turkey, is one of the country's primary economic drivers. In this regard, it is highly important to evaluate banks as it is necessary to present to what extent they use their resources efficiently. The main purpose of the study is to analyze the efficiencies of Turkish banks by the two-stage data envelopment analysis (DEA) and Malmquist productivity index (MPI).

The authors aim to analyze both the efficiency and productivity of Turkish banks by two-stage DEA and the MPI, which enable decomposing into sub-sections of production processes. Hence, more detailed insight into the Turkish banking system can be presented through two-stage efficiency and production approaches.

DEA results indicate that two out of three state-owned banks achieved resource efficiency while none of the investigated banks performed profit efficiency throughout the investigated period. Besides, average resource efficiency is found higher than average profit efficiency in Turkish banks. MPI results reveal that both technological and technical improvement prospects exist for Turkish banks.

The original contribution of this paper is to employ two-stage DEA and the MPI, which reflect both the static and dynamic performance of the Turkish banking sector. In this regard, this study aims to be a pioneer by both reflecting the static and dynamic performance analysis of Turkish banks.

Very large-scale integration (VLSI) digital signal processing became very popular and is predominantly used in several emerging applications. The optimal design of multirate filter with improvement in performance parameters such as less area, high speed and less power is the challenging task in most of the signal processing applications. This study aims to propose several effective multirate filter structures to accomplish sampling rate conversion.

The multirate filter structures considered in this work are polyphase filter and coefficient symmetry-based finite impulse response filter. The symmetry scheme particularly brings down the complexity to significant extent. To bring improvement in speed, delay registers are inserted at appropriate path with the help of pipelining and retiming scheme.

In this paper, the three tasks have been considered. First, the polyphase coefficient symmetry and modified polyphase (MP) structure is designed. Second, the pipelining is applied to the polyphase structure and the obtained results are compared with the polyphase structure. In third, retiming is applied to the polyphase structure and the performance comparison is carried out. The structures are realized for various orders, and the comparative analysis is carried out with the filter order N = 12, 30, 42, 8, 11 and 24 and the results are stated. The performance of all the accomplished structures is analyzed using Altera Quartus with the family cyclone II, device EP2C70F672C6. The results show that the multirate filter using pipelining and retiming offers better performance when examining with the conventional structures. Retimed and pipelined MP structure achieves a speed enhancement of about 33.81% when examining with the conventional polyphase (CP) structure with retiming and pipelining for N = 24 and M = 5. Likewise, the 2/3 structure of pipelined coefficient symmetry approach offers area reduction of about 54.76% over 2/3 structure of pipelined polyphase approach for N = 30 with little reduction in power. The fine grain pipelined and retimed MP structure with N = 11 and M = 3 avails critical path delay reduction of about 28.15% when examining with the corresponding fine grain pipelined and retimed CP structure.

The proposed distinct structures offer better alternative to conventional structures because of the symmetric coefficients, performance enhancement using pipelining and retiming based rate conversion structures. The suggested structures can be used for achieving different rates in software radios.

Each production unit is responsible for the protection of the environment. The restricted undesirable production effects lower environmental damage. This paper emphasizes a proportional reduction of the undesirable outputs, and it supports the growth of desirable outputs as much as possible as well. The two-stage proposed model not only considers the viewpoint of the managers to follow the environmental regulations but also it assigns some bounds on producing undesirable factors according to international environmental protocols. Additionally, the restricted bounds on the undesirable outputs, in both stages, enhance the discriminatory ability of the model.

Two-stage network structure based on Data Envelopment Analysis (DEA) is applied as the main methodology for this paper. The advantages of the proposed model are appointed to assess the environmental units.

Comparing with the existing models, the proposed approach presents a new two-stage model to deal with the environmental issues. Furthermore, the discriminatory ability of the efficiency scores is improved. The distribution of this model is greater than the existing ones.

This paper is fully written, submitted and revised during limitations caused by coronavirus .

The proposed method is employed in two different cases. The efficiency scores of 25 power plants and 13 poultry farms are determined. In fact, the undesirable outputs never meet zero in the process of production but they can be reduced. The results of this research support the effect of the undesirable factors' restriction on the reduction scenario. Both of the examples show that imposing the upper bounds for the undesirable products provide low-efficiency results in comparison with the existing model. On the other hand, the results cover the arguments of sustainability in the evaluation of environmental efficiency.

In the production process, desirable outputs and undesirable factors are produced jointly so undesirable factors never meet zero. This paper develops a new two-stage method to reduce the undesirable outputs at each stage. First, the model confirms the reduction of undesirable outputs. Second, this model imposes restrictions on intermediate and final undesirable outputs according to environmental rights and the concerns of the managers. The model increases the discrimination of the efficiency assessment of real-life two-stage environmental systems as well. Then it focuses on the production of desirable outputs. The new objective function is defined according to the aim of the proposed model that not only declares better efficiency decomposition to the individual system but also the efficiency score is evaluated for each stage.

As returns to scale (RTS) describes the long run connection of the changes of outputs relative to increases in the inputs, the purpose of this study is to answer the following questions: If the proportionate changes exist in the inputs, what is the rate of changes in outputs with respect to the inputs’ variations in the two-stage networks over the long term? How can the authors investigate quantitative RTS in the two-stage networks? In other words, the purpose of this research is to introduce a different approach to estimate the performance, RTS and scale economies (SE) in network structures.

This paper proposes a novel non-radial approach based on data envelopment analysis to analyze the performance and to investigate RTS and SE in two-stage processes.

The findings show that the range adjusted measure (RAM)/RTS approach can identify reference sets for overall systems and each stage. In addition, the models presented in this paper can classify decision-making units and determine the increasing/decreasing trends of RTS.

The majority of previous RTS studies have been examined in black-box structures and have been discussed in a radial framework. Therefore, in this study, RTS and SE in the two-stage networks are dealt with using an extended RAM approach. Actually, the efficiency and RTS for each stage and the overall model are calculated using the proposed technique.