Impacts of organizational arrangements on conservation agriculture: insights from interpretive structural modeling in Iran

Conservation agriculture (CA) has been promoted worldwide as an approach to sustainable resource management and better productivity. Promotion and adoption of CA in Iran have been receiving increased attention from the national government over recent years. Therefore, to speed up development of CA as a basis for sustainable development, drivers that influence the development of CA need to be identified and modeled. The main aim of this study is to present a comprehensive model for CA development in Iran by identifying the institutional drivers that influence its promotion and determining the relationship between drivers. At first, the drivers identified from the literature and interviews with experts, and the relationships among the drivers were explored and clarified using Interpretative Structural Modeling (ISM). A cross-impact matrix multiplication was applied to classification (MICMAC) analysis, which was then used to categorize the drivers in four sub-groups. The results showed that creating a suitable organizational structure is a very significant driving factor for CA development in Iran. Strong driving power and weak dependence associated with this factor should be treated as a critical driver. If CA shall expand more rapidly in future, then Iran’s government should invest in an appropriate organizational structure for it

The Measurement of Technical Efficiency and Effective Factors in Cucumber Greenhouse (Case Study: Eastern Azarbayjan Province)

The purpose of this study was to estimate technical efficiency of cucumber greenhouses in Eastern Azarbayjan. In economic literature, it means the ratio of maximum output to the inputs. The objective of this research was to determinate the effective factors influencing it's inefficiency. The method of determination of deterministic and stochastic technical efficiency is corrected ordinary least squares (COLS) and maximum likelihood (ML) respectively. The average of technical efficiency in province’s cucumber greenhouse is approximately about 57 and 93 percent for deterministic and stochastic frontier method respectively. Production types had positive influence on technical inefficiency whereas experience of manager have negative influence on technical inefficiency

Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding

In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve a decision-making process at each step, including bit-flipping (BF) decoding, residual belief propagation, and anchor decoding. We then illustrate how such algorithms can be mapped to Markov decision processes allowing for data-driven learning of optimal decision strategies, rather than basing decisions on heuristics or intuition. As a case study, we consider BF decoding for both the binary symmetric and additive white Gaussian noise channel. Our results show that learned BF decoders can offer a range of performance-complexity trade-offs for the considered Reed-Muller and BCH codes, and achieve near-optimal performance in some cases. We also demonstrate learning convergence speed-ups when biasing the learning process towards correct decoding decisions, as opposed to relying only on random explorations and past knowledge

Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding

In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve a decision-making process at each step, including bit-flipping (BF) decoding, residual belief propagation, and anchor decoding. We then illustrate how such algorithms can be mapped to Markov decision processes allowing for data-driven learning of optimal decision strategies, rather than basing decisions on heuristics or intuition. As a case study, we consider BF decoding for both the binary symmetric and additive white Gaussian noise channel. Our results show that learned BF decoders can offer a range of performance-complexity trade-offs for the considered Reed-Muller and BCH codes, and achieve near-optimal performance in some cases. We also demonstrate learning convergence speed-ups when biasing the learning process towards correct decoding decisions, as opposed to relying only on random explorations and past knowledge

Digital transmission techniques

This chapter discusses the single carrier modulation techniques which have been proposed and used in first generation narrowband PLC systems. It emphasizes the recent insight and research focusing first on frequency/phase shift keying combined with permutation coding. The chapter covers multicarrier modulation which is at the heart of latest narrowband and broadband PLC systems. Several known and more recent schemes are here described including OFDM, FMT, Pulse-shaped OFDM, Wavelet OFDM, OQAM-OFDM, and Cyclic block FMT. The voltage and current modulation is discussed as a simple and effective means to transmit data at low speed and over long distances. If multiple wires are available, multiple input multiple output (MIMO) transmission schemes can be used to exploit spatial diversity. The chapter also discusses the noise mitigation and error control techniques currently found in various PLC protocols and standards, as well as other promising techniques that are still only found in the literature