Technological Progress and Emergence of Policies with Priorities for the Development of Land-Poor Farmers in Bangladesh

This macro-level research analyzed sequential changes in agricultural policies and evaluated their impacts among various groups of farmers classified based on the land ownership. All supply-side agricultural policies from their origins to current year were divided into four phases where, government supports for agriculture were changed from adverse circumstances support, to direct enormous support, to reform-embedded support, and finally to collaborative support with private sector and Non-government Organizations (NGOs). The changing policies favored all types of farmers among whose reform policies contributed more. The small farmers in the past were not benefited from government policies but they were lately more benefited from coherent policies emphasized on the development of land-poor farmers.

Ligand based sustainable composite material for sensitive nickel(II) capturing in aqueous media

© 2019 Elsevier Ltd. Organic ligand based sustainable composite material was prepared for the detection and removal of nickel (Ni(II)) ion from contaminated water. The ligand was anchored based on the building-block approach. The carrier silica and ligand embedded composite material were characterized systematically. The detection and removal of Ni(II) ion operation was evaluated according to the solution pH, reaction time, detection limit, initial Ni(II) concentration and diverse co-existing metal ions. The detection limit of Ni(II) ion by the proposed composite material was 0.41 μg L-1. The detection and removal of Ni(II) ion was significantly influenced by the solution pH. However, the neutral pH 7.0 was chosen for sensitive and selective detection and removal of Ni(II) ion. The co-existing diverse metal ions were not interfered during the detection and removal of Ni(II) ion because of the high affinity of Ni(II) ion to composite material at the optimum experimental conditions. The Langmuir adsorption isotherm model was selected based on the materials morphology and applied to validate the adsorption isotherms according to the homogeneous ordered frameworks. The adsorption capacity was 199.19 mg g-1 as expected due to the high surface area of material. The adsorbed Ni(II) ion was completely eluted from the composite material with the eluent of 0.50 M HCl and the regenerated material was used in several cycles without deterioration in its initial performances. Therefore, it is expected to that the Facile composite material may hold huge potentials in applications and may be scaled up for commercial applications, including environmental detection and removal of Ni(II) ion

Jatropha Biofuel Industry: The Challenges

Considering environmental issues and to reduce dependency on fossil fuel many countries have politicized to replenish fossil fuel demand from renewable sources. Citing the potential of Jatropha mostly without any scientific and technological backup, it is believed to be one of the most suitable biofuel candidates. Huge grants were released by many projects for huge plantation of Jatropha (millions of hectares). Unfortunately, there has been no significant progress, and Jatropha did not contribute much in the energy scenario. Unavailability of high-yielding cultivar, large-scale plantation without the evaluation of the planting materials, knowledge gap and basic research gap seem to be the main reasons for failure. Thus, the production of Jatropha as a biofuel has been confronted with various challenges such as production, oil extraction, conversion and also its use as a sustainable biofuel. In this chapter, we disclose the challenges and possible remedy for the contribution in the biofuel industry

Tropical Legume Crop Rotation and Nitrogen Fertilizer Effects on Agronomic and Nitrogen Efficiency of Rice

Bush bean, long bean, mung bean, and winged bean plants were grown with N fertilizer at rates of 0, 2, 4, and 6 g N m−2 preceding rice planting. Concurrently, rice was grown with N fertilizer at rates of 0, 4, 8, and 12 g N m−2. No chemical fertilizer was used in the 2nd year of crop to estimate the nitrogen agronomic efficiency (NAE), nitrogen recovery efficiency (NRE), N uptake, and rice yield when legume crops were grown in rotation with rice. Rice after winged bean grown with N at the rate of 4 g N m−2 achieved significantly higher NRE, NAE, and N uptake in both years. Rice after winged bean grown without N fertilizer produced 13–23% higher grain yield than rice after fallow rotation with 8 g N m−2. The results revealed that rice after winged bean without fertilizer and rice after long bean with N fertilizer at the rate of 4 g N m−2 can produce rice yield equivalent to that of rice after fallow with N fertilizer at rates of 8 g N m−2. The NAE, NRE, and harvest index values for rice after winged bean or other legume crop rotation indicated a positive response for rice production without deteriorating soil fertility

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System