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Abiotic stresses, for example, water stress, salinity, chilling, high temperature, and nutrient imbalance may influence the molecular and physiological process of plants in declining crop production. The molecular and physiological changes that hinder crop growth are foremost concerned since it affects directly meeting the global food demand. Abiotic stress reveals a profound impact on plant proteins, including alteration in posttranscriptional, cellular localization, and at the posttranslational levels. Understanding and identifying the role of differentially expressed protein during abiotic stress will be comparatively easier to deal with plant signaling to stresses. A proteomics approach like identification of abiotic stress–specific biomarkers, study of the selected protein accumulation pattern in response to different stresses, and elucidation of functional protein pathways is most important. Currently, gel electrophoresis, in addition to high throughput mass spectrometry (LC-MS-MS, MALDI-TOF) coupled with bioinformatics, is a widely used proteomics approach in plant-protein identification. This chapter focuses on advanced quantitative proteomics approaches like isobaric tag for relative and absolute quantitation (Iraq) and inductively coupled plasma-mass spectrometry approaches used to find novel and differentially expressed proteins. The proteomics approach and findings will enhance our understanding of the cellular processes and the pathways associated with abiotic-stress responses to develop and improve important agronomical traits.

The agricultural is an important sector and shares a major fraction of any nations’ gross national product and, thus, has a key role in the development. Due to the green revolution, crop production has increased to a greater extent in the past decades and simulated to be continued in the coming years. But, at present, one of the challenging issues is food security and maintaining the equitable standard of living for the coming generations. In this regard, one of the major global environmental issues in front of us is human-induced climate change, which threatens our ability to meet the demand. In the past decades, the high growth rate of industrialization, urbanization, and anthropogenic activities has resulted in adverse climate changes, significantly affecting the global temperature, increased greenhouse gases, seasonal variation, and irregular precipitation patterns. According to the Intergovernmental Panel on Climate Change (IPCC) global warming prediction report, the global mean temperature will be increased by 1.0–3.5°C by 2100, and CO2 doubling would be increased temperature by 1.5–4.5°C. Global mean surface temperature anomalies were also recorded to be increasing consistently with time since 1880 to the year 2019; 2016 ranks as the warmest on record (NASA GISS). Thus, increase in temperature and CO2 can be a big challenge in front of farmers and result in a major threat to agriculture and food security. It has the potential to affect agriculture positively as well as negatively in terms of yield, depending upon the variations in various factors. The share of agriculture in global emissions, the need for further global mitigation efforts, and continued projected agricultural emissions growth in many countries all combined together to underline the necessity for stronger and more effective policies. Thus, this chapter is focused on the impact of climate change on the agricultural sector and strategies for mitigating climate change in the agriculture system. In this context, there is an urgent need to analyze the impact of climate change on agriculture productivity and project sustainable approaches to adapt the climate change and meet the future food demand.

Increase in the cost of process water and stringent environmental norms result in the requirement for water reducing technologies in process industries. Batch processes have been widely used in process industry (such as pharmaceuticals, fine and specialty chemicals, etc.) as batch processing is appropriate to produce a specialized low amount of production and is flexible to adjust according to the dynamism of the market. In recent years, significant research efforts are made to minimize resources such as energy and water in batch processes. Optimizing the storage requirement is an essential aspect while considering capital investment and space availability. This paper focuses on minimizing freshwater and storage requirement simultaneously. In this paper, a mathematical formulation is proposed to solve the proposed bi-objectives problem. The primary objective of this work is to generate a Pareto optimal front for these two objectives via using a ϵ-constraint method. The proposed linear programming formulation includes demand satisfaction, source availability and quality constraints. The trade-off between storage and resource requirements is captured through a Pareto optimal front. This Pareto optimal front facilitates decision-makers to select an appropriate operating point as per suitability.

Metals being one of the essential ingredients for plant growth and development play a dual role. At the optimum level, they intend to improve the plants nutritional level and yield while in access being toxic displays, direct and indirect adverse impacts resulting in lower quality and quantity. Nowadays due to industrialization and modernization, metal contamination in agricultural land (soil) is a foremost concern since it directly affects the human health. Most of the edible crops, including tubers, fruits, and vegetables, accumulate metals at the toxic levels since they are grown on metal-contaminated soil. Taking this into consideration, here we address different approaches in horticultural crops to mitigate the effect of metal stress. The use of various chemical immobilization materials such as organic, inorganic, and nanoparticles is generally used approaches. However, eco-friendly and cost-effective microbe-assisted bioremediation and phytoremediation employing hyperaccumulator plants to remove the specific metal contaminants from the soil and water are successful. Additionally, we will discuss metal uptake and transport mechanisms, including important genes involved in metal accumulation and detoxification. Such genes might be useful in future research by improving phytoremediation potential using genetic transformation to mitigate the metal stress in plants.

The quantification of spectral components of electroencephalogram (EEG) signals plays a crucial role in various clinical and scientific real-time applications for brain–computer interface (BCI) systems. For effective implementation of such systems identification of motor imagery (MI) signals from the frequency patterns obtained from various brain oscillations play a key role. The main objective of this chapter is to perform a comparative analysis of various feature extraction techniques and classification algorithms that are best suited for obtaining the MI signals. Linear features such as power spectral density, band power, as well as mean and maximum power of MI signals are analyzed along with some nonlinear features such as correlation coefficient and approximate entropy. An attempt has been made to check the different linear and nonlinear features from the EEG signals. These features are then fed to different classifiers such as support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN) to determine the best features providing maximum separability between different classes. In order to further enhance the classification accuracy, particle swarm optimization (PSO)-based SVM classifier is implemented. Band power features exhibit best performance over all other feature extraction techniques having a classification accuracy of 87.78% and 94.44% using SVM and PSO-SVM classifiers, respectively, for the BCI dataset. The case studies and results validate the efficacy of the proposed approach for real-time neuro-aid applications such as in moving a wheelchair or a robotic arm and other prosthetic devices. Also as the existing algorithms sometimes prove to be computationally slow, therefore, optimization techniques used may aid in enhancing the speed and accuracy of the systems.