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Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS).

The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies.

The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R² of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R² of 0.887 and a slight overestimation bias of +0.01.

While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research.

Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs.

Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment.

This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.

The case describes the performance evaluation system that has been put in place by Ravi Kumar, the MD to ensure that Oystar Hassia is able to design, deliver, service, sell its packaging machines seamlessly in all parts of the world. The performance evaluation system is periodic, regular, able to take track the progress of the people within the system. The benefits accrued from performance evaluation system are also detailed in this case.

This paper aims to investigate the influence of advertised reference price (ARP) and sales price (SP) as anchor points on the latitude of expected price, and subsequently on purchase intention (PI). The research involves the theoretical lens of selective anchoring mechanism, which allows investigation of the influence of ARP and SP in a situation where price estimation task is a “non-thoughtful processes”.

On the basis of quasi-experimental design, the study involves intercept survey of 142 shoppers.

The study finds that due to anchoring effect, the highest and the lowest expected prices shift toward ARP and SP, respectively. Consequently, it influences the latitude of expected price, which in turn influences purchase intention. In addition, the study proposes and tests a method to forecast expansion and contraction of the latitude of expected price.

It suggests a new mechanism to understand the simultaneous influence of ARP and SP, provides a mechanism to understand shifts in price latitude’s end-points and investigates a phenomenon with two externally provided anchors.

The study highlights the role of the latitude of expected price in understanding consumers’ response. Results suggest that a plausible ARP, when joined with an above-expectation SP, can fetch better consumer responses.

The study uniquely investigates a problem with two anchor points and two estimation targets, and proposes a construct of internal price uncertainty (IPU).

Mangroves constitute an important ecosystem because of their global extent and high productivity. These plants thrive in the intertidal zones of the tropics and subtropics that are characterized by regular tidal inundation and fluctuating salinity. Mangrove species are well adapted, both morphologically and physiologically, to survive under saline conditions, but in hypersaline environment their growth is reduced. The present chapter is a critical analysis on the impact of salinity on the growth of a common mangrove species (Hertiera fomes). The analysis has been carried out in the framework of Indian Sundarbans, which has contrasting salinity profiles in different segments owing to barrage discharge and siltation phenomena. Analysis of the decadal profile of salinity indicates a gradual lowering in the western Indian Sundarbans due to Farrakka barrage discharge and run-off from catchments. The central sector, however, exhibits a contrasting picture of increment of aquatic salinity through time, mainly due to disconnection of the Bidyadhari River with the Ganga–Bhagrirathi–Hooghly River system (in the western part). This has made the Matla River in the central Indian Sundarbans hypersaline in nature (that used to get water from the Bidyadhari River) finally leading to an insecure ecological condition for the growth and survival of mangroves. The possible remedial measures to combat the situation have also been listed considering the ecological framework of the study zone.