Tourist-Group Consideration in Tourism Carrying Capacity Assessment: a New Approach for Saint Martin’s Island, Bangladesh

Everything has a capacity that it can tolerate or afford, beyond which it cannot serve. Tourism development and management should also be based on the recognition of the limits of a destination. As a result, tourist destinations must have their carrying capacity assessed and managed accordingly with a view to protecting them from getting exploited. In assessing tourism carrying capacity of a particular destination, the formulae given by Cifuentes (1992) and recommended by IUCN (Ceballos-Lascuráin, 1996) are widely used around the world. The formulae recognize the ‘necessary distance between two tourists’ as a factor that affects tourist satisfaction; and thus, consider it in assessing physical carrying capacity (PCC) of a destination. But Tran Nghi et al. (2007) argue that, the necessary distance between two groups on a route, as well as distance between two people, affects tourist's comfort and should be considered when assessing tourism carrying capacity (TCC). Tran Nghi et al. (2007) tried to make some adjustments to the formulae and used some techniques to consider tourists as in separate groups- not as an individual in a mass. When it is about assessing carrying capacity of a trekking trail or walking route where tourists have to be in a line one behind another, carrying capacity can be calculated for group of tourists using this adjusted formula. But when it is about a beach or a park where tourists are not in queues or lines- rather scattered in the whole area, carrying capacity cannot be calculated based on tourist-group, -rather as a mass. To calculate TCC of such destinations (beach/park) a new technique is proposed and used on the basic formulae of Cifuentes in this study.  This new technique is applied in calculating the TCC of the Saint Martin’s Island, the only coral island of Bangladesh, as a case study. Keywords: tourism carrying capacity (TCC), Cifuentes' methodology, tourist-group, Saint Martin’s Island

Modeling Emerging Semiconductor Devices for Circuit Simulation

Circuit simulation is an indispensable part of modern IC design. The significant cost of fabrication has driven researchers to verify the chip functionality through simulation before submitting the design for final fabrication. With the impending end of Moore’s Law, researchers all over the world are looking for new devices with enhanced functionality. A plethora of promising emerging devices has been proposed in recent years. In order to leverage the full potential of such devices, circuit designers need fast, reliable models for SPICE simulation to explore different applications. Most of these new devices have complex underlying physical mechanism rendering the model development an extremely challenging task. For the models to be of practical use, they have to enable fast and accurate simulation that rules out the possibility of numerically solving a system of partial differential equations to arrive at a solution. In this chapter, we show how different modeling approaches can be used to simulate three emerging semiconductor devices namely, silicon- on- insulator four gate transistor(G4FET), perimeter gated single photon avalanche diode (PG-SPAD) and insulator-metal transistor (IMT) device with volatile memristance. All the models have been verified against experimental /TCAD data and implemented in commercial circuit simulator

Sensors and low power signal processing

Offers a general overview of a sensor's working principle and a discussion of various sensor technologies including chemical, electro-chemical and MEMS based sensors. This book includes a discussion on design challenges associated with low-power analog circuits and the schemes to overcome them

Energy Harvesting in Implantable and Wearable Medical Devices for Enduring Precision Healthcare

Modern healthcare is transforming from hospital-centric to individual-centric systems. Emerging implantable and wearable medical (IWM) devices are integral parts of enabling affordable and accessible healthcare. Early disease diagnosis and preventive measures are possible by continuously monitoring clinically significant physiological parameters. However, most IWM devices are battery-operated, requiring replacement, which interrupts the proper functioning of these devices. For the continuous operation of medical devices for an extended period of time, supplying uninterrupted energy is crucial. A sustainable and health-compatible energy supply will ensure the high-performance real-time functioning of IWM devices and prolong their lifetime. Therefore, harvesting energy from the human body and ambient environment is necessary for enduring precision healthcare and maximizing user comfort. Energy harvesters convert energy from various sources into an equivalent electrical form. This paper presents a state-of-the-art comprehensive review of energy harvesting techniques focusing on medical applications. Various energy harvesting approaches, working principles, and the current state are discussed. In addition, the advantages and limitations of different methods are analyzed and existing challenges and prospects for improvement are outlined. This paper will help with understanding the energy harvesting technologies for the development of high-efficiency, reliable, robust, and battery-free portable medical devices

Greenhouse Gas Emissions in the Industrial Processes and Product Use Sector of Saudi Arabia—An Emerging Challenge

The Kingdom of Saudi Arabia has been experiencing consistent growth in industrial processes and product use (IPPU). The IPPU’s emission has been following an increasing trend. This study investigated time-series and cross-sectional analyses of the IPPU sector. Petrochemical, iron and steel, and cement production are the leading source categories in the Kingdom. In recent years, aluminum, zinc, and titanium dioxide production industries were established. During the last ten years, a significant growth was observed in steel, ethylene, direct reduce iron (DRI), and cement production. The growth of this sector depends on many factors, including domestic and international demand, socioeconomic conditions, and the availability of feedstock. The emissions from IPPU without considering energy use was 78 million tons of CO2 equivalent (CO2eq) in 2020, and the cement industry was the highest emitter (35.5%), followed by petrochemical (32.3%) and iron and steel industries (16.8%). A scenario-based projection analysis was performed to estimate the range of emissions for the years up to 2050. The results show that the total emissions could reach between 199 and 426 million tons of CO2eq in 2050. The Kingdom has started initiatives that mainly focus on climate change adaptation and economic divergence with mitigation co-benefits. In general, the focus of such initiatives is the energy sector. However, the timely accomplishment of the Saudi Vision 2030 and Saudi Green Initiative will affect mitigation scenarios significantly, including in the IPPU sector. The mitigation opportunities for this sector include (i) energy efficiency, (ii) emissions efficiency, (iii) material efficiency, (iv) the re-use of materials and recycling of products, (v) intensive and longer use of products, and (vi) demand management. The results of this study will support the Kingdom in developing an appropriate climate change mitigation roadmap

A Multivariate Machine Learning Model of Adsorptive Lindane Removal from Contaminated Water

It is challenging to use conventional one-variable-at-time (OVAT) batch experiments to evaluate multivariate/inter-parametric interactions between physico-chemical variables that contribute to the adsorptive removal of contaminants. Thus, chemometric prediction approaches for multivariate calibration and analysis reveal the impact of multi-parametric variation on the process of concern. Hence, we aim to develop an artificial neural network (ANN), and stepwise regression (SR) models for multivariate calibration and analysis utilizing OVAT data prepared through experimentation. After comparing the models’ performance, ANN was the superior model for this application in our work. The standard deviations (SD) between the observed and ANN-predicted values were very close. The average correlation coefficient (R2) between observed and ANN-predicted values for the training dataset was 96.9%. This confirms the ability of our developed ANN model to forecast lindane removal accurately. The testing dataset correlation coefficients (89.9% for ANN and 67.75% for SR) demonstrated a better correlation between observed and predicted ANN values. The ANN model training and testing dataset RMSE values were 1.482 and 2.402, lower than the SR values of 4.035 and 3.890. The MAPE values for the ANN model’s training and testing datasets, 0.018 and 0.031, were lower than those for the SR model. The training and testing datasets have low RSR and PBIAS values, implying model strength. The R2 and WIA values are above 0.90 for both datasets, proving the ANN model’s accuracy. Applying our developed ANN model will reduce the cost of removing inorganic and organic impurities, including lindane, and optimize chemical utilization

A Critical, Temporal Analysis of Saudi Arabia’s Initiatives for Greenhouse Gas Emissions Reduction in the Energy Sector

The per capita greenhouse gas (GHG) emissions of Saudi Arabia were more than three times the global average emissions in 2019. The energy sector is the most dominant GHG-emitting sector in the country; its energy consumption has increased over five times in the last four decades, from over 2000 quadrillion joules in 1981 to around 11,000 quadrillion joules in 2019, while the share of renewable energy in 2019 was only 0.1%. To reduce GHG emissions, the Saudi Arabian government has undertaken initiatives for improving energy efficiency and increasing the production of renewable energies in the country. However, there are few investigative studies into the effectiveness of these initiatives in improving energy efficiency and reducing greenhouse gas emissions. This study provides an overview of the various energy efficiency and renewable energy initiatives undertaken in Saudi Arabia. Then, it evaluates the effectiveness of energy-related policies and initiatives using an indicator-based approach. In addition, this study performs temporal and econometrics analyses to understand the trends and the causal relationships among various drivers of energy sector emissions. Energy intensity and efficiency have improved moderately in recent years. This study will support policymakers in identifying significant policy gaps in reducing the emissions from the energy sector; furthermore, this study will provide a reference for tracking the progress of their policy initiatives. In addition, the methodology used in this study could be applied in other studies to evaluate various climate change policies and their progress

A Critical, Temporal Analysis of Saudi Arabia’s Initiatives for Greenhouse Gas Emissions Reduction in the Energy Sector

The per capita greenhouse gas (GHG) emissions of Saudi Arabia were more than three times the global average emissions in 2019. The energy sector is the most dominant GHG-emitting sector in the country; its energy consumption has increased over five times in the last four decades, from over 2000 quadrillion joules in 1981 to around 11,000 quadrillion joules in 2019, while the share of renewable energy in 2019 was only 0.1%. To reduce GHG emissions, the Saudi Arabian government has undertaken initiatives for improving energy efficiency and increasing the production of renewable energies in the country. However, there are few investigative studies into the effectiveness of these initiatives in improving energy efficiency and reducing greenhouse gas emissions. This study provides an overview of the various energy efficiency and renewable energy initiatives undertaken in Saudi Arabia. Then, it evaluates the effectiveness of energy-related policies and initiatives using an indicator-based approach. In addition, this study performs temporal and econometrics analyses to understand the trends and the causal relationships among various drivers of energy sector emissions. Energy intensity and efficiency have improved moderately in recent years. This study will support policymakers in identifying significant policy gaps in reducing the emissions from the energy sector; furthermore, this study will provide a reference for tracking the progress of their policy initiatives. In addition, the methodology used in this study could be applied in other studies to evaluate various climate change policies and their progress