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A third-order discrete time sigma delta modulator (SDM) is proposed with optimum performance by addressing instability and power dissipations issues, and a novel SDM architecture is designed and verified in behavioural modelling in MATLAB/SIMULINK environment. Simulation results show that performance parameters of proposed modulator achieved SNR of 105.41 dB, SNDR of 101.96 dB and DR of 17 bits for the signal bandwidth of 20 kHz.

This paper describes single-loop SDM design with optimum selection of integrator weights for physiological signal processing in IoT applications.

The proposed discrete time modulator designed with 1-bit quantizer and optimum oversampling ratio proved as power efficient. Integrator scaling coefficients are generated in LabVIEW environment for pure third-order noise shaping.

This paper contains the novelty in the work, and it is suitable for cognitive Internet of Things applications.

This paper attempted a novel approach for system-level modeling and simulation of sigma-delta modulator for low-frequency CMOS integrated analog to digital interfaces. Comparative analysis of various architectures topologies, circuit implementation techniques are described with analytical procedure for effective selection of topologies for targeted specifications.

Virtual instruments are presented in labview environment to analyze the correlation of circuit-level non-ideal effects with key design parameters over sampling ratio, coarse quantizer bits and loop filter order. A fourth-order single-loop sigma-delta modulator is designed and verified in MATLAB simulink environment with careful selection of integrator weights to meet stable desired performance.

The proposed designed achieved SNDR of 122 dB and 20 bit resolution satisfying high-resolution requirements of low-frequency biomedical signal processing applications. Even though the simulation performed at behavioral level, the results obtained are considered as accurate, by including all non-ideal and non-linear circuit errors in simulation process.

Virtual instruments using labview environment used to analyze the correlation of circuit-level non-ideal effects with key design parameters over sampling ratio, coarse quantizer bits and loop filter order for accurate design.

Rapid urbanization and development of pilgrimage cities cause significant problems for the environment and society, leading to long-term challenges. Despite several discussions on city sustainability, the literature does not address some of the specific problems of pilgrimage cities. Hence, this study attempts at developing a method to examine the growth pattern and sustainability of pilgrimage cities in southern part of India.

The benchmarking method and the social, economic and environmental dimensions of sustainability are considered to construct the Pilgrimage City Sustainability Index (PCSI). Appropriate variables and categories are identified through a literature review and expert opinion survey. The benchmark values of the variables are derived by contemplating the pilgrimage cities of Tamil Nadu, one of the states with the largest tourist arrivals in India. Subsequently, three prominent pilgrimage cities from Tamil Nadu were chosen for the case study and the method was tested.

The result reveals that the cities investigated are performing above average in the sustainability index, with slight variations in their dimension scores. While the category scores of cities assist in identifying macro-level issues, the variable scores provide an insight into micro-level issues. Furthermore, the gap analysis between the benchmark and the present value of each variable discloses the immediate area of attention in each city. Thus, the cities could set more specific targets, frame strategies and/or collaborate with matching cities to bridge these gaps.

This index assessment provides a comparison of the pros and cons of these pilgrimage cities and helps identify their demand and supply. Policymakers can find appropriate tools and approaches that aid in sustainable urban development and tourism management.

To the best of our knowledge, this is the first study in emphasizing the application of the benchmarking method to assess the sustainability of Indian pilgrimage sites. With appropriate modifications, this method can be used in varied contexts across the globe.

The purpose of this paper is to examine the impact of market timing and pseudo market timing on equity issuance decisions of IPOs in an emerging economy – India. Indian new issues market provides a perfect setting to test market timing against pseudo market timing due to two reasons. First, the US literature shows that most underpriced IPOs are highly overvalued and in India, the authors have the evidence of underpricing of IPOs. But whether Indian IPOs are overvalued or not it is yet to be tested. Second, majority of IPOs were issued in India only after the 1991 economic reforms which may signal the evidence for pseudo market timing hypothesis.

The authors use direct test to examine the impact of market timing and pseudo market timing variables on the IPO activity. The direct tests of market timing and pseudo market timing hypotheses are based on the positive relation of market timing variables and market conditions variables with IPO activity. The authors examine the long-run performance of IPOs by using the calendar-time regression approach to test market timing against pseudo market timing. This serves as indirect test of market timing and pseudo market timing. Evidence of market timing using indirect test shows that there is a decline in the long-run stock performance of IPOs.

The results show that in India, firms issue equity not just due to market conditions but they also issue equity in order to time the market. The results of market timing are also supported by the calendar-time approach results. However, the authors find that the evidence of market timing is stronger for hot issue markets as compared to cold issue markets.

This is the first study to comprehensively examine market timing and pseudo market timing using direct and indirect tests for an emerging market context.

The purpose of this paper is to study the reallocation of initial public offering (IPO) shares to retail investors, non-institutional buyers (NIBs) and qualified institutional buyers (QIBs). The authors examine how the reallocation process is related to the pricing decision of the underwriter. The authors also examine the long-run performance of the IPOs classified on the basis of the highest reallocation by retail investors, NIBs and QIBs.

The authors use regression analysis as well as 2SLS and three-stage least squares models to test the hypotheses. For long-run performance analysis, the authors adopt Carhart’s (1997) four-factor model.

First, the authors provide evidence that the reallocation of IPO shares for retail investors, NIBs and QIBs is frequent. Second, all three categories of investors are treated differently in the reallocation of underpriced shares. Third, the authors find that the reallocation and pricing strategies are interdependent and both the strategies are used by the underwriter to reward and favor retail investors for showing high level of demand. The authors find that in India, underwriters reward retail investors. Lastly, even though underwriters favor retail investors for reallocation, the authors find that IPOs which receive highest reallocation to retail investors perform poorly in the long run.

This paper is the first paper to show evidence of reallocation of IPO shares by underwriters for an emerging market. The paper is different from other papers as the regulatory regime present in the Indian markets is different from other markets.

Despite carbon reduction commitments, many constraints hinder the delivery of low-carbon buildings (LCBs) in high-rise high-density cities. The collaborative commitment of relevant stakeholders is vital to effectively address and mitigate these constraints. Hence, this study aims to comprehensively explore the required stakeholder collaboration attributes to address and mitigate the “common” constraints of delivering LCBs by focussing on several high-rise high-density cities.

A list of 21 “significant and common” constraints was identified through a systematic literature review followed by a questionnaire survey covering five economies (Hong Kong, Singapore, Australia, Qatar and the UAE). Nineteen influential stakeholders/stakeholder categories were identified through the literature, and their ability to influence the 21 constraints was mapped and identified through a two-round Delphi survey of 15 experienced professionals. The Delphi survey findings were analysed through social network analysis (SNA) methods to assess the stakeholder engagement and collaboration attributes.

The SNA results revealed the ability of stakeholders to influence the constraints, required collaborative stakeholder networks to address the constraints, significance of stakeholders according to the SNA centrality measures, core and periphery stakeholders and individual co-affiliation networks of core stakeholders.

While achieving the planned primary target of exploring stakeholder collaboration and their significance through SNA, this study also presents a useful sequential methodological approach for future researchers to conduct similar studies in different contexts. The findings also provide a foundation for accelerating the delivery of LCBs by strengthening stakeholder collaboration.

Vast areas have been studied toward combustion and emission analysis in vegetable oil methyl esters and quite a few in algal oil biodiesel. To analyze the better alternate source for diesel engine, this study aims to investigate the combustion behavior and emission characteristics between cottonseed biodiesel and algal oil biodiesel on comparison with mineral diesel in a compression ignition engine.

The fuel properties like density, kinematic viscosity, calorific value and Cetane number have met the biodiesel standards for both algal and cottonseed biodiesel. At rated power, engine was operated on all three test fuels, where combustion analysis describing in-cylinder pressure, peak pressure, rate of pressure rise and rate of heat release and emission characteristics including hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx) and smoke for both biodiesel comparing mineral diesel.

Algal and cottonseed biodiesel showed up to 2-3°CA delayed start of combustion comparing mineral diesel curve. The in-cylinder pressure of algal biodiesel was found to be 68 bar, whereas cottonseed biodiesel exhibited 65 bar at full load condition. Similarly, the rate of pressure rise and rate of heat release of algal biodiesel depicted 7.9 and 10.7 per cent rise than cottonseed biodiesel, respectively. As the load increased, ignition delay showed decreasing trend, while combustion duration showed an increasing trend. HC, CO and smoke emissions were seen to be lower than mineral diesel with noticeable increase in NOx emission.

In this present investigation, biodiesel from Stoechospermum Marginatum, a marine marco algae, was used to fuel the compression ignition engine. Its combustion behavior and emission characteristics are compared with cottonseed biodiesel, a vegetable oil-based biodiesel having similar physio-chemical characteristics to understand the suitability of algal biodiesel in compression ignition engine. This study involves the assessment of straight biodiesel from macro algae and cottonseed oil on standard operating conditions.

Through the use of the Markov Decision Model (MDM) approach, this study uncovers significant variations in the availability of machines in both faulty and ideal situations in small and medium-sized enterprises (SMEs). The first-order differential equations are used to construct the mathematical equations from the transition-state diagrams of the separate subsystems in the critical part manufacturing plant.

To obtain the lowest investment cost, one of the non-traditional optimization strategies is employed in maintenance operations in SMEs in this research. It will use the particle swarm optimization (PSO) algorithm to optimize machine maintenance parameters and find the best solutions, thereby introducing the best decision-making process for optimal maintenance and service operations.

The major goal of this study is to identify critical subsystems in manufacturing plants and to use an optimal decision-making process to adopt the best maintenance management system in the industry. The optimal findings of this proposed method demonstrate that in problematic conditions, the availability of SME machines can be enhanced by up to 73.25%, while in an ideal situation, the system's availability can be increased by up to 76.17%.

The proposed new optimal decision-support system for this preventive maintenance management in SMEs is based on these findings, and it aims to achieve maximum productivity with the least amount of expenditure in maintenance and service through an optimal planning and scheduling process.

The conventional textile dyeing process requires various operational characteristics, and determining the most reliable factor in dyeing performance has always been a challenge for the textile industry. Thus, the present paper aimed to evaluate the process sensitivity of C. I. Reactive Blue 194 dyeing of cotton fabric using a statistical technique.

An L₂₇ orthogonal array-based Taguchi's methodology was used with six parameters and three levels of each parameter. The signal-to-noise (S/N) ratio and analysis of variance were studied using total fixation efficiency (T%) as the response of the process sensitivity.

Results showed that dyebath pH was the most influential factor on the process and total fixation efficiency (p-value = 0.00 and contribution percentage 45.03%), followed by dye-fixing temperature, dye mass, electrolyte concentration, dye-fixing time and material to liquor ratio.

Overall this study provides a foundation for the determination of dyeing process sensitivity that will be useful in textile industries toward further development.

The purpose of this study is to fabricate and compare the mechanical and thermal properties of Sansevieria and Kaans fiber reinforced polyester matrices composites.

Treated Sansevieria and Kaans fiber was used as reinforcement for the fabrication of polymer matrix composites. Kaans fiber, which was available plenty in the delta region, but physical and mechanical properties of Kaans fiber were low when it compared with Sansevieria fiber. To make use of Kaans fiber for the fabrication of composite, the physical and mechanical properties have to be enhanced. So Egg shell powder was selected as a filler material to enhance the Kaans fiber reinforced composite. The selected fibers were properly weaved after alkali treatment. A three-layered (0°/45°/0°) Sansevieria fiber reinforced polymer (S-FRP) and Kaans fiber reinforced polymer (K-FRP) composite plates were fabricated using the compression molding method. As per American Society for Testing and Materials standards, the specimens were cut and mechanical, thermal and absorption properties of Sansevieria and Kaans fiber composites were investigated experimentally.

Tensile and flexural test reveals that K-FRP composite has good ductility and bending property than S-FRP composite plate. But from the other test results, S-FRP possesses high elongation capability than K-FRP. Thermo gravimetric analysis, moisture absorption and swelling test too done which clearly appeared S-FRP composite plate has prevalent execution than K-FRP composite plate.

This original research study enlists the mechanical, thermal properties and absorption properties of fabricated S-FRP and K-FRP composite plates.