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Adoption of integrated MOS based pseudo-resistor (PR) structures instead of using off-chip passive poly resistors for analog circuits in complementary metal oxide semiconductor technology (CMOS) is an area-efficient way for realizing larger time constants. However, issue of common-mode voltage shifting and excess dependency on the process and temperature variations introduce nonlinearity in such structures. So there is dire need to not only closely look for the origin of the problem with the help of a thorough mathematical analysis but also suggest the most suitable PR structure for the purpose catering broadly to biomedical analog circuit applications.

In this work, incremental resistance (IR) expressions and IR range for balanced PR (BPR) structures operating in the subthreshold region have been closely analyzed for broader range of process-voltage-temperature variations. All the post-layout simulations have been obtained using BSIM3V3 device models in 0.18 µm standard CMOS process.

The obtained results show that the pertinent problem of common-mode voltage shifting in such PR structures is completely resolved in scaled gate linearization and bulk-driven quasi-floating gate (BDQFG) BPR structures. Among all BPR structures, BDQFG BPR remarkably shows constant IR value of 1 TΩ over −1 V to 1 V voltage swing for wider process and temperature variations.

Various balanced PR design techniques reported in this work will help the research community in implementing larger time constants for analog-mixed signal circuits.

The PR design techniques presented in the present piece of work is expected to be used in developing tunable and accurate biomedical prosthetics.

The BPR structures thoroughly analyzed and reported in this work may be useful in the design of analog circuits specifically for applications such as neural signal recording, cardiac electrical impedance tomography and other low-frequency biomedical applications.

This paper compares the web accessibility of Indian museum websites assessing the level of compliance to the Web Content Accessibility Guidelines 2.1 (WCAG 2.1). The study aims to assess the accessibility of Indian museum websites in terms of their severity.

The accessibility evaluation of 11 Indian museum websites was conducted using Siteimprove Accessibility Checker (SAC), an automated website testing tool.

The study results indicate that the mean conformance score for the Indian museum websites for level A was 19.27, while for level AA and AAA, it came out to be 2.63 and 4.09, respectively, with the highest severity scores for error (16.3) followed by review (5.81) criteria and warning (3.81). The major findings indicate that the websites of Indian museums poorly conform to web accessibility guidelines.

The study's findings will assist the museum's website creators, managers and administrators in determining the level of conformity as per standard accessibility guidelines helping them make strategic decisions to improve the accessibility.

Most of the studies available in the field of website accessibility range from the web accessibility of educational institutions, tourism websites, municipal websites and ministerial websites, but there are very few studies investigating the accessibility of museum websites. A quantitative evaluation of different aspects of accessibility was conducted in the study, which can pave the way for the better design of web sites by addressing deficiencies.

The study aims to investigate the document selection rationale of library and information science students to satisfy their academic needs.

The descriptive survey design was used for the study using questionnaire as the data collection instrument. The collected data are analysed by frequency distribution and presented using charts and tables.

The results of the study reveal the preferences of Library and Information Science (LIS) students and hints what they want from LIS literature. A majority of students prefer books in comparison to journal articles as reading material. The survey revealed a high preference towards books written by authors from national origin than international authors. Similarly, students prefer books published by the national publishers than the international publishers. The findings further suggest that the affiliation of the author is a major factor, as the students prefer books written by authors from central universities more than those written by authors belonging to state universities or authors from industry. Further, students seem to be more impressed with the books written by LIS academics than the books written by practising librarians.

One limitation of the study is that it was conducted in India, taking the case of LIS students from only two universities. To generalise the impact, similar studies should be replicated by taking a larger sample to gain a complete perspective of LIS students.

The findings of the study have both theoretical and practical implications. The theoretical understanding of the behaviour of LIS students will help faculty members to understand the selection rationale of students. The practical aspect of this study is that the findings will help the acquisition librarians to take informed decisions in collection building for LIS collection in their libraries.

Most of the studies available in LIS literature related to reading preferences, study the general aspects such as frequency of library visit, the favourite place of reading, the purpose of library visit, gender-wise distribution of library visit, and time spent in the library. There are very few studies investigating the document selection rationale of LIS students.

With asymmetric investments in exchange (i.e. sourcing) relationships, both sourcing firms and suppliers invest but one party invests more than the other. This paper aims to examine the associations between asymmetric (i.e. unequal) investments in exchange relationships and the tendency of the strategic supplier base to shirk as perceived by the sourcing firm, as well as the moderation effects of cross-functional information sharing within a sourcing firm on these associations.

The authors analyzed survey data from 500 US middle-market manufacturers via ordinary least squares (OLS) estimation. Besides appropriate controls, the authors also employed the heteroskedasticity-based instrumental variable approach to ensure that analytical inferences are not influenced by endogeneity.

On average, when a sourcing firm invests more than its strategic supplier base into their exchange relationships, the perceived tendency of the strategic supplier base to shirk decreases. This negative association is more pronounced when a sourcing firm facilitates cross-functional information sharing. Conversely, when the strategic supplier base invests more than the sourcing firm into their exchange relationships, the perceived tendency of the strategic supply base to shirk is not detected unless the sourcing firm facilitates cross-functional information sharing.

Prior research reveals that investments by a sourcing firm or by suppliers influence supplier shirking. This paper provides new evidence as to how and why asymmetric investments in exchange relationships relate to the perceived tendency of the strategic supplier base to shirk and new evidence as to how and why cross-functional information sharing safeguards against this tendency when investments in exchange relationships are unequal.

In this paper, the authors introduce supply disruption ambiguity as the inability of a sourcing firm to attach probability point estimates to the occurrence of and to the magnitude of loss from supply disruptions. The authors drew on the “ambiguity in decision-making” literature to define this concept formally, connected it to relevant supply disruption information deficit, positioned it relative to supply chain risk assessment and hypothesized and tested its negative associations with both supply base ties and inventory turnover.

The authors analysed survey data from 171 North American manufacturers and archival data for a subset (88 publicly listed) of these manufacturers via Ordinary Least Squares (OLS) estimation after ensuring that methodological concerns with survey research have been addressed. They used appropriate controls and employed the heteroskedasticity-based instrumental variable (HBIV) approach to ensure that inferences from our results are not unduly influenced by endogeneity.

Strong supply base ties decrease supply disruption ambiguity, which, in turn, increases inventory turnover. Moreover, strong supply base ties and data integration with the supply base have indirect and positive effects on inventory turnover. As sourcing firms strengthen ties and integrate data exchange with their supply base, their inventory turnover improves from access to information relevant to detect and diagnose supply disruptions effectively.

Research on supply disruption management has paid more attention to the “disruption recovery” stage than to the “disruption discovery” stage. In this paper, the authors add novel insights regarding the recognition and diagnosis aspects of the “disruption discovery” stage. These novel insights reveal how and why sourcing firms reduce their overall ambiguity associated with detecting and assessing losses from supply disruptions through establishing strong ties with their supply base and how and why reducing such ambiguity improves inventory turnover performance.

This study examined the impact of the Glasgow Climate Pact on the abnormal returns of global clean energy stocks. Further, this study examines which country-specific and firm-specific variables drive the cumulative abnormal returns (CARs) of clean energy stocks.

The authors used the event study method and cross-sectional multivariate regression model. The clean energy stocks in this study are limited to 81 constituent firms of the S&P Global Clean Energy Index across 17 nations. The final sample includes 80 firms and the sample period ranges from January 26, 2021, to December 07, 2021.

The study finds that the Glasgow Climate Pact negatively affects the stock returns of clean energy firms. Moreover, the climate change performance index (CCPI) positively impacts cumulative abnormal returns (CARs), signifying that clean energy investors react positively to firms in nations with good CCPI scores. The environmental, social and governance (ESG) measure for the shorter window (−1, +1) exhibited a negative relationship with CARs. The firm-specific variables (BTM, stock liquidity, size and past returns) exhibit a negative relationship with CARs in different event windows.

The authors use the CCPI as a proxy for the stringency of environmental policies in any nation. The authors extend the existing literature by employing firm-specific variables and supporting previous findings. Their findings have policy implications for clean energy investors, policymakers and other market participants.

Climate risks impact the global financial market, so the findings have implications for global regulatory bodies. Currently, there are bankruptcy cases due to climate risks. Because financial markets must play a critical role in shifting the economy toward a green one, regulators can use the cross-sectional drivers of this study to shape policy. It is also critical for regulators to reduce stock price volatility in the event of the implementation of environmental regulations and improve environmental disclosures by publicly traded companies. Furthermore, governments are interested in researching the effects of environmental regulations to protect stakeholders' interests. These regulations significantly impact emerging markets because they lack the same solid institutional frameworks as developed markets.

The authors provide evidence that firms with better ESG scores and larger firm sizes have experienced fewer abnormal returns, as these firms have stable financial and non-financial fundamentals. This timely study on the ongoing regulatory shift in environmental policy will help investors, policymakers, firms and other stakeholders make relevant decisions.

The purpose of the present study is to examine the inter-relationship between the multi-dimensional poverty and water poverty using household level data for Indian states.

A modified water poverty index (MWPI) for both rural and urban households was created using the five components approach and various quantifiable proxies. Principal component analysis (PCA) has been used for the construction of MWPI. Multidimensionality of poverty (MPI) is measured using the Alkire and Foster methodology. Further, the study has utilized correlation and Tobit regression analysis to show the relationship between MWPI and MPI.

The empirical findings suggest that there is a positive and significant relationship between multidimensional poverty and water poverty, with the extent of relationship being greater in rural areas. The results show that in rural areas all the components of water poverty has significant impact on multidimensional poverty, whereas in urban areas except use component all others have significant impact on multidimensional poverty. Further, components of multidimensional poverty were also found to be significantly impacting water poverty.

The study suggests that policymakers cannot treat both forms of poverty in isolation. If India aims to reduce poverty, then it needs to pay significant attention to improving water conditions.

This is a pioneering attempt to construct water poverty index at the household level while accounting for micro-level differences for Indian economy. It highlights that water poverty leads to multi-dimensional poverty and vice-versa.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2021-0731.

The purpose of this paper is to provide a picture of the water situation of the states of India and to identify key areas in which intervention is necessary for sustainable development and poverty elevation.

To understand the trend and situation of water across the states, Water Poverty Index (WPI) has been constructed. WPI has been computed for the years 2012 and 2018 to get a picture of temporal change happening in the region. Further, descriptive statistics were used to show the required changes.

Jharkhand and Rajasthan continue to be the worst performer in both time periods. Water poverty was the least in the states of Goa and Chandigarh for both time periods. Although owing to improvement in access and capacity component, the water status of India as a whole improved from 2012 to 2018 but few states have witnessed a decline in their water situation mainly due to deterioration in the environment and resource components.

This paper adds to the relatively scarce literature on the water situation conducted for the states of India. The findings of the paper provide insights into the lacking areas responsible for the deterioration in water poverty status. The results can be utilized for framing proper policies to combat the water woes of the country.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

The purpose of this paper is to identify and analyze the interactions among different enablers of agile manufacturing system (AMS). The existing enablers available in the past literature are scattered and not able to meet specific requirements of the customers. So, it becomes a necessity to encapsulate these enablers in appropriate proportions to enable traditional organizations to AMS. To fill this gap total interpretive structural modeling (TISM) and MICMAC analysis-based framework model have been developed to understand the mutual interactions between among the enablers.

Identification of enablers followed by application of TISM, which is an innovative version of ISM and MICMAC analysis, is used to study and analyze the mutual interactions between identified enablers.

The result reveals that top management support, organizational structure and information technology integration have strong driving power and weak dependence power and are at the lowest level in the TISM model hierarchy, while the outcome enablers of AMS have low-driving power but have high-dependence power.

This model is developed on the basis of inputs from few experts and may not reflect the opinion of whole industry community.

Top management must stress on enablers having strong driving power for efficient implementation of AMS. Managers in the area of manufacturing may drive useful insights from the empirical study presented in this paper. Managers should plan an effective strategy for proper implementation of AMS which makes organization more agile, productive, competitive and profitable.

TISM-based framework structural model has been proposed for industry or organization which is a new effort for implementation of AMS.