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This paper aims to propose a new lector-based domino and examine it with inputs and clock signal combination in a 45-nm dual-threshold footerless domino circuit for reduced leakage current.

In this technique, p-type and n-type leakage control transistors (LCTs) are introduced between pull-up and pull-down networks, and the gate of one is controlled by the source of the other. A high-threshold transistor is used in the input for reducing gate oxide leakage current, which becomes dominant in nanometre technology. Simulations were based on a 45-nm BISM 4 model using an HSPICE simulator for proposed domino circuits.

The result shows that CHIL (clock high and input low) state is ineffective for lowering leakage current and the conventional CHIH (clock high and input high) state is only effective to suppress the leakage at low temperature for wide fan-in domino circuits. At high temperature, CLIL (clock low and input low) state is preferable to reduce the leakage current for low fan-in domino, but for high fan-in domino, CHIH state is preferred. The proposed circuit technique for AND2, OR2, OR4 and OR8 circuits reduces the active power consumption by 50.94 to 75.68 per cent and by 64.85 to 86.57 per cent at low and high die temperatures, respectively, when compared to the standard dual-threshold voltage domino logic circuits.

The research proposes a new leakage reduction technique used in domino circuits and also evaluates the state for leakage reduction which can be used for low-power dynamic circuits.

Drawing on the results of the previous chapters, this chapter looks at current progress in terms of climate disaster risk incorporation into development planning and practice at three levels (national government, municipalities, and communities) and analyzes gaps, challenges, and opportunities. The chapter also discusses potential factors for enhancing local disaster risk management (DRM) capacity by collaborating with three levels of stakeholders.

The global research evidences indicate that the technology adoption in case of agribusiness has a potential to enhance the performance and bring operational efficiency. India is the world’s largest producer as well as consumer of milk but struggles with yield per cattle, overall productivity, low rate of technology acceptance and adoption, health detection of milching units, animal data recording and presence of dairy products in the global market. The purpose of this study is to focus on identifying the challenges of technology adoption in dairy farms and constructing a hierarchical model using soft systems methodology.

This study uses nominal group technique-based discussion with domain experts and personal interviews with dairy farm owners/managers for the identification of challenges, fuzzy interpretative structural modeling as well as FMICMAC to develop a hierarchical model of challenging elements and to divide the identified elements into four categories based on the dominance of driving-dependence power.

This research has developed a list of 12 challenges affecting the technology adoption in a dairy farm business unit, identified through the personal interviews with 60 dairy farms across three highest milk-producing states of India in terms of annual milk output – Haryana, Punjab and Uttar Pradesh. Lack of government support followed by lack of educational opportunities in dairy-based education were found as the most crucial and high driving challenges, whereas high cost, huge investment and low acceptance of decision-maker were found as the most dependent challenges of technology adoption.

This research is one step ahead of interpretive structural modeling that considers the fuzzy-based dominance in the model to showcase the degree of relationship along with its existence, but it lacks to statistically validate the findings using techniques like SEM.

This paper has developed a list of challenges in adoption of technology along with their inter-relationships to highlight the required focus challenge that drives or is dependent on the other challenges. The goal is to bring performance improvement and assist Indian dairy farm business stakeholders or decision-makers in formulating strategic and action plans and help policy planners to make favorable policies based on the understanding of contextual relationship between challenges.

In Indian context, dairy is an important part of agriculture sector, and milk is an essential item that facilitates income generation to small and rural households and a source item for several other businesses and activities. The results of this research suggested the policy planners and government to ensure subsidized and insured technologies, training support and facilities, educational opportunities and efforts for promotion of technology adoption among dairy farmers. The suggestions are purely on the basis of the relevance of challenges in the hierarchy and can play a significant role in improving the level of technology adoption and can ultimately uplift the social and economic well-being from micro-level of farmers to macro-stage concerning economic development of India.

To the best of the authors’ knowledge, this study is purely original and outcome of the research conducted by authors.

The purpose of the study was to assess the nutritional and health status of Saharia and non-Saharia women.

The present study was undertaken to compare the nutritional status of Saharia versus non-Saharia women in Baran district, Rajasthan. The sample comprised married non-pregnant and non-lactating (NPNL) women (aged 18-35 years) from three groups, that is, Saharia (n = 100), non-Saharia (Meena tribe, n = 100) and general category (n = 30). The general category women, or reference group, were selected as the control group belonging to the same region. The data included general profile, physical measurement, biochemical hemoglobin estimation, dietary and nutrient intake assessment.

The mean hemoglobin value in Saharia (8.3 ± 1.4 g/dl) and Meena (8.1 ± 1.4 g/dl) women was found to be significantly lower (p < 0.01 at 99 per cent confidence level) than that of the reference group (9.5 ± 1.4 g/dl) and much below the standard value of 12 g/dl. Chronic energy deficiency (BMI < 18.5) was more prevalent in Saharia women (68 per cent) followed by Meena (∼24 per cent) than reference women (7 per cent). Only 29 per cent Saharia women were under normal BMI and majority of the reference group women (77 per cent) and Meena women (72 per cent) had normal BMI (18.5-24). Nutrient and dietary intake of both the tribal women groups were low when compared with suggested levels. In Saharia and Meena women, magnesium and thiamine were significantly higher (p < 0.01) and other nutrients were significantly lower (p < 0.01) than recommended dietary allowances.

Anemia is prevalent in all categories of women. Women’s health is poor especially among Saharia women who are still striving hard to meet the national health standards. A multidimensional approach is required to uplift the health status. Hemoglobin levels of all the women were found to be very low.

The case study is based on field data, including in-depth semi-structured interviews with the main protagonist and related stakeholders of a large government hospital in Western India. After informed consent, the interviews with the stakeholders were conducted, transcribed and analyzed verbatim. In addition, secondary data from policy reports, newspaper articles and government websites was used to create the case. Since the protagonist works in the government system, her identity and other identifying information are disguised to maintain confidentiality.

The case study investigates the leadership challenges in a healthcare facility/hospital in public health. It traces the evolution of Dr Meena Sharma (Dr Meena), a leader in the government hospital ecosystem facing challenges such as infrastructural deficiencies, manpower deficit, healthcare bureaucracy and heavy patient load. This first-generation medical practitioner who transitioned from a private practice to a governmental one juggles balancing her demanding clinical practice, administrative responsibilities and teaching in the government hospital with her family responsibilities setup. However, in the wake of the upcoming LaQshya – Labour Room Quality Improvement Initiative by the Ministry of Health & Family Welfare, she strives to put together and motivate her team to work toward improving the quality of care during delivery and the immediate postpartum period at her hospital. Various issues arise in the organizational leadership for a woman leader such as adopting appropriate leadership style and using appropriate motivation and communication strategies for optimal performance.

The case study is aimed at teaching/training a) departmental heads of public and private hospitals, b) health program managers at higher and middle-level leadership roles, c) health policymakers at various levels in the government and other organizations and d) graduate and postgraduate students of public health, hospital management/administration. In addition to this, it can also be used for general management programs to teach organizational behavior, communication and leadership courses.

In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features.

In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error.

The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness.

The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

This paper aims to propose a new fin field-effect transistor (FinFET)-based domino technique low-power series connected foot-driven transistors logic in 32 nm technology and examine its performance parameters by performing transient analysis.

In the proposed technique, the leakage current is reduced at footer node by a division of current to improve the performance of the circuit in terms of average power consumption, propagation delay and noise margin. Simulation of existing and proposed techniques are carried out in FinFET and complementary metal-oxide semiconductor technology at FinFET 32 nm technology for 2-, 4-, 8- and 16-input domino OR gates on a supply voltage of 0.9 V using HSPICE.

The proposed technique shows maximum power reduction of 77.74% in FinFET short gate (SG) mode in comparison with current-mirror-based process variation tolerant (CPVT) technique and maximum delay reduction of 51.34% in low power (LP) mode in comparison with CPVT technique at a frequency of 100 MHz. The unity noise gain of the proposed circuit is 1.10× to 1.54× higher in comparison with different existing techniques in FinFET SG mode and 1.11× to 1.71× higher in FinFET LP mode. The figure of merit of the proposed circuit is up to 15.77× higher in comparison with existing domino techniques.

The research proposes a new FinFET-based domino technique and shows improvement in power, delay, area and noise performance. The proposed design can be used for implementing high-speed digital circuits such as microprocessors and memories.

The current study uses an advanced machine learning method and aims to investigate whether auditors perceive financial statements that are principles-based as less risky. More specifically, this study aims to explore the association between principles-based accounting standards and audit pricing and between principles-based accounting standards and the likelihood of receiving a going concern opinion.

The study uses an advanced machine-learning method to understand the role of principles-based accounting standards in predicting audit fees and going concern opinion. The study also uses multiple regression models defining audit fees and the probability of receiving going concern opinion. The analyses are complemented by additional tests such as economic significance, firm fixed effects, propensity score matching, entropy balancing, change analysis, yearly regression results and controlling for managerial risk-taking incentives and governance variables.

The paper provides empirical evidence that auditors charge less audit fees to clients whose financial statements are more principles-based. The finding suggests that auditors perceive financial statements that are principles-based less risky. The study also provides evidence that the probability of receiving a going-concern opinion reduces as firms rely more on principles-based standards. The finding further suggests that auditors discount the financial numbers supplied by the managers using rules-based standards. The study also reveals that the degree of reliance by a US firm on principles-based accounting standards has a negative impact on accounting conservatism, the risk of financial statement misstatement, accruals and the difficulty in predicting future earnings. This suggests potential mechanisms through which principles-based accounting standards influence auditors’ risk assessments.

The authors recognize the limitation of this study regarding the sample period. Prior studies compare rules vs principles-based standards by focusing on the differences between US generally accepted accounting principles (GAAP) and international financial reporting standards (IFRS) or pre- and post-IFRS adoption, which raises questions about differences in cross-country settings and institutional environment and other confounding factors such as transition costs. This study addresses these issues by comparing rules vs principles-based standards within the US GAAP setting. However, this limits the sample period to the year 2006 because the measure of the relative extent to which a US firm is reliant upon principles-based standards is available until 2006.

The study has major public policy suggestions as it responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US Securities and Exchange Commission (SEC), to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the International Accounting Standards Board (IASB) Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks such as climate change.

The study has major public policy suggestions because it demonstrates the value of principles-based standards. The study responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US SEC, to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information as business transactions and investor needs continue to evolve globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the IASB Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks like climate change. The study fills the gap in the literature that auditors perceive principles-based financial statements as less risky and further expands the literature by providing empirical evidence that the likelihood of receiving a going concern opinion is increasing in the degree of rules-based standards.

The purpose of the paper is to study multiple viewpoints which are required to access the more informative similarity features among the tweets documents, which is useful for achieving the robust tweets data clustering results.

Let “N” be the number of tweets documents for the topics extraction. Unwanted texts, punctuations and other symbols are removed, tokenization and stemming operations are performed in the initial tweets pre-processing step. Bag-of-features are determined for the tweets; later tweets are modelled with the obtained bag-of-features during the process of topics extraction. Approximation of topics features are extracted for every tweet document. These set of topics features of N documents are treated as multi-viewpoints. The key idea of the proposed work is to use multi-viewpoints in the similarity features computation. The following figure illustrates multi-viewpoints based cosine similarity computation of the five tweets documents (here N = 5) and corresponding documents are defined in projected space with five viewpoints, say, v₁,v₂, v₃, v₄, and v₅. For example, similarity features between two documents (viewpoints v₁, and v₂) are computed concerning the other three multi-viewpoints (v₃, v₄, and v₅), unlike a single viewpoint in traditional cosine metric.

Healthcare problems with tweets data. Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding term frequency and inverse document frequency (TF–IDF) for unlabelled tweets.

Topic models play a crucial role in the classification of health-related tweets with finding topics (or health clusters) instead of finding TF-IDF for unlabelled tweets.