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A “state-driven” approach suggests that colonists use census categories to rule. However, a “society-driven” approach suggests that this state-driven perspective confers too much power upon states. A third approach views census-taking and official categorization as a product of state–society interaction that depends upon: (a) the population's lay categories, (b) information intellectuals' ability to take up and transform these lay categories, and (c) the balance of power between social and state actors. We evaluate the above positions by analyzing official records, key texts, travelogues, and statistical memoirs from three key periods in India: Indus Valley civilization through classical Gupta rule (ca. 3300 BCE–700 CE), the “medieval” period (ca. 700–1700 CE), and East India Company (EIC) rule (1757–1857 CE), using historical narrative. We show that information gathering early in the first period was society driven; however, over time, a strong interactive pattern emerged. Scribes (information intellectuals) increased their social status and power (thus, shifting the balance of power) by drawing on caste categories (lay categories) and incorporating them into official information gathering. This intensification of interactive information gathering allowed the Mughals, the EIC, and finally British direct rule officials to collect large quantities of information. Our evidence thus suggests that the intensification of state–society interactions over time laid the groundwork for the success of the direct rule British censuses. It also suggests that any transformative effect of these censuses lay in this interactive pattern, not in the strength of the British colonial state.

This study aims to present the consequences of activation energy and the chemical reactions on the unsteady MHD squeezing flow of an incompressible ternary hybrid nanofluid (THN) comprising magnetite (FE₃O₄), multiwalled carbon nano-tubes (MWCNT) and copper (Cu) along with water (H2O) as the base fluid. This investigation is performed within the framework of two moving parallel plates under the influence of magnetic field and viscous dissipation.

Due to the complementary benefits of nanoparticles, THN is used to augment the heat transmit fluid’s efficacy. The flow situation is expressed as a system of dimensionless, nonlinear partial differential equations, which are reduced to a set of nonlinear ordinary differential equations (ODEs) by suitable similarity substitutions. These transformed ODEs are then solved through a semianalytical technique called differential transform method (DTM). The effects of several changing physical parameters on the flow, temperature, concentration and the substantial measures of interest have been deliberated through graphs. This study verifies the reliability of the results by performing a comparison analysis with prior researches.

The enhanced activation energy results in improved concentration distribution and declined Sherwood number. Enhancement in chemical reaction parameter causes disparities in concentration of the ternary nanofluid. When the Hartmann number is zero, value of skin friction is high, but Nusselt and Sherwood numbers values are small. Rising nanoparticles concentrations correspond to a boost in overall thermal conductivity, causing reduced temperature profile.

Due to its firm and simple nature, its implications are in various fields like chemical industry and medical industry for designing practical problems into mathematical models and experimental analysis.

Deployment of the squeezed flow of ternary nanofluid with activation energy has significant consideration in nuclear reactors, vehicles, manufacturing facilities and engineering environments.

This study would be contributing significantly in the field of medical technology for treating cancer through hyperthermia treatment, and in industrial processes like water desalination and purification.

In this problem, a semianalytical approach called DTM is adopted to explore the consequences of activation energy and chemical reactions on the squeezing flow of ternary nanofluid.

The paper provides a critical analysis of the relationship between the growth of informal manufacturing activities and environmental sustainability. The research is grounded in the context of global economic trends, focusing specifically on the impact of these activities in India. The study highlights the significant role of unorganized manufacturing enterprises in the Indian economy. These enterprises, while contributing notably to the GDP, also lead to increased material and energy use, raising concerns about environmental sustainability. The paper underscores the trade-off between economic growth and environmental sustainability, particularly in the context of informal sector industrialization. The findings suggest that higher economic growth, driven by the informal manufacturing sector, correlates with greater environmental degradation. This relationship is primarily due to increased energy consumption and carbon intensity. The paper posits that the growth of the informal manufacturing sector, especially in developing countries like India, leads to higher CO₂ emissions and environmental un-sustainability. This is exacerbated by the sector's significant reliance on energy sources like kerosene, contributing to higher carbon emissions. The analysis indicates a significant negative correlation between the growth of informal manufacturing activities and GDP, highlighting the environmental costs of such economic activities. The paper emphasizes the need for policy interventions to address the challenges posed by the informal manufacturing sector. It calls for policies that balance economic growth with environmental sustainability, considering the significant role of the informal sector in countries like India. The research presents a nuanced understanding of the complexities involved in achieving sustainable economic growth in the face of growing environmental concerns.

This paper aims to develop an optimization model to enhance pipeline assembly performance. It focuses on predicting the pipeline’s assembly pose while ensuring compliance with clamp constraints.

The assembly pose of the pipeline is quantitatively assessed by a proposed indicator based on joint defects. The assembly interference between the pipeline and assembly boundary is characterized quantitatively. Subsequently, an analytical mapping relationship is established between the assembly pose and assembly interference. A digital fitting model, along with a novel indicator, is established to discern the fit between the pipeline and clamp. Using the proposed indicators as the optimization objective and penalty term, an optimization model is established to predict the assembly pose based on the reinforced particle swarm optimization, incorporating a proposed adaptive inertia weight.

The optimization model demonstrates robust search capability and rapid convergence, effectively minimizing joint defects while adhering to clamp constraints. This leads to enhanced pipeline assembly efficiency and the achievement of a one-time assembly process.

The offset of the assembly boundary and imperfections in pipeline manufacturing may lead to joint defects during pipeline assembly, as well as failure in the fit between the pipeline and clamp. The assembly pose predicted by the proposed optimization model can effectively reduce the joint defects and satisfy clamp constraints. The efficiency of pipeline modification and assembly has been significantly enhanced.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

Stock markets are considered as the largest and most important units for the development and growth of the economy. The present study attempts to provide a comprehensive view of factors influencing investment decision making process of stock market investors. A multi group analysis of gender is also carried out on the proposed model.

The data of 402 valid responses are collected through structured questionnaires from individual investors of North India. SPSS 23 is used to do the descriptive analysis and AMOS 22 is used to establish the validity of the constructs and for hypotheses testing. For performing multi group analysis, several invariance tests have also been conducted to check the robustness of the model.

The results reveal that all the factors such as firm image, accounting information, neutral information, advocate recommendation and personal financial needs significantly influence investment decision making concluding image of the firm being the most influential factor and advocate recommendation being the least influential factor for investment decisions. No significant differences between males and females were found.

The current study suffers from the limitation of restricted geographical area of North India. Moreover, there is also a scope to incorporate more demographic factors for predicting investment decisions.

This study incorporates a range of factors which covers all the aspects of investment decision making. This study also highlights the notion of signaling theory, thus contributing to the limited literature in Indian context.

The purpose of this review is to identify how health professionals perceive participation in implementation of new technology in healthcare organizations.

A qualitative systematic review based on the PRISMA diagram, was conducted using qualitative synthesis. NVivo software was used for thematic analysis. The searches were performed in PubMed, CINAHL and Scopus.

A total of 15 articles were included in the review, four themes describing how participation of health professionals in digital transformation affects the outcomes were identified, and three themes describing the factors that are necessary to promote participation. The underlying latent theme of an unmet desire to participate in the digital transformation was also identified in the analysis.

The digital transformation of healthcare is complex and faces many obstacles if not managed correctly. Professional participation in the implementation seems to be essential for success. Focus on increased resources and planning during early stages, as well as teamwork and ethical reflection is important addressing the challenges that professionals face in digital transformation of healthcare.

This paper aims to contextualize the process of public hospital providing services, based on the measurement of the performance of Federal University Hospitals (HUFs) of Brazil, using the technique of multivariate statistics of principal component analysis.

This research presented a descriptive and quantitative character, as well as exploratory purpose and followed the inductive logic, being empirically structured in two stages, that is, the application of principal component analysis (PCA) in four healthcare performance dimensions; subsequently, the full reapplication of principal component analysis in the most highly correlated variables, in module, with the first three main components (PC1, PC2 and PC3).

From the principal component analysis, considering mainly the component I, with twice the explanatory power of the second (PC2) and third components (PC3), it was possible to evidence the efficient or inefficient behavior of the HUFs evaluated, through the production of medical residency, by specialty area. Finally, it was observed the formation of two groups composed of seven and eight hospitals, that is, Groups II and IV, which shows that these groups reflect similarities, with respect to the scores and importance of the variables for both hospitals’ groups.

Among the main limitations, it was observed incomplete data for some HUFs, which made it impossible to search for information to explain and better contextualize certain aspects. More specifically, a limited number of hospitals with complete information was dealt with for 60% of SIMEC/REHUF performance indicators.

The use of PCA multivariate technique was of great contribution to the contextualization of the performance and productivity of homogeneous and autonomous units, represented by the hospitals. It was possible to generate a high quantity of information, to contribute with assumptions to complement the decision-making processes in these organizations.

Development of public policies, with emphasis on hospitals linked to teaching centers represented by university hospitals. The projection of improvements in the reach of the efficiency of the services of assistance to the public health, from the qualified formation of professionals, both to academy, as to clinical practice.

The originality of this paper for the scenarios, Brazilian public health sector and academic area involved the application of a consolidated performance analysis technique, that is, PCA, obtaining a rich work in relation to the extensive exploitation of techniques to support decision-making processes. In addition, the sequence and the way in which the content, formed by object of study and techniques, has been organized, generating a particular scenario for the measurement of performance in hospital organizations.

This qualitative work aims to explore the university students’ attitude toward advantages, drawbacks and prospects of ChatGPT.

This paper applies well accepted Colaizzi’s phenomenological descriptive method of enquiry and content analysis method to reveal the ChatGPT user experience of students in the higher education level.

The study’s findings indicate that ChatGPT enhances the quality of learning and facilitates faster learning among university students. However, despite numerous positive outcomes, it is noted that ChatGPT may diminish students' creativity by swiftly addressing their critical queries. Over time, students may experience a decline in patience and critical thinking skills as they excessively rely on ChatGPT, potentially leading to ethical misconduct.

This paper primarily explores the advantages and drawbacks of using ChatGPT in the university context of Bangladesh. The present study creates a platform for future research in this domain with comprehensive study design. The study results alert the policy makers to improve upcoming version of ChatGPT with convenient user experience and academicians as this paper unleash several positive as well as negative consequences of using this AI-enabled chatbot.