A Comparative Analysis of Human Capital Efficiency of Public and Private Banks in India

The purpose of the study is to make a comparative analysis of the human capital efficiency (HCE) of the private and public banks in India for the period 2005-06 to 2009-10. The study is based on the secondary data taken from the financial statements of the banks. Value added method has been used to measure the human capital efficiency of the banks. Exponential trend method, ANOVA and GAP Analysis has been used to measure the variation in the human capital efficiency of the private and public sector banks. The main finding of the study is that there is a reduction of 839.32 per cent in gap index of HCE between public and private banks. The Annual Compounded Growth Rate of public banks are more than the private banks which shows that public banks have made great efforts to be competent with private banks; by focusing on Business Process Re-engineering, providing Voluntary Retirement Scheme (VRS)  options to employees, competent compensation, and incurring development expenditures on employees to improve their skills and knowledge etc. But still the public banks need to adopt flexible recruitment policy to retain the talented staff and expansion in decision making powers to terminate the unproductive employees and elimination of overlapping branches. The study also suggests that there is a need of accounting standard for measuring, reporting and disclosing of the intellectual capital of the banks in the financial statements. Keywords: Human capital efficiency, Value added, Business Process Re-engineering, Compensation and GAP Analysis

Strategy for Hospitality Businesses in the Developing World

The purpose of this paper is to present an alternate framework for evaluating strategic decisions of hospitality businesses in developing nations, particularly small- and medium-sized enterprises (SMEs). While strategy literature is extensive and diverse, it remains focused on developed nation contexts. By default, so is the case with hospitality strategy literature. This has created a paucity of research for hospitality businesses in developing nations; these businesses are largely SMEs in dynamic environments seldom similar to the ones in developed nations. Therefore, the proposed framework emphasizes the role of environment, and its relationship to strategic choice, resource allocation, and strategy evaluation. A set of research questions is also proposed

Using the Power-Interruptions-Finances-Resources model to tackle the financial management problems of municipal corporations in India

Purpose – The paper aims to explore the problems related to the financial management of municipal corporations in India and to suggest solutions. Design/methodology/approach – The study is based on primary data collected from a sample of 577 employees of municipal corporations working in four metro cities of India, namely Chennai, Mumbai, Kolkata, and Delhi. Data were put through exploratory and confirmatory factor analysis for problem identification and inferences were classified and grouped to map the solutions for these problems. Findings – The study found that municipal corporations in India face four major problems or issues in their financial management. These problems are mainly related to the four dimensions: Power, Interruptions, Finances, and Resources. The model used to explore these four types of issues is named as “PIFR model” by the author. Originality/value – The findings suggest that real-world problems can be represented through a conceptual model that helps in identifying practical suggestions which can be implemented by municipal corporations at the ground level for better financial management

A survey of Patients and staff satisfaction with a Rapid Response Psychiatric Liaison Service in an Acute Hospital: Are Elderly Patients Easier to please?

Background and Objectives: The provision and quality of mental health services in Acute General Hospitals is a growing concern. Developing research to elicit the views of patients and staff will offer insights into service improvements.  The Rapid Assessment, Interface and Discharge service (RAID) developed in an Acute General Hospital to deliver a rapid-response, 24-hour, 7-day- a- week, age-inclusive intervention was evaluated for its impact on staff satisfaction, with emphasis on staff training; and patient satisfaction, with emphasis on the differences in satisfaction between working age (under 65 years) and older adults (over 65 years). Population: Staff working in acute hospital for patients with mental health needs, and patients presenting to acute hospitals, requiring clinical input for their mental health. Method: Data on patient satisfaction was collected through a structured telephone questionnaire including fixed and open-ended questions.  Data related to staff satisfaction regarding the service provided was collected by a semi-structured interview administered face-to-face with staff from wards referring to the team.  Training was evaluated using open-ended, Likert-scale and open-ended questionnaires. Results: Results show that the majority of working age patients rated the service as ‘good’ (42.2%), felt that the team was helpful in their care (84.8%), met their mental health needs (69.7%), and treated them with respect (96.1%). Overall, older adults rated the service as ‘excellent’ (58.3%), felt that the team was helpful in their care (85.7%), met their mental health needs (85.7%), treated them with respect (92.9%) and stated that they were seen in good time (100%). The difference in satisfaction levels between patients of working age and older patients was statistically significant. Common aspects staff rated as most helpful were advice on managing patients (12.0%), support of staff (11.0%) and advice on medication (11.0%). The majority of staff surveyed felt that their practice would be improved following the training, and rated it as either excellent (61.6%) or good (36.3%). Interpretation and Conclusion: This study highlighted the benefits of providing support and training to staff working directly with patients with mental health needs.  It is more challenging to measure the satisfactory effect of older people who continue to give favourable answers on satisfaction questionnaires

Rheumatic Heart Disease among Pregnant Women with Cardiac Diseases in a Tertiary Care Center of Nepal: A Descriptive Cross-sectional Study.

INTRODUCTION: Cardiac disease in pregnancy is a major cause of maternal mortality and morbidity in women, particularly in resource limited countries like Nepal. Rheumatic Heart Disease is the commonest cardiac disease complicating pregnancy. There is very limited data and evidence from Nepal regarding rheumatic heart disease complicating the pregnancy. The study aims to find out the prevalence of rheumatic heart disease among cardiac disease patients in a tertiary care hospital. METHODS: A descriptive cross-sectional study was conducted among 41 women with cardiac disease who delivered babies at Chitwan Medical College from 1st January 2018 to 31st December 2019, after taking ethical approval from the Institutional Review Committee. A convenient sampling method was used. Statistical Package for the Social Sciences was used for data analysis. Point estimate at 95% Confidence Interval was calculated along with frequency and proportion for binary data. RESULTS: Among 41 pregnant women with cardiac disease, 32 (78%) (95% Confidence Interval = 65.32-90.68) had rheumatic heart disease. The mean age of the affected pregnant women was 24.9±4.49 years. Out of 32 patients with rheumatic heart disease, postpartum haemorrhage was the most common maternal complication 5 (15.6%) followed by hypertension 4 (9.7%). CONCLUSIONS: Rheumatic Heart Disease was highly common among pregnant women with cardiac disease

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Involvement of NMDA receptor complex in the anxiolytic-like effects of chlordiazepoxide in mice

In the present study, we demonstrated that low, ineffective doses of N-methyl-d-aspartic acid (NMDA) receptor antagonists [competitive NMDA antagonist, CGP 37849, at 0.312 mg/kg intraperitoneally (i.p.), antagonist of the glycineB sites, L-701,324, at 2 mg/kg i.p., partial agonist of glycineB sites, d-cycloserine, at 2.5 mg/kg i.p.] administered jointly with an ineffective dose of the benzodiazepine, chlordiazepoxide (CDP, 2.5 mg/kg i.p.), significantly increased the percentage of time spent in the open arms of the elevated plus-maze (index of anxiolytic effect). Furthermore, CDP-induced anxiolytic-like activity (5 mg/kg i.p.) was antagonized by NMDA (75 mg/kg i.p.) and by an agonist of glycineB sites of the NMDA receptor complex, d-serine [100 nmol/mouse intracerebroventricularly (i.c.v.)]. The present study showed a positive interaction between γ-aminobutyric acid (GABA) and glutamate neurotransmission in the anxiolytic-like activity in the elevated plus-maze test in mice and this activity seems to particularly involve the NMDA receptors

Multitask Prompted Training Enables Zero-Shot Task Generalization

International audienceLarge language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models’ pretraining (Radford et al., 2019). Can zero-shot generalization instead be directly induced by explicit multitask learning? To test this question at scale, we develop a system for easily mapping any natural language tasks into a human-readable prompted form. We convert a large set of supervised datasets, each with multiple prompts with diverse wording. These prompted datasets allow for benchmarking the ability of a model to perform completely held-out tasks. We fine-tune a pre-trained encoder-decoder model (Raffel et al., 2020; Lester et al., 2021) on this multitask mixture covering a wide variety of tasks. The model attains strong zero-shot performance on several standard datasets, often outperforming models up to 16x its size. Further, our approach attains strong performance on a subset of tasks from the BIG-bench benchmark, outperforming models up to 6x its size. All trained models are available at https://github.com/bigscience-workshop/t-zero, and all prompts are available at https://github.com/bigscience-workshop/promptsource

Measurement of differential cross sections for top quark pair production using the lepton plus jets final state in proton-proton collisions at 13 TeV

National Science Foundation (U.S.

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License