Voluntary disclosures of intellectual capital: An empirical analysis

Purpose: This paper aims to investigate inter firm intellectual capital (IC) disclosures and its variations in top 20 listed pharmaceutical companies in India, study the category wise and element wise IC disclosures (ICD), find out the impact of ICD on the creation of IC in monetary terms, find out correlation between IC valuation and its disclosure, and test significance of correlation. Design/methodology/approach: This is an exploratory and empirical study of ICD by sample companies in 2009 using content analysis. IC is valued as market value minus book value. Five-point scale (0-4), mean disclosure score, range, Chi- squares, Karl Pearson's correlation and Student's t-test are used for analysis and interpretation. Findings: Although top 20 companies of knowledge-led industry, ICD are low, narrative and varying significantly among companies. ICD score varies in range of 4 to 36 against expected score of 96. External capital with mean score of 18.78 is the most disclosed category. Brands and business collaborations is most disclosed element of IC, followed by employee competence and internal organizational capital respectively. ICD leads to creation of IC in some companies. Markets reflected true valuations of ICD in seven companies, and high degree of inconsistency in 13 companies. Overall correlation between IC valuation and disclosure is negative, weak and insignificant. Practical implications: Sector-specific intangible asset monitors should be formulated to capture ICD. Originality/value: The paper measures ICD using five-point scaling technique, it uses Chi- square test (non-parametric test) to calculate inter-firm variations. The paper also correlates ICD and valuation of respective companies with Spearman's correlation for the first time in pharmaceutical companies in India. It proposes inclusion of fourth category i.e. sector-specific items in existing models of ICD. © Emerald Group Publishing Limited

Measurement of corporate social performance: An Indian perspective

Purpose - This paper aims to: design a comprehensive, review-based and statistically tested corporate social responsibility disclosure (CSRD) index; measure item-wise and theme-wise the social performance of the top 82 companies in India; and investigate item-wise and theme-wise the variations in CSRD. Design/methodology/approach - The paper presents an empirical study of CSRD in 2009-2010, using content analysis, Cronbach's α, the Kolmogorov-Smirnov and Shapiro-Wilk tests of normality and a six point scale (0-5), mean, skewness, kurtosis, and Levene's, Kruskal-Wallis's and Mood's median tests for analysis and interpretation. Findings - CSRD shows less satisfactory social performance, mainly narrative, and varies significantly among items and themes. Community development, with a mean score of 14.30, is the most disclosed theme, followed by HR, with a score of 11.20. The human element is the center of social performance in India. More than equal focus should be given to the environment and to emissions, which impact the greater interests of the world. Some burning global issues like water usage, alternative sources of energy, product safety and innovation have not received adequate attention. Research limitations/implications - The study offers ample scope for the further studies as each and every theme and item considered in the model/index requires individual focus to serve the future generations of mankind. Longitudinal/transnational studies in the area of CSR could be carried out to set the scene for further studies. Practical implications - The paper recommends mandatory CSR norms leading to improved disclosure, the sharing of innovative knowledge, cost reductions and enhanced effectiveness in managing scarce resources. Originality/value - The paper evaluates social performance in the economic, social, religious environment and highlights the emerging philanthropic attitude. The paper improves an existing model by incorporating an emerging dimension, i.e. Emissions of carbon and other harmful gases. The CSEEE index designed here is highly appropriate for developing economies like India. The paper measures CSRD using six-point scales for the first time. © Emerald Group Publishing Limited

Risk factors for driver distraction and inattention in tram drivers

Abstract. Tram driving is a complex task requiring high levels of workload, route knowledge, and attention. Metropolitan tram networks typically contain many routes and share roads with other road users. Collision potential is highest at road intersections and areas where the track runs along the road with no segregation and when collisions occur they can cause serious injury and disruption. A study was conducted on an Australian tram network to identify collision risk factors. The approach included focus groups and discussions with 22 drivers, and observations at two high-risk locations. Data were coded thematically using a recently published taxonomy for driver distraction and inattention. The majority of factors fell into the Driver Cursory Attention category, with a large representation also in the Misprioritised and Neglected Attention categories and instances of Diverted Attention were mainly driving-related. Findings are discussed in terms of potential mitigation strategies and their implications for further refinements to driver distraction and inattention taxonomies

Measuring intellectual capital performance of Indian banks: A public and private sector comparison

© 2016, © Emerald Group Publishing Limited. Purpose – The purpose of this paper is to measure the intellectual capital performance of Indian banks and established a relationship between intellectual capital and return on assets (ROA). The paper also compared the intellectual capital performance of public sector and private sector banks. Design/methodology/approach – This study is based on secondary data from the top 20 Indian banks. Ten banks were selected from each of the public and private sectors on the basis of paid-up equity capital. The analysis was made using the value added intellectual coefficient, the coefficient of variation, exponential growth rates, trend analysis, Yule’s coefficient, the coefficient of correlation, the F-test and the t-test. Findings – The study revealed that private sectors have performed relatively better regarding the creation of total information coefficient (IC). However, the ROA was still below the international benchmark of > 1 percent. The major cause of the lower IC and the reduced ROA is disproportionate to the increase in capital employed and escalating non-performing assets in the Indian banking sector. Practical implications – The study focussed on managers and identified the causes of lower performance. It proposed numerous strategies to improve the aggregate score of IC, which is closely related to bank profitability. Originality/value – This is the first study to make a comparative analysis of intellectual capital performance in public and private sector banks in India and in addition to the traditional style of measuring sectoral performance. Further, the study employed new statistical tools, such as Yule’s coefficient of association, to establish the association between performance variables

Risk factors for driver distraction and inattention in tram drivers

Abstract. Tram driving is a complex task requiring high levels of workload, route knowledge, and attention. Metropolitan tram networks typically contain many routes and share roads with other road users. Collision potential is highest at road intersections and areas where the track runs along the road with no segregation and when collisions occur they can cause serious injury and disruption. A study was conducted on an Australian tram network to identify collision risk factors. The approach included focus groups and discussions with 22 drivers, and observations at two high-risk locations. Data were coded thematically using a recently published taxonomy for driver distraction and inattention. The majority of factors fell into the Driver Cursory Attention category, with a large representation also in the Misprioritised and Neglected Attention categories and instances of Diverted Attention were mainly driving-related. Findings are discussed in terms of potential mitigation strategies and their implications for further refinements to driver distraction and inattention taxonomies

APT attacks on industrial control systems: A tale of three incidents

APT attacks on industrial control systems: A tale of three incident

Advances and challenges in thyroid cancer: the interplay of genetic modulators, targeted therapies, and AI-driven approaches

Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future.</p

Adapted cardiac rehabilitation for people with sub-acute, mild-to-moderate stroke: a mixed methods feasibility study

Objective: To determine the recruitment strategy, acceptability, adherence, outcome measures, and adverse events for a definitive study that will explore adapted cardiac rehabilitation (CR) for people post-stroke with mild-to-moderate severity stroke in the sub-acute stage of recovery. Design: Mixed methods feasibility study. Setting: Acute hospital setting, neurology outpatients and community hospitals. Participants: 32 participants with stroke (mean age: 64.4 years) of median National Institutes of Health Stroke Scale (NIHSS) score 2 (range: 0 to 6) within six months of stroke. Intervention: All participants attended six weeks, adapted CR within one to six months after a stroke. A combined class with people post cardiac event. Main outcome measures: Incremental shuttle walk test (ISWT), blood pressure, heart rate, weight, body mass index, quality of life, fatigue, anxiety and depression, tone, falls, stroke attitude and knowledge, physical activity (accelerometry) and functional ability. Qualitative: Interviews with participants, non-participants and people post-cardiac event. Focus groups with Stroke and CR teams. Results: 32 participants were recruited. The programme was acceptable to people with mild stroke (NIHSS 2. Clinical Trial Registration Number: ISRCTN14861846

An undergraduate-level electrochemical investigation of gold nanoparticles-modified physically small carbon electrodes

This paper reports an undergraduate experiment based on analytical chemistry, electrochemistry and materials science of carbon microelectrodes. The modification of the electroactive surface of the carbon microelectrode was done using gold nanoparticles electrodeposited from gold solution. To determine the changes on the surface, the electrode was subjected to simple optical microscopy. Next, the electrode was characterized using fast-scan cyclic voltammetry of two known electrochemical redox markers: hexaamineruthenium(III) chloride and potassium hexacyanoferrate (III), i.e. potassium ferricyanide. The redox behavior of both markers demonstrated the change in electrode surface. After modification, the ferricyanide reduction peaks were observed to increase significantly, as a consequence of accelerated electron transfer. Furthermore, changes in wave slope and half-wave potentials (E½) of the redox waves also confirmed an altered electrode surface that students can logically trace back to the modification. The electrode tip dimension was also determined using a modified form of the Cottrell equation, confirming the tip size to be 2.0 μm. The discussion of these results enables an understanding of electrochemistry, analytical chemistry and materials chemistry, and presents an excellent opportunity to apply these in an undergraduate setting

Potentials of data mining and cloud computing in the fish poisoning and relevant economic contexts: A conceptual review

Fish poisoning can be life threatening and it is important to detect the causes of fish poisoning, and the type of fish poisoning, reliably and accurately. However, the existing research on fish poisoning has generally focused on statistical methods and specific areas within the fish poisoning field. This review based research also highlights the possibilities of data mining in this area, especially by reflecting how the different data mining methods may address different issues of uncertainty linked to fish poisoning. The article also discusses the possibility of cloud computing in this context and the economic benefit that may be brought forth by using a data mining based system within cloud computing architecture. Additionally it demonstrates an example of utilizing data mining in the fish poisoning research area. The work is expected to guide empirical research, especially for the Fijian context. © 2016 IEEE