Effects of Corporate Governance on Intellectual Capital Disclosure: An Analysis of Indian Companies

This paper empirically explores the critical connection between intellectual capital disclosure and corporate governance variables, while having firm-specific control variables, for a sample of 137 Indian listed firms. Intellectual capital disclosure is recorded by a self-developed index score used on the annual reports of the sample companies, along with an attempt to capture the qualitative aspect of the intellectual capital disclosure in the form of semantic properties underpinned by Time Expectation theory and the overall word count of intellectual capital in the annual reports. The analysis is further widened by analyzing individually the corporate governance factors with all the three forms of Intellectual capital (Internal, External and Human). Most of the corporate governance variables within the analysis is associated with the disclosure practices of Intellectual capital be it quantitative or qualitative. Thus, this paper attempts to extend the previous literatures with a specialized case of an emerging economy along with contributing towards the agency theory by establishing the links between corporate governance and disclosure practices

Minimization of Energy and Service Latency Computation Offloading using Neural Network in 5G NOMA System

The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation

Minimization of Energy and Service Latency Computation Offloading using Neural Network in 5G NOMA System

The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation

A drug utilization study of antidepressant drugs in a tertiary care hospital

Background: In psychiatric practice, antidepressant drugs are widely used group of drugs. Number of drugs are available in this group with diverse type of mechanisms and efficacy/safety profile. The objective for the study was to identify the prescribing pattern of antidepressant drugs in psychiatric patients at a tertiary care hospital and to see the distribution of antidepressant drugs in the sociodemographic group.Methods: It was a record based descriptive study which was carried out at NKP Salve Institute of Medical Sciences and Research Centre, Nagpur. Case record files of all the patients suffering from depression and other psychiatric disorders and treated with antidepressant drugs during the period of from 1st January 2015 to 31st December 2015 by institutional psychiatrists were analyzed for prescription pattern of antidepressant drugs and their age and sex wise distribution.Results: A total no. of 300 patients received antidepressant drugs. The most commonly prescribed drug was escitalopram in 121 (40.3%) patients followed by fluoxetine in 61 (20.3%) and clomipramine in 30 (10%) patients. Other drugs that were prescribed were mirtazapine in 24 (8%), nortryptiline in 21 (7%), venlafaxine in 13 (4.3%), fluvoxamine in 12 (4%), amitryptiline in 9 (3%), dothiepin in 5 (1.6%) and duloxetine in 4 (1.3%) patients. Also patients in the age group of 31-40 yrs and 21-30 yrs received most number of antidepressants i.e. 129 (40.3%) and 83 (27.6%) patients respectively. Females have received more antidepressant drugs as compared to males.Conclusions: Selective Serotonin Reuptake Inhibitors (SSRIs) are the most commonly prescribed group of antidepressant drugs. Escitalopram followed by Fluoxetine were the most commonly prescribed drugs for the management of depression and other psychiatric disorders because of their better efficacy, safety, tolerability and less side effects as compared to TCAs. Females suffer more from depression and other psychiatric disorders as compared to males and the most vulnerable is 21 to 40 yrs of age

Effects of peppermint, chewing gum on memory, cognition, alertness, reaction time, arithmetic skills and athletic performance during laboratory induced stress in undergraduate medical students

Background: Many medical students are dealing with day to day stress in their lives and they need to be both physically and mentally active to counter and overcome their daily problems. In such scenario, a non-pharmacological adjunct could prove useful to counter all of it so that they can work more efficiently. Many universities do recommend the use of peppermints and chewing gums. This study is conducted to see if any of these items actually have an influence on cognitive and physical abilities.Methods: A non-randomized controlled trial was performed on the undergraduate medical students of  N.K.P. Salve Institute of Medical Sciences and Research Centre belonging to the age group of 18-21 years. They were given normal flavoured gum, mint flavoured gum and peppermint. A series of cognitive and physiological tests in both stressful and stress free environments were performed.Results: Consumption of peppermint in a stressful environment showed increase in attention span of the subjects and it increased the intelligence Wechsler Adult Intelligence Scale-R (WAIS-R) in both stress free and stressful environments. As compared to normal gum, peppermint and peppermint gum proved to be more effective. There was no significant change observed in alertness, reaction time, arithmetic ability, short term memory and fatigue index in any of the subjects.Conclusions: Peppermint has caused increase in the attention span and intelligence of medical students in a stressful work scenario. Peppermint gum was found to be more beneficial than normal gum

Spinal intramedullary cysticercosis mimicking spinal tumour

Neurocysticercosis is a relatively uncommon entity with even more rare spinal intramedullary variety. We present a case of cervico- dorsal intramedullary NCC mimicking spinal tumour with per operative finding mimicking abscess