Informatics Publishing Limited

Sikandar Kumar ¹, Saibalini Parida ², Sinmayee Kumari Devi ³

Affiliations
1 Psychiatric Nursing, Lord Jagannath Mission College of Nursing, Bhubaneswar, Odisha, IN
2 L.H.V Training School, Berhampur, Ganjam, Odisha, IN
3 Dept. of Obstetrics and Gynecological Nursing, Lord Jagannath Mission College of Nursing, Bhubaneswar, Odisha, IN

Abstract

A quasi experimental research design was undertaken among 50 caregivers of Schizophrenics Patients at mental health Institute, S.C.B. Medical College, Hospital Cuttack, Odisha, selected by Purposive Sampling technique, data were collected from Dtd. 10.11.2014 to 12.12.2014 by using Depressive Anxiety Stress Scale (DASS) to assess the level of stress among the caregivers of schizophrenic patient in the form of structured interview schedule. The collected data were analyzed by using descriptive and inferential statistics. Findings revealed that in Pre-test, 54% of the caregivers had moderate stress and 34% of them had mild stress. But in post test majority 62% of caregivers have no stress and 36% have mild level of stress.

Factor wise comparison showed that there is effectiveness of pranayam in decreasing stress. Highly significant difference was found between Pre and Post stress Score. No significant association was found. No significant association was found between post test stress and demographic variables like sex, religion, educational, type of family, duration of care given, relationship, monthly income, residence, family history of schizophrenic patient and previous exposure to programme of pranayama. But there was an association between post test stress with age and marital status.

Keywords

Stress, Pranayam , Caregivers, Schizophrenic Patient.

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Sikandar Kumar ¹

Affiliations
1 L.J.M College of Nursing, Bhubaneswar, IN

Abstract

The purpose of the study was to assess and improve the knowledge among the mothers of under five children regarding upper respiratory tract infection. 30 samples were selected from the bhubaneswar. The subjects who were between 20-40 yrs having children below age of 5 yrs were selected by purposive sampling. The closed ended questionnaire knowledge regarding URTI were administered. Information related to prevention and management of URTI was given through planned teaching programme. The findings showed that during pretest the knowledge of the subjects regarding URTI was inadequate where as in post test the score the knowledge was adequate. There was no significant difference (p=0.05) between the post test score and their selected demographic variable. Finding reveals that most of the mothers were in the age group of between 30-35 years and most of them have the educational qualification of secondary level. Most of them were Hindu religion and majority of them were housewife. Majority of them were belongs from nuclear family all of them line in rural area and majority of them having the monthly income of 1001-5,000. In pre-test, overall level of knowledge on management of URTI among the mothers were 40%. Where as in post test knowledge score for the mother were 87%. Hence, it depicts that intervention was effective for the gaining of the knowledge regarding management of URTI mong the mother of under five children.

Keywords

Management, Respiratory Tract Infection, Mothers, Under Five Children.

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Sinmayee Kumari Devi ¹, Sikandar Kumar ¹

Affiliations
1 Dept. of OBG, Lord Jagannath Mission College of Nursing, Rasulgarh-10, Bhubaneswar, Odisha, IN

Abstract

A quasi experimental study with pre and post test with without control group design was under taken in HI-TECH school of nursing, BBSR to assess the effectiveness of SIM regarding high risk pregnancy on knowledge among the ANM students. 42 ANM students were selected by simple random sampling technique and Data was collected by using closed ended questionnaire from Dt-27.02.2014 to Dt-13.03.2014 and collected data were analyzed by using descriptive and inferential statistics. Findings revealed that highest percentage 50% of the ANM students were in the age group of 18-20 years. All were female and unmarried Majorities 98% of them are Hindus and 2% are Christians. The overall pre test mean score was (9.42±6.8) which is 31% of the total score reveals poor knowledge where as it was (16.85±9.7) which is 56% in posttest revealing 25% of enhancement knowledge score. Area wise highest post test mean score (4.9±5.2) which is 49% was obtained for the area of "risk factor" where as the lowest post test mean score (1.4±1.6) which is 46.6% was obtained for the area of "complication". Highly significant (p<0.01) difference was found between pre and post test knowledge scores and no significant (p>0.05) association was found between post test knowledge scores in relation to demographic variables of ANM students.

Keywords

High Risk Pregnancy, Auxiliary Nurses and Midwifery Student, Self Instructional Module.

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Ram Bilash Prajapati ¹, Rabindra Kumar Sinha ¹, Sikandar Kumar ¹, R.N. Gupta ², Divya Divya ³

Affiliations
1 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, 826004, IN
2 National Institute of Rock Mechanics, Bangalore, Karnataka 560070, IN
3 Mahatma Gandhi Medical College and Hospital, Jamshedpur, Jharkhand, 831012, IN

Abstract

Depillaring of coal seams is of prime importance for coal mining industry in view of depleting superior quality coal reserve and increasing import of foreign coal. Depillaring in conjunction with caving is the most hazardous operation due to sudden roof fall. Some researchers have focused their work on roof fall risk assessment using statistical methods with a view to safety of men and machinery and to minimize accidents, down time and loss of production. Extensive research has not been done to predict roof caving sequence which is the basic requirement for successful caving operation for achieving production with zero harm potential. Roof caving is the result of interactions of all geotechnical and mining parameters including extraction area which is its main cause and contributory parameter. In this research, Random Forest, a supervised ensemble machine learning algorithm along with grid search and cross-validation is used to process the interactions among various parameters and to predict the sequential occurrence of roof caving and characterize the same as local or main fall with considerable and reliable accuracy.

Keywords

Depillaring with caving, grid search, feature selection, local fall, machine learning, main fall, random forest, roof fall risk.

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Sandhyarani Behera ¹, Sikandar Kumar ²

Affiliations
1 Sr. Tutor, Department of Psychiatric Nursing, Mental Health Institute (COE), SCBMCH, Cuttack. Mangalabag, Cuttack, Odisha., IN
2 Asst. Professor and HOD, Department of Psychiatric Nursing, Mental Health Institute (COE), SCBMCH, Cuttack. Mangalabag, Cuttack, Odisha., IN

Abstract

A descriptive study with quantitative approach was under taken on 50 significant family members of mentally ill patients selected by non probability convenient sampling technique at Mental Health Institute (COE), SCBMCH, Cuttack to assess the psychosocial problems and coping strategies of significant family members of mentally ill patients. Data was collected from 10.02.2020 to 10.03.2020 through questionnaire on psychosocial problems formulated in the form of 4-point likert scale. and COPE Inventory by Carver et al. rated on a 4-point scale format. Collected data were analyzed by using descriptive and inferential statistics. Findings revealed that Highest Percentage (40%) of the family members were in the age group of 48–60 years. A majority (66%) of them were male and (92%) of them were Hindus and (8%) of them were Muslim. Majority (60%) of them were married (36%) of them were farmer. Highest percentage (30%) of them were illiterate and majority (50%) of them were having income ≤ Rs.5000 and (56%) of them from nuclear family. Highest percentage (58%) of them were from rural area and (44%) of them were mother. Majority (38%) of them had >5 years of illness and (76%) of them were having no family history. Most of the significant family members of mentally ill patients (84%) under this study had moderate problem whereas (8%) of them had mild and also (8%) severe problems. The coping strategy most often used by the significant family members of mentally ill patients was restraint coping mean score (15.64±0.66) and instrumental social support mean score (15.64±0.52) and the least used was Humor mean score (4.04±0.28) and Alcohol disengagement mean score (4.38±0.28). The internal consistency of COPE Inventory exhibited Cronbach’s alpha (α) coefficients ranging from 0.93 (Emotional social support) and Instrumental social support (0.90) to 0.41 (Restraint coping). However, the (Restraint coping) shows lower alfa (α). Mostly Problem focused coping strategies (14.12±1.37) was used by the significant family members of the mentally ill patients.

Keywords

Psychosocial problems, Coping strategies, Significant family members, Mentally ill patients.

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Ram Bilash Prajapati ¹, Rabindra Kumar Sinha ¹, R. N. Gupta ², Sikandar Kumar ¹, Deepti Prajapati ³

Affiliations
1 Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad – 826004, Jharkhand, IN
2 National Institute of Rock Mechanics, Bangalore – 560070, Karnataka, IN
3 All India Institute of Medical Sciences, Patna – 801507, Bihar, IN

Abstract

Depillaring with caving method of mining is a common practice in Indian coalfields and so is the occurrence of fall in goaf area, which can be considered as a boon in disguise as it allows wining of coal from large reserves but this becomes a curse just because of its unpredicted occurrence. Various empirical and statistical models are developed after idealization of several complicated mechanisms but they are not able to predict roof fall accurately especially in caving panels. Therefore, a new approach based on Artificial Intelligence is used to predict the sequence of local and main fall in caving panel taking into account a host of geotechnical and mining parameters of the mine. Mathematical equations and hidden calculations of artificial neural networks are known to have the capability of learning and analyzing records endlessly. Two different models have been deployed after optimal hyper parameter optimization to predict the occurrence of fall and to characterize the nature of fall (local or main) with considerable and reliable accuracy.

Keywords

Bord and Pillar, Caving, Deep Learning Algorithm, Deep Neural Network, Hyper Parameter Optimization, Local Fall, Main Fall, Talos

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Sikandar Kumar ¹, Rabindra Kumar Sinha ¹, Mohammad Jawed ¹

Affiliations
1 Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad - 826004, India;, IN

Abstract

Seams amenable to opencast mining is on the verge of exhaustion globally and to meet the future demand of coal, underground mining method is the only viable technique left. In India, also quite a large number of coal seams have been extensively developed and standing on pillars for a long time. Extraction of these standing pillars with reasonable safety has been a challenge to mining engineers for most of reasons. Presently, depillaring in Indian mines is mostly carried out through cyclic unit operations involving drilling, blasting, loading with either a Side Discharge Loader (SDL) or a Load Haul Dumper (LHD), transport and hauling. Fully mechanized depillaring panels are limited in number. Mechanized depillaring using continuous miner and shuttle car is being used in a few mines in India with a view to achieving bulk production and high productivity. Still we are far behind with our Output per Man Shift (OMS), to a tune of 2.01 compared to the global OMS of 12 tonnes per man-shift in depillaring districts. Different strata, along with hostile geo-mining factors, are considered to be this prime case of low productivity. This paper seeks to highlight the existing depillaring practices in India and other major coal-producing countries namely USA, Australia and South Africa. The authors also present a case study on conventional depillaring practice in the Indian context and a few methods being practiced in major coal-producing countries.

Keywords

Bord and Pillar, Coal Pillar, Depillaring, Underground Mining

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