An investigation on prevalence of Anemia in relation to BMI and nutrient intake among adult rural population of West Bengal, India

Background: Anemia is now globally recognized major public health problem. The present study was undertaken to investigate the prevalence of anemia and interrelationship of anemia with BMI among adult rural population of West Bengal state, India. Method: Anthropometric measures, nutritional parameters, heart rate and hemoglobin were measured from 310 adult subjects by using standard technique. Results: The prevalence of anemia was high among both sexes and more than the global prevalence. The anemic individuals consumed nutritionally deficient diet than the non-anemic individuals. The prevalence of anemia was significantly high in underweight and normal groups. The Odds ratios became significantly higher in underweight group than normal and overweight/obese groups of both sexes. Conclusions: The prevalence of anemia in the adult rural population might be related to dietary deficiency and poor dietary patterns. The present study was hypothesized that the subjects with low BMI would have a higher risk of anemia compared to normal or overweight subjects

Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.publishedVersio

Nano to micrometer range particle size effect on the electrical and piezoelectric energy harvesting performances of hydroxide mediated crosslinked PVDF composites

The polar phase and piezoelectric response of filler-loaded PVDF-based composites very often depend on the interfacial interaction between the filler surface and PVDF dipoles. In this regard, hydrogen bonding interaction has shown a much stronger effect compared to other interactions. In order to induce hydrogen bonding interaction, the filler surface is commonly modified by various modifiers. In the present work, instead of filler surface modification, we introduce ZnSn(OH)6 filler (hydroxide filler having a high number of -OH groups) into the PVDF matrix in order to facilitate the hydrogen bonding interaction. Not only the hydroxide fillers but the effect of wide particle size variation (from nano to micrometer range) into PVDF has also been shown here for the very first time. ZnSn(OH)6 fillers with similar morphology but different sizes have been synthesized by using a variety of techniques and then incorporated into the PVDF matrix. The microstructural defects of the composite films have been found to be gradually increased with the increase in filler size which in turn caused to gradually increase their space charge polarization. Filler, with 915 nm size has shown the best polar phase formation (-84 %), dielectric permittivity (-10 at 1 kHz), and piezoelectric energy harvesting performance (output voltage -20 V) of the resulting PVDF-based composite and hence has been used for various real-life applications. All of these results have been suitably explained here on the basis of interfacial interaction, microstructural defect, and the mechanism of formation of space charge polarization

Air-plasma discharged PVDF based binary magnetoelectric composite for simultaneously enhanced energy storage and conversion efficiency

Different nanomaterials and their modified forms are very often added into a poly(vinylidene fluoride) (PVDF) matrix in order to improve the energy storage and conversion efficiency of the system. The improvement in energy storage density caused by this secondary nanomaterial addition is most often found to be accompanied by the reduction in energy storage efficiency due to increased amounts of space charges. Here, we show that both the capacitive energy storage density and efficiency can be simultaneously improved by air-plasma discharging on the PVDF based composite system. The energy storage density and efficiency of a 5 wt. % BiFeO3 loaded PVDF film (5BF) have been found to be increased to similar to 1.55 J/cm(3) and similar to 73%, respectively, from the values of similar to 1.36 J/cm(3) and 59% after air-plasma discharging. The dipole rotation caused by air-plasma discharging also helped in improving the mechanical to electrical energy conversion efficiency and magnetoelectric coupling of the studied composite system. Upon similar periodic applied stress, the pristine and air-plasma discharged 5BF film showed similar to 3 and 9.6 mu W/cm(2) of output electrical power density with similar to 13.5 and 19.2 V of open circuit output voltage, respectively. The air-plasma discharged 5BF film (5BFD) has also shown an excellent magnetoelectric coupling coefficient (alpha(33)) of similar to 35 mV cm(-1) Oe(-1) at 1 kHz frequency of fixed AC magnetic field (similar to 3 Oe) and 4 kOe of DC bias field. The simultaneous improvement of all of these parameters of the studied composite system caused by air-plasma discharging proves its multifunctional applicability in a variety of real life applications

Estimation of stature from hand dimensions in Bengalee population, West Bengal, India

Stature estimation from decomposing bodies and incomplete skeletons particularly in personal identification is considered as one of the biggest aspects of forensic science. This issue has gained importance in recent times due to mass disasters like terrorist attacks, mass murders, transport accidents, floods and earthquakes. Thus, the present study was undertaken to set up a standard formulae to estimate stature from hand dimensions in the Bengalee population. Measurements of different hand dimensions and statures were taken from 1662 adult Bengalee women aged from 20 to 40 years following the standard technique and appropriate landmarks. There was no statistically significant bilateral variation of the measurements. The correlation coefficients between stature and all variables were positive and statistically significant (p < 0.001). The hand length and palm length showed a better correlation with stature than the other variables. Simple linear regression equations and multiple linear regression equation were formulated for stature estimation using the hand dimensions. The derived equations were applied to the control group and it was noted that the percentage difference between true stature of the control and the estimated stature ranged from 0.01% to 0.15%. The multiple linear regression equation was more reliable than the simple linear regression equations as a lower standard error of estimate and higher value of determination coefficient and multiple correlation coefficient. From the results of the present study, it may be concluded that hand dimensions can be successfully used for estimating stature of adult Bengalee women in forensic practice by enforcement agencies and forensic scientists

Ergonomic Evaluation of Work Related Musculoskeletal Disorders and Postural Stress among Male Potato Cultivators of West Bengal, India

Background: Apart from cereal production (rice, wheat, etc.) agricultural labourers are engaged in vegetable cultivation in West Bengal, India. Potato cultivation is one of the important vegetable cultivation in India. The potato cultivation tasks are repetitive in nature and those are carried out mainly by manual efforts. Most of the tasks of potato cultivation are monotonous, strenuous, physiologically demanding as well as time-consuming.Objective: The present study was aimed to evaluate musculoskeletal disorders (MSDs) and postural stress among male workers engaged in different potato cultivation jobs. Methods: The study was conducted on 163 male potato cultivators in different districts of West Bengal state. A modified Nordic Questionnaire and Body Part Discomfort scale were applied to identify MSDs in different body parts. The postural pattern was assessed by direct observation method. The postural stress was analyzed by OWAS, REBA and QEC methods. Result: The results show that the prevalence of MSDs was very high among the workers and the most affected areas were back and upper extremity. Postural analysis indicated that the subjects had to adopt different stressful postures during performing different potato cultivation jobs. During potato harvesting operation the workers were subjected to greater postural stress than that of other tasks.Conclusion: Postural stress might be the reason of the occurrence of MSDs. From this study it has been recommended that workers should avoid bad work postures as far as possible during work for reducing job related health hazards.International Journal of Occupational Safety and Health, Vol 4 No 2 (2014) 05 – 1

Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring

Identification of a suitable working posture for female workers engaged in golden thread work

Background: The golden thread (jori) work is one of the major home based industries providing employment to thousands of artisans spread over several rural areas of the West Bengal. The study is essentially labor intensive and thus workers are prone to suffer from work related health problems. Objective: The present study was aimed to evaluate the musculoskeletal disorders (MSDs) and postural stress of the female golden thread workers. Methods: The study was carried on 123 adult female golden thread workers in different districts of West Bengal. The prevalence of MSD, body part discomfort rating (BPD) and postural stress (by OWAS, RULA, REBA method) of the workers were evaluated by standard methods. The EMG of shoulder and back muscle was recorded with the help of the BIOPAC system. Result: The results showed that the prevalence of MSDs, BPD rating and postural stress were comparatively lower in case of sitting on the floor with folded legs than squatting and sitting on the floor with stretched legs. The EMG and RMS values of EMG voltage of shoulder and back muscle were comparatively lower in this posture than that of other two postures. Conclusion: It was therefore, concluded that sitting on the floor with folded legs was less hazardous and it imposed less postural stress in comparison to other sitting postures adopted by the golden thread workers.International Journal of Occupational Safety and Health, Vol 4 No 2 (2014) 24 – 3