The Himalayan region has been experiencing stark impacts of climate change,demographic and livelihood pattern changes.The analysis of land use and land cover(LULC)change provides insights into the shifts in spatial an...The Himalayan region has been experiencing stark impacts of climate change,demographic and livelihood pattern changes.The analysis of land use and land cover(LULC)change provides insights into the shifts in spatial and temporal patterns of landscape.These changes are the combined effects of anthropogenic and natural/climatic factors.The present study attempts to monitor and comprehend the main drivers behind LULC changes(1999-2021)in the Himalayan region of Pithoragarh district,Uttarakhand.Pithoragarh district is a border district,remotely located in the north-east region of Uttarakhand,India.The study draws upon primary and secondary data sources.A total of 400 household surveys and five group discussions from 38 villages were conducted randomly to understand the climate perception of the local community and the drivers of change.Satellite imagery,CRU(Climatic Research Unit)climate data and climate perception data from the field have been used to comprehensively comprehend,analyze,and discuss the trends and reasons for LULC change.GIS and remote sensing techniques were used to construct LULC maps.This multifaceted approach ensures comprehensive and corroborated information.Five classes were identified and formed viz-cultivation,barren,settlement,snow,and vegetation.Results show that vegetation and builtup have increased whereas cultivation,barren land,and snow cover have decreased.The study further aims to elucidate the causes behind LULC changes in the spatially heterogeneous region,distinguishing between those attributed to human activities,climate shifts,and the interconnected impacts of both.The study provides a comprehensive picture of the study area and delivers a targeted understanding of local drivers and their potential remedies by offering a foundation for formulating sustainable adaptation policies in the region.展开更多
Mountain ecosystem, on the earth, has plenty of natural resources. In Himachal Pradesh all the rivers are snowfed and therefore rich in water resources. These resources have been supporting enough for the generation o...Mountain ecosystem, on the earth, has plenty of natural resources. In Himachal Pradesh all the rivers are snowfed and therefore rich in water resources. These resources have been supporting enough for the generation of electricity through introducing hydropower projects since the last decade However, every developmental activity has its own negative impacts on the surrounding environment. Due to the fragile nature of topography and delicacy of ecology of the Himalaya, it results in lot of disturbances because of high degree of human interferences like construction of major hydropower projects. The increased extent of geological hazards, such as landslides, rock fall and soil erosion, have mainly due to alike developmental interventions in the natural ecosystem. So understanding and analysing such impacts of the hydropower projects have mainly been on the environment in various forms but natural hazards have been frequent ones. The present study, therefore, focuses mainly on the Parbati Stage II （800 MW） and the Parbati Stage III （520 MW） hydropower projects; both of which fall within the Kullu district of Himachal Pradesh. Based on the perception survey of the local communities, the existing land use pattern, status of total acquired land of the residents by hydropower projects, frequent natural hazards and resultant loss to the local communities due to upcoming construction of hydropower projects surrounding to the Parbati Stage II and III have been analysed in the paper. Also, the preventive measures to mitigate these adverse impacts have been suggested to strengthen these projects in eco-friendly manner in the mountain context.展开更多
Introduction: Serum Thyrotropin (TSH) level is used to assess adequacy of levothyroxine dosing for patients with hypothyroidism. Some patients have raised TSH levels despite being on an adequate dose of levothyroxine ...Introduction: Serum Thyrotropin (TSH) level is used to assess adequacy of levothyroxine dosing for patients with hypothyroidism. Some patients have raised TSH levels despite being on an adequate dose of levothyroxine (100 mcg/day - 200 mcg/day). Aim: To evaluated the effect of advising patients to take their levothyroxine 45 - 60 minutes before breakfast on raised serum TSH levels. Patients and Methods: Rather than increase the dose, patients with raised TSH levels were asked to take their levothyroxine at least 45 - 60 minutes before breakfast and other oral medications. Thyroid Function Tests were assessed at base line and repeated after two months. Results: Data from ten patients who presented between 2008 and 2010 were analyzed (9 females, 1 male): With median (IQR) age: 39 (33 - 49) years and duration of hypothyroidism: 6 (3 - 7.8) years. Median (IQR) levothyroxine dose was 175 (144 - 250) mcg, serum free-Thyroxine (free-T4): 13 (10.5 - 17.1) pmol/L and serum TSH: 12.63 (6.2 - 48.3) mIU/L. After two months all patients demonstrated biochemical improvement;a decrease in serum TSH to 3.15 (0.4 - 6.1) mIU/L accompanied by an increase in serum free-T4 to 17.7 (14.8 - 21.3) pmol/L. Both changes were statistically significant (p < 0.05 and p < 0.01, respectively). The median (IQR) percentage TSH reduction was 83.5 (40.3 - 95.8) mIU/L and this bore no significant correlation with the initial TSH level (rs = 0.2, p = 0.58). Conclusion: Changing levothyroxine administration to 45 - 60 minutes before breakfast and other oral medications reduced TSH levels by 40% - 96% in all patients. We recommend this advice for all patients with hypothyroidism on adequate doses of levothyroxine but still appear biochemically under-replaced.展开更多
Wet cupping is a simple and minor procedure practiced in Ayurveda and various traditional medicine system worldwide.In Ayurveda wet cupping therapy is practiced under the scope of Raktamo Kshana(therapeutic bloodletti...Wet cupping is a simple and minor procedure practiced in Ayurveda and various traditional medicine system worldwide.In Ayurveda wet cupping therapy is practiced under the scope of Raktamo Kshana(therapeutic bloodletting)which is adopted to remove vitiated Rakta(blood).The present work is aimed to explore the wet cupping therapy from Ayurveda perspective along with global scenario.In this review,classical Ayurveda text and PubMed,Cochrane library,science direct,Google scholar and DHARA database were scrutinized for worldwide work on wet cupping therapy.The Ayurveda science can utilize these researches in completing its lost knowledge and also provide integrative effort in re-validation and enrichment of WCT which are required at large for greater benefit of the mankind.The method of WCT application,principles,indications,contraindications,complications and probable mode of action from Ayurveda perspective and global scenario were introduced and summarized.展开更多
This paper introduces a deep learning(DL)algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing(OFDM)communication systems affected by hardware imp...This paper introduces a deep learning(DL)algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing(OFDM)communication systems affected by hardware impairments(HIs).In practice,hardware imperfections are present at the transceivers,which are modeled as direct current(DC)offset,carrier frequency offset(CFO),and in-phase and quadrature-phase(IQ)imbalance at the transmitter and the receiver in OFDM system.In HIs,the explicit system model could not be mathematically derived,which limits the performance of conventional least square(LS)or minimum mean square error(MMSE)estimators.Thus,we consider time-frequency response of a channel as a 2D image,and unknown values of the channel response are derived using known values at the pilot locations with DL-based image super-resolution,and image restoration techniques.Further,a deep neural network(DNN)is designed to fit the mapping between the received signal and transmit symbols,where the number of outputs equals to the size of the modulation order.Results show that there are no significant effects of HIs on channel estimation and signal detection in the proposed DL-assisted algorithm.The proposed DL-assisted detection improves the OFDM performance as compared to the conventional LS/MMSE under severe Hls.展开更多
Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce.Manual detection of blight disease can be cumbersome and may require trained experts.T...Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce.Manual detection of blight disease can be cumbersome and may require trained experts.To overcome these issues,we present an automated system using the Mask Region-based convolutional neural network(Mask R-CNN)architecture,with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions.The approach uses transfer learning,which can generate good results even with small datasets.The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day.The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf.The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches,which can confound the outcome of binary classification.To improve the detection performance,the original RGB dataset was then converted to HSL,HSV,LAB,XYZ,and YCrCb color spaces.A separate model was created for each color space and tested on 417 field-based test images.This yielded 81.4%mean average precision on the LAB model and 56.9%mean average recall on the HSL model,slightly outperforming the original RGB color space model.Manual analysis of the detection performance indicates an overall precision of 98%on leaf images in a field environment containing complex backgrounds.展开更多
文摘The Himalayan region has been experiencing stark impacts of climate change,demographic and livelihood pattern changes.The analysis of land use and land cover(LULC)change provides insights into the shifts in spatial and temporal patterns of landscape.These changes are the combined effects of anthropogenic and natural/climatic factors.The present study attempts to monitor and comprehend the main drivers behind LULC changes(1999-2021)in the Himalayan region of Pithoragarh district,Uttarakhand.Pithoragarh district is a border district,remotely located in the north-east region of Uttarakhand,India.The study draws upon primary and secondary data sources.A total of 400 household surveys and five group discussions from 38 villages were conducted randomly to understand the climate perception of the local community and the drivers of change.Satellite imagery,CRU(Climatic Research Unit)climate data and climate perception data from the field have been used to comprehensively comprehend,analyze,and discuss the trends and reasons for LULC change.GIS and remote sensing techniques were used to construct LULC maps.This multifaceted approach ensures comprehensive and corroborated information.Five classes were identified and formed viz-cultivation,barren,settlement,snow,and vegetation.Results show that vegetation and builtup have increased whereas cultivation,barren land,and snow cover have decreased.The study further aims to elucidate the causes behind LULC changes in the spatially heterogeneous region,distinguishing between those attributed to human activities,climate shifts,and the interconnected impacts of both.The study provides a comprehensive picture of the study area and delivers a targeted understanding of local drivers and their potential remedies by offering a foundation for formulating sustainable adaptation policies in the region.
文摘Mountain ecosystem, on the earth, has plenty of natural resources. In Himachal Pradesh all the rivers are snowfed and therefore rich in water resources. These resources have been supporting enough for the generation of electricity through introducing hydropower projects since the last decade However, every developmental activity has its own negative impacts on the surrounding environment. Due to the fragile nature of topography and delicacy of ecology of the Himalaya, it results in lot of disturbances because of high degree of human interferences like construction of major hydropower projects. The increased extent of geological hazards, such as landslides, rock fall and soil erosion, have mainly due to alike developmental interventions in the natural ecosystem. So understanding and analysing such impacts of the hydropower projects have mainly been on the environment in various forms but natural hazards have been frequent ones. The present study, therefore, focuses mainly on the Parbati Stage II （800 MW） and the Parbati Stage III （520 MW） hydropower projects; both of which fall within the Kullu district of Himachal Pradesh. Based on the perception survey of the local communities, the existing land use pattern, status of total acquired land of the residents by hydropower projects, frequent natural hazards and resultant loss to the local communities due to upcoming construction of hydropower projects surrounding to the Parbati Stage II and III have been analysed in the paper. Also, the preventive measures to mitigate these adverse impacts have been suggested to strengthen these projects in eco-friendly manner in the mountain context.
文摘Introduction: Serum Thyrotropin (TSH) level is used to assess adequacy of levothyroxine dosing for patients with hypothyroidism. Some patients have raised TSH levels despite being on an adequate dose of levothyroxine (100 mcg/day - 200 mcg/day). Aim: To evaluated the effect of advising patients to take their levothyroxine 45 - 60 minutes before breakfast on raised serum TSH levels. Patients and Methods: Rather than increase the dose, patients with raised TSH levels were asked to take their levothyroxine at least 45 - 60 minutes before breakfast and other oral medications. Thyroid Function Tests were assessed at base line and repeated after two months. Results: Data from ten patients who presented between 2008 and 2010 were analyzed (9 females, 1 male): With median (IQR) age: 39 (33 - 49) years and duration of hypothyroidism: 6 (3 - 7.8) years. Median (IQR) levothyroxine dose was 175 (144 - 250) mcg, serum free-Thyroxine (free-T4): 13 (10.5 - 17.1) pmol/L and serum TSH: 12.63 (6.2 - 48.3) mIU/L. After two months all patients demonstrated biochemical improvement;a decrease in serum TSH to 3.15 (0.4 - 6.1) mIU/L accompanied by an increase in serum free-T4 to 17.7 (14.8 - 21.3) pmol/L. Both changes were statistically significant (p < 0.05 and p < 0.01, respectively). The median (IQR) percentage TSH reduction was 83.5 (40.3 - 95.8) mIU/L and this bore no significant correlation with the initial TSH level (rs = 0.2, p = 0.58). Conclusion: Changing levothyroxine administration to 45 - 60 minutes before breakfast and other oral medications reduced TSH levels by 40% - 96% in all patients. We recommend this advice for all patients with hypothyroidism on adequate doses of levothyroxine but still appear biochemically under-replaced.
文摘Wet cupping is a simple and minor procedure practiced in Ayurveda and various traditional medicine system worldwide.In Ayurveda wet cupping therapy is practiced under the scope of Raktamo Kshana(therapeutic bloodletting)which is adopted to remove vitiated Rakta(blood).The present work is aimed to explore the wet cupping therapy from Ayurveda perspective along with global scenario.In this review,classical Ayurveda text and PubMed,Cochrane library,science direct,Google scholar and DHARA database were scrutinized for worldwide work on wet cupping therapy.The Ayurveda science can utilize these researches in completing its lost knowledge and also provide integrative effort in re-validation and enrichment of WCT which are required at large for greater benefit of the mankind.The method of WCT application,principles,indications,contraindications,complications and probable mode of action from Ayurveda perspective and global scenario were introduced and summarized.
基金supported by the Ministry of Science and Technology,SERB under Grant SRG/2021/000199 and by the Indian National Academy of Engineering(INAE)Project with Sanction under Grant 2023/INTW/10.
文摘This paper introduces a deep learning(DL)algorithm for estimating doubly-selective fading channel and detecting signals in orthogonal frequency division multiplexing(OFDM)communication systems affected by hardware impairments(HIs).In practice,hardware imperfections are present at the transceivers,which are modeled as direct current(DC)offset,carrier frequency offset(CFO),and in-phase and quadrature-phase(IQ)imbalance at the transmitter and the receiver in OFDM system.In HIs,the explicit system model could not be mathematically derived,which limits the performance of conventional least square(LS)or minimum mean square error(MMSE)estimators.Thus,we consider time-frequency response of a channel as a 2D image,and unknown values of the channel response are derived using known values at the pilot locations with DL-based image super-resolution,and image restoration techniques.Further,a deep neural network(DNN)is designed to fit the mapping between the received signal and transmit symbols,where the number of outputs equals to the size of the modulation order.Results show that there are no significant effects of HIs on channel estimation and signal detection in the proposed DL-assisted algorithm.The proposed DL-assisted detection improves the OFDM performance as compared to the conventional LS/MMSE under severe Hls.
基金the Government of India’s Department of Biotechnology under the FarmerZone™initiative(#BT/IN/Data Reuse/2017-18)the Ramalingaswami Re-entry fellowship(#BT/RLF/Re-entry/44/2016).
文摘Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce.Manual detection of blight disease can be cumbersome and may require trained experts.To overcome these issues,we present an automated system using the Mask Region-based convolutional neural network(Mask R-CNN)architecture,with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions.The approach uses transfer learning,which can generate good results even with small datasets.The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day.The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf.The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches,which can confound the outcome of binary classification.To improve the detection performance,the original RGB dataset was then converted to HSL,HSV,LAB,XYZ,and YCrCb color spaces.A separate model was created for each color space and tested on 417 field-based test images.This yielded 81.4%mean average precision on the LAB model and 56.9%mean average recall on the HSL model,slightly outperforming the original RGB color space model.Manual analysis of the detection performance indicates an overall precision of 98%on leaf images in a field environment containing complex backgrounds.
