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In this paper, we propose a reconfigurable intelligent surface (RIS) that can dynamically switch the transmission and reflection phase of incident electromagnetic waves in real time to realize the dual-beam or quad-beam and convert the polarization of the transmitted beam. Such surfaces can redirect a wireless signal at will to establish robust connectivity when the designated line-of-sight channel is disturbed, thereby enhancing the performance of wireless communication systems by creating an intelligent radio environment. When integrated with a sensing element, they are integral to performing joint detection and communication functions in future wireless sensor networks. In this work, we first analyze the scattering performance of a reconfigurable unit element and then design a RIS. The dynamic field scattering manipulation capability of the RIS is validated by full-wave electromagnetic simulations to realize six different functions. The scattering characteristics of the proposed unit element, which incorporates two p-i-n diodes have been substantiated through practical implementation. This involved the construction of a simple prototype and the subsequent examination of its scattering properties via the free-space measurement method. The obtained transmission and reflection coefficients from the measurements are in agreement with the anticipated outcomes from simulations.

Present research entails an in-depth analysis on the effect of two secondary host plants of eri silkworm viz., borpat (Ailanthus grandis Prain) and borkesseru (Ailanthus excels Roxb.) and seasons on yarn parameters of eri silk as quality of silk depends on food quality and rearing season. Seasons had significant effect on all the yarn parameters viz., yarn size, breaking load, tenacity and elongation except twist per inch. Significantly the highest yarn size (10.26s) was observed in autumn season which was at par with the spring season. But the maximum breaking load (0.67 kg) and tenacity (1.40 g/denier) were recorded in spring season and elongation (26.54%) in early summer season. Regardless of the seasons, significantly the higher breaking load (0.64 kg) and tenacity (1.51 g/denier) were recorded on the borkesseru leaves. The interaction effect of seasons and two host plants on eri silk yarn was found to be non-significant. Diversiform effect of both the seasons and the host plants pertaining to yarn characters were witnessed. It can be ensured that all the seasons and host plants are relevant with slight alteration in the yarn quality.

Present research entails an in-depth analysis on the effect of two secondary host plants of eri silkworm viz., borpat (Ailanthus grandis Prain) and borkesseru (Ailanthus excels Roxb.) and seasons on yarn parameters of eri silk as quality of silk depends on food quality and rearing season. Seasons had significant effect on all the yarn parameters viz., yarn size, breaking load, tenacity and elongation except twist per inch. Significantly the highest yarn size (10.26s) was observed in autumn season which was at par with the spring season. But the maximum breaking load (0.67 kg) and tenacity (1.40 g/denier) were recorded in spring season and elongation (26.54%) in early summer season. Regardless of the seasons, significantly the higher breaking load (0.64 kg) and tenacity (1.51 g/denier) were recorded on the borkesseru leaves. The interaction effect of seasons and two host plants on eri silk yarn was found to be non-significant. Diversiform effect of both the seasons and the host plants pertaining to yarn characters were witnessed. It can be ensured that all the seasons and host plants are relevant with slight alteration in the yarn quality.

Present research entails an in-depth analysis on the effect of two secondary host plants of eri silkworm viz., borpat (Ailanthus grandis Prain) and borkesseru (Ailanthus excels Roxb.) and seasons on yarn parameters of eri silk as quality of silk depends on food quality and rearing season. Seasons had significant effect on all the yarn parameters viz., yarn size, breaking load, tenacity and elongation except twist per inch. Significantly the highest yarn size (10.26s) was observed in autumn season which was at par with the spring season. But the maximum breaking load (0.67 kg) and tenacity (1.40 g/denier) were recorded in spring season and elongation (26.54%) in early summer season. Regardless of the seasons, significantly the higher breaking load (0.64 kg) and tenacity (1.51 g/denier) were recorded on the borkesseru leaves. The interaction effect of seasons and two host plants on eri silk yarn was found to be non-significant. Diversiform effect of both the seasons and the host plants pertaining to yarn characters were witnessed. It can be ensured that all the seasons and host plants are relevant with slight alteration in the yarn quality.

Present research entails an in-depth analysis on the effect of two secondary host plants of eri silkworm viz., borpat (Ailanthus grandis Prain) and borkesseru (Ailanthus excels Roxb.) and seasons on yarn parameters of eri silk as quality of silk depends on food quality and rearing season. Seasons had significant effect on all the yarn parameters viz., yarn size, breaking load, tenacity and elongation except twist per inch. Significantly the highest yarn size (10.26s) was observed in autumn season which was at par with the spring season. But the maximum breaking load (0.67 kg) and tenacity (1.40 g/denier) were recorded in spring season and elongation (26.54%) in early summer season. Regardless of the seasons, significantly the higher breaking load (0.64 kg) and tenacity (1.51 g/denier) were recorded on the borkesseru leaves. The interaction effect of seasons and two host plants on eri silk yarn was found to be non-significant. Diversiform effect of both the seasons and the host plants pertaining to yarn characters were witnessed. It can be ensured that all the seasons and host plants are relevant with slight alteration in the yarn quality.

A study was undertaken to compare the awareness about muga culture among the college students in Jorhat district of Assam. Significant differences were found between the male and female students, and between rural and urban students. On the other hand, significant differences were found in the case of awareness on muga culture between Arts and Science stream students, and Arts & Commerce stream students.

A study was undertaken to compare the awareness about muga culture among the college students in Jorhat district of Assam. Significant differences were found between the male and female students, and between rural and urban students. On the other hand, significant differences were found in the case of awareness on muga culture between Arts and Science stream students, and Arts & Commerce stream students.

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.