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Static random access memory (SRAM)-based physically unclonable functions (PUFs) utilize unpredictable start-up values (SUVs) for key generation, making them widely adopted in cryptographic systems. This unpredictability in SUVs is accompanied by device noise that escalates with process-voltage–temperature (PVT) variations, resulting in significant deviations from the golden response collected at ambient conditions, thereby increasing the bit-error-rate (BER) of the PUF responses. To reduce this high- $(\geq 15\%)$ BER, either an involved error correcting code (ECC) circuitry with significant overhead is required, or more helper information needs to be generated at varying operating conditions, resulting in increased information leakage. We address this issue by proposing the first reported application of machine learning to recalibrate the responses by predicting the golden responses of the SRAM-based PUF (SRAM-PUF) at different operating conditions with high accuracy. Our recalibration technique is based on a novel collective decision that involves observing the neighborhood cells of the SRAM-PUF, as opposed to the traditional single-cell approach. By leveraging a memory map exhibiting a high correlation in ambient reliability amongst neighboring cells, we indirectly use the physical co-location of SRAM cells to assist neighborhood error prediction. It leads to efficient post-processing for SRAM-PUFs by using helper data generated at ambient conditions only while employing a fixed ECC designed for the same. Subsequently, to justify our claims and validate the efficacy of our proposed methodology, we demonstrate extensive experimentation results over multiple SRAM-PUF instances implemented on the Arduino UNO (an 8-bit microcontroller unit) and its scaled-up version, the Arduino Zero (a 32-bit microcontroller unit) boards, by varying supply voltages from 3.8 to 6.2 V and 7 to 12 V, respectively, and temperature from −25° to 70° C in both cases. Our observations show a vast drop in BER from 17.02% to $\approx 1\%$ . Although worst-case conditions with both voltage and temperature variations at play resulted in a BER of 20%, using our proposed approach reduces it to $\approx 1{\text {-}} 2\%$ , in turn demonstrating the high efficacy of our scheme.

The Moyna basin of West Bengal is experiencing a rapid transformation in land use and land cover (LULC) as waterbodies are rapidly increasing at the expense of low-lying agricultural lands. The transformation in the LULC pattern in Moyna basin has been studied earlier based on the focus of climate change, biodiversity and other environmental issues. However, very little is known about the implications of this transformation on gender roles, responsibilities and livelihoods. This study examines the interconnectedness between LULC transformation and its differential impact on the livelihoods and workload of men and women using an intersectional perspective in three villages across Moyna block. The data were collected using a mixed-method approach and comprised of three major sources of information—observations, photographs and interviews. Results show that the changes in LULC have occurred partly as an impact of climate change and partly due to the inclination towards aquaculture due to decline in profitability of agriculture and farming practices. In the context of these livelihood changes, a fundamental shift in gendered roles has occurred. It has been found that the switch to aquaculture has led to two prominent outcomes, that is, depeasantisation of agricultural workers and defeminisation of the overall workforce. While women and men were equally displaced from agriculture with the advent of aquaculture, women were faced with additional challenges of unequal social gender norms and limited opportunities for alternative employment. It further shows that, the impacts of land use change on livelihoods are diverse and is a complex process as factors, like—class, caste, patriarchal family relations, family structure and the nature of the labour market intersect with gender and it also makes some women more vulnerable than other men and women.

Nanodimensional glass of composition 35Li2O·65SiO2was grown within the nanochannels of diameter 5.5 nm of mesoporous silica SBA-15 by suitable heat treatment of the required precursor sols. A dc electrical conductivity of 10–4S·cm–1was measured for the nanoglass at room temperature. This also shows a low activation energy of 0.1 eV for the lithium ion migration. This has been explained as arising due to oxygen ion vacancies caused by the presence of Si2+and Si4+species in the mesoporous silica at its interface with the nanoglass. This causes a repulsive interaction between the defects and the lithium ions, thereby reducing the attractive electrostatic force between the nonbridging oxygen ions and the lithium ions. These nanocomposites will be suitable for applications in lithium-ion batteries for storage of renewable energy.