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Two-dimensional ferroelectric monolayers are promising candidates for compact memory devices and flexible electronics. Here, through first-principles calculations, we predict room temperature ferroelectricity in AB-type monolayers comprising group III (A = Al, In, Ga) and group V (B = As, P, Sb) elements. We show that their spontaneous polarization, oriented out-of-plane, ranges from 9.48 to 13.96 pC m -1 , outperforming most known 2D ferroelectrics. We demonstrate an electric field tunable Berry curvature dipole and nonlinear Hall current in these monolayers. Additionally, we highlight their applicability in next-generation memory devices by forming efficient ferroelectric tunnel junctions, especially in InP, which supports high tunneling electroresistance. Our findings motivate further exploration of these monolayers for studying the interplay between the Berry curvature and ferroelectricity and for integrating these ferroelectric monolayers in next-generation electronic devices.

Single-port ferroelectric FET (FeFET) that performs write and read operations on the same electrical gate prevents its wide application in tunable analog electronics and suffers from read disturb, especially in the high-threshold voltage ( V TH ) state as the retention energy barrier is reduced by the applied read bias. To address both issues, we propose to adopt a read disturb-free dual-port FeFET where the write is performed on the gate featuring a ferroelectric layer and the read is done on a separate gate featuring a nonferroelectric dielectric. Combining the unique structure and the separate read gate, read disturb is eliminated as the applied field is aligned with polarization in the high- V TH state, thus improving its stability, while it is screened by the channel inversion charge and exerts no negative impact on the low- V TH state stability. Comprehensive theoretical and experimental validation has been performed on fully depleted silicon-on-insulator (FDSOI) FeFETs integrated on a 22 nm platform, which intrinsically has dual ports with its buried oxide layer acting as the nonferroelectric dielectric. Novel applications that can exploit the proposed dual-port FeFET are proposed and experimentally demonstrated for the first time, including FPGA that harnesses its read disturb-free feature and tunable analog electronics (e.g., frequency tunable ring oscillator in this work) leveraging the separated write and read paths.

Advancements in AI led to the emergence of in-memory-computing architectures as a promising solution for the associated computing and memory challenges. This study introduces a novel in-memory-computing (IMC) crossbar macro utilizing a multi-level ferroelectric field-effect transistor (FeFET) cell for multi-bit multiply and accumulate (MAC) operations. The proposed 1FeFET-1R cell design stores multi-bit information while minimizing device variability effects on accuracy. Experimental validation was performed using 28 nm HKMG technology-based FeFET devices. Unlike traditional resistive memory-based analog computing, our approach leverages the electrical characteristics of stored data within the memory cell to derive MAC operation results encoded in activation time and accumulated current. Remarkably, our design achieves 96.6% accuracy for handwriting recognition and 91.5% accuracy for image classification without extra training. Furthermore, it demonstrates exceptional performance, achieving 885.4 TOPS/W-nearly double that of existing designs. This study represents the first successful implementation of an in-memory macro using a multi-state FeFET cell for complete MAC operations, preserving crossbar density without additional structural overhead., (© 2023. Springer Nature Limited.)

Deep neural networks (DNNs) form a critical infrastructure supporting various systems, spanning from the iPhone neural engine to imaging satellites and drones. The design of these neural cores is often proprietary or a military secret. Nevertheless, they remain vulnerable to model replication attacks that seek to reverse engineer the network's synaptic weights. In this article, we propose SCANet (Superparamagnetic-MTJ Crossbar Array Networks), a novel defense mechanism against such model stealing attacks by utilizing the innate stochasticity in superparamagnets. When used as the synapse in DNNs, superparamagnetic magnetic tunnel junctions (s-MTJs) are shown to be significantly more secure than prior memristor-based solutions. The thermally induced telegraphic switching in the s-MTJs is robust and uncontrollable, thus thwarting the attackers from obtaining sensitive data from the network. Using a mixture of both superparamagnetic and conventional MTJs in the neural network (NN), the designer can optimize the time period between the weight updation and the power consumed by the system. Furthermore, we propose a modified NN architecture that can prevent replication attacks while minimizing power consumption. We investigate the effect of the number of layers in the deep network and the number of neurons in each layer on the sharpness of accuracy degradation when the network is under attack. We also explore the efficacy of SCANet in real-time scenarios, using a case study on object detection.

In this study, we examined the relationships between extremes of low temperatures and chickpea yield in 12 field experiments conducted at six sites in the subtropical environment of southeast Queensland (SEQ) from 2014 to 2019. Three commercial chickpea cultivars, PBA-Boundary, PBA-HatTrick and PBA-Seamer, were grown in all the experiments. Cultivars PBA-Pistol, PBA-Monarch and Kyabra were also included in three of these experiments conducted in 2015. In these experiments, the crop experienced a total of 8 to 41 frosts (minimum temperature <  = 0 °C), 2 to 41 pre-flowering frosts, 2 to 19 frosts during the critical period, 0 to 13 frosts and 2 to 71 low-temperature days (< = 15 °C) after flowering. The mean yield, which varied from 1 to 3 t/ha, was negatively related to post-flowering frosts (r =  - 0.74, p < 0.01) and low-temperature days (r =  - 0.76, p < 0.01), and positively related to pre-flowering frosts (r = 0.67, p < 0.05). Each post-flowering frost was associated with a 5% decrease and a low-temperature day with a 1% decrease in yield. The cultivar × site interaction was significant only in the three experiments with six commercial cultivars. This interaction was most likely due to an increase in the sensitivity range with additional cultivars, as indicated by frost damage scores and their relationships with yield. The results imply that extreme low-temperature events after flowering could negatively impact chickpea yield in SEQ and similar subtropical environments. Overcoming these effects through management and breeding should increase and stabilise chickpea yield., (© 2022. Crown.)

The recent discovery of synthetic two-dimensional materials has opened up a new paradigm for exploring novel transport and optical properties, beyond those found in naturally occurring materials. Here, we present a detailed investigation of the acoustic phonon limited intrinsic carrier mobility in MA 2 Z 4 series (M = Cr, Mo, W; A = Si, Ge; and Z = N, P) monolayers. We find that out of the twelve monolayers studied, only two are metallic (CrGe 2 N 4 and CrGe 2 P 4 ), and the rest of them are semiconducting. We demonstrate that the carrier mobilities in these monolayers are anisotropic and show a large variation, ranging from a small value of ∼90 cm 2 V -1 s -1 to a large value of ∼10 4 cm 2 V -1 s -1 . In addition, we show that strain engineering in these materials can further change the electronic band structure drastically, and change the carrier mobilities by up to a factor of 20. Our detailed and systematic study provides a useful platform for designing electronic devices based on the MA 2 Z 4 family of materials.

Water deficit often hastens flowering of pulses partially because droughted plants are hotter. Separating temperature-independent and temperature-dependent effects of drought is important to understand, model, and manipulate phenology. We define a new trait, drought effect on phenology (DEP), as the difference in flowering time between irrigated and rainfed crops, and use FST genome scanning to probe for genomic regions under selection for this trait in chickpea (Cicer arietinum). Owing to the negligible variation in daylength in our dataset, variation in phenology with sowing date was attributed to temperature and water; hence, genomic regions overlapping for early- and late-sown crops would associate with temperature-independent effects and non-overlapping genomic regions would associate with temperature-dependent effects. Thermal-time to flowering was shortened with increasing water stress, as quantified with carbon isotope composition. Genomic regions on chromosomes 4-8 were under selection for DEP. An overlapping region for early and late sowing on chromosome 8 revealed a temperature-independent effect with four candidate genes: BAM1, BAM2, HSL2, and ANT. The non-overlapping regions included six candidate genes: EMF1, EMF2, BRC1/TCP18, BZR1, NPGR1, and ERF1. Modelling showed that DEP reduces the likelihood of drought and heat stress at the expense of increased likelihood of cold stress. Accounting for DEP would improve genetic and phenotypic models of phenology., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

During the reproductive stage, chilling temperatures and frost reduce the yield of chickpea and limit its adaptation. The adverse effects of chilling temperature and frost in terms of the threshold temperatures, impact of cold duration, and genotype-by-environment-by-management interactions are not well quantified. Crop growth models that predict flowering time and yield under diverse climates can identify combinations of cultivars and sowing time to reduce frost risk in target environments. The Agricultural Production Systems Simulator (APSIM-chickpea) model uses daily temperatures to model basic crop growth but does not include penalties for either frost damage or cold temperatures during flowering and podding stages. Regression analysis overcame this limitation of the model for chickpea crops grown at 95 locations in Australia using 70 years of historic data incorporating three cultivars and three sowing times (early, mid, and late). We modified model parameters to include the effect of soil water on thermal time calculations, which significantly improved the prediction of flowering time. Simulated data, and data from field experiments grown in Australia (2013 to 2019), showed robust predictions for flowering time (n = 29; R 2  = 0.97), and grain yield (n = 22; R 2  = 0.63-0.70). In addition, we identified threshold cold temperatures that significantly affected predicted yield, and combinations of locations, variety, and sowing time where the overlap between peak cold temperatures and peak flowering was minimal. Our results showed that frost and/or cold temperature-induced yield losses are a major limitation in some unexpected Australian locations, e.g., inland, subtropical latitudes in Queensland. Intermediate sowing maximise yield, as it avoids cold temperature, late heat, and drought stresses potentially limiting yield in early and late sowing respectively., (© 2021. The Author(s).)

Pigeonpea [ Cajanus cajan (L.) Millsp.] is an important rainfed pulse crop of tropics and sub-tropics, and during its long growth cycle of 6-9 months it encounters a number of biotic and abiotic stresses. The recently developed CMS-based pigeonpea hybrids have demonstrated large gains in yield and stability over the traditional inbred cultivars. In this review, the authors argue that the heterosis expressed in traits like seed germination, radicle growth, root biomass production and moisture retention during water stress confers advantages to hybrid plants in negotiating a few abiotic and biotic stresses in much better way than pure line cultivars., (Copyright © 2020 by JAPANESE SOCIETY OF BREEDING.)

Matching crop phenology to environment is essential to improve yield and reduce risk of losses due to extreme temperatures, hence the importance of accurate prediction of flowering time. Empirical evidence suggests that soil water can influence flowering time in chickpea and wheat, but simulation models rarely account for this effect. Adjusting daily thermal time accumulation with fractional available soil water in the 0-60 cm soil layer improved the prediction of flowering time for both chickpea and wheat in comparison to the model simulating flowering time with only temperature and photoperiod. The number of post-flowering frost events accounted for 24% of the variation in observed chickpea yield using a temperature-photoperiod model, and 66% of the variation in yield with a model accounting for top-soil water content. Integrating the effect of soil water content in crop simulation models could improve prediction of flowering time and abiotic stress risk assessment.

Pulmonary alveolar proteinosis (PAP) is a rare lung disease characterized by accumulation of excessive lung surfactant in the alveoli leading to restrictive lung functions and impaired gas exchange. Whole lung lavage (WLL) is the treatment modality of choice, which is usually performed using double lumen endobronchial tube insertion under general anesthesia and alternating unilateral lung ventilation and washing with normal saline. It may be difficult to perform WLL in patients with severe hypoxemia wherein patients do not tolerate single lung ventilation. Extracorporeal membrane oxygenation support (ECMO) has been used in such patients. We report a patient with autoimmune PAP following renal transplant who presented with marked hypoxemia and was managed by WLL under ECMO support.

High-performance liquid chromatography (HPLC) is replacing electrophoresis for identification of hemoglobin variants. Our objective was to identify unknown tall peaks with elution times and shapes of hemoglobin Barts found on hemoglobin chromatograms that could not be confirmed by alkaline and acid gel electrophoresis. Of 90 specimens identified with this peak, 86 were from patients with hemoglobin SS. Regression of the height of the unknown peaks to serum bilirubin concentrations, diminution of the unknown peaks by washing the specimens, and chromatographic similarity of a total bilirubin serum calibrator, a bilirubin proficiency testing specimen, and 3 patients' serum samples with markedly elevated bilirubin to hemoglobin Barts provide evidence the peak was bilirubin. We suggest exclusion of bilirubin before HPLC results are reported as consistent with hemoglobin Barts.