SAFEGUARDING AGAINST IMPROPER CONFIGURATIONS FOR LORAWAN GATEWAYS

Presented herein are safeguard features for Long Range wide-area network (LoRaWAN) deployments on a LoRa gateway. The techniques presented herein provide a solution to prevent end users from wrongly or accidentally deploying LoRa gateways in unlawful Industrial, Scientific and Medical (ISM) radio bands. The techniques presented herein require little computational and storage resources to be integrated into any LoRa gateway embedded system, operate automatically based on the standard LNS protocol, and are sufficiently flexible to suit different use cases

Interplay between ferromagnetism, surface states, and quantum corrections in a magnetically doped topological insulator

The breaking of time-reversal symmetry by ferromagnetism is predicted to yield profound changes to the electronic surface states of a topological insulator. Here, we report on a concerted set of structural, magnetic, electrical and spectroscopic measurements of \MBS thin films wherein photoemission and x-ray magnetic circular dichroism studies have recently shown surface ferromagnetism in the temperature range 15 K $\leq T \leq 100$ K, accompanied by a suppressed density of surface states at the Dirac point. Secondary ion mass spectroscopy and scanning tunneling microscopy reveal an inhomogeneous distribution of Mn atoms, with a tendency to segregate towards the sample surface. Magnetometry and anisotropic magnetoresistance measurements are insensitive to the high temperature ferromagnetism seen in surface studies, revealing instead a low temperature ferromagnetic phase at $T \lesssim 5$ K. The absence of both a magneto-optical Kerr effect and anomalous Hall effect suggests that this low temperature ferromagnetism is unlikely to be a homogeneous bulk phase but likely originates in nanoscale near-surface regions of the bulk where magnetic atoms segregate during sample growth. Although the samples are not ideal, with both bulk and surface contributions to electron transport, we measure a magnetoconductance whose behavior is qualitatively consistent with predictions that the opening of a gap in the Dirac spectrum drives quantum corrections to the conductance in topological insulators from the symplectic to the orthogonal class.Comment: To appear in Phys. Rev.

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.</p

Deriving Hourly Evapotranspiration Rates with SEBS: A Lysimetric Evaluation

Numerous energy balance (EB) algorithms have been developed to use remote sensing data for mapping evapotranspiration (ET) on a regional basis. Adopting any single or combination of these models for an operational ET remote sensing program requires a thorough evaluation. The Surface Energy Balance System (SEBS) was evaluated for its ability to estimate hourly ET rates of summer tall and short crops grown in the Texas High Plains by using 15 Landsat 5 Thematic Mapper scenes acquired during 2006 to 2009. Performance of SEBS was evaluated by comparing estimated hourly ET values with measured ET data from four large weighing lysimeters, each located at the center of a 4.3 ha field in the USDA-ARS Conservation and Production Research Laboratory in Bushland, TX. The performance of SEBS in estimating hourly ET was good for crops under both irrigated and dryland conditions. A locally derived, surface albedo-based soil heat flux (G) model further improved the G estimates. Root mean square error and mean bias error were 0.11 and −0.005 mm h−1, respectively, and the Nash–Sutcliff model efficiency was 0.85 between the measured and calculated hourly ET. Considering the equal or better performance with a minimal amount of ancillary data as compared to with other EB algorithms, SEBS is a promising tool for use in an operational ET remote sensing program in the semiarid Texas High Plains. However, thorough sensitivity and error propagation analyses of input variables to quantify their impact on ET estimations for the major crops in the Texas High Plains under different agroclimatological conditions are needed before adopting the SEBS into operational ET remote sensing programs for irrigation scheduling or other purposes

Past, present and future: perspectives on an oral history of intellectual disability nursing

Thirty-one participants engaged in this oral history research study aimed at exploring the lived experience of intellectual disability nurses and healthcare assistants’ knowledge of the trajectory of intellectual disability nursing over the last 30 years in the Republic of Ireland and England. This paper documents some of these experiences offering perspectives on intellectual disability nursing and what is important for the future. Findings from Ireland consider the nature of intellectual disability services and the registered nurse in intellectual disability. Findings from England focus on opportunities and restrictions in intellectual disability nursing, shared visions, the changing context within which work took place and also the internal and external supports that impacted their roles. It is evident that intellectual disability nurses must be responsive to the changing landscape of service provision and also the requirements for contemporary new roles to meet the changing needs of people with intellectual disabilities

‘Intellectual disability nursing, the Cinderella relation of nursing’: marginality explored through the oral histories of intellectual disability nurses.

This paper draws on work undertaken for an oral history project during 2018 - 2020, which sought to explore the careers of 31 intellectual disability nurses from England, UK and the Republic of Ireland, with at least thirty years experience. In both jurisdictions some participants had worked within long-stay institutions built in previous centuries, which have now all but closed; being replaced with smaller living configurations. Few practising intellectual disability nurses have experience of working in such institutions, and this makes it apposite to hear their important stories. Their narratives provide new insights into how these nurses have been marginalised from their professional group by a complex interplay of hegemonic tensions, parallel stigmatisation, recruitment issues, and emotional labour