Enhancing Generic Skills Development in Higher Education in the Era of Large Language Model Artificial Intelligence

The recently developed large language model artificial intelligence (AI) systems, such as ChatGPT, are likely to become widely used over the next few years as core tools in many areas of business. Higher education graduates will need to be adequately prepared with the necessary generic skills to exploit this technology and a detailed understanding of the risks of using it and related ethical aspects to maximize their employability. Higher education institutions will need to develop training to support this and a conceptual multi-layer framework is proposed for generic skills development in higher education relevant to the emerging new era of large language model AI. This model encapsulates traditional generic skills training for employability, the extended skill sets needed for operating as independent knowledge-workers in increasingly fragmented working contexts, and the new sets of skills in areas such as prompt engineering, output verification and bias detection, which are needed specifically for operating as effective users of advanced AI systems in business

Research Issues in the Development of Advanced Mobile Web-Based Geospatial Information Services

This paper considers the development of the next generation of mobile web-based geospatial information services and the underlying research issues that need to be tackled. These include the human factors, (including device related issues and geosemantics), architectural issues and efficiency, complexity of processing and security issues

HySenS data exploitation for urban land cover analysis

This paper addresses the use of HySenS airborne hyperspectral data for environmental urban monitoring. It is known that hyperspectral data can help to characterize some of the relations between soil composition, vegetation characteristics, and natural/artificial materials in urbanized areas. During the project we collected DAIS and ROSIS data over the urban test area of Pavia, Northern Italy, though due to a late delivery of ROSIS data only DAIS data was used in this work. Here we show results referring to an accurate characterization and classification of land cover/use, using different supervised approaches, exploiting spectral as well as spatial information. We demonstrate the possibility to extract from the hyperspectral data information which is very useful for environmental characterization of urban areas

Crop yield prediction using multipolarization radar and multitemporal visible / infrared imagery

This paper describes research undertaken on the improvement of within-field late season yield forecasting for crops such as wheat using multi-temporal visible/infrared satellite imagery and multi-polarization radar satellite imagery. Experiments have been carried out using ASAR imagery from Envisat combined with nine bands of ASTER imagery from the NASA Terra satellite. An experimental test site in an agricultural area in the county of Lincolnshire, UK, has been used. The satellite imagery has been integrated using artificial neural networks which have been trained as predictors of the spatial distributions of yield per unit area in a variety of fields. Ground truth data in the form of yield maps from GPS-enabled combine harvesters have been used to train the neural networks and to evaluate accuracy. The results show that the combinations of ASTER and ASAR imagery can provide enhanced yield predictions with overall correlations of up to 0.77 between predicted and actual yield patterns. The results also show that the use of dual polarization radar data alone is not sufficient to give reasonable yield predictions even in a multi-temporal mode. It has also been shown that varying the architectures of the neural networks with ensembles can improve the overall results

Risk Factors for Acquired Rifamycin and Isoniazid resistance: A systematic review and meta-analysis

BACKGROUND: Studies looking at acquired drug resistance (ADR) are diverse with respect to geographical distribution, HIV co-infection rates, retreatment status and programmatic factors such as regimens administered and directly observed therapy. Our objective was to examine and consolidate evidence from clinical studies of the multifactorial aetiology of acquired rifamycin and/or isoniazid resistance within the scope of a single systematic review. This is important to inform policy and identify key areas for further studies. METHODS: Case-control and cohort studies and randomised controlled trials that reported ADR as an outcome during antitubercular treatment regimens including a rifamycin and examined the association of at least 1 risk factor were included. Post hoc, we carried out random effects Mantel-Haenszel weighted meta-analyses of the impact of 2 key risk factors 1) HIV and 2) baseline drug resistance on the binary outcome of ADR. Heterogeneity was assessed used I 2 statistic. As a secondary outcome, we calculated median cumulative incidence of ADR, weighted by the sample size of the studies. RESULTS: Meta-analysis of 15 studies showed increased risk of ADR with baseline mono- or polyresistance (RR 4.85 95% CI 3.26 to 7.23, heterogeneity I 2 58%, 95% CI 26 to 76%). Meta-analysis of 8 studies showed that HIV co-infection was associated with increased risk of ADR (RR 3.02, 95% CI 1.28 to 7.11); there was considerable heterogeneity amongst these studies (I 2 81%, 95% CI 64 to 90%). Non-adherence, extrapulmonary/disseminated disease and advanced immunosuppression in HIV co-infection were other risk factors noted. The weighted median cumulative incidence of acquired multi drug resistance calculated in 24 studies (assuming whole cohort as denominator, regardless of follow up DST) was 0.1% (5 th to 95 th percentile 0.07 to 3.2%). CONCLUSION: Baseline drug resistance and HIV co-infection were significant risk factors for ADR. There was a trend of positive association with non-adherence which is likely to contribute to the outcome of ADR. The multifactorial aetiology of ADR in a programmatic setting should be further evaluated via appropriately designed studies

Effect of HIV-1 infection on T-Cell-based and skin test detection of tuberculosis infection

RATIONALE: Two forms of the IFN-gamma release assay (IFNGRA) to detect tuberculosis infection are available, but neither has been evaluated in comparable HIV-infected and uninfected persons in a high tuberculosis incidence environment. OBJECTIVE: To compare the ability of the T-SPOT.TB (Oxford Immunotec, Abingdon, UK), QuantiFERON-TB Gold (Cellestis, Melbourne, Australia), and Mantoux tests to identify latent tuberculosis in HIV-infected and uninfected persons. METHODS: A cross-sectional study of 160 healthy adults without active tuberculosis attending a voluntary counseling and testing center for HIV infection in Khayelitsha, a deprived urban South African community with an HIV antenatal seroprevalence of 33% and a tuberculosis incidence of 1,612 per 100,000. MEASUREMENTS AND MAIN RESULTS: One hundred and sixty (74 HIV(+) and 86 HIV(-)) persons were enrolled. A lower proportion of Mantoux results was positive in HIV-infected subjects compared with HIV-uninfected subjects (p < 0.01). By contrast, the proportion of positive IFNGRAs was not significantly different in HIV-infected persons for the T-SPOT.TB test (52 vs. 59%; p = 0.41) or the QuantiFERON-TB Gold test (43 and 46%; p = 0.89). Fair agreement between the Mantoux test (5- and 10-mm cutoffs) and the IFNGRA was seen in HIV-infected people (kappa = 0.52-0.6). By contrast, poor agreement between the Mantoux and QuantiFERON-TB Gold tests was observed in the HIV-uninfected group (kappa = 0.07-0.30, depending on the Mantoux cutoff). The pattern was similar for T-SPOT.TB (kappa = 0.18-0.24). Interpretation: IFNGRA sensitivity appears relatively unimpaired by moderately advanced HIV infection. However, agreement between the tests and with the Mantoux test varied from poor to fair. This highlights the need for prospective studies to determine which test may predict the subsequent risk of tuberculosis

HySenS data exploitation for urban land cover analysis

This paper addresses the use of HySenS airborne hyperspectral data for environmental urban monitoring. It is known that hyperspectral data can help to characterize some of the relations between soil composition, vegetation characteristics, and natural/artificial materials in urbanized areas. During the project we collected DAIS and ROSIS data over the urban test area of Pavia, Northern Italy, though due to a late delivery of ROSIS data only DAIS data was used in this work. Here we show results referring to an accurate characterization and classification of land cover/use, using different supervised approaches, exploiting spectral as well as spatial information. We demonstrate the possibility to extract from the hyperspectral data information which is very useful for environmental characterization of urban areas