The Change in Classification of Asperger Syndrome: An Exploration of its Effects on Self-Identity

Recently, the American Psychiatric Association eliminated Asperger Syndrome (AS) and introduced the Autism Spectrum Disorder (ASD) diagnostic framework. This change in nosology socially implicates people who self-identify with and derive personal meaning from their AS diagnosis. The current study explored the opinions of adults with AS regarding their identity related to the diagnostic terminology of ASD. Twelve adults with AS completed a semi-structured interview that was transcribed and analyzed qualitatively using Thematic Analysis. The analysis revealed six themes: (a) Derived Meaning, (b) Knowledge and Understanding, (c) Perceptions and Labels, (d) Social Identity, (e) Opinions and Reactions to ASD, and (f) Barriers to Funding and Service Provision. Many participants socially identified and self-categorized as part of the AS community because their challenges matched those described by the DSM-IV. Importantly, many participants described the removal of AS as a threat to their identity, social status, and access to supports. Implications are discussed

Provenance for the people: an HCI perspective on the W3C PROV standard through an online game

In the information age, tools for examining the validity of data are invaluable. Provenance is one such tool, and the PROV model proposed by the World Wide Web Consortium in 2013 offers a means of expressing provenance in a machine readable format. In this paper, we examine from a user’s standpoint notions of provenance, the accessibility of the PROV model, and the general attitudes towards history and the verifiability of information in modern data society. We do this through the medium of an online-game designed to explore these issues and present the findings of the study along with a discussion of some of its implications

Allometric Equations to Estimate Aboveground Biomass in Spotted Gum (Corymbia citriodora Subspecies variegata) Plantations in Queensland

Accurate equations are critical for estimating biomass and carbon accumulation for forest carbon projects, bioenergy, and other inventories. Allometric equations can provide a reliable and accurate method for estimating and predicting biomass and carbon sequestration. Cross-validatory assessments are also essential to evaluate the prediction ability of the selected model with satisfactory accuracy. We destructively sampled and weighed 52 sample trees, ranging from 11.8 to 42.0 cm in diameter at breast height from three plantations in Queensland to determine biomass. Weighted nonlinear models were used to explore the influence of different variables using two datasets: the first dataset (52 trees) included diameter at breast height (D), height (H) and wood density (ρ); and the second dataset (40 trees) also included crown diameter (CD) and crown volume (CV). Cross validation of independent data showed that using D alone proved to be the best performing model, with the lowest values of AIC = 434.4, bias = −2.2% and MAPE = 7.2%. Adding H and ρ improved the adjusted. R2 (Δ adj. R2 from 0.099 to 0.135) but did not improve AIC, bias and MAPE. Using the single variable of CV to estimate aboveground biomass (AGB) was better than CD, with smaller AIC and MAPE less than 2.3%. We demonstrated that the allometric equations developed and validated during this study provide reasonable estimates of Corymbia citriodora subspecies variegata (spotted gum) biomass. This equation could be used to estimate AGB and carbon in similar spotted gum plantations. In the context of global forest AGB estimations and monitoring, the CV variable could allow prediction of aboveground biomass using remote sensing datasets

Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research

Agricultural price prediction is crucial for farmers, policymakers, and other stakeholders in the agricultural sector. However, it is a challenging task due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by improving accuracy, real-time prediction, customization, and integration. This paper reviews recent research on machine learning algorithms for agricultural price prediction. We discuss the importance of agriculture in developing countries and the problems associated with crop price falls. We then identify the challenges of predicting agricultural prices and highlight how machine learning algorithms can support better prediction. Next, we present a comprehensive analysis of recent research, discussing the strengths and weaknesses of various machine learning techniques. We conclude that machine learning has the potential to revolutionize agricultural price prediction, but further research is essential to address the limitations and challenges associated with this approach

Species-Specific Allometric Equations for Predicting Belowground Root Biomass in Plantations: Case Study of Spotted Gums (Corymbia citriodora subspecies variegata) in Queensland

Spotted gum (Corymbia citriodora spp. variegata; CCV) has been widely planted, has a wide natural distribution, and is the most important commercially harvested hardwood species in Queensland, Australia. It has a great capacity to sequester carbon, thus reducing the impact of CO2 emissions on climate. Belowground root biomass (BGB) plays an important role as a carbon sink in terrestrial ecosystems. To explore the potential of biomass and carbon accumulation belowground, we developed and validated models for CCV plantations in Queensland. The roots of twenty-three individual trees (size range 11.8–42.0 cm diameter at breast height) from three sites were excavated to a 1-m depth and were weighed to obtain BGB. Weighted nonlinear regression models were most reliable for estimating BGB. To evaluate the candidate models, the data set was cross-validated with 70% of the data used for training and 30% of the data used for testing. The cross-validation process was repeated 23 times and the validation of the models were averaged over 23 iterations. The best model for predicting spotted gum BGB was based on a single parameter, with the diameter at breast height (D) as an independent variable. The best equation BGB = 0.02933 × D2.5805 had an adjusted R2 of 0.854 and a mean absolute percentage error of 0.090%. This equation was tested against published BGB equations; the findings from this are discussed. Our equation is recommended to allow improved estimates of BGB for this species

Feasibility of achieving the 2025 WHO global tuberculosis targets in South Africa, China, and India: a combined analysis of 11 mathematical models

Background The post-2015 End TB Strategy proposes targets of 50% reduction in tuberculosis incidence and 75% reduction in mortality from tuberculosis by 2025. We aimed to assess whether these targets are feasible in three high-burden countries with contrasting epidemiology and previous programmatic achievements. Methods 11 independently developed mathematical models of tuberculosis transmission projected the epidemiological impact of currently available tuberculosis interventions for prevention, diagnosis, and treatment in China, India, and South Africa. Models were calibrated with data on tuberculosis incidence and mortality in 2012. Representatives from national tuberculosis programmes and the advocacy community provided distinct country-specifi c intervention scenarios, which included screening for symptoms, active case fi nding, and preventive therapy. Findings Aggressive scale-up of any single intervention scenario could not achieve the post-2015 End TB Strategy targets in any country. However, the models projected that, in the South Africa national tuberculosis programme scenario, a combination of continuous isoniazid preventive therapy for individuals on antiretroviral therapy, expanded facility-based screening for symptoms of tuberculosis at health centres, and improved tuberculosis care could achieve a 55% reduction in incidence (range 31–62%) and a 72% reduction in mortality (range 64–82%) compared with 2015 levels. For India, and particularly for China, full scale-up of all interventions in tuberculosis-programme performance fell short of the 2025 targets, despite preventing a cumulative 3·4 million cases. The advocacy scenarios illustrated the high impact of detecting and treating latent tuberculosis. Interpretation Major reductions in tuberculosis burden seem possible with current interventions. However, additional interventions, adapted to country-specifi c tuberculosis epidemiology and health systems, are needed to reach the post-2015 End TB Strategy targets at country level

HAC-ER: a disaster response system based on human-agent collectives

This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emergency responders by enabling humans and agents, using state-of-the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC-ER utilises crowdsourcing combined with machine learning to extract situational awareness information from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a prototype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations

Molecular charge distributions in strong magnetic fields: a conceptual and current DFT study

The effect of strong magnetic fields on the charge distribution of the hydrogen halides, H2O and NH3 is studied in the context of recent extensions of conceptual density functional theory to include additional variables such as external magnetic fields. From conceptual DFT studies on atoms in strong magnetic fields, changes in electronegativity and hardness suggest a reversal in polarity for all three diatomic molecules under these conditions. This is confirmed by current DFT calculations on these molecules in the presence of strong magnetic fields parallel and perpendicular to the internuclear axis; in the former case the electric dipole moment only undergoes small changes whereas in the latter case it changes significantly and also reverses in direction, doing so at lower field strength if the geometry is relaxed. The absence of a dipole moment induced perpendicular to the bond when a magnetic field is applied in this direction is understood by consideration of time reversal symmetry. Similar results are obtained for H2O and NH3; this may be an important point to consider in future studies focused on the unresolved question on the behaviour of hydrogen bonding in applied magnetic fields

Understanding ground and excited-state molecular structure in strong magnetic fields using the maximum overlap method

The maximum overlap method (MOM) provides a simple but powerful approach for performing calculations on excited states by targeting solutions with non-Aufbau occupations from a reference set of molecular orbitals. In this work, the MOM is used to access excited states of (Formula presented.) and (Formula presented.) in strong magnetic fields. The lowest (Formula presented.), (Formula presented.) and (Formula presented.) states of (Formula presented.) in the absence of a field are compared with the corresponding states in strong magnetic fields. The changes in molecular structure in the presence of the field are examined by performing excited state geometry optimisations using the MOM. The (Formula presented.) state is significantly stabilised by the field, becoming the ground state in strong fields with a preferred orientation perpendicular to the applied field. Its potential energy surface evolves from being repulsive to bound, with an equilateral equilibrium geometry. In contrast, the (Formula presented.) state is destabilised and its structure distorts to an isosceles form with the longest H−H bond parallel to the applied field. Comparisons are made with the (Formula presented.) state of H3, which also becomes bound with an equilateral geometry at high fields. The structures of the high-spin ground states are rationalised by orbital correlation diagrams constructed using constrained geometry optimisations