Representation without informative signalling

Various writers have attempted to use the sender–receiver formalism to account for the representational capacities of biological systems. This article has two goals. First, I argue that the sender–receiver approach to representation cannot be complete. The mammalian circadian system represents the time of day, yet it does not control circadian behaviours by producing signals with time of day content. Informative signalling need not be the basis of our most basic representational capacities. Second, I argue that representational capacities are primarily about control, and only when specific conditions obtain does this control require informative signalling

When Vegas comes to Wall Street: Associations between stock price volatility and trading frequency among gamblers

Both gambling and trading involve risk-taking in exchange for potential financial gains. In particular, speculative high-risk high-frequency trading closely resembles disordered gambling behaviour by attracting the same individuals who tend to be overconfident, sensation-seekers, and attracted to quick large potential payoffs. We build on these studies via an incentivised experiment, in which we examine how manipulated levels of market volatility affected trading frequency. Gamblers (N=604) were screened based on the existence of household investments and recruited across the four categories of the Problem Gambling Severity Index. The volatility of stocks was manipulated between-participants (high vs. low). Participants traded fictitious stocks and were provided bonuses based on the results of their trading activity (M=US$4.77, range=[0, 16.99]). Participants traded more often in the high-volatility market, and this finding remained robust after controlling for financial literacy, overconfidence, age, and gender. Many investors trade more frequently than personal finance guides advise, and these results suggest that individuals are more likely to commit this error in more volatile markets. Exploratory analyses suggest that the effect of the volatility manipulation was strongest amongst gamblers who were at low-risk of experiencing gambling harms. As they might be otherwise considered low-risk, these individuals could be overlooked by protective gambling interventions yet nonetheless suffer unmitigated financial harms due to unchecked excessive trading

PestGPT: Leveraging large language models and IoT for timely and customized recommendation generation in sustainable pest management

In response to the escalating food crisis caused by global population growth, precision or smart agriculture has been proposed as a promising solution to achieve efficient and sustainable agricultural production through the Internet of Things (IoT), Data Analytics, and Artificial Intelligence (AI). Although much work has been proposed and proven to be effective in practical application scenarios, such as optimised water utilisation through smart irrigation systems, numerous studies exploring agricultural pest management solutions are limited to pest monitoring and lack the integration of management recommendation generation due to the reliance on agricultural expert knowledge. With the advancement of computer technology, especially for the large language models (LLMs), the combination of IoT and LLMs provides a feasible solution to this limitation by automating the observation data analysis process of agricultural experts. Therefore, this article proposes a framework that integrates with IoT and LLMs to provide users with accurate and customised pest management recommendations based on environmental information from edge devices. In the proposed framework, LLMs incorporate an explicit expert knowledge base to mitigate hallucinations. The proposed framework is deployed in an online farm management platform and is evaluated in a real-world application scenario. After quantitative analysis and validation with real-world application scenarios, we report on the limitations and future directions of the LLMs in the agricultural sector

The role of uncertainty in planning for self-funded social care for older people with a diagnosis of dementia

This paper considers how a diagnosis of dementia affects people’s planning for future social care needs and associated costs. It addresses the gap in knowledge about how people recently diagnosed with dementia, and their family carers, engage with planning for social care needs that are uncertain in timing and scale. The paper also considers people’s attitudes to planning for care that they may need to pay for privately, and what facilitates or hinders acting on such plans. We conducted and undertook thematic qualitative analysis of 39 in-depth interviews with 27 people newly diagnosed with dementia and/or their carers over a two year period. Topics included current care and support, planning and co-ordinating care, paying for care, and expectations and planning for the future. The research took place in England. Our findings indicate that whilst people recognised they would have future care needs, with associated financial implications, this knowledge did not necessarily translate into actively planning for care or its cost. A key reason that recognition did not translate into action was uncertainty, manifested in three areas: the timescales and trajectory of their dementia and thus need for care; the potential for care needs to change and so negate care planning efforts; and uncertainty over their own capabilities to plan for and access paid for care, given the perceived complexity of social care and associated financial arrangements. The paper discusses how anticipated regret may affect decision-making and contributes to debates about appropriate professional support for older self-funders with dementia. It suggests the task for those involved in the care of people with dementia is to identify the points and places in the care system where worries about future self-funded care can be addressed, and carers and the people they care for can be prompted and supported to act

Carbon accumulation rate peaks at 1,000-m elevation in tropical planted and regrowth forests

Tropical planted and regrowth forests (TPRFs) are one of the most low-cost components for recovering biomass-stored carbon in the tropics. Nevertheless, challenges persist in pinpointing which elevational ranges exhibit the largest carbon accumulation rate ( ) due to the highly inconsistent previous assessments. This prevents the selection of optimal locations for implementing large-scale reforestation in the tropics. Here, we proposed a refined approach that used a carbon accumulation threshold (<80% of the maximum value) to quantify in TPRFs at various elevations. We find that increases with elevations from 300 to 1,000 m and declines at elevations >1,000 m. TPRFs at elevations ∼1,000 m exhibit three times more than lowland TPRFs. This optimal elevation, highly dependent on background temperatures, varies slightly but significantly across different mountains. These findings provide guidelines for policymakers to determine the optimal elevations from regional to continental scales when implementing reforestation initiatives in the tropics

Prospective piperacillin lymphocyte transformation testing in patients with cystic fibrosis receiving regular and desensitization courses of piperacillin-tazobactam

Background Piperacillin-tazobactam is used in patients with cystic fibrosis to treat recurrent respiratory infections. Exposure is associated with a high frequency of non-immediate hypersensitivity. Objective To assess the applicability of the lymphocyte transformation test (LTT) for the diagnosis of piperacillin hypersensitivity and the influence of desensitization on piperacillin-specific T-cell responses. Methods Study-arm one was an analysis of LTT responses from 58 naïve/baseline tolerant patients with samples collected over a three-year interventional phase. In study-arm two, seventeen hypersensitive patients were recruited and LTTs were conducted before and post-desensitization. Clinical hypersensitivity reactions in both arms were monitored over an eight-year observational period. Results Fifty-eight patients in study arm one received 611 (range, 2-40; mean±SD, 10.5±8.1) piperacillin-tazobactam courses during the interventional phase, of which 11 developed hypersensitivity. The patients that remained tolerant received 236 piperacillin-tazobactam courses in the observational period, of which 9 developed hypersensitivity. Ten/eleven interventional phase hypersensitive patients had a positive LTT, while one remained negative. 136 negative LTTs were recorded with 39 tolerant patients, while eight patients recorded a positive LTT, with 4 developing hypersensitivity during the observational period. Ten LTT positive patients in study arm two underwent piperacillin-tazobactam desensitization, with seven tolerating the drug. The strength of the LTT decreased during desensitization and negative results were recorded for a minimum of 14-days. During follow-up, eight patients tolerated 62 piperacillin-tazobactam courses through desensitization. Conclusions LTT is a sensitive marker of drug sensitisation that could be used to inform future patient management. Desensitization is associated with attenuation of the piperacillin-specific T-cell response

Divergences and convergences across European musical preferences: how taste varies within and between countries

When investigating relational structures in culture, research in Europe has often either mapped the relationship between cultural tastes in a particular context, or mapped differences in cultural tastes (measured consistently) in different countries, without assessing how these differences can vary across them. Indeed, the idea of national homology (namely that the structures of cultural capital would be fairly similar in nations across Europe) has never been really tested, probably due to a lack of cross-national research on cultural preferences. Using data from the EUCROSS survey that took place in Denmark, Germany, Italy, Romania, Spain and the UK (2012–2013, n = 6016), we first use multiple correspondence analysis to estimate the relationships between a set of items on musical tastes. We then extend this through the use of class-specific analysis, to investigate how these relationships vary in each of the six countries. Finally, we analyse the relationships between the underlying dimensions of music tastes and different components of cosmopolitanism, compared with key demographic variables. We show that the musical field significantly varies across the nations represented in the survey, demonstrating that musical preferences remain largely anchored in national contexts. Cultural preferences are shaped by historical and social dynamics specific to each country, with significant variations in the symbolic value and demographic associations of music genres

Neural ordinary differential equations for predicting the temporal dynamics of a ZnO solid electrolyte FET

Efficient storage and processing are essential for temporal data processing applications to make informed decisions, especially when handling large volumes of real-time data. Physical reservoir computing provides effective solutions to this problem, making them ideal for edge systems. These devices typically necessitate compact models for device-circuit co-design. Alternatively, machine learning (ML) can quickly predict the behaviour of novel materials/devices without explicitly defining any material properties and device physics. However, previously reported ML device models are limited by their fixed hidden layer depth, which restricts their adaptability to predict varying temporal dynamics of a complex system. Here, we propose a novel approach that utilizes a continuous-time model based on neural ordinary differential equations to predict the temporal dynamic behaviour of a charge-based device, a solid electrolyte FET, whose gate current characteristics show a unique negative differential resistance that leads to steep switching beyond the Boltzmann limit. Our model, trained on a minimal experimental dataset successfully captures device transient and steady state behaviour for previously unseen examples of excitatory postsynaptic current when subject to an input of variable pulse width lasting 20–240 milliseconds with a high accuracy of 0.06 (root mean squared error). Additionally, our model predicts device dynamics in ∼5 seconds, with 60% reduced error over a conventional physics-based model, which takes nearly an hour on an equivalent computer. Moreover, the model can predict the variability of device characteristics from device to device by a simple change in frequency of applied signal, making it a useful tool in the design of neuromorphic systems such as reservoir computing. Using the model, we demonstrate a reservoir computing system which achieves the lowest error rate of 0.2% in the task of classification of spoken digits