Understanding and predicting animal movements and distributions in the Anthropocene

Predicting animal movements and spatial distributions is crucial for our comprehension of ecological processes and provides key evidence for conserving and managing populations, species and ecosystems. Notwithstanding considerable progress in movement ecology in recent decades, developing robust predictions for rapidly changing environments remains challenging. To accurately predict the effects of anthropogenic change, it is important to first identify the defining features of human‐modified environments and their consequences on the drivers of animal movement. We review and discuss these features within the movement ecology framework, describing relationships between external environment, internal state, navigation and motion capacity. Developing robust predictions under novel situations requires models moving beyond purely correlative approaches to a dynamical systems perspective. This requires increased mechanistic modelling, using functional parameters derived from first principles of animal movement and decision‐making. Theory and empirical observations should be better integrated by using experimental approaches. Models should be fitted to new and historic data gathered across a wide range of contrasting environmental conditions. We need therefore a targeted and supervised approach to data collection, increasing the range of studied taxa and carefully considering issues of scale and bias, and mechanistic modelling. Thus, we caution against the indiscriminate non‐supervised use of citizen science data, AI and machine learning models. We highlight the challenges and opportunities of incorporating movement predictions into management actions and policy. Rewilding and translocation schemes offer exciting opportunities to collect data from novel environments, enabling tests of model predictions across varied contexts and scales. Adaptive management frameworks in particular, based on a stepwise iterative process, including predictions and refinements, provide exciting opportunities of mutual benefit to movement ecology and conservation. In conclusion, movement ecology is on the verge of transforming from a descriptive to a predictive science. This is a timely progression, given that robust predictions under rapidly changing environmental conditions are now more urgently needed than ever for evidence‐based management and policy decisions. Our key aim now is not to describe the existing data as well as possible, but rather to understand the underlying mechanisms and develop models with reliable predictive ability in novel situations

Determining absolute neutrino mass using quantum technologies

Next generation tritium decay experiments to determine the absolute neutrino mass require high-precision measurements of β-decay electron energies close to the kinematic end point. To achieve this, the development of high phase-space density sources of atomic tritium is required, along with the implementation of methods to control the motion of these atoms to allow extended observation times. A promising approach to efficiently and accurately measure the kinetic energies of individual β-decay electrons generated in these dilute atomic gases, is to determine the frequency of the cyclotron radiation they emit in a precisely characterised magnetic field. This cyclotron radiation emission spectroscopy (CRES) technique can benefit from recent developments in quantum technologies. Absolute static-field magnetometry and electrometry, which is essential for the precise determination of the electron kinetic energies from the frequency of their emitted cyclotron radiation, can be performed using atoms in superpositions of circular Rydberg states. Quantum-limited microwave amplifiers will allow precise cyclotron frequency measurements to be made with maximal signal-to-noise ratios and minimal observation times. Exploiting the opportunities offered by quantum technologies in these key areas, represents the core activity of the Quantum Technologies for Neutrino Mass (QTNM) project. Its goal is to develop a new experimental apparatus that can enable a determination of the absolute neutrino mass with a sensitivity on the order of 10~meV/c2

Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations

The shape of a hydrodynamic particle separator has been optimized using a parallelized and robust formulation of Bayesian optimization, with data from an unsteady Eulerian flow field coupled with Lagrangian particle tracking. The uncertainty due to the mesh, initial conditions, and stochastic dispersion in the Eulerian-Lagrangian simulations was minimized and quantified. This was then translated across to the error term in the Gaussian process model and the minimum probability of improvement infill criterion. An existing parallelization strategy was modified for the infill criterion and customized to prefer exploitation in the decision space. In addition, a new strategy was developed for hidden constraints using Voronoi penalization. In the approximate Pareto Front, an absolute improvement over the base design of 14% in the underflow collection efficiency and 10% in the total collection efficiency was achieved, which resulted in the filing of a patent.* The corresponding designs were attributed to the effective distribution of residence time between the trays via the removal of a vertical plume. The plume also reduced both efficiencies by creating a flow path in a direction that acted against effective settling. The concave down and offset tray shapes demonstrated the value of Bayesian optimization in producing useful and non-intuitive designs

The Effect of Endurance Exercise and its Intensity in Middle- aged Runners; Are they Thrombogenic?

Introduction Despite the well documented benefits of regular exercise, acute exercise induces a transient hypercoagulable state with increasing risk of thrombotic disease with age and intensity. While prior studies have used various conventional coagulation tests in studying the influence of exercise on coagulation, limited attention has been given to clot microstructure and contraction profile in well-trained individuals of middle to older age. Our aim was to identify effects of exercise on these variables using hemorheological biomarkers. Materials and methods Twenty-eight male and female runners aged over 40 years completed a 10 km run at moderate intensity. Of these runners,14 were reinvited to complete a 3 km run to exhaustion. Blood samples were drawn at three time-points, baseline, immediately after exercise and after 1 hour of recovery. Structural biomarker df and measurements of mature clot mechanical properties (Maximum Contractile Force and G’Max) were analysed alongside conventional coagulation markers. Results While df remained stable following long moderate intensity exercise, higher intensity exercise caused an increase in df indicating a hypercoagulable phase. Following an hour of rest, df returned to baseline. These results indicate that the effect of acute exercise on hypercoagulability is intensity dependent and transient. Maximum Contractile Force (CFMax) was reduced by exercise, irrespective of intensity. This effect was lower after an hour of rest, suggesting that some unknown initial compensatory mechanisms are outlasted by a longer period of reduced contractile force. Conclusion df and CFMax detected the hypercoagulable phase that occurred in trained older individuals as a result of exercise. Investigating these effects in more sentient populations could allow risk stratification of exercise rehabilitation programmes and their intensity

Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological Stances

From applications in automating credit to aiding judges in presiding over cases of recidivism, deep-learning powered AI systems are becoming embedded in high-stakes decision-making processes as either primary decision-makers or supportive assistants to humans in a hybrid decision-making context, with the aim of improving the quality of decisions. However, the criteria currently used to assess a system’s ability to improve hybrid decisions is driven by a utilitarian desire to optimise accuracy through a phenomenon known as ‘complementary performance’. This desire puts the design of hybrid decision-making at odds with critical subjective concepts that affect the perception and acceptance of decisions, such as fairness. Fairness as a subjective notion often has a competitive relationship with accuracy and as such, driving complementary behaviour with a utilitarian belief risks driving unfairness in decisions. It is our position that shifting epistemological stances taken in the research and design of human-AI environments is necessary to incorporate the relationship between fairness and accuracy into the notion of ‘complementary behaviour’, in order to observe ‘enhanced’ hybrid human-AI decisions

Exploring South Korean Foreign Direct Investment Motives and State-Level Location Decisions: US Evidence 1995-2008

This study uses a novel application of panel fuzzy-set qualitative comparative analysis (fsQCA) in the international management field. utilizing a unique database capturing reasons for foreign direct investment (FDI), and state-level location, we explain location decisions of high-technology South Korean (henceforth Korean) multinational enterprises (MNEs), when first entering the United States of America (henceforth US), from 1995 until the 2008 financial crisis. Various home country conditions, combined with a desire for technological upgrading, encouraged firms to seek locational advantages. Additionally, rather than assuming FDI to be driven by a single purpose over time, the addition of regional characteristics allows a typology of reasons for Korean FDI to be developed. We show evolving Korean FDI trends in the US with home country and regional perspectives interacting to attract FDI into US states with different characteristics, arguing this is consistent with US policy seeking to attract inward investment to foster economic development

A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly

Background: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China as a sample to predict the risk factors of depression in the elderly population from both holistic and individual level. Methods: A total of participants met the inclusion criteria from the fourth waves of the China Health and Retirement Longitudinal Study (CHARLS) were analyzed with ML algorithms. The level of depression was assessed by the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Results: The current study found top 5 factors that were important for predicting depression in the elderly population in China, including average sleep time, gender, age, social activities and nap time during the day. The results also provide reliable diagnostic likelihood at the individual level to support clinicians identify the most impactful factors contributing to patient depression. Our findings also suggested that activities such as interacting with friends and play ma-Jong, chess or join community clubs may have a positive collaborative effect for elderly's mental health. Conclusions: Holistic approaches are an effective method of deriving and interpreting sophisticated models of mental health in elderly populations. More detailed information about a patient's demographics, medical history, sleeping patterns and social/leisure activities can help to inform policy and treatment interventions on a population and individual level. Large scale surveys such as CHARLS are effective methods for testing the most accurate models, however, further research using professional clinical input could further advance the field

Unravelling the origin of enhanced CO2 selectivity in amine-PIM-1 during mixed gas permeation

Previously, it has been reported that amine-PIM-1, a polymer of intrinsic microporosity obtained by reduction of nitrile groups of PIM-1 to primary amine groups, shows enhanced CO2 selectivity during mixed gas permeation studies with respect to single gas measurements for gas pairs involving CO2. This distinct and potentially useful behaviour was ascribed to the affinity of CO2 for the polymer amine groups. Here, we demonstrate that enhanced selectivity originates from both CO2 physisorption and chemisorption. A combination of 13C and 15N solid-state NMR spectroscopic analyses of a CO2-loaded amine-PIM-1 membrane allowed the identification and quantitative determination of both chemisorbed and physisorbed species and the characterization of polymer-CO2 interactions. Experiments with 13C isotopically enriched CO2 unequivocally demonstrated the conversion of 20% of the NH2 groups into carbamic acids at 298 K and a CO2 pressure of 1 bar. Chemisorption was supported by the strong heat of CO2 adsorption for amine-PIM-1 that was estimated as 50 kJ mol−1. Molecular dynamics simulations with models based on the experimentally determined polymer structure gave a detailed description of intra- and interchain hydrogen bond interactions in amine-PIM-1 after chemisorption, as well as of the effect of chemisorption on polymer porosity and physisorption

Electrolyte tailoring and interfacial engineering for safe and high-temperature lithium-ion batteries

The deployment of lithium-ion batteries, essential for military and space exploration applications, faces restrictions due to safety issues and performance degradation stemming from the uncontrollable side reactions between electrolytes and electrodes, particularly at high temperatures. Current research focuses on interfacial modification and non-flammable electrolyte development, which fails to simultaneously improve both safety and cyclic performance. This work introduces a synergistic approach by incorporating weakly polar methyl 2,2-difluoro-2-(fluorosulfonyl)acetate (MDFSA) and non-flammable 2-(2,2,2-trifluoroethoxy)-1,3,2-dioxaphospholane 2-oxide (TFP) to achieve a localized high-concentration electrolyte (LHCE) that can stabilize both anode and cathode interfaces and thus improve the cycling life and safety of batteries, particularly at evaluated temperatures. As a result, the NCM811|Gr pouch cell with MDFSA-containing LHCE exhibits a high capacity retention rate of 79.6% at 60 °C after 1200 cycles due to the formation of thermally and structurally stable interfaces on the electrodes, outperforming pouch cells utilizing commercial carbonate-based (capacity retention: 23.7% after 125 cycles). Additionally, pouch cells in the charging state also exhibit commendable safety performance, indicating potential for practical applications

Professional Registration of Probation Practitioners in a Devolved Welsh Probation Service

In this paper, we consider the newly implemented Probation Professional Register Policy Framework in the context of the intent of the Welsh Government (WG) to work towards the devolution of justice, including probation. Thus, we reflect on devolution and its potential implications, the specific forms of partnership working and development in Wales, probation organisational culture, and questions of probation's legitimacy. We suggest that to make the most of the professional register's potential for professionalisation of probation practice, it needs to be embedded in an organisational structure and culture that fully owns and promotes the ethics and values of partnership working, taking a rights-based approach in the support for those who cause harm to victims and communities, and evidence-based practice