There are six species of flamingos in the world, all under pressure from human activities in their wetland habitats. Obtaining global population estimates for flamingos is challenging because of their broad geographical range, nomadic movements, capacity for long-distance flight, and the complexity of international monitoring. Two species, the Andean Flamingo Phoenicoparrus andinus and Puna Flamingo P. jamesi, during key parts of their life cycle, use wetlands in the Andes of South America, where they coexist at various sites. We compiled historical information on population estimates and ecology for these two species and integrated data collected on regional simultaneous censuses to describe population trends, current and emerging threats, and provide recommendations for conservation action. Long-term population trends have been difficult to establish given the unreliability of population estimates prior to the late 1990s. Systematic, regional censuses carried out regularly since 1997 have produced robust population estimates for the Andean and Puna flamingos (most recently, 78,000 and 154,000, respectively) and show populations of both species to be stable and increasing. Increasingly rapid changes in wetlands caused by human activities such as industrial-scale mining in breeding and foraging sites in the high Andes wetlands, and agro-industrial activities in their lowland wintering sites, focused on areas of the highest concentrations of flamingos pose threats to their survival and ability to reproduce. In addition, climate change is projected to reduce wetland habitats and some localised effects have already been detected. Continued research on the ecological drivers of flamingo abundance, movements, and population genetics to understand population structure and dynamics are necessary, as well as the identification of response variables to changing environmental conditions. Interdisciplinary and systems-level approaches in the context of international collaboration in monitoring and conservation planning among a diversity of stakeholders will be required to safeguard flamingo populations and wetland habitats.

The care crisis intersects with economic, social, and refugee crises, necessitating focused attention to bolster care infrastructure and address the multifaceted challenges. Women bear a disproportionate burden of unpaid domestic work, exacerbating gender inequalities in labor markets and education. This paper applies the International Labour Organization–UN Women (2021) policy tool to Turkish data, estimating coverage gaps in education and healthcare, associated costs, and employment generation potential in the care sectors and related sectors. We identify a coverage gap in education affecting 5.8 million children. The required investment to address this gap is estimated at 2.28 percent of gross domestic product (GDP). In all, 303,000 healthcare workers are needed, requiring an investment of 1.23 percent of GDP. These investments have the potential to generate 1.740 million direct and 152,000 indirect jobs. This would result in a substantial 6.7 percent increase in total employment. Considering the current gender composition, women are expected to fill 65 percent of these jobs, leading to a 14 percent improvement in female employment. Incorporating 3.7 million Syrian refugees, Turkey’s investment cost rises to 3.74 percent of GDP, creating 1.878 million new direct jobs – an 8 percent boost over the non-inclusive scenario. Prioritizing public investments in care services promises to promote gender equality, human development, and inclusive economic growth.

We introduce a systematic approach for designing ultrathin, flexible, and polarization-insensitive metasurface absorbers (MSAs), suitable for aviation applications, such as radar cross-section reduction of unmanned aerial vehicles. Metal-backed resistive patches are arranged on a flexible polyethylene terephthalate substrate of thickness about 1/100 of the operating wavelength, classifying the absorbers as ultrathin. The ultralow weight of the proposed MSAs is crucial for the targeted aviation applications, to ensure airworthiness. A narrowband uniform MSA is designed to achieve maximum absorption and serves as a starting point to synthesize a broadband and polarization-insensitive $3 \times 3$ absorber supercell. The non-uniform absorber is systematically designed by a fast semi-analytical method. The proposed absorbers have been fabricated and experimentally tested both on flat and cylindrical curved surfaces, with measurements being in very good agreement with the corresponding simulations, and corroborate the high absorption and broadband behavior of the proposed non-uniform ultrathin and flexible absorber.

In order to make a fast and accurate response to gas leakage event, e.g. gas leakage in hydrogen storage station, it is very important to identify and locate the leakage source accurately and quickly. Due to the flexibility and the adaptability of robots to harsh environments, leakage source tracing based on mobile robots has attracted more and more attention. However, the existing ground robots are limited by the ground environment and thus it is difficult to trace and locate the leakage in the complex environment with ground robots. Although unmanned aerial vehicle (UAV) can overcome the limitation of ground obstacles, there are still some problems in the accuracy and reliability of gas sampling due to the interference of flow field caused by UAV rotors to the surrounding gases. Based on computational fluid dynamic simulation, a simulation model of UAV with four rotors was established. Combined with test experiments, the influence of flow field around UAV on gas sampling under different UAV speeds, rotors assembly structures, leakage, and sampling conditions was analyzed and investigated. The optimized UAV assembly structure and gas sensor installation position were determined and verified by the simulations and experiments. The results showed that the sensor was less affected by the rotor airflow when the UAV rotor was reversely assembled and the gases were sampled above the UAV. This research can provide a guidance for gas sampling for emission source tracing with UAV for process safety management of energy gas storage.

In order to improve the performance of $k - \omega $ SST model in turbomachinery, previous studies have used the machine-learning (ML) technique to obtain turbulence models (for example, the ML-RANS EQ model). However, these models do not lead to satisfactory results in complex flows in turbomachinery. In this study, we use non-equilibrium training dataset to obtain a new turbulence model (i.e., the ML-RANS TR-NE-EQ model). Calculations in various cases of turbine cascade flows show that ML-RANS TR-NE-EQ model performs obviously better than ML-RANS EQ model as well as $k - \omega $ SST model.

Until recently, statistical consultants did not have to worry about being replaced by artificial intelligence. There was no statistical analogue to ‘Dr Google’ before ChatGPT arrived on the scene. Although ChatGPT (most of the time) adequately responds to basic queries such as the assumptions of different statistical tests or summarises relevant manuals on statistical software providing clear instructions with point-and-click software such as SPSS, there are many important aspects of statistical consulting that ChatGPT does not cover. This tutorial article is about these aspects: a summary of what statistical consulting is, its purpose and possible settings during the empirical research cycle, the role and responsibilities of the consultant and the client, how to ensure a good consulting experience, how to prepare for a consulting session, typical questions and more. The article was written for researchers who are considering contacting a statistician for the first time and aims to facilitate a good and fruitful consulting experience for all parties involved.

This article addresses the localization problem in robotic autonomous luggage trolley collection at airports and provides a systematic evaluation of different methods to solve it. The robotic autonomous luggage trolley collection is a complex system that involves object detection, localization, motion planning and control, manipulation, etc. Among these components, effective localization is essential for the robot to employ subsequent motion planning and end-effector manipulation because it can provide a correct goal position. This article explores four popular and representative localization methods for object localization in luggage trolley collection: radio frequency identification (RFID), Keypoints, ultrawideband (UWB), and Reflectors. A qualitative evaluation framework is constructed to assess performance, encompassing Localization Accuracy, Mobile Power Supplies, Coverage Area, Cost, and Scalability. Furthermore, a series of quantitative experiments concerning Localization Accuracy and Success Rate have been conducted on a real-world robotic autonomous luggage trolley collection system. The performance of various localization methods is further analyzed based on experimental results, indicating that the Keypoints method is optimally suited for indoor environments to facilitate luggage trolley collection. Significantly, these experiment results provide a valuable reference point, extending the application of indoor localization methods across diverse scenarios. A website about this work is available at https://sites.google.com/view/localization-evaluation/.

Nutrition is the critical nongenetic factor that has a major influence on the health status of an organism. The nutritional status of the mother during gestation and lactation plays a vital role in defining the offspring’s health. Undernutrition during these critical periods may induce chronic metabolic disorders like obesity and cardiovascular diseases in mothers as well as in offspring. The present study aims to evaluate the impact of undernutrition during gestational and lactational periods on the plasma metabolic profile of dams. Additionally, we investigated the potential synergistic mitigating effects of astaxanthin and docosahexaenoic acid (DHA) on dysregulated plasma metabolic profiles. Evaluation of plasma lipid profile revealed that undernourishment resulted in elevated levels of total cholesterol, triglycerides, low density and very low-density lipoproteins in dams. Liquid chromatography-tandem mass spectrometry (LC–MS/MS) based untargeted metabolomics illustrated that pathways related to lipid metabolism, such as cholesterol metabolism, steroid biosynthesis and metabolism of amine-derived hormones, were dysregulated by undernourishment. Additionally, pathway enrichment analysis predicted that there is a high incidence of development of desmosterolosis, hypercholesterolaemia, lysosomal acid lipase deficiency and Smith–Lemli–Opitz syndrome in the offspring, reflecting predisposition in mothers. However, synergistic supplementation of astaxanthin and DHA ameliorated these adverse effects by regulating a separate set of metabolic pathways associated with lipid metabolism. They included branched chain amino acid degradation such as valine, leucine and isoleucine, metabolism of alpha-linolenic acid, lipoic acid, lysine degradation, biosynthesis, elongation and degradation of fatty acids.

Disasters, armed conflicts, and disease outbreaks often overwhelm normal corpse-handling capacities, highlighting the importance of mass fatality management in emergency preparedness and response. This paper examines principles, practices, and challenges of ensuring dignified corpse management after catastrophic events leading to sudden mass fatalities. It draws insights from Nepal’s experience with the 2015 earthquakes, as well as other recent disasters worldwide. The discussion reveals planning and policy gaps that undermine the dignity of the deceased and prolong trauma for survivors. Recommendations are provided for improving global preparedness to accord proper respect to the dead amid immense tragedy. As climate change escalates disasters, all vulnerable nations must enhance their capacities for systematic and empathetic mass fatality management. Even when protocols exist, overwhelmed systems lead to a breakdown in practical implementations, violating cultural norms. By building robust preparedness through strategic plans, training, infrastructure, and international cooperation, we can preserve humanity even amidst utter inhumanity.

Given the complexity of unpaid care work in the Indian context, this study employs advanced machine learning techniques to unveil hidden patterns within the 2019 time-use survey dataset. The study pursues a dual objective: (1) assessing the superior predictive capability of machine learning over traditional statistical methods in estimating unpaid care work time, and (2) unveiling the sociodemographic determinants of extended unpaid care work durations. The results emphasise the exceptional predictive performance of machine learning, notably the random forest analysis, with a noteworthy 9 per cent improvement in forecast accuracy. Key determinants influencing unpaid care work time encompass gender, employment status, marital situation, and age. Findings underscore the heightened vulnerability of young married women without employment, who face amplified unpaid care work demands, exacerbating related challenges and risks. It further highlights the country’s imperative for a comprehensive care framework to mitigate caregiving constraints hindering women’s equitable participation in evolving economic paradigms.

Meeting social need is usually associated in social policy with the provision of benefits and public services, and the role of taxation often confined to an acknowledgement of its revenue-raising function for the purpose of funding them. Against a backdrop of multiple concurrent challenges shared by many high-income societies, including inadequate social care for an ageing population and unprecedented waiting lists for health care, the UK’s experience of the short-lived Health and Social Care Levy is used as a case study to reveal how the relationship between taxation and social need is complex, mediated by a range of factors, and how these contributed to its abolition. The article proposes five different relationships between taxation and social need evident in the story of the rise and fall of the Levy.

Why did the Liberal Party of Canada (LPC) and the New Democratic Party (NDP) enter into a supply-and-confidence agreement in March 2022? Interparty cooperation among federal parties is rare during minority governments, and yet the agreement created a formal alliance in the House of Commons. In this article, we argue that ideational factors led to the 2022 agreement. We examine the role of programmatic beliefs and strategic learning during the COVID-19 crisis and the 2019-2021 election sequence to shed light on changes in federal parliamentary strategies in Canada. From ad-hoc voting coalitions to extended cooperation on social policymaking, the LPC and the NDP learned how to work together in the House of Commons while using the agreement as a tool to compete with each other in anticipation of the next federal election.