Explore the vast range of titles available.

Food systems are at the heart of at least 12 of the 17 Sustainable Development Goals (SDGs). The wide scope of the SDGs call for holistic approaches that integrate previously “siloed” food sustainability assessments. Here we present a first global-scale analysis quantifying the status of national food system performance of 156 countries, employing 25 sustainability indicators across 7 domains as follows: nutrition, environment, food affordability and availability, sociocultural well-being, resilience, food safety, and waste. The results show that different countries have widely varying patterns of performance with unique priorities for improvement. High-income nations score well on most indicators, but poorly on environmental, food waste, and health-sensitive nutrient-intake indicators. Transitioning from animal foods toward plant-based foods would improve indicator scores for most countries. Our nation-specific quantitative results can help policy-makers to set improvement targets on specific areas and adopt new practices, while keeping track of the other aspects of sustainability. The development of sustainable food systems requires an understanding of potential trade-off between various objectives. Here, Chaudhary et al. examine how different nations score on food system performance across several domains, including environment, nutrition, and sociocultural wellbeing.

Demand side interventions, such as dietary change, can significantly contribute towards the achievement of 2030 national sustainable development goals. However, most previous studies analysing the consequences of dietary change focus on a single dimension of sustainability (e.g., environment) using a limited number of indicators and dietary scenarios. A multi-dimension and multi-indicator analysis can identify the potential trade-offs. Here, starting from the current food consumption data (year 2011), we first designed nine alternative dietary scenarios (healthy Swiss diet, healthy global diet, vegetarian, vegan, pescatarian, flexitarian, protein-oriented and meat-oriented diets and a food greenhouse gas tax diet). Next we calculated three nutritional quality (nutrient balance score, disqualifying nutrient score, percent population with adequate nutrition), five environmental (greenhouse gas, water, land, nitrogen and phosphorus use), one economic (daily food expenditure) and one human health indicator (DALYs) for current and alternative diets. We found that transition towards a healthy diet following the guidelines of Swiss society of nutrition is the most sustainable option and is projected to result in 36% lesser environmental footprint, 33% lesser expenditure and 2.67% lower adverse health outcome (DALYs) compared with the current diet. On the other extreme, transition towards a meat or protein oriented diet can lead to large increases in diet related adverse health outcomes, environmental footprint, daily food expenditure and a reduction in intakes of essential nutrients (for Vitamin C, Fibre, Potassium and Calcium). We found that shifting to the vegetarian and vegan diet scenarios might lead to a reduction in intakes of certain micronutrients currently supplied primarily by animal-sourced foods (Vitamin B12, Choline and Calcium). Results show that achieving a sustainable diet would entail a high reduction in the intake of meat and vegetable oils and a moderate reduction in cereals, roots and fish products and at the same time increased intake of legumes, nuts, seeds, fruits and vegetables. We identify several current data and research gaps that need to be filled in order to get more accurate results. Overall, our analysis underscores the need to consider multiple indicators while assessing the dietary sustainability and provides a template to conduct such studies in other countries and settings. Future efforts should focus on assessing the potential of different interventions and policies that can help transition the population from current to sustainable dietary patterns.

Forests managed for timber have an important role to play in conserving global biodiversity. We evaluated the most common timber production systems worldwide in terms of their impact on local species richness by conducting a categorical meta-analysis. We reviewed 287 published studies containing 1008 comparisons of species richness in managed and unmanaged forests and derived management, taxon and continent specific effect sizes. We show that in terms of local species richness loss, forest management types can be ranked, from best to worse, as follows: selection and retention systems, reduced impact logging, conventional selective logging, clear-cutting, agroforestry, timber plantations, fuelwood plantations. Next, we calculated the economic profitability in terms of the net present value of timber harvesting from 10 hypothetical wood-producing Forest Management Units (FMU) from around the globe. The ranking of management types is altered when the species loss per unit profit generated from the FMU is considered. This is due to differences in yield, timber species prices, rotation cycle length and production costs. We thus conclude that it would be erroneous to dismiss or prioritize timber production regimes, based solely on their ranking of alpha diversity impacts.

Transitioning toward sustainable agriculture production is key in achieving sustainable development goals. However, quantifying the sustainability status of current agricultural mix and figuring out a future sustainable crop and livestock production mix that is nutritionally adequate, low in environmental impact and profitable to farmers, is a formidable challenge for any country. Here through a case-study on Indian agriculture, we demonstrate the application of a framework to first characterize the current agricultural mix by comparing 31 sustainability indicators with respective benchmarks across nutrition (social), environment and economic dimensions in each of the 36 Indian states. Next, we demonstrate the application of mathematical optimization algorithms to calculate the sustainable production mix in each state with the objective of maximizing the farmers’ profit under the constraint that the total national agricultural production meets the nutritional requirements of population without exceeding the current environmental footprint levels. Characterization results show that India does not produce enough of certain micronutrients (vitamin-E, and choline) and the carbon, nitrogen and freshwater footprints exceed their planetary boundaries while the farmers’ income remains below national targets. Optimization algorithm generated the sustainable production amounts of 138 crop and livestock items in each state and showed that fruits, vegetables, legumes, and oilseeds production needs to increase by 50%–100% while the cereals production amounts need to reduce to 50% of current levels in India. This will result in an increase in farmer’s income by 25% on average, fulfil the nutritional requirements of population and reduce the environmental footprint by 10%. Our approach can act as a template for other countries in collecting necessary data on sustainability indicators and determining optimum crop and livestock mix.

The rapid growth of generative artificial intelligence (AI) has enabled diverse applications but also introduced new attack techniques. Similar to deepfake media, generative AI can be exploited to create AI-generated traffic that evades existing intrusion detection systems (IDSs). This paper proposes a Dual Detection System to detect such synthetic network traffic in the Message Queuing Telemetry Transport (MQTT) protocol widely used in Internet of Things (IoT) environments. The system operates in two stages: (i) primary filtering with a Long Short-Term Memory (LSTM) model to detect malicious traffic, and (ii) secondary verification with a Transformer–MLP ensemble to identify AI-generated traffic. Experimental results show that the proposed method achieves an average accuracy of 99.1 ± 0.6% across different traffic types (normal, malicious, and AI-generated), with nearly 100% detection of synthetic traffic. These findings demonstrate that the proposed dual detection system effectively overcomes the limitations of single-model approaches and significantly enhances detection performance.

Currently, ~65% of extant primate species ( ca 512 species) distributed in 91 countries in the Neotropics, mainland Africa, Madagascar, South Asia and Southeast Asia are threatened with extinction and 75% have declining populations as a result of deforestation and habitat loss resulting from increasing global market demands, and land conversion for industrial agriculture, cattle production and natural resource extraction. Other pressures that negatively impact primates are unsustainable bushmeat hunting, the illegal trade of primates as pets and as body parts, expanding road networks in previously isolated areas, zoonotic disease transmission and climate change. Here we examine current and future trends in several socio-economic factors directly or indirectly affecting primates to further our understanding of the interdependent relationship between human well-being, sustainable development, and primate population persistence. We found that between 2001 and 2018 ca 191 Mha of tropical forest (30% canopy cover) were lost as a result of human activities in the five primate range regions. Forty-six percent of this loss was in the Neotropics (Mexico, Central and South America), 30% in Southeast Asia, 21% in mainland Africa, 2% in Madagascar and 1% in South Asia. Countries with the greatest losses ( ca 57% of total tree cover loss) were Brazil, Indonesia, DRC, China, and Malaysia. Together these countries harbor almost 50% of all extant primate species. In 2018, the world human population was estimated at ca 8bn people, ca 60% of which were found in primate range countries. Projections to 2050 and to 2100 indicate continued rapid growth of the human populations in these five primate range regions, with Africa surpassing all the other regions and totaling ca 4bn people by the year 2100. Socioeconomic indicators show that, compared to developed nations, most primate range countries are characterized by high levels of poverty and income inequality, low human development, low food security, high levels of corruption and weak governance. Models of Shared Socioeconomic Pathway scenarios (SSPs) projected to 2050 and 2100 showed that whereas practices of increasing inequality (SSP4) or unconstrained growth in economic output and energy use (SSP5) are projected to have dire consequences for human well-being and primate survivorship, practices of sustainability-focused growth and equality (SSP1) are expected to have a positive effect on maintaining biodiversity, protecting environments, and improving the human condition. These results stress that improving the well-being, health, and security of the current and future human populations in primate range countries are of paramount importance if we are to move forward with effective policies to protect the world’s primate species and promote biodiversity conservation.

We introduce a concept of dissipative measure-valued martingale solution to the stochastic Euler equations describing the motion of an inviscid incompressible fluid. These solutions are characterized by a parametrized Young measure and a concentration defect measure in the total energy balance. Moreover, they are weak in the probabilistic sense i.e., the underlying probability space and the driving Wiener process are intrinsic parts of the solution. We first exhibit the relative energy inequality for the incompressible Euler equations driven by a multiplicative noise and then demonstrate the pathwise weak-strong uniqueness principle. Finally, we also provide a sufficient condition, á la Prodi (Ann Mat Pura Appl 48:173–182, 1959) and Serrin (in: Nonlinear problems, University of Wisconsin Press, Madison, Wisconsin, pp 69–98, 1963), for the uniqueness of weak martingale solutions to the stochastic Navier–Stokes system in the class of finite energy weak martingale solutions.

As a consequence of recent human activities. populations of approximately 75% of the world’s primates are in decline, and more than 60% of species ( n = 512) are threatened with extinction. Major anthropogenic pressures on primate persistence include the widespread loss and degradation of natural habitats caused by the expansion of industrial agriculture, pastureland for cattle, logging, mining, and fossil fuel extraction. This is the result of growing global market demands for agricultural and nonagricultural commodities. Here, we profile the effects of international trade of forest-risk agricultural and nonagricultural commodities, namely soybean, oil palm, natural rubber, beef, forestry products, fossil fuels, metals, minerals, and gemstones on habitat conversion in the Neotropics, Africa, and South and Southeast Asia. Total estimated forest loss for these regions between 2001 and 2017 was ca 179 million ha. The average percent of commodity-driven permanent deforestation for the period 2001–2015 was highest in Southeast Asia (47%) followed by the Neotropics (26%), South Asia (26%), and Africa (7%). Commodities exports increased significantly between 2000 and 2016 in all primate range regions leading to the widespread conversion of forested land to agricultural fields and an increase in natural resource extraction. In 2016, US $1.1 trillion of natural-resource commodities were traded by countries in primate range regions. The Neotropics accounted for 41% of the total value of these exports, Southeast Asia for 27%, Africa 21%, and South Asia 11%. Major commodity exporters in 2016 were Brazil, India, Indonesia, Malaysia and South Africa, countries of high primate diversity and endemism. Among the top 10 importers were China, the US, Japan, and Switzerland. Primate range countries lag far behind importer nations in food security and gross domestic product per capita, suggesting that trade and commodity-driven land-use have done little to generate wealth and well-being in primate habitat countries. Modeling of land-use and projected extinction of primate species by 2050 and 2100 under a business as usual scenario for 61 primate range countries indicate that each country is expected to see a significant increase in the number of species threatened with extinction. To mitigate this impending crisis, we advocate the “greening” of trade, a global shift toward a low-meat diet, reduced consumption of oil seed, diminished use of tropical timber, fossil fuels, metals, minerals, and gemstones from the tropics, accompanied by a stronger and sustained global resolve to regulate and reverse the negative impacts of growing unsustainable global demands and commodity trade on income inequality, and the destruction of primates and their habitats.

Integrating artificial intelligence (AI) with wearable sensor technologies can revolutionize the monitoring and management of various chronic diseases and acute conditions. AI-integrated wearables are categorized by their underlying sensing techniques, such as electrochemical, colorimetric, chemical, optical, and pressure/stain. AI algorithms enhance the efficacy of wearable sensors by offering personalized, continuous supervision and predictive analysis, assisting in time recognition, and optimizing therapeutic modalities. This manuscript explores the recent advances and developments in AI-powered wearable sensing technologies and their use in the management of chronic diseases, including COVID-19, Diabetes, and Cancer. AI-based wearables for heart rate and heart rate variability, oxygen saturation, respiratory rate, and temperature sensors are reviewed for their potential in managing COVID-19. For Diabetes management, AI-based wearables, including continuous glucose monitoring sensors, AI-driven insulin pumps, and closed-loop systems, are reviewed. The role of AI-based wearables in biomarker tracking and analysis, thermal imaging, and ultrasound device-based sensing for cancer management is reviewed. Ultimately, this report also highlights the current challenges and future directions for developing and deploying AI-integrated wearable sensors with accuracy, scalability, and integration into clinical practice for these critical health conditions.