ObjectiveThe low-altitude economy covers low-altitude manufacturing, flight operations, support services and comprehensive service industries, and has the characteristics of spatially three-dimensional, regional dependence, digital ecology, industrial integration and radiation driving. With the continuous development of digitization and informatization, these characteristics increasingly affect the interaction between low-altitude activities and urban and natural environments, which provides new ideas for the development of landscape architecture, and the coordinated development of the two is an important issue in the transformation of landscape architecture.MethodsThe knowledge map method was used to analyze the research status of environmental low-altitude economy at home and abroad. By combing and reading relevant literature at home and abroad, the research trends of low-altitude economy in logistics, aviation, ecological monitoring and other fields were systematically analyzed by reviewing literature research, case studies and comparative analysis. ResultsBy analyzing the domestic and foreign research, the internal mechanism of the cross research between landscape architecture and low-altitude economy is revealed four aspects. In the aspect of technology application, low-altitude aircraft tend to be transformed from perception tools to "design intelligence" which can guide ecological design and space construction. In the aspect of spatial planning, landscape architecture will realize the spatial transformation from plane extension to three-dimensional reconstruction under the development of low-altitude economy. In the aspect of ecological impact, the research focus has changed from identifying environmental risks by using low-altitude facilities to systematic assessment and control of ecology. In the aspect of humanistic experience, the combination of low-altitude economy and cultural narrative has further stimulated the vitality of landscape architecture discipline. It is found that under the influence of low-altitude economy, the development of landscape architecture faces some new problems, such as imperfect policies and regulations, insufficient adaptability of spatial planning system, systematic lack of ecological protection, technical bottleneck restriction, homogenization dilemma of cultural and tourism integration, including lack of unified standards for low-altitude facility design, traditional two-dimensional planning being difficult to meet the needs of air-space coordination, ecological destruction caused by noise and habitat disturbance, lack of ecological protection system, etc. There are technical bottlenecks in data processing and flight stability; homogenization of tourism products, focusing on tourism over cultural innovation. In order to promote the coordinated development of the two, this paper puts forward the implementation path of the integration of low-altitude economy and landscape architecture: to ensure the adaptation of policies and regulations, to improve policies and regulations and technical standards, to formulate low-altitude greening design and ecological evaluation standards, and to lay a foundation for the integration of landscape architecture and low-altitude economy; Conduct spatial value evaluation, clarify the use, development subject and income distribution mechanism of each low-altitude area, promote the market-oriented operation of public resources, and use unmanned aerial vehicles to carry out ecological background analysis and evaluate the ecological carrying capacity of low-altitude activities; Conduct functional compound planning, make landscape architecture break through the limitation of traditional ground perspective, bring low-altitude airspace into the vertical space system of landscape architecture, add low-altitude related infrastructure in the garden, and realize efficient coordinated utilization of airspace and ground resources; In ecological service monitoring, we should increase the investment in R&D of UAV technology, low-altitude aircraft noise reduction technology, flight safety guarantee technology, etc., improve the intelligent level of landscape architecture monitoring and management, and establish a new ecological assessment mode; Promote the integrated development of culture and tourism, break through the homogenization dilemma from three dimensions of cultural empowerment, spatial differentiation and experience depth, and integrate garden cultural elements into low-altitude experience links, to improve the overall operation effect. ConclusionBased on the research, we have drawn conclusions in three aspects. 1) The global low-altitude economy industry will become the next development hotspot, and it should accelerate the integration with transportation logistics, cultural tourism and other formats, expand more application scenarios, promote the integration of landscape architecture and related industries, and activate the new vitality of landscape architecture. 2) Green space and parks in cities will become important carriers for low-altitude transportation in the future, and planning and design of low-altitude composite public space will be the focus of research, and research will move from two-dimensional garden aesthetic space to three-dimensional traffic spatial pattern. 3) The development of unmanned aerial vehicle and related technologies provide refined intelligent solutions for intelligent garden management, and low-altitude monitoring data provide new tools for ecological value assessments of gardens, significantly improve ecological monitoring and management levels.
Aesthetics of cities. City planning and beautifying, Architectural drawing and design
The global surge in illicit drug use has intensified the demand for rapid, portable, and reliable on-site detection technologies. Traditional analytical approaches, such as laboratory-based instrumentation and biological sample assays, while accurate, are often constrained by high costs, long processing times, and the need for specialized equipment, rendering them unsuitable for field applications. This review highlights recent progress in chemical sensor technologies designed for the detection of widely misused drugs such as methamphetamine, cocaine, fentanyl, and heroin. Parallel advancements in the detection of environmental contaminants, particularly concerning micro- and nanoplastics, are also discussed. Emerging sensing platforms employing nanoparticle functionalization, graphene nanosheets, MXenes, metal–organic frameworks (MOFs), and supramolecular colorimetric assays demonstrate significant potential for achieving high sensitivity, selectivity, and operational simplicity in portable formats. These innovations enable real-time detection with minimal user expertise, thereby advancing applications in forensic analysis, environmental monitoring, and public health protection. The review also addresses current limitations related to detection accuracy, reagent stability, and matrix interferences and proposes future directions for optimizing sensor robustness and performance under diverse field conditions.
Modified theories of gravity often introduce geometric structure beyond general relativity in order to address unresolved problems in the gravitational sector without invoking ad hoc matter fields. Mimetic gravity, for example, generates an effective cosmological dark sector by isolating the conformal mode of the metric, while Horava--Lifshitz gravity attains power-counting renormalizability by endowing spacetime with a preferred dynamical foliation. Although chronology protection was not the original motivation for either theory, both enforce it classically through stable causality. This suggests that chronology protection itself may be elevated from a derived property to a guiding principle for constructing modified gravitational theories, especially if its implementation at the quantum-gravitational level leaves infrared imprints in the effective action. Motivated by this possibility, we introduce a toy metric--affine gravity model that modifies only the geometric sector. The model realizes stable causality by dynamically generating a global time function via breaking of projective invariance. We further show that mimetic gravity is recovered as a special case, while a broader dark sector emerges naturally.
We study environment-dependent clustering using the marked correlation function applied to Hu-Sawicki $f(R)$ modified gravity simulations. This gravity theory enriches the structure formation by enhancing gravity in a scale-dependent form. By employing a multi-scale cosmic structure finder algorithm, we define the cosmic environments divided in: nodes, filaments, walls and voids. We find a stronger impact of modified gravity in nodes and filament, which together dominate the information content by more than a factor of four relative to other environments. Combining environmental information further enhances the expected signal-to-noise ratio for CMASS- and DESI-like mock samples, particularly in configurations including filaments. Overall, marked correlation functions that incorporate environmental structure increase the information content by about a factor of two compared to standard density-based marks applied to the full galaxy sample. These results demonstrate the importance of environmental information, especially from filaments, in improving the constraining power of galaxy clustering tests of modified gravity.
ObjectivesAs an essential sustainable energy technology, renewable energy-powered water electrolysis for hydrogen production has attracted widespread attention due to its advantages in environmental protection and low carbon emissions. However, conventional water electrolysis technologies for hydrogen production face challenges in terms of efficiency and cost, the rapid development of artificial intelligence (AI) provides an effective way to solve the difficult problems of hydrogen production technology through electrolysis of water. To address this, this study aims to explore the key applications and development prospects of AI for optimizing the efficiency and economic performance of water electrolysis systems for hydrogen production.MethodsCommon AI tools such as MATLAB, Python, and SimuNPS are employed for algorithm development, deep learning model training, and multi-physics simulation in water electrolysis systems for hydrogen production. By integrating AI technologies, applications such as output prediction, system capacity optimization and scheduling, and fault diagnosis are implemented to improve system performance and stability. A comparative analysis of performance of different AI models in various real-world scenarios is conducted to explore their specific roles and implementation methods in enhancing system performance and controllability.ConclusionsAI technology offers new avenues for enhancing the efficiency and intelligent scheduling of renewable energy-powered water electrolysis hydrogen production systems. Future research should focus on the application of AI in output forecasting, scheduling optimization, and fault diagnosis, promoting deep integration between AI and system operation. Moreover, innovative applications of AI in intelligent monitoring, automatic control, and multi-source coordination should be explored to provide strong support for the development of efficient, stable, and low-carbon hydrogen energy systems.
Applications of electric power, Production of electric energy or power. Powerplants. Central stations
Christo Kurisummoottil Thomas, Omar Hashash, Kimia Ehsani
et al.
Aligning with the global mandates pushing towards advanced technologies with reduced resource consumption and environmental impacts, the sustainability of wireless networks becomes a significant concern in 6G systems. To address this concern, a native integration of sustainability into the operations of next-generation networks through novel designs and metrics is necessary. Nevertheless, existing wireless sustainability efforts remain limited to energy-efficient network designs which fail to capture the environmental impact of such systems. In this paper, a novel sustainability metric is proposed that captures emissions per bit, providing a rigorous measure of the environmental footprint associated with energy consumption in 6G networks. This metric also captures how energy, computing, and communication resource parameters influence the reduction of emissions per bit. Then, the problem of allocating the energy, computing and communication resources is posed as a multi-objective (MO) optimization problem. To solve the resulting non-convex problem, our framework leverages MO reinforcement learning (MORL) to maximize the novel sustainability metric alongside minimizing energy consumption and average delays in successfully delivering the data, all while adhering to constraints on energy resource capacity. The proposed MORL methodology computes a global policy that achieves a Pareto-optimal tradeoff among multiple objectives, thereby balancing environmental sustainability with network performance. Simulation results show that the proposed approach reduces the average emissions per bit by around 26% compared to state-of-the-art methods that do not explicitly integrate carbon emissions into their control objectives.
The integration of AI tools into medical applications has aimed to improve the efficiency of diagnosis. The emergence of large language models (LLMs), such as ChatGPT and Claude, has expanded this integration even further despite a concern for their environmental impact. Because of LLM versatility and ease of use through APIs, these larger models are often utilised even though smaller, custom models can be used instead. In this paper, LLMs and small discriminative models are integrated into a Mendix application to detect Covid-19 in chest X-rays. These discriminative models are also used to provide knowledge bases for LLMs to improve accuracy. This provides a benchmark study of 14 different model configurations for comparison of diagnostic accuracy and environmental impact. The findings indicated that while smaller models reduced the carbon footprint of the application, the output was biased towards a positive diagnosis and the output probabilities were lacking confidence. Meanwhile, restricting LLMs to only give probabilistic output caused poor performance in both accuracy and carbon footprint, demonstrating the risk of using LLMs as a universal AI solution. While using the smaller LLM GPT-4.1-Nano reduced the carbon footprint by 94.2% compared to the larger models, this was still disproportionate to the discriminative models; the most efficient solution was the Covid-Net model. Although it had a larger carbon footprint than other small models, its carbon footprint was 99.9% less than when using GPT-4.5-Preview, whilst achieving an accuracy of 95.5%, the highest of all models examined. This paper contributes to knowledge by comparing generative and discriminative models in Covid-19 detection as well as highlighting the environmental risk of using generative tools for classification tasks.
Chris C. Lim, Richard B. Hayes, Jiyoung Ahn
et al.
Abstract Rationale Many studies have linked short-term exposure to ozone (O3) with morbidity and mortality, but epidemiologic evidence of associations between long-term O3 exposure and mortality is more limited. Objectives To investigate associations of long-term (annual or warm season average of daily 8-h maximum concentrations) O3 exposure with all-cause and cause-specific mortality in the NIH-AARP Diet and Health Study, a large prospective cohort of U.S. adults with 17 years of follow-up from 1995 to 2011. Methods The cohort (n = 548,780) was linked to census tract–level estimates for O3. Associations between long-term O3 exposure (averaged values from 2002 to 2010) and multiple causes of death were evaluated using multivariate Cox proportional hazards models, adjusted for individual- and census tract–level covariates, and potentially confounding copollutants and temperature. Measurements and Main Results Long-term annual average exposure to O3 was significantly associated with deaths caused by cardiovascular disease (per 10 ppb; hazard ratio [HR], 1.03; 95% confidence interval [CI], 1.01–1.06), ischemic heart disease (HR, 1.06; 95% CI, 1.02–1.09), respiratory disease (HR, 1.04; 95% CI, 1.00–1.09), and chronic obstructive pulmonary disease (HR, 1.09; 95% CI, 1.03–1.15) in single-pollutant models. The results were robust to alternative models and adjustment for copollutants (fine particulate matter and nitrogen dioxide), although some evidence of confounding by temperature was observed. Significantly elevated respiratory disease mortality risk associated with long-term O3 exposure was found among those living in locations with high temperature (Pinteraction < 0.05). Conclusions This study found that long-term exposure to O3 is associated with increased risk for multiple causes of mortality, suggesting that establishment of annual and/or seasonal federal O3 standards is needed to more adequately protect public health from ambient O3 exposures.
Krzysztof Michalski, Magdalena Kośka-Wolny, Krzysztof Chmielowski
et al.
Biogas has improved due to technological advancements, environmental awareness, policy support, and research innovation, making it a more cost-effective and environmentally friendly renewable energy source. The Generalized Linear Model (GLM) was employed to examine the relationship between purchased and generated energy from 2007 to 2023. Metrics such as deviance, log likelihood, and dispersion phi were examined to assess model fit. The Mann–Kendall test was utilized to detect trends in energy datasets. Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) reduction was significant, exceeding 97% from 2014 to 2023. However, treated sewage displayed limited susceptibility to biological degradation, with COD to BOD5 ratios increasing from 2.28 to 6.59 for raw sewage and from 2.33 to 7.05 for treated sewage by 2023. Additionally, the efficiency of sewage purification processes was calculated, and multivariate regression analysis was conducted on gas composition data. Principal Coordinate Ordination (PCO) and k-means clustering were used for dimensionality reduction and biogas component clustering, respectively. This research showed that biogas from the waste water treatment process can be used, particularly in methane production. Technological advancements have made biogas production more efficient, enhancing energy generation within a circular economy framework.
Monitoring the annual variation of antibiotic resistance genes (ARGs) in livestock wastewater is important for determining the high-risk period of transfer and spread of animal-derived antibiotic resistance into the environment. However, the knowledge regarding the variation patterns of ARGs, especially intracellular ARGs (iARGs) and extracellular ARGs (eARGs), over time in livestock wastewater is still unclear. Herein, we conducted a year-round study to trace the profiles of ARGs at a Chinese-intensive dairy farm, focusing on the shifts observed in different months. The results showed significant differences in the composition and variation between iARGs and eARGs. Tetracycline, sulfonamide, and macrolide resistance genes were the major types of iARGs, while cfr was the major type of eARG. The environmental adaptations of the host bacteria determine whether ARGs appear as intracellular or extracellular forms. The total abundance of ARGs was higher from April to September, which can be attributed to the favorable climatic conditions for bacterial colonization and increased antibiotic administration during this period. Integron was found to be highly correlated with most iARGs, potentially playing a role in the presence of these genes within cells and their similar transmission patterns in wastewater. The intracellular and extracellular bacterial communities were significantly different, primarily because of variations in bacterial adaptability to the high salt and anaerobic environment. The intracellular co-occurrence network indicated that some dominant genera in wastewater, such as Turicibacter, Clostridium IV, Cloacibacillus, Subdivision5_genera_incertae_sedis, Saccharibacteria_genera_incertae_sedis and Halomonas, were potential hosts for many ARGs. To the best of our knowledge, this study demonstrates, for the first time, the annual variation of ARGs at critical points in the reuse of dairy farm wastewater. It also offers valuable insights into the prevention and control of ARGs derived from animals.
Low-cost monitors have made possible for the first time measurements of long-term (months to years) potential indoor exposures to fine particles. Indoor and outdoor measurements made over nearly 5 years (2017–2021) by the largest network of low-cost monitors in the United States (PurpleAir) are compared to the prevalence of adult smokers in 1650 Zip codes within the three West Coast states of California, Oregon, and Washington. The results show that mean potential indoor exposures above the 75th percentile of adult smoking prevalence are more than 50 % higher than those below the 25th percentile. Mean outdoor concentrations are also elevated, but by a smaller amount (∼ 20 %). Both comparisons are significant at the p < 0.001 level. The elevation of PM2.5 concentrations with increasing smoking prevalence is evidence of environmental disparities in income, education, and other socioeconomic indices. The relatively stronger effect on indoor rather than outdoor PM2.5 exposures highlights the importance of including indoor measurements when possible in environmental justice studies.
This study introduces LLM4DESIGN, a highly automated system for generating architectural and environmental design proposals. LLM4DESIGN, relying solely on site conditions and design requirements, employs Multi-Agent systems to foster creativity, Retrieval Augmented Generation (RAG) to ground designs in realism, and Visual Language Models (VLM) to synchronize all information. This system resulting in coherent, multi-illustrated, and multi-textual design schemes. The system meets the dual needs of narrative storytelling and objective drawing presentation in generating architectural and environmental design proposals. Extensive comparative and ablation experiments confirm the innovativeness of LLM4DESIGN's narrative and the grounded applicability of its plans, demonstrating its superior performance in the field of urban renewal design. Lastly, we have created the first cross-modal design scheme dataset covering architecture, landscape, interior, and urban design, providing rich resources for future research.
When there is a fault, the protection system automatically removes one or more transmission lines on a fast time scale of less than one minute. The outaged lines form a pattern in the transmission network. We extract these patterns from utility outage data, determine some key statistics of these patterns, and then show how to generate new patterns consistent with these statistics. The generated patterns provide a new and easily feasible way to model the overall effect of the protection system at the scale of a large transmission system. This new generative modeling of protection is expected to contribute to simulations of disturbances in large grids so that they can better quantify the risk of blackouts. Analysis of the pattern sizes suggests an index that describes how much outages spread in the transmission network at the fast timescale.
This paper investigates the faithfulness of multimodal large language model (MLLM) agents in a graphical user interface (GUI) environment, aiming to address the research question of whether multimodal GUI agents can be distracted by environmental context. A general scenario is proposed where both the user and the agent are benign, and the environment, while not malicious, contains unrelated content. A wide range of MLLMs are evaluated as GUI agents using a simulated dataset, following three working patterns with different levels of perception. Experimental results reveal that even the most powerful models, whether generalist agents or specialist GUI agents, are susceptible to distractions. While recent studies predominantly focus on the helpfulness of agents, our findings first indicate that these agents are prone to environmental distractions. Furthermore, we implement an adversarial environment injection and analyze the approach to improve faithfulness, calling for a collective focus on this important topic.
Robert Oleniacz, Marek Bogacki, Mateusz Rzeszutek
et al.
Constantly changing vehicle stock, modification of road infrastructure, and other conditions result in a need to update the knowledge on the effectiveness of individual traffic management strategies, which could form the basis for actions taken by local authorities to improve air quality in crowded city centers, especially in street canyons. The article presents research results that evaluate the theoretical effects of introducing select traffic reorganization scenarios in the example of four street canyons located in Krakow (Poland) that are different in terms of vehicle traffic volume and canyon geometry. These scenarios were based on a reduction in the average traffic speed, road capacity or the admission of cars meeting certain exhaust emission standards. The authors estimated changes in emissions of nitrogen oxides (NO, NO<sub>2</sub> and total NO<sub>x</sub>) and particulate matter (PM<sub>10</sub> and PM<sub>2.5</sub>) as well as investigated the effect of these changes on air quality in the canyons using the Operational Street Pollution Model (OSPM). Significant effects in terms of improving air quality were identified only in scenarios based on a significant reduction in traffic volume and the elimination of passenger cars and light commercial vehicles with internal combustion engines that did not meet the requirements of the Euro 4, Euro 5 or Euro 6 emission standards. For these scenarios, depending on the variant and canyon analyzed, the emission reduction was achieved at a level of approximately 36–66% for NO, 28–77% for NO<sub>2</sub>, 35–67% for NO<sub>x</sub> and 44–78% for both PM<sub>10</sub> and PM<sub>2.5</sub>. The expected effect of improving air quality in individual street canyons for these substances was 15–44%, 5–14%, 11–36% and 3–14%, respectively. The differences obtained in the percentage reduction of emissions and pollutant concentrations in the air were the result of a relatively high background of pollutants that suppress the achieved effect of improving air quality to a large extent.
Since 2008, people’s livelihoods and well-being has undergone profound changes, especially the Yellow River basin in China. Under the background of coordinated development, it is of great significance to research the temporal-spatial variation mechanism of people’s livelihoods and well-being. This study establishes a “society-ecology-policy” ternary system to analyze the coordinated development in nine provinces situated along the Yellow River from 2009 to 2018. Furthermore, it explores the system’s coordinated development mechanism with respect to people’s livelihoods and well-being in different regions based on the geographically and temporally weighted regression theory(GTWR). The results were as follows. First, the impacts of social development (SDi), watershed ecological protection (WEPi), and policy support (PSi) in the middle and lower reaches are generally stronger than those in other regions. Shandong showed the highest average comprehensive benefit. Second, from the perspective of coupling coordinated development, Qinghai and Shanxi were classified as slightly uncoordinated; Sichuan, Ningxia, Gansu, Shanxi, Shaanxi, and Henan were basically coordinated; and Inner Mongolia and Shandong were moderately coordinated. Third, clear temporal-spatial differences existed in the impact of degrees of coordinated development on people’s livelihoods and well-being. Social development provided a driving force for the economy, yet excessive use of natural resources had a significant negative impact. While ecological debt and long payback periods of environmental protection investment may negatively impact people’s livelihoods and well-being, the rapid response and precise execution of policies are key guarantees for improving the same.
IntroductionIssues related to fat, oil, and grease from kitchen waste (KFOG) in lipid-containing wastewater are intensifying globally. We reported a novel denitrifying bacterium Pseudomonas CYCN-C with lipid-utilizing activity and high nitrogen-removal efficiency. The aim of the present study was aim to explore the metabolic mechanism of the simultaneous lipid-utilizing and denitrifying bacterium CYCN-C at transcriptome level.MethodsWe comparatively investigated the cell-growth and nitrogen-removal performances of newly reported Pseudomonas glycinae CYCN-C under defined cultivation conditions. Transcriptome analysis was further used to investigate all pathway genes involved in nitrogen metabolism, lipid degradation and utilization, and cell growth at mRNA levels.ResultsCYCN-C could directly use fat, oil, and grease from kitchen waste (KFOG) as carbon source with TN removal efficiency of 73.5%, significantly higher than that (60.9%) with sodium acetate. The change levels of genes under defined KFOG and sodium acetate were analyzed by transcriptome sequencing. Results showed that genes cyo, CsrA, PHAs, and FumC involved in carbon metabolism under KFOG were significantly upregulated by 6.9, 0.7, 26.0, and 19.0-folds, respectively. The genes lipA, lipB, glpD, and glpK of lipid metabolic pathway were upregulated by 0.6, 0.4, 21.5, and 1.3-folds, respectively. KFOG also improved the denitrification efficiency by inducing the expression of the genes nar, nirB, nirD, and norR of denitrification pathways.ConclusionIn summary, this work firstly provides valuable insights into the genes expression of lipid-utilizing and denitrifying bacterium, and provides a new approach for sewage treatment with reuse of KFOG wastes.
Vasiliki Kompoura, Ioanna Prapa, Paraskevi B. Vasilakopoulou
et al.
The present study aimed at investigating the possible benefits of a dietary intervention with Corinthian currants, a rich source of phenolic compounds, on type 1 diabetes (T1D) using the animal model of the streptozotocin-(STZ)-induced diabetic rat. Male Wistar rats were randomly assigned into four groups: control animals, which received a control diet (CD) or a diet supplemented with 10% <i>w</i>/<i>w</i> Corinthian currants (CCD), and diabetic animals, which received a control diet (DCD) or a currant diet (DCCD) for 4 weeks. Plasma biochemical parameters, insulin, polar phenolic compounds, and inflammatory factors were determined. Microbiota populations in tissue and intestinal fluid of the caecum, as well as fecal microbiota populations and short-chain fatty acids (SCFAs), were measured. Fecal microbiota was further analyzed by 16S rRNA sequencing. The results of the study showed that a Corinthian currant-supplemented diet restored serum polar phenolic compounds and decreased interleukin-1b (IL-1b) (<i>p</i> < 0.05) both in control and diabetic animals. Increased caecal lactobacilli counts (<i>p</i> < 0.05) and maintenance of enterococci levels within normal range were observed in the intestinal fluid of the DCCD group (<i>p</i> < 0.05 compared to DCD). Higher acetic acid levels were detected in the feces of diabetic rats that received the currant diet compared to the animals that received the control diet (<i>p</i> < 0.05). Corinthian currant could serve as a beneficial dietary component in the condition of T1D based on the results coming from the animal model of the STZ-induced T1D rat.
Fabian Obster, Christian Heumann, Heidi Bohle
et al.
We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data. We illustrate this by using environmental, social, human and biophysical data to predict the financial vulnerability of farmers in Chile and Tunisia against climate hazards. We show how group structures can be considered and how interactions can be found in high-dimensional datasets using a novel 2-step boosting approach. The advantages and efficacy of the proposed method are shown and discussed. Results indicate that the presence of interaction effects only improves predictive power when included in two-step boosting. The most important variable in predicting all types of vulnerabilities are natural assets. Other important variables are the type of irrigation, economic assets and the presence of crop damage of near farms.
Amy Altshuler, Ofek Lauber Bonomo, Nicole Gorohovsky
et al.
Search processes in the natural world are often punctuated by home returns that reset the position of foraging animals, birds, and insects. Many theoretical, numerical, and experimental studies have now demonstrated that this strategy can drastically facilitate search, which could explain its prevalence. To further facilitate search, foragers also work as a group: modifying their surroundings in highly sophisticated ways e.g., by leaving chemical scent trails that imprint the memory of previous excursions. Here, we design a controlled experiment to show that the benefit coming from such ``environmental memory'' is significant even for a single, non-intelligent, searcher that is limited to simple physical interactions with its surroundings. To this end, we employ a self-propelled bristle robot that moves randomly within an arena filled with obstacles that the robot can push around. To mimic home returns, we reset the bristle robot's position at constant time intervals. We show that trails created by the robot give rise to a form of environmental memory that facilitates search by increasing the effective diffusion coefficient. Numerical simulations, and theoretical estimates, designed to capture the essential physics of the experiment support our conclusions and indicate that these are not limited to the particular system studied herein.