Human Life Research Articles

АРХЕТИПЫ И МИФОЛОГЕМЫ КОЛЛЕКТИВНОГО СОЗНАНИЯ В ФРАЗЕОЛОГИЗМАХ И МЕТАФОРАХ РУССКОГО ЯЗЫКА

In modern linguistics, one of the pressing issues is the problem of determining the essence of mythologemes and archetypes, as well as their functions. This article examines the archetypes and mythologems that form the deep semantics of Russian phraseological units and metaphors. Phraseologisms of the Russian language, which constitute a separate special layer of the vocabulary as a whole, have always been of interest to researchers. The formation of their stable semantics is the result of the historical development of mankind. Phraseological units of language and metaphors of collective consciousness always appeal to human experience. Since ancient times, humanity has sought to find an answer to the question of the creation of the world and man, to explain the reality around it, and this desire remains relevant for modern human society. Man's original desire to explain the essence of the universe is reflected in myths. Since man’s first attempts to explain the phenomena of reality are enshrined in myths, we turn to concepts such as “archetype” and “mythologem”, which are directly units of myth. This work analyzes several phraseological units and metaphors of the Russian language, the meanings of which are motivated by archetypes and mythologems of mass consciousness. From time immemorial, myths have never lost their relevance in human life and society. After all, a myth always establishes an idea as an ideal and at the same time is a guarantee of the stability of this ideal. It is in myths that the basic ideas of morality are contained (the ideas of good - evil, friend - stranger, Divine - Demonic, etc.), according to which orders are established in modern society. It is these ideas of morality, the binary oppositions of good and bad, which are preserved in mythologems, that perform the function of controlling the semantics of phraseological units and metaphors in the language. By comparing language and the mythological consciousness of society, we can determine the semantic core of phraseological units and the logic of the appearance in the language of certain metaphors that are associated with prototypes in the collective unconscious.

Research Progress on the Mechanism for Improving Glucose and Lipid Metabolism Disorders Using Phenolic Acid Components from Medicinal and Edible Homologous Plants.

Glucose and lipid metabolism disorders are the core pathological mechanism of a variety of metabolic diseases, and the incidence of related diseases is increasing year by year, which seriously threatens human life and health. Traditional Chinese medicine with medicinal and edible properties refers to Chinese medicinal resources that have both medicinal and edible characteristics. Due to its safety and its health-promoting and medicinal functions, traditional Chinese medicine has received increasing attention in the development of functional health foods. Phenolic acids are important secondary metabolites that are ubiquitous in medicinal and edible homologous plants, and the regulation of glycolipid metabolism is an important activity and plays a key role in many diseases. In this paper, we focus on the alleviation of glycolipid disorders using MEHH phenolic acids, which regulate glucose metabolism and lipid metabolism, improve insulin resistance, inhibit inflammatory responses, alleviate oxidative stress, and regulate intestinal flora; additionally, we summarize the mechanism in order to provide a reference for MEHH phenolic acids in the treatment of glycolipid metabolism diseases.

Engineering interkingdom communication for next-generation therapeutic approaches

Engineering interkingdom communication for next-generation therapeutic approaches Brian Snyder and Christopher H. Contag, from Michigan State University, discuss engineering interkingdom communication, which is not for palace intrigue, but for next-generation therapeutic approaches they argue. Our gut microbiota, once seen as ‘harmless passengers’, is now redefined as a vital symbiotic organ integral to sustaining human life. This microbiome consists of an estimated 1x1014 microbial symbionts. With an approximate 1:1 ratio of human to bacterial cells, it is the largest “organ” in the human body by cell count. Facilitating digestion, nutrient absorption, immune responses, and metabolic function, microbial symbionts are crucial for maintaining human health. Even minor changes in the composition of our microbiome have been implicated in a myriad of diseases, including cancers, metabolic diseases, autoimmune disorders, and neurological conditions (Clemente, Ursell, et al. 2012; Gomaa 2020).

Research on Low-Voltage Arc Fault Based on CNN–Transformer Parallel Neural Network with Threshold-Moving Optimization

Low-voltage arc fault detection can effectively prevent fires, electric shocks, and other accidents, reducing potential risks to human life and property. The research on arc fault circuit interrupters (AFCIs) is of great significance for both safety in production scenarios and daily living disaster prevention. Considering the diverse characteristics of loads between the normal operational state and the arc fault condition, a parallel neural network structure is proposed for arc fault recognition, which is based on a convolutional neural network (CNN) and a Transformer. The network uses convolutional layers and Transformer encoders to process the low-frequency current and high-frequency components, respectively. Then, it uses Softmax classification to perform supervised learning on the concatenated features. The method combines the advantages of both networks and effectively reduces the required depth and computational complexity. The experimental results show that the accuracy of this method can reach 99.74%, and with the threshold-moving method, the erroneous judgment rate can be lower. These results indicate that the parallel neural network can definitely detect arc faults and also improve recognition efficiency due to its lean structure.

Study on the Constraint Effect of Vegetation on Ecosystem Services in the Yellow River Basin

Ecosystem services (ESs) serve as the foundation for sustaining human life and development, with vegetation status playing a crucial role in influencing the supply of these services. This study focuses on the Yellow River Basin (YRB), where we quantitatively examined the main ESs indicators from 2010 to 2020. We explored the trends in fractional vegetation cover (FVC) and ESs, as well as the constraint relationship between FVC and total ecosystem services (TES). The findings are as follows. (1) From 2010 to 2020, FVC, landscape aesthetics (LA), soil conservation (SC), food production (FP), and TES in the YRB demonstrated an upward trend, whereas water yield (WY) exhibited a downward trend. (2) A constraint relationship exists between FVC and LA, SC, WY, and TES, with the constraint line taking on a hump-like shape. (3) The threshold value of the constraint line between FVC and LA, SC, WY, and TES are approximately 80%. Below this value, FVC does not impose a constraint effect on LA, SC, WY, and TES, but above 80%, a strong constraint effect emerges, leading to a reduction in LA, SC, WY, and TES. These results offer a valuable data reference for guiding future vegetation restoration and ecological engineering efforts in the region.

Green slag cement with nano copper oxide and nano silica: Enhanced antimicrobial and structural properties

The global construction industry is facing challenges related to sustainability, environmental impact, and the demand for innovative materials with multifunctional properties. A major concern is the durability of cement structures, which is often compromised by harmful bacteria and algae. These microorganisms accelerate the deterioration of structures, increase maintenance costs, and pose safety risks. Addressing these issues is essential to prolong the life of cement-based structures and reduce their environmental and carbon footprint. Cement in moist environments is particularly vulnerable to microbial growth, leading to structural weaknesses and degradation. This degradation necessitates costly repairs, shortens the lifespan of infrastructure, creates a threat to human lives, and contributes to increased carbon emissions. To tackle these challenges, researchers have tried to develop low-carbon cement ((green cement) with antimicrobial properties. Nanomaterials like nano-copper oxide (nCuO) and nano-silica (nSiO₂) have shown great potential in enhancing both the structural performance and microbial resistance of green cement. When nanomaterials, nCuO is incorporated into Portland slag cement (PSC), which already has a lower carbon footprint compared to traditional cement, enhances antibacterial and antialgal properties but adversely impacts the mechanical performance and to counter the adverse effect of nCuO, nSiO2 added to improve the mechanic performance. PSC is known for its ability to reduce industrial waste and CO₂ emissions, and the addition of nCuO and nSiO₂ makes it an even higher-performance material. Effects of nCuO and nSiO₂ on PSC have been studied by using isothermal calorimetry to assess hydration kinetics, X-ray diffraction to observe composition changes during hydration, scanning electron microscopy (SEM) to analyze microstructural changes, and mechanical testing to evaluate the strength of the material. The effectiveness of antimicrobial properties is studied in lab and field experiments. The results revealed improved compressive strength and significant resistance to bacterial and algal growth. The combination of nCuO's antimicrobial properties and nSiO₂'s ability to enhance the cement's strength created a synergistic effect that optimizes both performance and durability. Additionally, the study proposed a model to better understand the interaction between these nanomaterials, offering valuable insights into the mechanisms of hydration and microbial inhibition.

Design, Synthesis, and Antimicrobial Evaluation of New Thiopyrimidine-Benzenesulfonamide Compounds.

Bacterial infection poses a serious threat to human life due to the rapidly growing resistance of bacteria to antibacterial drugs, which is a significant public health issue. This study was focused on the design and synthesis of a new series of 25 analogues bearing a 5-cyano-6-oxo-4-substituted phenyl-1,6-dihydropyrimidine scaffold hybridized with different substituted benzenesulfonamides through the thioacetamide linker M1-25. The antimicrobial activity of the new molecules was studied against various Gram-positive, Gram-negative, and fungal strains. All the tested compounds showed promising broad-spectrum antimicrobial efficacy, especially against K. pneumoniae and P. aeruginosa. Furthermore, the most promising compounds, 6M, 19M, 20M, and 25M, were subjected to minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays. In addition, the antivirulence activity of the compounds was also examined using multiple biofilm assays. The new compounds promisingly revealed the suppression of microbial biofilm formation in the examined K. pneumoniae and P. aeruginosa microbial isolates. Additionally, in silico ADMET studies were conducted to determine their oral bioavailability, drug-likeness characteristics, and human toxicity risks. It is suggested that new pyrimidine-benzenesulfonamide derivatives may serve as model compounds for the further optimization and development of new antimicrobial and antisepsis candidates.

Concepts of justice beyond suum cuique

The concept of justice is fundamental in many spheres of human life and in academic disciplines such as law, philosophy, and theology. This article surveys a range of concepts of justice from these disciplines, threading a path from the idea of justice as fairness or proportionality in the works of Hart, Kelsen, and Rawls, to the notion of justice applied in a legal system (juridical justice) as understood by Gunther Teubner, then to concepts of justice beyond equivalence in the work of Ágnes Heller, either seeing justice as a step toward the good or understanding justice as righteousness. The article ends up discussing the idea of love as justice with the help of Paul Tillich. This paves the way for the possibility of critiquing worldly law and juridical justice from the perspective of an ontological concept of justice. The aim of the article is to put the understanding of justice which dominates the conceptual apparatus of the legal discipline in a new and critical light.

Remediation quantum of organic amendments to immobilize potentially toxic heavy metals in wastewater-contaminated soils through maize cultivation

Wastewater is considered a good reservoir of mineral elements that can be used for agriculture, aquaculture, and some other activities after adopting suitable measures. The gap between supply and demand for water is increasing exponentially because of the abrupt boost to the world’s population. This poses a threat to human life as it has reached alarming levels in some parts of the globe. Normally, wastewater consists of liquid waste produced by commercial or industrial sources for daily use, consumption, and production. It is time to refocus our attention on a kind of circulating water system by reusing municipal wastewater for agricultural purposes, particularly irrigation. The recycled or treated water would be used as an alternative to fresh water. In the current study, the impact of various organic amendments was studied to mitigate the toxic effects of pollutants present in wastewater by cultivating maize as a test crop. The present study comprised five treatments replicated four times with a randomized complete block design under field conditions. In this experiment, the treatments included T1 (treatment 1) = control (wastewater-polluted soil without the application of any amendment), T2 = farmyard manure (FYM) at 2.5 tons ha-1 (hectare-1), T3 = FYM at 5.0 tons ha-1, T4 = compost at 2.5 tons ha-1, and T5 = compost at 5.0 tons ha-1. The application of FYM at 5.0 tons ha-1 (T3) was recorded as being the most effective as the maximum improvement was observed in soil characteristics such as pH, electrical conductivity (EC), sodium adsorption ratio (SAR), and organic matter, and for T3, these were 7.33, 2.22 dS m-1, 8.16, and 0.94%, respectively. T3 remained most superior in reducing the concentration of heavy metals in the soil; for example, lead, cadmium, nickel, and arsenic for T3 were 8.64, 1.34, 10.44, and 2.25 mg kg-1 (milligrams per kg), respectively. Maximum fresh biomass (fodder yield) of 9.98 tons ha-1 was harvested when FYM was applied at 5.0 tons ha-1 to the soil compared to 6.2 tons ha-1 in the control plot. The highest contents of nitrogen (1.20%), phosphorus (0.41%), and potassium (3.97%) were observed in maize plants for T3. In maize plants (T3), the concentration of lead, cadmium, nickel, and arsenic was reduced to levels of 1.92, 0.23, 2.28, and 1.25 mg kg-1, respectively. Therefore, it can be concluded from the findings of the experiment that the application of FYM significantly reduced heavy metal concentrations and improved soil health, along with maize crop growth and productivity.

Synergetic effect of bioglass and nano montmorillonite on 3D printed nanocomposite of polycaprolactone/gelatin in the fabrication of bone scaffolds

Nowadays, bone injuries and disorders have increased all over the world and can reduce the quality of human life. Bone tissue engineering repair approaches require new biomaterials and methods to construct scaffolds with the required structural properties as well as improved performance. As potential therapeutic strategies in bone tissue engineering, 3D printed scaffolds have been developed. Polycaprolactone/Ceramic composites have attracted considerable attention due to their cytocompatibility, biodegradability, and physical properties. In this study, a 3D printing process was used to create polycaprolactone (PCL)-Gelatin (GEL) scaffolds containing varying concentrations of Bioglass (BG) and Nano Montmorillonite (MMT). This mixture was then loaded into a 3D printer, and the scaffolds were printed layer by layer. After constructing the scaffolds, they were then examined for their physical, chemical, and biological characteristics. Surface appearance was analyzed with a scanning electron microscope (SEM), which revealed that NC increased the diameter of pores from 465 to 480 μm. The elements in the scaffolds were evaluated by EDX analysis, and a uniform dispersion of nano montmorillonite particles was observed. The compressive strength reached 76.43 MPa for PCL/G/35 %MMT/15 %BG scaffold. Also, the rate of water absorption, biodegradability and bioactivity of PCL-GEL scaffolds increased significantly in the presence of NC. According to the MTT cell test results, adding BG and NC increased cell proliferation, adhesion and cell viability to 127.7 %. These findings indicated that the 3D printed PCL/G/35 %MMT/15 %BG scaffold has promising strategies for bone repair applications. Also, polynomial curve fitting shows that scaffold degradability after soaking in PBS can be predicted using the initial weight and soaking time. Adding more variables and data could improve prediction accuracy, reducing the need for experiments and conserving resources.

An optimal set of criteria for the allocation of territories within the Arctic zone of the Russian Federation

The article examines the criteria for classifying regions in the Far North as part of the Arctic Zone within the Russian Federation. It evaluates a range of factors, including natural and climatic conditions, socio-economic aspects, and the medical and biological characteristics of the territories. Additionally, the article presents the outcomes of zoning the northern territories of the Russian Federation based on the natural and climatic conditions that impact human life. This analysis resulted in the identification of three criteria for determining the inclusion of territories in the Arctic Zone of the Russian Federation: the vegetation component of landscapes in its zonal distribution; the CAFF boundary, which proposed by the Arctic Council’s Arctic Flora and Fauna Conservation Working Group; and the level of ultraviolet deficiency, which reflects the direct impact of latitude on the natural environment and human health. These criteria are characterized by their clarity and ease of interpretation, and do not require the development or endorsement of supplementary methodological guidelines. They are easily grasped by both regional leaders and residents regarding the inclusion or exclusion of areas from the Arctic Zone of the Russian Federation. Consequently, these criteria can be effectively integrated into federal legislation as directly applicable norms.

Healthy aging: A novel therapy to reverse age-related damage

Healthy aging: A novel therapy to reverse age-related damage What if we could turn back the clock on age-associated dysfunctions by using a therapy that not only treats symptoms but acts to correct the underlying pathology and restores cells to normal function? Lori A. Birder and Edwin K. Jackson from the University of Pittsburgh School of Medicine, explain how this could be a possibility. Increases in our aging population will put substantive burdens on the public health system as well as on medical and social services. Though age is a risk factor for disease, it is often overlooked in clinical practice. Many older people suffer from more than one disease, with the likelihood of having two or more conditions rising to 60% by 75 years of age, and both aging and multimorbidity contribute to frailty, which is associated with a risk of other complications. While several pharmacologic or cell-based strategies show promise for slowing the progression of several age-associated disorders, most ‘mask’ symptoms but do not reverse the condition or restore healthy form and function. Despite the prevalence and impact of age-related disorders on quality of life in humans, a multitude of age-related conditions continue to be undertreated simply because there are few successful therapeutic options, especially minimally invasive ones, and no preventative strategies nor known modulating interventions that reverse or delay physiologic changes. To further compound the problem, the prevalence of polypharmacy in the aged population can increase the risk of drug-drug interactions, which may complicate the diagnosis of many age-associated disorders.

Enhanced Staphylococcus aureus protection by uncoupling of the α-toxin-ADAM10 interaction during murine neonatal vaccination

Staphylococcus aureus remains a leading global cause of bacterial infection-associated mortality and has eluded prior vaccine development efforts. S. aureus α-toxin (Hla) is an essential virulence factor in disease, impairing the T cell response to infection. The anti-Hla antibody response is a correlate of human protective immunity. Here we observe that this response is limited early in human life and design a vaccine strategy to elicit immune protection against Hla in a neonatal mice. By targeted disruption of the interaction of Hla with its receptor ADAM10, we identify a vaccine antigen (HlaH35L/R66C/E70C, HlaHRE) that elicits an ~100-fold increase in the neutralizing anti-Hla response. Immunization with HlaHRE enhances the T follicular helper (TFH) cell response to S. aureus infection, correlating with the magnitude of the neutralizing anti-toxin response and disease protection. Furthermore, maternal HlaHRE immunization confers protection to offspring. Together, these findings illuminate a path for S. aureus vaccine development at the maternal-infant interface.

Human history: A socio-cultural examination of handshaking

Human history: A socio-cultural examination of handshaking Penelope J. Corfield, from Royal Holloway, University of London, offers an intriguing exploration of the social and cultural significance of handshaking throughout human history. All aspects of human life have a history. Really? All of them? Yes, really. All humans live in history and make history as they do so. Yet it’s also true that not all aspects of human history are equally easy to study. Governments, taxes and warfare between them have a significant impact – and leave many records. So they tend to be well-studied. By contrast, other aspects of human life flourish ‘under the radar’. Yet readers today who are interested in (say) the history of silence, can find an excellent book on that theme – while another fine study explains the evolutionary role of humanity’s ‘curious behaviour’, such as yawning, laughing and hiccupping.

Protecting subterranean ecosystems for the wellbeing of surface environments and humanity

Protecting subterranean ecosystems for the wellbeing of surface environments and humanity Stefano Mammola, a researcher at the Italian National Research Council and coordinator of the Biodiversa+ project ‘DarCo’, emphasizes the importance of better incorporation of subterranean biodiversity in conservation agendas. Beneath our feet lies a vast and intricate network of caves, aquifers, fissures, and other voids teeming with unique life forms crucial to both human life and the environment. Groundwaters, for instance, represent the largest unfrozen freshwater reserve, serving as the primary water source for over half of the world’s population. Despite their importance, subterranean ecosystems receive minimal attention in current biodiversity and climate change agendas – a concerning oversight that demands urgent action. (1)

Bio-inspired feature selection for early diagnosis of Parkinson's disease through optimization of deep 3D nested learning.

Parkinson's disease (PD) is one of the most common neurodegenerative disorders that affect the quality of human life of millions of people throughout the world. The probability of getting affected by this disease increases with age, and it is common among the elderly population. Early detection can help in initiating medications at an earlier stage. It can significantly slow down the progression of this disease, assisting the patient to maintain a good quality of life for a more extended period. Magnetic resonance imaging (MRI)-based brain imaging is an area of active research that is used to diagnose PD disease early and to understand the key biomarkers. The prior research investigations using MRI data mainly focus on volume, structural, and morphological changes in the basal ganglia (BG) region for diagnosing PD. Recently, researchers have emphasized the significance of studying other areas of the human brain for a more comprehensive understanding of PD and also to analyze changes happening in brain tissue. Thus, to perform accurate diagnosis and treatment planning for early identification of PD, this work focuses on learning the onset of PD from images taken from whole-brain MRI using a novel 3D-convolutional neural network (3D-CNN) deep learning architecture. The conventional 3D-Resent deep learning model, after various hyper-parameter tuning and architectural changes, has achieved an accuracy of 90%. In this work, a novel 3D-CNN architecture was developed, and after several ablation studies, the model yielded results with an improved accuracy of 93.4%. Combining features from the 3D-CNN and 3D ResNet models using Canonical Correlation Analysis (CCA) resulted in 95% accuracy. For further enhancements of the model performance, feature fusion with optimization was employed, utilizing various optimization techniques. Whale optimization based on a biologically inspired approach was selected on the basis of a convergence diagram. The performance of this approach is compared to other methods and has given an accuracy of 97%. This work represents a critical advancement in improving PD diagnosis techniques and emphasizing the importance of deep nested 3D learning and bio-inspired feature selection.

Investigating the role of biomass energy consumption and forest products trade on the forest footprint in Finland: an environmental Kuznets curve analysis

AbstractForests are essential in mitigating climate change by absorbing carbon dioxide and maintaining ecosystems, mainly through preserving biodiversity. For this reason, the sustainability of forests is essential for human life. This paper examines the role of biomass energy consumption, forest product trade, and economic growth on Finland’s forest footprint from 1970 to 2018. By employing Fourier-based unit root and cointegration tests, we confirm the Environmental Kuznets Curve (EKC) hypothesis, demonstrating a long-term relationship in which economic growth first raises, then reduces the forest footprint. Biomass energy consumption and forest products trade show an insignificant long-term effect on the forest footprint. We found unidirectional causality from the forest footprint to biomass energy consumption and economic growth. This research fills a gap by focusing on the under-researched forest footprint within the EKC framework, particularly in Finland’s context of stringent forestry policies. Our findings emphasize the need for interdisciplinary collaboration to enhance environmental policy and sustainability practices. We further contribute to the broader literature on sustainable forest management and offer actionable policy recommendations for enhancing Finland’s environmental strategies.

Using Social Robotics to Identify Educational Behavior: A Survey

The advancement of social robots in recent years has opened a promising avenue for providing users with more accessible and personalized attention. These robots have been integrated into various aspects of human life, particularly in activities geared toward students, such as entertainment, education, and companionship, with the assistance of artificial intelligence (AI). AI plays a crucial role in enhancing these experiences by enabling social and educational robots to interact and adapt intelligently to their environment. In social robotics, AI is used to develop systems capable of understanding human emotions and responding to them, thereby facilitating interaction and collaboration between humans and robots in social settings. This article aims to present a survey of the use of robots in education, highlighting the degree of integration of social robots in this field worldwide. It also explores the robotic technologies applied according to the students’ educational level. This study provides an overview of the technical literature in social robotics and behavior recognition systems applied to education at various educational levels, especially in recent years. Additionally, it reviews the range of social robots in the market involved in these activities. The objects of study, techniques, and tools used, as well as the resources and results, are described to offer a view of the current state of the reviewed areas and to contribute to future research.

Adolescent autonomy to make a ‘catastrophically bad’ decision informed by religious beliefs: To respect or protect?

This article examines the Australian case law in which medical treatment has been authorised against the wishes of a Gillick competent adolescent, in circumstances where the adolescent’s decision to refuse treatment has been informed by their religious beliefs. We analyse three factors that have shaped the Australian jurisprudence – medical evidence regarding the adolescent’s prognosis and risk of death; the role of religion in adolescents’ medical treatment decision-making; and the adolescent’s own views and wishes – to expose arguably uncomfortable, value-laden judicial attempts to achieve a ‘nice balance’ between the sanctity of human life and individual autonomy. We argue for greater judicial transparency about when, if ever, an adolescent’s Gillick competence will be respected in refusal of medical treatment cases shaped by the adolescent’s faith.

Time-optimal ergodic search: Multiscale coverage in minimum time

Search and exploration capabilities are essential for robots to inspect hazardous areas, support scientific expeditions in extreme environments, and potentially save human lives in natural disasters. The variability of scale in these problems requires robots to reason about time alongside their dynamics and sensor capabilities to effectively assess and explore for information. Recent advances in ergodic search methods have shown promise in supporting trajectory planning for exploration in continuous, multiscale environments with dynamics consideration. However, these methods are still limited by their inability to effectively reason about and adapt the time to explore in response to their environment. This ability is crucial for adapting exploration to variable-resolution information-gathering tasks. To address this limitation, this paper poses the time-optimal ergodic search problem and investigates solutions for fast, multiscale, and adaptive robotic exploration trajectories. The problem is formulated as a minimum-time problem with an ergodic inequality constraint whose upper bound specifies the amount of coverage needed. We show the existence of optimal solutions using Pontryagin’s conditions of optimality, and we demonstrate effective, minimum-time coverage numerically through a direct transcription optimization approach. The efficacy of the approach in generating time-optimal search trajectories is demonstrated in simulation under several nonlinear dynamic constraints, and in a physical experiment using a drone in a cluttered environment. We find that constraints such as obstacle avoidance are readily integrated into our formulation, and we show through an ablation study the flexibility of search capabilities at various scales. Last, we contribute a receding-horizon formulation of time-optimal ergodic search for sensor-driven information-gathering and demonstrate improved adaptive sampling capabilities in localization tasks.