Representation Of Environment Research Articles

Nature, ideology, and the ecocritical enterprise: Wangari Maathai’s The Green Belt Movement and Nadine Gordimer’s Get a Life

The dominant knowledge about African environments informed by Western literature—scientific reports, travelogues, memoirs, journalism, and fiction—has constructed an image of Africa as a pristine wilderness of exotic biodiversity on the verge of destruction due to Africa’s ignorance-based environmental culture. Contrary to this, Wangari Maathai’s and Nadine Gordimer’s environmental discourse, in The Green Belt Movement (2003) and Get a Life (2005), respectively, reveal Africa’s environmental decline as the direct consequence of the long history of colonial and capitalist exploitation of its natural resources, and the transformation of its environment into a resource base for industrial production. Drawing on the theoretical concepts of post-colonial ecocriticism, this article argues that environmental representations are mediated by the ideological configurations that generate them. Consequently, Maathai’s and Gordimer’s environmental discourse repudiates the dominant knowledge of African environments, offering alternative ways of engaging with its ecological issues while highlighting the dangers of capitalist resource exploitation on Africans’ environments, lives, and livelihoods. The image of the environment, in their works, ties politics and ecology together, providing an understanding of how the environment enables a rethinking of socio-political justice in dealing with Africa’s ecological crisis. (This article is published in the thematic collection ‘African ecologies: literary, cultural and religious perspectives’, edited by Adriaan von Klinken, Simon Manda, Damaris Parsitau and Abel Ugba.)

Towards Personally Relevant Navigation: The Differential Effects of Cognitive Style and Map Orientation on Spatial Knowledge Development

Under emergencies such as floods and fires or during indoor navigation where cues from local landmarks and a Global Positioning System (GPS) are no longer available, the acquisition of comprehensive environmental representation becomes particularly important. Several studies demonstrated that individual differences in cognitive style might play an important role in creating a complete environmental representation and spatial navigation. However, this relationship between cognitive style and spatial navigation is not well researched. This study hypothesized that a specific type of map orientation (north-up vs. forward-up) might be more efficient for individuals with different cognitive styles. Forty participants were recruited to perform spatial tasks in a virtual maze environment to understand how cognitive style may relate to spatial navigation abilities, particularly the acquisition of survey and route knowledge. To measure survey knowledge, pointing direction tests and sketch map tests were employed, whereas, for route knowledge, the landmark sequencing test and route retracing test were employed. The results showed that both field-dependent and field-independent participants showed more accurate canonical organization in their sketch map task with a north-up map than with a forward-up map, with field-independent participants outperforming field-dependent participants in canonical organization scores. The map orientation did not influence the performance of Field-Independent participants on the pointing direct test, with field-dependent participants showing higher angular error with north-up maps. Regarding route knowledge, field-independent participants had more accurate responses in the landmark sequencing tests with a north-up map than with a forward-up map. On the other hand, field-dependent participants had higher accuracy in landmark sequencing tests in the forward-up map condition than in the north-up map condition. In the route retracing test, however, the map orientation had no statistically significant effect on different cognitive style groups. The results indicate that cognitive style may affect the relationship between map orientation and spatial knowledge acquisition.

Measurement-based V2V propagation modeling in highway, suburban, and urban environments

Vehicle-to-vehicle (V2V) communication is one of the promising communication applications designed to optimize traffic conditions and has played a crucial role in the improvement of intelligent transportation technologies. Since there is still some uncertainty regarding generalized models that provide a more accurate representation of propagation environments, the existing literature emphasizes the need for additional experimental studies in various countries and propagation environments. This study aims to investigate the low-density and high-density characteristics of V2V channels for highway, suburban, and urban propagation environments in Türkiye. Thus, first, channel measurements were conducted for all propagation scenarios. Then, after the estimation of path loss parameters, the best-fitted path loss model was determined for each propagation scenario by comparing log-distance, two-ray, and log-ray models. It was observed that the log-ray model offered remarkably better performance than the two-ray model, especially in the majority of scenarios with two-ray characteristics. In addition, small-scale modeling and shadowing were also examined, and the outcomes were compared to relevant literature. Last, generalized path loss models were developed for six propagation scenarios and compared with previous studies. Providing additional experimental data on the impact of traffic and road environments that vary across countries on the V2V channel, this study not only validated and compared existing propagation models but also improved the representing accuracy and generalizability of the newly proposed propagation models. Here, all findings were presented in detail to support the motivation of the research.

Emergence of integrated behaviors through direct optimization for homeostasis

Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeostatic RL organizes the behaviors of autonomous embodied agents according to the demands of the internal dynamics of their bodies, coupled with the external environment. Thus, it provides a basis for real-world autonomous agents, such as robots, to continually acquire and learn integrated behaviors for survival. However, prior studies have generally explored problems pertaining to limited size, as the agent must handle observations of such coupled dynamics. To overcome this restriction, we developed an advanced method to realize scaled-up homeostatic RL using deep RL. Furthermore, several rewards for homeostasis have been proposed in the literature. We identified that the reward definition that uses the difference in drive function yields the best results. We created two benchmark environments for homeostasis and performed a behavioral analysis. The analysis showed that the trained agents in each environment changed their behavior based on their internal physiological states. Finally, we extended our method to address vision using deep convolutional neural networks. The analysis of a trained agent revealed that it has visual saliency rooted in the survival environment and internal representations resulting from multimodal input.

Think Holistically, Act Down-to-Earth: A Semantic Navigation Strategy With Continuous Environmental Representation and Multi-Step Forward Planning

Think Holistically, Act Down-to-Earth: A Semantic Navigation Strategy With Continuous Environmental Representation and Multi-Step Forward Planning

Dense Feature Matching for Hazard Detection and Avoidance Using Machine Learning in Complex Unstructured Scenarios

Exploring the Moon and Mars are crucial steps in advancing space exploration. Numerous missions aim to land and research in various lunar locations, some of which possess challenging surfaces with unchanging features. Some of these areas are cataloged as lunar light plains. Their main characteristics are that they are almost featureless and reflect more light than other lunar surfaces. This poses a challenge during navigation and landing. This paper compares traditional feature matching techniques, specifically scale-invariant feature transform and the oriented FAST and rotated BRIEF, and novel machine learning approaches for dense feature matching in challenging, unstructured scenarios, focusing on lunar light plains. Traditional feature detection methods often need help in environments characterized by uniform terrain and unique lighting conditions, where unique, distinguishable features are rare. Our study addresses these challenges and underscores the robustness of machine learning. The methodology involves an experimental analysis using images that mimic lunar-like landscapes, representing these light plains, to generate and compare feature maps derived from traditional and learning-based methods. These maps are evaluated based on their density and accuracy, which are critical for effective structure-from-motion reconstruction commonly utilized in navigation for landing. The results demonstrate that machine learning techniques enhance feature detection and matching, providing more intricate representations of environments with sparse features. This improvement indicates a significant potential for machine learning to boost hazard detection and avoidance in space exploration and other complex applications.

Surrendering the Self

In his novel Annihilation, author Jeff Vandermeer provides a science-fiction narrative on nature as an unstoppable and uncontrollable environment where plants, animals, humans, and the land exist as a collective and connected entity of interactions. The novel utilizes Lovecraftian horror elements of an uncontrollable nature, human contamination, and an unknowable future controlled by nonhuman forces to portray both a multispecies environment and the posthuman future. Read through a multispecies lens and framed by Donna Haraway’s Staying with the Trouble: Making Kin in the Chthulucene, this essay is an analysis of how Annihilation’s setting—Area X—necessitates the removal of human-centered processes and the human concept of individualism for favor of a flourishing multispecies environment. Its analysis exemplifies the genre of science fiction as a method to expand the boundaries of our perceived human-centered world. The narrative and rhetorical structures utilized by Vandermeer in his representation of real-world environments and natural processes as uncanny horrors and an off-center reality accurately represent the unknown future beyond the human species.

RELIABLE OPERATION OF CYBER-PHYSICAL SYSTEM WITH ACCOMPANIED BY A DIGITAL TWIN

The reliability of cyber-physical systems (CPS) operation is based on the general problem minimization of multi-factor risks, the margin of permissible risk, the forecast of the destabilizing dynamics of risk factors, principles, hypotheses, and axioms that are directly related to the analysis of abnormal situations, accidents, and disasters. To ensure the reliable operation of the CPS, a digital twin is created, which accompanies the operation of the CPS throughout its life cycle. The design of digital twins is based on simulation modelling methods that provide the most realistic representation of a physical environment or object in the virtual world.

Indoor view graph: A model to capture route and configurational information

This paper presents a graph model that simultaneously stores route and configurational information about indoor spaces. Existing indoor information models either capture route information to compute shortest paths and to generate route descriptions (i.e., answering how-to-get-to questions), or they store configurational information about objects and places and their spatial relationships to enable spatial querying and inference (i.e., answering where-questions). Consequently, multiple representations of an indoor environment must be stored in information systems to address the various information needs of their users. In this paper, we propose a graph that can capture both configurational and route information in a unified manner. The graph is the dual representation of connected lines of sight, or views. Views can represent continuous movement in an indoor environment, and at the same time, the visible configurational information of each view can be explicitly captured. In this paper, we discuss the conceptual design of the model and an automatic approach to derive the view graph from floorplans. Finally, we demonstrate the capabilities of our model in performing different tasks such as calculating shortest paths, generating route descriptions, and deriving place graphs.

Coupling an agent-based model and an ensemble Kalman filter for real-time crowd modelling.

Agent-based modelling has emerged as a powerful tool for modelling systems that are driven by discrete, heterogeneous individuals and has proven particularly popular in the realm of pedestrian simulation. However, real-time agent-based simulations face the challenge that they will diverge from the real system over time. This paper addresses this challenge by integrating the ensemble Kalman filter (EnKF) with an agent-based crowd model to enhance its accuracy in real time. Using the example of Grand Central Station in New York, we demonstrate how our approach can update the state of an agent-based model in real time, aligning it with the evolution of the actual system. The findings reveal that the EnKF can substantially improve the accuracy of agent-based pedestrian simulations by assimilating data as they evolve. This approach not only offers efficiency advantages over existing methods but also presents a more realistic representation of a complex environment than most previous attempts. The potential applications of this method span the management of public spaces under 'normality' to exceptional circumstances such as disaster response, marking a significant advancement for real-time agent-based modelling applications.

Using agro-ecological zones to improve the representation of a multi-environment trial of soybean varieties.

This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data and records of soybean varietal trials, this research generates a zonal environmental characterization which captures major components of the growing environment which affect the range of adaptation of soybean varieties. These findings have immediate applications for optimizing multi-environment soybean trials. This characterization allows breeders to assess the environmental representation of a multi-environmental trial of soybean varieties, and to strategize the distribution of testing and the placement of test sites accordingly. This application is demonstrated with a historical scenario of a soybean multi-environment trial, using two resource allocation models: one targeted towards improving the general adaptation of soybean varieties, which focuses on widely cultivated areas, and one targeted towards specific adaptation, which captures diverse environmental conditions. Ultimately, the study aims to improve the efficiency and impact of soybean breeding programs, leading to the development of cultivars resilient to variable and changing climates.

Learning Generalizable and Composable Abstractions for Transfer in Reinforcement Learning

Reinforcement Learning (RL) in complex environments presents many challenges: agents require learning concise representations of both environments and behaviors for efficient reasoning and generalizing experiences to new, unseen situations. However, RL approaches can be sample-inefficient and difficult to scale, especially in long-horizon sparse reward settings. To address these issues, the goal of my doctoral research is to develop methods that automatically construct semantically meaningful state and temporal abstractions for efficient transfer and generalization. In my work, I develop hierarchical approaches for learning transferable, generalizable knowledge in the form of symbolically represented options, as well as for integrating search techniques with RL to solve new problems by efficiently composing the learned options. Empirical results show that the resulting approaches effectively learn and transfer knowledge, achieving superior sample efficiency compared to SOTA methods while also enhancing interpretability.

Abstract 3198: An analysis of breast cancer membrane lipid-modified nanoliposomes for enhanced targeting of triple-negative breast cancer

Abstract Background: Every breast cancer diagnosis is unique in its own way. Triple-negative breast cancer (TNBC), has a highly aggressive phenotype characterized by a rapid growth rate profile, increased risk of metastasis and recurrence. TNBC is devoid of epidermal growth factor receptor 2, estrogen, and progesterone receptors. All three are commonly present in other forms of breast cancer disease. The aim of this study was to develop a cell membrane lipid-extracted nanoliposomes (CLENs) for selective targeting compared to conventional nanoliposomal varieties. An additional objective was to investigate the role of microenvironment on targeting using cell model of TNBC, and representations of non-target tissue environments for comparison. Method: Lipid extracts (LE) obtained from 4T1, a murine mammary carcinoma cell line from a BALB/cfC3H mouse was used to formulate the CLENs (cell membrane lipid-extracted nanoliposomes) with different ratios of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC), a phospholipid used in most conventional drug delivery liposomes. Different lipid compositions and ratios were employed to evaluate cellular uptake in target, non-target and off-target cells. The target cell line, 4T1, off-target and non-target cell populations included CRL-2089, normal breast fibroblasts, SKBR3 a (human breast cancer), and A549 a (lung cancer) cell lines. Other studies involved determination of molecular charge and zeta potential of various nanoliposomal preparations and corresponding fluorescence studies in vitro. Results: The physicochemical properties of CLENs such as particle size and surface charge characteristics were vital when assessing benefit of the lipid-based preparations. The size of the CLENs typically ranged between 130 - 165 nm and zeta potential values were negatively-charged. The inclusion of 70 mol% of LE in preparation of 4T1-CLENs demonstrated increased cellular uptake and binding by the 4T1 target cells compared to the control DOPC (100%). The highest degree of selectivity was observed in early cell binding events, and with studies involving more extended incubation time points under different experimental conditions. Off-target cells demonstrated reduced uptake by comparison. Overall, 4T1-CLENs was most efficient when applied against intended target 4T1 cells, when compared to relevant control cell populations. In conclusion, studies performed to date suggest the notion that lipid extracts derived from TNBC improves targeting. Citation Format: Christiana Ayertey, Parmida Amid, Mohammed H. Almozain, Robert B. Campbell. An analysis of breast cancer membrane lipid-modified nanoliposomes for enhanced targeting of triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3198.

Rapid altitude displacement induce zebrafish appearing acute high altitude illness symptoms

Rapid ascent to high-altitude areas above 2500 m often leads to acute high altitude illness (AHAI), posing significant health risks. Current models for AHAI research are limited in their ability to accurately simulate the high-altitude environment for drug screening. Addressing this gap, a novel static self-assembled water vacuum transparent chamber was developed to induce AHAI in zebrafish. This study identified 6000 m for 2 h as the optimal condition for AHAI induction in zebrafish. Under these conditions, notable behavioral changes including slow movement, abnormal exploration behavior and static behavior in the Novel tank test. Furthermore, this model demonstrated changes in oxidative stress-related markers included increased levels of malondialdehyde, decreased levels of glutathione, decreased activities of superoxide dismutase and catalase, and increased levels of inflammatory markers IL-6, IL-1β and TNF-α, and inflammatory cell infiltration and mild edema in the gill tissue, mirroring the clinical pathophysiology observed in AHAI patients. This innovative zebrafish model not only offers a more accurate representation of the high-altitude environment but also provides a high-throughput platform for AHAI drug discovery and pathogenesis research.

Implementation of advanced techniques in production and manufacturing sectors through support vector machine algorithm with embedded system

Our study set out to explore the integration of digital twins into manufacturing and production sectors with the aim of enhancing sustainability and operational efficiency. Employing a structured methodology, we utilized advanced 3D modeling techniques to create virtual representations of physical environments and products, forming the basis of our digital twin ecosystem. Through virtual product implementation, we simulated product behavior and performance, facilitating comprehensive testing and analysis before physical manufacturing. Leveraging artificial intelligence, particularly Support Vector Machine (SVM) optimization, we conducted performance analysis by processing data from interconnected sources, including social media platforms, to optimize digital transformation processes. Our results indicate that digital twins significantly enhance operational efficiency by detecting inefficiencies early, reducing production costs, and expediting time-to-market. Moreover, they contribute to sustainability efforts by minimizing resource wastage and environmental impact during manufacturing processes. The adoption of digital twins also fosters a culture of innovation and continuous improvement, enabling technological advancements and adaptability in corporate and industrial settings. In conclusion, our findings underscore the transformative potential of digital twins in driving positive change and optimizing industrial processes.

DEVELOPING 4D MALAYSIAN MARINE CADASTRE DATA MODEL BASED ON LADM - PRELIMINARY WORKS

Abstract. The complex interactions between terrestrial and aquatic dimensions make effective land management in coastal and marine settings particularly difficult. For thorough spatial data administration, it has been acknowledged that the traditional Land Administration Domain Model (LADM) must incorporate temporal and three-dimensional (3D) elements. The main goal of this work is to develop a 4D Malaysian marine cadastre data model based on LADM that integrates the temporal dimensions (3D + T= 4D) to the current 3D Malaysian marine cadastre data model and to assess how well the extended model handles issues with the representation of 3D marine environment. The depiction and management of temporal data related to shifting coastlines, fluctuating tides, and seasonal fluctuations in the maritime environment are some of the key questions investigated. The findings of this study are expected to provide valuable insights for augmenting land administration methodologies in dynamic marine settings. The outcomes of this research hold significance for sustainable marine development and effective spatial data management in coastal regions.

Analyzing the impact of grid structures on traffic flow optimization in autonomous transport systems

A fundamental element of a centralized autonomous vehicle control process is the representation of the transport environment. The road surface has to be partitioned to be able to ensure the main safety requirements of the transport process: a vehicle can only be in one place at a time, and only one vehicle can be in one place at a time. The occupancy grid concept is widely used for this purpose; however, previous concepts mainly utilize square grid representations, considered as a static component of the traffic models. Our research focuses on discovering and introducing the impact of different grid representations by evaluating their efficiency, computational complexity, and safety characteristics. This result provides the basis for real-time optimization of self-organizing road transport networks, as our research gives clear information on the impact of different grid structures. It has been pointed out that applying other network structures than the traditional square grid structure can achieve better results. Accordingly, some of the newly introduced structures (such as the hexagon-based, triangle-based or combined structures) are proved to have significant advantages compared to the classical grid representation.

A heuristic autonomous exploration method based on environmental information gain during quadrotor flight

Autonomous exploration is a widely studied fundamental application in the field of quadrotor, which requires them to automatically explore unknown space to obtain complete information about the environment. The frontier-based method, one of the representative works on autonomous exploration, drives autonomous determination by the definition of frontier information so that complete information about the environment is available to the quadrotor. However, existing frontier-based methods are able to accomplish the task but still suffer from inefficient exploration, and how to improve the efficiency of autonomous exploration is the focus of research nowadays. Slow frontier generation affecting real-time viewpoint determination and insufficient determination methods affecting the quality of viewpoints are typical of these problems. Therefore, to overcome the aforementioned problems, this article proposes a two-level viewpoint determination method for frontier-based autonomous exploration. First, a sampling-based frontier detection method is presented for faster frontier generation, improving the immediacy of environmental representation compared to traditional traversal-based methods. Second, the access to environmental information during flight is considered for the first time, and an innovative heuristic evaluation function is designed to decide on high-quality viewpoint as the next local navigation target in each exploration iteration. Extensive benchmark and real-world tests are conducted to validate our method. The results confirm that our method optimizes the frontier search time by 85%, the exploration time by around 20%–30%, and the exploration path by 25%–35%.

METAMORPHOSIS: A DIGITAL APPROACH TO TRANSFORMING COMMUNITIES THROUGH PHOTOGRAMMETRY AND METAVERSE

Abstract. This research employs a digital strategy to counter rural population decline and urban migration by integrating drone photogrammetry, metaverse, and blockchain technology. The focus is creating detailed 3D models of the settlements, which are subsequently optimized for metaverse platforms such as Voxels and Mona. Voxels facilitates participatory design, utilizing voxel parcels and Rhino- Grasshopper to involve all the stakeholders for renovation design work. Mona transforms parameterized projects into an immersive virtual experience, fuelled by blockchain technology, providing non-fungible token (NFT) marketplaces and economic transitions. These virtual and real realms serve purposes such as decision-making, cultural preservation, economy generation, and virtual tourism, fostering rural revival, virtual economy, and digital representation of real-world environments.

Foundations of spatial perception for robotics: Hierarchical representations and real-time systems

3D spatial perception is the problem of building and maintaining an actionable and persistent representation of the environment in real-time using sensor data and prior knowledge. Despite the fast-paced progress in robot perception, most existing methods either build purely geometric maps (as in traditional SLAM) or “flat” metric-semantic maps that do not scale to large environments or large dictionaries of semantic labels. The first part of this paper is concerned with representations: we show that scalable representations for spatial perception need to be hierarchical in nature. Hierarchical representations are efficient to store, and lead to layered graphs with small treewidth, which enable provably efficient inference. We then introduce an example of hierarchical representation for indoor environments, namely a 3D scene graph, and discuss its structure and properties. The second part of the paper focuses on algorithms to incrementally construct a 3D scene graph as the robot explores the environment. Our algorithms combine 3D geometry (e.g., to cluster the free space into a graph of places), topology (to cluster the places into rooms), and geometric deep learning (e.g., to classify the type of rooms the robot is moving across). The third part of the paper focuses on algorithms to maintain and correct 3D scene graphs during long-term operation. We propose hierarchical descriptors for loop closure detection and describe how to correct a scene graph in response to loop closures, by solving a 3D scene graph optimization problem. We conclude the paper by combining the proposed perception algorithms into Hydra, a real-time spatial perception system that builds a 3D scene graph from visual-inertial data in real-time. We showcase Hydra’s performance in photo-realistic simulations and real data collected by a Clearpath Jackal robots and a Unitree A1 robot. We release an open-source implementation of Hydra at https://github.com/MIT-SPARK/Hydra .