Complex Environmental Systems Research Articles

Influence of non-adiabatic effects on linear absorption spectra in the condensed phase: Methylene blue.

Modeling linear absorption spectra of solvated chromophores is highly challenging as contributions are present both from coupling of the electronic states to nuclear vibrations and from solute-solvent interactions. In systems where excited states intersect in the Condon region, significant non-adiabatic contributions to absorption line shapes can also be observed. Here, we introduce a robust approach to model linear absorption spectra accounting for both environmental and non-adiabatic effects from first principles. This model parameterizes a linear vibronic coupling (LVC) Hamiltonian directly from energy gap fluctuations calculated along molecular dynamics (MD) trajectories of the chromophore in solution, accounting for both anharmonicity in the potential and direct solute-solvent interactions. The resulting system dynamics described by the LVC Hamiltonian are solved exactly using the thermalized time-evolving density operator with orthogonal polynomials algorithm (T-TEDOPA). The approach is applied to the linear absorption spectrum of methylene blue in water. We show that the strong shoulder in the experimental spectrum is caused by vibrationally driven population transfer between the bright S1 and the dark S2 states. The treatment of the solvent environment is one of many factors that strongly influence the population transfer and line shape; accurate modeling can only be achieved through the use of explicit quantum mechanical solvation. The efficiency of T-TEDOPA, combined with LVC Hamiltonian parameterizations from MD, leads to an attractive method for describing a large variety of systems in complex environments from first principles.

Control Method of Complex Environmental Engineering Machinery Cooling System Integrating Deep Learning and Feature Clustering

Control Method of Complex Environmental Engineering Machinery Cooling System Integrating Deep Learning and Feature Clustering

Data Analytics for Environmental Science and Engineering Research.

The advent of new data acquisition and handling techniques has opened the door to alternative and more comprehensive approaches to environmental monitoring that will improve our capacity to understand and manage environmental systems. Researchers have recently begun using machine learning (ML) techniques to analyze complex environmental systems and their associated data. Herein, we provide an overview of data analytics frameworks suitable for various Environmental Science and Engineering (ESE) research applications. We present current applications of ML algorithms within the ESE domain using three representative case studies: (1) Metagenomic data analysis for characterizing and tracking antimicrobial resistance in the environment; (2) Nontarget analysis for environmental pollutant profiling; and (3) Detection of anomalies in continuous data generated by engineered water systems. We conclude by proposing a path to advance incorporation of data analytics approaches in ESE research and application.

Viral abundance, community structure and correlation with bacterial community in soils of different cover plants

Microbial abundance, diversity, metabolic activities, and interactions are central to the ecosystem functions and biogeochemical processes in soil. Viruses can impose top-down control over microbial communities and thus regulate the microbial-mediated processes of the system. However, the understanding of the linkage between viral community, virus-host interactions and environmental conditions of soil is still lacking which imposes a major knowledge gap in microbial ecology of this complex environmental system. Here, we investigated the viral and bacterial communities under different plant cover types. Soil properties such as organic matter and nitrate content showed significant correlations with virus-like particles (VLPs) abundance, bacterial abundance, and inducible lysogenic fractions (LF) suggesting that the environmental factors might be influencing viral/bacterial abundance and reproduction strategy in soils. The bacterial communities were shaped by the land management, i.e., cover plants and agricultural practices. Soil physicochemical properties, VLP abundance, and LF were significant factors accounting for the difference of bacterial community composition in soils. The variances of the viral community composition were closely related to cover plants corresponding to similar patterns in bacterial community composition suggesting that viral and bacterial community composition are tightly linked. Our study provides insight in the ways how environmental factors shape virus-host interactions and further influence soil viruses in soil ecosystems.

A multi-robot path-planning algorithm for autonomous navigation using meta-reinforcement learning based on transfer learning

The adaptability of multi-robot systems in complex environments is a hot topic. Aiming at static and dynamic obstacles in complex environments, this paper presents dynamic proximal meta policy optimization with covariance matrix adaptation evolutionary strategies (dynamic-PMPO-CMA) to avoid obstacles and realize autonomous navigation. Firstly, we propose dynamic proximal policy optimization with covariance matrix adaptation evolutionary strategies (dynamic-PPO-CMA) based on original proximal policy optimization (PPO) to obtain a valid policy of obstacles avoidance. The simulation results show that the proposed dynamic-PPO-CMA can avoid obstacles and reach the designated target position successfully. Secondly, in order to improve the adaptability of multi-robot systems in different environments, we integrate meta-learning with dynamic-PPO-CMA to form the dynamic-PMPO-CMA algorithm. In training process, we use the proposed dynamic-PMPO-CMA to train robots to learn multi-task policy. Finally, in testing process, transfer learning is introduced to the proposed dynamic-PMPO-CMA algorithm. The trained parameters of meta policy are transferred to new environments and regarded as the initial parameters. The simulation results show that the proposed algorithm can have faster convergence rate and arrive the destination more quickly than PPO, PMPO and dynamic-PPO-CMA.

The DiscoverFramework freeware toolkit for multivariate spatio-temporal environmental data visualization and evaluation

The freeware DiscoverFramework provides new tools to build spatial and temporal data visualization applications accessible to stakeholders, policy makers, scientists, and educators. By focusing on environmental data and supporting applications accessible via laptops, tablets, and cell phones, the DiscoverFramework can be used to increase public awareness and inspire responsible use of complex environmental systems upon which human society depends. DiscoverFramework enables computer-savvy domain scientists to develop interactive applications using “Elements” and workflows defined to make visualization easy and address common problems such as spatio-temporal scales and user engagement. Two applications are used to demonstrate DiscoverFramework: DiscoverWater and DiscoverHABs. DiscoverWater uses Map, Chart, and Text Elements to relate streamflow changes to groundwater withdrawals. DiscoverHABs uses the Scenario Element to aid stakeholders, such as resource managers and users, struggling to identify when and where harmful algal blooms (HABs) are likely given that causal relations in these systems remain poorly understood.

Adaptive coverage control for multi-USV system in complex environment with unknown obstacles

Aiming at the shortcomings of the existing control law based on the global information, this article studies the coverage problem of a given region in the plane using a team of USVs. The coverage goal, which is to cover a given search domain using multiple mobile sensors so that each point is surveyed until a certain preset level is achieved, is formulated in a mathematically precise problem statement. The adaptive control law is presented which enables multi-USV to navigate in a complex environment in the presence of unknown obstacles and guarantees that a fully connected multi-USV system attains the coverage goal. In particular, the dangerous area was divided into two different parts in order to enhance the searching efficiency. Finally, simulation results are presented to validate the feasibility and efficiency of the proposed approach.

The Deinstitutionalization of Persons with Psychosocial Disabilities from the Perspective of Ecological Systems Theory

The deinstitutionalization of social care in Lithuania started in 2012 after the adoption of the strategic guidelines by the Ministry of Social Security and Labour. The goal of this reform was to improve the care conditions and introduce new community-based services for persons with disabilities. Almost ten years of the reform resulted in only five percent of persons with disabilities who moved to community settings, mainly group-living homes. The slow-motion of the reform, as well as the tensions in the communities, suggests the need for a thorough analysis of the process of deinstitutionalization and its improvement.
 Bronfenbrenner’s ecological theory is applied as a conceptual and methodological tool for understanding the roles of deinstitutionalization agents at different levels, including the Ministry of Social Security and Labour, municipalities, non-governmental organizations, social care institutions, and local communities. All of these agents are involved and diversely interact among themselves during the transformation process of the social care system. The ecological theory provides the necessary integrated approach to the analysis of the process of deinstitutionalization of the social care system at the micro-, meso-, exo-, and macro levels.
 Deinstitutionalization and the trajectories of its participants reveal resilient connections with different fields of the ecological system and show that different system components not only represent different systems but become microsystems themselves that affect all elements in the ecological system.
 The complexity of environmental systems constitutes the basis of ecological systems theory. It serves as a lens to guide the analysis of the transformation of a particular person’s life in the context of deinstitutionalization. Herewith, it is an appropriate tool for understanding the impact of deinstitutionalization on specific local communities.

RETRACTED ARTICLE: Construction of complex environmental art design system based on 3D virtual simulation technology

A new advent has been achieved with the development of the perception of virtual reality in the field of multimedia for improving the modern environmental art design. Due to the excessive dependence on digital virtual technology for designing traditional environmental art, a new research method of environmental art design based on digital virtual technology is proposed in this work. The environmental art design is strengthened by combining virtual reality technology with the environmental art designing. In-depth consideration of the two environmental art attributes of environmental landscape and environmental form, obtain graphics and image layers, based on the proportion. The size of the three-dimensional environmental area obtains a two-dimensional vector map of the environmental area, and merge all the textures obtained by shooting at a small angle. The overall environment image texture is obtained, and on this basis, the Google Sketchup platform is used to draw the preliminary model of the virtual environment. The preliminary environment model is merged to construct a three-dimensional environment landscape model, by embedding the two-dimensional vector diagram in the terrain utilizing Google Sketchup platform. This work uses the three-dimensional environment landscape model based chaotic generation method for the generation and realization of the virtual environment form. The virtual reality view of the environmental form design drawing is obtained through the evolution of the environment experience. The experimental results show that the 3D modeling error of the proposed method is minimized to 1.2%, and the environmental art, fidelity, efficiency, and cost indicators of technical design are better.

Real-time feedback improves multi-stakeholder design for complex environmental systems

We test whether providing quantitative real-time feedback relating design decisions to system objectives improves group solutions in an interdependent energy-water design task. While prior research suggests an important role of real-time feedback on task performance, few studies have examined the role of real-time feedback in the design of complex environmental systems. We tested a real-time feedback approach using a mixed within- and between-subject experiment (n = 88 Carnegie Mellon University students, divided into 22 groups of four). When compared to individual designs and informal collaborations, real-time performance feedback yielded solutions closer to the Pareto frontier and reduced both financial (by 26% and 21%) and environmental cost (by 34% and 12%). In addition, informal collaboration did not improve group decision-making when compared to individual designs. The results suggest that optimal solutions to meeting energy and water demand while minimizing cost and environmental impact can be obscured in informal collaborations, but that real-time feedback to system designers can help avoid waste of public resources.

Missing the outbreak for the germs

Leafy greens cause a growing proportion of foodborne illness outbreaks despite heavy investment in surveillance technologies designed to control pathogenic hazards in agriculture. To understand how the governing regime maintains authority despite continual lapses in control, I examine a deadly 2018 outbreak of Escherichia coli O157: H7 linked to romaine lettuce. By comparing the outbreak investigation and regulatory response to the questions not asked and actions not taken, I show how the regime’s methods of understanding the outbreak also organized its ignorance of dangers outside its carefully constructed field of vision. Applying agnotology theory, I argue that the industrial organization of leafy greens agriculture and the institutionalized non-knowledge of emergent social–ecological vulnerabilities coproduce one another, allowing the industrial food regime to avoid fundamental reforms that might enhance resilience. This case demonstrates that critical examination of organized non-knowledge in complex environmental governance systems can reveal limits to institutional learning and systemic reflexivity that impede sustainability transitions.

Development and Design of Crowd Evacuation Simulation System in Complex Environment

In order to better evacuate people in emergencies and ensure the safety of people’s lives and properties, we designed and developed a simulation system for crowd evacuation in complex environments based on the RVO algorithm. The system has completed the functions of scene construction, crowd simulation, optimal path planning, evacuation process simulation and playback, and heat map analysis. After the evacuation process is over, the evacuation drawings, evacuation process and related content can be saved in the form of the entire project if the creator chooses to save it, which is convenient for future review.

Autonomous Robotic Exploration and Navigation System using Tri-layered Mapping and Geometrical Path Planning Techniques

In the recent decade, robotic exploration is emerging as a vast research platform as it prolongs to be a challenging task, especially in unknown dynamic environments. Exploration plays a vital role in collecting data from the areas inaccessible to the human. The operation of manual exploration systems in complex environments is cumbersome due to ineffective noise filtering, poor visibility and shuffling of map characteristics. In order to overcome such drawbacks, our approach focuses on the development of autonomous robotic exploration by designing a robust SLAM-based system, incorporating tri-layered mapping technique for automatic map generation and a new geometrical boundary-based path-planning technique to improve the mobility of the robot during navigation. The proposed path-planning algorithm yields better trajectory planning and obstacle avoidance using the pose, odometry and orientation parameters of the robot. The performance of the developed robot is evaluated through real-time experimentation, and it is evident from the results that, the proposed approach is preferable for autonomous exploration applications.

A State‐Domain Robust Chi‐Square Test Method for GNSS/INS Integrated Navigation

Aiming at abrupt faults in GNSS/INS integrated systems in complex environments, classical fault detection algorithms are mostly developed from the measurement domain. A robust chi‐square test method based on the state domain is proposed in this paper. The fault detection statistic is built based on the difference between the prior state estimation and the posterior state estimation in Kalman filtering. To improve the calculation stability, singular value decomposition (SVD) is used to factor the covariance matrix of the difference. The relevant formulas of the proposed method were theoretically derived, and the relationship between the proposed method and the existing innovation chi‐square test method was revealed. The proposed method was compared with state‐of‐the‐art chi‐square test methods and verified by GNSS/INS integrated navigation experiments using simulation data and real data. The experimental results show that the proposed method (a) directly works in the state domain, (b) does not require the known real system state, (c) has computational efficiency and good robustness, and (d) accurately detects abrupt faults.

Open-source computational simulation of moth-inspired navigation algorithm: A benchmark framework

Olfactory navigation is defined as a task of a self-propelled navigator with some sensors capabilities to detect odor (or scalar concentration) convected and diffused in a windy environment. Known for their expertise in locating an odor source, male moths feature a bio-inspirational model of olfactory navigation using chemosensory. Many studies have developed moths-inspired algorithms based on proposed strategies of odor-sourcing. However, comparing among various bio-inspired strategies is challenging, due to the lack of a componential framework that allows statistical comparison of their performances, in a controlled environment. This work aims at closing this gap, using an open source, freely accessible simulation framework. To demonstrate the applicability of our simulated framework as a benchmarking tool, we implemented two different moth-inspired navigation strategies; for each strategy, specific modifications in the navigation module were carried out, resulting in four different navigation models. We tested the performance of moth-like navigators of these models through various wind and odor spread parameters in a virtual turbulent environment. The performance of the navigators was comprehensively analyzed using bio-statistical tests. This benchmark-ready simulation framework could be useful for the biology-oriented, as well as engineering-oriented studies, assisting in deducing the evolutionary efficient strategies and improving self-propelled autonomous systems in complex environments.•The open-source framework `Mothpy' provides a computational platform that simulates the behavior of moth-like navigators, using two main inputs to be modified by the user: (1) flow condition; and (2) navigation strategy.•`Mothpy' can be used as a benchmarking platform to compare the performance of multiple moth-like navigators, under various physical environments, and different searching strategies.•Method name: Mothpy 0.0.1' - an open-source moth-inspired navigator simulator.

A framework for ecosystem resilience in policy and practice: DECCA

Ecosystem resilience is increasingly considered within political responses to environmental problems and is a key element of recent environmental legislation in Wales. The actual mechanisms of ecosystem resilience are complex making it difficult, from a management perspective, to meaningfully describe or report on them for ecosystems at a national scale. For this reason, the legislation and associated policies in Wales have taken a pragmatic approach, using environmental attributes that have previously been causally linked with ecosystem resilience as a framework for description and reporting. These attributes are diversity, extent, condition, connectivity and adaptability, and referred to as ‘DECCA’. The framework has proved useful and influential, and provides a novel example of how established and relatively simple scientific principles can inform and put into practice legislation about complex environmental systems; the Welsh case serves as the first example of a national government implementing resilience policy. However, the attributes remain proxies for actual resilience and knowledge gaps for converting theory to practice remain. These include fundamental understanding of the underlying mechanisms of resilience and related concepts such as environmental tipping points, and methodological issues such as how resilience can be quantified and confidently reported on. There is a need to develop a research framework for addressing these issues, linked to policy cycles to ensure new evidence and understanding are appropriately interpreted and adopted.

Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of multi-agent learning and long-term autonomy. Towards this direction, a novel cognitive architecture for multi-agent LfD robotic learning is introduced in this paper, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments. In particular, the designed architecture capitalizes on the recent advances in the Artificial Intelligence (AI) (and especially the Deep Learning (DL)) field, by establishing a Federated Learning (FL)-based framework for incarnating a multi-human multi-robot collaborative learning environment. The fundamental conceptualization relies on employing multiple AI-empowered cognitive processes (implementing various robotic tasks) that operate at the edge nodes of a network of robotic platforms, while global AI models (underpinning the aforementioned robotic tasks) are collectively created and shared among the network, by elegantly combining information from a large number of human-robot interaction instances. Regarding pivotal novelties, the designed cognitive architecture a) introduces a new FL-based formalism that extends the conventional LfD learning paradigm to support large-scale multi-agent operational settings, b) elaborates previous FL-based self-learning robotic schemes so as to incorporate the human in the learning loop and c) consolidates the fundamental principles of FL with additional sophisticated AI-enabled learning methodologies for modelling the multi-level inter-dependencies among the robotic tasks. The applicability of the proposed framework is explained using an example of a real-world industrial case study (subject to ongoing research activities) for agile production-based Critical Raw Materials (CRM) recovery.

Model Predictive Temperature and Humidity Control of Greenhouse with Ventilation

Facility gardening with complex environmental control systems has potential in improving productivity and producing high value-added agricultural products; however, its introduction is expensive. Thus, this study proposes a greenhouse temperature and humidity control system using relatively inexpensive heaters, humidifiers, and ventilation fans. Humidity control is expected to promote photosynthesis and prevent plant disease. The concept of model predictive control (MPC) was applied to a small greenhouse to minimise the sum of squared errors. Compared with the two conventional methods, the simulation results showed that MPC reduced the relative RMS error of the temperature and humidity deficit to 23.5% and 13.1%, respectively.

Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives

Biological wastewater treatment using algae–bacteria consortia for nutrient uptake and resource recovery is a ‘paradigm shift’ from the mainstream wastewater treatment process to mitigate pollution and promote circular economy. The symbiotic relationship between algae and bacteria is complex in open or closed biological wastewater treatment systems. In this regard, machine learning algorithms (MLAs) have found to be advantageous to predict the uncertain performances of the treatment processes. MLAs have shown satisfactory results for effective real-time monitoring, optimization, prediction of uncertainties and fault detection of complex environmental systems. By incorporating these algorithms with online sensors, the transient operating conditions during the treatment process including disruptions or failures due to leaking pipelines, malfunctioning of bioreactors, unexpected fluctuations of organic loadings, flow rate, and temperature can be forecasted efficiently. This paper reviews the state-of-the-art MLA approaches for the integrated operation of biological wastewater treatment systems combining algal biomass production and nutrient recovery from municipal wastewater.

Reliability theory for microbial water quality and sustainability assessment

Microbial surface water contamination can disrupt critical ecosystem services such as recreation and drinking water supply. Prediction of water contamination and assessment of sustainability of water resources in the context of water quality are needed but are difficult to achieve – with challenges arising from the complexity of environmental systems, and stochastic variability of processes that drive contaminant fate and transport. In this paper we use reliability theory as a framework to address these issues. We define failure as exceedance of regulatory water contamination limits, and system components as reaches in the surface water network. We then methodically study the reliability of each component in the context of water quality, as well as the impact of individual components on overall water quality and sustainability. We obtain spatially distributed probability- and physics-based sustainability measures of reliability, vulnerability, resilience and the sustainability index. Finally, we use GIS as a platform to present these measures as geospatial products in an effort to foster public acceptance of probability-based methods in contaminant hydrology.