System Environment Research Articles

Methods for Implementing Tactile Sensations in Virtual Space by Integrating Physical Objects with Their Computer Models

The paper presents original methods and approaches for integrating, from the user's point of view, three-dimensional virtual models visible to them in a virtual reality headset, and their physical prototypes. This allows for the addition of some important elements of physical reality to the virtual environment, such as tactile and muscle-motor sensations. Developed solutions are based on the use of such wireless devices as HTC Vive Trackers, which determine their position and orientation in space, and can be placed on various objects of the real world. An original approach to making a tracking system by means of two HTC Base Stations and several Vive Trackers with selecting one anchor element is proposed, as well as new methods for real-time integration of physical reality objects with their virtual models by using functional control schemes with own blocks for trackers. Based on methods and approaches proposed in this work, software modules were implemented. Approbation of these modules in the VirSim virtual environment system, which was developed in Scientific Research Institute for System Analysis of the Russian Academy of Sciences, showed that the solutions developed are adequate and effective, and can have practical application in virtual environment systems and training complexes.

Re-imagining performance spaces and locations in ecological dynamics: Implications for pedagogical practices in physical education

Many places and locations for physical education, and the associated pedagogies, limit the enrichment of the individual–environment system. Strengthening this relationship can provide children and youth with greater functionality to navigate a diverse range of environments more effectively, leading to a positive relationship with sport and physical activity across the lifespan. The purpose of this position paper is to draw attention to the role that environmental constraints play in shaping the relationship between places, locations, and pedagogical practice in physical education. More specifically, we demonstrate how the design of spaces for physical education, and movement competencies more generally, emerges under political, historical, cultural, and socioeconomic factors. Ecological dynamics is presented as an explanatory framework to position the concept of open spaces as a means of enriching individual–environment interactions via guided discovery and exploratory behaviour. In unpacking these key concepts, we highlight innovative approaches to the design of open spaces for physical activity and sport, exemplified by the Skills Garden and PLAYCE initiatives. To move beyond the theoretical arguments presented in this article, we encourage practitioners and applied scientists to collaborate and integrate sub-disciplines, applied ideas, and research methods to re-imagine the design of spaces for learning. To take full advantage of redesigned spaces in strengthening the individual–environment system, practitioners need to adopt a model of physical education that complements these contemporary learning spaces.

Optimal planning for electric vehicle fast charging stations placements in a city scale using an advantage actor-critic deep reinforcement learning and geospatial analysis

The transition to Electric Vehicles (EVs) for reducing urban greenhouse gas emissions is hindered by the lack of public charging infrastructure, particularly fast-charging stations. Given that electric vehicle fast charging stations (EVFCS) can burden the electricity grid, it is crucial for EVFCS to adopt sustainable energy supply methods while accommodating the growing demands of EVs. Despite recent research efforts to optimize the placement of renewable-powered EV charging stations, current planning methods face challenges when applied to a complex city scale and integrating with renewable energy resources. This study thus introduces a robust decision-making model for optimal EVFCS placement planning integrated with solar power supply in a large and complex urban environment (e.g., Chicago), utilizing an advantage actor-critic (A2C) deep reinforcement learning (DRL) approach. The model balances traffic demand with energy supply, strategically placing charging stations in areas with high traffic density and solar potential. As a result, the model is used to optimally place 1,000 charging stations with a random starting search approach, achieving total reward values of 74.30 %, and estimated the capacities of potential EVFCS. This study can inform the identification of suitable locations to advance the microgrid-based charging infrastructure systems in large urban environments.

Impact of group work on the hidden curriculum that induces students’ unprofessional behavior toward faculty

BackgroundHidden curriculum (HC) can limit the effects of professionalism education. However, the research on how HC triggers unprofessional behavior among medical students is scant. Furthermore, there is no established approach for how faculty members may create a context, such as an educational environment and education system, that prevents students’ unprofessional behavior. This study aimed to develop an educational approach to prevent unprofessional behavior and clarify how faculty members consider HC that triggers students’ unprofessional behavior.MethodsThe study sample comprised 44 faculty members and eight medical students from the Chiba University School of Medicine. The participants were divided into groups and asked the following question: “What attitudes, statements, and behaviors of senior students, physicians, and faculty members trigger medical students’ unprofessional behavior?” The responses were collected using the affinity diagram method. The group members discussed the causes and countermeasures for the selected attitudes, statements, and behaviors of senior students, physicians, and faculty members based on the affinity diagram. The impact of the group work on the faculty members was surveyed using questionnaires immediately after its completion and six months later. Furthermore, the cards in the group work were analyzed using content analysis.ResultsThe responses to the questionnaire on group work indicated that some faculty members (43.8%) improved HC, while others suggested conducting group work with more participants. The content analysis revealed six categories – inappropriate attitude/behavior, behavior encouraging unprofessional behavior, lack of compliance with regulations, harassment of other medical staff, inappropriate educational environment/supervisor, and inappropriate self-control – and 46 subcategories.ConclusionsThe HC that triggers students’ unprofessional behavior includes the words and actions of the educator, organizational culture, and educational environment. Group work makes faculty members aware of the HC that triggers unprofessional behavior, and induces behavioral change for HC improvement in the educational activities. Educators should refrain from using words and actions that encourage unprofessional behavior, such as personal anecdotes, as they reduce students’ learning motivation.

Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server

With machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resources to such algorithms. Graph neural networks (GNNs), for example, have provided astounding improvements in extracting complex signatures from data and are now widely used in a variety of applications, such as particle jet classification in high energy physics (HEP). However, GNNs also come with an enormous computational penalty that requires the use of GPUs to maintain reasonable throughput. At shared computing facilities, such as those used by physicists at Fermi National Accelerator Laboratory (Fermilab), methodical resource allocation and high throughput at the many-user scale are key to ensuring that resources are being used as efficiently as possible. These facilities, however, primarily provide CPU-only nodes, which proves detrimental to time-to-insight and computational throughput for workflows that include machine learning inference. In this work, we describe how a shared computing facility can use the NVIDIA Triton Inference Server to optimize its resource allocation and computing structure, recovering high throughput while scaling out to multiple users by massively parallelizing their machine learning inference. To demonstrate the effectiveness of this system in a realistic multi-user environment, we use the Fermilab Elastic Analysis Facility augmented with the Triton Inference Server to provide scalable and high-throughput access to a HEP-specific GNN and report on the outcome.

Semi-autonomous sensor fusion-based strategy for unmanned aerial atmospheric surveillance system in open field environments

<p>This paper explores the convergence of semi-autonomous systems and sensor fusion for monitoring hazardous atmospheric substances in open and semi-confined environments such as open-pit mines. As the heart of this system, a universal and flexible device leveraging wireless technologies to collect and analyze large volumes of data, designed by the authors, is proposed. The article outlines the key components of the platform, emphasizing its potential for increasing personnel safety levels as stipulated by the international public exposure guidelines. In modern mines, it becomes crucial to monitor elevated concentrations of nitrogen and carbon oxides, as well as other pollutants after blasting and the toxic gas emissions produced by the heavy transportation equipment such as mining trucks and excavators. In this paper, industrial-grade electrochemical sensors are compared with price-affordable but less precise microelectrochemical ones. The performed experiments for lowering computational requirements show promising results. The integration of an ultrasonic anemometer enhances the system’s capabilities, contributing to a comprehensive understanding of atmospheric conditions. In parallel, the research discusses the role of computer vision in autonomous control systems, with a focus on the architecture and processing pipeline of the state-of-the-art system-on-module Kria by AMD. Advocating the potential of adaptive computing for enhanced efficiency in dynamic environments, the paper underlines the importance of the integrated approach in developing a semi-autonomous atmospheric surveillance system and highlights the impact of adaptive computing in dynamic scenarios. The main part of the measuring equipment and methods has been experimented with in real conditions in open-pit mines in Bulgaria. This is an initial phase of ongoing research aimed at serving as a foundation for future improvements and the elaboration of a fully autonomous prototype for hazardous substances evaluation in the atmosphere of open-pit mines.<strong></strong></p>

QM/CG-MM: Systematic Embedding of Quantum Mechanical Systems in a Coarse-Grained Environment with Accurate Electrostatics.

Quantum Mechanics/Molecular Mechanics (QM/MM) can describe chemical reactions in molecular dynamics (MD) simulations at a much lower cost than ab initio MD. Still, it is prohibitively expensive for many systems of interest because such systems usually require long simulations for sufficient statistical sampling. Additional MM degrees of freedom are often slow and numerous but secondary in interest. Coarse-graining (CG) is well-known to be able to speed up sampling through both reduction in simulation cost and the ability to accelerate the dynamics. Therefore, embedding a QM system in a CG environment can be a promising way of expediting sampling without compromising the information about the QM subsystem. Sinitskiy and Voth first proposed the theory of Quantum Mechanics/Coarse-grained Molecular Mechanics (QM/CG-MM) with a bottom-up CG mapping. Mironenko and Voth subsequently introduced the DFT-QM/CG-MM formalism to couple a Density Functional Theory (DFT) treated QM system and to an apolar environment. Here, we present a more complete theory that addresses MM environments with significant polarity by explicitly accounting for the electrostatic coupling. We demonstrate our QM/CG-MM method with a chloride-methyl chloride SN2 reaction system in acetone, which is sensitive to solvent polarity. The method accurately recapitulates the potential of mean force for the substitution reaction, and the reaction barrier from the best model agrees with the atomistic simulations within sampling error. These models also have generalizability. In two other reactive systems that they have not been trained on, the QM/CG-MM model still achieves the same level of agreement with the atomistic QM/MM models. Finally, we show that in these examples the speed-up in the sampling is proportional to the acceleration of the rotational dynamics of the solvent in the CG system.

Planning Green Infrastructure: Provisions and Alteration in Prominent Cities of Nigeria

Abstract: Green infrastructure provides physical aesthetics, healthier and quality environment for urban settlements globe. However, many urban centres in the third world countries are deprived the benefits of green infrastructure and subjected to unhealthy conditions that endanger human safety, land uses and the entire ecological system of both macro and micro environment due to poor planning and government policies. This research therefore, compared the effect of green infrastructure alteration on the physical environment of two major cities (Port Harcourt and Calabar) Nigeria. The study sampled 6 green zones and 250 residents for both cities under investigation while Duncan Multiple Comparism analysis (DMCA), mathematical percentage (MP) and Analysis of Variance (ANOVA) was adopted as the statistical analysis implements. The findings established that calabar benefits from green infrastructure that improves the city aesthetics and other aspect of the environment through proper physical planning and government policies than Port Harcourt city of Nigeria at (P<0.05). In furtherance, the study revealed that usage understanding, budget constraint, wrong cost benefit analysis, and poor environmental management and maintenance strategies were identified as the key determinants of naturally retained and planned green infrastructure alteration in both cities, and discovered that, the city of Calabar was built on smart and resilient development strategies than Port Harcourt metropolis of Nigeria. The study recommended that town planning authorities should be charge with the responsibilities of smart and resilient city building through green infrastructure development and avoidance of informal alteration of such infrastructure. Also, suggested the need for frequent acquisition of data on green infrastructure development and inclusion in all land use development.

Groundwater health risk assessment and its temporal and spatial evolution based on trapezoidal fuzzy number-Monte Carlo stochastic simulation: A case study in western Jilin province

The United States Environmental Protection Agency (USEPA) Four-step-Method (FSM) is a straightforward and extensively utilized tool for evaluating regional health risks, However, the complex and heterogeneous groundwater environment system causes great uncertainty in the assessment process. Triangular stochastic simulation (TSS) possesses certain advantages in solving uncertainty problems, but its inadequacy with discrete data reveals limitations in this aspect. To solve the above problems, this study proposes to construct trapezoidal fuzzy number-Monte Carlo stochastic simulation (TFN-MCSS) to compensate for the shortcomings of the first two methods. This method adopted trapezoidal fuzzy number (TFN) analysis to comprehensively consider the characteristics of a large dispersion of water quality monitoring data and the uncertainty of the human health risk assessment (HHRA) process. Concurrently, to overcome the subjectivity and uncertainty of artificially determining the interval of TFN in traditional methods, the slope was used to select the most probable interval value (TMPIV) of TFN combined with the α-truncated set technique (α-TST) and MCSS. Based on these, a TFN-MCSS was constructed and applied to groundwater HHRA in western Jilin Province. First, the groundwater chemical characteristic determination and water quality evaluation in western Jilin were performed to identify the main pollution indicators, and the health risk effects of pollutants in groundwater of different aquifers at different time periods on adults and children were evaluated using the TFN-MCSS. The uncertainty and sensitivity were analyzed, and the primary risk control indicators were identified and compared to FSM and TSS. The results reveal that TFN-MCSS was more sensitive to data and could reduce the uncertainty of assessment process. It indicated that over a 10-year period, the health risks associated with unconfined groundwater (UW) and confined water (CW) decreased by greater than 52 %. However, the highest total non-carcinogenic risk index (THI) was 1.3-fold higher than the safety threshold, and this posed a health risk.

Research on Coupling Effect Measurement and Coupling Risk Simulation of Green Building Construction Safety Risk Factors

The construction of green buildings is an important direction for the transformation and development of the construction industry, but it is beset with problems such as a lack of construction experience, immature new technologies, and unstable material properties; these issues bring risks to the construction stage of green buildings, and the coupling of uncertain risk factors in the construction process of green buildings may lead to unfavorable results. The purpose of this study is to explore the coupling degree of green building construction safety risk factors and the changing trend in their coupling combinations at the system risk level. First, the risk factor index system was defined by reading the literature and gathering expert opinions, and the coupling degree between risk factors was measured using an improved coupling degree model. Then, a system dynamics model was established to simulate and analyze the coupling effects among the risk factors and determine the combinations with the greatest influence. The results show that the risk probability is proportional to the risk coupling value, the human–environment coupling value is the largest, and the material equipment–management coupling value is the smallest. The human–environment system simulation shows that reducing the coupling value of system factors will promote a decrease in the total level of system risk. According to the research conclusions, measures to prevent risk coupling are proposed, which offer theoretical references for green building practitioners carrying out risk management; these measures hold a certain guiding significance for the risk control and future development of green buildings.

Dynamics of the epidemiological Predator-Prey system in advective environments.

This paper aims to establish the existence of traveling wave solutions connecting different equilibria for a spatial eco-epidemiological predator-prey system in advective environments. After applying the traveling wave coordinates, these solutions correspond to heteroclinic orbits in phase space. We investigate the existence of the traveling wave solution connecting from a boundary equilibrium to a co-existence equilibrium by using a shooting method. Different from the techniques introduced by Huang, we directly prove the convergence of the solution to a co-existence equilibrium by constructing a special bounded set. Furthermore, the Lyapunov-type function we constructed does not need the condition of bounded below. Our approach provides a different way to study the existence of traveling wave solutions about the co-existence equilibrium. The existence of traveling wave solutions between co-existence equilibria are proved by utilizing the qualitative theory and the geometric singular perturbation theory. Some other open questions of interest are also discussed in the paper.

요양보호사의 근무환경이 직무만족에 미치는 영향

Objectives The purpose of this study is to analyze the mediating effect of job enthusiasm in the relationship between the work environment and job satisfaction of the nursing care workers who work in long-term care homes for the elderly. Methods This study conducted a survey on 330 caregivers working at long-term care facilities for the elderly in Yeongdong-gun, and analyzed the collected questionnaire data using SPSS 24.0 and AMOS 24.0 statistical programs. Results First, out of the working environment factors of nursing care workers at long-term care homes, all of them, namely the human environment, physical environment, reward systems, and job expertise factors, had a significant and positive impact on job enthusiasm. Second, of the work environment factors of the nursing care workers of long-term care homes, the physical environment and reward system factors had a significant and positive impact on job satisfaction. Third, the job enthusiasm of the nursing care workers at long-term care homes turned out to have a similarly significant and positive impact on job satisfaction. Fourth, in the relationship between work environment factors of nursing care workers at long-term care homes, such as the human environment, physical environment, reward system, job expertise, and job satisfaction, it was shown that, upon all of these factors, job enthusiasm had a significant mediating effect. Fifth, with regard to the groups where the work experience was less than five years and those of five years or longer, the difference between these two groups was confirmed in the paths of [reward system→job enthusiasm], [reward system→job satisfaction], [job enthusiasm→job satisfaction]. Conclusions This study is significant in that it identified the working environment of nursing care workers and sought specific action plans to increase job satisfaction by increasing job enthusiasm.

Movement-driven modelling reveals new patterns in disease transmission networks.

Interspecific interactions are highly relevant in the potential transmission of shared pathogens in multi-host systems. In recent decades, several technologies have been developed to study pathogen transmission, such as proximity loggers, GPS tracking devices and/or camera traps. Despite the diversity of methods aimed at detecting contacts, the analysis of transmission risk is often reduced to contact rates and the probability of transmission given the contact. However, the latter process is continuous over time and unique for each contact, and is influenced by the characteristics of the contact and the pathogen's relationship with both the host and the environment. Our objective was to assess whether a more comprehensive approach, using a movement-based model which assigns a unique transmission risk to each contact by decomposing transmission into contact formation, contact duration and host characteristics, could reveal disease transmission dynamics that are not detected with more traditional approaches. The model was built from GPS-collar data from two management systems in Spain where animal tuberculosis (TB) circulates: a national park with extensively reared endemic cattle, and an area with extensive free-range pigs and cattle farms. In addition, we evaluated the effect of the GPS device fix rate on the performance of the model. Different transmission dynamics were identified between both management systems. Considering the specific conditions under which each contact occurs (i.e. whether the contact is direct or indirect, its duration, the hosts characteristics, the environmental conditions, etc.) resulted in the identification of different transmission dynamics compared to using only contact rates. We found that fix intervals greater than 30 min in the GPS tracking data resulted in missed interactions, and intervals greater than 2 h may be insufficient for epidemiological purposes. Our study shows that neglecting the conditions under which each contact occurs may result in a misidentification of the real role of each species in disease transmission. This study describes a clear and repeatable framework to study pathogen transmission from GPS data and provides further insights to understand how TB is maintained in multi-host systems in Mediterranean environments.

Transforming experimental radiology: Design and implementation of an innovative ePACS image storage system for AI imaging research environments

Introduction and purpose: We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security. Methods and results: The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing. Discussion and conclusions: The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology.

The Relationship between Urbanization and the Water Environment in the Chengdu-Chongqing Urban Agglomeration

Ensuring the harmonization between urbanization and water environment systems is imperative for fostering sustainable regional development in the future. With urban agglomerations and metropolitan areas increasingly dominating urbanization trends in China, it is crucial to explore the interdependent relationship between urbanization and the water environment. Such exploration holds significant implications for water resource management and the formulation of urbanization policies. This study utilizes a comprehensive index system encompassing urbanization and the water environment. It examines the coupled and coordinated spatial and temporal dynamics of these systems within the Chengdu-Chongqing Urban Agglomeration from 2011 to 2019. This analysis employs the Coupled Coordination Degree model alongside the spatial autocorrelation model. The results show that there is still much room for improving the urbanization development level and the water environment quality. During the study period, a nonlinear and nearly U-shaped evolutionary trajectory was observed between the two systems. The results suggest that there is a progression from basic to more advanced coordination between urbanization and water environment at the city cluster scale. Urbanization appears to generally lag behind the water environment in terms of coordination. At the municipal scale, there is a gradient in which some cities show better coordination compared to others. Spatially, the coupling and coordination of this region exhibited dual-core development characteristics centered around Chengdu and Chongqing. The region is in the transition stage towards a core-type networked and decentralized development mode, which has not yet formed an integrated pattern. This offers a theoretical and technical framework for harmonizing water environments and urbanization in similar regions globally.

Multi-Scenario Research on the Coupled and Coordinated Development of the Economic–Energy–Environmental (3E) System under the Reconstruction of the Power System—New Exploration Based on the “Dual Triangle” Theory

Under the background of building a new-type power system, as the core of the energy system, the restructuring of the power system will inevitably have a linked impact on the coordinated development of the economy–energy–environment system. A “Dual Triangle” theoretical framework consisting of the “Energy Impossibility Triangle” and “Sustainable Development Triangle” was constructed in this paper. By combining historical statistical analysis with scenario predictive analysis, the historical characteristics of the “Dual Triangle” and future trends under different scenarios were analyzed, and the coupled interaction relationship of the “Dual Triangle” was also studied. Conclusions were as follows: The energy system gradually inclined towards economy and cleanliness from 2010 to 2022, and the operation status of the “Dual Triangle” was stable. A positive coupling relationship within the “Dual Triangle” in vigorously promoting renewable energy scenarios is close, which can be regarded as the preferred path for the reconstruction of the power system. The security and low-carbon phased development scenario is a development model that dynamically mitigates the challenges of the “Energy Impossibility Triangle”. Security has become the core contradiction of the “Energy Impossibility Triangle”. This paper conducts an integrated and innovative application of the “Dual Triangle” theory and provides a case basis for how to achieve the coupled interaction of the “Dual Triangle”.

TRNet: Two-level Refinement Network leveraging speech enhancement for noise robust speech emotion recognition

One persistent challenge in Speech Emotion Recognition (SER) is the ubiquitous environmental noise, which frequently results in deteriorating SER performance in practice. In this paper, we introduce a Two-level Refinement Network, dubbed TRNet, to address this challenge. Specifically, a pre-trained speech enhancement module is employed for front-end noise reduction and noise level estimation. Later, we utilize clean speech spectrograms and their corresponding deep representations as reference signals to refine the spectrogram distortion and representation shift of enhanced speech during model training. Experimental results validate that the proposed TRNet substantially promotes the robustness of the proposed system in both matched and unmatched noisy environments, without compromising its performance in noise-free environments.

Bridging computer and education sciences: A systematic review of automated emotion recognition in online learning environments

Emotions play an important role in the learning process. With intelligent technology support, identification and intervention of learners’ cognition have made great achievement, but the care of emotion has been in the absence for a long time. In recent years, the use of affective computing technology to solve affective loss in online education has become a key research topic. To date, a growing number of studies have investigated automated emotion recognition (AER) in online environments. However, AER has been mainly studied from the perspective of computer science focusing on technical characteristics of developing AI technology while its pedagogical value and educational application has been overlooked. Therefore, this systematic literature review aimed to bring together educational and technical aspects of AER. Following PRISMA methodology, a comprehensive search of AER research from 2010 to 2024 in three databases (Web of Science, Science Direct and IEEE Xplore) identified 117 studies that met inclusion criteria. The articles were coded for report characteristics, educational characteristics (tech platform, pedagogy, assessment, content), technical characteristics (emotion model, emotion category, emotion measurement channel, database, algorithm model) and outcome characteristics (technical result, educational application). We found that the primary purpose of these studies was to develop and evaluate systems for AER, rather than implementing these systems in real online learning environments. Furthermore, our findings indicated a lack of integration between computer science and educational science in the realm of AER. Despite the fact that most algorithm models demonstrated high accuracy in AER, the interpretability of the results was significantly constrained by the quality of the databases used, along with the scarcity of studies focusing on the effective and real-time application of AER results. These findings provide essential guidance for shaping future research and development pathways in this field.

A bi-level model for coal power decarbonization via biomass co-firing considering CO[formula omitted] emission trading system

Faced with the challenges posed by the Paris Agreement targets, biomass co-firing offers an efficient measure to decarbonize coal-fired power generation. Although economic burden of biomass co-firing has severely limited the large-scale application, effective CO2 emission trading system has the capability to subsidize and incentivize. Existing studies related to biomass co-firing under CO2 emission trading system fail to consider multiple decision makers and uncertain decision-making environment. To address these limitations, this paper proposes a bi-level model for coal power decarbonization via biomass co-firing considering CO2 emission trading system in an uncertain decision-making environment. It considers the interactive relationship between multiple decision makers. The upper-level model focuses on the formulation of CO2 emission trading system, and the lower-level model decides biomass co-firing and emission reduction. Fuzzy set theory is employed to describe the uncertain parameters and convert them into exact values. To calculate the model, a bi-level interactive method based on satisfactory solution is developed. A case study is conducted to illustrate the effectiveness and practicality of the proposed model. Results show that the price of initial quotas fluctuates between 38.01 and 48.7 CNY/tonne, and the lowest carbon intensity is 0.741 kg/kWh. Management recommendations are provided to support CO2 emission reduction.

Exact and Approximate Moment Derivation for Probabilistic Loops With Non-Polynomial Assignments

Many stochastic continuous-state dynamical systems can be modeled as probabilistic programs with nonlinear non-polynomial updates in non-nested loops. We present two methods, one approximate and one exact, to automatically compute, without sampling, moment-based invariants for such probabilistic programs as closed-form solutions parameterized by the loop iteration. The exact method applies to probabilistic programs with trigonometric and exponential updates and is embedded in the Polar tool. The approximate method for moment computation applies to any nonlinear random function as it exploits the theory of polynomial chaos expansion to approximate non-polynomial updates as the sum of orthogonal polynomials. This translates the dynamical system to a non-nested loop with polynomial updates, and thus renders it conformable with the Polar tool that computes the moments of any order of the state variables. We evaluate our methods on an extensive number of examples ranging from modeling monetary policy to several physical motion systems in uncertain environments. The experimental results demonstrate the advantages of our approach with respect to the current state-of-the-art.