Environment Model Research Articles

Reducing Uncertainty of Groundwater Redox Condition Predictions at National Scale, for Decision Making and Policy.

Understanding hydrogeochemical heterogeneity, associated with natural nitrate attenuation, is an integral part of implementing integrated land and water management on a regional or national scale. Redox conditions are a key indicator of naturally occurring denitrification in the groundwater environment, and often used to inform spatial planning and targeted regulation. This work describes the development of a statistical redox condition model for the groundwater environment at a national scale, using spatially variable physiochemical descriptors as predictors. The proposed approach builds on previous work, by complementing the available data with expert knowledge, in the form of synthetic data. Special care is given so that the synthetic data do not overfit and create further imbalances to the training dataset. The predictor dataset is further complemented by the results of a data driven model of the water table developed for this study, which is used both as a predictive parameter and a reference level for groundwater redox condition predictions at different depths. The developed model predicted the redox class for 84% of the samples in the out-of-bag datasets. We also propose an alternative approach for the communication of prediction uncertainty. We use the concept of a discriminate function to identify model classifications that may be ambiguous. Our results show a marked reduction in prediction uncertainty at shallow depths, with uncertainty in reduced environments decreasing from 76 to 12%, and overall uncertainty reduced by approximately 20%, though improvements at greater depths are less pronounced. We conclude that this approach can highlight robust model predictions that are defendable for decision making and can identify areas where monitoring or sampling efforts can be focused for improved outcomes.

An intelligent virtual machine allocation optimization model for energy-efficient and reliable cloud environment

An intelligent virtual machine allocation optimization model for energy-efficient and reliable cloud environment

Applying opponent and environment modelling in decentralised multi-agent reinforcement learning

Multi-agent reinforcement learning (MARL) has recently gained popularity and achieved much success in different kind of games such as zero-sum, cooperative or general-sum games. Nevertheless, the vast majority of modern algorithms assume information sharing during training and, hence, could not be utilised in decentralised applications as well as leverage high-dimensional scenarios and be applied to applications with general or sophisticated reward structure. Thus, due to collecting expenses and sparsity of data in real-world applications it becomes necessary to use world models to model the environment dynamics, using latent variables — i.e. use world model to generate synthetic data for training of MARL algorithms. Therefore, focusing on the paradigm of decentralised training and decentralised execution, we propose an extension to the model-based reinforcement learning approaches leveraging fully decentralised training with planning conditioned on neighbouring co-players’ latent representations. Our approach is inspired by the idea of opponent modelling. The method makes the agent learn in joint latent space without need to interact with the environment. We suggest the approach as proof of concept that decentralised model-based algorithms are able to emerge collective behaviour with limited communication during planning, and demonstrate its necessity on iterated matrix games and modified versions of StarCraft Multi-Agent Challenge (SMAC).

On Transforming Reinforcement Learning With Transformers: The Development Trajectory.

Transformers, originally devised for natural language processing (NLP), have also produced significant successes in computer vision (CV). Due to their strong expression power, researchers are investigating ways to deploy transformers for reinforcement learning (RL), and transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and dissect recent advances concerning the transformation of RL with transformers (transformer-based RL (TRL)) to explore the development trajectory and future trends of this field. We group the existing developments into two categories: architecture enhancements and trajectory optimizations, and examine the main applications of TRL in robotic manipulation, text-based games (TBGs), navigation, and autonomous driving. Architecture enhancement methods consider how to apply the powerful transformer structure to RL problems under the traditional RL framework, facilitating more precise modeling of agents and environments compared to traditional deep RL techniques. However, these methods are still limited by the inherent defects of traditional RL algorithms, such as bootstrapping and the "deadly triad". Trajectory optimization methods treat RL problems as sequence modeling problems and train a joint state-action model over entire trajectories under the behavior cloning framework; such approaches are able to extract policies from static datasets and fully use the long-sequence modeling capabilities of transformers. Given these advancements, the limitations and challenges in TRL are reviewed and proposals regarding future research directions are discussed. We hope that this survey can provide a detailed introduction to TRL and motivate future research in this rapidly developing field.

How far back shall we peer? Optimal air handling unit control leveraging extensive past observations

Heating, Ventilation, and Air Conditioning (HVAC) systems play a critical role in ensuring occupant comfort in buildings. Traditional Rule-Based Feedback Control (RBFC) systems, while widely deployed for their simplicity, suffer from low adaptability. Recent improvements, such as Model Predictive Control (MPC) methods demand complex mathematical modeling and substantial expert knowledge, creating a high barrier for system design and optimization. Reinforcement Learning (RL) emerges as a promising solution with its adaptability and model-free nature, albeit challenged by sample inefficiency and suboptimal convergence. Given the intrinsic delayed effects from previous actions and prolonged thermal inertia in the HVAC systems, this study introduces an innovative deep RL framework that can leverage historical observations to refine RL agent performance. By incorporating a state-of-the-art (SOTA) Transformer model, we capture the temporal patterns in HVAC data and build a more precise RL training environment. Implemented on high-resolution, real-world HVAC datasets, our framework showed superior performance in both HVAC system modeling and RL control performance. Specifically, compared to the two baseline models—Bidirectional Long Short-Term Memory (Bi-LSTM) and vanilla Transformer, our proposed RL environment model achieved an average of 30.5% and 35.8% prediction accuracy improvement, respectively. Moreover, our optimal past observable RL agent swiftly delivers 35.3% of electricity saving and 54.4% of thermal comfort improvement over traditional RBFC. These results show the effectiveness of the pioneering integration of extensive historical observations for HVAC operation optimization.

Green building interior design based on digital image processing and thermal environment simulation

With the increasingly severe global climate change and energy crisis, this study aims to explore effective strategies for green building interior design, especially in terms of thermal environment control, through digital image processing and thermal environment simulation. The study simulates indoor scenes based on image processing technology, using methods such as image acquisition, localization, and object detection to improve the accuracy of scene reconstruction, and conducts in-depth analysis of detection effects, laying the foundation for subsequent thermal environment simulations. In the design of the thermal energy environment control system, the calculation of indoor cooling load was studied and compared with existing thermal environment models to determine the impact of user activities on the indoor thermal environment. By establishing a thermal energy environment control model and implementing precise environmental regulation for different usage scenarios, indoor comfort and energy efficiency have been significantly improved. The experimental results show that the use of digital image processing simulation technology can effectively improve the accuracy and flexibility of indoor thermal environment control in green buildings, optimize energy efficiency, emphasize the importance of thermal factors in interior design, and provide theoretical basis and practical guidance for future building design practices.

Immersive learning of the young generation using a mobile learning lab

In order to counter modern threats, knowledge and ablity to act in the face of emerging dangers are necessary. It is not possible to solve these problems without proper training of the population, including the younger generation, in the field of life safety. The development of the modern education system involves the introduction of modern technologies and innovative educational projects into the educational process, including immersive teaching methods. Immersive learning is the most effective and safe way to develop sustainable skills for safe behavior in various emergency and dangerous situations. The aim of this study is to increase the level of training of the younger generation in the field of life safety. To achieve the aim set, the authors: analyzed the main normative legal acts regulating the issues of training the population in the field of life safety; studied previously completed scientific papers and publications in this field of research; the problematic issues of training the younger generation in the field of life safety are considered; identified the most effective ways of training the younger generation based on the use of modern technologies. To solve the identified problems and achieve the research aim, the authors propose using the latest achievements and trends in the educational environment to develop a unique educational product – the mobile educational laboratory «Country of Safety». The paper substantiates the relevance and effectiveness of the creation and use of a mobile training laboratory in the education system, analyzes the experience of using similar complexes on the topic of road safety in Moscow and the Moscow region. The main emphasis is put on the organization of practical training of the younger generation on the basis of a mobile educational laboratory using game forms of learning, virtual reality technologies and environment modeling using 3D visualization. As conclusions, the planned results of the development and implementation of the mobile training laboratory «Country of Safety» in the activities of both the Ministry of Emergency Situations of Russia and educational organizations are proposed.

Transforming industrial automation: voice recognition control via containerized PLC device

The article discusses the impact of voice recognition and containerization technologies in the industrial sector, particularly on Programmable Logic Controller (PLC) devices. It highlights how voice assistants like Alexa, Siri, Cortana, and Google Assistant are pioneering and pushing the future of human–machine interfaces, with applications moving from smart homes to industrial automation. Containerization, illustrated by Docker, is transforming software deployment practices, offering benefits such as enhanced portability, modular architecture, and improved security when applied to industrial PLCs. The article introduces a novel approach to enhancing human–machine interfaces (HMIs) within industrial applications, leveraging voice recognition and containerization technologies on Programmable Logic Controllers (PLCs). Unlike traditional systems, this article integrates voice assistant with industrial PLCs through a containerized IoT architecture. This innovative framework enables efficient deployment on edge devices, supporting modular, portable, and secure operations aligned with Industry 4.0 and 5.0 paradigms. The study further includes a detailed implementation on microcontrollers and industrial PLCs, validating its application in a controlled laboratory environment and virtual model.

Physics-consistent input convex neural network-driven reinforcement learning control for multi-zone radiant ceiling heating and cooling systems: An experimental study

Radiant ceiling heating and cooling system is a technology used for space heating and cooling. Owing to the variable weather conditions, occupant behavior, and thermal lag of the system, it is challenging to design a control strategy to reduce air-conditioning energy consumption while maintaining the thermal environment. This study is the first pilot implementation of a physics-consistent input convex neural network (PCICNN)-driven reinforcement learning (RL) approach for real-world multi-zone radiant ceiling heating and cooling systems. A multi-zone PCICNN based on a graph neural network (GNN) was developed to simulate the zone temperature. The radiant panel load was simulated using the physics-based ε-NTU method. The PCICNN-driven RL agent was based on the soft actor-critic algorithm and trained in an environment model comprising the PCICNN and ε-NTU models. The proposed controller was deployed in real-time on one floor of an office building for one month. The real-world implementation showed that the proposed PCICNN-driven RL control can reduce the radiant panel cooling load by up to 33% compared with the inherited baseline control strategy under similar weather conditions. This study provides a comprehensive demonstration of real-world data-driven building controls and leverages future research on advanced building control.

The co-receptor Tetraspanin12 directly captures Norrin to promote ligand-specific β-catenin signaling.

Wnt/β-catenin signaling directs animal development and tissue renewal in a tightly controlled, cell- and tissue- specific manner. In the mammalian central nervous system, the atypical ligand Norrin controls angiogenesis and maintenance of the blood-brain barrier and blood-retina barrier through the Wnt/β-catenin pathway. Like Wnt, Norrin activates signaling by binding and heterodimerizing the receptors Frizzled (Fzd) and Low-density lipoprotein receptor-related protein 5 or 6 (LRP5/6), leading to membrane recruitment of the intracellular transducer Dishevelled (Dvl) and ultimately stabilizing the transcriptional coactivator β-catenin. Unlike Wnt, the cystine-knot ligand Norrin only signals through Fzd4 and additionally requires the co-receptor Tetraspanin12 (Tspan12); however, the mechanism underlying Tspan12-mediated signal enhancement is unclear. It has been proposed that Tspan12 integrates into the Norrin-Fzd4 complex to enhance Norrin-Fzd4 affinity or otherwise allosterically modulate Fzd4 signaling. Here, we measure direct, high-affinity binding between purified Norrin and Tspan12 in a lipid environment and use AlphaFold models to interrogate this interaction interface. We find that Tspan12 and Fzd4 can simultaneously bind Norrin and that a pre-formed Tspan12/Fzd4 heterodimer, as well as cells co-expressing Tspan12 and Fzd4, more efficiently capture low concentrations of Norrin than Fzd4 alone. We also show that Tspan12 competes with both heparan sulfate proteoglycans and LRP6 for Norrin binding and that Tspan12 does not impact Fzd4-Dvl affinity in the presence or absence of Norrin. Our findings suggest that Tspan12 does not allosterically enhance Fzd4 binding to Norrin or Dvl, but instead functions to directly capture Norrin upstream of signaling.

Towards Open-Set NLP-Based Multi-Level Planning for Robotic Tasks

This paper outlines a conceptual design for a multi-level natural language-based planning system and describes a demonstrator. The main goal of the demonstrator is to serve as a proof-of-concept by accomplishing end-to-end execution in a real-world environment, and showing a novel way of interfacing an LLM-based planner with open-set semantic maps. The target use-case is executing sequences of tabletop pick-and-place operations using an industrial robot arm and RGB-D camera. The demonstrator processes unstructured user prompts, produces high-level action plans, queries a map for object positions and grasp poses using open-set semantics, then uses the resulting outputs to parametrize and execute a sequence of action primitives. In this paper, the overall system structure, high-level planning using language models, low-level planning through action and motion primitives, as well as the implementation of two different environment modeling schemes—2.5 or fully 3-dimensional—are described in detail. The impacts of quantizing image embeddings on object recall are assessed and high-level planner performance is evaluated using a small reference scene data set. We observe that, for the simple constrained test command data set, the high-level planner is able to achieve a total success rate of 96.40%, while the semantic maps exhibit maximum recall rates of 94.69% and 92.29% for the 2.5d and 3d versions, respectively.

Lithofacies and microfacies and depositional environment model of the Cenozoic carbonate platform: an example from the Upper Jafnayn Formation of Jafnayn area in north-east Oman

Lithofacies and microfacies and depositional environment model of the Cenozoic carbonate platform: an example from the Upper Jafnayn Formation of Jafnayn area in north-east Oman

Development of an e-module based on the Self-Organized Learning Environment (SOLE) model assisted by Artificial Intelligence (AI) on kinematics material

This development research aims to develop an e-module based on the Self-Organized Learning Environment (SOLE) model assisted by Artificial Intelligence (AI) on kinematics material. The research and development method uses the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) approach created by Robert and Maribe Brach. This model includes the following stages: 1) Analysis, 2) Design, 3) Development, 4) Implementation, 5) Evaluation. The subjects of this research were students from two schools, and the data collection instruments were questionnaires provided to expert validators and students. The data generated is qualitative and analyzed using assessment criteria guidelines to determine the quality of the product. The results of this research produced teaching materials and determined the quality of the developed product: 86% from material experts (categorized as highly feasible), 95% from media experts (categorized as highly feasible), 87% from educators, and 93% from students in small-scale trials, with 87% in large-scale trials, all categorized as very attractive. Based on these results, it can be concluded that the E-module based on the Self-Organized Learning Environment (SOLE) model assisted by Artificial Intelligence (AI) for kinematics material is highly feasible and attractive for use in high school physics learning for 11th-grade students.

Контурні моделі середовища електронної книги в системі «JETIQ VNTU»

The article contains the results of the development of contour models for the e-book environment in the JetIQ VNTU information ecosystem. The authors continued the study of contour models of the educational electronic information environment, one of the elements of which is an electronic book. It is considered as a separate environment for independent and mixed learning, it forms an environment for studying specific topics and performing practical tasks. Such electronic textbooks or laboratory practices have built-in tests, allow the teacher to enter tasks, determine the time for completing tasks, and integrate the obtained grades into an electronic journal. The distribution of contours by type allows researchers to focus their attention on the development of each of the contours - functional, communication, managerial, motivational and emotional. The relationship between them also shows the impact on the level of quality of the educational electronic environment. In addition, it is important to develop a security circuit to preserve one's own knowledge base based on an e-book, to develop author's electronic educational materials.

An innovative traffic flow detection model based on temporal video frame analysis and grayscale aggregation quantification

AbstractCurrent traffic status detection methods heavily rely on historical traffic flow data and vehicle counts. However, these methods often fail to meet the stringent real‐time requirements of state detection, especially on edge devices with limited computing resources.To address these challenges, this study develops a traffic alert model using temporal video frame analysis and grayscale aggregation quantization techniques. Initially, the model uses distance mapping between pixel features and frames of road traffic videos to construct a comprehensive road environment and vehicle segmentation model. The model also establishes a mapping between pixel equidistant lines and actual distances, enabling precise congestion detection. This approach significantly reduces costs associated with traditional traffic detection methods as it does not rely on historical data. Performance evaluation using fixed‐point road monitoring data indicates that the proposed model outperforms traditional traffic state detection models, with a performance improvement of approximately 4.7% to 9.5%. Additionally, the model improves computing resource efficiency by approximately 72.5% and demonstrates substantial real‐time detection capabilities.

FACTORS INFLUENCING SOCIAL COMMERCE ADOPTION : A TOE FRAMEWORK ANALYSIS

The increasing use of social media has led to a new business trend called social commerce. This study investigates the factors that influence the adoption of social commerce in Solo Raya SMEs using the technology, organizational, and environment (TOE) model. This research is a quantitative research using primary data. Data collection techniques use questionnaires via Google Form. The population of this research is SMEs in Solo Raya that have used social commerce technology. The sampling technique in this study used purposive sampling technique with a sample size of 132 respondents. The data analysis technique in this study uses structure equation modeling partial least square (SEM - PLS) with the help of smartPLS 3.0 software. The results of this study indicate that perceived usefulness, security concerns, top management support, organizational readiness, consumer pressure, and trading partner pressure have a positive effect on the behavioral intention of social commerce adoption. These findings can help SMEs in utilizing digital technology to increase their competitiveness in today's digital era.

Cutting-edge techniques in 3D modeling and animation: Leveraging mathematical models and advanced software tools

Abstract. This paper explores the advancements in computer animation technology, focusing on 3D modeling and animation. It delves into the core aspects of character design, environment modeling, and animation rigging, emphasizing the mathematical models and algorithms that enhance realism and efficiency. Key techniques discussed include Bezier curves and spline interpolation for anatomy and proportions, Perlin noise and fractal algorithms for texturing, and Inverse Kinematics (IK) and Forward Kinematics (FK) for rigging. Additionally, the paper examines procedural generation and fluid dynamics simulations for environment modeling and the integration of motion capture data. The use of software tools like Blender, Autodesk Maya, and Houdini is highlighted. This study aims to provide a comprehensive understanding of the current state of 3D animation technology and its future directions.

Olfactory bulb tracks breathing rhythms and place in freely behaving mice.

Vertebrates sniff to control the odor samples that enter their nose. These samples can not only help identify odorous objects, but also locations and events. However, there is no receptor for place or time. Therefore, to take full advantage of olfactory information, an animal's brain must contextualize odor-driven activity with information about when, where, and how they sniffed. To better understand contextual information in the olfactory system, we captured the breathing and movements of mice while recording from their olfactory bulb. In stimulus- and task-free experiments, mice structure their breathing into persistent rhythmic states which are synchronous with statelike structure in ongoing neuronal population activity. These population states reflect a strong dependence of individual neuron activity on variation in sniff frequency, which we display using "sniff fields" and quantify using generalized linear models. In addition, many olfactory bulb neurons have "place fields" that display significant dependence of firing on allocentric location, which were comparable with hippocampal neurons recorded under the same conditions. At the population level, a mouse's location can be decoded from olfactory bulb with similar accuracy to hippocampus. Olfactory bulb place sensitivity cannot be explained by breathing rhythms or scent marks. Taken together, we show that the mouse olfactory bulb tracks breathing rhythms and self-location, which may help unite internal models of self and environment with olfactory information as soon as that information enters the brain.

Computer-aiding evaluation of north wall effects of a solar greenhouse: Multiphysics modelling of the indoor environment

The north wall (NW) of a solar greenhouse (SG), oriented away from direct sunlight, plays a crucial role in thermal insulation; however, impacts of the NW internal surface structure (ISS) on the SG indoor environment remains underexplored. Driven by in situ measurements, the temperature (Ti) and relative humidity (RHi) of a SG are evaluated for three typical NW designs: flat wall (FW), striped wall (SW) and alveolate/honeycombed wall (AW). In both sunny and cloudy scenarios, the AW setup ensures a more favourable thermal environment, well aligned with the power spectral analysis indicating enhanced thermal energy capture. The time-lagged cross correlation highlights solar radiation as the primary driver of SG internal thermal variability, with outdoor temperature being of secondary relevance. Additionally, the AW setup consistently maintains the lowest RHi on sunny days, promoting optimal conditions for plant growth, and ensures more stable humidity levels during cloudy nights. The revealed response of SG internal environments to modified NW ISS should offer some insights into SG design, particularly in optimising conditions for plant growth through modifications to the NW structure.

Integrating material flow analysis into hydrological model for water environment management of large-scale urban-rural mixed catchment

Simultaneous simulation of urban and rural hydrological processes is important for water environment management of mixed land-uses catchments. However, the discharge paths of pollution in the urban drainage system are not described in traditional catchment hydrological models. In this study, an urban-rural water environment (URWE) model is developed through incorporating the material flow analysis (MFA) and the soil and water assessment tool (SWAT) into a general framework. The URWE model is an advancement with respect to traditional hydrological models in terms of simultaneously simulating the urban organized and rural decentralized discharges of pollution. Due to the low data requirement and high computational efficiency of MFA, URWE model is applicable to large-scale catchment with wide urban area. The URWE model is applied to a typical urban-rural mixed catchment, the Dianchi Catchment (China), where the pollution characteristics are analyzed and the pollution control measures are investigated. Results indicate that the URWE model outperforms the conventional SWAT model for both water quantity and quality simulations, with an 8.5 % improvement in average coefficient of determination (R2) and a 67.4 % improvement in average Nash coefficient (NSE). Rural best management practice, rainwater-sewage separation, and storage capacity expansion are identified as the most cost-effective measures for COD, TN, and TP reduction, respectively. Contributions of this study are to improve the accuracy of water environment simulation in urban-rural mixed catchment, as well as to help decision-makers develop synergistic urban-rural water environment management measures.