Models Of Environments Research Articles

Systematic literature review of urban charging infrastructure planning over time

The transition from Internal Combustion Engine (ICE) vehicles to Electric Vehicles (EVs) is imperative to achieve the goal of reducing transport-related greenhouse gas emissions by 90 % in 2050. As urbanization intensifies, vehicle miles in urban environments increase and cities already consume 75 % of global energy, there is a pressing need for efficient charging infrastructure (CI) placement tailored to urban environments. Accordingly, this paper conducts a systematic literature review to outline prevailing research and derive requirements for a future CI model adaptable to urban environments. Analysis of N = 57 studies underscores the necessity for agent-based demand models to capture the intricate behaviors of EV drivers, which are currently underrepresented due to their data-heavy nature (n = 28 flow-based; n = 18 node-based). Furthermore, with a projected surge of 800 % in CI installations in Europe by 2030, strategic placement according to demand and urban-specific requirements is paramount. Still, multi-periodicity considerations are largely absent in current literature (n = 50). Geometric segmentation is presented as a solution to mitigate partial coverage issues. Ultimately, agent-based models, coupled with geometric segmentation, emerge as pivotal requirements for future CI models in urban environments, facilitating the transition to EVs, aligning with emission reduction targets, ensuring resource efficiency, and fostering urban sustainability.

Low-quality image object detection based on reinforcement learning adaptive enhancement

The task of object detection for low-quality images in multiple scenarios remains a challenging task. To alleviate this problem, a novel approach to improve the detection performance of the model on low-quality images using deep reinforcement learning is proposed. The approach differs from methods that involve data labeling and retraining, as the detection results of the model are utilized to formulate a reward function. This enables deep reinforcement learning to transform low-quality images into high-quality images through image enhancement, ultimately leading to an improvement in the model's recognition performance on low-quality images. An image enhancement tool chain (IETC) has been prepared, offering a flexible and lightweight algorithm selection for the Dueling Deep Q-Network-based Tool Selector (DDQN-TS). By adding a “Pass” option, Enable DDQN-TS to learn the relative relationship between performing and not performing image enhancement. To mitigate the potential inconsistency between the output of the detection model and the actual situation caused by negative samples, which can lead to incorrect decisions made by DDQN-TS, the introduction of a “thresh” parameter regulates the level of intervention in target detection by DIE. The experimental results demonstrate the effectiveness and portability of the DIE model in environments with noise, fog, and uneven brightness.

From knowledge to inclusion: Culturally responsive TPACK (TPACCK)

Technological pedagogical content knowledge (TPACK) is one of the most widely used frameworks mobilized to support technology integration. Building on recent scholarship that calls for more inclusive and contextualized TPACK to improve technological integration at the local level, this study situates and empirically analyzes the influence of cultural variables in TPACK implementation by a Canadian EFL teacher working in the Japanese university context. It uses analytic autoethnography that draws on culturally responsive teaching and Western and Japanese cultural theories. The results highlight the critical limitations of the current TPACK model in intercultural environments and argue for the inclusion of cultural knowledge as a distinct but overlapping knowledge construct (TPACCK). This model would give greater attention to teachers’ cultural knowledge, thus better equipping educators and organizations to overcome cultural challenges that arise in implementing pedagogies, content, and technologies in intercultural contexts.

Analysis of Digital Security Governance under the Objectives of Digital Ecology: A Three-Party Evolutionary Game Approach

With the rapid development of the digital economy, there has been an explosion in the amount of data generated. Data have become a vital resource for nations, just as tangible assets and human capital are crucial factors of production. Consequently, protecting digital security has become paramount. However, the increasing frequency of various data security incidents in recent years has exposed issues such as inadequate platform governance, lack of government regulation, and incomplete digital security governance (DSG) mechanisms. This study aims to create a healthy and open digital ecosystem and proposes a new digital security governance framework. It subdivides traditional government departments into local governments and the central government. Together with third-party platforms, they are analyzed as participants in an evolutionary game. The study examines the evolutionary stability of strategy choices made by each party and explores the relationship between key factors such as negative externalities, data security incident probabilities, and their impacts on strategy selection using numerical simulations. The research findings indicate the following. (1) Key parameters such as implicit benefits, government subsidies, and negative externalities play a significantly positive role in the development of the digital ecosystem. (2) The central government consistently tends towards emergency response, considering the overall societal perspective, as long as it is capable of bearing the costs. (3) Local governments may exhibit free-riding behavior during the governance process. To address this, it is important to increase the willingness of third-party platforms to govern and oversee the participation of local governments, which is an effective way to prevent data crises. The study also identifies different governance models in various environments: (1) a digital security governance model in a stable market environment, involving increased central government intervention and supervision of local governments’ participation; and (2) a digital security governance model in the event of a data crisis, where the central government establishes subsidies that exceed the governance costs of local governments to enhance the willingness of third-party platforms to govern. Finally, recommendations and strategies are presented to enhance the level of digital security governance.

Humans adaptively select different computational strategies in different learning environments.

The Rescorla-Wagner rule remains the most popular tool to describe human behavior in reinforcement learning tasks. Nevertheless, it cannot fit human learning in complex environments. Previous work proposed several hierarchical extensions of this learning rule. However, it remains unclear when a flat (nonhierarchical) versus a hierarchical strategy is adaptive, or when it is implemented by humans. To address this question, current work applies a nested modeling approach to evaluate multiple models in multiple reinforcement learning environments both computationally (which approach performs best) and empirically (which approach fits human data best). We consider 10 empirical data sets (N = 407) divided over three reinforcement learning environments. Our results demonstrate that different environments are best solved with different learning strategies; and that humans adaptively select the learning strategy that allows best performance. Specifically, while flat learning fitted best in less complex stable learning environments, humans employed more hierarchically complex models in more complex environments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

VARIATIONAL ALGORITHMS OF DEEP KINEMATIC MIGRATION IN TWODIMENSIONAL MEDIA WITH HORIZONTAL VELOCITY GRADIENT

The paper considers three algorithms of kinematic migration (transformation of time fields of normal rays into reflecting boundaries) based on the variational theory of ray tracing developed by Professor of the Department of Seismometry and Geoacoustics of the Geological Faculty of Moscow State University T.I. Oblogina. The results of numerical experiments on theoretical models of layered media of varying complexity have revealed significant drawbacks of the “classical” variational algorithm for solving the inverse kinematic problem. Two mod- ifications of the “classical” variational algorithm are proposed in terms of calculating the starting angle of the rays’ exit from the Earth’s surface, leaving unchanged the principle of taking into account the curvature of seismic rays and refraction at intermediate boundaries: a variational algorithm using “image rays” and a kinematic migration algorithm for layered media with variable reservoir velocities, taking into account the slope of each boundary. The results obtained on theoretical models of complex environments demonstrated high efficiency of solving the inverse kinematic problem by a modified kinematic migration algorithm that takes into account the slope of each boundary.

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios.

Advanced intelligent monitoring technologies for animals: A survey

Effective animal intelligent monitoring is of great value in terms of ecological protection and endangered specie conservation. At present, computer vision technologies have shed light on animal intelligent monitoring. Especially, numerous deep learning-based models and methods have been developed to address various challenges, and have made substantial strides in this field. However, there are still several problems to be solved and related areas to be mined, such as exploring new strategies to enhance the robustness and generalization ability of models, designing novel models for complex environments, and establishing large-scale publicly available animal datasets for performance verification of models. Therefore, we comprehensively elaborated and analyzed existing works on animal intelligent monitoring based on advanced information science technologies, so as to provide useful information assistance for relevant researchers. In this paper, we focus on three primary task fields: precise animal localization, tracking and individual identification. Specifically, we elucidate the definition and significance of each monitoring task, and summarize the baseline models for addressing different problems. We provide a specific analysis of strategies and prototypes of the models and methods employed in each tasks following by the technical progression from traditional machine learning to deep learning. In addition, we make a comparison and analysis of the relevant methods, summarize their similarities and differences between them, and point out the advantages and disadvantages of these methods. Finally, we present several unresolved challenges and problems in animal intelligent monitoring and provide potential research directions in the future. We expect that our review can serve as reference and guidance for related research fields.

Optimization Algorithm for Multimedia Interaction of Computer-Aided Design Models in Virtual Reality Environments

Optimization Algorithm for Multimedia Interaction of Computer-Aided Design Models in Virtual Reality Environments

Revolutionizing Depth Sensing: A Review Study of Apple LiDAR Sensor for as-built Scanning Applications

Incorporating the LiDAR sensor in the most recent Apple devices represents a substantial development in 3D mapping technology. Meanwhile, Apple's Lidar is still a new sensor. Therefore, this article reviews the potential uses of the Apple Lidar sensor in various fields, including engineering and construction, focusing on indoor and outdoor as-built 3D mapping and cultural heritage conservation. The affordable cost and shorter observation times compared to traditional surveying and other remote sensing techniques make the Apple Lidar an attractive choice among scholars and professionals. This article highlights the need for continued research on the Apple LiDAR sensor technology while discussing its specifications and limitations. A comprehensive review found that the Apple LiDAR sensor has shown promise in capturing 3D point clouds of small to medium-sized objects with exceptional detail. This technology offers a cost-effective and accessible option to scan areas faster and analyze data more quickly and automatically for 3D mapping and modelling in indoor and outdoor environments, particularly in areas with restricted access when using other traditional techniques. It also opens the door for more sophisticated applications in future studies, including cultural heritage conservation, archaeological investigations and feature detection, building health monitoring and many more.

Performance Analysis of Deep Learning Model-Compression Techniques for Audio Classification on Edge Devices

Audio classification using deep learning models, which is essential for applications like voice assistants and music analysis, faces challenges when deployed on edge devices due to their limited computational resources and memory. Achieving a balance between performance, efficiency, and accuracy is a significant obstacle to optimizing these models for such constrained environments. In this investigation, we evaluate diverse deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for audio classification tasks on the ESC 50, UrbanSound8k, and Audio Set datasets. Our empirical findings indicate that Mel spectrograms outperform raw audio data, attributing this enhancement to their synergistic alignment with advanced image classification algorithms and their congruence with human auditory perception. To address the constraints of model size, we apply model-compression techniques, notably magnitude pruning, Taylor pruning, and 8-bit quantization. The research demonstrates that a hybrid pruned model achieves a commendable accuracy rate of 89 percent, which, although marginally lower than the 92 percent accuracy of the uncompressed CNN, strikingly illustrates an equilibrium between efficiency and performance. Subsequently, we deploy the optimized model on the Raspberry Pi 4 and NVIDIA Jetson Nano platforms for audio classification tasks. These findings highlight the significant potential of model-compression strategies in enabling effective deep learning applications on resource-limited devices, with minimal compromise on accuracy.

Causal inference for out‐of‐distribution recognition via sample balancing

AbstractImage classification algorithms are commonly based on the Independent and Identically Distribution (i.i.d.) assumption, but in practice, the Out‐Of‐Distribution (OOD) problem widely exists, that is, the contexts of images in the model predicting are usually unseen during training. In this case, existing models trained under the i.i.d. assumption are limiting generalisation. Causal inference is an important method to learn the causal associations which are invariant across different environments, thus improving the generalisation ability of the model. However, existing methods usually require partitioning of the environment to learn invariant features, which mostly have imbalance problems due to the lack of constraints. In this paper, we propose a balanced causal learning framework (BCL), starting from how to divide the dataset in a balanced way and the balance of training after the division, which automatically generates fine‐grained balanced data partitions in an unsupervised manner and balances the training difficulty of different classes, thereby enhancing the generalisation ability of models in different environments. Experiments on the OOD datasets NICO and NICO++ demonstrate that BCL achieves stable predictions on OOD data, and we also find that models using BCL focus more accurately on the foreground of images compared with the existing causal inference method, which effectively improves the generalisation ability.

A NOVEL HYBRID DECISION-MAKING MODEL: FUZZY AHP-TOPSIS APPROACH FOR PRIORITISING COPPER SMELTING PROCESSES

The construction of a copper smelting facility and its undisturbed and profitable business undoubtedly contribute to the development of each country’s economy. These facilities employ many workers and produce a large amount of copper, reducing imports and dependence on this important raw material, thereby improving the economic situation in a given country. More than a hundred copper smelters operate worldwide, many of which use different types of copper extraction processes. Strict legislation relating to ecology and environmental protection as well as stakeholder involvement in selecting and constructing copper smelting facilities limit the maximisation of short-term economic objectives. The prioritisation of technological processes for the extraction of copper must consider the impacts of often mutually opposing economic, technical and environmental objectives. No research from the available literature analyses the economic, technical and environmental parameters systematically. Studies have mainly dealt with exploring individual influences of factors through the use of one selection method. This paper presents the development of a novel hybrid AHP-TOPSIS model in fuzzy environments that will provide both informative decisions and optimum results of decision making.

Attention Based Energy Demand Forecasting in Smart Grid Environments

The smart grid is a crucial aspect of the modern energy landscape, providing a reliable, efficient, and sustainable way of meeting the growing energy demands. However, the vast amounts of data generated by smart grid technology necessitate the development of advanced data processing and analysis techniques. In this paper, we propose an attention-based time series workflow that combines dilated convolution and attention mechanisms for time series forecasting in smart grid applications. This workflow extracts temporal features from time series data using dilated convolutions and emphasizes significant temporal points in the hidden states using attention mechanisms. Experimental evaluations showed up to an 8% better performance for energy demand forecasting compared to commonly used deep learning-based methods. Note that our workflow achieved this gain by requiring 1/3 of the training time other models took. We have also improved performance by 42% in various domains, demonstrating the adaptability of our approach across different areas. This study may assist researchers in constructing accurate forecasting models for smart grid environments. Furthermore, it highlights that the attention-based approach can be employed to promote sustainable energy and optimize smart grid environments.

A Review of Robust Machine Scheduling

Robust optimization (RO) has been recognized as an effective means to deal with unanticipated events in highly uncertain and risky environments. This paper systematically reviews two types of emerging RO machine scheduling approaches—robust machine scheduling (R-MS) and distributionally R-MS (DR-MS) methods—which usually offer tractable formulations and analytical results for machine scheduling problems under uncertainty. First, after highlighting the advantages of RO methods over the stochastic approach in terms of tractability and robustness, we use the bibliometric method to analyze the literature related to R-MS/DR-MS problems and classify them from the following aspects: (1) uncertain factors, (2) uncertainty descriptions, (3) robustness criteria, (4) machine environments and (5) solution methods. Second, we discuss the uncertainty descriptions, and the robust feasibility and robust optimality criteria. We further provide a state-of-the-art review of R-MS/DR-MS models in different machine environments and discuss the performance of the R-MS/DR-MS models. Third, we review and discuss the existing exact, approximation, online, and heuristic solution methods for solving R-MS/DR-MS models. Finally, we present future research opportunities in two promising areas: green machine scheduling problems and machine learning-enabled algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Machine scheduling plays an essential role in industrial and service systems, such as manufacturing, power generation, transportation and medical systems. However, in practice, scheduling systems usually operate in highly uncertain environments due to noisy measurements, prediction errors, and implementation deviations. To ensure robust feasibility and robust optimality, robust machine scheduling (R-MS) and distributionally R-MS (DR-MS) approaches have been recently proposed to hedge against the uncertainties related to processing time, release time, due date, machine breakdown, etc. This paper provides a comprehensive review of the R-MS/DR-MS models and algorithms in different machine environments from the aspects of uncertainty descriptions, robustness criteria and solution methods. This paper further highlights the challenges of R-MS problems and provides promising and valuable research opportunities in terms of problem formulations and algorithm designs.

21st-century stewardship: infusing environmental stewardship education with global citizenship

Environmental stewardship education, as a locally oriented concept, benefits from being recast to account for the emergent, multidimensional, and interdisciplinary nature of today’s most pressing socio-ecological issues. In an effort to infuse environmental stewardship education with global citizenship, instructional materials on community-based conservation in Peru were delivered to high school students in New York, USA. The purpose of this study was to assess the impact of instructional materials on student learning outcomes related to the practice of global citizenship in environmental stewardship education. The CLIA model for powerful learning environments was used to convey the competencies, learning conditions, interventions, and assessments that structured the delivery of the instructional materials. To evaluate learning outcomes, written student responses were analyzed, using the qualitative coding methodology. Additionally, transformative learning was assessed using the Learning Activities Survey. Results suggest a trajectory past knowledge comprehension and toward transformative, empowered action, progressing from a cognitive understanding to affective and ultimately behavioral shifts. Based on these findings, this study conceptualized and operationalized the concept 21st-century stewardship – a term coined here as an updated stewardship model that is infused with global citizenship’s multi-scalar perspectives, global compacts, and layered identities to achieve its maximum impact in our interconnected world.

Self-distillation framework for document-level relation extraction in low-resource environments.

The objective of document-level relation extraction is to retrieve the relations existing between entities within a document. Currently, deep learning methods have demonstrated superior performance in document-level relation extraction tasks. However, to enhance the model's performance, various methods directly introduce additional modules into the backbone model, which often increases the number of parameters in the overall model. Consequently, deploying these deep models in resource-limited environments presents a challenge. In this article, we introduce a self-distillation framework for document-level relational extraction. We partition the document-level relation extraction model into two distinct modules, namely, the entity embedding representation module and the entity pair embedding representation module. Subsequently, we apply separate distillation techniques to each module to reduce the model's size. In order to evaluate the proposed framework's performance, two benchmark datasets for document-level relation extraction, namely GDA and DocRED are used in this study. The results demonstrate that our model effectively enhances performance and significantly reduces the model's size.

Monitoring of chlorophyll content in local saltwort species Suaeda salsa under water and salt stress based on the PROSAIL-D model in coastal wetland

As the invasion of alien species intensifies, the native salt marsh vegetation, especially the ecosystem of Suaeda salsa (S. salsa), in Chinese coastal wetlands has been severely disrupted, significantly impeding its functionality within coastal wetland ecosystems. Chlorophyll content (Cab) is an important parameter for monitoring the growth and health status of vegetation. However, the remote sensing mechanism of Cab for different phenotypes of green and red vegetation influenced by betacyanin under different water and salt conditions is not clear. Therefore, to further clarify the remote sensing mechanism of S. salsa and improve the generality of Cab prediction for different phenotypes under different water and salt conditions, we designed a growth experiment under different groundwater levels and salt concentrations. This experiment was used to simulate the growth of S. salsa in different habitats in nature and investigate the relationship between their physicochemical parameters, spectra, and environmental stress. We propose using the specific absorption coefficient of betacyanin (Beta) to adjust PROSAIL-D to better fit the spectral characteristics of S. salsa. The relationship between Beta and Cab was utilized to further optimize the simulation accuracy of the PROSAIL-D model in different environments. Feature extraction algorithms, including differential enhancement of two-dimensional correlation spectroscopy (FD-2DCOS) and three-band models, were introduced to construct multiple spectral features for establishing a Cab prediction model for S. salsa. The research results showed that: (1) S. salsa had the best response of Cab and spectra to salinity under a higher groundwater level and showed a gradual reddening trend. (2) The spectral curves simulated by the adjusted PROSAIL-D model were more consistent with the spectral characteristics of S. salsa under different water-salt environments, and the simulation effect could be further optimized by using the relationship between Beta and Cab. (3) The FD-2DCOS analysis method we proposed could highlight the useful spectral information related to Cab better than the original 2DCOS algorithm. The final eight spectral features of (1/R701–1/R540)R540, Mean(D590–600), Mean(D660–670), Mean(D677–685), FDVI_NRG, FDVI_NIR, Skewness (D677–750) and Position(D677–750) had the best response to Cab of S. salsa, and had the least influence on LAI and soil background. (4) The particle swarm optimization random forest regression (PSO-RFR) model based on eight spectral features and adding 70% measured spectral data performed the best in predicting the actual Cab of S. salsa, with an RMSE of 2.326 μg·cm−2. Although further optimization and calibration of the adjusted PROSAIL-D model and Cab prediction model of S. salsa are needed in the future, this study extended its application to S. salsa, a special local salt marsh vegetation, and promoted the development of Cab monitoring of salt marsh vegetation in coastal wetlands.

An efficient bandwidth-adaptive gradient compression algorithm for distributed training of deep neural networks

In distributed deep learning with data parallelism, communication bottleneck throttles the efficiency of model training. Recent studies adopt versatile gradient compression techniques, with communication sparsification standing out as an effective approach for reducing the number of gradients to be transmitted. However, the deployment of gradient sparsification is adversely influenced by the change of network environment in real systems, and existing methods either neglect bandwidth dynamics during training or experience drastic fluctuation of compression ratios. In this paper, we propose ACE, a novel adaptive gradient compression mechanism with high communication efficiency under bandwidth variation. ACE adapts the sparsification ratio to the average bandwidth in a time window, other than following its dynamics exactly. To accurately compute the compression ratio, we first profile the compression time and model a single iteration time consisting of communication, computation and compression operations. We then present a practical model to fit the needed training rounds till convergence, and formulate an optimization problem to compute the optimal sparsification ratio. We conduct experiments on different DNN models in different network environments and compare various methods in terms of convergence and model quality. The experimental results show that ACE achieves up to 9.39× and 1.28× training speedups over fixed and state-of-the-art adaptive compression methods.

A modified atmospheric scattering model and degradation image clarification algorithm for haze environments

Addressing the limitation of existing image dehazing algorithms based on atmospheric scattering models, which often overlook the underlying physical causes of color distortion in hazy images, resulting in suboptimal algorithm robustness and image clarity when faced with varying levels of haze pollution, this study introduces a refined atmospheric scattering model. It also presents a haze image clarity algorithm based on this modified atmospheric scattering model by analyzing the fundamental factors contributing to image degradation and distortion caused by haze. Initially, the hazy image undergoes conversion to a new HSI color space. The hazy image is aligned more closely with human visual perception characteristics by implementing an adaptive saturation enhancement technique and adjusting contribution coefficients for the three RGB channels to the luminance component. Subsequently, the hazy image is partitioned into dense haze and non-dense haze regions based on haze distribution characteristics within the image. A global light intensity computation method is devised for the hazy image utilizing a quad-tree segmentation algorithm. Following this, calculations are performed to determine the atmospheric light value and transmittance for dense and non-dense haze regions separately. The global atmospheric light and transmittance values are then derived based on the probability density function of the dense haze region using Alpha Fusion. Finally, corrections are applied to the global atmospheric light values using an atmospheric light correction vector, while denoising the global transmittance is achieved through a fast non-local mean filtering algorithm. Experimental findings demonstrate that the proposed haze image clarity algorithm, grounded in the modified atmospheric scattering model, exhibits adaptability across various degrees and types of haze images. Comparative analysis against other prominent image dehazing algorithms reveals its efficacy in addressing severe color distortion in dense haze scenarios, achieving a more natural clarity effect, and showcasing superior performance across all numerical evaluation metrics.