Mapping Model Research Articles

Performance Assessment of Individual and Ensemble Learning Models for Gully Erosion Susceptibility Mapping in a Mountainous and Semi-Arid Region

High-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts.

Multimodal HSI Combined with Multiblock Data Fusion: A New Tool for the Study of Time-Dependent Alteration Processes in Dyed Textiles.

The present study describes an innovative approach for the study of time-dependent alteration processes. It combines an advanced hyperspectral imaging (HSI) system, to collect visible reflectance and fluorescence spectral data sets sequentially, with a tailored multiblock data processing method. This enables the modeling of chemical degradation maps and the early, spatially resolved detection of dye alteration in textiles. A chemometric method based on data fusion and principal component analysis was employed to identify spectral features of dye degradation, combining and enhancing information from reflectance and fluorescence HSI data. The most significant spectral profiles extracted were used to develop an asymmetric Gaussian-based pixel-by-pixel fitting model applied to the HSI fluorescence data set, enabling the reconstruction of degradation maps for rapid and intuitive visualization. In particular, changes in intensities and horizontal shift of dye emission peaks were pixel-by-pixel evaluated and fitted for the reconstruction of the degradation maps. Artificially aged wool samples tinted with indigo carmine (IC) dye served as a case study. IC is extensively used in textiles, and it is notable for its light sensitive. The results show that this approach effectively identifies spatial variations and chemical changes in dyed wool fibers, offering potential for sustainable conservation of historical textiles and other types of time-dependent processes. Thus, by amplifying variation in spectral profiles induced over time by aging, even minimal changes at early stages can be easily detected and localized, offering powerful tools for future studies on food and drug shelf life and stability, as well as forensic trace analysis.

Bayesian nonparametric mixture of experts for inverse problems

Large classes of problems can be formulated as inverse problems, where the goal is to find parameter values that best explain some observed measures. The relationship between parameters and observations is typically highly non-linear, with relatively high dimensional observations and correlated multidimensional parameters. To deal with these constraints via inverse regression strategies, we consider the Gaussian Local Linear Mapping (GLLiM) model, a special instance of mixture of expert models. We propose a general scheme to design a Bayesian nonparametric GLLiM model to avoid any commitment to an arbitrary number of experts. A tractable estimation algorithm is designed using variational Bayesian expectation-maximisation. We establish posterior consistency for the number of mixture components after the merge-truncate-merge algorithm post-processing. Illustrations on simulated data show good results in terms of recovering the true number of experts and the regression function.

Three degrees of freedom rigid-soft coupling biomimetic hip joint driven by dielectric elastomer

Abstract Dielectric elastomer (DE) has an attractive combination of high energy density, large strain, and fast response. A growing number of DE actuators are being used as driving materials in rigid-soft coupling joints. For better structural design, the mapping relations from the musculoskeletal system of the human hip joint to the biomimetic hip joint mechanism have been established. Inspired by the human musculoskeletal system, the configuration of the three-degree-of-freedom rigid-soft coupling hip joint based on DE is proposed. The configuration includes six soft-driving limbs and one passive rigid limb. The soft-driving limb driven by the fiber-constrained dielectric elastomer actuator (FCDEA) does not contain passive rigid joints. Based on the Gent material model, the electromechanical coupling model of FCDEA is established. A mapping model is established between the voltage applied to the soft-driving limb of the biomimetic hip joint and the posture of the moving platform in the case of deflection and torsion. A single FCDEA is prepared and its electro-responsive deformation performance is tested through experiments. As a demonstration, a prototype of the biomimetic hip joint is developed. After applying voltage, the deflection angle and the torsion angle of the biomimetic hip joint are tested and the curve of voltage and rotation angle is drawn. The experimental results agree well with the theoretical predictions. This article can provide theoretical references for improving the performance of rigid-soft coupling hip joints.

Tactical intent-driven autonomous air combat behavior generation method

With the rapid development and deep application of artificial intelligence, modern air combat is incrementally evolving towards intelligent combat. Although deep reinforcement learning algorithms have contributed to dramatic advances in in air combat, they still face challenges such as poor interpretability and weak transferability of adversarial strategies. In this regard, this paper proposes a tactical intent-driven method for autonomous air combat behaviour generation. Firstly, this paper explores the mapping relationship between optimal strategies and rewards, demonstrating the detrimental effects of the combination of sparse rewards and dense rewards on policy. Built around this, the decision-making process of pilot behavior is analyzed, and a reward mapping model from intent to behavior is established. Finally, to address the problems of poor stability and slow convergence speed of deep reinforcement learning algorithms in large-scale state-action spaces, the dueling-noisy-multi-step DQN algorithm is devised, which not only improves the accuracy of value function approximation but also enhances the efficiency of space exploration and network generalization. Through experiments, the conflicts between sparse rewards and dense rewards are demonstrated. The superior performance and stability of the proposed algorithm compared to other algorithms are captured by our empirical results. More intuitively, the strategies under different intents exhibit strong interpretability and flexibility, which can provide tactical support for intelligent decision-making in air combat.

Development and rollout of a national plan on cardiovascular health. Spain's cardiovascular health strategy (ESCAV).

Cardiovascular disease (CVD) remains the leading cause of death in most countries; however, few specific political actions have been implemented to increase cardiovascular health at the national or international level. We aimed to describe the methodology used for the development and initial rollout of Spain's Cardiovascular Health Strategy of the National Health System (ESCAV, Estrategia en Salud Cardiovascular del Sistema Nacional de Salud). A multidisciplinary team comprising all relevant stakeholders developed a national strategy to reduce the burden of CVD in Spain. The team used qualitative methodologies to identify and select priorities and design strategic map models. The strategy was structured within a matrix composed of 6 cross-cutting themes (citizen empowerment; health promotion, prevention, and early CVD detection; comprehensive management of acute and chronic CVD; health care coordination; equity; and knowledge management) and 4 longitudinal lines (ischemic heart disease, heart failure, arrhythmias, and valve disease). These were framed by 3 overarching axes: continuity of care, patient safety, and leveraging of information systems. A total of 27 critical points were selected, leading to the definition of 99 strategic objectives (32 general and 67 specific) and 136 actions through iterative reviews and prioritization. Strategic maps were developed for the overall strategy and for each action line, incorporating 61 indicators to facilitate and monitor the development of the strategy. ESCAV is one of the most ambitious initiatives to change a nation's cardiovascular health. The steps taken and the methods used to develop this strategy may serve as a valuable reference for other countries seeking to implement similar initiatives. In addition, it may provide an incentive to advance efforts toward establishing a European plan for cardiovascular health.

CAUSAL HEALTH IMPACTS OF POWER PLANT EMISSION CONTROLS UNDER MODELED AND UNCERTAIN PHYSICAL PROCESS INTERFERENCE.

Causal inference with spatial environmental data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and nonlocal treatment. This is especially relevant when estimating the effect of power plant emissions controls on population health, as pollution exposure is dictated by: (i) the location of point-source emissions as well as (ii) the transport of pollutants across space via dynamic physical-chemical processes. In this work we estimate the effectiveness of air quality interventions at coal-fired power plants in reducing two adverse health outcomes in Texas in 2016: pediatric asthma ED visits and Medicare all-cause mortality. We develop methods for causal inference with interference when the underlying network structure is not known with certainty and instead must be estimated from ancillary data. Notably, uncertainty in the interference structure is propagated to the resulting causal effect estimates. We offer a Bayesian, spatial mechanistic model for the interference mapping, which we combine with a flexible nonparametric outcome model to marginalize estimates of causal effects over uncertainty in the structure of interference. our analysis finds some evidence that emissions controls at upwind power plants reduce asthma ED visits and all-cause mortality; however, accounting for uncertainty in the interference renders the results largely inconclusive.

Extended Beta models for poverty mapping. An application integrating survey and remote sensing data in Bangladesh

Extended Beta models for poverty mapping. An application integrating survey and remote sensing data in Bangladesh

XTC-DDPM: Diffusion Model for Tone Mapping and Pseudo-Color Imaging of Dual-Energy X-Ray Security Inspection Images

XTC-DDPM: Diffusion Model for Tone Mapping and Pseudo-Color Imaging of Dual-Energy X-Ray Security Inspection Images

Combining predictive soil mapping and process models to estimate future carbon sequestration potential under no-till

There is increasing interest in soil organic carbon (SOC) sequestration as a climate change mitigation strategy. There is a need to estimate the quantity of SOC sequestered historically due to no-till, and the remaining sequestration potential in Saskatchewan. To answer this question, predictive soil mapping results were linked with the Century model to predict SOC stock change over time to a depth of 20 cm considering three different future climate change scenarios. Climate scenarios included low, moderate, and high amounts of climate change and included estimated changes to monthly minimum, average, and maximum temperature, total monthly precipitation, and average monthly relative humidity at an 800 m × 800 m resolution. Historically, the modelled average SOC gain for Saskatchewan was 2.8 Mg ha−1. Future potential simulated SOC was lower over the next 30 years, with average SOC gains estimated to range from 1.4 to 1.7 Mg ha−1 by 2054 and 2.3 to 3.1 Mg ha−1 by 2100. There is also unequal spatial distribution of SOC stock gain potential, with the northern grain growing regions showing lower future potential. The predicted future gains will be at a lower rate than in the past with carbon sequestration rates dropping from 0.06 to less than 0.02 Mg ha−1 year−1. Additional management practices such as improved residue management and the introduction of crop varieties with increased below ground carbon inputs and more stable residues should be explored to offset the diminishing SOC returns from no-till.

Digital-driven in-situ monitoring for thermally-induced volumetric errors of CNC machine tools

In order to realize the in-situ monitoring of thermally-induced volumetric errors of CNC machine tools, a digital twin system is designed and developed through combined programming of C#, ANSYS, and MATLAB. The thermal error mapping models are constructed through superposing the axis motion-induced thermal errors and the spindle rotation-induced thermal deformations. The models of axis motion-induced thermal errors are established based on the orthogonal polynomial tabular modeling method, and real-timely corrected through correction models which are constructed based on the predicted and measured errors. The spindle rotation-induced thermal deformations are obtained via digital twin for thermal characteristics of the spindle system. The error distributions are generated by MATLAB through pre-creating the DLL file. Experimental results show that the prediction accuracy of digital twin-driven in-situ monitoring for thermally-induced volumetric errors is greater than 95 %, which effectively improvs the prediction accuracy of thermally-induced volumetric errors of CNC machine tools and provides a basis for thermal error compensation.

Satisfying Consumer Preferences: The Packaging Design of Guizhou Red Sour Soup

Red sour soup (RSS) is a traditional food with rich cultural connotations and nutritional value, unique to the Kaili region of Guizhou Province, China. However, the existing packaging design lacks cultural characteristics and visual appeal, which greatly limits its market potential. This study proposes a systematic research framework for RSS packaging design by integrating NLP, Kansei engineering, and semiotics. First, we mined consumers’ dual requirements by analyzing online reviews from e-commerce and tourism platforms using NLP and LDA. Second, we used Kansei engineering to construct a mapping model between consumers’ requirements and design elements. Notably, in the semantic space, we innovatively introduced the concept of a semantic network, considering the relationships between Kansei words for the first time. Finally, we proposed integrating the four dimensions of semiotics into the four stages of design, guiding the application of batik culture in packaging design. Based on this framework, we created a set of RSS packaging designs that integrate modern design concepts and traditional Miao cultural elements. The new design showed significant attractiveness in testing, with 100% of subjects preferring it, fully demonstrating the effectiveness and consumer acceptance of our approach. This study provides new methods and ideas for packaging in the food industry, which has positive significance for the modernized marketing of traditional foods.

Enhanced dynamic modeling of regenerative CO2 transcritical power cycles: Comparative analysis of Pham-corrected and conventional turbine models

Turbine modeling plays a critical role in assessing system power output and performance in CO₂ transcritical power cycle (CTPC) systems, particularly under dynamic operating conditions. Conventional turbine models often fail to provide accurate predictions when inlet parameters deviate from the design point, leading to discrepancies in system behavior. This paper introduces a novel methodology by integrating a Pham-based corrected turbine performance map into a regenerative CTPC dynamic system. This approach enhances turbine modeling accuracy compared to traditional models, such as the uncorrected turbine performance map, nozzle model, and empirical model. The proposed Pham-corrected model method improves system response stability, particularly in terms of inlet pressure and CO₂ mass flow rate under heat source temperature reductions, offering more reliable performance predictions. In contrast, the empirical model shows substantial deviations in net power output (32 kW or 4.0 %) and thermal efficiency (1.36 %), leading to longer system stabilization times. The Pham-corrected and uncorrected turbine performance map models exhibit similar responses under cooling water mass flow rate reductions but diverge in turbine inlet pressure trends compared to the nozzle and empirical models. Additionally, the uncorrected turbine performance map model shows significant deviations in net power (25.6 kW or 3.17 %) and thermal efficiency (0.5 %) under a 10 % reduction in pump speed. This study highlights the importance of correcting turbine inlet parameters and addressing prediction deviations in conventional models. The Pham-corrected turbine model improves reliability, performance prediction, and design optimization, especially under fluctuating heat sources. With its potential to enhance system stability and efficiency, this methodology offers significant prospects for future CTPC applications, including waste heat recovery and renewable energy integration.

Spectral-Spatial Deep Learning model for seaweed cultivation mapping using PlanetScope imagery in Pangkajene and Islands Regency

Spectral-Spatial Deep Learning model for seaweed cultivation mapping using PlanetScope imagery in Pangkajene and Islands Regency

Spatial Characteristics Visualization of Transfer Point Infrastructure

The government's efforts to increase public transport users in Palembang by providing public transport such as LRT, Teman Bus and angkot feeder LRT Musi Emas. The previous research shows that there are still deficiencies in public transport services. The condition of physical integration between public transport, such as no bus stop, inadequate sidewalk facilities and even in some locations they are not available, no crossing bridges, no lighting, no information on routes, schedules and several other things. Based on these conditions, this research will study physical integration between public transport modes by visualizing the spatial characteristics of transit point facilities in Palembang City. The method used for visualization is a mapping model using the GIS 10.8 application. The results of this research show that the districts with the most transfer points in Palembang are Ilir Timur I and Alang-Alang Lebar which have LRT stations, bus stops and river mode infrastructures. The results of this research can be used as a reference for improving public transport physical integration in Palembang in areas served by public transport. The visualization results of transfer point users in Palembang have shown that Ilir Barat I and Sukarame Districts have the highest number of users of transfer points infrastructure LRT station. Based on this research, it is known that the availability of transfer points infrastructure is not necessarily accompanied by a high number of public transport users in one sub-district when compared to other sub-districts. This is partly due to inadequate condition of physical integration between public transport.

Double Decomposition and Fuzzy Cognitive Graph-Based Prediction of Non-Stationary Time Series.

Deep learning models, such as recurrent neural network (RNN) models, are suitable for modeling and forecasting non-stationary time series but are not interpretable. A prediction model with interpretability and high accuracy can improve decision makers' trust in the model and provide a basis for decision making. This paper proposes a double decomposition strategy based on wavelet decomposition (WD) and empirical mode decomposition (EMD). We construct a prediction model of high-order fuzzy cognitive maps (HFCM), called the WE-HFCM model, which considers interpretability and strong reasoning ability. Specifically, we use the WD and EDM algorithms to decompose the time sequence signal and realize the depth extraction of the signal's high-frequency, low-frequency, time-domain, and frequency domain features. Then, the ridge regression algorithm is used to learn the HFCM weight vector to achieve modeling prediction. Finally, we apply the proposed WE-HFCM model to stationary and non-stationary datasets in simulation experiments. We compare the predicted results with the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models.For stationary time series, the prediction accuracy of the WE-HFCM model is about 45% higher than that of the ARIMA, about 35% higher than that of the SARIMA model, and about 16% higher than that of the LSTM model. For non-stationary time series, the prediction accuracy of the WE-HFCM model is 69% higher than that of the ARIMA and SARIMA models.

Mapping Crop Types for Beekeepers Using Sentinel-2 Satellite Image Time Series: Five Essential Crops in the Pollination Services

Driven by the widespread adoption of deep learning (DL) in crop mapping with satellite image time series (SITS), this study was motivated by the recent success of temporal attention-based approaches in crop mapping. To meet the needs of beekeepers, this study aimed to develop DL-based classification models for mapping five essential crops in pollination services in Quebec province, Canada, by using Sentinel-2 SITS. Due to the challenging task of crop mapping using SITS, this study employed three DL-based models, namely one-dimensional temporal convolutional neural networks (CNNs) (1DTempCNNs), one-dimensional spectral CNNs (1DSpecCNNs), and long short-term memory (LSTM). Accordingly, this study aimed to capture expert-free temporal and spectral features, specifically targeting temporal features using 1DTempCNN and LSTM models, and spectral features using the 1DSpecCNN model. Our findings indicated that the LSTM model (macro-averaged recall of 0.80, precision of 0.80, F1-score of 0.80, and ROC of 0.89) outperformed both 1DTempCNNs (macro-averaged recall of 0.73, precision of 0.74, F1-score of 0.73, and ROC of 0.85) and 1DSpecCNNs (macro-averaged recall of 0.78, precision of 0.77, F1-score of 0.77, and ROC of 0.88) models, underscoring its effectiveness in capturing temporal features and highlighting its suitability for crop mapping using Sentinel-2 SITS. Furthermore, applying one-dimensional convolution (Conv1D) across the spectral domain demonstrated greater potential in distinguishing land covers and crop types than applying it across the temporal domain. This study contributes to providing insights into the capabilities and limitations of various DL-based classification models for crop mapping using Sentinel-2 SITS.

Pseudorandom high‐frequency injection synchronous reluctance motor sensorless control with parameter variation consideration

AbstractThe sensorless control of synchronous reluctance motor (SynRM) in the zero and low‐speed domain using the traditional high‐frequency injection method has the problems of audible noise caused by the high‐frequency injection and high‐frequency loss caused by the change of working conditions, which limits the practical application of SynRM in the industrial field. To solve the problem of high‐frequency noise and loss, a pseudo‐random high‐frequency injection method considering parameter variation is proposed in this paper. Firstly, the signal injection form was adjusted to expand the power spectral density of the high‐frequency current, and the current energy spike was suppressed to reduce high‐frequency noise. Secondly, the current demodulation was combined with the flux map model to complete the injection voltage amplitude adjustment, so that the response current was kept constant under multiple operating conditions to reduce the high‐frequency loss. At the same time, the flux map model is applied to the observer to reduce the rotor position estimation error caused by the cross‐coupling effect. Under the condition of satisfying the dynamic and steady performance requirements of sensorless control, the high‐frequency loss and sharp noise caused by the high‐frequency injection method are effectively suppressed. Finally, experiments were carried out on a 1.5 kW SynRM drive platform to verify the feasibility and effectiveness of the sensorless control scheme in this paper.

West Iranian in historical perspective

Abstract The development of the Persian definitive object marker -rā from Old Persian rādī through Middle Persian benefactive rāy has been established as a model grammaticalization process by Bossong (1985). His explanations on other modern Iranian languages, and their interplay in the historical development, have received far less research attention. This article offers a novel and comprehensive account of the historical development of -rā in West Iranian, from Old Persian up to approximately 20 New Iranian languages. Based on the semantic map model, it demonstrates that concerning -rā, most West Iranian languages can be subdivided into three typological groups, two of which represent more or less clearly defined genetic subgroups within the continuum of West Iranian. The interplay of genetic and typological/functional factors is complex, and open questions remain. The present study aims to provide empirical data, and to raise relevant questions, for future in-depth studies.

RETRACTION: Supply Chain Operation Evaluation and Management Decision by Fuzzy Cognitive Map Model

Retraction: P. Zheng, “,” Expert Systems 41, no. (2024): . https://doi.org/10.1111/exsy.13022. The above article, published online on 15 May 2022, in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest‐edited special issue. Following publication, it has come to the attention of the journal that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors did not respond to the notice of retraction.