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26 pages, 5852 KiB

Open AccessArticle

A Model of Building Changes to Support Comparative Studies and Open Discussions on Densification

by Bénédicte Bucher, Juste Raimbault, Mouhamadou Ndim, Ana-Maria Raimond, Julien Perret, Sebastian Dembski and Mathias Jehling

ISPRS Int. J. Geo-Inf. 2025, 14(4), 155; https://doi.org/10.3390/ijgi14040155 (registering DOI) - 2 Apr 2025

Abstract

Densification is a widely used concept, but there is a lack of terminology and tools to facilitate discussions among data scientists, policy makers and citizens. This paper proposes a model of building changes based on building surveys undertaken in past decades to connect [...] Read more.

Densification is a widely used concept, but there is a lack of terminology and tools to facilitate discussions among data scientists, policy makers and citizens. This paper proposes a model of building changes based on building surveys undertaken in past decades to connect discussions about densification with shared evidence. A specific challenge is to process buildings in city regions and areas in a replicable way across different building data sources. Another challenge is to manage the quality of the representation, i.e., how well the maps represent changes to buildings and how well they can support discussions of densification. Building data and real buildings are different things that sometimes change in an independent way. Addressing these factors requires different forms of expertise, i.e., expertise about the realities depicted in the areas studied, about local data sources, and about advanced matching tools and state-of-the-art densification concepts. We present a collaborative dashboard through which to engage corresponding experts in the production of building change maps and the clarification of related concepts. Full article

21 pages, 5524 KiB

Open AccessArticle

Spatiotemporal Dynamics and Driving Factors of Urban Green Space in Texas (2001–2021): A Multi-Source Geospatial Analysis

by Tengfei Ma, Huakai Ye, Yujing Lai, Haoying Han, Yangguang Song and Yile Chen

Buildings 2025, 15(7), 1166; https://doi.org/10.3390/buildings15071166 (registering DOI) - 2 Apr 2025

Abstract

This study investigates the changes in urban green space coverage across 254 counties of varying types in Texas from 2001 to 2021, aiming to explore the spatial patterns of green space transformation and its socioeconomic driving factors. By analyzing Landsat remote sensing data [...] Read more.

This study investigates the changes in urban green space coverage across 254 counties of varying types in Texas from 2001 to 2021, aiming to explore the spatial patterns of green space transformation and its socioeconomic driving factors. By analyzing Landsat remote sensing data and building type datasets, combined with land use transition matrices, GIS spatial statistics tools, and regression analysis of population and GDP data, this study comprehensively examines the green space change patterns of different urban types. The results indicated significant differences in green space changes across different types of cities: (1) Urban areas with higher populations and rankings, as well as their surrounding regions, show a more pronounced trend of green space converting into built-up urban areas, particularly the expansion of medium and low-density areas. (2) In contrast, green space changes in smaller cities and rural areas occur at a slower pace. Further analysis reveals that the transformation of green spaces is primarily driven by residential land development, with about 39% of green space in high-density urban areas and over 65% in medium and low-density areas being replaced by residential land. (3) The regression analysis results indicate that population growth and GDP growth are the main driving factors for green space changes, explaining up to 86% and 84% of the green space changes, respectively. These findings provide important theoretical support and practical guidelines for urban green space conservation, planning, and sustainable development policies. Full article

(This article belongs to the Special Issue Research towards the Green and Sustainable Buildings and Cities)

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19 pages, 4836 KiB

Open AccessArticle

Glycemic Variability and Its Association with Traditional Glycemic Control Biomarkers in Patients with Type 1 Diabetes: A Cross-Sectional, Multicenter Study

by Sandra Lazar, Delia-Viola Reurean-Pintilei, Ioana Ionita, Vlad-Florian Avram, Andreea Herascu and Bogdan Timar

J. Clin. Med. 2025, 14(7), 2434; https://doi.org/10.3390/jcm14072434 (registering DOI) - 2 Apr 2025

Abstract

Background/Objectives: Glycemic variability (GV) is a novel concept in the assessment of the quality of glycemic control in patients with diabetes, with its importance emphasized in patients with type 1 diabetes. Its adoption in clinical practice emerged with the increased availability of continuous [...] Read more.

Background/Objectives: Glycemic variability (GV) is a novel concept in the assessment of the quality of glycemic control in patients with diabetes, with its importance emphasized in patients with type 1 diabetes. Its adoption in clinical practice emerged with the increased availability of continuous glycemic monitoring systems. The aim of this study is to evaluate the GV in patients with type 1 diabetes mellitus (T1DM) and to assess its associations with other parameters used to evaluate the glycemic control. Methods: GV indexes and classical glycemic control markers were analyzed for 147 adult patients with T1DM in a multicentric cross-sectional study. Results: Stable glycemia was associated with a higher time in range (TIR) (78% vs. 63%; p < 0.001) and a lower HbA1c (6.8% vs. 7.1%; p = 0.006). The coefficient of variation (CV) was reversely correlated with TIR (Spearman’s r = −0.513; p < 0.001) and positively correlated with hemoglobin A1c (HbA1c) (Spearman’s r = 0.349; p < 0.001), while TIR was reversely correlated with HbA1c (Spearman’s r = −0.637; p < 0.001). The composite GV and metabolic outcome was achieved by 28.6% of the patients. Conclusions: Stable glycemia was associated with a lower HbA1c, average and SD of blood glucose, and a higher TIR. A TIR higher than 70% was associated with a lower HbA1c, and SD and average blood glucose. Only 28.6% of the patients with T1DM achieved the composite GV and metabolic outcome, despite 53.7% of them achieving the HbA1c target, emphasizing thus the role of GV in the assessment of the glycemic control. Full article

(This article belongs to the Section Endocrinology & Metabolism)

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23 pages, 13995 KiB

Open AccessArticle

The Effect of Dopaminergic Therapy in Parkinson’s Disease: A Graph Theory Analysis

by Karthik Siva, Palanisamy Ponnusamy, Vishal Chavda and Nicola Montemurro

Brain Sci. 2025, 15(4), 370; https://doi.org/10.3390/brainsci15040370 (registering DOI) - 2 Apr 2025

Abstract

Background: Dopaminergic therapy (DT) is the gold standard pharmacological treatment for Parkinson’s disease (PD). Currently, understanding the neuromodulation effect in the brain of PD after DT is important for doctors to optimize doses and identify the adverse effects of medication. The objective [...] Read more.

Background: Dopaminergic therapy (DT) is the gold standard pharmacological treatment for Parkinson’s disease (PD). Currently, understanding the neuromodulation effect in the brain of PD after DT is important for doctors to optimize doses and identify the adverse effects of medication. The objective of this study is to investigate the brain connectivity alteration with and without DT in PD using resting-state EEG. Methods: Graph theory (GT) is an efficient technique for analyzing brain connectivity alteration in healthy and patient groups. We applied GT analyses on three groups, namely healthy control (HC), Parkinson with medication OFF (PD-OFF), and Parkinson with medication ON (PD-ON). Results: Using the clustering coefficient (CC), participation coefficient (PC), and small-worldness (SW) properties of GT, we showed that PD-ON patients’ brain connectivity normalized towards healthy group brain connectivity due to DT. This normalization effect appeared in the brain connectivity of all EEG frequency bands, such as theta, alpha, beta-1, beta-2, and gamma except the delta band. We also analyzed region-wise brain connectivity between 10 regions of interest (ROIs) (right and left frontal, right and left temporal, right and left parietal, right and left occipital, upper and lower midline regions) at the scalp level and compared across conditions. During PD-ON, we observed a significant decrease in alpha band connectivity between right frontal and left parietal (p-value 0.0432) and right frontal and left occipital (p-value 0.008) as well as right frontal and right temporal (p-value 0.041). Conclusion: These findings offer new insights into how dopaminergic therapy modulates brain connectivity across frequency bands and highlight the continuous elevation of both the segregation and small-worldness of the delta band even after medication as a potential biomarker for adverse effects due to medication. Additionally, reduced frontal alpha band connectivity is associated with cognitive impairment and levodopa-induced dyskinesia, highlighting its potential role in Parkinson’s disease progression. This study underscores the need for personalized treatments that address both motor and non-motor symptoms in PD patients. Full article

(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)

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32 pages, 2384 KiB

Open AccessArticle

A Fuzzy Decision-Making Approach to the Health Assessment and Optimization of Architecture-Dominated Outdoor Spaces in High-Density Urban Environments

by Yanyan Huang, Yi Yang, Kangwei Tu and Tiancheng Yang

Buildings 2025, 15(7), 1165; https://doi.org/10.3390/buildings15071165 (registering DOI) - 2 Apr 2025

Abstract

As urbanization rapidly increases, the design of outdoor spaces in high-density urban environments has become crucial for promoting public health. This study investigates the health impacts of architecture-dominated outdoor spaces, particularly focusing on small decentralized spaces around buildings. The research aims to develop [...] Read more.

As urbanization rapidly increases, the design of outdoor spaces in high-density urban environments has become crucial for promoting public health. This study investigates the health impacts of architecture-dominated outdoor spaces, particularly focusing on small decentralized spaces around buildings. The research aims to develop a comprehensive health evaluation framework that quantifies the influence of various design factors such as comfort, safety, diversity, and ecology. Using a fuzzy Delphi method (FDM) and the Analytic Hierarchy Process (AHP), 31 key design indicators are identified and weighted based on expert opinions. A multi-level fuzzy comprehensive evaluation model is then applied to assess the outdoor space of Wuhan Citizen’s Home. The results show that the space performs well in promoting health, particularly in comfort, safety, and ecological design. However, there are areas for improvement, such as enhancing cultural representation and increasing the frequency of health-promoting activities. This study concludes that the proposed evaluation framework can provide valuable insights for optimizing the design of outdoor public spaces, supporting healthier urban environments, and improving residents’ physical and mental well-being, offering valuable reference for future urban space design. Full article

(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

22 pages, 5383 KiB

Open AccessArticle

Landslide Segmentation in High-Resolution Remote Sensing Images: The Van–UPerAttnSeg Framework with Multi-Scale Feature Enhancement

by Chang Li, Quan Zou, Guoqing Li and Wenyang Yu

Remote Sens. 2025, 17(7), 1265; https://doi.org/10.3390/rs17071265 (registering DOI) - 2 Apr 2025

Abstract

Among geological disasters, landslides are a common and extremely destructive disaster. Their rapid identification is crucial for disaster analysis and response. However, traditional methods of landslide recognition mainly rely on visual interpretation and manual recognition of remote sensing images, which are time-consuming and [...] Read more.

Among geological disasters, landslides are a common and extremely destructive disaster. Their rapid identification is crucial for disaster analysis and response. However, traditional methods of landslide recognition mainly rely on visual interpretation and manual recognition of remote sensing images, which are time-consuming and susceptible to subjective factors, thereby limiting the accuracy and efficiency of recognition. To overcome these limitations, for high-resolution remote sensing images, this method first uses online equalization sampling and enhancement strategy to sample high-resolution remote sensing images to ensure data balance and diversity. Then, it adopts an encoder–decoder structure, where the encoder is a visual attention network (Van) that focuses on extracting discriminative features of different scales from landslide images. The decoder consists of a pyramid pooling module (PPM) and feature pyramid network (FPN), combined with a convolutional block attention module (CBAM) module. Through this structure, the model can effectively integrate features of different scales, achieving precise positioning and recognition of landslide areas. In addition, this study introduces a sliding window algorithm based on Gaussian fusion as a post-processing method, which optimizes the prediction of landslide edge in high-resolution remote sensing images and ensures the context reasoning ability of the model. In the validation set, this method achieved a significant landslide recognition effect with a Dice score of 84.75%, demonstrating high accuracy and efficiency. This result demonstrates the importance and effectiveness of the research method in improving the accuracy and efficiency of landslide recognition, providing strong technical support for analysis and response to geological disasters. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

6 pages, 205 KiB

Open AccessEditorial

Featured Papers in Inorganic Materials 2024

by Roberto Nisticò and Torben R. Jensen

Inorganics 2025, 13(4), 110; https://doi.org/10.3390/inorganics13040110 (registering DOI) - 2 Apr 2025

Abstract

After the impressive success of the Special Issue “10th Anniversary of Inorganics: Inorganic Materials”, published in 2023 [...] Full article

(This article belongs to the Special Issue Featured Papers in Inorganic Materials 2024)

20 pages, 1636 KiB

Open AccessArticle

Spatial Divergence of Forestry Green Total Factor Productivity in China Under the Constraint of Carbon Emissions

by Ansheng Huang, Zexi Xue, Ya Liu, Ruoxuan Lin and Yan Huang

Forests 2025, 16(4), 625; https://doi.org/10.3390/f16040625 (registering DOI) - 2 Apr 2025

Abstract

In the dual-carbon context, forestry green total factor productivity (FGTFP) serves as a key indicator of the quality and efficiency of forestry development. Based on New Economic Geography Theory, this study explores FGTFP and its spatial divergence under the constraint of carbon emissions. [...] Read more.

In the dual-carbon context, forestry green total factor productivity (FGTFP) serves as a key indicator of the quality and efficiency of forestry development. Based on New Economic Geography Theory, this study explores FGTFP and its spatial divergence under the constraint of carbon emissions. We analyzed panel data from 30 Chinese provinces between 2004 and 2022. The Directional Distance Function (DDF) model was applied to measure FGTFP, and the Global Malmquist–Luenberger (GML) model was applied to measure FGTFP’s decomposition index. The Dagum Gini coefficient was employed to analyze the degree of spatial divergence of FGTFP and identify its sources. Using Porter’s model and Sustainable Development Theory, the geo-detector was applied to examine the driving factors of FGTFP and its decomposition index. The study’s findings indicate that (1) FGTFP in China generally trended upward from 2004 to 2022, with significant heterogeneity observed at both interprovincial and regional levels; (2) Technological Improvement (TI) was the primary driver of FGTFP growth in the eastern, northeastern and central regions, while Efficiency Change (EC) was the key driver in the western region; (3) FGTFP exhibited distinct spatial divergence patterns in China, with hypervariable density as the primary source, followed by interregional differentiation, and regional differentiation contributing the least; and (4) green energy transition factors consistently showed a significant “two-factor enhancement effect” and a “non-linear enhancement trend”, while external environmental factors exhibited strong interaction effects but demonstrated a “non-linear weakening trend”. Therefore, it is essential to promote the green transformation of production modes, facilitate structural adjustments and upgrades in the forestry industry, enhance regional collaboration, and advance the “dual enhancement” of technological progress and efficiency. Additionally, leveraging regional comparative advantages will promote coordinated development. Full article

(This article belongs to the Section Forest Ecology and Management)

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22 pages, 2595 KiB

Open AccessArticle

New Insights into the Adsorption Mechanism of Vanadium Through Quaternary Ammonium Salt-Functionalized SiO₂: Synergistic Experiments Utilizing Energy Decomposition Analysis

by Qiang Fu, Jianhua Tian, Jinjun Yang, Jie Wang, Meitong Li, Gangzhen Jiao, Yuhong Xie, Wenjiao Yuan and Cuihong Wang

Molecules 2025, 30(7), 1593; https://doi.org/10.3390/molecules30071593 (registering DOI) - 2 Apr 2025

Abstract

Introducing organic functional groups to adsorbent surfaces enhances vanadium adsorption, an effective strategy for vanadium enrichment. In a quest for a profounder comprehension of the above adsorption mechanism, this study synthesized five types of quaternary ammonium salt-functionalized silica (QAS-SiO₂) and investigated [...] Read more.

Introducing organic functional groups to adsorbent surfaces enhances vanadium adsorption, an effective strategy for vanadium enrichment. In a quest for a profounder comprehension of the above adsorption mechanism, this study synthesized five types of quaternary ammonium salt-functionalized silica (QAS-SiO₂) and investigated the influence of functional groups, pH values, contact time, and temperature on vanadium (V) adsorption. The results indicated that the optimal QAS-SiO₂ (SiO₂@DMOA) achieved a vanadium adsorption rate of 99.40% and a maximum adsorption capacity of 39.16 mg g⁻¹. SiO₂@DMOA exhibited favorable adsorption selectivity for V over chromium (Cr), with a maximum separation factor (β_V/Cr) of 135.42 at pH 3.3. SiO₂@DMOA maintained efficient adsorption performance over five repeated cycles. A fusion of adsorption trials with energy decomposition analysis (EDA) tentatively unveiled that both chemical bonds and non-bonding interactions contributed to the interaction energy between organic functional groups and vanadium. Among them, chemical bonds accounted for 80.26%, while non-bonding interactions accounted for 19.74%. Based on EDA analysis, the interaction characteristics of different structural quaternary ammonium salts with vanadium in adsorption and extraction processes are discussed. Additionally, steric hindrance, the charge of the vanadium species, polarizability, and solvation effects, all played significant roles in the adsorption process. Full article

26 pages, 12666 KiB

Open AccessArticle

Gaslike Social Motility: Optimization Algorithm with Application in Image Thresholding segmentation

by Oscar D. Sanchez, Luz M. Reyes, Arturo Valdivia-González, Alma Y. Alanis and Eduardo Rangel-Heras

Algorithms 2025, 18(4), 199; https://doi.org/10.3390/a18040199 (registering DOI) - 2 Apr 2025

Abstract

This work introduces a novel and practical metaheuristic algorithm, the Gaslike Social Motility (GSM) algorithm, designed for optimization and image thresholding segmentation. Inspired by a deterministic model that replicates social behaviors using gaslike particles, GSM is characterized by its simplicity, minimal parameter requirements, [...] Read more.

This work introduces a novel and practical metaheuristic algorithm, the Gaslike Social Motility (GSM) algorithm, designed for optimization and image thresholding segmentation. Inspired by a deterministic model that replicates social behaviors using gaslike particles, GSM is characterized by its simplicity, minimal parameter requirements, and emergent social dynamics. These dynamics include: (1) attraction between similar particles, (2) formation of stable particle clusters, (3) division of groups upon reaching a critical size, (4) inter-group interactions that influence particle distribution during the search process, and (5) internal state changes in particles driven by local interactions. The model’s versatility, including cross-group monitoring and adaptability to environmental interactions, makes it a powerful tool for exploring diverse scenarios. GSM is rigorously evaluated against established and recent metaheuristic algorithms, including Particle Swarm Optimization (PSO), Differential Evolution (DE), Bat Algorithm (BA), Artificial Bee Colony (ABC), Artificial Hummingbird Algorithm (AHA), AHA with Aquila Optimization (AHA-AO), Colliding Bodies Optimization (CBO), Enhanced CBO (ECBO), and Social Network Search (SNS). Performance is assessed using 22 benchmark functions, demonstrating GSM’s competitiveness. Additionally, GSM’s efficiency in image thresholding segmentation is highlighted, as it achieves high-quality results with fewer iterations and particles compared to other methods. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

19 pages, 8881 KiB

Open AccessArticle

Global Warming Drives Shifts in the Suitable Habitats of Subalpine Shrublands in the Hengduan Mountains Region in China

by Huayong Zhang, Yunyan Yu, Xiande Ji, Zhongyu Wang and Zhao Liu

Forests 2025, 16(4), 624; https://doi.org/10.3390/f16040624 (registering DOI) - 2 Apr 2025

Abstract

Subalpine shrubland is an important vegetation type in the Hengduan Mountains region of China, and its distribution has been substantially influenced by global warming. In this research, four subalpine shrub communities in the Hengduan Mountains were selected: Rhododendron heliolepis Franch. scrub, Rhododendron flavidum [...] Read more.

Subalpine shrubland is an important vegetation type in the Hengduan Mountains region of China, and its distribution has been substantially influenced by global warming. In this research, four subalpine shrub communities in the Hengduan Mountains were selected: Rhododendron heliolepis Franch. scrub, Rhododendron flavidum Franch. scrub, Quercus monimotricha (Hand.-Mazz.) Hand.-Mazz. scrub, and Pinus yunnanensis var. pygmaea (Hsueh ex C. Y. Cheng, W. C. Cheng & L. K. Fu) Hsueh scrub. A MaxEnt model was used to assess the suitable habitats and their primary drivers of four subalpine shrublands in China under different climate scenarios. Our results indicate the following: (1) The suitable habitat areas of the four subalpine shrublands exhibit a predominant distribution within the Hengduan Mountains region, with small populations in the Himalayas and Wumeng Mountain. Temperature and precipitation are identified as the primary drivers influencing the suitable habitat areas of the four subalpine shrublands, and the temperature factor is more influential than the precipitation factor. Furthermore, the contribution rate of slope to Quercus monimotricha scrub is 19.2%, which cannot be disregarded. (2) Under future climate scenarios, the total suitable habitats of the four subalpine shrublands show an expanding trend. However, the highly suitable areas of three shrublands (Rhododendron flavidum scrub, Quercus monimotricha scrub, and Pinus yunnanensis var. pygmaea scrub) show a contracting trend under the high-carbon-emission scenario (SSP585). (3) Driven by global warming, the suitable habitat areas of Rhododendron heliolepis scrub, Rhododendron flavidum scrub, and Pinus yunnanensis var. pygmaea scrub shift toward higher elevations in the northwest, while the distribution of Quercus monimotricha scrub varies under different carbon emission scenarios, with a much smaller shift range than the other three scrubs. Our study contributes valuable insights into the spatiotemporal dynamics of subalpine shrublands in China under climate change, providing scientific guidance for biodiversity conservation and ecosystem restoration. Full article

(This article belongs to the Section Forest Meteorology and Climate Change)

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34 pages, 11375 KiB

Open AccessReview

Advanced Financial Fraud Malware Detection Method in the Android Environment

by Jaeho Shin, Daehyun Kim and Kyungho Lee

Appl. Sci. 2025, 15(7), 3905; https://doi.org/10.3390/app15073905 (registering DOI) - 2 Apr 2025

Abstract

The open-source structure and ease of development in the Android platform are exploited by attackers to develop malicious programs, greatly increasing malicious Android apps aimed at committing financial fraud. This study proposes a machine learning (ML) model based on static analysis to detect [...] Read more.

The open-source structure and ease of development in the Android platform are exploited by attackers to develop malicious programs, greatly increasing malicious Android apps aimed at committing financial fraud. This study proposes a machine learning (ML) model based on static analysis to detect malware. We validated the significance of private datasets collected from Bank A, comprising 183,938,730 and 11,986 samples of benign and malicious apps, respectively. Undersampling was performed to adjust the proportion of benign applications in the training data because the data on benign and malicious apps were unbalanced. Moreover, 92 datasets were compiled through daily training to evaluate the proposed approach, with benign app data updated over 70 days (D-70 to D-1) and malware app data cumulatively aggregated to address the imbalance. Five ML algorithms were used to evaluate the proposed approach, and the optimal hyperparameter values for each algorithm were obtained using a grid search method. We then evaluated the models using common evaluation metrics, such as accuracy, precision, recall, F1-Score, etc. The LightGBM model was selected for its superior performance, achieving high accuracy and effectiveness. The optimal decision threshold for determining whether an application was malicious was 0.5. Following re-evaluation, the LightGBM model obtained accuracy and F1-Score values of 99.99% and 97.04%, respectively, highlighting the potential of using the proposed model for real-world financial fraud detection. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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17 pages, 6662 KiB

Open AccessArticle

Mechanical Force Triggers Macrophage Pyroptosis and Sterile Inflammation by Disrupting Cellular Energy Metabolism

by Hao Tan, Guoyin Yang, Ye Zhu, Xinyi He, Lan Yang, Yun Hu and Leilei Zheng

Int. J. Mol. Sci. 2025, 26(7), 3321; https://doi.org/10.3390/ijms26073321 (registering DOI) - 2 Apr 2025

Abstract

Mechanical force regulates tissue remodeling during orthodontic tooth movement (OTM) by inducing macrophage-mediated sterile inflammatory responses. Pyroptosis, as an inflammatory form of programmed cell death, triggers a robust inflammatory cascade by activating the inflammasome. Although recent reports have demonstrated that pyroptosis can be [...] Read more.

Mechanical force regulates tissue remodeling during orthodontic tooth movement (OTM) by inducing macrophage-mediated sterile inflammatory responses. Pyroptosis, as an inflammatory form of programmed cell death, triggers a robust inflammatory cascade by activating the inflammasome. Although recent reports have demonstrated that pyroptosis can be activated by mechanical force, it remains unclear whether and how orthodontic force induces macrophage pyroptosis and sterile inflammation. In this study, by establishing a rat OTM model and a force-loaded macrophage model, we found that force induces Caspase1-dependent pyroptosis in macrophages and activates sterile inflammation both in vivo and in vitro. Mechanistically, we uncovered that mechanical force disrupts macrophage energy metabolism, characterized by an imbalance between lactate dehydrogenase A (LDHA) and pyruvate dehydrogenase (PDH), as well as mitochondrial dysfunction. Notably, inhibiting pyruvate dehydrogenase kinase 1 (PDK1) effectively restored this metabolic balance, thereby alleviating pyroptosis and sterile inflammation in force-stimulated macrophages. Overall, this study elucidates that force induces macrophage pyroptosis and sterile inflammation, and further identifies imbalances in the LDHA/PDH ratio and mitochondrial dysfunction as pivotal mechanistic features. These insights offer novel perspectives and potential therapeutic targets for the precise and effective modulation of OTM. Full article

(This article belongs to the Section Molecular Immunology)

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28 pages, 1442 KiB

Open AccessArticle

The Impact of Energy Intensity, Renewable Energy, and Financial Development on Green Growth in OECD Countries: Fresh Evidence Under Environmental Policy Stringency

by Tugba Nur, Emre E. Topaloglu, Sureyya Yilmaz-Ozekenci and Erol Koycu

Energies 2025, 18(7), 1790; https://doi.org/10.3390/en18071790 (registering DOI) - 2 Apr 2025

Abstract

This study examines the impact of financial development, renewable energy, energy intensity, and stringent environmental policies on green growth in twenty-three Organization for Economic Cooperation and Development countries from 2000 to 2023. Additionally, it examines how stringent environmental policies moderate the link between [...] Read more.

This study examines the impact of financial development, renewable energy, energy intensity, and stringent environmental policies on green growth in twenty-three Organization for Economic Cooperation and Development countries from 2000 to 2023. Additionally, it examines how stringent environmental policies moderate the link between financial development and green growth. Economic complexity, trade openness, and green technology variables are also included in the model as control variables. The index is constructed using economic growth, education, health, CO₂ emissions, net forest, and mineral components for green growth, the main variable explained in the research. The Fully Modified Ordinary Least Squares method is applied to estimate elasticity coefficients in the study. The findings show that financial development and energy intensity have a negative impact on green growth, whereas strict environmental policies and renewable energy support green growth. Moreover, the interaction between financial development and stringent environmental policies promotes green growth. At the same time, the control variables of trade openness and economic complexity have a negative impact on green growth, while green technology makes a positive contribution. Furthermore, financial development and energy intensity have the most significant quantitative impact on green growth, while trade openness and stringent environmental policies have the least impact. In line with these findings, environmentally friendly financial instruments and green investments should be supported instead of directing financial resources only to industry-intensive sectors in Organization for Economic Cooperation and Development countries. In this context, implementing energy efficiency policies and increasing incentives for renewable energy are of great importance. Full article

(This article belongs to the Special Issue Economic and Financial Transformations in the Global Energy Transition: Market Mechanisms, Systemic Risks, and Adaptive Policy Frameworks)

13 pages, 2345 KiB

Open AccessArticle

Effect of Al₂O₃ Particle Addition on Fluidized Bed Thermochemical Heat Storage Performance of Limestone: From Instability Mitigation to Efficiency Enhancement

by Hongmei Yin, Yang Liu, Liguo Yang, Yingjie Li, Xiaoyi Zhu, Lei Zhang, Yu Ruan, Ming Ma and Xiaoxu Fan

Energies 2025, 18(7), 1791; https://doi.org/10.3390/en18071791 (registering DOI) - 2 Apr 2025

Abstract

This study elucidates the mechanism of fluidization instability during limestone carbonation under a 100% CO₂ atmosphere and determines the influence of Al₂O₃ fluidization aids (dosage and particle size) on exothermic performance. The experiments demonstrate that rapid CO₂ absorption [...] Read more.

This study elucidates the mechanism of fluidization instability during limestone carbonation under a 100% CO₂ atmosphere and determines the influence of Al₂O₃ fluidization aids (dosage and particle size) on exothermic performance. The experiments demonstrate that rapid CO₂ absorption in the emulsion phase, coupled with insufficient gas replenishment from the bubble phase, disrupts the balance between drag force and buoyancy, leading to localized defluidization. This instability impedes gas exchange between the bubble and emulsion phases, resulting in bubble coalescence and channeling across the bed. The fluidization instability reduces the maximum exothermic temperature and causes significant temperature heterogeneity in the bed. With repeated thermal cycles (20 cycles), the CO₂ absorption capacity of limestone diminishes (the effective conversion rate drops to 0.25), and the instability disappears. The addition of 5wt.% Al₂O₃ (particle size: 0.05–0.075 mm) stabilizes the fluidization state during carbonation, significantly homogenizing the bed temperature distribution, with maximum and average temperature differentials reduced by 63% and 89%, respectively, compared to pure limestone systems. Full article

(This article belongs to the Section D: Energy Storage and Application)

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19 pages, 556 KiB

Open AccessArticle

Bayesian FDOA Positioning with Correlated Measurement Noise

by Wenjun Zhang, Xi Li, Yi Liu, Le Yang and Fucheng Guo

Remote Sens. 2025, 17(7), 1266; https://doi.org/10.3390/rs17071266 (registering DOI) - 2 Apr 2025

Abstract

In this paper, the problem of source localization using only frequency difference of arrival (FDOA) measurements is considered. A new FDOA-only localization technique is developed to determine the position of a narrow-band source. In this scenario, time difference of arrival (TDOA) measurements are [...] Read more.

In this paper, the problem of source localization using only frequency difference of arrival (FDOA) measurements is considered. A new FDOA-only localization technique is developed to determine the position of a narrow-band source. In this scenario, time difference of arrival (TDOA) measurements are not normally useful because they may have large errors due to the received signal having a small bandwidth. Conventional localization algorithms such as the two-stage weighted least squares (TSWLS) method, which jointly exploits TDOA and FDOA measurements for positioning, are thus no longer applicable since they will suffer from the thresholding effect and yield meaningless localization results. FDOA-only localization is non-trivial, mainly due to the high nonlinearity inherent in FDOA equations. Even with two FDOA measurements being available, FDOA-only localization still requires finding the roots of a high-order polynomial. For practical scenarios with more sensors, a divide-and-conquer (DAC) approach may be applied, but the positioning solution is suboptimal due to ignoring the correlation between FDOA measurements. To address these challenges, in this work, we propose a Bayesian approach for FDOA-only source positioning. The developed method, referred to as the Gaussian division method (GDM), first converts one FDOA measurement into a Gaussian mixture model (GMM) that specifies the prior distribution of the source position. Next, the GDM assumes uncorrelated FDOA measurements and fuses the remaining FDOAs sequentially by invoking nonlinear filtering techniques to obtain an initial positioning result. The GDM refines the solution by taking into account and compensating for the information loss caused by ignoring that the FDOAs are in fact correlated. Extensive simulations demonstrate that the proposed algorithm provides improved performance over existing methods and that it can attain the Cramér–Rao lower bound (CRLB) accuracy under moderate noise levels. Full article

24 pages, 1800 KiB

Open AccessReview

Lentinula edodes as a Source of Bioactive Compounds with Therapeutical Potential in Intestinal Inflammation and Colorectal Cancer

by Mikołaj Bugajewski, Norbert Angerhoefer, Leszek Pączek and Beata Kaleta

Int. J. Mol. Sci. 2025, 26(7), 3320; https://doi.org/10.3390/ijms26073320 (registering DOI) - 2 Apr 2025

Abstract

Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a rising global health issue. Chronic intestinal inflammation is an important risk factor for colorectal cancer (CRC). Despite significant progress in IBD and CRC treatment, numerous patients remain resistant to [...] Read more.

Inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), is a rising global health issue. Chronic intestinal inflammation is an important risk factor for colorectal cancer (CRC). Despite significant progress in IBD and CRC treatment, numerous patients remain resistant to standard pharmacotherapy or experience severe side effects that prevent them from continuing treatment. There is evidence suggesting that bioactive substances in Lentinula edodes have immunomodulatory and anticancer properties. This fungus is currently classified as a functional food, considering its beneficial effects on human health and special nutritional value. Studies conducted in vitro and in animal models demonstrated that L. edodes bioactive compounds, in particular glucans, have anti-inflammatory and antioxidant effects, induce apoptosis of cancer cells, reduce tumor angiogenesis, restore gut microbiome heterogeneity and improve gut barrier dysfunction. Moreover, clinical trials confirmed that these compounds combined with standard chemotherapy have a significant effect in improving the prognosis of CRC patients. In addition, L. edodes glucans increase microbial diversity and enhance interferon (IFN)-γ production by immune cells. Future studies must be focused on understanding the pathways and mechanisms associated with the observed effects. Moreover, both randomized trials and long-term follow-up studies are needed to confirm their effectiveness in the treatment of IBD and CRC. Full article

(This article belongs to the Special Issue The Effects of Dietary Supplements, Functional Foods and Nutraceuticals in the Immune System)

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21 pages, 8363 KiB

Open AccessArticle

A New Species of Tachymenoides (Serpentes: Dipsadidae: Tachymenini) from Peru with Comments on the Taxonomic Status of Galvarinus tarmensis (Walker, 1945)

by Edgar Lehr, Mikael Lundberg, Juan Carlos Cusi, Jack W. Sites, Jr., Claudia Torres and César Aguilar-Puntriano

Taxonomy 2025, 5(2), 18; https://doi.org/10.3390/taxonomy5020018 (registering DOI) - 2 Apr 2025

Abstract

We describe a new species of snake of the genus Tachymenoides using molecular and morphological evidence. The description is based on 21 specimens (4 females, 17 males) obtained in the regions of Pasco, Junín, and Puno between 2190 and 3050 m elevation. A [...] Read more.

We describe a new species of snake of the genus Tachymenoides using molecular and morphological evidence. The description is based on 21 specimens (4 females, 17 males) obtained in the regions of Pasco, Junín, and Puno between 2190 and 3050 m elevation. A maximum likelihood phylogenetic tree based on two mitochondrial (12S and cyt-b) genes and one nuclear (c-mos) gene shows that the new species is the sister taxon of T. affinis and distinct from Galvarinus tarmensis, which we transfer back to Tachymenis. The new species has smooth dorsal scales without apical pits usually in 19/17/15 series, 1 preocular, 2 postoculars, 1 loreal undivided nasal scale, 8 supralabials (4th and 5th in contact with the eye), 9 infralabials, 1–2+2–3 temporals, 139–157 ventrals, 52–67 subcaudals, and a divided cloacal scale. The longest specimen, a male, had a total length of 559 mm. Two females contained six and five eggs with small embryos. In life, the dorsum and flanks are olive brown to pale grayish brown with scattered black and cream flecks and no longitudinal stripes. Ventral coloration is highly variable, nearly uniformly black, mottled gray and dark-gray, mottled pale gray and tan, or pale grayish tan. Usually, three irregularly shaped, narrow, longitudinal ventral stripes are present. The iris is brown with a distinct yellowish-tan ringlet. Full article

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14 pages, 264 KiB

Open AccessArticle

Exploring the Determinants of Treatment Completion Among Youth Who Received Medication-Assisted Treatment in the United States

by Esther A. Adeniran, Megan Quinn, Ying Liu, Billy Brooks and Robert P. Pack

Healthcare 2025, 13(7), 798; https://doi.org/10.3390/healthcare13070798 (registering DOI) - 2 Apr 2025

Abstract

Background: An effective treatment for Opioid Use Disorder is Medication-Assisted Treatment (MAT). However, in the United States (US), this is still being underutilized by youth. Research indicates the need to develop strategies to reduce treatment barriers among these youth. Thus, we explored the [...] Read more.

Background: An effective treatment for Opioid Use Disorder is Medication-Assisted Treatment (MAT). However, in the United States (US), this is still being underutilized by youth. Research indicates the need to develop strategies to reduce treatment barriers among these youth. Thus, we explored the rates of treatment completion and dropout among youth receiving MAT in US substance use treatment facilities and examined factors associated with treatment completion and dropout. Methods: This study used the 2019 Treatment Episode Data Set—Discharges. Our analysis was restricted to youth (12–24 years) who primarily used heroin at admission. Log-binomial regression was used to examine various determinants of treatment completion and dropout, guided by Andersen’s Behavioral Model. Results: The final sample size was 4917. Among youth reporting heroin use with receipt of MAT, those showing a higher likelihood of treatment completion than dropout were males [ARR: 1.23; 95% CI: 1.088–1.381; p = 0.0008], had 1–7 times [ARR: 1.33; 95% CI: 1.115–1.584; p = 0.0015] and 8–30 times self-help group participation [ARR: 1.50; 95% CI: 1.246–1.803; p < 0.0001], had co-occurring mental and substance use disorders [ARR: 1.28; 95% CI: 1.133–1.448, p < 0.0001], were admitted to detoxification [ARR: 2.80; 95% CI: 2.408–3.255; p < 0.0001] and residential/rehabilitation settings [ARR: 2.05; 95% CI: 1.749–2.400; p < 0.0001], and were located in the Midwest/West [ARR: 1.18; 95% CI: 1.030–1.358; p = 0.0173]. Conversely, other races (excluding Whites and Blacks/African Americans) [ARR: 0.75; 95% CI: 0.609–0.916; p = 0.0051], those who used MAT [ARR: 0.81; 95% CI: 0.694–0.946; p = 0.0077], and youth in the South [ARR: 0.45; 95% CI: 0.369–0.549; p < 0.0001] were less likely to report treatment completion than dropout. Conclusions: The majority of youth receiving MAT dropped out of treatment. We identified various factors that should be prioritized to address youth underutilization of MAT in the US. Full article

(This article belongs to the Special Issue Adolescent Substance Use: Prevention and Intervention Strategies)

25 pages, 2077 KiB

Open AccessReview

Sustainable Transition of the Global Semiconductor Industry: Challenges, Strategies, and Future Directions

by Yilong Yin and Yi Yang

Sustainability 2025, 17(7), 3160; https://doi.org/10.3390/su17073160 (registering DOI) - 2 Apr 2025

Abstract

The semiconductor industry is essential to information technology and the ongoing artificial intelligence transformation but also poses significant environmental challenges, including greenhouse gas emissions, air pollution, solid waste, and high water and energy consumption. This review identifies key emission sources in semiconductor manufacturing, [...] Read more.

The semiconductor industry is essential to information technology and the ongoing artificial intelligence transformation but also poses significant environmental challenges, including greenhouse gas emissions, air pollution, solid waste, and high water and energy consumption. This review identifies key emission sources in semiconductor manufacturing, focusing on the release of fluorinated gases from chemical-intensive processes and the sector’s substantial energy demands. We evaluate the effectiveness and limitations of current mitigation strategies, such as process optimization, clean energy adoption, and material substitution. We also examine supply chain interventions, including green procurement, logistics optimization, and intelligent management systems. While technological innovation is crucial for the sustainable transition of the global semiconductor industry, the high cost of upgrading to greener production processes remains a major obstacle. Despite progress in clean energy integration and material alternatives, significant challenges persist in reducing emissions across the entire value chain. This review underscores an urgent need for collaborative, integrated approaches to drive the sustainable transition of the semiconductor sector and its upstream supply chain. Full article

(This article belongs to the Special Issue Pollution Reduction and Green Low-Carbon Transformation of Industrial System)

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27 pages, 12281 KiB

Open AccessArticle

Research on Sensorless Technology of a Magnetic Suspension Flywheel Battery Based on a Genetic BP Neural Network

by Weiyu Zhang and Fei Guo

Actuators 2025, 14(4), 174; https://doi.org/10.3390/act14040174 (registering DOI) - 2 Apr 2025

Abstract

The research object of this paper is a new type of multi-functional, air-gap-type, vehicle-mounted magnetic suspension flywheel battery. It is a new energy storage technology with a long working life, high energy conversion efficiency, multiple charging and discharging times, low carbon and environmental [...] Read more.

The research object of this paper is a new type of multi-functional, air-gap-type, vehicle-mounted magnetic suspension flywheel battery. It is a new energy storage technology with a long working life, high energy conversion efficiency, multiple charging and discharging times, low carbon and environmental protection. However, when the vehicle-mounted flywheel battery is operating, it will inevitably be disturbed by road conditions, resulting in loose sensors and feedback errors, thereby reducing the control accuracy and reliability of the system. To solve these problems, a sensorless control system came into being. It samples the current of the magnetic bearing coil through the hardware circuit and converts it into displacement for real-time control, eliminating the risk of sensor failure. However, the control accuracy of the traditional sensorless system is relatively low. Therefore, this paper adopts a BP (backpropagation) neural network PID controller based on genetic algorithm optimization on the basis of the sensorless control system. Through the joint simulation of the dynamic simulation software ADAMS/VIEW2018 and MATLAB2022b, the optimal PID control parameter database for complex road conditions is established. Through sensorless technology, the current of the flywheel battery is converted into the position error for extensive training so that the genetic BP neural network PID controller can accurately identify the current complex road conditions according to the position error, so as to provide the optimal PID control parameters corresponding to the road conditions to carry out accurate real-time stability control of the flywheel rotor. The experimental results show that the method can effectively reduce feedback errors, improve the control accuracy, and output optimal control parameters in real time under complex road conditions, which significantly improves the reliability and control performance of the vehicle flywheel battery system. Full article

(This article belongs to the Special Issue Advanced Technologies on the Control Method of Electromagnetic Actuator—Second Edition)

10 pages, 246 KiB

Open AccessArticle

Primary Investigation on the Synergistic Effects of Methyl Bromide and 1 °C Cold Treatment for Two-Spotted Spider Mite (Tetranychus urticae) and the Citrus Mealybug (Planococcus citri)

by Jin-Sung Yoo, Jae-Ho Ban, Ji-Eun Choi, Bong-Su Kim and Jun-Ran Kim

Insects 2025, 16(4), 377; https://doi.org/10.3390/insects16040377 (registering DOI) - 2 Apr 2025

Abstract

Since the use of methyl bromide has been prohibited globally because of environmental concerns, several alternative fumigants have been newly developed and applied to fresh products. However, single treatment with a methyl bromide alternative fumigant cannot completely replace methyl bromide treatment for some [...] Read more.

Since the use of methyl bromide has been prohibited globally because of environmental concerns, several alternative fumigants have been newly developed and applied to fresh products. However, single treatment with a methyl bromide alternative fumigant cannot completely replace methyl bromide treatment for some products because of issues associated with long treatment times and phytotoxicity. In this study, we compared the mortality of two agricultural pests, Tetranuchus urticae and Planococcus citri, after single treatment with methyl bromide and combined application of methyl bromide and cold treatment to confirm the synergistic effects of chemical and physical treatments. The combined application of methyl bromide and cold treatment was effective against the nymph and adult stages of T. urticae, but no synergism was observed at the egg stage. For P. citri, the required dosage of methyl bromide decreased when methyl bromide treatment was followed by low temperature, possibly because of the susceptibility of P. citri to cold treatment. These results indicate that the synergism of fumigants with cold treatment can differ by pest species and growth stage, and further studies on other pests are needed to reduce methyl bromide usage. Full article

(This article belongs to the Section Insect Pest and Vector Management)

21 pages, 1663 KiB

Open AccessArticle

Investigating the Impact of Sowing Date on Wheat Leaf Morphology Through Image Analysis

by Junfan Chen, Jianliang Wang, Jiacheng Wang, Zhian Wang, Lihan Zhao, Yaohua Yan, Jiayue Li, Hanzeyu Xu, Chengming Sun and Tao Liu

Agriculture 2025, 15(7), 770; https://doi.org/10.3390/agriculture15070770 (registering DOI) - 2 Apr 2025

Abstract

The morphology of wheat leaves is a key indicator of crop stand quality and photosynthetic capacity, with sowing date being a critical factor influencing leaf morphology. To investigate the effects of sowing time on wheat growth, development, and leaf phenotypes, this study utilized [...] Read more.

The morphology of wheat leaves is a key indicator of crop stand quality and photosynthetic capacity, with sowing date being a critical factor influencing leaf morphology. To investigate the effects of sowing time on wheat growth, development, and leaf phenotypes, this study utilized image analysis technology to systematically extract key phenotypic traits of winter wheat leaves, including effective leaf area, leaf color, and leaf shape. The results demonstrated that delayed sowing significantly affected the morphology and color characteristics of winter wheat leaves. Specifically, leaf length and width exhibited a quadratic decreasing trend, resulting in an average reduction in leaf area of over 59%. Additionally, the greenness index (EXG) decreased by 25.84%, while the red pigment index (EXR) increased by 21.69%. Significant differences in leaf color changes were observed among the varieties. This study provides reliable data for determining the optimal sowing period for winter wheat and offers valuable guidance for optimizing field management strategies to enhance crop yield and quality. Full article

(This article belongs to the Special Issue AI-Powered UAVs and Imaging Systems for Precision Wheat and Rice Management)

33 pages, 11763 KiB

Open AccessArticle

An Estimation Model of Emissions from Burning Areas Based on the Tier Method

by Barbara Dobosz, Kamil Roman and Emilia Grzegorzewska

Remote Sens. 2025, 17(7), 1264; https://doi.org/10.3390/rs17071264 (registering DOI) - 2 Apr 2025

Abstract

The emissions of particulates from burning agricultural fields threaten the environment and human health, contributing to air pollution and increasing the risk of respiratory and cardiovascular diseases. An analysis of total suspended particulate (TSP), PM2.5, and PM10 emissions from crop residue burning is [...] Read more.

The emissions of particulates from burning agricultural fields threaten the environment and human health, contributing to air pollution and increasing the risk of respiratory and cardiovascular diseases. An analysis of total suspended particulate (TSP), PM2.5, and PM10 emissions from crop residue burning is presented in this study. A primary goal is to improve emission estimation accuracy by integrating satellite imagery from modes of Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometers (VIIRS) with traditional data. Particulate emissions were estimated using Tier 1 and Tier 2 methodologies outlined in the EEA/EMEP Emission Inventory Guidebook based on thermal anomaly data from satellite observations. According to the findings, burning wheat, maize, barley, and rice residue accounts for most emissions, with significant variations identified in India, China, and the United States. The variations highlight the need for a location-specific approach to emission management. Particulate emissions cause adverse environmental and health impacts, which can be minimized by targeting mitigation strategies at key emission hotspots. The research provides important insights to inform policymakers and support developing strategies to reduce fine particulate agricultural emissions. Full article

(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications (Second Edition))

19 pages, 555 KiB

Open AccessArticle

Acoustic Source Localization Based on the Two-Level Data Aggregation Technology in a Wireless Sensor Network

by Yuwu Feng, Guohua Hu and Lei Hong

Sensors 2025, 25(7), 2247; https://doi.org/10.3390/s25072247 (registering DOI) - 2 Apr 2025

Abstract

The inherent energy constraints of sensor nodes render energy efficiency optimization a critical challenge in wireless sensor network deployments. This study presents an innovative acoustic source localization framework incorporating a two-level data aggregation technology, specifically designed to minimize energy expenditure while prolonging network [...] Read more.

The inherent energy constraints of sensor nodes render energy efficiency optimization a critical challenge in wireless sensor network deployments. This study presents an innovative acoustic source localization framework incorporating a two-level data aggregation technology, specifically designed to minimize energy expenditure while prolonging network lifetime. A mixed noise model is proposed to describe the characteristics of abnormal noise in real environments. Subsequently, the novel two-level data aggregation technology is proposed. The first level is implemented at individual sensors, where a large number of similar measurements may be collected. The second level data aggregation technology is performed at the cluster head nodes to eliminate the data redundancy between different sensor nodes. After the novel two-level data aggregation, most of the redundant data are eliminated and a significant amount of energy is saved. Then, a nonlinear iterative weighted least squares algorithm is applied to complete the final acoustic source location estimation based on the real remaining sensor measurements. Finally, through extensive simulation experiments, it was verified that the two-level data aggregation technology reduced energy consumption by at least 51% and 43%, respectively, and that the RMSE is less than 0.96. Full article

(This article belongs to the Special Issue Sensor Fusion Applications for Navigation and Indoor Positioning)

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