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Search Results (2,118)

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23 pages, 3967 KiB  
Article
Drawing a Long Shadow: Analyzing Spatial Segregation of Afghan Immigrants in Tehran
by Noureddin Farash, Rasoul Sadeghi and Hamidreza Rabiei-Dastjerdi
Soc. Sci. 2024, 13(11), 611; https://doi.org/10.3390/socsci13110611 (registering DOI) - 11 Nov 2024
Abstract
Although recent dramatic political changes in Afghanistan have brought that country to global attention, migration from Afghanistan to Iran has a long history. Nearly three quarters of Afghan immigrants in Iran are located in cities, particularly in Tehran’s metropolitan area. However, despite the [...] Read more.
Although recent dramatic political changes in Afghanistan have brought that country to global attention, migration from Afghanistan to Iran has a long history. Nearly three quarters of Afghan immigrants in Iran are located in cities, particularly in Tehran’s metropolitan area. However, despite the long-term presence of Afghan immigrants in Iran, research on patterns and drivers of spatial segregation of immigrants has been very limited. The research method involves a secondary analysis of census data. Therefore, this article utilizes 2006 Iran census tract data to examine patterns of spatial segregation of Afghan immigrants in the Tehran metropolis. The required data for two-group segregation indices, Getis–Ord statistics, and Geographically Weighted Regression, were analyzed as a map using ArcMap and Geo-Segregation Analyzer. The results reveal that the spatial segregation of Afghans is high and that most live in lower-SES census tracts. Multivariable analyses indicate that the extent of segregation can be explained by education, job class, and generation status. It can be concluded that generational transition and access to human capital have reduced various indicators of spatial segregation of Afghan immigrants in Tehran. Full article
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17 pages, 3803 KiB  
Article
Location Preferences and Changes in Pollution-Intensive Firms from the Yangtze River Economic Belt, China
by Chang Liu, Huixin Zhou, Zitong Li, Dingyang Zhou, Yingying Tian and Guanghui Jiang
Land 2024, 13(11), 1883; https://doi.org/10.3390/land13111883 - 11 Nov 2024
Abstract
This study examined the location preferences and changes in pollution-intensive firms by analyzing the spatiotemporal distribution and drivers in the Yangtze River Economic Belt, a transitional manufacturing region in China. To analyze the distribution of firms under natural growth conditions prior to the [...] Read more.
This study examined the location preferences and changes in pollution-intensive firms by analyzing the spatiotemporal distribution and drivers in the Yangtze River Economic Belt, a transitional manufacturing region in China. To analyze the distribution of firms under natural growth conditions prior to the implementation of the national “Great Protection of the Yangtze River” policy in 2016, this study utilized data on newly expanded industrial land use from 2007 to 2016. The results indicated that new pollution-intensive firms predominantly focused on water pollution, occupying over 40% of the total area annually. The new pollution-intensive firms preferred the geographic agglomeration siting strategy, mostly along the Yangtze River or in urban agglomerations, while gradually moving westward. The total area and number of new pollution-intensive firms in the Yangtze River Economic Belt showed an overall trend of “inverted U-shaped” variation during the study period, and the average size of the pollution-intensive firms gradually decreased. GeoDetector analysis revealed that geographical factors have always been significant. Local economic factors attracted new pollution-intensive firms, but later in the study period, these factors showed some inhibitory effect on the increase in pollution-intensive firms in the lower reaches. Government intervention worked less effectively but was significantly enhanced after interaction with other factors. Finally, the results suggested that local governments should build a stronger synergy between industrial land policies and environmental regulations to ensure sustainable growth and rational allocation of pollution-intensive firms. Full article
(This article belongs to the Section Land Environmental and Policy Impact Assessment)
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29 pages, 4990 KiB  
Article
Forest Fire Severity and Koala Habitat Recovery Assessment Using Pre- and Post-Burn Multitemporal Sentinel-2 Msi Data
by Derek Campbell Johnson, Sanjeev Kumar Srivastava and Alison Shapcott
Forests 2024, 15(11), 1991; https://doi.org/10.3390/f15111991 - 11 Nov 2024
Abstract
Habitat loss due to wildfire is an increasing problem internationally for threatened animal species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endangered in most of its range, and large areas of forest were burnt by widespread wildfires in [...] Read more.
Habitat loss due to wildfire is an increasing problem internationally for threatened animal species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endangered in most of its range, and large areas of forest were burnt by widespread wildfires in Australia in 2019/2020, mostly areas dominated by eucalypts, which provide koala habitats. We studied the impact of fire and three subsequent years of recovery on a property in South-East Queensland, Australia. A classified Differenced Normalised Burn Ratio (dNBR) calculated from pre- and post-burn Sentinel-2 scenes encompassing the local study area was used to assess regional impact of fire on koala-habitat forest types. The geometrically structured composite burn index (GeoCBI), a field-based assessment, was used to classify fire severity impact. To detect lower levels of forest recovery, a manual classification of the multitemporal dNBR was used, enabling the direct comparison of images between recovery years. In our regional study area, the most suitable koala habitat occupied only about 2%, and about 10% of that was burnt by wildfire. From the five koala habitat forest types studied, one upland type was burnt more severely and extensively than the others but recovered vigorously after the first year, reaching the same extent of recovery as the other forest types. The two alluvial forest types showed a negligible fire impact, likely due to their sheltered locations. In the second year, all the impacted forest types studied showed further, almost equal, recovery. In the third year of recovery, there was almost no detectable change and therefore no more notable vegetative growth. Our field data revealed that the dNBR can probably only measure the general vegetation present and not tree recovery via epicormic shooting and coppicing. Eucalypt foliage growth is a critical resource for the koala, so field verification seems necessary unless more-accurate remote sensing methods such as hyperspectral imagery can be implemented. Full article
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12 pages, 270 KiB  
Article
Knowledge, Attitude, and Practice of Healthcare Providers Towards Preventive Chemotherapy Neglected Tropical Diseases in the Forécariah Health District, Guinea, 2022
by Fatoumata Diaraye Diallo, Tamba Mina Millimouno, Hawa Manet, Armand Saloum Kamano, Emmanuel Camara, Bienvenu Salim Camara and Alexandre Delamou
Trop. Med. Infect. Dis. 2024, 9(11), 273; https://doi.org/10.3390/tropicalmed9110273 - 11 Nov 2024
Viewed by 101
Abstract
Background: Neglected tropical diseases (NTDs) are a diverse group of twenty diseases that occur in tropical and subtropical regions that particularly affect vulnerable and often marginalised populations. Five of these are classified as “preventive chemotherapy” (PC) diseases such as trachoma, onchocerciasis, geo-helminthiasis, lymphatic [...] Read more.
Background: Neglected tropical diseases (NTDs) are a diverse group of twenty diseases that occur in tropical and subtropical regions that particularly affect vulnerable and often marginalised populations. Five of these are classified as “preventive chemotherapy” (PC) diseases such as trachoma, onchocerciasis, geo-helminthiasis, lymphatic filariasis, and schistosomiasis. This study aimed to describe the knowledge, attitudes, and practices of healthcare providers in the Forecariah health district with respect to PC-NTDs in Guinea in 2022. Methods: A descriptive cross-sectional study was conducted from 7 to 22 November 2022 among healthcare providers in the health district of Forécariah in Guinea. Data on participants’ socio-demographic characteristics and knowledge of and attitudes and practices regarding PC-NTDs were collected using an electronic (KoboToolbox) semi-structured questionnaire and analysed using descriptive statistics. Results: Among the 86 healthcare providers who participated in this study, nurses (44.2%) and young adults aged between 25 and 49 years (81.4%) were mostly represented. The majority of respondents declared having already heard about onchocerciasis (70.7%) and lymphatic filariasis (60.0%) but only the minority declared having already heard about geo-helminthiasis (30.7%), schistosomiasis (21.3%), and trachoma (9.3%). Only a few respondents knew how to prevent PC-NTDs (onchocerciasis 26.7%, lymphatic filariasis 26.7%, geo-helminthiasis 29.3%, and schistosomiasis 17.3%). Many healthcare providers reported they would refer cases of onchocerciasis (50.6%), lymphatic filariasis (58.7%), and schistosomiasis (46.7%) to a management centre. Conclusions: This study highlights the varying levels of knowledge, attitudes, and practices among healthcare providers in dealing with PC-NTDs, suggesting areas for improvement in training and resource allocation. Full article
(This article belongs to the Special Issue Insights on Neglected Tropical Diseases in West Africa)
20 pages, 575 KiB  
Article
Large Language Model-Driven Structured Output: A Comprehensive Benchmark and Spatial Data Generation Framework
by Diya Li, Yue Zhao, Zhifang Wang, Calvin Jung and Zhe Zhang
ISPRS Int. J. Geo-Inf. 2024, 13(11), 405; https://doi.org/10.3390/ijgi13110405 - 10 Nov 2024
Viewed by 226
Abstract
Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a [...] Read more.
Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a benchmark for generating structured and formatted spatial outputs from LLMs with a focus on enhancing spatial information generation. We present a multi-step workflow designed to improve the accuracy and efficiency of spatial data generation. The steps include generating spatial data (e.g., GeoJSON) and implementing a novel method for indexing R-tree structures. In addition, we explore and compare a series of methods commonly used by developers and researchers to enable LLMs to produce structured outputs, including fine-tuning, prompt engineering, and retrieval-augmented generation (RAG). We propose new metrics and datasets along with a new method for evaluating the quality and consistency of these outputs. Our findings offer valuable insights into the strengths and limitations of each approach, guiding practitioners in selecting the most suitable method for their specific use cases. This work advances the field of LLM-based structured spatial data output generation and supports the seamless integration of LLMs into real-world applications. Full article
(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)
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16 pages, 8178 KiB  
Article
A New Probiotic Formulation Promotes Resolution of Inflammation in a Crohn’s Disease Mouse Model by Inducing Apoptosis in Mucosal Innate Immune Cells
by Carlo De Salvo, Abdullah Osme, Mahmoud Ghannoum, Fabio Cominelli and Luca Di Martino
Int. J. Mol. Sci. 2024, 25(22), 12066; https://doi.org/10.3390/ijms252212066 - 10 Nov 2024
Viewed by 230
Abstract
The interaction between gut-residing microorganisms plays a critical role in the pathogenesis of Crohn’s disease (CD), where microbiome dysregulation can alter immune responses, leading to unresolved local inflammation. The aim of this study is to analyze the immunomodulatory properties of a recently developed [...] Read more.
The interaction between gut-residing microorganisms plays a critical role in the pathogenesis of Crohn’s disease (CD), where microbiome dysregulation can alter immune responses, leading to unresolved local inflammation. The aim of this study is to analyze the immunomodulatory properties of a recently developed probiotic + amylase blend in the SAMP1/YitFc (SAMP) mouse model of CD-like ileitis. Four groups of SAMP mice were gavaged for 56 days with the following treatments: 1) probiotic strains + amylase (0.25 mg/100 µL PBS); 2) only probiotics; 3) only amylase; PBS-treated controls. Ilea were collected for GeoMx Digital Spatial Profiler (DSP) analysis and histological evaluation. Histology assessment for inflammation indicated a significantly reduced level of ileitis in mice administered the probiotics + amylase blend. DSP analysis showed decreased abundance of neutrophils and increased abundance of dendritic cells, regulatory T cells, and macrophages, with a significant enrichment of five intracellular pathways related to apoptosis, in probiotics + amylase-treated mice. Increased apoptosis occurrence was confirmed by (TdT)- deoxyuridine triphosphate (dUTP)-biotin nick end labeling assay. Our data demonstrate a beneficial role of the probiotic and amylase blend, highlighting an increased apoptosis of innate immunity-associated cell subsets, thus promoting the resolution of inflammation. Hence, we suggest that the developed probiotic enzyme blend may be a therapeutic tool to manage CD and therefore is a candidate formulation to be tested in clinical trials. Full article
(This article belongs to the Special Issue The Role of Microbiota in Immunity and Inflammation)
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21 pages, 7978 KiB  
Article
Combining Sentinel-2 Data and Risk Maps to Detect Trees Predisposed to and Attacked by European Spruce Bark Beetle
by Per-Ola Olsson, Pengxiang Zhao, Mitro Müller, Ali Mansourian and Jonas Ardö
Remote Sens. 2024, 16(22), 4166; https://doi.org/10.3390/rs16224166 - 8 Nov 2024
Viewed by 281
Abstract
The European spruce bark beetle is a major disturbance agent in Norway spruce forests in Europe, and with a changing climate it is predicted that damage will increase. To prevent the bark beetle population buildup, and to limit further spread during outbreaks, it [...] Read more.
The European spruce bark beetle is a major disturbance agent in Norway spruce forests in Europe, and with a changing climate it is predicted that damage will increase. To prevent the bark beetle population buildup, and to limit further spread during outbreaks, it is crucial to detect attacked trees early. In this study, we utilize Sentinel-2 data in combination with a risk map, created from geodata and forestry data, to detect trees predisposed to and attacked by the European spruce bark beetle. Random forest models were trained over two tiles (90 × 90 km) in southern Sweden for all dates with a sufficient number of cloud-free Sentinel-2 pixels during the period May–September in 2017 and 2018. The pixels were classified into attacked and healthy to study how detection accuracy changed with time after bark beetle swarming and to find which Sentinel-2 bands are more important for detecting bark beetle attacked trees. Random forest models were trained with (1) single-date data, (2) temporal features (1-year difference), (3) single-date and temporal features combined, and (4) Sentinel-2 data and a risk map combined. We also included a spatial variability metric. The results show that detection accuracy was high already before the trees were attacked in May 2018, indicating that the Sentinel-2 data detect predisposed trees and that the early signs of attack are low for trees at high risk of being attacked. For single-date models, the accuracy ranged from 63 to 79% and 84 to 94% for the two tiles. For temporal features, accuracy ranged from 65 to 81% and 81 to 92%. When the single-date and temporal features were combined, the accuracy ranged from 70 to 84% and 90 to 96% for the two tiles, and with the risk map included, the accuracy ranged from 83 to 91% and 92 to 97%, showing that remote sensing data and geodata can be combined to increase detection accuracy. The differences in accuracy between the two tiles indicate that local differences can influence accuracy, suggesting that geographically weighted methods should be applied. For the single-date models, the SWIR, red-edge, and blue bands were generally more important, and the SWIR bands were more important after the attack, suggesting that they are most suitable for detecting the early signs of a bark beetle attack. For the temporal features, the SWIR and blue bands were more important, and for the variability metric, the green band was generally more important. Full article
(This article belongs to the Section Forest Remote Sensing)
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13 pages, 3622 KiB  
Article
RF Exposure Assessment by Drone-Based Technology
by Jesús M. Paniagua-Sánchez, Christopher Marabel-Calderón, Francisco J. García-Cobos, Antonio Gordillo-Guerrero, Montaña Rufo-Pérez and Antonio Jiménez-Barco
Appl. Sci. 2024, 14(22), 10203; https://doi.org/10.3390/app142210203 - 7 Nov 2024
Viewed by 263
Abstract
There is growing international interest in assessing population exposure to radiofrequency electromagnetic fields, especially those generated by mobile-phone base stations. The work presented here is an experimental study in which we assess exposure to radiofrequency electromagnetic fields in a university environment, where there [...] Read more.
There is growing international interest in assessing population exposure to radiofrequency electromagnetic fields, especially those generated by mobile-phone base stations. The work presented here is an experimental study in which we assess exposure to radiofrequency electromagnetic fields in a university environment, where there is a site with mobile-phone antennas and where a large number of people live on a daily basis. The data were collected with a personal exposure meter in two samplings, one walking at ground level and the other using an aerial vehicle at a height higher than the buildings. The geo-referenced electric-field data were subjected to a process in which a theoretical model was adjusted to the experimental variograms, and heat maps were obtained using kriging interpolation. The research carried out is of great relevance, since it provides detailed measurements of the electromagnetic radiation levels both at ground level and at significant heights, using innovative methodologies such as the use of drones. Furthermore, the results obtained allow for contextualizing the exposures in relation to international safety limits, highlighting the importance of rigorous monitoring in everyday environments. Full article
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36 pages, 46209 KiB  
Article
Subsidence and Uplift in Active and Closed Lignite Mines: Impacts of Energy Transition and Climate Change
by Artur Guzy
Energies 2024, 17(22), 5540; https://doi.org/10.3390/en17225540 - 6 Nov 2024
Viewed by 352
Abstract
This study examines the combined effects of decommissioning lignite mining operations and long-term climate trends on groundwater systems and land surface movements in the Konin region of Poland, which is characterised by extensive open-pit lignite extraction. The findings reveal subsidence rates ranging from [...] Read more.
This study examines the combined effects of decommissioning lignite mining operations and long-term climate trends on groundwater systems and land surface movements in the Konin region of Poland, which is characterised by extensive open-pit lignite extraction. The findings reveal subsidence rates ranging from −26 to 14 mm per year within mining zones, while land uplift of a few millimetres per year occurred in closed mining areas between 2015 and 2022. Groundwater levels in shallow Quaternary and deeper Paleogene–Neogene aquifers have declined significantly, with drops of up to 26 m observed near active mining, particularly between 2009 and 2019. A smaller groundwater decline of around a few metres was observed outside areas influenced by mining. Meteorological data show an average annual temperature of 8.9 °C from 1991 to 2023, with a clear warming trend of 0.0050 °C per year since 2009. Although precipitation patterns show a slight increase from 512 mm to 520 mm, a shift towards drier conditions has emerged since 2009, characterised by more frequent dry spells. These climatic trends, combined with mining activities, highlight the need for adaptive groundwater management strategies. Future research should focus on enhanced monitoring of groundwater recovery and sustainable practices in post-mining landscapes. Full article
(This article belongs to the Section B: Energy and Environment)
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14 pages, 4324 KiB  
Article
Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region
by Anatoly Zeyliger, Konstantin Muzalevskiy, Olga Ermolaeva, Anastasia Grecheneva, Ekaterina Zinchenko and Jasmina Gerts
Sustainability 2024, 16(21), 9606; https://doi.org/10.3390/su16219606 - 4 Nov 2024
Viewed by 446
Abstract
In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to [...] Read more.
In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture. Full article
(This article belongs to the Special Issue Biotechnology on Sustainable Agriculture)
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25 pages, 18080 KiB  
Article
Comprehensive Analysis and Verification of the Prognostic Significance of Cuproptosis-Related Genes in Colon Adenocarcinoma
by Yixiao Gu, Chengze Li, Yinan Yan, Jingmei Ming, Yuanhua Li, Xiang Chao and Tieshan Wang
Int. J. Mol. Sci. 2024, 25(21), 11830; https://doi.org/10.3390/ijms252111830 - 4 Nov 2024
Viewed by 388
Abstract
Colon adenocarcinoma (COAD) is a frequently occurring and lethal cancer. Cuproptosis is an emerging type of cell death, and the underlying pathways involved in this process in COAD remain poorly understood. Transcriptomic and clinical data for COAD patients were collected from The Cancer [...] Read more.
Colon adenocarcinoma (COAD) is a frequently occurring and lethal cancer. Cuproptosis is an emerging type of cell death, and the underlying pathways involved in this process in COAD remain poorly understood. Transcriptomic and clinical data for COAD patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We investigated alterations in DNA and chromatin of cuproptosis-related genes (CRGs) in COAD. In order to identify predictive differentially expressed genes (DEGs) and various molecular subtypes, we used consensus cluster analysis. Through univariate, multivariate, and Lasso Cox regression analyses, four CRGs were identified. A risk prognostic model for cuproptosis characteristics was constructed based on four CRGs. This study also examined the association between the risk score and the tumor microenvironment (TME), the immune landscape, and drug sensitivity. We distinguished two unique molecular subtypes using consensus clustering analysis. We discovered that the clinical characteristics, prognosis, and TME cell infiltration characteristics of patients with multilayer CRG subtypes were all connected. The internal and external evaluations of the predicted accuracy of the prognostic model built using data derived from a cuproptosis risk score were completed at the same time. A nomogram and a clinical pathological analysis make it more useful in the field of medicine. A significant rise in immunosuppressive cells was observed in the high cuproptosis risk score group, with a correlation identified between the cuproptosis risk score and immune cell infiltration. Despite generally poor prognoses, the patients with a high cuproptosis risk but low tumor mutation burden (TMB), cancer stem cell (CSC) index, or microsatellite instability (MSI) may still benefit from immunotherapy. Furthermore, the cuproptosis risk score positively correlated with immune checkpoint gene expression. Analyzing the potential sensitivity to medications could aid in the development of clinical chemotherapy regimens and decision-making. CRGs are the subject of our in-depth study, which exposed an array of regulatory mechanisms impacting TME. In addition, we performed additional data mining into clinical features, prognosis effectiveness, and possible treatment medications. COAD’s molecular pathways will be better understood, leading to more precise treatment options. Full article
(This article belongs to the Special Issue Molecular Advances in Cancer and Cell Metabolism)
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16 pages, 1197 KiB  
Article
Fiscal Adjustment Heterogeneity in Inflationary Conditions in the Eurozone: A Non-Stationary Heterogeneous Panel Approach
by Olgica Glavaški, Emilija Beker Pucar, Marina Beljić and Jovica Pejčić
J. Risk Financial Manag. 2024, 17(11), 493; https://doi.org/10.3390/jrfm17110493 - 3 Nov 2024
Viewed by 310
Abstract
In recent years, fiscal policy in the Eurozone (EZ) has faced challenges posed by the strong and rapid increase in inflation as a consequence of the COVID-19 pandemic and other geo-political crises. Due to the fear of “fiscal inflation” present during episodes of [...] Read more.
In recent years, fiscal policy in the Eurozone (EZ) has faced challenges posed by the strong and rapid increase in inflation as a consequence of the COVID-19 pandemic and other geo-political crises. Due to the fear of “fiscal inflation” present during episodes of fiscal stimulus during the pandemic crisis, this paper assesses the relationship between discretionary fiscal policy and inflation in developed EZ economies, taking into consideration the rise in energy prices as a control variable. This study considers the econometric framework of heterogeneous, non-stationary panels (Pooled Mean Group (PMG) and Common Correlated Effects Mean Group (CCEMG) estimators). Using quarterly panel data for the period 2015q1–2024q1, the results show that, in the long run, the effects of fiscal policy on inflation are insignificant. However, covering only the pandemic and other geo-political crises (2020q1–2024q1), research shows a significant negative long-run relationship between fiscal expenditure and inflation and heterogeneous short-run fiscal adjustments due to the lack of a fiscal union in the EU economies. Hence, accompanied by monetary policy, the discretionary response of fiscal policy to inflationary shock was oriented in the same direction—the reduction in inflationary pressures during a geo-political crisis. Fiscal policy mitigated inflationary pressures in these recent crises, while in the long run, it did not affect nominal variables, indicating that there is no evidence of fiscal inflation in the sample of EZ economies during a stabilization period or under crisis conditions. Full article
(This article belongs to the Special Issue Emerging Issues in Economics, Finance and Business—2nd Edition)
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21 pages, 4546 KiB  
Article
Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate
by Essam Ghamry, Dedalo Marchetti and Mohamed Metwaly
Atmosphere 2024, 15(11), 1318; https://doi.org/10.3390/atmos15111318 - 2 Nov 2024
Viewed by 763
Abstract
In this study, we analysed lithospheric, atmospheric, and top-side ionospheric magnetic field data six months before the three earthquake doublets occurred in the last ten years around the Arabian tectonic plate. They occurred in 2014, close to Dehloran (Iran), in 2018, offshore Kilmia [...] Read more.
In this study, we analysed lithospheric, atmospheric, and top-side ionospheric magnetic field data six months before the three earthquake doublets occurred in the last ten years around the Arabian tectonic plate. They occurred in 2014, close to Dehloran (Iran), in 2018, offshore Kilmia (Yemen) and in 2022, close to Bandar-e Lengeh (Iran). For all the cases, we considered the equivalent event in terms of total released energy and mean epicentral coordinates. The lithosphere was investigated by calculating the cumulative Benioff strain with the USGS earthquake catalogue. Several atmospheric parameters (aerosol, SO2, CO, surface air temperature, surface latent heat flux humidity, and dimethyl sulphide) have been monitored using the homogeneous data from the MERRA-2 climatological archive. We used the three-satellite Swarm constellation for magnetic data, analysing the residuals after removing a geomagnetic model. The analysis of the three geo-layers depicted an interesting chain of lithosphere, atmosphere, and ionosphere anomalies, suggesting a geophysical coupling before the Dehloran (Iran) 2014 earthquake. In addition, we identified interesting seismic accelerations that preceded the last 20 days, the Kilmia (Yemen) 2018 and Bandar-e Lengeh (Iran) 2022 earthquake doublets. Other possible interactions between the geolayers have been observed, and this underlines the importance of a multiparametric approach to properly understand a geophysical complex topic as the preparation phase of an earthquake. Full article
(This article belongs to the Special Issue Ionospheric Sounding for Identification of Pre-seismic Activity)
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16 pages, 6435 KiB  
Article
Tree Species Classification by Multi-Season Collected UAV Imagery in a Mixed Cool-Temperate Mountain Forest
by Ram Avtar, Xinyu Chen, Jinjin Fu, Saleh Alsulamy, Hitesh Supe, Yunus Ali Pulpadan, Albertus Stephanus Louw and Nakaji Tatsuro
Remote Sens. 2024, 16(21), 4060; https://doi.org/10.3390/rs16214060 - 31 Oct 2024
Viewed by 390
Abstract
Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of [...] Read more.
Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of UAV aerial imagery offer an alternative to tedious ground surveys. However, the timing (season) of the aerial surveys, input variables considered for classification, and the model type affect the classification accuracy. This work evaluates how the seasons and input variables considered in the species classification model affect the accuracy of species classification in a temperate broadleaf and mixed forest. Among the considered models, a Random Forest (RF) classifier demonstrated the highest performance, attaining an overall accuracy of 83.98% and a kappa coefficient of 0.80. Simultaneously using input data from summer, winter, autumn, and spring seasons improved tree species classification accuracy by 14–18% from classifications made using only single-season input data. Models that included vegetation indices, image texture, and elevation data obtained the highest accuracy. These results strengthen the case for using multi-seasonal data for species classification in temperate broadleaf and mixed forests since seasonal differences in the characteristics of species (e.g., leaf color, canopy structure) improve the ability to discern species. Full article
(This article belongs to the Section Forest Remote Sensing)
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18 pages, 5982 KiB  
Article
Multi-Source Monitoring and Numerical Simulation Deformation on Highway Steep Slopes Under Rainfall Effects
by Peijun Li, Qing Li, Qingshan Feng, Zhendong Huang, Xun Gan, Haibin Ding and Changjie Xu
Buildings 2024, 14(11), 3473; https://doi.org/10.3390/buildings14113473 - 30 Oct 2024
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Abstract
Rainfall is one of the most important factors affecting slope stability. This study employed multi-source monitoring devices to observe the slope displacements in real time under rainfall infiltration and performed numerical simulations to investigate the effects of different rainfall conditions and anti-slip pile [...] Read more.
Rainfall is one of the most important factors affecting slope stability. This study employed multi-source monitoring devices to observe the slope displacements in real time under rainfall infiltration and performed numerical simulations to investigate the effects of different rainfall conditions and anti-slip pile configurations on slope stability. Specifically, multi-source monitoring operations were conducted on the high and steep slopes along the Yunmao Expressway. Real-time data on slope deformation, rainfall, and displacement at the tops of anti-slip piles were collected and analyzed, and numerical simulations were conducted using Geo Studio finite-element software. The findings indicated that abrupt deformation of slopes occurs once a threshold rainfall amount is surpassed and sustained over a specific duration. Slope displacement decreased with increasing slope depth above the potential slip fracture surface, with a more rapid reduction in deformation rates observed in slopes reinforced with anti-slip piles. For equivalent rainfall amounts, short-duration, intense rainfalls led to a rapid decrease in the slope safety factor, which also recovered rapidly once the rainfall ceased, in contrast to long-duration, mild rainfalls. The presence and location of anti-slip piles significantly influenced slope stability; therefore, project implementation should carefully consider factors such as cost and duration for optimal decision making. Full article
(This article belongs to the Special Issue New Reinforcement Technologies Applied in Slope and Foundation)
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