Spatial Statistical Analysis Research Articles

Cortical structural degeneration and functional network connectivity changes in patients with subcortical vascular cognitive impairment.

To explore the structural basis of functional network connectivity (FNC) changes and early cortical degenerative patterns in subcortical vascular cognitive impairment (SVCI). We prospectively included SVCI cases and healthy controls (HCs). FNC alterations were evaluated using group-independent component analysis of resting-state functional MRI data. Cortical microstructural and macrostructural alterations were assessed using gray matter-based spatial statistics analysis with neurite orientation dispersion and density imaging and cortical thickness analysis with FreeSurfer software on T1-weighted images, respectively. Spearman correlation analyses were performed to assess relationships between FNC alterations and cortical microstructural/macrostructural alterations and between FNC, cortical thickness, or neurite density index (NDI)/orientation dispersion index (ODI) alterations and cognitive performance. Forty-six SVCI patients and 73 HCs were recruited. FNC analysis showed lower network connectivity between the visual network (VN) and sensorimotor network (SMN) in SVCI, positively correlated with information processing speed (p=0.008) and negatively with summary SVD score (p = 0.037). Cortical microstructural analyses exhibited a lower NDI, mainly in the VN and default mode network (DMN) areas (PFWE < 0.05, cluster > 100 voxels), and lower ODI, mainly in the SMN and DMN areas (PFWE < 0.05, cluster > 100 voxels) in SVCI, both of which were related to cognitive function (p < 0.05). However, cortical thickness did not differ between groups. Lower NDI in the lateral occipital cortex was linked to lower VN-SMN connectivity in SVCI (p = 0.002). Cortical microstructural alterations may serve as the basis for FNC changes in SVCI.

Revealing the rural multifunctionality declining and its causes in depopulated regions of Northeast China: a case study of Heilongjiang Province

As the main form of global rural decline, the impact of rural population loss on the overall level and trend of rural multifunction is still unclear. Thus, it is crucial to reveal the characteristics and influencing factors of rural multifunction changes in depopulation areas which is important to actively deal with depopulation and promote sustainable development in rural areas. Based on an evaluation of rural multifunctionality from 2000 to 2020, the spatial statistical analysis method and a geographical model were used to reveal the declining characteristics of rural multifunction in the depopulation area, as well as the effects of the influencing factors in the whole region and under various population size areas. The results show that demographic changes had a significant impact on rural multifunction development. Compared to areas with population growth, the weakening of rural multifunctionality was more likely to occur in counties with depopulation. The negative impact of population shrinkage on rural multifunction development was shown in our results as an inverted “U” shape. In the process of demographic shrinkage, the dominant factors affecting rural multifunctionality in the whole region tended to be diversified, but the population urbanization rate was always the main factor. Among regions with different population sizes, natural factors played important roles in the development of rural multifunction, and depopulation enhanced the impact of economic factors. The interaction effect of natural, social, and economic factors was stronger than that of a single factor in rural multifunction. In the process of depopulation, the interaction effect was more and more polarized. The interaction effect of economic and natural factors enhanced, but which was the most stable in mid-population-size areas. Thus, to alleviate rural decline and realize rural revitalization, the trend of demographic change should be given attention to keep a moderate population size, as well as we should actively deal with the impact of population shrinkage on rural multifunction according to different stages of population shrinkage.

Geospatial Assessment of Trophic Status From a Dam Under Significant Agricultural Drainage at the Mid-Anatolia, Türkiye

The population of Anatolia is continuously growing and developing and it is under the deep influence of global warming. It is increasingly evident that more fresh water will be needed for drinking, irrigation and domestic use. This study aims to assess the water quality and trophic status of a dam (Bayramhacılı dam lake) under excessive agricultural irrigation threat using spatial and multivariate statistical analysis. In the study, the periodic changes in the eutrophic state of the dam lake were determined using global index categories, Carlson Trophic Status Index (CTSI = 66.7), Burns Trophic Level Index (BTLI = 6.4), and Shu Trophic State Index (STSI = 65.6). The two periodical averages of total phosphorus (TP), total nitrogen (TN), biological oxygen demand (BOD), (chemical oxygen demand (COD) and chlorophyll a (Chl_a) concentrations, which serve as an indicator of anthropogenic nutrient input, were determined to be 0.23, 31.28, 1.83, 8.99 and 48.1 µg/L, respectively. Trophic index distribution maps demonstrate that the dam’s surface water displays considerable alterations, particularly during the dry season. This evidence supports the implementation of a local management model that addresses the issue of eutrophication. It is therefore imperative that on-site measures are taken without delay.

Exploring the vitality of Tianjin’s downtown based on the Light GBM-SHAP model

In the age of stock planning, urban vitality is a key indication of a city’s health and vitality. Using the central city of Tianjin as an example, the study uses multi-source data, such as Weibo check-ins, points of interest, etc., to quantify urban vitality. The Light GBM-SHAP model is chosen to measure the non-linear effects of each indicator on urban vitality in four dimensions: crowd vitality, economic vitality, facility vitality, and environmental vitality. The study also applies spatial visualization and spatial statistical analysis to analyze the vitality of the central city of Tianjin in terms of time and space scales. The findings indicate that: (1) There is clear temporal and geographical variation in the vitality distribution in Tianjin’s core urban region. Over time, spring, particularly April, is marked by a surge of vitality brought on by the tourist season and holiday effects; there is a double-peak time in the morning and evening, and the nighttime vitality is particularly strong; and, in terms of space, urban vitality tends to decline from the Heping District outward. (2) Public facility density, living facility density, and building density are the three indicators that most strongly influence urban vitality; each indication has a negligible impact on the temporal dimension of urban vitality. (3) The indicators in Tianjin’s core urban region have a substantial impact on urban vitality. Their threshold effect and non-linear influence are evident, and managing the indicators within a suitable range may effectively promote urban vitality. The study’s findings might serve as a foundation for Tianjin’s core city’s urban planning and design.

A multisite validation of brain white matter pathways of resilience to chronic back pain.

Chronic back pain (CBP) is a global health concern with significant societal and economic burden. While various predictors of back pain chronicity have been proposed, including demographic and psychosocial factors, neuroimaging studies have pointed to brain characteristics as predictors of CBP. However, large-scale, multisite validation of these predictors is currently lacking. In two independent longitudinal studies, we examined white matter diffusion imaging data and pain characteristics in patients with subacute back pain (SBP) over 6- and 12-month periods. Diffusion data from individuals with CBP and healthy controls (HC) were analyzed for comparison. Whole-brain tract-based spatial statistics analyses revealed that a cluster in the right superior longitudinal fasciculus (SLF) tract had larger fractional anisotropy (FA) values in patients who recovered (SBPr) compared to those with persistent pain (SBPp), and predicted changes in pain severity. The SLF FA values accurately classified patients at baseline and follow-up in a third publicly available dataset (Area under the Receiver Operating Curve ~0.70). Notably, patients who recovered had FA values larger than those of HC suggesting a potential role of SLF integrity in resilience to CBP. Structural connectivity-based models also classified SBPp and SBPr patients from the three data sets (validation accuracy 67%). Our results validate the right SLF as a robust predictor of CBP development, with potential for clinical translation. Cognitive and behavioral processes dependent on the right SLF, such as proprioception and visuospatial attention, should be analyzed in subacute stages as they could prove important for back pain chronicity.

A multisite validation of brain white matter pathways of resilience to chronic back pain

Chronic back pain (CBP) is a global health concern with significant societal and economic burden. While various predictors of back pain chronicity have been proposed, including demographic and psychosocial factors, neuroimaging studies have pointed to brain characteristics as predictors of CBP. However, large-scale, multisite validation of these predictors is currently lacking. In two independent longitudinal studies, we examined white matter diffusion imaging data and pain characteristics in patients with subacute back pain (SBP) over 6- and 12-month periods. Diffusion data from individuals with CBP and healthy controls (HC) were analyzed for comparison. Whole-brain tract-based spatial statistics analyses revealed that a cluster in the right superior longitudinal fasciculus (SLF) tract had larger fractional anisotropy (FA) values in patients who recovered (SBPr) compared to those with persistent pain (SBPp), and predicted changes in pain severity. The SLF FA values accurately classified patients at baseline and follow-up in a third publicly available dataset (Area under the Receiver Operating Curve ~0.70). Notably, patients who recovered had FA values larger than those of HC suggesting a potential role of SLF integrity in resilience to CBP. Structural connectivity-based models also classified SBPp and SBPr patients from the three data sets (validation accuracy 67%). Our results validate the right SLF as a robust predictor of CBP development, with potential for clinical translation. Cognitive and behavioral processes dependent on the right SLF, such as proprioception and visuospatial attention, should be analyzed in subacute stages as they could prove important for back pain chronicity.

Evaluation of Meteorological Drought Using SPI and SPEI on Wheat Yield in Southwestern Iran

The objective of this research is to determine the meteorological drought index and the effective rainfall model that exhibit the highest correlation with the yield of rainfed wheat in the Karkheh watershed. Additionally, using spatial statistics analysis, the trend and status of drought in various parts of the watershed will be identified. This will allow for the determination of suitable areas for rainfed wheat cultivation in the near future. In this research, meteorological drought monitoring was conducted using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) drought indices at 34 synoptic stations of the Karkheh watershed in southwestern Iran from 1981 to 2016. Effective precipitation (EPCP) was calculated using four methods, namely the United States bureau of reclamation method (USBR), the simplified version of soil conservation service of the United States department of agriculture method (USDA-SCS simplified), the food and agriculture organization of the United Nations method (FAO), and the CROPWAT version of the USDA-SCS method (USDA-SCS CROPWAT). The correlation between SPI and SPEI and between wheat yield and these indices was performed using Pearson’s correlation coefficient (R) to select the best index for assessing the effects of meteorological droughts on the agricultural sector. The analysis of the drought features showed that the SPEI reported milder and longer droughts in most synoptic stations. The spatial pattern of drought in the Karkheh watershed was determined using spatial statistics methods, and global Moran’s I statistic showed that the drought in the Karkheh watershed had spatial autocorrelation and a clustered pattern with a 99% confidence level. The results of global and local Moran’s I and the Getis-Ord statistic (Gi*) showed that milder droughts prevailed in the northern clusters and more severe droughts in the southern clusters. Also, the correlation between wheat yield and the SPI and SPEI was positive in all stations except for the stations of Kermanshah province and Aqa Jan Bolaghi, which is due to the existence of numerous dams and springs in these areas. Considering the impact of temperature (Tmean) on meteorological drought, it is suggested that in addition to the SPI, the SPEI should also be used to predict droughts in the Karkheh watershed.

Land Use Challenges in Emerging Economic Corridors of the Global South: A Case Study of the Laos Economic Corridor

Economic corridors play a crucial role in promoting economic growth and facilitating coordinated regional development. However, land use changes associated with the development of emerging economic corridors have become a prominent source of conflict in regional integration in the Global South. This study takes the Laos Economic Corridor as a case study to explore the characteristics and driving mechanisms of land use changes in emerging economic corridor regions. Using global land cover data from 2000 to 2020 (GlobeLand30) and employing spatial statistical analysis, the Random Forest (RFC) algorithm, and the CA-Markov model, the study follows a Pattern–Process–Mechanism–Trend analytical framework to reveal the spatial distribution characteristics and transformation paths of land use within the corridor. The study results indicate that (1) The land use pattern in the Laos Economic Corridor has gradually shifted from a “single-core radial” structure to a “dumbbell-shaped” structure, promoting coordinated regional economic development. (2) A significant unidirectional flow of land use has been established, with forestland being converted into cultivated land and cultivated land being further converted into artificial surfaces. (3) In addition to the natural geographical constraints, the transport infrastructure and the spatial layout of industries are the main drivers for the expansion of ecological land, agricultural land, and built-up land. (4) Spatial planning interventions are essential and urgent: the establishment of land management rules based on the principles of forest conservation and intensive development can effectively control the uncontrolled expansion of artificial areas, significantly reduce the loss of forestland, and ensure the rational allocation of land resources for long-term development. The findings of this study offer valuable insights and reference points for the Global South, enhancing understanding of the spatial development dynamics of economic corridors, informing the optimization of land-use policies, and supporting efforts to promote regional integration and sustainable development.

Assessment of ecological status of Damsa Dam (Nevşehir, Türkiye) using trophic classification indexes, geospatial and statistical techniques

In this study, the water quality and trophic status of Damsa reservoir were evaluated using spatial and multivariate statistical analysis. When the thematic spatial distribution maps created for each parameter were analysed, it was determined that the water quality of Damsa Dam was class I water (very good) in terms of temperature, pH and dissolved oxygen and class III water (moderate) in terms of total N concentration. In the study, the changes on eutrophic status were analysed by global index categorisations and it was determined that Carlson trophic status index was CTSI=66.6, Burns trophic level index was BTLI=6.2 and Shu trophic status index was STSI=64.7. The results showed that Damsa Reservoir can reach the limits of ‘eutrophic’ according to CTSI classification scale, ‘eutrophic’ according to STSI classification scale and ‘hypereutrophic’ according to TLI classification scale. For this reason, the water quality of Damsa reservoir should be monitored continuously in certain periods and necessary interventions should be made in case of pollution.

KETERKAITAN ANTARA KETERSEDIAAN PELAYANAN DASAR PENUNJANG PERMUKIMAN PERKOTAAN DAN GUNA LAHAN PERMUKIMAN (Studi Kasus: Kecamatan Rangkasbitung, Banten)

Rapid urbanization has become a significant global phenomenon, affecting not only major cities but also suburban areas, with profound impacts on various aspects of urban life. This article explores the correlation between the increase in population as an impact from urbanization and the availability of basic services in Rangkasbitung, which consists of 16 villages. The purpose of this research is to determine the effect of the availability of basic daily services on the amount of residential land use in Rangkasbitung. Basic services are assessed using the 20-Minute Neighbourhood parameter, which represents an area designed to provide essential services within a short walk for residents. This research uses quantitative descriptive methodology, with spatial analysis and descriptive statistical analysis. The study is limited to the availability of residential land use, basic services, and public transportation as the three main components in the development of urban residential areas based on the 20-Minute Neighbourhood parameter. The 20-Minute Neighbourhood is a city planning concept defined as an area where residents can access most activities necessary for a good life without traveling outside the area. The results indicate that the availability of basic services in an area affects residents' decisions on where to live.

Impacts of environments on school myopia by spatial analysis techniques in Wuhan

The prevalence of myopia in China has increased significantly in recent years, and the age of onset has become younger. Previous studies have indicated that various environmental factors can influence the development of school myopia. However, the environmental impacts on school myopia remains to be investigated. Discoveries in this field may contribute to better urban planning. This study involved 7,610 students (aged 6–12 years, 4084 boys and 3526 girls) from six primary schools in Wuhan, China. We evaluated the associations between school myopia and the environment by analyzing the geographical distribution of myopic children. We utilized the spatial statistical analysis model. The Normalized Difference Vegetation Index (NDVI) risk coefficient for a 5,000-m radius around target schools was 0.379 (p = 0.008), while the NDVI risk coefficient for a 100-m radius around target schools was 0.241 (p = 0.047). The sports area risk coefficient for a 5,000-m radius around target schools was 0.234 (p = 0.016). We found that the specific buffers of NDVI and sports area around schools were associated with the prevalence of school myopia in schools, which worth further research to guide future initiatives on school myopia from an environmental perspective.

Diabetes may contribute to cognitive impairment in Parkinson's disease via damaging white matter tracts.

Parkinson's disease with dementia (PDD) severely affects the quality of life of patients with Parkinson's disease (PD) in the later stages. Recently, PD patients with diabetes were found to have a higher risk of cognitive decline and developing dementia with a faster progression, but the underlying mechanism remains unclear. Diabetes-related white matter damage may partially explain the mechanism by which diabetes participates in PDD. Seventy PD patients were included. PD patients underwent diffusion tensor imaging from The Second Affiliated Hospital of Chongqing Medical University were collected and were divided into four groups: PD with diabetes without dementia, PD with dementia without diabetes, PD without dementia and diabetes. Tract-based spatial statistics analysis and region-of-interest-based analysis were performed. Factorial analysis with diabetes and dementia taken as the main effects was performed, and the differences between the white matter fibers of PD patients from the four groups were also analyzed. The interaction between diabetes and dementia in the damage of white matter in PD patients was also analyzed. We found that both diabetes and dementia were found to be related to the damage in internal capsule, corona radiata, and thalamic radiation of the PD patients. There is an interaction between diabetes and dementia in the white matter damage of PD patients. Both diabetes and dementia were found to be related to the damage in internal capsule, corona radiata, and thalamic radiation of the PD patients. Diabetes may participate in cognitive decline in PD patients via damaging cognition-related white matter tracts.

Deciphering the point source carbon footprint puzzle: Land use dynamics and socio-economic drivers

Point source carbon emissions account for approximately 80 % of total emissions. Investigating the influence of land use and socio-economic indicators on these emissions is crucial for achieving sustainable development goals. Existing research faces challenges such as focusing on specific regions, mixing variables that may exhibit multicollinearity, and lacking sufficient land use information. This study takes China, the largest emitting country, as a case study, utilizing geospatial big data to subdivide land use into 11 categories based on emission sectors. The impacts of land use and socio-economic indicators on different emission sectors are discussed from the perspectives of bivariate and spatial statistical analysis, with spatial hotspots identified. Hierarchical regression is used to evaluate the explanatory power of the indicators and to establish models, and potential carbon reduction strategies are further explored. Key findings reveal: (1) Significant multicollinearity between land use and socio-economic indicators was demonstrated, with land use explaining 57.1 % of emissions compared to 37.4 % explained by socio-economic indicators. The spatial consistency between land use and emissions exceeds 80 %, and the spatiotemporal variability is relatively low, making land use a more advantageous factor in explaining point source carbon emissions. (2) Agricultural mechanization increases emission intensity, but this efficient farming method helps convert surplus plowland, the largest influencing factor (Coefficient = 0.717), into carbon sinks, thereby controlling agricultural emissions. (3) Land intensification helps control industrial land, the main factor influencing industrial emissions (Coefficient = 0.392). It also contributes to the efficient use of carbon reduction technologies and industrial supporting land. (4) Mixed commercial and residential land has the greatest impact on commercial, service, and household emissions. However, its relationship with the economy (Correlation = 0.479) is stronger than its relationship with emissions (Correlation = 0.182), making it more applicable to cities that serve as economic growth hubs.

The Influence of Physical Geography Parameters on Hydrometeorological Disasters (Case Study: Banjarmasin City)

Abstract Data from the National Disaster Management Agency (BNPB) 2022 show that there were 1.381 occurrences between January and April 26. Of them, 536 incidents were flood catastrophes. The largest flood of 2021 happened in South Kalimantan at the start of the year, with losses projected to be as high as IDR 1.2 trillion. In addition, South Kalimantan Province National Board for Disaster Management reported that the Banjarmasin city remained under water, with water level above ± 50 cm and 1.5 m. 108,524 individuals had been affected, and 35,138 dwellings were affected. In order to address the hydrometeorological (flood) catastrophe risks in Banjarmasin City, this research uses spatial statistical analysis to examine the key factors that contribute to flood disasters. GWR stands for Geographically Weighted Regression. Rainfall, soil type, and land usage are among the criteria that are used. Following the GWR spatial statistical assessment, recommendations are offered that may be pursued further in the form of a research on hydrometeorology and related strategic issues for disaster management policies.

An in-depth multiscale analysis of farmland abandonment and recultivation dynamics in the Yangtze River Delta, China: A landscape ecology perspective empowered by google earth engine

Farmland abandonment represents the extreme utilization of marginal land and results from the interplay between humans and the environment. In the Yangtze River Delta region of China, a significant economic hub, understanding the landscape patterns and driving mechanisms of farmland abandonment and recultivation land is crucial for regional sustainable development. This study aims to map the extent of farmland abandonment in 2020 and recultivation land in 2021 across the Yangtze River Delta, analyze the landscape patterns of farmland abandonment and recultivation land on multiple scales, and explore their driving mechanisms. Utilizing multi-source data and the Land Use Trajectory Change Method on the Google Earth Engine platform, we classified land use and identified farmland abandonment, fallow land, active farmland, and recultivation land. Spatial statistical analysis was conducted to investigate the distribution and patterns of farmland abandonment and recultivation land at Yangtze River Delta, provincial, and municipal scales. Additionally, a stepwise multiple linear regression model was employed to dissect the main driving forces of farmland abandonment and recultivation land. Results revealed that farmland abandonment in the Yangtze River Delta region exceeded an abandonment rate of 10%, with a reclamation rate in the following year of less than 30%. The spatial distribution of farmland abandonment and recultivation land varied significantly across different scales. On the Yangtze River Delta scale, farmland abandonment was positively influenced by altitude, distance to rivers, and temperature, while recultivation land was positively correlated with night light intensity. At the provincial and municipal scales, the driving forces exhibited scaling effects, with different factors becoming significant. Our findings contribute to a deeper understanding of the complexity and diversity of farmland abandonment in the Yangtze River Delta region. The results provide a scientific basis for formulating land use policies, promoting sustainable agricultural development, and ensuring ecological balance. By uncovering the land use status and ecosystem balance influenced by human-environment interactions, this study offers insights into achieving a harmonious coexistence between economy, society, and ecology in the Yangtze River Delta region.

Linkages between the Availability of Basic Services Supporting Urban Settlements and Settlement Land Use (Case Study: Rangkasbitung District, Lebak Regency, Banten Province)

Rapid urbanization has become a significant global phenomenon, affecting major cities and suburban areas and profoundly impacting urban life. This article explores the correlation between the increase in population as an effect of urbanization and the availability of essential services in Rangkasbitung, which consists of 16 villages. This research aims to determine the impact of the availability of crucial daily services on the amount of residential land use in Rangkasbitung. Essential services are assessed using the 20-minute neighborhood parameter, representing an area designed to provide essential services within a short walk for residents. This research uses a quantitative descriptive methodology with spatial and descriptive statistical analysis. The study is limited to the availability of residential land use, essential services, and public transportation as the three main components in the development of urban residential areas based on the 20-Minute Neighborhood parameter. The 20-minute neighborhood is a city planning concept defined as an area where residents can access most activities necessary for a good life without traveling outside the area. The results indicate that the availability of essential services in an area affects residents' decisions on where to live.

Statistical Methods for Interpreting Spatial and Temporal Heterogeneity of Martian Tropical Water Ice Informed by Properties of Crater Ejecta Types

AbstractThe martian tropical water ice spatial and temporal distribution was characterized using impact crater ejecta type, location, size, and age in one of two epochs, Ga and Ga, using statistical models designed for spatial and temporal correlation structures. The indicator thought to identify the presence of ice is craters with layered ejecta, while the indicator thought to identify no ice is craters with radial ejecta. These indicators imply the location (longitude and latitude) and, potentially, depth (crater diameter as a proxy) of ice, and when the ice was present. The spatial and temporal distribution of layered ejecta versus radial ejecta may inform on the geography and evolution of ice. A statistical spatial point analysis was conducted on a 54‐sample data set (craters with diameters 2.77–10.00 km) for an equatorial region ( to S, and E to W. The analysis shows the spatial and temporal distribution of tropical ice in the study region is most likely random.

Warming trends in the Nile Delta: A high-resolution Spatial statistical approach

The Nile Delta, a region of historical significance, is facing significant environmental changes driven by climate change. This study employs a novel pixel-level spatial statistical analysis to assess the intensity and trends in the daytime and nighttime urban heat island (UHI) from 2003 to 2021. We employed high-resolution data from the Global Artificial Impermeable Area (GAIA) dataset (30 m), land surface temperature (LST) from the MODIS Aqua satellite 1000 m, and the MOD13A3 Normalized Difference Vegetation Index (NDVI) (1000 m). Bivariate choropleth maps were used to illustrate the spatial relationships between daytime and nighttime LST and NDVI. Ordinary least squares (OLS) regression method was used to calculate the trend for each pixel and the Mann-Kendall test was used to assess the statistical significance of the trend at 95% confidence level (p < 0.05). The central and southern regions of the delta experienced significant LST increases, highlighting the risk of warming due to vegetation degradation. Specifically, the diurnal LST trend ranged from −0.46 °C to 0.34 °C/year, while the nocturnal trend ranged from −0.12 °C to 0.26 °C/year. Spatially, the study also indicates cooling trends in coastal cities such as Port Said, New Damietta and Alexandria due to the moderate influence of the Mediterranean Sea. In contrast, the inland and southern Delta cities are warming rapidly. The relationship between diurnal UHI average and the NDVI showed a modest negative correlation (R = −0.31, p < 0.0001). This association was much stronger at night, with a negative correlation of (R = −0.71, P < 0.0001) A strong negative correlation between diurnal UHI trend and NDVI (R = −0.68, p < 0.0001). The relationship between nocturnal UHI trend and NDVI is negative (R = −0.61, p < 0.0001). The analysis reveals that 13 cities exhibited significant warming during the daytime, compared to 35 cities at night. The results highlight the importance of pixel-level data to accurately assess environmental changes and inform urban planning strategies to mitigate the effects of warming on the Nile Delta.

Determining the Correlation between Abaca (Musa textilis) Distribution Patterns and Bunchy Top Disease Prevalence in the Caraga Region through Spatial Point Pattern and Statistical Analysis

Determining the Correlation between Abaca (Musa textilis) Distribution Patterns and Bunchy Top Disease Prevalence in the Caraga Region through Spatial Point Pattern and Statistical Analysis

Wafer-Scale Atomic Layer-Deposited TeOx/Te Heterostructure P-Type Thin-Film Transistors.

There is an increasing demand for p-type semiconductors with scalable growth, excellent device performance, and back-end-of-line (BEOL) compatibility. Recently, tellurium (Te) has emerged as a promising candidate due to its appealing electrical properties and potential low-temperature production. So far, nearly all of the scalable production and integration of Te with complementary metal oxide semiconductor (CMOS) technology have been based on physical vapor deposition. Here we demonstrate wafer-scale atomic layer-deposited (ALD) TeOx/Te heterostructure thin-film transistors with high uniformity and integration compatibility. The wafer-scale uniformity of the film is evidenced by spatial Raman mappings and statistical electrical analysis. Furthermore, surface accumulation-induced good ohmic contact has been observed and explained by the unique band alignment of the charge neutrality level inside the Te valence band. These results demonstrate ALD TeOx/Te as a promising p-type semiconductor for monolithic three-dimensional integration in BEOL CMOS applications incorporated with well-established n-type ALD oxide semiconductors.