High-high Research Articles

Identifying Spatiotemporal Heterogeneity of PM2.5 Concentrations and the Key Influencing Factors in the Middle and Lower Reaches of the Yellow River

Fine particulate matter (PM2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM2.5 pollution in the study area, the PM2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM2.5, while elevation makes a strong negative impact on PM2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.

Geochemical Association Rules of Elements Mined Using Clustered Events of Spatial Autocorrelation: A Case Study in the Chahanwusu River Area, Qinghai Province, China

The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we proposed an association rule mining method based on clustered events of spatial autocorrelation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high–high), LL (low–low), HL (high–low), and LH (low–high) groups by using local Moran’s I clustering map (LMIC). Then, the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Middle Triassic granodiorite is enriched in sulfophile elements, e.g., Zn, Ag, and Cd, and the Early Permian granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining coexistence patterns of elements and provides an insight into their enrichment mechanisms.

Spatial Analysis for the Identification of Priority Areas Dengue in West Sumatera Province, Indonesia

Dengue is transmitted by mosquitoes in the genus Aedes, particularly Aedes aegypti and Aedes albopictus. West Sumatra Province is a tropical area in Indonesia where the two main mosquito genera are endemic in almost all regions year-round. This study analysed distribution of dengue, and defined the priority dengue areas in West Sumatra in 2017 using spatial autocorrelation (Moran’s I) and Moran’s scatter plot. The result revealed the global Moran’s index of the case patterns to be a cluster or positive correlation (I > 0, p < 0.01), and Moran scatter plot indicated associations between districts and their neighbours. Dengue cases in each district were described as high-high (HH), high-low (HL, hotspot), low-high (LH, cold spot), or low-low (LL). Districts identified as HH should be prioritized, and dengue must be controlled in HL and LH districts. In LL districts, dengue programs should be maintained. The results of this study could help implement strategies to prevent and control dengue.

HYBRID DISCRETE WAVELET TRANSFORM AND TEXTURE ANALYSIS METHODS FOR FEATURE EXTRACTION AND CLASSIFICATION OF BREAST DYNAMIC THERMOGRAM SEQUENCES

Breast cancer is a common cancer that hits women causing thousands of casualties every year. A cancerous tumor causes an increase of temperature near the region of the tumor. The heat generated by the temperature transferred to the skin surface. The temperature in the tumor area is warmer than in the healthy area. Detecting breast cancer in early stages can save women’s lives and lower the burden on the cost. Thermography is an imaging technique used for breast cancer detection. A dynamic thermography technique which is used to generate infrared images over a fixed time measured in minutes to detect the difference between the normal and cancerous areas in images. In this research, we propose a methodology to deal with the changes of temperature in patient's breasts by defining a set of efficient features resulted from extraction and reduction of coefficients obtained from breast thermogram images followed by classification. Texture feature methods (Histogram of Oriented Gradients (HOG) and Discrete Curvelet transform) are applied separately using the HH (high-high) and HL (high-low) sub band images of Discrete Wavelet transform (DWT). HOG-based features and Curvelet features are extracted by reducing coefficients’ vectors returned by the two methods. Finally, Support Vector Machine (SVM) binary classifier is used to classify the images to either normal or abnormal. The proposed work has successfully achieved an Accuracy of 98.2%, Sensitivity of 97.7%, and Specificity of 98.2% through empirical studies using dynamic breast thermogram dataset.

Characteristics and Driving Forces of Non-Grain Production of Cultivated Land from the Perspective of Food Security

A large proportion of the cultivated land in China has been used for non-grain production purposes. As food insecurity is worsening worldwide, this issue has attracted attention from the Chinese government. In order to curb this trend and to ensure food security, this paper explores the quantitative characteristics and spatial distribution of cultivated land used for non-grain purposes in Liyang City, Jiangsu Province, and discusses the clustering characteristics and mechanisms behind this based on spatial autocorrelation analysis and geographically weighted regression (GWR). The results show that most of the cultivated land in Liyang City has not been used for non-grain purposes, and the cultivated land reserve is abundant. Among all land types, irrigable land has the largest non-grain production rate of cultivated land. There is no significant spatial correlation of cultivated land for non-grain purposes in most towns in Liyang, among which Kunlun Street is in the High-High (HH) zone and Daibu Town in the Low-High (LH) zone. It is also found that the same factor has various impacts on the non-grain production of cultivated land in different towns, and the number of enterprises is the core factor that leads to the non-grain use of cultivated land in Liyang city. Low food prices lead some farmers to plant other crops with higher economic benefits, and also lead to the outflow of the rural labor force. This will not only accelerate the non-grain production of cultivated land, but also cause a large amount of cultivated land to be in a state of unmanned cultivation, further aggravating the proportion of non-grain production in cultivated land.

Early Monitoring and Subsequent Gain of Tacrolimus Time-In-Therapeutic Range May Improve Clinical Outcomes After Living Kidney Transplantation.

The early identification of recipients at high risk of graft loss is clinically relevant after kidney transplantation. The authors explored whether the earlier monitoring of tacrolimus (Tac) time-in-therapeutic range (TTR) is predictive of and a subsequent gain in TTR improves transplant outcomes. The TTR within 3, 6, 9, and 12 months was evaluated. Multivariate Cox analyses were performed to explore when TTR was predictive of transplant outcomes. Patients were divided into 3 groups based on incremental TTR change [TTR gain (increase >10%), TTR stable (maintained within 10%), and TTR loss (decrease >10%)] and 4 groups based on predefined cutoff values [low-low (LL), low-high (LH), high-low (HL), and high-high (HH)] using 6- and 12-month TTRs. Death-censored graft loss and patient death were primary outcomes. Nonlinear associations were observed between 6-, 9-, and 12-month TTR and death-censored graft and patient survival rates. In multivariate analysis, every 10% increase in 6-, 9-, and 12-month TTRs was associated with reduced patient death [hazard ratio (HR): 0.83; HR: 0.68; HR: 0.61, respectively] and graft loss (HR: 0.88; HR: 0.73; HR: 0.66, respectively). A nonlinear relationship was observed between transplant outcomes and incremental changes in TTR. TTR gain and stable TTR contributed to higher graft survival (HR: 0.20; HR: 0.21) and patient survival (HR: 0.14; HR: 0.15) rates than TTR loss, whereas the former 2 had comparable outcomes. Furthermore, compared with those in the HH group, the LL and HL groups had inferior graft survival (HR: 3.33; HR: 5.17) and patient survival (HR: 5.15; HR: 8.94) rates, whereas the LH group had similar outcomes (P = 0.63, P = 0.97). Nonadherence was the main controllable risk factor for low TTR. The 6-month TTR identified patients at higher risk of worse outcomes. The subsequent gain of TTR may contribute to better transplant outcomes.

Spatial-temporal variations of ecological vulnerability in Jilin Province (China), 2000 to 2018

As the main grain production area in China, Jilin Province has a significant ecological function in Northeast China. Scientifically understanding the spatial and temporal characteristics of ecological vulnerability can aid in effectively managing environmental changes, guiding rational use of land resources and developing strategies for regional environmental protection. Applying the Sensitivity Resilience Pressure (SRP) conceptual model of ecological vulnerability, integrated with meteorological, remote sensing, and statistical data, a comprehensive evaluation index system for ecological vulnerability was established in Jilin Province. An entropy weight model constructed with analytic hierarchy process (AHP) and principal component analysis (PCA) was employed to calculate weights of each indicator. Using a geographical information system, the geospatial and temporal features of ecological vulnerability were analyzed from 2000 to 2018 in the study area. Spatial autocorrelation analysis was utilized to probe the spatial relationship. The results indicate that the ecological vulnerability was categorized a potential and light within the study area and that variations among different regions increased gradually from east to west. High levels of ecological vulnerability were mainly distributed in the western area, displaying a significant global spatial autocorrelation with high-high (HH) aggregation. Over time, the proportion of high vulnerability areas decreased from 14% in 2000 to 9% in 2018, while low vulnerability areas increased from 26% in 2000 to 29% in 2018. The results reflect the condition of the regional ecological environment, which could have implications for ecological protection and sustainable development in the Jilin Province.

Spatiotemporal distribution of varicella in the Republic of Korea

Varicella is a highly contagious disease caused by the varicella-zoster virus (VZV). Given its tendency to cluster geographically, spatial analyses may provide a better understanding of the pattern of varicella transmission. We investigated the spatial characteristics of varicella in Korea and the risk factors for varicella at a national level. Using national surveillance and demographic data, we examined the spatial distribution of incidence rates and their spatial autocorrelation and calculated Moran's index. Spatial regression analysis was used to identify sociodemographic predictors of varicella incidence at the district level. An increasing tendency in the annual incidence of varicella was observed over a 12-year period (2006-2018), with a surge in 2017. There was a clear positive spatial autocorrelation of the varicella incidence rate during the surveillance period. During 2006-2014, High-High (HH) clusters were mostly confined to the northeast region and neighboring districts. The spatial error model showed that population density had a negative coefficient and childhood percentage, percentage of children under 12 years of age among the total population, had positive coefficient, whereas vaccine coverage was insignificant. The varicella incidence according to geographic region varied with population density, childhood percentage, suggesting the importance of community-level surveillance and monitoring strategies.

Spatial Spillover Effects of Renewable Energy on Carbon Emissions in Less-developed Areas of China.

The purpose of this paper is to determine the spatial spillover effects of renewable energy on carbon emissions in China's less-developed areas. However, few studies have considered this issue from the perspective of less-developed areas. Based on panel data of 21 provinces in China from 2000 to 2017, this paper analyzes the spatial spillover effects of renewable energy on carbon emissions using Moran's I and Spatial Durbin Model (SDM). The results suggest that, first, Moran's I ranges from 0.378 to 0.519, Moran scatter plot presents that provinces are located in the high-high (HH) and low-low (LL) quadrants, indicating provincial carbon emissions in the study area have a significant spatial correlation and agglomeration. Second, under the three matrices, the direct effect coefficients of renewable energy are -0.2522, -0.2639 and -0.2601, this shows that renewable energy is beneficial to local carbon emissions reduction. In contrast, the indirect effect coefficients of renewable energy are 0.0605, 0.1012 and 0.1125, which means higher renewable energy consumption in a single area is conducive to the improvement of carbon emissions to neighbouring areas. Third, urbanization, industrialization, physical capital and other variables have different impacts on local and nearby carbon emissions. This study provides empirical evidence to achieve carbon emission reduction targets by government policymakers.

Indeks Pembangunan Berkelanjutan Lokal dan Sebaran Spasialnya di Kabupaten Mandailing Natal, Provinsi Sumatera Utara

The concept of sustainable development is very important to increase our quality of life. Measurement of development based on specific indicators is one of the approaches to describe sustainable development. At the local level, measuring sustainable development using specific indicators is still rare. Since each location has their own unique characteristics, measuring sustainability at the regional level could not always be applied locally. The availability of data at the village level strongly encourages the local measurement of sustainable development index. The economic, social, and environmental dimensions of Mandailing Natal Regency's development are still constrained. Using 21 variables that can reflect economic, social, and environmental aspects, this study aims to measure the sustainable development index at the local level. The Local Sustainable Development Index will be generated by analyzing these variables using the Factor Analysis (FA) method and Local Indicator of Spatial Autocorrelation (LISA) analysis. This index measurement is useful in considering the direction of location-based (spatial) development for decision makers at the local level and describe development sustainability. In general, IPBL economic value was in the medium category in 2011, and several villages in the low category were able to enhance their economic development sustainability in 2018. In the northwest, IPBL environment cluster type High-high (HH). In 2018, the percentage spatial distribution pattern of villages with the Low-low (LL) cluster type of IPBL economic and IPBL environment decreased. However, as can be seen from the spatial distribution pattern of IPBL social with cluster type LL, the percentage increased in 2018.

Impact of safe drinking water and clean fuels on health and wellbeing in Pakistan: A spatial analysis

Access to safe drinking water and clean fuels remains a setback for low- and middle-income countries. It has substantially impacted people's health and wellbeing worldwide and its improvement would enhance the economic development of any country. In this study, we constructed four versions of the District Health Index (DHI) each with a different set of indicators by adopting the Urban Health Index (UHI) framework and Geographic Information System (GIS) tools to (i) determine the effect of safe drinking water and clean fuels on districts wellbeing, (ii) measure the impact of safe drinking water and clean fuels on disparities/inequalities (disparity ratios and disparity slopes) concerning DHI scores and, (iii) examine the role of safe drinking water and clean fuels in the spatial relationship among contiguous districts in terms of DHI scores. Results of the study indicated that on average, only 17.72 and 18.29 percent of people in each district had access to safe water and clean fuel respectively. Less accessibility towards these indicators reduced the mean score of the Pakistan district's health from 0.37 to 0.25. The inclusion of safe water widened the disparity ratio and diminished the disparity slope. Spatial analysis revealed that Pakistan's district health status was spatially correlated with contiguous districts. Global Moran's I scores confirmed the declined spatial dependence, when safe drinking water indicator was included in composite index. Most of the districts were concentrated in high-high (HH) and low-low (LL) quadrants. A spatial cluster of low-low (deprived/unhealthy) districts in Baluchistan confirmed that comparatively, Baluchistan is the most deprived province which needs government attention and policy maker's prioritizations.

Spatial Pattern Analysis of Malaria Cases in Muara Enim Regency using Moran Index and Local Indicator Spatial Autocorrelation

BACKGROUND: Malaria is a disease which still becomes a global health issue, including in Indonesia, because of its potential vector which can infect and spread causing a wide impact. Until currently, malaria still becomes a serious threat to people living in tropical and subtropical areas. Muara Enim Regency is a malaria-endemic regency with the second highest positive case in South Sumatra Province, with an API value in 2019 was 0.18/1000 population. AIM: The current research was performed to identify and change the spatial pattern of malaria cases, environmental variability (rainfall), population density using Moran index and local indicator spatial autocorrelation (LISA), and habitat in Muara Enim Regency. METHODS: This research employed a quantitative research design with an analytical survey research method and a case–control approach. This research method was designed using a geographic information systems approach. RESULTS: The results of the study showed that malaria cases in Muara Enim Regency in 2017 occurred in groups with a Moran index of 0.263, indicating a positive autocorrelation. Meanwhile, based on the LISA index, it was found that there were three districts categorized as high-high (HH) (quadrant 1), those are Lawang Kidul District, Muara Enim District, and Gunung Megang District, while in the low-high (LH) category (quadrant 2), there was Benakat District. In 2018, it also occurred in groups where the Moran index was 0.129, indicating a positive autocorrelation, while the LISA index found that there was one district categorized as HH (quadrant 1) which is Lawang Kidul District, and district categorized as LH (quadrant 2) was Gunung Megang District. In 2019, it happened randomly or spread with a Moran index of −0.022 indicating a negative autocorrelation, while based on the LISA index, it was found that there was one subdistrict categorized as HH category (quadrant 1) which is Lawang Kidul District and two districts categorized as LH (quadrant 2) which are Semende Darat Laut and Rambang Niru. CONCLUSION: There was a change in the results of Moran index from a positive autocorrelation in 2017, 2018, to a negative autocorrelation in 2019 with the results of the LISA index for malaria cases in 2017–2019 in one subdistrict, namely, Lawang Kidul District categorized as a HH category (quadrant 1).

Landscape Ecological Risk Assessment Based on LUCC—A Case Study of Chaoyang County, China

The ecological environment is suffering from great human disturbance. Scientific assessment of landscape ecological risks can provide scientific guidance for land use management. This study focused on Chaoyang County in China, used ecological risk assessment methods to characterize the impact of land use/land cover (LUCC) change, and revealed the risk aggregation pattern with the help of spatial autocorrelation analysis. The results showed that ecological risk was increased from 2000 to 2010 but decreased from 2010 to 2018. The ecological risk of the Daling River and Xiaoling River basin was at a relatively high level, and low in the northwest and southeast of the study which covered by forest land. Occupying cultivated land for built-up and large-scale deforestation were two of the main factors to contribute to the increase of ecological risk. The distribution of High-High (HH) and Low-Low (LL) risk agglomeration areas was basically the same as risk levels, but the scope is smaller and more precise. Thus, HH and LH risk agglomeration area should be paid more attention to prevent the adverse impact of adjacent areas. Our study gave a novel perspective to investigate the pattern of ecological risk in order for government managers to identify key risk areas.

Research on the Temporal and Spatial Distribution and Influencing Factors of Forestry Output Efficiency in China

Forestry output efficiency is key to forestry development. China is now promoting the development of forestry, and thus the research on forestry output efficiency is of practical significance. Through the data envelopment analysis (DEA)-Malmquist index, spatial autocorrelation model, and fixed effect model of panel data, in this study, we analyzed the forestry output efficiency of China with indicators, such as the fixed asset input, employed personnel, total output value, and timber output, and drew the following conclusions. In the time series, the forestry total-factor productivity (TFP) in China saw a rapid increase, which is attributed to the technological progress change (TC), whereas the efficiency change (EC) imposed negative influences upon the forestry TFP. In the spatial distribution, there was a difference in the increase in the forestry output efficiency among the eastern, central, and western regions of China, with the eastern region having the fastest growth and the central region having the slowest growth. According to the spatial autocorrelation, there was spatial aggregation (high–high (HH) and low–low (LL)) with a significant positive correlation. Through the optimized fixed effect regression model, the fixed asset input, employed personnel, total output value, and timber output all had significant influences on the comprehensive technical efficiency of the forestry output, wherein the input indicators had negative influences, and the output indicators had positive influences.

Spatio-Temporal Differentiation Mode and Threshold Effect of Yangtze River Delta Urban Ecological Well-Being Performance Based on Network DEA

Improving ecological well-being performance (EWP) is the essential requirement of green transformation development and ecological civilization construction. With the help of a network DEA model and threshold panel regression model, this study evaluated urban ecological well-being performance to explore the evolution process, impact, and optimization path of urban ecological well-being performance in the Yangtze River Delta from 2001 to 2017. The findings are as follows: (1) The ecological well-being performance of all cities in the region had been steadily improving since 2001, most cities had realized the transformation from a low performance level to a higher performance level, and there was an obvious spatial mismatch between urban economic development level and ecological well-being performance. (2) A regional urban ecological well-being performance development mode was constantly optimized, sustainable urban development ability was improved, and the number of high-high (HH) mode cities was gradually increasing while the number of low-low (LL) mode cities was gradually decreasing. (3) Under the influence of threshold variables of population density, industrialization level, and environmental regulation, the impact of urbanization on ecological well-being performance had different threshold characteristics. Economic growth, industrialization, and government macro-control had significantly negative restraining effects, while consumption level, industrial structure upgrading, energy efficiency, and technological innovation had significantly positive driving effects.

STUDENTS’ LANGUAGE AWARENESS ON LINGUISTICS PROBLEMS DURING NEGOTIATION OF MEANING IN PAIR WORK DISCUSSION.

The objectives of this study are :1) to describe linguistics problems produced by the students during the negotiation of meaning in pair work discussion, and 2) to describe the students’ language awareness (willingness and unwillingness to correct the linguistics problems). This study applied descriptive quantitative design and the subjects are 3 pairs consisting of high-high (H-H) learner, high and low (H-L) learner and low- low (L-L) learner. All of them are the fourth semester students of English department of teachers training faculty of Lampung University in academic 2018/2019. Each pair was given a topic (chosing 5 personality traits that a teacher should have). Each pair discussed the topic and during the discussion, all their utterances were recorded, transcribed and were analyzed based on relevant theories. The results show that each pair produced linguistics problems, such as grammatical and vocabulary. Most of the learners, especially H-L and L-L are not aware of linguistics problems because they focus more on the message across. Eventhough some of the pairs are aware of linguistics problems and are willing to correct them, it does not guarantee that the input can be internalized by their pairs since they focus more on meaning during the interaction. Therefore, it is suggested further researchers investigate this phenomena.

The spatial ecology dynamics of groundwater quality: a case study of the urban environment, Yogyakarta City, Indonesia

Ecological vulnerability assessment is essential to environmental degradation and resource management, particularly in the urban environment that given recent global warming concerns. In this study, we propose to evaluate the spatial ecology dynamics of groundwater quality with a typical ecologically in the urban environment, Yogyakarta city, as a case study. The proposed method is based on the definition of the Intergovernmental Panel on Climate Change and Water (IPCC VI) using software GeoDA 10.4. The results of the research study indicated the ecological spatial dynamics of quality groundwater in Yogyakarta City had variations in spatial correlation distribution. The overall result is 19% of the High-High (HH) classification areas appeared mainly between high concentration and the high population densities, where the economic activities have been developing rapidly. As well, 27% of the Low-Low (LL) classification areas appeared mainly between low concentration and the low population densities. The applied analysis indicates a spatial correlation between groundwater quality index and population densities index in the urban environment with positive autocorrelation. Based on result research that indicates the urgency of applying the sanitation facilities to convince local society for anticipating climate change regarding groundwater quality.

Forest Land Quality Evaluation and the Protection Zoning of Subtropical Humid Evergreen Broadleaf Forest Region Based on the PSO-TOPSIS Model and the Local Indicator of Spatial Association: A Case Study of Hefeng County, Hubei Province, China

Forest land is the carrier for growing forests. It is of great significance to evaluate the forest land quality scientifically and delineate forestland protection zones reasonably for realizing better forest land management, promoting ecological civilization construction, and coping with global climate change. In this study, taking Hefeng County, Hubei Province, a subtropical humid evergreen broad-leaved forest region in China, as the study area, 14 indicators were selected from four dimensions—climatic conditions, terrain, soil conditions, and socioeconomics—to construct a forest land quality evaluation index system. Based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model, we introduced the Particle Swarm Optimization (PSO) algorithm to design the evaluation model to evaluate the forest land quality and analyze the distribution of forest land quality in Hefeng. Further, we used the Local Indicator of Spatial Association (LISA) to explore the spatial distribution of forest land quality and delineate the forest land protection zones. The results showed the following: (1) the overall quality of forest land was high, with some variability between regions. The range of Forest Land Quality Index (FLQI) in Hefeng was 0.4091–0.8601, with a mean value of 0.6337. The forest land quality grades were mainly first and second grade, with the higher-grade forest land mainly distributed in the central and southeastern low mountain regions of Zouma, Wuli, and Yanzi. The lower-grade forest land was mainly distributed in the northwestern middle and high mountain regions of Zhongying, Taiping, and Rongmei. (2) The global spatial autocorrelation index of forest land quality in Hefeng County was 0.7562, indicating that the forest land quality in the county had a strong spatial similarity. The spatial distribution of similarity types high-high (HH) and low-low (LL) was more clustered, while the spatial distribution of dissimilarity types high-low (HL) and low-high (LH) was generally dispersed. (3) Based on the LISA of forest land quality, forest land protection zones were divided into three types: key protection zones (KPZs), active protection zones (APZs), and general protection zones (GPZs). The forest land protection zoning basically coincided with the forest land quality. Combining the characteristics of self-correlated types in different forestland protection zones, corresponding management and protection measures were proposed. This showed that the PSO-TOPSIS model can be effectively used for forest land quality evaluation. At the same time, the spatial attributes of forest land were incorporated into the development of forest land protection zoning scheme, which expands the method of forest land protection zoning, and can provide a scientific basis and methodological reference for the reasonable formulation of forest land use planning in Hefeng County, while also serving as a reference for similar regions and countries.

Dynamic Evaluation of Urban Sustainability Based on ELECTRE: A Case Study from China

Sustainable urban evaluation is an important management tool to grasp the status of urban development in real time and to make policy adjustments. In this study, the evaluation indicator system is constructed from the three dimensions of economy, society, and environment. The ELECTRE (elimination et choice translation reality) model based on information entropy weighting is employed to evaluate urban sustainability. The model applies three-dimensional data to explore the dynamics of sustainable urban development. The spatial measurement model is used to explore the spatial effects of cities. Finally, 17 cities in Henan Province from 2013 to 2017 are used as case studies for urban sustainable development evaluation. The results show that, in 2013–2017, the sustainability of cities such as Zhengzhou, Luoyang, and Sanmenxia was stable at a high level, while the sustainability levels of Kaifeng, Luohe, and Xinyang showed a fluctuating downward trend, and the sustainability levels of Puyang, Nanyang, and Xinxiang showed a fluctuating upward trend. Among the 17 cities, Zhengzhou has the highest sustainability level and its economic and social sustainability levels are significantly better than other cities. Zhoukou is the city with the lowest level of sustainability. In addition, from 2013 to 2016, the level of urban sustainability was not spatially correlated but gradually presented positive spatial correlation and the characteristics of clustering distribution in 2017. The cities such as Zhengzhou, Luoyang, and Jiaozuo are mainly represented by “high-high (H-H)” agglomeration. In contrast, Shangqiu, Zhoukou, and Zhumadian are mainly represented by “low-low (L-L)” agglomeration. This research provides suggestions and decision-making support for promoting urban sustainability.

Risk and success factors for good udder health of early lactation primiparous dairy cows

We compared the management and housing of dairy heifers from calf to calving in herds that were very successful versus less successful in preventing mastitis in early lactation primiparous cows. This retrospective observational study included 170 Swedish dairy herds. Eligible herds were identified from the Swedish official milk recording scheme (SOMRS). Each herd had at least 60 cows per year, production data from 3 consecutive years, and at least 10 primiparous cows per year with their first milk recording 5 to 35 d after calving and their second milk recording 20 to 40 d after the first milk recording. In each herd, primiparous cows with a low (≤75,000 cells/mL) cow somatic cell count (CSCC) at both the first and second milk recording were categorized as low-low (LL); those with a high (>100,000 cells/mL) CSCC at both recordings were categorized as high-high (HH). Cows with high CSCC at the first recording and low at the second were categorized as high-low (HL). The annual proportions of LL, HL, and HH cows within each herd were calculated. Herds with an above-median proportion of LL, HL, or HH cows during the first year of a 3-yr selection period, and above the third quartile proportion of LL, HL, or HH cows, respectively, during the second and third year were identified. These herds (LL herds, n = 129; HL herds, n = 92; HH herds, n = 139) were contacted until a maximum of 60 herds per category had agreed to participate. Field technicians/veterinarians visited each herd once in the mid to late indoor season to collect data on housing and management of the heifers from birth to calving. Additional data were retrieved from the SOMRS. Associations between herd category (LL, HL, or HH) and variables collected were analyzed in 8 multivariable multinomial logistic regression submodels covering herd characteristics, milk-fed calves, heifers in early pregnancy, heifers in late pregnancy, calving and colostrum period, miscellaneous factors, summarized heifer housing data, and general health, culling, and fertility data. A final multivariable model was built from the results of the submodels and univariable analyses. The final model showed that having a standard operating procedure for colostrum feeding was more common in LL and HL herds than in HH herds; the mean bulk milk SCC and overall culling rate due to udder health was higher in HH herds than in LL and HL herds; and automatic milking was less common in LL herds than in HL and HH herds. Several herd and management variables differed between herd categories in the submodels. In conclusion, we identified several success factors for herds with good udder health among early lactation primiparous cows. This knowledge can be used to improve preventive measures in dairy herds to ensure sustainable and economic milk production.