Representative Areas Research Articles

Source analysis of chlorine in municipal solid waste under waste classification: a case study of Hangzhou, China.

Inorganic chlorine is susceptible to water and soil salinization due to its non-degradability and high mobility. To clarify the environmental risks associated with the active inorganic chlorine in municipal solid waste (MSW), the specific characteristics and contributions of inorganic chlorine in different MSW categories were investigated in this study. MSW samples were collected from eight representative waste classification residential areas in Hangzhou, China. It was found that the inorganic chlorine content in different MSW categories varied significantly (0-113 mg/g). Perishable waste, paper, and plastic were found to be the main sources of inorganic chlorine in MSW. A four-category classification system was used to quantify the contribution of inorganic chlorine from each waste category. It was found that the misclassification of inorganic chlorine contributions from perishable waste and other waste accounted for 51.96% and 48.04%, respectively. However, when correctly classified into the four-category system, their contributions were reduced to 67.14% and 30.65%, respectively. Therefore, MSW classification showed a significant reduction in the overall contribution of inorganic chlorine. The misclassification reduces the contribution of inorganic chlorine to 48.04%, while correct classification increases the reduction to 69.35%.

Revealing the Spatial Interactions and Driving Factors of Ecosystem Services: Enlightenments under Vegetation Restoration

Large-scale vegetation restoration has caused complex changes in ecosystem service (i.e., ES) interactions. However, current analysis on the spatial interactions of ESs and their driving mechanisms remains deficient, limiting the adaptive management in vegetation restoration areas. This study focused on a representative restoration area (Yan’an) to analyze the relationships among carbon sequestration, water yield, baseflow regulation, and soil conservation from 1990 to 2020. Employing the bivariate boxplot and spatial autocorrelation methods, we identified the overall changes and spatial patterns of ES interactions. The geographically and temporally weighted regression (i.e., GTWR) model was applied to elucidate the driving factors of these spatial ES interactions. The results indicated the following: (1) Over the past three decades, synergies between carbon sequestration and water yield emerged as the joint results of spatial ‘low–low’ interactions and ‘high–high’ interactions between the two ESs, while other ES pairs generally exhibited comparatively weaker synergies, due to their spatial ‘low–high’ interactions in southern semi-humid areas. (2) In the northern semi-arid areas, both fractional vegetation cover (i.e., FVC) and climatic factors consistently exerted negative influences on all ‘low–low’ ES interactions, which caused a reduced area in synergies, while in the southern semi-humid areas, FVC suppressed the ‘low–high’ trade-offs between ESs, indicating the adaptability of grassland restoration efforts. (3) The impact of human activities on ES interactions has increased in the last 10 years, and exhibited positive effects on the ‘low–low’ ES interactions in northern semi-arid areas. However, the expansion of trade-off between soil conservation and carbon sequestration warrants attention. This study offers important insights into understanding the spatial interactions among carbon, water, and soil-related ESs in drylands.

Quality Evaluation, health risk Assessment, and geographic origin tracing of Ophiocordyceps sinensis through mineral element analysis

Ophiocordyceps sinensis (OS) is a unique and precious medicinal fungus endemic to the Tibetan Plateau, growing underground for the majority of its lifecycle. The mineral composition of OS is a distinctive feature, yet there is currently a lack of comprehensive mineral profiles. The concerns regarding excessive heavy metals and associated health risks significantly impact the development of the OS industry. Furthermore, OS samples from various production areas exhibit distinct qualities, resulting in significant price differences. Mineral profiles hold great potential for origin tracing. Consequently, there is an urgent need for quality evaluation, health risk assessment, and geographic origin tracing of OS by mineral element analysis. Here, 96 OS samples were collected from the representative producing areas in China to investigate their mineral profiles by inductively coupled plasma-optical emission spectrometry (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS). Potassium (K) was found to be the most abundant mineral in the OS samples, with a mean content of 10.73 g/kg. High levels of phosphor (P), sulfur (S) and magnesium (Mg) were also detected with concentrations exceeding 1 g/kg. The mean concentrations of total arsenic in all OS samples were determined to be 8.33 mg/kg. The OS from Nagqu exhibited a relatively high quality with lower levels of lead (Pb), cadmium (Cd) and arsenic. Health risk assessment based on the total target hazard quotient (THQ) value of heavy metals of Zn, Mn, Fe, copper (Cu), nickel (Ni), chromium (Cr), Pb and Cd indicated that consumption of OS from China did not pose an obvious risk to human health at the ingestion rate declared by the Chinese Pharmacopeia. Finally, three orthogonal partial least squares discriminant analysis (OPLS-DA) models were constructed using 75 samples to trace the geographic origins. These models successfully authenticated OS samples from six producing areas and confirmed by 21 blind test samples with 90.48 % accuracy. These results affirmed that OS from different regions possessed distinct metal profiles, which could be utilized to authenticate the geographic origins of O. sinensis.

Understanding Forearm Muscle Activity during Everyday Common Grasps: Insights for Rehabilitation, Prosthetic Control, and Human–Machine Interaction

The specific role of forearm muscles in the development of activities of daily living (ADL) remains unknown. Consequently, studying forearm muscle activity during the most commonly used grasps in ADL would yield valuable insights for hand function evaluation, rehabilitation, and advancements in prosthetic control. In this study, forearm muscle activity was analyzed in 22 healthy subjects, examining seven representative forearm areas during the performance of seven types of grasps at 50% of maximum effort. A Scheirer–Ray–Hare test revealed significant differences for grasp, spot, and their interaction (α < 0.05), but not for repetition (and its interactions). Specific significant differences between grasps were found in specific spots by means of Bonferroni post hoc analyses, ensuring the possibility to discriminate between grasps, which is key to identifying the person’s intention to perform a particular grasp. The median values ranged from 4.4% to 32.8%, depending on the spot and grasp, with small 95% confidence intervals (0.5% to 5.5%). Cylindrical grasp requires the highest muscle activity among all spots, while lateral pinch demands the least. The findings elucidate the contribution, coordination, and function of each muscle in relation to each grasp, with implications for rehabilitation, prosthetics, and telerobotic and teleoperation systems.

Exploring the Diagnostic Challenges of Uterine Lesions in Frozen Section: A Comprehensive Study at a Tertiary Care Center.

Introduction Uterine masses are commonly submitted for frozen section, to guide the surgeon, regarding the type and extent of the procedure during surgery. Despite the technical difficulties in processing, sectioning, and staining of frozen section samples, it remains a fairly reliable intraoperative tool. Aim This study aims to analyze the diverse spectrum of uterine masses sent for frozen sections for two years. In addition, it aims to analyze the histomorphology of the uterine masses sent for the frozen section and correlate it with that of the routine histopathological findings, thereby justifying the diagnostic value of the frozen section with this study. Furthermore, the study aims to classify the lesions into benign and malignant, quantify their frequency, and listthe most common lesions seen in the uterine mass specimens sent for frozen section analysis. Methodology This retrospective descriptive study includes data from January 2021to December 2022, retrieved from the archives of the Department of Pathology at Saveetha Medical College.This study includes a total of 76 cases, including all the uterine masses sent for frozen section analysis during the study period. Results Of the total of 76 cases received, 17 (22.4%) were malignant and 59 (77.6%) were benign. Of the malignant cases reported, the most common was endometrial carcinoma, and the least common entities encountered were carcinosarcoma and leiomyosarcoma. Of the benign cases, benign endometrial polyp was the most common endometrial lesion and leiomyoma with and without degeneration was the most common myometrial lesion encountered. Of the 50 cases of leiomyoma encountered, 16 had extensive degenerative changes. The most common degeneration seen in the fibroid was hyaline degeneration, and the least common was xanthogranulomatous degeneration. Conclusions The intraoperative frozen section analysis is a very important diagnostic tool, but we need to be aware of its limitations. The accuracy, sensitivity, and specificity rates were found to be high. Thus, frozen section diagnoses can be very valuable in the clinical management of uterine tumors. Careful gross examination, sampling from representative areas, and good communication between the pathologist and surgeon may help in avoiding its limitations.

Antimicrobial Susceptibility Pattern of Bacterial Pathogens in Our Setup: Ear, Nose and Throat Perspective

With a high prevalence of infections involving the ear, nose, and throat, and the occurrence of drug resistance the antimicrobial susceptibility pattern of bacterial pathogens is of immense importance. Objective: To determine the antimicrobial sensitivity profile of pathogenic bacteria isolated from representative infected areas of patients with ear, nose, and throat infections. Methods: This cross-sectional observational study was conducted at Ear, Nose and Throat (ENT) Outpatient Department (OPD), Capital Hospital Islamabad, over two years. The sample included 639 pathogenic bacterial culture specimens, grown from the representative infected ear, nose, or throat of patients who attended ENT outpatients of the hospital. The bacterial cultures were subjected to the standard disc agar diffusion method to know the antimicrobial susceptibility profile. Data collected included the patient’s age, gender, area/ site of infection, bacterial pathogen isolated, and sensitivity to antibiotics. Results: The sample included 49.30% males and 50.70% females and mean age of 30.13±19.24 years. Gram-positive organisms were predominant [n=441, (69.01%)] with Staphylococcus aureus being the commonest isolate (67.92%) followed by Pseudomonas spp. (26.13%). Gram-positive isolates were sensitive to Cefoperazone+Sulbactam, Ceftazidime, Cefoperazone, Amikacin, Pipercillin+Tazobactam, Vancomycin, Gentamycin, Linezolid, Amoxicillin + Clavulanate, and Ceftriaxone with resistance to Cefixime, while the Gram-negative isolates were sensitive to Vancomycin and Meropenum, Ciprofloxacin, Levofloxacin, Ceftriaxone and Ceftazidime and highly resistant to Cefixime, Cefuroxime, Amoxicillin+Clavulanate and Co-trimoxazole. Conclusions: Since ENT infections are predominated by Staphylococcus aureus and Pseudomonas spp. When unavoidable the empirical therapy should cover these pathogens, however culture and sensitivity studies are justified keeping in view the growing resistance to antimicrobials.

2021年山东长岛毗邻海域底层渔业生物监测数据集

As one of the most representative areas of the shelf-edge sea, the waters adjacent to offshore islands are important sea-land transition areas. The ecosystems in these areas are diverse and complex due to the dual regulation of natural environmental variabilities and human activities. The adjacent waters of the Changdao Island are characterized by typical island ecology and are key migration channels, spawning grounds and habitats of fishery resources in the Bohai Sea and Yellow Sea, which are of great significance to the food web process of fishery organisms in this area. From March to December 2021, the Shandong Changdao Fishery Resources National Observation and Research Station conducted a month-by-month survey of bottom fishery stocks with 10 stations per cruise and sample measurements in the waters adjacent to the Changdao Island. Afterwards, the index of relative importance (IRI), species replacement rate, catch per unit effort (CPUE), D (Margalef richness index), H′ (Shannon-Wiener diversity index) and J′ (Pielou evenness index) were calculated from the biological measurement data of the catches that formed the present dataset. The review and proofreading of this dataset was thoroughly conducted through extensive full-sample cross-checks by multiple reviewers to guarantee the standardization and accuracy. This dataset can provide data support for the study of spatial and temporal patterns of bottom fishery stocks in the Bohai Sea and Yellow Sea and the study of special island fishery ecosystems with migratory passage characteristics.

경찰공무원의 직무만족에 미치는 영향요인 연구

In modern society, police officials are responsible for key security tasks such as crime prevention, crime response, and civil safety, and play a role in maintaining national safety and stability. Therefore, it can be said that the job satisfaction of police officials is an important factor that has a great influence on social stability and the effectiveness of law enforcement. In this study, an in-depth study was conducted to analyze the relationship between police officials' job satisfaction based on Herzberg's motivation and hygiene theory to examine the factors affecting police officials' job satisfaction. Herzberg's motivation and hygiene theory is one of the important theories for explaining and improving job satisfaction, and classifies job satisfaction and job dissatisfaction as other factors. According to the analysis, higher growth potential, promotion, praiseㆍrecognition, and achievement, which are Herzberg's motivating factors, can have a positive effect on job satisfaction, and higher hygiene factors such as communication, work environment, policyㆍ supervision, and salaryㆍallowance can have a positive effect on job satisfaction. These results support Herzberg's motivation and hygiene theory, and suggest that police organizations need to improve job satisfaction by strengthening organizational management and support. As active and active police activities of individual police officials are required, the following implications are derived based on the analysis results of this study. The first is a sense of accomplishment. As shown in the analysis results, it was found that the sense of achievement had a high effect on job satisfaction. If you feel rewarded and accomplished through your current work as a police officer, this will soon help you improve your ability and expertise. Second is the rationalization of the police personnel management system. A representative area of human resources management is promotion. This is because police officials, unlike other organizations, are a vertical class culture and have a large difference in authority and responsibility for performing their duties depending on the class. Therefore, measures should be devised to increase the rationality of the performance evaluation system and the fairness of promotion.

Novel scoring system provides high separation of diploidy and triploidy to aid partial hydatidiform mole diagnosis: an adaption of HER2 D-DISH for ploidy analysis

AimsDiagnosis of hydatidiform mole or molar pregnancy based on morphology alone can be challenging, particularly in early gestation, necessitating the use of ancillary techniques for accurate diagnosis. We sought to...

Severe nitrogen leaching and marked decline of nitrogen cycle-related genes during the cultivation of apple orchard on barren mountain

Limited available lands have led to the widespread practice of establishing orchards on barren mountains to expand apple cultivation in China. However, improper nitrogen (N) management strategies in intensive orchards escalate costs and also contribute to environmental contamination. A long-term field experiment was conducted in a representative apple-producing area in Shandong Province, China, to assess the environmental risks associated with newly established orchards. This research utilized lysimeters and soil metagenomic sequencing to investigate N leaching loss and the functional microbial profiles associated with the N cycle during apple cultivation on barren mountains. Five treatments were implemented: no inorganic N fertilizer, traditional farmer's fertilization, drip irrigation with a water-soluble fertilizer, resin-coated urea, and grass interplanting. Among treatments involving inorganic N, the total N leaching rate ranged from 16.5% to 30.7% of the total N fertilizer input. Notably, in treatments with equivalent N fertilizer applications, the water-soluble fertilizer treatment exhibited the highest N leaching loss rate -(20.2–29.0%), while the grass interplanting and resin-coated urea treatments showed significantly lower rates of N leaching loss (17.0–24.9% and 15.2–24.5%, respectively). The abundance of N cycle-related genes experienced a marked decline (17.1–18.8%) during the cultivation of orchards on barren mountains. The water-soluble fertilizer treatment displayed the highest potential for nitrification, contributing to elevated N leaching. Both resin-coated urea and traditional farmer's fertilization treatments exhibited the highest abundance of genes associated with N2O emission risk to the atmosphere. However, the grass interplanting treatment, while effectively reducing N leaching, also triggered the most pronounced soil acidification. Spearman correlation analysis revealed that the decline in pH, coupled with an increase in soil NO3--N concentration and electrical conductivity (EC), were the primary factors contributing to the reduction in the abundance of key N cycle functions. In summary, excessive use of inorganic N fertilizer in orchard environments led to increased N leaching, altered soil physicochemical properties, and significant impacts on N cycle-related genes. The elevated ionic strength in soils might be responsible for both the high N leaching loss and the decrease in N cycle function abundance. In intensive orchard cultivation, each N fertilization strategy mentioned above comes with its environmental drawbacks. Further investigation is warranted to achieve a balance between productivity and environmental protection.

Multisource elevations strategy obtaining robust seed points and reference surfaces for ground points extraction in complex terrain area

The appearance of unmanned aerial vehicle photogrammetry and airborne lidar makes it possible to obtain measurement data for complex terrains such as gullies and mountainous regions. However, extracting ground points from these abundant and massive measurement datasets is challenging. In traditional extractions, their essence is to determine the surfaces that can describe the terrain from the seed points in the grid and use them as the basis for separating non-ground points. For effective extraction, this study proposes a multisource elevations strategy (MES) obtaining robust seed points and reference surfaces. First, two-level extended grids were constructed as the basic units. Then, to select more robust values between measurement and interpolation elevations, an elevation-determination rule was established for seed points. After, based fitting and interpolation elevations of grid nodes, the correction range is determined and the elevation is corrected for reference surfaces. In two representative complex terrain areas, when non-ground points were marked as seed points, the MES effectively reduced the phenomenon of seed points moving away from the ground. Reference surfaces can also accurately represent the global change trend and local elevation of the ground in areas where the terrain changes rapidly. This strategy provides a new thinking for ground point extraction from point cloud.

Weather and the City: Machine Learning for Predicting and Attributing Fine Scale Air Quality to Meteorological and Urban Determinants.

Urban air quality persists as a global concern, with critical health implications. This study employs a combination of machine learning (gradient boosting regression, GBR) and spatial analysis to better understand the key drivers behind air pollution and its prediction and mitigation strategies. Focusing on New York City as a representative urban area, we investigate the interplay between urban characteristics and weather factors, showing that urban features, including traffic-related parameters and urban morphology, emerge as crucial predictors for pollutants closely associated with vehicular emissions, such as elemental carbon (EC) and nitrogen oxides (NOx). Conversely, pollutants with secondary formation pathways (e.g., PM2.5) or stemming from nontraffic sources (e.g., sulfur dioxide, SO2) are predominantly influenced by meteorological conditions, particularly wind speed and maximum daily temperature. Urban characteristics are shown to act over spatial scales of 500 × 500 m2, which is thus the footprint needed to effectively capture the impact of urban form, fabric, and function. Our spatial predictive model, needing only meteorological and urban inputs, achieves promising results with mean absolute errors ranging from 8 to 32% when using full-year data. Our approach also yields good performance when applied to the temporal mapping of spatial pollutant variability. Our findings highlight the interacting roles of urban characteristics and weather conditions and can inform urban planning, design, and policy.

Contribution of different land use catchments on the microplastic pollution in detention basin sediments

The assessment of microplastic (MP) pollution in urban areas is essential considering its abundance in freshwater, particularly due to urban wet weather discharge. The precise sources of MPs must be identified to better understand its characteristics. This study examines the relationship between MP pollution in detention basin sediments and land use in the investigated catchments. The study of stormwater management infrastructure, mainly in detention basins, has enabled the quantification of MP abundance in sediments conveyed by stormwater in urban areas. Sediment sampling was conducted in ten detention basins and one combined sewer overflow (CSO) structure in the Lyon metropolitan area, France. These basins correspond to stormwater outlets of representative urban catchment areas. MP extraction involves densimetric separation and organic matter degradation. MPs were then characterized using micro-Fourier infrared spectroscopy and siMPle software. This protocol identified MPs between 50 and 500 μm in the study sites. This study highlights the high abundance in the collected sediment samples, ranging from 2,525 to 1,218,82 MP kg−1 by dry weight sediment. The MPs found have a median size around 115 μm, making them very small MPs that are mainly composed of polypropylene followed by polyethylene and polystyrene or polyethylene terephthalate. The abundance of MPs in sediments is associated with the land use type. Catchments in predominantly industrial and commercial zones were more significantly polluted with MPs compared with those in predominantly agricultural or heterogeneous zones. Finally, statistical analyses revealed links between sedimentary and urban parameters and MPs concentrations. Several recommendations are given for future research, notably concerning the analyzing of stormwater sediments to understand the sources of MP pollution.

Coupling effect of key factors on ecosystem services in border areas: a study of the Pu'er region, Southwestern China.

The coupling effects created by transboundary and local factors on ecosystem services are often difficult to determine. This poses great challenges for ecosystem protection and management in border areas. To decrease uncertainty, it is crucial to quantify and spatialize the impact multiple factors have on ecosystem services within different scenarios. In this study, we identified key transboundary and local factors from a set of 15 sorted factors related to four main ecosystem services. We employed a Bayesian Network-Geographic Information System (BN-GIS) model to simulate 90 scenarios with multiple factors combinations, quantifying and spatializing the coupling effects on the main ecosystem services. These simulations were conducted in the Pu'er region, which is situated alongside three countries, and serves as a representative border area in southwest China. The results showed that: (1) The coupling effects of multiple factors yield significant variations when combined in different scenarios. Managers can optimize ecosystem services by strategically regulating factors within specific areas through the acquisition of various probabilistic distributions and combinations of key factors in positive coupling effect scenarios. The outcome is a positive coupling effect. (2) Among the four main ecosystem services in the Pu'er region, food availability and biodiversity were affected by key transboundary and local factors. This suggests that the coupling of transboundary and local factors is more likely to have a significant impact on these two ecosystem services. Of the 45 combination scenarios on food availability, the majority exhibit a negative coupling effect. In contrast, among the 45 combination scenarios on biodiversity, most scenarios have a positive coupling effect. This indicates that food availability is at a higher risk of being influenced by the coupling effects of multiple factors, while biodiversity faces a lower risk. (3) Transboundary pests & diseases, application of pesticides, fertilizer & filming , population density, and land use were the key factors affecting food availability. Bio-invasion, the normalized differential vegetation index, precipitation, and the landscape contagion index were the key factors affecting biodiversity. In this case, focusing on preventing transboundary factors such as transboundary pests & disease and bio-invasion should be the goal. (4) Attention should also be paid to the conditions under which these transboundary factors combine with local factors. In the areas where these negative coupling effects occur, enhanced monitoring of both transboundary and local factors is essential to prevent adverse effects.

Explaining deep learning models for ozone pollution prediction via embedded feature selection

Ambient air pollution is a pervasive global issue that poses significant health risks. Among pollutants, ozone (O3) is responsible for an estimated 1 to 1.2 million premature deaths yearly. Furthermore, O3 adversely affects climate warming, crop productivity, and more. Its formation occurs when nitrogen oxides and volatile organic compounds react with short-wavelength solar radiation. Consequently, urban areas with high traffic volume and elevated temperatures are particularly prone to elevated O3 levels, which pose a significant health risk to their inhabitants. In response to this problem, many countries have developed web and mobile applications that provide real-time air pollution information using sensor data. However, while these applications offer valuable insight into current pollution levels, predicting future pollutant behavior is crucial for effective planning and mitigation strategies. Therefore, our main objectives are to develop accurate and efficient prediction models and identify the key factors that influence O3 levels. We adopt a time series forecasting approach to address these objectives, which allows us to analyze and predict future O3 behavior. Additionally, we tackle the feature selection problem to identify the most relevant features and periods that contribute to prediction accuracy by introducing a novel method called the Time Selection Layer in Deep Learning models, which significantly improves model performance, reduces complexity, and enhances interpretability. Our study focuses on data collected from five representative areas in Seville, Cordova, and Jaen provinces in Spain, using multiple sensors to capture comprehensive pollution data. We compare the performance of three models: Lasso, Decision Tree, and Deep Learning with and without incorporating the Time Selection Layer. Our results demonstrate that including the Time Selection Layer significantly enhances the effectiveness and interpretability of Deep Learning models, achieving an average effectiveness improvement of 9% across all monitored areas.

Bibliometric Overview of ChatGPT: New Perspectives in Social Sciences

This study delves into a bibliometric analysis of ChatGPT, an AI tool adept at analysing and generating text, highlighting its influence in the realm of social sciences. By harnessing data from the Scopus database, a total of 814 relevant publications were selected and scrutinised through VOSviewer, focusing on elements such as co-citations, keywords and international collaborations. The objective is to unearth prevailing trends and knowledge gaps in scholarly discourse regarding ChatGPT’s application in social sciences. Concentrating on articles from the year 2023, this analysis underscores the rapid evolution of this research domain, reflecting the ongoing digital transformation of society. This study presents a broad thematic picture of the analysed works, indicating a diversity of perspectives—from ethical and technological to sociological—regarding the implementation of ChatGPT in the fields of social sciences. This reveals an interest in various aspects of using ChatGPT, which may suggest a certain openness of the educational sector to adopting new technologies in the teaching process. These observations make a contribution to the field of social sciences, suggesting potential directions for future research, policy or practice, especially in less represented areas such as the socio-legal implications of AI, advocating for a multidisciplinary approach.

Characterization of temperature and humidity effects on extreme heat stress under global warming and urban growth in the Pearl and Yangtze River Deltas of China

With global warming accelerating, the heavily populated and rapidly urbanized coastal regions of the Pearl River Delta (PRD) and the Yangtze River Delta (YRD) stand as representative areas with mounting concerns about extreme heat stress. This study analyzes differentiated effects of temperature (TAS) and relative humidity (RH) on human heat stress measured by wet-bulb globe temperature (WBGT) in those urban regions based on machine learning and mathematical derivation, while also examining the impacts of global warming and urbanization on prospective heat risks. To generate fine-scale climate projections targeted at the PRD and YRD, two global projections forced by Representative Concentration Pathway (RCP) 8.5 scenario are dynamically downscaled using non-hydrostatic Regional Climate Model version 4.7 (RegCM4), with the urban density and extent updated every year based on Shared Socioeconomic Pathways 5-8.5 (SSP5) scenario, thereby incorporating the transient urban growth into future projections. The bias-corrected downscaled simulations effectively capture the distinct interdependencies between TAS and RH on WBGT across different regions, similar to the observed patterns during the historical period. While the absolute contribution of TAS to WBGT is larger than RH regardless of warming levels and regions, the relative increase in RH becomes more pronounced with warming. Under RCP8.5 scenario, unprecedentedly extreme WBGT is projected to emerge in the far future (2080–2099). In contrast, the effect of urbanization appears to be more dominant in the near future (2030–2049) as urban density under SSP5 scenario is projected to peak around the 2040s and gradually decrease afterwards. The reduction of RH is found in the intensely urbanized areas locally, but it does not significantly lower WBGT because the positive contribution of increased TAS is more dominant. As a result, highly urbanized regions still exhibit higher WBGT compared to other areas. In addition, urban heat island effect is more pronounced for compact areas with high urban density (i.e., PRD) and at night. Despite the smaller temperature increase from urban heat island effect compared to global warming, it can play a critical role in exacerbating heat stress, adding to the already dangerous humid and hot conditions.

Identifying soil groups and selecting a high-accuracy classification method based on multi-textural features with optimal window sizes using remote sensing images

Determining the spatial distribution of soil groups accurately is crucial for managing soil resources. However, limitations persist in the mapping of soil groups using multi-textural features derived from remote sensing images. Identification of the optimal window size for multi-textural feature extraction and the most effective classification method for soil group recognition using remote sensing multi-textural features remains unresolved. In this study, we investigated soil groups in a representative area of the Jiaodong Peninsula. We extracted the mean and entropy texture parameters for various window sizes (3 × 3 to 25 × 25 in odd increments) from Landsat 8 images to determine the optimal sizes for multi-textural feature extraction. The efficacy of identifying soil groups via textural features was analyzed using maximum likelihood classification (MLC), support vector machine (SVM), artificial neural network (ANN), and random forest (RF) methods to ascertain the most suitable classification approach. The results indicate that the optimal window sizes were 19 × 19 for the mean parameter and 23 × 23 for the entropy parameter. The SVM method outperformed the MLC, ANN, and RF methods in terms of the classification accuracy. Notably, the SVM classification method reached a peak accuracy of 71.61% when combining multi-textural features with the optimal windows. This demonstrates the feasibility of different soil groups using multi-textural information from remote sensing images. These findings have notable implications in guiding digital soil mapping using multi-textural features.

Cluster-based oversampling with area extraction from representative points for class imbalance learning

Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class. Resampling methods have been used to adjust the class distribution, but they often have limitations for small disjunct minority subsets. This paper introduces AROSS, an adaptive cluster-based oversampling approach that addresses these limitations. AROSS utilizes an optimized agglomerative clustering algorithm with the Cophenetic Correlation Coefficient and the Bayesian Information Criterion to identify representative areas of the minority class. Safe and half-safe areas are obtained using an incremental k-Nearest Neighbor strategy, and oversampling is performed with a truncated hyperspherical Gaussian distribution. Experimental evaluations on 70 binary datasets demonstrate the effectiveness of AROSS in improving class imbalance learning performance, making it a promising solution for mitigating class imbalance challenges, especially for small disjunct minority subsets.

An observational network of ground surface temperature under different land-cover types on the northeastern Qinghai–Tibet Plateau

Abstract. Ground surface temperature (GST), measured at approximately 5 cm in depth, is a key controlling parameter for subsurface biophysical processes at the land–atmosphere boundary. This work presents a valuable dataset of GST observations at various spatial scales in the Headwater Area of the Yellow River (HAYR), a representative area of high-plateau permafrost on the northeastern Qinghai–Tibet Plateau (QTP). GST was measured every 3 h using 72 iButton temperature loggers (DS1922L) at 39 sites from 2019 to 2020. At each site, GST was recorded in two plots at distances from 2 to 16 m under similar and different land-cover conditions (steppe, meadow, swamp meadow, and bare ground). These sensors proved their reliability in harsh environments because there were only 165 biased measurements from a total of 210 816. A high significant correlation (>0.96, p<0.001) was observed between plots, with a mean absolute error (MAE) of 0.2 to 1.2 °C. The daily intra-plot differences in GST were mainly <2 °C for sites with similar land cover in both plots and >2 °C when GST of bare ground was compared to that of sites with vegetation. From autumn to spring, the differences in GST could increase to 4–5 °C for up to 15 d. The values of the frost number (FN) were quite similar between the plots with differences in FN <0.05 for most of the sites. This dataset complements the sparse observations of GST on the QTP and helps to identify the permafrost distribution and degradation at high resolution as well as to validate and calibrate the permafrost distribution models. The datasets are openly available in the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.272945, Şerban and Jin, 2022).