Contaminated sites are a main cause of urban soil problems and have led to increasing pollution and public risk in China as a result of the rapid growth of industrial and urban land use. Because land pollution involves extensive multi-source heterogeneous information, identifying the risk of urban soil pollution efficiently and predicting pollution-related events are important for urban environmental management. Knowledge graphs (KGs) have unique advantages in dealing with massive amounts of information. This study attempts to construct a KG of contaminated sites in South China to explore its feasibility and effectiveness in urban soil environmental management. The results demonstrate that KGs have a favorable effect in information retrieval, knowledge reasoning, and visualization. Studied cases in this article demonstrate that the KG model can achieve many functions, including the display of global information of polluted sites, and discovery of regional distribution of characteristic pollutants and main pollutants of specific industries, based on special query syntax. However, this approach is limited by some technical difficulties, such as knowledge mining of natural resources, which must be overcome in future studies to improve the operability of KG technologies.

Background Polycystic ovary syndrome (PCOS) is a common indication for in vitro fertilization (IVF). Previous studies have often assessed risk factors independently, overlooking the multifactorial interactions that frequently influence clinical pregnancy outcomes. We retrospectively analyzed electronic medical record (EMR) data from PCOS patients who were undergoing IVF. Methods Key clinical variables (age, body mass index [BMI], duration of infertility, hormonal or metabolic disorders, tubal or uterine abnormalities, ovarian conditions including luteinized unruptured follicle syndrome [LUFS], and treatment details) were one-hot-encoded. Apriori association rule mining (ARM) was applied to identify patterns linked to clinical pregnancy failure, using thresholds of support ≥ 0.05, confidence ≥ 0.60, and lift > 1. This approach revealed that multifactorial risk associations were not evident in traditional single-variable analyses. Results The overall clinical pregnancy success rate in the cohort was ~ 40%. ARM revealed several clinically meaningful patterns; notably, maternal age > 35 years frequently appeared in high-risk combinations, often with metabolic or anatomical abnormalities. For instance, the combination of LUFS and tubal obstruction was strongly associated with failure, suggesting a synergistic negative effect. Many of these multifactorial associations may be overlooked using a traditional single-variable analysis approach. Conclusions Apriori rule mining effectively identified complex combinations of risk factors for in vitro fertilization failure in polycystic ovary syndrome, informing individualized strategies. Clinically, identifying advanced-age patients with specific reproductive or metabolic abnormalities may support targeted interventions. This study shows the broader potential of combining ARM with electronic medical record data to reveal hidden patterns for personalized clinical decision-making.

Background: Polycystic ovary syndrome (PCOS) is a leading cause of anovulatory infertility and a frequent indication for in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI). Multivariable regression estimates adjusted associations, whereas association rule mining (ARM) can describe frequently co-occurring attributes; however, ARM outputs are unadjusted and may be sensitive to analytic specification. Objectives: We used ARM alongside multivariable logistic regression and internal robustness analyses to describe combinations of clinical and treatment-related variables that co-occurred with clinical pregnancy failure among women with PCOS undergoing IVF/ICSI. Methods: We retrospectively analyzed 733 deidentified patients with PCOS from the Reproductive Hospital of Guangxi. The original one-hot-encoded analytic file was linked to a supplemental clinical record file containing continuous age, BMI, infertility duration, anti-Müllerian hormone, total gonadotropin dose, fertilization method, embryo-transfer day, number of embryos transferred, and embryo score. Embryo quality was derived from D3 cleavage-stage and blastocyst morphology records. Multivariable logistic regression was used to estimate adjusted associations. ARM results were assessed using Fisher’s exact test, Benjamini–Hochberg false discovery rate (FDR) correction, 1000 bootstrap resamples, fivefold cross-validation, and threshold/cutoff sensitivity analyses. Results: In the multivariable model (n = 730), age remained associated with clinical pregnancy failure (adjusted odds ratio [aOR] = 1.04 per year, 95% CI 1.00–1.08, p = 0.031), whereas BMI was not independently associated with failure (aOR = 1.00 per kg/m², 95% CI 0.96–1.05, p = 0.860). Transfer of two embryos (aOR = 0.55, 95% CI 0.35–0.85, p = 0.008), D5 blastocyst transfer (aOR = 0.49, 95% CI 0.32–0.75, p = 0.001), and transfer of at least one high-quality embryo (aOR = 0.63, 95% CI 0.43–0.93, p = 0.020) were associated with lower odds of failure. Under the primary host-factor specification, age > 35 years + BMI ≥ 24 kg/m² + pelvic adhesion defined 48 patients, 30 of whom had clinical pregnancy failure (support = 0.07; confidence = 0.63; lift = 1.38; FDR q = 0.098). In the secondary treatment-inclusive analysis, infertility duration > 5 years + no high-quality embryo transferred defined 43 patients with 30 failures (support = 0.06; confidence = 0.70; lift = 1.54; FDR q = 0.026). Lift represents the subgroup failure proportion divided by the overall failure proportion and is not an adjusted risk ratio. The rule composition and the number of FDR-supported rules changed under alternative cutoffs and confidence thresholds. Conclusions: ARM described transparent but specification-sensitive co-occurrence profiles that complement, rather than compete with, regression. Treatment-inclusive profiles combine baseline and downstream cycle characteristics and are therefore especially exploratory. These findings require prospective multicenter validation before any clinical decision-support use.

This dataset supports the research article titled "Exploring housing price dynamics in sustainable cities through a cooperated big data driven machine learning method: case study on a typical city in China", published in City and Environment Interactions. The data were collected from multiple sources, including web-scraped real estate listings, air quality monitoring stations, public amenities using the Gaode Map API, and population data from the LandScan global dataset. The dataset includes variables describing property characteristics, accessibility, environmental quality, and land use patterns. Random Forest modeling and SHAP values were used to interpret the contribution of each feature to housing price volatility. This dataset is valuable for urban economists, planners, and data scientists studying housing market dynamics, land use policy, or spatial machine learning. It enables replication, benchmarking, and comparative studies in similar urban contexts across developing cities.

Mineral prospectivity mapping (MPM) increasingly integrates geological, geochemical, geophysical, remote-sensing, and structural evidence through machine-learning workflows. Predictive accuracy alone, however, does not establish that a prospectivity map is geologically credible or useful for exploration decisions. This critical narrative review examines explainable artificial intelligence (XAI) for MPM through the connections among model behavior, mineral-system knowledge, sampling, spatial validation, uncertainty, and field evidence. We distinguish methods demonstrated in representative MPM studies from general explanation tools and proposed applications. Study-level comparisons show that SHAP and permutation-based attribution can support evidence-layer auditing and target interpretation, while their meaning depends on correlated predictors, label construction, and evaluation design. Spatially separated evaluation tests a different generalization problem from random splitting; neither replaces newly acquired field evidence. Geological plausibility, model faithfulness, explanation stability, and decision utility therefore require separate assessment. We synthesize practical pathways for geological knowledge embedding and three-dimensional modeling, identify limits in current graph explanations and uncertainty reporting, and propose a minimum reporting checklist. Future priorities include geospatial foundation models, source-traceable language tools, three-dimensional prospectivity and four-dimensional extensions incorporating geological time, knowledge-guided hypothesis generation, integrated exploration systems, and field-based evaluation of explanations.

Background: Polycystic ovary syndrome (PCOS) is a major indication for in‑vitro fertilisation (IVF). Previous studies typically evaluate candidate risk factors in isolation, obscuring the multi‑factor interactions that often govern clinical pregnancy outcomes. Methods: This study retrospectively analyzed EMR data from PCOS patients undergoing IVF. Key clinical variables (age, body mass index, years of infertility, hormonal/metabolic disorders, tubal or uterine abnormalities, ovarian conditions including luteinized unruptured follicle syndrome [LUFS], and IVF treatment details) were one-hot-encoded. Apriori association rule mining was then used to identify patterns associated with clinical pregnancy failure, with thresholds of support ≥ 0.05, confidence ≥ 0.60, and lift > 1 to ensure robust rules. This novel approach enabled the detection of multifactorial risk associations that were not apparent in a traditional single-factor analysis. Results: The overall clinical pregnancy success rate in the cohort was ~40%. Association rule mining uncovered several clinically meaningful patterns; notably, maternal age > 35 years was a recurrent component of high-risk combinations, often alongside other factors (e.g., metabolic or anatomical abnormalities). For example, a combination of LUFS and tubal obstruction was strongly associated with failure, indicating a synergistic negative effect. Many of these multifactorial associations would have been missed by analyzing variables individually. Conclusions: Apriori rule mining effectively identified complex risk factor combinations for IVF failure in PCOS, informing individualized treatment strategies. Clinically, recognizing patients with advanced age coupled with specific reproductive or metabolic abnormalities can guide tailored interventions to improve IVF success. More broadly, this work demonstrates the potential of integrating association rule mining with EMR data for clinical decision support, enabling the discovery of hidden patterns to enhance personalized medicine.