Model Assessment Research Articles

Development of individualized risk assessment models for predicting post-traumatic epilepsy 1 and 2 years after moderate-to-severe traumatic brain injury: A traumatic brain injury model system study.

Although traumatic brain injury (TBI) and posttraumatic epilepsy (PTE) are common, there are no prospective models quantifying individual epilepsy risk after moderate-to-severe TBI (msTBI). We generated parsimonious prediction models to quantify individual epilepsy risk between acute inpatient rehabilitation for individuals 2 years after msTBI. We used data from 6089 prospectively enrolled participants (≥16 years) in the TBI Model Systems National Database. Of these, 4126 individuals had complete seizure data collected over a 2-year period post-injury. We performed a case-complete analysis to generate multiple prediction models using least absolute shrinkage and selection operator logistic regression. Baseline predictors were used to assess 2-year seizure risk (Model 1). Then a 2-year seizure risk was assessed excluding the acute care variables (Model 2). In addition, we generated prognostic models predicting new/recurrent seizures during Year 2 post-msTBI (Model 3) and predicting new seizures only during Year 2 (Model 4). We assessed model sensitivity when keeping specificity ≥.60, area under the receiver-operating characteristic curve (AUROC), and AUROC model performance through 5-fold cross-validation (CV). Model 1 (73.8% men, 44.1 ± 19.7 years, 76.1% moderate TBI) had a model sensitivity = 76.00% and average AUROC = .73 ± .02 in 5-fold CV. Model 2 had a model sensitivity = 72.16% and average AUROC = .70 ± .02 in 5-fold CV. Model 3 had a sensitivity = 86.63% and average AUROC = .84 ± .03 in 5-fold CV. Model 4 had a sensitivity = 73.68% and average AUROC = .67 ± .03 in 5-fold CV. Cranial surgeries, acute care seizures, intracranial fragments, and traumatic hemorrhages were consistent predictors across all models. Demographic and mental health variables contributed to some models. Simulated, clinical examples model individual PTE predictions. Using information available, acute-care, and year-1 post-injury data, parsimonious quantitative epilepsy prediction models following msTBI may facilitate timely evidence-based PTE prognostication within a 2-year period. We developed interactive web-based tools for testing prediction model external validity among independent cohorts. Individualized PTE risk may inform clinical trial development/design and clinical decision support tools for this population.

Brachial-Ankle Pulse Wave Velocity as a Predictor of Diabetes Development: Elevated Risk Within Normal Range Values in a Low-Risk Population.

Recent studies have suggested that increased brachial-ankle pulse wave velocity (baPWV) is a risk factor for diabetes. Exploring its relationship with insulin resistance is of interest, necessitating further studies across different sexes and age groups. This cohort study involved 119 170 Korean adults with an average age of 39.8 years, none of whom had diabetes at baseline. As part of a health screening, baPWV measurements were taken. Over a median follow-up period of 5.6 years, fasting blood glucose, glycated hemoglobin, insulin levels, and questionnaire responses were collected. The risk of developing diabetes was evaluated using a flexible parametric proportional hazards model with data stratified by sex and age group (<40 versus ≥40 years). During the follow-up period, diabetes was diagnosed in 5966 participants (5.0%). A fully adjusted model found that the hazard ratios for diabetes onset associated with baPWV quartiles Q2 (1171.0-1270.5 cm/s), Q3 (1271.0-1376.0 cm/s), and Q4 (≥1376.5 cm/s) compared with Q1 (<1171.0 cm/s) were 1.06 (95% CI, 0.96-1.17), 1.25 (1.14-1.38), and 1.48 (1.34-1.62), respectively (P for trend <0.001). A significant sex-based interaction was noted in this association, with women showing a higher risk of diabetes development. Furthermore, higher baPWV quartiles were associated with an increased risk of developing insulin resistance, defined as the homeostatic model assessment of insulin resistance. These findings highlight the importance of arterial stiffness, as measured by elevated baPWV, in the development of diabetes and insulin resistance. Notably, this study highlighted a strong association, particularly among women.

Metabolic, genetic and immunological features of relatives of type 1 diabetes patients with elevated insulin resistance.

Insulin resistance plays a pivotal role in the preclinical stages of type 1 diabetes (T1D). This study aims at exploring the genetic, metabolic, and immunological features associated with insulin resistance among individuals at risk of developing T1D. We retrospectively selected relatives of individuals with T1D from participants in the TrialNet Pathway to Prevention study. They were categorized into two groups: high-H (n = 27) and low-H (n = 30), based on the upper and lower quartiles of insulin resistance assessed using the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Genetic predisposition was determined using the T1D Genetic Risk Score 1 (GRS1). Additionally, glucose control was evaluated through an oral glucose tolerance test and levels of metabolic hormones and inflammatory cytokines were measured in the serum. Flow cytometry analysis was employed to assess frequency and phenotype of islet-specific CD8 T cells. While GRS1 were similar between the low-H and high-H groups, high-H individuals displayed a distinct metabolic profile, characterized by compensatory hyperinsulinemia, even while maintaining normoglycemia. Circulating cytokine levels were similar between the two groups. However, immune profiling revealed a central memory and activated profile of GAD65-specific CD8 T cells, along with an increased frequency of insulin-specific CD8 T cells in high-H individuals. The enrichment in insulin-specific CD8 T cells was independent of body mass. These findings highlight the intricate interplay between insulin resistance, genetic factors, and immune activation in the context of T1D susceptibility, indicating potential connections between insulin resistance and immune responses specific to islet cells.

Classification of speech arrests and speech impairments during awake craniotomy: a multi-databases analysis.

Awake craniotomy presents a unique opportunity to map and preserve critical brain functions, particularly speech, during tumor resection. The ability to accurately assess linguistic functions in real-time not only enhances surgical precision, but also contributes significantly to improving postoperative outcomes. However, today, its evaluation is subjective as it relies on a clinician's observations only. This paper explores the use of a deep learning based model for the objective assessment of speech arrest and speech impairments during awake craniotomy. We extracted 1883 3-second audio clips containing the patient's response following direct electrical stimulation from 23 awake craniotomies recorded from two operating rooms of the Tokyo Women's Medical University Hospital (Japan) and two awake craniotomies recorded from the University Hospital of Brest (France). A Wav2Vec2-based model has been trained and used to detect speech arrests and speech impairments. Experiments were performed with different datasets settings and preprocessing techniques and the performances of the model were evaluated using the F1-score. The F1-score was 84.12% when the model was trained and tested on Japanese data only. In a cross-language situation, the F1-score was 74.68% when the model was trained on Japanese data and tested on French data. The results are encouraging even in a cross-language situation but further evaluation is required. The integration of preprocessing techniques, in particular noise reduction, improved the results significantly.

Assessment of sustainable development models for rural watershed areas with a focus on environmental components

&lt;p&gt;From the point of view of the systemic approach in sustainable rural development programs, all components should be considered concerning each other, but one of the most important components is the environmental index in development programs. Considering that the lack of attention to the environmental components in the development plans of many projects has failed and many challenges have been created in the environmental components. The current research aims to evaluate the role of environmental components in the sustainable development of rural areas. For this purpose, a questionnaire was compiled and using Cochran’s formula, a sample size of 100 copies of the questionnaire was completed from household heads, and in addition, 20 copies of the questionnaire were completed to survey the relevant officials. SPSS software was used to classify and analyze data and information, and the AHP model was used to examine the relationship between variables and prioritize them. The studied area was located in the north of Tehran-Mashhad asphalt road and about 50 km east of Garmsar city. The results of the study indicate that there is a significant relationship between the environmental components and the sustainable development of the rural areas of the studied area, which requires the special attention of the officials in this sector. It has the most importance in this region. At the end, suggestions for better planning and sustainable development are given.&lt;/p&gt;

Is publishing gold open-access worth it? An assessment of hybrid and gold open-access publishing models of medical journals on their impact

Background Hybrid and gold open-access (OA) are the most common publishing models. The latter requires fees to allow full-text visibility upon publishing, whereas hybrid journals offer the option to publish gold OA or for free (subscription-based) where only users with access can get the full-text. We aimed to evaluate the impact of the publishing model and other factors on medical journals’ impact. Methods A sample of hybrid and gold OA medical journals indexed in Web of Science (WOS) and Scopus were included. The effect of the publishing model and other factors on journals’ impact factor (IF), CiteScore, quartile, and number of citations was assessed. Results 402 journals were included, 201 in each group. Hybrid and gold OA journals had a median age of 32 and 21 years, respectively (P&lt;0.001). The median publishing cost in gold OA journals was $2,690, and 46.3% of them publish continuously. Publishing model, journal’s age, being of an organization/society, and EMBASE indexation didn’t affect IF, CiteScore, number of citations, and WOS quartile (P&gt;0.05). However, gold OA model wasn’t significantly associated with Q1 ranking in Scopus (OR, 0.49; 95% CI, 0.25-0.94; P=0.032), which indicates that hybrid journals were more likely to have such ranking. Conclusion These findings indicate that gold OA publishing doesn’t necessarily result in higher impact, which contradicts the claim that such model enhances citations. Therefore, authors can continue to publish in hybrid journals without being concerned about getting cited. Gold OA journals are encouraged to reduce their fees to facilitate global research access.

КОМБІНОВАНА МОДЕЛЬ ПРОЦЕСУ РОЗРОБЛЕННЯ АПАРАТНО-ПРОГРАМНОГО МОДУЛЯ ОБРОБКИ ДАНИХ СТУДЕНТСЬКОГО НАНОСУПУТНИКА CUBESAT

The CubeSat nanosatellite concept has been a game-changer in the field of space science research and the development of cutting-edge space technologies. The primary factors for their success are low cost, relative simplicity of production, and predictable lifecycle. CubeSats are highly important for preparing future engineers: bachelor's and master's students of aerospace universities. Therefore, the use of CubeSats is a cost-effective way to explore near space and conduct scientific work. However, there are many issues related to the effective development of software, ensuring software quality at the system level, maintenance, and reuse of software code. The advantages and disadvantages of both classical and modern agile (Agile) software development lifecycle models are considered. To enhance flexibility and reduce the complexity of CubeSat projects, a combined model for the development of a hardware-software data processing module is proposed, which combines the advantages of two models: the waterfall model for hardware development and the agile model for software development. For a comprehensive assessment of the combined model of the data processing hardware-software module development process, the SWOT analysis methodology is used, which is a popular strategic planning tool. For its implementation, the strengths and weaknesses, external opportunities, and threats of the combined model of the data processing hardware-software module development process were formulated. The proposed combined model will allow student teams with limited experience to develop hardware-software data processing modules with minimal risks, ensuring code reuse and increasing the attractiveness of student CubeSat projects.

Geospatial mapping and entropy-based analysis for groundwater evaluation with estimation of potential health risks due to nitrate and fluoride exposure.

Groundwater is a vital source of freshwater, but its quality is often compromised by various physiochemical factors. In the Mid-Gangetic Plains, there is a concerning escalation in the degradation of groundwater quality due to anthropogenic interventions. However, there remains a paucity of comprehensive knowledge concerning groundwater quality and the associated health hazards it poses. In response to this gap, the current study focuses on Nalanda district (Bihar), where 78 groundwater samples were collected across district in the month of May 2022 and their various water quality parameters were quantified as per standard methods. The adequacy of groundwater for human use was assessed using an entropy-based water quality index (EWQI), which also evaluated the potential human health risk stemming from nitrate and fluoride contamination. Furthermore, an empirical Bayesian kriging (EBK) driven geostatistical approach was utilized to predict water quality parameters at ungauged sites. The analysis of results disclosed that the ionic dominance in groundwater followed the sequence as cations Ca2+ > Mg2+, and anions HCO3- > SO42- > Cl- > NO3- > F- > PO43-. The concentration of NO3- and F- exceeded the permissible BIS levels by 11.5% and 6.5% of the samples respectively. The analysis of EBK models suggested K-Bessel as the best-fit model for pH, Mg2+, TH, F-, NO3-, and SO42- spatial interpolation while exponential EBK model for EC, Cl-, and PO43- and whittle EBK model for TDS, Ca2+, and HCO3- spatial interpolation. Spearman's correlation analysis revealed that elevated TDS and EC levels, coupled with correlations between NO3-, SO42-, and Cl-, suggest anthropogenic influences. The EWQI of the groundwater samples ranged from 36.28 to 180.80. The analysis of EWQI values revealed predominantly fair to good groundwater quality across the study area, suitable for drinking purposes. The hazard quotients for NO3- and F- indicate that non-carcinogenic health risks are more significant with nitrate pollution. The combined health impact was assessed using total hazard index (HI), ranging from 0.20 to 3.29 for children, 0.19 to 3.05 for males, and 0.17 to 2.70 for females. The cumulative probability distribution revealed total hazard index (HI) > 1 in 41.56%, 34.62%, and 28.21% of samples for children, males, and females. The HI analysis indicated a substantially higher risk for children compared to adults within the study area. This study offers a novel combination of entropy-based water quality assessment and geostatistical EBK modeling to evaluate groundwater quality and health risks in ungauged areas. The findings provide valuable insights for improved groundwater management and health risk mitigation.

Novel junctionless GAA negative capacitance FET based on gate engineering aspects: analytical modeling and performance assessment

Novel junctionless GAA negative capacitance FET based on gate engineering aspects: analytical modeling and performance assessment

Polyethylene glycol loxenatide modulates lipid metabolism and insulin resistance through lncRNA steroid receptor RNA activator/cellular nucleic acid binding protein/Rho-associated coiled-coil kinase 2 axis in type 2 diabetes mellitus.

Polyethylene glycol loxenatide (PEG-Loxe) is applied in treating type 2 diabetes mellitus. Nevertheless, the effect and mechanism of PEG-Loxe on lipid metabolism disorder and insulin resistance in type 2 diabetes mellitus are not fully understood. Type 2 diabetes mellitus rats developed by high-fat diet/streptozotocin injection were treated with PEG-Loxe (0.3 or 1 mg/kg). Insulin resistance was evaluated by fasting blood glucose (FBG), oral glucose tolerance test, fasting insulin, homeostasis model of assessment for insulin resistance and for insulin sensitivity. Immunohistochemistry, hematoxylin and eosin staining, and biochemistry measurements were performed to assess lipid metabolism. Inflammatory response and oxidative stress were assessed by inflammatory cytokines and reactive oxygen species. Genes' expressions were tested using RT-qPCR, western blot, and in situ hybridization. Relationships of molecules were validated by pull-down assay and RNA immunoprecipitation. mRNA stability was examined by actinomycin D assay. High-PEG-Loxe decreased FBG and ameliorated glucose tolerance, hyperinsulinemia, and insulin resistance. Low-PEG-Loxe partly while high-PEG-Loxe apparently relieved hepatocyte injury, reduced lipase I, triglyceride, total cholesterol and leptin, and increased adiponectin in type 2 diabetes mellitus rats. PEG-Loxe mitigated inflammatory response and oxidative stress. High-PEG-Loxe reduced RhoA and Rho-associated coiled-coil kinase 2 (ROCK2) in liver tissues of type 2 diabetes mellitus rats, while both doses of PEG-Loxe decreased steroid receptor RNA activator (SRA). SRA overexpression reversed the protective functions of high-PEG-Loxe. SRA cooperated with cellular nucleic acid binding protein (CNBP) to enhance ROCK2 mRNA stability. High-PEG-Loxe relieves insulin resistance and lipid metabolism disorder in type 2 diabetes mellitus through SRA/CNBP/ROCK2 axis. This research provides a molecular mechanism of PEG-Loxe for treating type 2 diabetes mellitus.

The Benefits of Hierarchical Ecosystem Models: Demonstration Using EcoState, a New State‐Space Mass‐Balance Model

ABSTRACTEcosystem models predict changes in productivity and status for multiple species, and are important for incorporating climate‐linked dynamics in ecosystem‐based fisheries management. However, fishery regulations are primarily informed by single‐species stock assessment models, which estimate unexplained variation in dynamics (e.g., recruitment, survival, fishery selectivity, etc) using random effects. We review the general benefits of estimating random effects in ecosystem models: (1) better representing biomass cycles and trends for focal species; (2) conditioning interactions upon observed biomass for predators and prey; (3) easier replication of model results using formal estimation rather than informal model “tuning;” and (4) attributing process errors via comparison amongst different models. We then demonstrate these by introducing a new state‐space model EcoState (and associated R‐package) that extends mass balance dynamics from Ecopath with Ecosim. This model estimates mass balance (Ecopath) and time‐dynamics (Ecosim) parameters directly via their fit to time‐series data (biomass indices and fisheries catches) while also estimating the magnitude of process errors using RTMB. A real‐world application involving Alaska pollock (Gadus chalcogrammus) in the eastern Bering Sea suggests that fluctuations in krill consumption are associated with cycles of increased and decreased pollock production. A self‐test simulation experiment confirms that estimating process errors can improve estimates of productivity (growth and mortality) rates. Overall, we show that state‐space mass‐balance models can be fitted to time‐series data (similar to surplus‐production stock assessment models), and can attribute time‐varying productivity to both bottom‐up and top‐down drivers including the contribution of individual predator and prey interactions.

Research on Climate Drivers of Ecosystem Services’ Value Loss Offset in the Qinghai–Tibet Plateau Based on Explainable Deep Learning

As one of the highest and most ecologically vulnerable regions in the world, the Qinghai–Tibet Plateau (QTP) presents significant challenges for the application of existing ecosystem service value (ESV) assessment models due to its extreme climate changes and unique plateau environment. Current models often fail to adequately account for the complex climate variability and topographical features of the QTP, making accurate assessments of ESV loss deviations difficult. To address these challenges, this study focuses on the QTP and employs a modified ESV loss deviation model, integrated with explainable deep learning techniques (LSTM-SHAP), to quantify and analyze ESV loss deviations and their climate drivers from 1990 to 2030. The results show that (1) between 1990 and 2020, the offset index in the eastern QTP consistently remained low, indicating significant deviations. Since 2010, low-value clusters in the western region have significantly increased, reflecting a widening range of ecological damage caused by ESV losses, with no marked improvement from 2020 to 2030. (2) SHAP value analysis identified key climate drivers, including temperature seasonality, diurnal temperature variation, and precipitation patterns, which exhibit nonlinear impacts and threshold effects on ESV loss deviation. (3) In the analysis of nonlinear relationships among key climate drivers, the interaction between diurnal temperature range and precipitation in wet seasons demonstrated significant effects, indicating that the synergistic action of temperature variation and precipitation patterns is critical to ecosystem stability. Furthermore, the complex nonlinear interactions between climate factors exacerbated the volatility of ESV loss deviations, particularly under extreme climate conditions. The 2030 forecast highlights that wet season precipitation and annual rainfall will become key factors driving changes in ESV loss deviation. By combining explainable deep learning methods, this study advances the understanding of the relationship between climate drivers and ecosystem service losses, providing scientific insights for ecosystem protection and sustainable management in the Qinghai–Tibet Plateau.

Research on Deflection Risk Assessment for Steel Box Girder Bridges Based on GA-BP Neural Network

Steel box girder bridges constitute a pivotal structural component in modern bridge engineering, confronting intricate mechanical environments and dynamic conditions during construction, with a particularly notable risk of deflection. Risk assessments predominantly rely on traditional mechanical analyses and empirical judgments, which need help to fully capture the dynamic construction changes and latent risks. This study introduces an innovative risk assessment methodology grounded in finite element analysis (FEA) and optimized by a genetic algorithm-enhanced back propagation neural network (GA-BP) to address these limitations. This approach entails constructing an FEA model to precisely simulate and predict the mechanical behavior during the construction phase, with field data validation ensuring the model’s accuracy. The GA-BP assessment model is established by further incorporating the genetic algorithm to optimize the BP neural network, enabling comprehensive, systematic, and efficient risk assessment. Through practical application case studies, this methodology demonstrates the ability to accurately identify the critical risk factors influencing deflection during the construction phase of steel box girder bridges, providing a scientific basis for construction control. This research holds significant theoretical value and practical significance, and it offers a scientific foundation for risk management, construction optimization, and safety assurance in future bridge engineering projects, thereby enhancing the overall quality and safety of bridges.

The waist-to-height ratio is a good predictor for insulin resistance in women with polycystic ovary syndrome

ObjectiveThis study aimed to explore the role of the waist-to-height ratio (WHtR) in assessing insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS).Materials and methodsWe enrolled 882 PCOS-afflicted women in a cross-sectional analysis to evaluate the association of the WHtR with IR. Their demographic characteristics, anthropometric parameters, and fasting blood samples were collected and measured. Moreover, IR was evaluated by homeostatic model assessment of insulin resistance (HOMA-IR). We estimated the relationship between the WHtR and IR and the cut-off thresholds of the WHtR for IR using multivariable linear regression and logistic regression models, respectively.Result(s)The prevalence rate of IR was 51.9%. The patients with PCOS and IR displayed significantly increased values for body mass index (BMI), waist circumference (WC), WHtR, systolic blood pressure (SBP), diastolic blood pressure (DBP), free androgen index (FAI), HOMA-IR, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (ApoB). However, the patients with PCOS and IR showed a reduction in estradiol (E2), luteinizing hormone (LH), LH/FSH ratio, sex hormone binding globulin (SHBG), and high-density lipoprotein (HDL-C) values than those without IR. Moreover, BMI (log-transformed), WC, and HOMA-IR (log-transformed) were positively correlated with the WHtR. When adjusting for potential confounding variables, the WHtR was significantly associated with HOMA-IR (log-transformed), with a standardized regression coefficient of 0.271. Furthermore, the WHtR was significantly associated with an increased risk of IR, with the adjusted odds ratio (OR) of 3.15 (WHtR multiplied by 10). Additionally, the WHtR helped to identify IR in women with PCOS with an optimal cut-off point of 0.519 (Youden index = 0.433).Conclusion(s)The WHtR had a positive association with IR in women with PCOS. Hence, we suggest that the WHtR, as a simple, practical, and reliable anthropometric measure, can be used to predict the risk of IR in patients with PCOS.

Comprehensive Assessment of the Health Risk of Cadmium Exposure from Soil-wheat System in Wheat Fields in Northern China

Accurately assessing the health risks of cadmium （Cd） exposure in the soil-wheat system is of great importance for the formulation of regional health risk prevention measures. Based on regional large-scale survey and Monte Carlo simulation methods, a case study on the health risk of Cd exposure in a soil-wheat system in a wheat production area of North China Plain was conducted by constructing a comprehensive health risk assessment （CHRA） model. The results showed that 73% of the soil samples in the study area exceeded the screening value of Cd for farmland soil （GB 15618-2018） and 57.8% of the wheat samples exceeded the national standard （GB 2762-2017） for Cd. The hand-mouth ingestion pathway was the main route of soil Cd exposure for adults and children in the region, and under this pathway, 34.7% of adults and 57.4% of children had a cancer risk higher than the safe threshold recommended by USEPA （1E-06）. The comprehensive non-carcinogenic and carcinogenic risks of cadmium exposure from the soil-wheat system in the region were higher than the safe domain values for both adults and children, with the probabilities of 26.4% and 100% for adults and 62.5% and 100% for children, respectively. The overall trends of the comprehensive risks showed that children had higher risks than those of adults. The combined application of the comprehensive health risk assessment model and the uncertainty random simulation method helps to improve the accuracy of risk decision-making.

Assessment of Non-Linear Modeling of Ladle Furnace Transformer Using Finite Element Analysis

This paper assesses a non-linear model of a three-phase Ladle Furnace Transformer, on slow front transients under no-load conditions. The model is designed to maintain accuracy and reduce complexity in estimating equivalent circuit parameters using three methods: analytical calculations, finite element analysis, and test measurement. The results reveal that analytical and finite element methods show discrepancies lower than 1%. Tests measurement, on the other hand, shows discrepancies higher than 5%, when compared to ones obtained from analytical and finite elements methods. Such discrepancy is particularly high in the estimation of leakage inductances and capacitances, and it is attributed mainly to differences between the transformer design and its actual assembly. Additionally, there are inherent inaccuracies in test procedures and instrumentation errors. The proposed model does not require difficult-to-obtain parameters and incorporates the non-linearity of magnetizing inductance, contributing to more accurate simulations. This simplified model is suitable for analyzing slow front transients and can be integrated into future studies addressing vacuum circuit breaker switching in electric arc furnace power systems, contributing to performance improvements in industrial applications. Additionally, the methodology for parameter determination can be applied to conventional power transformers, highlighting its versatility.

Enrichment Characteristics, Risk Evaluation, and Source Apportionment of Heavy Metals in Wabu Lake of Yangtze River to Huaihe River Water Diversion Project

Wabu Lake serves as a crucial junction within the Yangtze River to Huai River Water Diversion Project （Yangtze-Chao Lake-Wabu Lake-Huai River）, where clarifying the status and risk levels of heavy metal contamination in the lake is crucial for the aquatic ecological safety along the route of the project. Surface water and sediment samples from 24 locations in Wabu Lake were collected and analyzed for the contents of heavy metals （As, Zn, Pb, Ni, Cu, Cr, Fe, and Cd）. Risk evaluation and source apportionment of heavy metals in the lake were conducted using the human risk assessment （HRA） model, sediment quality guidelines （SQGs）, and positive matrix factorization （PMF） method. The results showed that the average concentrations of heavy metals （except for Cd and Pb） in the surface water were lower than the class III limits of the surface water quality standard （GB 3838-2002）. The average concentration of heavy metals （As, Zn, Pb, Ni, Cu, Cr, Fe, and Cd） in interstitial water were lower than the Criteria Continuous Concentration （CCC） of the National Recommended Water Quality Criteria. The average contents of As, Zn, Pb, Ni, Cu, Cr, and Cd in the sediment exceeded the background values by factors of 1.78, 1.85, 1.42, 1.77, 1.41, 1.38, and 3.46, respectively. In addition, the heavy metals Pb, Cd, and Cr in the Wabu Lake water posed severe risks to human health, with the cancer risk （CR） value for Cr reaching 6.14×10-5. The sediment quality guidelines （SQGs） indicated that the probability of adverse effects on lakes by As, Ni, and Cr was relatively high. The spatial distribution of risk areas showed that the risk control zones for As, Cr, and Ni in Wabu Lake covered almost the entire lake area. The PMF analysis indicated that the primary sources of heavy metals in Wabu Lake were exogenous inputs from industrial, agricultural, and residential activities in surrounding areas, followed by endogenous pollution caused by enclosed aquaculture. The subsequent focus should be on the pollution of heavy metals Cr, Cd, and Pb in the overlying water of Wabu Lake as well as the sediment ecological safety of As, Cr, and Ni in the sediment.

Risk Assessment of Heavy Metals in Soil Surrounding Manganese Tailings Pond Based on Source-oriented Analysis

To analyze the source apportionment, potential ecological risk, and health risk of heavy metals in soils surrounding a manganese tailings pond in Chongqing, a positive matrix factorization （PMF） model, potential ecological risk index, and health risk assessment model were used. Further, all three models were combined to explore the risks of heavy metals in soils by different pollution sources to determine the priority control factors. The results showed that except for the Cr concentration, the average values of Mn, Cd, As, Pb, Cu, Zn, and Ni concentration were higher than their corresponding background values. Using the PMF model analysis, mining, natural, agricultural, and industrial sources were identified as the determinants for the accumulation of heavy metals in soils, with contribution rates of 23.9%, 30.2%, 18.8%, and 27.1%, respectively. Using potential ecological risk index analysis, the ecological risk was predominantly categorized as "strong" risk, with the rate of 77.8%. The health risk assessment model analysis revealed that, the carcinogenic risks of adults and children were tolerable, whereas the non-carcinogenic risks were acceptable. According to the analysis results of the relationship among heavy metals, pollution sources, potential ecological risk, and health risks showed that agricultural sources were identified as the priority control sources, with Cd as the primary control factor. Mining sources and industrial sources were identified as secondary control sources, with Mn and As as the secondary control factors.

Risk Assessment of Small-Diameter Shield Construction in a Deep Drainage Tunnel Based on an ISM–CRITIC–Cloud Model

The deep drainage tunnel project is an important measure to alleviate urban waterlogging. The construction of a deep drainage tunnel is a complicated process, involving many influencing factors, and there are correlations among these influencing factors, so the risk assessment is difficult. In this study, the ISM method, CRITIC method and cloud model are combined to build a risk assessment model for the small-diameter shield construction of a deep drainage tunnel. Firstly, the risk index system of small-diameter shield construction in a deep drainage tunnel is put forward. Secondly, the ISM method is used to divide the risk indicators and extract the key risk factors. Then, these key risk factors are weighted with the CRITIC method, and the cloud model is used to evaluate the construction risk of a small-diameter shield of a deep drainage tunnel. Finally, the feasibility and accuracy of the proposed method are verified by a practical case. It was found that the risk assessment method proposed in this study can not only effectively assess the level of security risk, but also identify the key risk factors and rank the importance of these factors. The results of this study can reduce the interference items and workload of risk assessment to a certain extent, and help provide managers with an accurate decision-making basis.

Edge Computing Architecture for the Management of Underwater Cultural Heritage

Underwater cultural heritage (UCH) is a valuable resource that preserves humanity’s historical legacy, offering insights into traditions and civilisations. Despite its significance, UCH faces threats from inadequate regulatory frameworks, insufficient conservation technologies, and climate-induced environmental changes. This paper proposes an innovative platform combining the internet of underwater things and edge computing technologies to enhance UCH’s real-time monitoring, localisation, and management. The platform processes data through a central unit installed on a buoy near heritage sites, enabling efficient data analysis and decision making without relying on cloud connectivity. Integrating acoustic communication systems, LoRa technology, and nonterrestrial networks supports a robust multilayered communication infrastructure for continuous operation, even in remote maritime areas. The platform’s edge node deploys artificial intelligence models for real-time risk assessment, focusing on key environmental parameters to predict and mitigate corrosion rates and climate-related threats. A case study illustrates the system’s capabilities in underwater localisation, demonstrating how edge computing and acoustic triangulation techniques enable precise tracking.