Model Assessment Research Articles

A New Model to Assess Compensation for Eco‐Environmental Damage

ABSTRACTTo improve procedures to initiate compensation for ecological damage, and to more quickly and efficiently quantify damage during the initial stages of damage, inspiration is drawn from the US Natural Resources Damage Assessment System to construct an ecological environment assessment model that includes indicators such as pollutant hazards, environmental functions, exceedance, pollution duration, pollutant quantity, and receptor sensitivity. According to rating values, a particular incident (a “case”) is apportioned to one of three levels: significantly minor, minor, and significant damage. These levels are used as a starting point for refining the case. We applied 126 cases from City A to this model. Results indicate that it is highly consistent with the actual situation. This model has good maneuverability. Based on existing standard specifications and practical cases, this model is validated. This model can be applied to provide technical guidance and a standardized response strategy to managers in instances of environmental impact.

Significant risk factors for oral mucosa cancer in the Altai Krai population

Relevance. Squamous cell carcinoma of the head and neck, including oral and oropharyngeal cancer, ranks as the seventh most common cancer worldwide, contributing to over 660,000 new cases and 325,000 deaths annually. Understanding the interplay of adverse factors, their association with disease development, and the creation of mathematical risk prediction models can play a crucial role in enhancing screening efforts and advancing primary prevention of malignant neoplasms.Objective. This study aimed to identify significant risk factors for oral mucosa cancer in the Altai Krai population and to develop a mathematical model for disease risk assessment.Material and methods. The study included 184 patients diagnosed with oral mucosa cancer, along with a control group of 416 healthy volunteers with no history or current diagnosis of oncological diseases. A total of 39 potential risk factors were analyzed across all participants. Statistical analyses were conducted to identify region-specific risk factors for Altai Krai. Binary logistic regression and ROC analysis were used to construct the risk prediction model.Results. Comparative analysis between the patient group and the control group revealed differences in 19 of the 39 evaluated factors. However, the final risk prediction model identified five key factors significantly influencing disease development. Advanced age, smoking status, the number of cigarettes smoked, and alcohol consumption were found to substantially increase the risk of oral mucosa cancer, while engagement in intellectual work-related activities was associated with a reduced risk. The resulting formula demonstrated high predictive accuracy, with an Area Under the ROC Curve (AUC) of 0.91, a standard error of 0.024, and a 95% confidence interval of 0.856–0.955 (z-statistic: 17.50; significance level: P (area = 0.5) < 0.001). Both the sensitivity and specificity of the model were high.Conclusion. The developed risk assessment model shows great promise for helping screen and diagnose oral cancer in the Altai Krai population. This tool could give dentists and healthcare providers a simple, practical way to identify individuals at risk early by using well-established risk factors.

Glucose Metabolic Abnormalities and Their Interaction With Defective Phosphate Homeostasis in Tumor-induced Osteomalacia.

Phosphate homeostasis was compromised in tumor-induced osteomalacia (TIO) due to increased fibroblast growth factor 23 (FGF23) secretion. Nevertheless, the glucose metabolic profile in TIO patients has not been investigated. This work aimed to clarify the glucose metabolic profiles in TIO patients and explore their interaction with impaired phosphate homeostasis. 20 TIO patients, 20 individuals with normal glucose tolerance, and 20 patients with type 2 diabetes mellitus (DM) were enrolled and underwent an oral glucose tolerance test (OGTT). Serum phosphate and FGF23 concentration were monitored during OGTT. In patients with TIO, 60% (12/20) exhibited impaired glucose tolerance (IGT) and 5% (1/20) had type 2 DM. Those with IGT or type 2 DM experienced more ambulatory difficulties (69.2% vs 42.9%), lower phosphate concentrations (0.43 ± 0.10 vs 0.53 ± 0.10, P = .042), and lower calcium concentrations (2.20 ± 0.08 vs 2.30 ± 0.40, P = .001) compared to TIO patients without these conditions. According to correlation analysis, serum phosphate levels were negatively correlated with plasma glucose levels at 60 minutes (P < .001), fasting plasma insulin levels (P < .05), and homeostasis model assessment for insulin resistance (P < .05). Those with high FGF23 levels had a higher glucose level at 60 minutes (10.5 [9.3, 12.3] vs 7.3 [6.4, 10.1], P = .048) than that of low group. After glucose loading, both FGF23 and phosphate levels exhibited a decreasing trend. The development of diabetes in TIO patients may be predisposed by ambulatory issues, low phosphate, and elevated FGF23 levels. Dysglycemia might further aggravate hypophosphatemia.

Anti-Inflammatory and Pain-Relieving Effects of Arnica Extract Hydrogel Patch in Carrageenan-Induced Inflammation and Hot Plate Pain Models

Arnica montana (AM), which belongs to the daisy family Asteraceae, has a longstanding traditional use in Europe and North America for pain and inflammation treatment. This study investigates the inhibitory effects of ‘Arnica montana extract hydrogel patch (AHP)’ on Carrageenan-induced paw edema and hot plate-induced pain models. AHP exhibited transdermal permeability without the occurrence of issues like crystal precipitation. This study employed two animal model assessments using AHP, in comparison with Arnicare Gel (AG), to evaluate anti-inflammatory and pain relief effects. AHP treatment for 2 days showed a decrease in paw edema thickness in mice as compared to vehicle or AG groups; Carrageenan-induced swelling increased maximally at 1 h with the AHP group demonstrating a higher reduction. Thus, the AHP group exhibited a lower ratio of right/left paw thickness and a superior reduction in swelling, supportive of its ability to diminish edema. A histological analysis showed that AHP treatment reduced inflammatory cell infiltration. Consistently, the mRNA levels of inflammatory markers (tnfa, il1b, and il6) were decreased to a greater extent than the AG group. Particularly, tnfa inhibition was better in the AHP group, and the levels of il1b and il6 transcripts showed ~80% and 40% lower. Likewise, AHP reduced pain scores in a hot plate-induced rat model, although AG failed to do so. Together, these results demonstrate that AHP has long-lasting inhibitory effects on fluid effusion and edema formation, the production of inflammatory mediators, and pain-sensation, supporting its anti-inflammatory and pain-relieving pharmacological effects.

Subclinical Carotid Disease Is Associated with Low Serum Vitamin D in Nondiabetic Middle-Aged Hypertensive Patients

Subclinical carotid artery disease anticipates major cardiovascular events, and previous studies show that low vitamin D levels are associated with arterial stiffening in hypertension. The aim of the study was to examine the relationship of 25-hydroxyvitamin D [25(OH)D] levels with subclinical carotid disease in hypertensive patients. In 223 middle-aged, nondiabetic, primary hypertensive patients free of major cardiovascular and renal complications, we measured 25(OH)D and parathyroid hormone (PTH) and assessed subclinical carotid arteries changes by B-mode ultrasonography. The carotid intima-media thickness (IMT) and presence of plaques were assessed together with measurements of indexes of carotid artery distensibility (coefficient of distensibility) or stiffening (Young’s elastic modulus; β-stiffness). Lower 25(OH)D levels were associated with older age (p &lt; 0.001), longer duration of hypertension (p = 0.019), higher fasting plasma glucose (p = 0.037), and insulin (p = 0.044), Homeostatic Model Assessment (HOMA) index (p = 0.044), and PTH (p &lt; 0.001). Insufficient and deficient 25(OH)D were associated with progressively greater carotid IMT (p &lt; 0.001), frequency of carotid plaques (p = 0.026), Young’s elastic modulus (p = 0.002), and β-stiffness (p &lt; 0.001), and progressively lower carotid coefficient of distensibility (p &lt; 0.001). Serum levels of 25(OH)D were negatively correlated with age (p &lt; 0.001), duration of hypertension (p = 0.006), fasting glucose (p &lt; 0.001), HOMA index (p = 0.032), PTH (p &lt; 0.001), carotid IMT (p &lt; 0.001), Young’s elastic modulus (p = 0.025), and β-stiffness (p &lt; 0.001), and positively related with carotid coefficient of distensibility (p &lt; 0.001). Multivariate regression analysis showed that both higher carotid IMT (p = 0.004) and lower coefficient of distensibility (p = 0.002) were related to lower 25(OH)D independent of age, severity, and duration of hypertension and metabolic variables. In conclusion, deficiency/insufficiency of 25(OH)D independently predicts subclinical carotid disease in uncomplicated, middle-aged, hypertensive patients and might predispose these patients to major cardiovascular complications.

Kidney Dysfunction Prediction by Serum Adropin in Type 2 Diabetes Iraqi Patients

Abstract Background: Diabetic nephropathy (DN) is a problem that arises from microvascular complications with type 2 diabetes mellitus (T2DM). It especially denotes the deterioration of renal function. Adropin, a regulatory peptide hormone, has attracted interest for its potential role in regulating metabolism, specifically glucose metabolism and insulin resistance. Objective: To assess Adropin’s role in the identification and severity of T2DM nephropathy. Methods: A cross-sectional study which involved an 88 patients with DN was conducted in the National Diabetes Center/Mustansiriyah University during the period from October 2023 to April 2024. Body mass index, blood analysis for sugar levels, lipid profile, renal function test, and albumin to creatinine ratio in urine were conducted. In addition, the content of human adropin was evaluated using an enzyme-linked immunosorbent assay. Results: Much more fasting blood sugar, glycated hemoglobin, insulin, homeostasis model assessment insulin resistance, cholesterol, triglycerides, low-density lipoprotein (LDL) and very LDL, urea, and creatinine were found in the microalbuminuria and macroalbuminuria groups of DN patients than in the normoalbuminuria group. The amount of adropin in the blood was lowered in people with microalbuminuria and macroalbuminuria (245.91 ± 59.47 and 179.86 ± 51.96 pg/ml, respectively) than in people with normoalbuminuria (377.97 ± 98.69 pg/ml) as well. While the subject attempts to identify the difference between the groups of DN, adropin works very well and is very sensitive. Conclusion: This study establishes a correlation between reduced adropin levels and impaired kidney function in individuals diagnosed with T2DM. Adropin content in the serum is able to serve as a biomarker for the early recognition of DN, by its role in glucose metabolism and insulin resistance.

Effects of Physical Exercise on Neurofilament Light Chain and Glial Fibrillary Acidic Protein Level in Patients with Multiple Sclerosis: A Systematic Review and Bayesian Network Meta-Analysis

Background: The prognosis of people with multiple sclerosis (MS) has improved substantially in recent decades due to advances in diagnosis and treatment. Due to the unpredictable course and heterogenous treatment response in MS, there is a clear need for biomarkers that reflect disease activity in the clinical follow-up of these patients. We conducted a systematic review with Bayesian network meta-analysis with the aim of analyzing the effects of physical exercise on neurofilaments (NfL) and glial fibrillary acidic protein (GFAP) levels in patients with MS. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, starting with a PICO (patient/population, intervention, comparison, and outcome) question: what are the clinical effects of physical exercise (with independence of the type) on NfL and/or GFAP levels in patients with MS compared with other interventions or no intervention whatsoever? A systematically comprehensive literature search was conducted from January to March 2024 to identify original studies that answered the PICO question, using the main data sources. The quality of the studies included was assessed using the Quality Index of Downs &amp; Black. For studies included in the systematic review that followed a randomized controlled trial (RCT) design, the methodological quality of each paper was assessed using the Physiotherapy Evidence Database (PEDro) Scale. Risk of bias was also explored by two independent reviewers. Finally, all articles were classified according to the levels of evidence and grades of recommendation for diagnosis studies established by the Oxford Center for Evidence-Based Medicine. For continuous outcome measures with enough comparisons and a methodological quality greater than or equal to good according to the PEDro scale, a Bayesian network meta-analysis (NMA) was applied. The statistical analyses were performed in R (version 4.1.3, R Core Team 2023) using the “BUGSnet” and “gemtc” packages. Bayesian NMA can be used to obtain a posterior probability distribution of all the relative treatment effects, which allows us to quantify the uncertainty of parameter estimates and to rank all the treatments in the network. Results: Eight studies were included in this systematic review and six articles in the NMA, and they were appraised for quality. The characteristics of the included studies, types of training and described protocols, methodological quality, risk of bias, and clinical effects on the studied biomarkers were outlined. Qualitative synthesis, effects of different exercise modalities in NfL with the Bayesian NMA, selection of the final model and model assessment, and ranking of interventions are also shown. Conclusions: Our findings indicated that moderate-intensity exercise is more likely to reduce NfL concentration compared to high-intensity exercise, and, in turn, high-intensity exercise is more likely to reduce NfL concentration than low-intensity exercise. However, the effects of high-intensity exercise on GFAP levels were inconclusive.

Temporal dynamics hidden within species baselines – multifaceted decline of a lichen bioindicator (Bryoria fuscescens)

Abstract Rapid advances in species distribution modelling have been facilitated by open availability of ‘big data’ and powerful statistical methods. A key consideration remains the time window over which field recorded occurrence data are sampled to develop a baseline species distribution. Too narrow, and distributions are incomplete and affected by sampling bias, too broad and distributions may fail to meet an assumption of equilibrium, having been affected by dynamic change across a range of different predictors. Lichens are a case in point; being diverse, functionally important and the subject of bioclimatic modelling for conservation assessment, they are nevertheless a specialist taxonomic group that is comparatively less well recorded compared to birds, mammals or vascular plants, for example. In this study, we examined the distribution of the ‘hair-lichen’ Bryoria fuscescens, based on UK record data. We partitioned records into sub-decadal periods (1970s, 1990s, 2010s), and accounting for recording effort, we compared these distributions to three predictors: an historical reconstruction of two different pollutants (sulphur dioxide and nitrogen deposition), and the climate (minimum mean temperature). We asked whether the strength of evidence for the effect of environmental predictors on Bryoria fuscescens distribution varied among the different decades, while also considering a potential for lag-effects. We show that a Bryoria fuscescens distribution that appears static, is dynamic when referenced against patterns of field recording effort. Climate was consistently important in explaining Bryoria fuscescens distribution, which was also affected by the changing pattern of pollution over time. This included a lag-effect of peak sulphur dioxide in the 1970s, and accrued effects of nitrogen deposition that strengthen over time. Overall, we conclude that Bryoria fuscescens has undergone a long-term decline in extent over the last six decades, caused by complex multivariate effects of air pollution, probably combined with climate warming. The ability to resolve these trends for assessment against future conservation targets depends critically on maintaining field identification skills and a sufficiently robust recording effort.

Electrical Performance and Photoresponse of Thin Film Transistor Based on Hybrid Blend P3HT:Mg-Doped ZnO: Assessment of Gaussian DOS Distribution and Surface Potential Models

Electrical Performance and Photoresponse of Thin Film Transistor Based on Hybrid Blend P3HT:Mg-Doped ZnO: Assessment of Gaussian DOS Distribution and Surface Potential Models

A three-stage plasma model based on one-way coupling of plasma dynamics, ionic motion, and fluid flow: Application to DBD plasma actuators

The scope of this paper is to present a comprehensive approach for simulating low-temperature atmospheric dielectric barrier discharge plasmas. The proposed methodology categorizes the primary physical phenomena: (i) discharge dynamics, (ii) ionic motion, and (iii) fluid flow, according to their respective time scales and simulates each independently. This allows for the use of distinct solution procedures tailored to each of the three stages of the problem. Such separation offers significant flexibility in choosing appropriate models and numerical schemes for each stage, enabling the simulation of complex geometries and large-scale applications without the excessive computational costs associated with a monolithic approach. As a case study, we apply the proposed algorithm to the surface dielectric barrier discharge plasma actuator for flow control, which is powered by alternating high voltages. The algorithm successfully described the actuator’s behavior while maintaining low computational cost. Additionally, a parametric study is conducted to examine the effect of key input parameters on the generated electrohydrodynamic force and the resulting velocity. Finally, an overall assessment of the three-stage model is provided, highlighting its efficiency and accuracy.

Assessment model of ozone pollution based on SHAP-IPSO-CNN and its application

The problem of ground-level ozone (O3) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems. Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorological conditions and atmospheric chemical processes. To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms. Firstly, an atmospheric dispersion model is utilized to predict the distribution concentration of VOCs emitted by enterprises in the park at the target monitoring stations based on the ozone generation mechanism. Then three mainstream machine learning models are compared for SHAP analysis to obtain the significance results of relevant features. Finally, the IPSO algorithm is combined with SHAP analysis to dynamically adjust the training features to optimize the performance of the CNN model. The model integrates atmospheric pollutants and related meteorological data to explore the nonlinear influence relationship of ozone formation in depth. The performance of the model is validated by the comprehensive evaluation indexes of R2, MAE and RMSE, and the results show that the present model outperforms the IPSO-CNN and SHAP-PSO-CNN models with the performance indexes of R2 of 0.9492, MAE of 0.0061 mg/m3 and RMSE of 0.0084 mg/m3. This study not only advances the understanding of ozone pollution formation mechanisms, but also provides an assessment of the impact of VOCs emissions from enterprises in the park, which provides empirical support for environmental management.

Hormonal Influences on ADC Values in Breast Tissues: A Scoping Review of DWI in Pre- and Post-menopausal Women

Background Breast cancer remains a significant global health concern, with early diagnosis and risk factor identification crucial for improving outcomes. Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) measurements have emerged as promising tools in breast cancer diagnostics. However, the influence of hormonal status on these measurements remains unclear. Objective This scoping review aims to synthesize current evidence on how hormonal changes in pre- and post-menopausal women influence ADC values of benign, malignant, and fibroglandular breast tissues. Method Following the Arksey and O’Malley framework, we conducted a comprehensive search of Scopus, Embase, and PubMed databases for relevant studies published between January 2000 and 2021. Inclusion criteria encompassed 1.5 Tesla MRI studies reporting ADC values in female subjects, considering menopausal status. Results Six studies meeting the inclusion criteria, involving 612 patients, were analyzed. Findings suggest that menopausal status may influence ADC values, with postmenopausal women generally showing lower ADC values in both normal fibroglandular tissue and breast lesions. The impact of menstrual cycle phases on ADC values was less consistent across studies. Conclusions This review highlights the potential influence of hormonal status on ADC values in breast tissues. While DWI with ADC mapping shows promise as a reliable diagnostic tool across different hormonal states, further research is needed to fully understand and account for hormonal influences on ADC measurements. Future studies should focus on longitudinal designs, standardization of DWI protocols, and integration of hormonal status information into breast cancer risk assessment models.

Modulation of placental angiogenesis by metformin in a rat model of gestational diabetes.

Gestational diabetes mellitus (GDM) significantly disrupts placental structure and function, leading to complications such as intrauterine growth restriction (IUGR) and preeclampsia. This study aimed to investigate the effects of GDM on placental histology, angiogenesis, and oxidative stress, as well as evaluate metformin's protective role in mitigating these changes. A total of 60 pregnant Sprague-Dawley rats were divided into four groups: control, metformin-treated, GDM, and GDM with metformin. GDM was induced using streptozotocin (STZ) at 40mg/kg, and metformin was administered at 200mg/kg from gestational day (GD) 4 to GD17. Blood glucose and insulin levels were assessed, and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) was calculated. Placentae were weighed and subjected to histological, immunohistochemical, and molecular analyses, focusing on key angiogenesis markers (VEGF, VEGFR, CD31, KLF2) and oxidative stress indicators (MDA, eNOS). GDM increased placental weight, angiogenesis (elevated VEGF, VEGFR, CD31), and oxidative stress (elevated MDA, eNOS). Histopathological changes included villous edema, membrane rupture, and hemosiderin deposition. Metformin treatment reduced placental weight; normalized VEGF, KLF2, and PlGF expression; and improved placental architecture. Additionally, oxidative stress was significantly reduced in metformin-treated GDM rats. In conclusion, GDM induces placental abnormalities, promoting excessive angiogenesis and oxidative stress, potentially leading to IUGR and other complications. Metformin showed protective effects by reducing placental overgrowth and restoring vascular and oxidative balance. These findings suggest that metformin may play a therapeutic role in improving placental health in GDM pregnancies, warranting further investigation into its long-term effects on fetal development and maternal health.

Markers for Pressure Injury Risk in Individuals with Chronic Spinal Cord Injury: A Pilot Study.

To identify markers associated with pressure injury (PrI) history in individuals with spinal cord injury (SCI) using two approaches: skin blood flow (SBF) response toward localized heating, and serum marker for insulin resistance. For this cross-sectional, observational study of adults with chronic traumatic SCI at T12 and above, researchers recruited two groups of participants: with history of PrI (group 1), and without history of PrI (group 2). The study protocol included obtaining fasting blood samples and measurement of SBF at bilateral heels with localized heating of 42 °C for 30 minutes from all participants. Primary SBF outcomes were initial peak and plateau SBF normalized to baseline SBF. The primary outcome for insulin resistance was Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), calculated from fasting plasma glucose and insulin. Secondary outcomes included demographic and SCI information. Researchers used the Fisher exact test and Wilcoxon-Mann-Whitney test to compare the intergroup difference of categorical and continuous variables, respectively. Sixteen adults completed this study (group 1, n = 7; group 2, n = 9). In comparison with group 2, group 1 had significantly higher HOMA-IR (3.90 ± 0.71 vs 1.45 ± 0.71), suggesting higher insulin resistance, and longer duration of injury (22.54 ± 7.24 vs 7.98 ± 6.58 years). There were no between-group differences in SBF or other secondary outcomes. HOMA-IR is a novel serum index associated with PrI history in persons with chronic SCI. Future longitudinal study is warranted to examine the role of insulin resistance in increasing PrI risk for the SCI population.

Assessment of Deep Neural Network Models for Direct and Recursive Multi-Step Prediction of PM10 in Southern Spain

Western Europe has been strongly affected in the last decades by Saharan dust incursions, causing a high PM10 concentration and red rain. In this study, dust events and the performance of seven neural network prediction models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), have been analyzed in a PM10 concentration series from a monitoring station in Córdoba, southern Spain. The models were also assessed here for recursive multi-step prediction over different forecast periods in three different situations: background concentration, a strong dust event, and an extreme dust event. A very important increase in the number of dust events has been identified in the last few years. Results show that CNN models outperform the other models in terms of accuracy for direct 24 h prediction (RMSE values between 10.00 and 10.20 μg/m3), whereas the recursive prediction is only suitable for background concentration in the short term (for 2–5-day forecasts). The assessment and improvement of prediction models might help the development of early-warning systems for these events. From the authors’ perspective, the evaluation of trained models beyond the direct multi-step predictions allowed to fill a gap in this research field, which few articles have explored in depth.

A Machine Learning-Based Risk Assessment Study of Unsafe Behavior of Workers in Nuclear Power Plants Under Construction

Most accidents during the construction of nuclear power plants are caused by human unsafe behavior. How to scientifically determine the risk management priority of human unsafe behaviors is the basis for effectively preventing accidents in under-construction nuclear power plants. Although employees are adopted for control in under-construction nuclear power plants, the records of unsafe behaviors are mostly recorded by inspectors, and the records of behaviors may have missing values. To overcome the above problems, this paper applies machine learning algorithms to construct an employee behavioral risk assessment model. Firstly, by analyzing the influencing factors of unsafe behaviors, the assessment indexes are proposed, then the Random Forest algorithm is used to obtain the characteristic importance of the proposed indexes and exclude those with smaller characteristic importance. Finally, the harmony search (HS) algorithm is used to optimize the back propagation (BP) neural network to construct an assessment model and compare with the BP evaluation model. The results show that the HS-BP model is more accurate and efficient. The results show that the method can comprehensively and effectively analyze workers‘ unsafe behaviors, and the BP neural network is optimized to construct the assessment model using the Harmonic Search algorithm, which is more accurate than the original model. The use of the machine learning method to assess workers’ behaviors can objectively output the risk level and overcome the one-sidedness and subjectivity of the traditional expert evaluation method.

Social Media Data-Based Rapid Hazard Assessment of Urban Waterlogging Event: A Case Study of Guilin 6.19 Waterlogging

Traditional methods for assessing urban waterlogging hazards lack real-time efficiency. This study develops a rapid hazard assessment method for urban waterlogging events using social media data and large language models, with the urban waterlogging event in Guilin in 2024 as a case study. We constructed a keyword database for urban waterlogging, combined with web crawling techniques to collect related data from social media platforms. This study applied the BERT-BILSTM-CRF model, the GPT-3.5-turbo model, and the ERNIE-3.5-128K model to extract waterlogging locations, comparing them with the official waterlogging location data to validate and select the optimal model. The model was then used for urban waterlogging hazard assessment at the township scale. The results indicate that the establishment of a keyword database facilitates access to more social media data. The GPT-3.5-turbo model demonstrates superior performance in waterlogging location identification, with a coverage of 93% compared with the actual waterlogging locations, and an F1 score of 0.8066. During the Guilin 6.19 waterlogging, areas with high hazard intensities mainly concentrated in the city center; meanwhile, the hazard intensities of some townships in the suburbs intensified from 17 June to 21 June. This study validates the effectiveness and timeliness of using social media data and large language models for rapid hazard assessment of urban waterlogging events through practical cases, which can provide reliable support for future urban disaster prevention and reduction efforts.

Digital transformation project risks assessment using hybrid picture fuzzy distance measure-based additive ratio assessment method

Digital transformation (DT) has become vital for companies trying to remain competitive in the recent ever-changing technological environment. DT is the integration of digital technologies into all disciplines of business from regular activities to strategic decision making. Risk management planning requires projects to assess possible risks that may negatively or positively affect a DT project. The purpose of the study is to introduce a hybridized decision support system (DSS) by combining the distance measure, ranking comparison (RANCOM) model and additive ratio assessment (ARAS) approach in the context of a picture fuzzy set (PFS). In this framework, the decision experts’ significance values are computed using a picture fuzzy score function-based formula. With the combination of objective weight using distance measure and subjective weight through the RANCOM model, a combined weight-determining approach is developed to determine the significance values of considered DT risks under picture fuzzy environment, while a hybrid ARAS model is developed to evaluate and rank DT projects from the risks perspective. To exhibit the feasibility of the introduced framework, a case study of a DT projects assessment problem is discussed in the context of picture fuzzy sets. A sensitivity study is also discussed over different values of the strategy coefficient, which confirms the strength of the proposed model. Further, a comparison with the existing picture fuzzy information-based methods is presented to prove the robustness of the developed decision-making framework.

Correlation of anti-oxidant and pro-oxidant enzymes with insulin resistance in diabetic nephropathy

Objective: To assess the changes in the expression of anti-oxidant and pro-oxidant enzymes in patients with type 2 diabetes and diabetic nephropathy, and to examine their correlation with insulin resistance. Method: The case-control study was conducted from March to November 2021 at the King Abdulaziz University Hospital, Jeddah, Saudi Arabia, and comprised adult patients of either gender with diabetic nephropathy in DN group, patients with type 2 diabetes but without diabetic nephropathy in T2D group, and non-diabetic individuals in the control group. Serum insulin levels were measured using a modular analyser, while nicotinamide adenine dinucleotide phosphate oxidase, glutathione S-transferase and superoxide dismutase 3 levels were measured using enzyme-linked immunosorbent assay. Data was analysed using SPSS 29.0.1. Results: Of the 74 subjects, 45(60.8%) were females and 29(39.2%) were males. The overall mean age was 53±14 years. There were 20(27%) patients in DN group with mean age 60±11 years, 29(39.2%) patients in T2D group with mean age 56±12 years, and 25(33.8%) in the control group with mean age 43±11 years. Nicotinamide adenine dinucleotide phosphate oxidase levels were significantly lower in T2D and DN groups than in the control group (p&lt;0.05). Glutathione S-transferase levels were significantly lower in the DN group than in the control group (p&lt;0.05). T2D and DN groups had significantly lower superoxide dismutase 3 levels than the control group (p&lt;0.05). Glutathione S-transferase levels correlated positively with glycated haemoglobin levels in the DN group, and negatively with fasting blood glucose levels in the T2D group (p&lt;0.05). Superoxide dismutase 3 levels were negatively correlated with insulin and homeostatic model assessment of insulin in T2D group (p&lt;0.05). Conclusion: Type 2 diabetes and diabetic nephropathy induced changes in the levels of superoxide dismutase 3, glutathione S-transferase and nicotinamide adenine dinucleotide phosphate oxidase. Low levels of superoxide dismutase 3 in type 2 diabetes correlated with insulin resistance, suggesting the need for anti-oxidant replacement therapy as part of diabetes control measures to prevent diabetic nephropathy. Key Words: Type 2 diabetes mellitus, Diabetic nephropathy, Insulin resistance, Oxidative stress, Anti-oxidant.

Multidimensional Index Model Development for Assessing Exacerbation Prognosis in COPD

Background: Acute exacerbations are a major cause of poor outcomes in patients with Chronic Obstructive Pulmonary Disease (COPD). However, attention to early recognition of acute exacerbation symptoms has been limited to FEV1 (Forced Expiratory Volume in 1 second). COPD is a complex disease that involves many dimensions, so early symptom recognition requires a more comprehensive multidimensional assessment. This study is a retrospective cohort study to develop a multidimensional index model in assessing the prognosis of exacerbations in COPD patients. Objectives: This study aims to find a comprehensive index model in the early assessment of exacerbations to reduce mortality and morbidity in COPD patients. Methods: This study is a retrospective cohort study by conducting non-interventional observations. A total of 28 COPD patients based on GOLD (Global Initiative for Chronic Obstructive Lung Disease) criteria were included as respondents. Results: The Cox Regression survival analysis for the quartiles showed a significant effect of this multidimensional index on predict exacerbation prognosis. Conclusion: The BODEX + sPO2 provided an adequate match between predicted and observed exacerbation prognosis in COPD.