Curve Method Research Articles

Linking IL-6 and hsCRP among Indian patients with myocardial infarction

The association between serum interleukin-6 (IL-6) and highly sensitive C - reactive protein (hsCRP) as predictors of the risk factors for Myocardial Infarction. The study included a total of 50 patients with Myocardial Infarction, aged between 25 to 74 years. The levels of hsCRP were measured using the immunoturbidimetry method, while Interleukin 6 was estimated using the sandwich ELISA method. Statistical analysis was conducted using SPSS version 21.0, with p values calculated using Quartile ratio, ANOVA unpaired t-test, and Kaplan-Meier Curve Method. A p-value of less than 0.05 was considered statistically significant. All participants underwent a questionnaire, physical examination, medical history assessment, and laboratory tests. The results of the study showed that there was a significant correlation between IL-6 and hsCRP levels in the Quartile groups, as well as with lipid profiles. The Kaplan-Meier method also demonstrated a significant association between IL-6 and hsCRP levels in participants. The comparison of biomarkers further supported these findings. Thus, data shows that elevated levels of hsCRP and IL-6 could serve as valuable diagnostic markers for predicting Acute Myocardial Infarction. Our study strongly suggests that IL-6 could be a powerful marker in evaluating the Myocardial Infarction.

Quantifying of Radioactive Elements in Soil, Water and Plant Samples using Laser Induced Breakdown Spectroscopy (LIBS) Technique

In this study, the laser-induced breakdown spectroscopy (LIBS) technique was applied to detect radioactive elements on surface soil, plant leaves (Tamarindus indica), and flow water collected from Bala, Mayo-Kebbi Quest, Chad. The survey process indicates that the samples contain radioactive elements U, Fr, Ce, Ac, Cm, Tb, and Pm. The concentrations of the detected elements in samples were determined using the calibration curve method, while the plasma temperatures (Texc) and electron density (Ne) of the detected radioactive elements were calculated from Boltzmann linear plots. Except for uranium, all elements in the soil sample had amounts below the acceptable limit. The concentration of uranium in soil samples ranges from 0.024236 to 0.23439 ppm, with a safety limit of 0.03 ppm. The average plasma temperature Texc for uranium (U) is 700, while the electron density Ne is 500 cm-3 .

Load-bearing capacity prediction of the orthogonal double corrugated SPSW under combined compression and shear load

The orthogonal double corrugated steel plate shear wall (ODC-SPSW) is composed of two corrugated steel plates placed orthogonally and connected by high-strength bolts at the corrugation valley regions. The load-bearing capacity prediction method of the ODC-SPSW subjected to both axial compression and shear should be investigated for engineering design. Firstly, with considering the embedded ODC-SPSW being simply supported on both sides and fixed on other both sides, its compressive elastic buckling load is derived using the equilibrium method, and the formula of the compressive elastic buckling load with a correction coefficient considering the differences in boundary simplification is corrected and verified through the finite element (FE) elastic buckling analysis to obtain the calculation formula of normalized high-thickness ratio. Additionally, the compression load-bearing capacity of the ODC-SPSW is approximately equal to the sum of the compression load-bearing capacity of the embedded ODC-SPSW and edge members. Finally, the load-bearing capacity of the ODC-SPSW under the combined load of the axial compression and shear is studied, and the design method of compression-shear interactive curves is proposed to guide engineering applications.

Multi-objective optimization of variable-stiffness composite for CFRP-winding buckle arrestor by using NSGA-Ⅲ

This study introduces a multi-objective optimization method utilizing the NSGA-III algorithm to tailor the fiber shape of variable-stiffness CFRP (Carbon Fiber Reinforced Plastics) shell structures. Combined with offshore oil and gas engineering, this method is employed to perform fiber shape optimization design for CFRP-winding buckle arrestors. Based on cubic polynomial path functions' contour lines, curved fibers are represented and fitted using the Curvature Minimum Bi-Arc (CMBiArc) interpolation NURBS curve method. Employing the NSGA-III algorithm, the fiber shapes of a CFRP laminate are optimized under manufacturing constraints, targeting mean compliance, fundamental frequency, and fiber curvature. With the study of numerical modeling, buckling crossover mode and arresting efficiency, multi-objective optimization is carried out for fiber shape of CFRP-winding buckle arrestors designed for subsea pipelines. With the goal of maximizing arresting efficiency, structural fundamental frequency, and minimizing fiber curvature, the method yields an enhanced CFRP-winding arrestor design, which demonstrates a high practicability through a layered design approach. By parametric study of optimization result and comparison with slip-on arrestor, the feasibility of the optimization method and the practical value of the novel improved arrestor are substantiated.

Research on the trajectory planning and global optimization strategy of cold spraying technique for complex products coating preparation

PurposeThis paper aims to present a set of processes for obtaining the global spraying trajectory of a cold spraying robot on a complex surface.Design/methodology/approachThe complex workpiece surfaces in the project are first divided by triangular meshing. Then, the geodesic curve method is applied for local path planning. Finally, the subsurface trajectory combination optimization problem is modeled as a GTSP problem and solved by the ant colony algorithm, where the evaluation scores and the uniform design method are used to determine the optimal parameter combination of the algorithm. A global optimized spraying trajectory is thus obtained.FindingsThe simulation results show that the proposed processes can achieve the shortest global spraying trajectory. Moreover, the cold spraying experiment on the IRB4600 six-joint robot verifies that the spraying trajectory obtained by the processes can ensure a uniform coating thickness.Originality/valueThe proposed processes address the issue of different parameter combinations, leading to different results when using the ant colony algorithm. The two methods for obtaining the optimal parameter combinations can solve this problem quickly and effectively, and guarantee that the processes obtain the optimal global spraying trajectory.

Mining Trajectory Planning of Unmanned Excavator Based on Machine Learning

Trajectory planning plays a crucial role in achieving unmanned excavator operations. The quality of trajectory planning results heavily relies on the level of rules extracted from objects such as scenes and optimization objectives, using traditional theoretical methods. To address this issue, this study focuses on professional operators and employs machine learning methods for job trajectory planning, thereby obtaining planned trajectories which exhibit excellent characteristics similar to those of professional operators. Under typical working conditions, data collection and analysis are conducted on the job trajectories of professional operators, with key points being extracted. Machine learning is then utilized to train models under different parameters in order to obtain the optimal model. To ensure sufficient samples for machine learning training, the bootstrap method is employed to adequately expand the sample size. Compared with the traditional spline curve method, the trajectories generated by machine learning models reduce the maximum speeds of excavator boom arm, dipper stick, bucket, and swing joint by 8.64 deg/s, 10.24 deg/s, 18.33 deg/s, and 1.6 deg/s, respectively; moreover, the error does not exceed 2.99 deg when compared with curves drawn by professional operators; and, finally, the trajectories generated by this model are continuously differentiable without position or velocity discontinuities, and their overall performance surpasses that of those generated by the traditional spline curve method. This paper proposes a trajectory generation method that combines excellent operators with machine learning and establishes a machine learning-based trajectory-planning model that eliminates the need for manually establishing complex rules. It is applicable to motion path planning in various working conditions of unmanned excavators.

Long-term survival in stroke patients: insights into triglyceride-glucose body mass index from ICU data

BackgroundThe Triglyceride Glucose-Body Mass Index (TyG-BMI) has been established as a robust indicator of insulin resistance (IR), reflecting metabolic health across various populations. In general, lower TyG-BMI values are often associated with better metabolic health outcomes and a reduced risk of adverse health events in non-critically ill populations. Previous studies have highlighted a significant negative association between TyG-BMI and all-cause mortality (ACM) among critically ill atrial fibrillation patients. Given the high prevalence and severe outcomes associated with stroke, understanding how TyG-BMI at the time of ICU admission correlates with ACM in critically ill stroke patients becomes imperative. This study aims to assess the correlation between TyG-BMI and ACM in this specific patient cohort, exploring how traditional associations between TyG-BMI and metabolic health may differ in the context of acute, life-threatening illness.MethodsPatient data were retrieved by accessing the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.2) database, categorizing patients into three groups on the basis of TyG-BMI tertiles. The study evaluated both primary and secondary outcomes: the primary outcomes included the 90-day, 180-day, and 1-year ACM, while secondary outcomes encompassed ICU, in-hospital, and 30-day ACM. Our study employed the Kaplan–Meier (K–M) curve method for outcome comparison across the groups while utilizing multivariate Cox proportional-hazards regression models and restricted cubic splines (RCS) to explore TyG-BMI association with these outcomes. Additionally, interaction and subgroup analyses were performed, focusing on different mortality time points.ResultsAmong a cohort of 1707 individuals diagnosed with stroke, the average age was 68 years (interquartile range [IQR]: 58–78 years), with 946 (55.42%) of the participants being male. The analysis of K-M curves suggested that patients having a lower TyG-BMI level faced a heightened risk of long-term ACM, whereas the short-term ACM exhibited no statistically significant differences across the three TyG-BMI groups. Furthermore, Cox proportional-hazards regression analysis validated a statistically significant increased risk of long-term ACM among patients belonging to the lowest TyG-BMI tertile. Additionally, RCS analysis results demonstrated L-shaped correlations between the TyG-BMI index and both short- and long-term ACM. These findings underscore the TyG-BMI predictive value for long-term mortality in stroke patients, highlighting a nuanced relationship that varies over different time frames. The results revealed no interactions between TyG-BMI and the stratified variables, with the exception of age.ConclusionIn our study, lower TyG-BMI levels in critically ill stroke patients are significantly related to a higher risk of long-term ACM within the context of the United States. This finding suggests the potential of TyG-BMI as a marker for stratifying long-term risk in this patient population. However, it's crucial to note that this association was not observed for short-term ACM, indicating that the utility of TyG-BMI may be more pronounced in long-term outcome prediction. Additionally, our conclusion that TyG-BMI could serve as a reliable indicator for managing and stratifying stroke patients over the long term is preliminary. To confirm our findings and assess the universal applicability of TyG-BMI as a prognostic tool, it is crucial to conduct rigorously designed research across various populations.

Model of decomposition of the native absorption spectrum of Porphyridium purpureum culture

A model of the native absorption spectrum of the red seaweed Porphyridium purpureum culture was developed in this work. The mathematical model of each pigment is the sum of Gaussian curves. To level the light scattering, the spectra of the culture were recorded on a spectrophotometer with an integrating sphere. To verify the model, a series of parallel measurements of photosynthetic pigment concentrations using standard biochemical methods and the Gaussian curve method were performed. It was shown that the proposed model with sufficient accuracy makes it possible to determine the concentration of the main photosynthetic pigments of Porphyridium purpureum culture without interfering with its growth processes.

Analysis of spatial distribution characteristics of A-level tourist attractions in Yunnan Province based on GIS

Based on GIS geographic information technology, this paper analyses and researches the spatial distribution characteristics of tourist attractions in Yunnan Province based on the data of Class A tourist attractions in Yunnan Province in 2022, using the nearest-neighbour index, kernel density analysis, geographic concentration index, imbalance index and Lorenz curve method. The results show that: (1) The spatial distribution type of A-grade tourist scenic spots in Yunnan Province is agglomeration, and the balance is low and the concentration degree is high, and the Lorentz curve shows a significant upward trend. From the perspective of the geographical area of Yunnan Province, the number of A-level tourist attractions in Kunming, Dalizhou, Puer, Qujing and Lijiang exceeds 50% of the province, showing obvious imbalance. (2) The spatial distribution of tourist attractions in Yunnan Province is obviously different, and the overall distribution characteristics of more northwest, less eastern edge, and more central area are mainly concentrated in central Yunnan and western Yunnan, while eastern Yunnan and northeast Yunnan are less distributed.

Flood susceptibility mapping contributes to disaster risk reduction: A case study in Sindh, Pakistan

Floods are a widespread and damaging natural phenomenon that causes harm to human lives, resources, property and has agricultural, eco-environmental, and economic impacts. Therefore, it is crucial to perform flood susceptibility mapping (FSM) to identify susceptible zones to mitigate and reduce damage. This study assessed the damage caused by the 2022 flash flood in Sindh and identified flood-susceptible zones based on the frequency ratio (FR) and analytical hierarchy process (AHP) models. Flood inventory maps were generated, containing 150 flood sampling points, which were manually selected from Landsat imagery. The flood sampling points were split into 70 % for training and 30 % for validating the results. Furthermore, fourteen flood conditioning factors were considered in the analysis for developing FSM. The final FSM is categorized into five zones, representing levels from very low to very high. The results identified that the areas under very high flood-susceptible zones in Ghotki (FR 4.42 % and AHP 5.66 %), Dadu (FR 21.40 % and AHP 21.29 %), and Sanghar (FR 6.81 % and AHP 6.78 %). Ultimately, FSM accuracy was evaluated using the receiver operating characteristics and area under the curve method, resulting in Ghotki (FR 82 %, AHP 83 %), Dadu (FR 91 %, AHP 90 %), and Sanghar (FR 96 %, AHP 95 %). This study enhances the scientific understanding of flash flood impacts across diverse regions and emphasizes the importance of accurate FSM for informed decision-making. The findings provide important informations and are supportive for policymakers, agricultural planners, and stakeholders engaged in flood risk management to mitigate the adverse consequences of flash floods.

Newton Geometric Iterative Method for B-Spline Curve and Surface Approximation

We introduce a progressive and iterative method for B-spline curve and surface approximation, incorporating parameter correction based on the Newton iterative method. While parameter corrections have been used in existing Geometric Approximation (GA) methods to enhance approximation quality, they suffer from low computational efficiency. Our approach unifies control point updates and parameter corrections in a progressive and iterative procedure, employing a one-step strategy for parameter correction. We provide a theoretical proof of convergence for the algorithm, demonstrating its superior computational efficiency compared to current GA methods. Furthermore, the provided convergence proof offers a methodology for proving the convergence of existing GA methods with location parameter correction.

Efficacy of Xianling Gubao capsule vs. its combination therapy in the treatment of primary osteoporosis: A network meta-analysis of randomized controlled trials

ObjectiveThis study aimed to evaluate the efficacy of the Xianling Gubao (XLGB) capsule alone and its combination therapy in primary osteoporosis (POP). MethodsDatabases including PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Data, VIP, and SinoMed were searched from their inception to January 16, 2024, for randomized controlled trials (RCTs) investigating the XLGB treatment for POP. A network meta-analysis (NMA) was performed to evaluate the efficacy and safety of multiple interventions in the treatment of POP. The Cochrane risk-of-bias tool was used to assess the quality of RCTs included in the meta-analysis. Software Stata (version 15.0) was used for statistical analysis. The surface under the cumulative ranking curve (SUCRA) method was used to present the findings from this NMA numerically and graphically by ranking multiple interventions. ResultsA total of 107 RCTs were included in the meta-analysis, involving 10,032 participants and 21 interventions. Meta-analysis showed that XLGB + calcium (Ca) + calcitonin (99.9 %) was the most desirable treatment option for improving clinical efficacy. XLGB + Ca + bisphosphonate (BP) was most effective for improving bone mineral density (BMD) at the lumbar spine, femoral neck BMD, and serum bone Gla protein (BGP). SUCRA values for improving these three outcome measures by XLGB + Ca + BP were 87.4 %, 77.2 %, and 84.3 %, respectively. XLGB + calcitonin was the optimal option in terms of safety evaluation and improving visual analogue scale (VAS), with the SUCRA values being 89.6 % and 94.9 %, respectively. ConclusionsThe XLGB combination therapy is a desirable option for treating POP as it can effectively improve the therapeutic effects, BMD, and serum BGP, as well as relieve pain in patients with POP.

Association between predicted fat mass, predicted lean mass, predicted percent fat and type 2 diabetes mellitus in Japanese adults: a retrospective study.

Type 2 diabetes mellitus (T2DM) is known to have obesity as a risk factor. Body mass index cannot distinguish between lean mass and fat mass. We aimed to examine the association between predicted fat mass, predicted lean mass, predicted percent fat and risk of T2DM in Japanese adults. We also explored whether these three new parameters could predict T2DM better than other obesity markers. This present study is a secondary data analysis. The study enrolled 20,944 Japanese individuals who participated in the NAGALA medical assessment program between 2004 and 2015. 15,453 participants who are eligible and have complete information were included to our analysis. Through the use of Kaplan-Meier curve, restricted cubic spline and univariate and multivariate Cox regression analysis, the relationship between predicted fat mass, predicted lean mass, predicted percent fat and T2DM risk was examined. The area under the curve method was used to assess the differences between these markers of obesity. A total of 373 cases of T2DM occurred over a median time of 5.4 years. In the male group, we found a U-shaped connection between predicted fat mass, predicted lean mass, and T2DM onset (p value, non-linearity < 0.05). A linear relationship was found between predicted percent fat and T2DM onset. The linear relationship was also found in the female group for predicted fat mass, and predicted percent fat. And for women, predicted lean mass was not an independent predictor. The area under the curve (AUC) for predicted fat mass, predicted lean mass, predicted percent fat in men was 0.673 (95%CI: 0.639 ~ 0.707), 0.598 (95%CI: 0.561 ~ 0.635), 0.715 (95%CI: 0.684 ~ 0.745), respectively. In males, WHtR was the strongest predictor (AUC 0.7151, 95%CI: 0.684 ~ 0.746), followed by predicted percent fat (AUC 0.7150, 95%CI: 0.684 ~ 0.745). In the females, WHtR was also the strongest predictor (AUC 0.758, 95%CI: 0.703 ~ 0.813), followed by body mass index (AUC 0.757, 95%CI: 0.704 ~ 0.811) and predicted percent fat (AUC 0.742, 95%CI: 0.687 ~ 0.798). Predicted fat mass, predicted lean mass, predicted percent fat were strongly connected with an increased risk for developing T2DM in Japanese, particularly in males. WHtR and predicted percent fat had a slightly better discrimination than other common obesity indicators in males. In the females, predicted fat mass and predicted percent fat were associated with T2DM risk, WHtR and body mass index had the slightly higher predictive power.

Effect of Alkyl Chain Length on the Corrosion Inhibition Performance of Imidazolium-Based Ionic Liquids for Carbon Steel in 1 M HCl Solution: Experimental Evaluation and Theoretical Analysis.

In this study, five kinds of 1-alkyl-3-methylimidazolium bromide ([CXami]Br) ionic liquids with different alkyl chain lengths (8, 10, 12, 14, and 16) were selected as inhibitors. Then, their corrosion inhibition performances for Q235 steel in 1.0 mol L-1 HCl solution were investigated via a weight loss test, polarization curve method, and surface analysis techniques. The results show that these five imidazolium-based ionic liquids are all mixed-type inhibitors, and they can be spontaneously adsorbed onto the Q235 steel surface. The adsorption process follows the Langmuir model and involves mixed physical-chemical adsorption. Theoretical calculations confirm that the increase in alkyl chain length is conducive to the imidazolium-based ionic liquids exhibiting stronger chemical bonding abilities and forming denser adsorption films. The inhibition efficiency significantly increases below the critical micelle concentration (CMC) with an increase in alkyl chain length, and the highest inhibition efficiency is 95.17% for the [C16ami]Br inhibitor at the concentration of 0.005 mM. However, above the CMC, the inhibition efficiency is minimally affected by the alkyl chain length since all ionic liquid inhibitors have reached adsorption saturation on the steel surface.

Minimal important clinical difference values are not uniformly valid in the active duty military population recovering from shoulder surgery

There are multiple methods for calculating the minimal clinically important difference (MCID) threshold, and previous reports highlight heterogeneity and limitations of anchor-based and distribution-based analyses. The Warfighter Readiness Survey assesses the perception of a military population's fitness to deploy and may be used as a functional index in anchor-based MCID calculations. The purpose of the current study in a physically demanding population undergoing shoulder surgery was to compare the yields of 2 different anchor-based methods of calculating MCID for a battery of PROMs, a standard receiver operating characteristic (ROC) curve-based MCIDs and baseline-adjusted ROC curve MCIDs. All service members enrolled prospectively in a multicenter database with prior shoulder surgery that completed pre- and postoperative PROMs at a minimum of 12 months were included. The PROM battery included Single Assessment Numeric Evaluation (SANE), American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form (ASES), Patient Reported Outcome Management Information System (PROMIS) physical function (PF), PROMIS pain interference (PI), and the Warfighter Readiness Survey. Standard anchor-based and baseline-adjusted ROC curve MCIDs were employed to determine if the calculated MCIDs were both statistically and theoretically valid (95% confidence interval [CI] either completely negative or positive). A total of 117 patients (136 operations) were identified, comprising 83% males with a mean age of 35.7±10.4 years and 47% arthroscopic labral repair/capsulorrhaphy. Using the standard, anchor-based ROC curve MCID calculation, the area under the curve (AUC) for SANE, ASES, PROMIS PF, and PROMIS PI were greater than 0.5 (statistically valid). For ASES, PROMIS PF, and PROMIS PI, the calculated MCID 95% CI all crossed 0 (theoretically invalid). Using the baseline-adjusted ROC curve MCID calculation, the MCID estimates for SANE, ASES, and PROMIS PI were both statistically and theoretically valid if the baseline score was less than 70.5, 69, and 65.7. When MCIDs were calculated and anchored to the results of standard, anchor-based MCID, a standard ROC curve analysis did not yield statistically or theoretically valid results across a battery of PROMs commonly used to assess outcomes after shoulder surgery in the active duty military population. Conversely, a baseline-adjusted ROC curve method was more effective at discerning changes across a battery of PROMs among the same cohort.

The rich-poor divide: Unravelling the spatial complexities and determinants of wealth inequality in India

This study presents the first in-depth analysis of spatial differences and factors influencing wealth distribution among households in India. It uses data from the latest National Family Health Survey, covering 707 districts. Techniques like the Lorenz curve, Gini coefficient, Location Quotient, Morans statistics, and Univariate and Bivariate LISA methods explore inequalities, concentration, and clustering patterns of rich-poor households at the district level. Additionally, spatial regression models such as OLS, GWR, and MGWR help to uncover spatial disparities and variability. Our findings demonstrate significant regional disparities, with the affluent household concentration being notably higher in north-western and southern India, while central, eastern, and northeastern regions exhibit greater inequality. Key factors impacting wealth inequality include rurality, low female literacy rates, educational level of household heads and prevalence of Scheduled Castes/Tribes. This study highlights the spatial dimensions of wealth inequality and provides a nuanced understanding of the factors contributing to these patterns. The GWR and MGWR models prove most effective, explaining more than 90% of the variation in wealth distribution factors. This study sheds light on the spatial dynamics and factors behind wealth disparities in India, offering strategic insights for equitable growth initiatives targeting diverse socio-economic sectors.

Effect of surface microstructure for corrosion resistance and magnetic properties of an amorphous cobalt-based Co-Si-Fe-Cr-Al ALLOY

The surface of an amorphous cobalt-based alloy of nominal composition Co75Si15Fe5Cr4.5Al0.5 was modified by nanostructures at anodizing in an ionic liquid – bis(trifluoromethane sulfonyl)imide 1-butyl-3-methyl- imidazolium. The magnetic (saturation specific magnetization and coercive force) and corrosion (corrosion potential and resistance) characteristics of an amorphous alloy before and after electrochemical modification of the surface by nanostructures are compared. Modification of the alloy surface partially changes its magnetic properties. After corrosion tests, an increase in the value of coercive force is observed. Corrosion tests were carried out by the method of polarization curves in Ringer’s solution. The corrosion resistance of alloys modified by oxide nanostructures is higher than the corrosion resistance of a polished alloy. The increase in corrosion resistance is mainly determined by the presence of nanostructures.

Rounding theory and method for the pitch curves of the variable center distance non-circular gear pair based on working conditions

Rounding theory and method for the pitch curves of the variable center distance non-circular gear pair based on working conditions

Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis

Ethiopia's varied landscape, significant rainfall, and diverse geological characteristics pose risks of landslides. The specific research area spans 40 km2 within the Lake Abaya catchment area in the Rift Valley of Ethiopia. This investigation aimed to map landslide susceptibility using remote sensing information, GIS technology, and frequency ratio analysis. It evaluated multiple factors influencing landslide susceptibility. The process involved meticulous mapping of thematic layers, utilizing GIS techniques and diverse data sources, including primary data, satellite imagery, and secondary sources. A combination of Google Earth image analysis and field surveys was used to map landslide susceptibility in inaccessible areas. It was determined that 138 landslide sites existed. Of these, 30% (41 points) were assigned to the test of the model and another 30% to the training of the model, for a total of 97 points. The landslide susceptibility was classified into five categories based on frequency ratio analysis of the landslide susceptibility index (LSI): very low, low, moderate, high, and very high. The northeastern sector of the study area demonstrated a comparatively diminished susceptibility to landslides, ranging from low to moderate, whereas the central and southern regions showcased markedly elevated vulnerability. An evaluation of the model's accuracy using the area under the curve (AUC) method based on test inventory landslide data produced encouraging results: 84.8% accuracy on the success rate curve and 78.8% accuracy on the prediction rate curve. Based on the frequency ratio model, a susceptibility map is derived to represent susceptibility levels accurately.

ANALYSIS OF MERCURY (HG) CONTENT IN URINE AND ITS IMPACT ON HEALTH DISORDERS OF ILLEGAL GOLD MINING WORKERS IN MALOMBA VILLAGE, DONDO SUB-DISTRICT, TOLITOLI DISTRICT

Mercury is a heavy metal element that very easily enters the bodies of living creatures and joins the food chain. The living creatures most likely to be directly exposed to mercury are traditional miners who use the amalgam method to extract gold ore. The danger of poisoning posed by mercury for humans can be diagnosed through analysis of mercury levels in urine. Furthermore, this research aims to determine mercury levels in the urine of gold mine workers and to obtain information regarding the mercury content in the urine and the health problems of gold mine workers. The method used in this research was the calibration curve method with Cold Vapor Atomic Absorption Spectophotometry (CV-AAS) analysis and descriptive methods from the results of questionnaire interviews. The results show that the mercury levels in mine workers have exceeded the threshold value set by WHO of 4 ppb while the highest mercury level obtained is 44.1 ppb, the lowest mercury level is 8.38 ppb and the control sample is 6.32 ppb, and there are 2 respondents that have symptoms of mercury exposure, namely respondent OP showing symptoms of mercury exposure in the form of frequent tingling, itching, easy fatigue, headache and eye irritation, and respondent YP showing symptoms of mercury exposure in the form of tremors, frequent tingling, itching, weakness of taste buds, difficulty swallowing, diarrhea, and headache. Based on the research results, it is found that the bodies of mining workers in Malomba Village show symptoms of exposure to mercury.