Inverse Distance Research Articles

Acceleration of Numerical Modeling of Uranium In Situ Leaching: Application of IDW Interpolation and Neural Networks for Solving the Hydraulic Head Equation

The application of In Situ Leaching (ISL) has significantly boosted uranium production in countries like Kazakhstan. Given that hydrodynamic and chemical processes occur underground, mining enterprises worldwide have developed models of reactive transport. However, modeling these complex processes demands considerable computational resources. This issue is particularly significant in the context of numerical analyses of mining processes or when modeling production scenarios in uranium mining by the ISL technique, given that a substantial portion of computational resources is allocated to solving the hydraulic head equation. This work aims to explore the applicability of PINNs to accelerate hydrodynamic simulations of the ISL process. The solution of the Poisson equation is accelerated by generating an initial approximation for the iterative method through the application of the Inverse Distance Weighting (IDW) interpolation and PINNs. The impact of various factors, including the computational grid and the spacing between wells, on both the accuracy and efficiency of initial approximation and the overall solution of the elliptic equation are explored. Employing the hydraulic head distribution obtained through PINNs as the initial approximation led to a significant reduction in computation time and a decrease in the number of iterations by a factor of 2.8 to 7.10.

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 μg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS

The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (Ri) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.•The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.•The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.•The method is easiest and suitable for short term operation research.

Spatio-Temporal Model to Forecast COVID-19 Confirmed Cases in High-Density Areas of Malaysia

The coronavirus 2019 disease has spread across the world. The number of coronaviruses 2019 (COVID-19) cases throughout Malaysia is high in the densely populated state of Selangor. In assisting the early preventive measures, this study utilises time series methods to model and forecast the number of daily positive cases in three Selangor districts: Petaling, Hulu Langat, and Klang. Specifically, the study compares the effectiveness of the Autoregressive Integrated Moving Average (ARIMA), a univariate model and the Generalized Space-Time autoregressive integrated (GSTARI), a multivariate model. For the GSTARI model, uniform and inverse distance weights represent the relationship between locations. The analysed data are from January to August 2021, and the lowest root mean square error (RMSE) is chosen as the best model. The results show GSTARI (1,1) with both spatial weights outperformed ARIMA (0,1,1) in Petaling and Klang but not in Hulu Langat. However, the average RMSE values show that the most accurate and effective for forecasting the number of daily confirmed positive cases in Selangor is using GSTARI. In conclusion, by utilising advanced time series methods such as spatial analysis, this study provides important insights into forecasting trends of infectious diseases like COVID-19 and can help in early preventive measures.

Algorithm for solving multi-objective hybrid flow shop scheduling problem

Aiming at the multi-objective unrelated parallel machine hybrid flow shop scheduling problem, a multi-objective mathematical model is established with the goal of minimizing the maximum completion time, total machine energy consumption and machine processing cost. An improved multi-objective evolution algorithm based on decomposition (IMOEAD) is proposed. The uniform design table is used to generate the initial weight vector to improve the population diversity. Normal distribution crossover and adaptive Gaussian mutation are used to improve the global and local search capabilities of the algorithm. Individuals are selected in the neighborhood of the weight vector to generate new solutions. The non-dominated rank and crowding distance are used to update the external archive. The inverse generation distance, generation distance and number of non-dominated solutions are used as performance indicators. Through a large number of case simulations, the algorithm is compared with the non-dominated sorting genetic algorithm II and the multi-objective evolution algorithm based on decomposition. The results verify the effectiveness of the algorithm.

Traffic noise prediction model using GIS and ensemble machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus.

This study represents a pioneering effort to integrate geographic information systems (GIS) and ensemble machine learning methods to predict noise levels on a university campus. Three ensemble models including random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) were developed to predict traffic noise based on data collected over a 4-week period at the Universiti Teknologi Malaysia (UTM) campus. Noise measurements were obtained during peak morning hours (7:30 to 9:30 a.m.) on weekdays within the UTM campus in Johor. Additional predictor variables, including data from the digital elevation model (DEM) and land use, were incorporated to capture the complex nonlinear relationships influencing noise levels. The models were optimized through hyperparameter tuning, resulting in high precision, as evidenced by performance metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The XGB model emerged as the most accurate, with R2 = 0.96, MAE = 0.9, and MSE = 0.3. Noise maps generated using the inverse distance weighting (IDW) interpolation technique highlighted the spatial distribution of noise levels, classified into five classes considering WHO standards. The findings identified distance from roads, the number of light vehicles, and proximity to green areas as the most significant predictors. However, challenges remain in accurately predicting noise levels associated with other predictors. The outcomes of the study indicate the superior performance of the XGB model compared to the GB and RF models. The study recommends several measures to manage and control noise pollution on the UTM campus, including raising awareness, regulating and enforcing vehicle speed limits, reevaluating land use, installing sound insulation systems, and planting trees and vegetation buffer zones around and within educational buildings.

Relativistic artificial molecule of two coupled graphene quantum dots at tunable distances

In a molecule formed by two atoms, energy difference between bonding and antibonding orbitals depends on distance between the two atoms. However, exploring molecular orbitals of two natural atoms with tunable distance has remained an outstanding experimental challenge. Graphene quantum dots can be viewed as relativistic artificial atoms, thus offering a unique platform to study molecular physics. Here, through scanning tunneling microscope, we create and directly visualize the formation process of relativistic artificial molecules based on two coupled graphene quantum dots with tunable distance. Our study indicates that energy difference between the bonding and antibonding orbitals of the lowest quasibound state increases linearly with inverse distance between the two graphene quantum dots due to the relativistic nature of the artificial molecule. For quasibound states with higher orbital momenta, the coupling between these states leads to half-energy spacing of the confined states because the length of the molecular-like orbit is approximately twice that of the atomic-like orbit. Evolution from ring-like whispering-gallery modes in the artificial atoms to figure-eight orbitals in the artificial molecules is directly imaged. The ability to resolve the coupling and orbitals of the relativistic artificial molecule at the nanoscale level yields insights into the behavior of quantum-relativistic matter.

Field-scale variability and dynamics of soil moisture in Southwestern Nigeria

This research leverages field-based sensors and Geographic Information Systems (GIS) to elucidate soil moisture dynamics across diverse land uses in Southwestern Nigeria, including Cocoa, Cassava, Oil Palm, and Riparian ecosystems. The study synthesizes soil moisture data and geospatial coordinates from strategically selected land covers with satellite-derived soil moisture records from NASA's Prediction of Worldwide Energy Resources (POWER) and the US Geological Survey. Through a robust analytical framework combining cross-correlation functions, wavelet analysis, and inverse distance weighting interpolation, we revealed distinct soil moisture patterns ranging from 0 to 47.9% in arable farms, 0 to 46.8% in built-up areas, 0 to 49.9% in cocoa farms, 0.2 to 49.7% in oil palm farms, and 5.6 to 94.1% in riparian vegetation. The coefficient of determination values between 0.7 and 0.97 (p ≤ 0.05) underscore the significant influence of periodic land use on soil moisture during the study period. A comparative analysis of ground-based and satellite data revealed a potential overestimation of minimum soil moisture values by 66% and an underestimation of maximum values by 4%. This investigation provides critical insights into the interplay between hydrological variables and agro-meteorological factors, informing the management and assessment of agricultural resources.

The effects of tropical cyclone on the container shipping network: A case study of typhoon Ma-on (2022)

As climate change intensifies, tropical cyclones pose increasing challenges to container shipping networks (CSN). However, research related in this area remains insufficient. In this study, we analyzed the effects of Typhoon Ma-on on the CSN using over 776 million Automatic Identification System (AIS) trajectory signals. Our findings reveal that the typhoon not only altered the network structure of the CSN but also affected the importance of container ports, vessel flow, and travel distance fluctuations along shipping routes. After the typhoon, the recovery of the network’s connectivity and stability, as well as travel time fluctuations, was less robust compared to other aspects. Additionally, the impact ranges for port importance, vessel flow, and travel distance fluctuations were 1,750 km, 500 km, and 2,250 km, respectively, following an inverse distance decay pattern. These findings offer new insights for stakeholders to enhance the effectiveness of disaster prevention and mitigation policies.

Evaluating Spatial Interpolation Maps of the Age Structure of the Population of Thi Qar Governorate Using Geographic Information Systems (GIS) Technologies

The research aims to harness spatial interpolation techniques to produce maps with a high level of perceptual accuracy in representing the population data of the study area. This is achieved after exploring the statistical and spatial nature of the databases used, analyzing them, and determining their distribution using a variety of spatial data exploration tools available within the GIS (Geographic Information Systems) environment. These tools contribute to evaluating the characteristics, distribution, and analysis of data, including testing data distribution, identifying its direction, and uncovering its spatial correlations. This highlights the importance of the study and understanding the distribution pattern of the population data in the study area, thereby facilitating the preparation of future that serve spatial organization by building spatial models for the numerical distribution of the data for age groups: young, middle-aged, and elderly, at the level of administrative units of the study area by relying on the inductive approach and the quantitative analysis approach. This is done using Geostatistical Analysis methods within the concept of spatial interpolation in GIS. The research also seeks to predict the population maps for Thi Qar Governorate by comparing spatial interpolation methods, namely Inverse Distance Weighting (IDW) and Simple Kriging (SK). The study concluded, after verifying the accuracy of the results using the Cross-Validation curve, that the Simple Kriging (SK) method is the most accurate model in spatial prediction of population data for representing the data of the young age group in the study area, as it had the lowest Root Mean Square Error (RMSE) of 10.012. It was followed by the Inverse Distance Weighting (IDW) method for representing the data of the elderly age group with an RMSE of 350.48. This indicates that the use of statistical criteria improves the accuracy of spatial interpolation and that the spatial prediction of the population data for the study area using the Simple Kriging (SK) method was more accurate than the Inverse Distance Weighting (IDW) method based on statistical indicators.

Factors affecting Kaposi’s sarcoma-associated herpesvirus transmission in rural Ugandan households, a longitudinal study

BackgroundWe report the impact of HIV infection within a household on oral Kaposi’s sarcoma-associated herpesvirus (KSHV) shedding.MethodsWe enrolled 469 individuals from 90 households. Mouthwash rinse samples collected at three monthly visits were analyzed for KSHV DNA using quantitative polymerase chain reaction (qPCR). Generalized linear mixed effects logistic models were applied to analyze factors associated with KSHV ever shedding, and among shedders, always versus intermittent shedding. Linear mixed effects models were applied to models of KSHV viral loads. Intraclass correlation coefficients (ICCs) were calculated to assess the contribution of household-level factors to variations in shedding probabilities. Hotspot analyses of geospatial feature clusters were calculated using Getis-Ord Gi* statistic and visualized using inverse distance weighted interpolation.ResultsAnalyses included 340 KSHV seropositive individuals, aged 3 + years, with qPCR results from 89 households. Forty households had 1 + persons living with HIV (PLWH), while 49 had none. Among participants, 149(44%) were KSHV ever shedders. Of 140 who shed KSHV at two or more visits, 34(24%) were always shedders. Increasing number of KSHV seropositive household members was significantly associated with ever shedding [Odds ratio(OR) (95% Confidence Interval(95%CI)):1.14(1.03,1.26);p = 0.013]. Among KSHV shedders, a statistically significant age-related trend was identified with 10–19 years being more likely to be always shedders (type III test p = 0.039) and to have higher viral loads (type III test p = 0.027). In addition, higher viral loads were significantly associated with increasing number of household members [coefficient(95%CI):0.06(0.01,0.12);p = 0.042], increasing number of KSHV seropositive members [coefficient(95%CI):0.08(0.01,0.15);p = 0.021], and living in households with 1 + PLWH [coefficient(95%CI):0.51(0.04,0.98);p = 0.033]. Always shedders exhibited higher viral loads than intermittent shedders [coefficient(95%CI):1.62(1.19,2.05);p < 0.001], and viral loads increased with the number of visits where KSHV DNA was detected in saliva (type III test p < 0.001). Household-level factors attributed for 19% of the variability in KSHV shedding (ICC:0.191;p = 0.010). Geospatial analysis indicated overlapping hotspots of households with more KSHV seropositive individuals and KSHV shedders, distinct from areas where PLWH were clustered.DiscussionKSHV oral shedding is influenced by multiple factors at the individual, household, and regional levels. To mitigate ongoing KSHV transmission a comprehensive understanding of factors contributing to oral KSHV reactivation and transmission within households is needed.

The Dynamics of Vegetation Evapotranspiration and Its Response to Surface Meteorological Factors in the Altay Mountains, Northwest China

The Altay Mountains’ forests are vital to Xinjiang’s terrestrial ecosystem, especially water regulation and conservation. This study evaluates vegetation evapotranspiration (ET) from 2000 to 2017 using temperature, precipitation, and ET data from the China Meteorological Data Sharing Service. The dataset underwent quality control and was interpolated using the inverse distance weighted (IDW) method. Correlation analysis and climate trend methodologies were applied to assess the impacts of temperature, precipitation, drought, and extreme weather events on ET. The results indicate that air temperature had a minimal effect on ET, with 68.34% of the region showing weak correlations (coefficients between −0.2 and 0.2). Conversely, precipitation exhibited a strong positive correlation with ET across 98.91% of the area. Drought analysis, using the standardized precipitation evapotranspiration index (SPEI) and the Temperature Vegetation Dryness Index (TVDI), showed that ET was significantly correlated with the SPEI in 96.47% of the region, while the TVDI displayed both positive and negative correlations. Extreme weather events also significantly influenced ET, with reductions in the Simple Daily Intensity Index (SDII), heavy precipitation days (R95p, R10), and increases in indicators like growing season length (GSL) and warm spell duration index (WSDI) leading to variations in ET. Based on the correlation coefficients and their significance, it was confirmed that the SII (precipitation intensity) and R95p (heavy precipitation) are the main factors causing vegetation ET increases. These findings offer crucial insights into the interactions between meteorological variables and ET, essential information for sustainable forest management, by highlighting the importance of optimizing water regulation strategies, such as adjusting species composition and forest density to enhance resilience against drought and extreme weather, thereby ensuring long-term forest health and productivity in response to climate change.

Assessing the Impact of Rainfall Inputs on Short-Term Flood Simulation with Cell2Flood: A Case Study of the Waryong Reservoir Basin

This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson’s r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson’s r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins.

Simplified inverse distance weighting-immersed boundary method for simulation of fluid-structure interaction

When simulating fluid-structure interaction (FSI) problems involving moving objects, the implicit inverse distance weighting-immersed boundary method (IDW-IBM) developed by Du et al. [1] has to construct a large square correlation matrix and solve its inversion at each time step. In this work, a simplified inverse distance weighting-immersed boundary method (SIDW-IBM) is proposed to eliminate the intrinsic limitations in the original implicit IDW-IBM. Through error analysis using Taylor series expansion, a second order approximation can be derived, which allows us to approximate the large square correlation matrix into a diagonal matrix; thereby, we proposed the SIDW-IBM based on this second order approximation to explicitly evaluate the velocity corrections, where the needs to assemble the large correlation matrix and inverse it are circumvented. Owing to the fact that the inverse distance weighting interpolation removes the limitations in the Dirac delta function, the proposed SIDW-IBM has been successfully implemented on the non-uniform meshes to further improve the computational efficiency. The proposed SIDW-IBM is integrated with the reconstructed lattice Boltzmann flux solver (RLBFS) [2] to simulate some classic incompressible viscous flows, including flow past an in-line oscillating cylinder, flow past a heaving airfoil, and flow past a three-dimensional flexible plate. The good agreement between the present results and reference data demonstrates the capability and feasibility of the SIDW-IBM for simulating FSI problems with moving boundaries and large deformations.

How to combine different types of prefabricated components in a building to reduce construction costs and carbon emissions?

In the context of industrialized construction and carbon emission reduction globally, prefabrication has gained considerable attention. However, most studies cared the carbon emissions and construction costs from the perspective of a whole prefabricated building rather than the prefabricated components. It makes it difficult to understand the role of prefabricated components play and make improvements from the prefabricated components level. To fill this gap, this study proposes a model to optimize the selection and combination of prefabricated components in a building project to simultaneously reduce the construction costs and carbon emissions. Six common component types were considered: shear wall, beam, floor slab, stair, balcony, and air-conditioning slab. Their costs and carbon emissions were quantified when adopting prefabrication and cast-in-situ technologies. To obtain the optimal solution, we developed an improved multi-objective mayfly algorithm (IMOMA), which incorporates enhanced circle chaos mapping and a global best guiding strategy to generate Pareto solution sets. The performance of IMOMA was evaluated using the Zitzler-Deb-Thiele series test functions, demonstrating its advantages in convergence, diversity, and comprehensive performance through metrics such as generation distance, diversity metric Δ, and inverse generational distance. A case study validated the effectiveness of this proposed method, demonstrating an average reduction of 9.05% in total construction costs and 8.04% in carbon emissions when compared to the initial scheme. Further study revealed that different prefabrication rates correspond to different optimal components combination schemes, with different trends in the costs and carbon emission curves as the prefabrication rate increases. Additionally, carbon trading policies influence the components combination, with construction costs reduced by 10.88% at a carbon trading price of 42.80 CNY/t CO2eq. This study provides valuable guidance for general contractors to design optimal component combinations and for governments to promote the sustainable development of prefabricated construction.

Groundwater quality assessment for drinking and irrigation purposes and its human health risks in the Sevathur mine region, south India

In this research, 35 samples of groundwater were collected and subjected to analysis with the aim of assessing its appropriateness for both drinking and irrigation purposes in Sevathur mine region, south India. Spatial depiction illustrates the widespread dispersal of primary positively charged ions and negatively charged ions formulated through the Inverse Distance Weighting (IDW) technique within a Geographic Information System (GIS). The Piper and Gibbs plots reveal a variety of water compositions in the surveyed region, encompassing combinations of mixed Ca-Mg-Cl, Ca-Mg-Cl-SO4, Na-K-Cl-SO4, and a few instances falling within the category of Ca-Mg-HCO3. The current features of hydrogeochemical facies result from the interplay between geological formations and water, alongside the occurrence of ion exchange, dissolution and weathering of calcium carbonate, calc-silicate minerals, and halite within subterranean aquifers. The water quality index unveiled that 23% of the samples fall into the poor category, while a substantial 11% are categorized as very poor. Additionally, 3% are undesirable, 17% are unfit for drinking, 23% exhibit a good, and 23% are distinguished by excellent categories. SAR values of subsurface water samples belong to the excellent class. Additionally, the RSC value indicates that 94% of the samples are categorized as suitable class, while 6% are classified as marginal for irrigation purposes. Individuals are advised to avoid drinking groundwater with fluoride levels above the WHO limit (>1.5mg/l), as 26% of samples exceeded this threshold. The region's groundwater chemistry is influenced by natural geological processes, such as mineral dissolution, and ion exchange, as well as human activities, including agricultural practices. Advanced water treatment techniques are being considered to improve water quality and ensure safer drinking water for the community.

Spatial Analysis of Sulfur Dioxide (SO2) and Nitrogen Dioxide (NO2) Distribution Using Getis-Ord Gi* in DKI Jakarta Region, Indonesia

Abstract The increasing mobility of population and industry is the main problem in this study, so it will affect the spatial pattern in the distribution of pollutants. The parameters to measure air quality are Sulfur Dioxide and Nitrogen Dioxide. This study aims to (1) analyze air quality distribution patterns with SO2 and NO2 concentration parameters in the DKI Jakarta area in 2021, (2) analyze ambient air quality distribution patterns based on air quality threshold values, and (3) analyze the spatial distribution of SO2 and NO2 in the DKI Jakarta area in 2021. The data used for this research is the average concentration data of SO2 and NO2 pollutants in DKI Jakarta in 2021. This research uses the interpolation method using Inverse Distance Weighting and Getis-Ord Gi*. The results of this research show that the concentration of pollutants that have been interpolated using Inverse Distance Weighting produces higher NO2 than SO2. From the interpolated image, a Hot Spot analysis (Getis-Ord Gi*) was conducted. This analysis showed significant clustering of nearest neighbors, with high concentrations of SO2 in East Jakarta and NO2 in West Jakarta. Therefore, air quality needs to be monitored and managed to maintain environmental stability.

Characterization of Serum Biochemical Changes in Response to Alcohol Consumption Using FT‐Raman Spectroscopy

ABSTRACTAlcohol is one of the most widely used stimulants globally, with increasing consumption rates annually. Prolonged alcohol consumption leads to significant physiological and neurological changes, potentially resulting in various diseases, which could be detect in serum. Therefore, FT‐Raman spectroscopy was used to measure chemical changes in the serum of heavy drinkers, and these results were compared with the serum composition of a control group of non‐drinkers. Obtained results showed a significant increase in lipids and a decrease in the amide/lipid ratio after alcohol administration. Additionally, higher amide III/amide II and lower amide II/amide I ratios were observed in the serum of alcohol‐addicted patients. These shifts of 892, 966, 1286, 1459 and 2940 cm−1 peaks indicate alterations in the protein‐lipid balance due to alcohol consumption. Principal component analysis (PCA) was employed to further analyze the spectral data, with the first three principal components accounting for 95.36% of total data variability. A Bayesian‐optimized k‐nearest neighbor (BO‐KNN) model was applied for classification. The optimal hyperparameters—two neighbors, correlation distance metric, and squared inverse distance weight—were determined after seven iterations, resulting in a remarkably low classification error of 0.0384. The BO‐KNN model achieved training and test accuracies of 95.92% and 90.48%, respectively. Receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of 1.00 for the training set and 0.90 for the test set, demonstrating the model's high precision and robustness. In conclusion, FT‐Raman spectroscopy effectively revealed significant chemical changes in the serum of alcohol‐addicted individuals, providing valuable insights into the biochemical impact of alcohol consumption.

Gm-APD Lidar Distance Image Recovery Based on Intensity Image Target Edge Guidance

Abstract Gm-APD LiDAR in the detection of long-distance targets, the laser by the atmospheric effects of attenuation is serious, so that the target echo sparse, easy to cause the target distance like edge missing, details of fuzzy and other problems, the traditional method can’t make targeted treatment of the above problems. Therefore, this paper proposes a Gm-APD LIDAR distance image recovery method based on intensity image target edge guidance. The method introduces one-dimensional intensity information as an aid, combines the inverse distance weighting function to guide the reconstruction of the target edge contour information of the distance image, and then applies morphological image processing to correct the target distance value in a partition, and recovers a distance image with sharp edges and clear details. Experiments are carried out on the distance targets, and the results show that the method improves the target restoration by 19.21% and the peak signal-to-noise ratio by 3.12% compared with the concave morphology restoration algorithm, which verifies the advantages and effectiveness of the method proposed in this paper.

Evaluation of the geochemical background of soil in a hyper-arid zone using a multivariate statistical methodology: The case of the city of Antofagasta in the Atacama Desert

This study investigates the geochemical background and factors influencing the variability of 19 environmentally relevant elements in the soils of Antofagasta, Chile, a region known for its extensive mining activities. Employing robust multivariate statistical techniques on a dataset of 94 soil samples, we identified four main factors explaining 70% of the total variance in elemental concentrations. These four factors reflect the influence of Jurassic volcanic rocks, intrusive rocks, marine sediments, and mafic to intermediate intrusive rocks. Cluster analysis revealed three distinct geochemical populations, each reflecting a unique combination of natural and anthropogenic influences. We established background concentrations for each element within these clusters using robust statistical methods. Geostatistical analysis, employing inverse distance weighted interpolation, produced factor distribution maps that, when integrated with geological data, provided insights into lithological and anthropogenic influences on soil geochemistry. Our findings highlight the complex interplay between natural geological processes, the region's unique arid climate, and anthropogenic activities in shaping the geochemical landscape of Antofagasta. This study contributes to the understanding of geochemical backgrounds in mining-intensive, arid regions and provides a methodological framework applicable to similar environments worldwide.