Sampling Points Research Articles

Characteristics of PM2.5-bound water-soluble inorganic ions generated during cooking

Commercial cooking is an important source of PM2.5, which adversely affects air quality, posing threats to human health. However, few studies have examined the effects of commercial cooking on outdoor air quality. We aimed to analyze characteristics of outdoor PM2.5 and ions from commercial cooking during cooking and non-cooking times in a popular restaurant area at Nankai University. The acidity and alkalinity of atmospheric PM2.5 based on the equivalent values of anions and cations and the formed compounds based on the ion concentrations and correlations between them were determined. The results showed that the mean PM2.5 concentration at the snack street during cooking times was 120.39 ± 58.52 μg/m3, approximately 1.4 times higher than at the comparison point. The total concentration of water-soluble inorganic ions accounted for approximately half of the PM2.5 emissions at both sampling points. High concentrations of Na+, NO3−, and SO42− were found at both sampling points. We found that NH4NO3, CaSO4, and CaCl2 formed at the snack street, whereas Ca(NO3)2, NH4NO3, and (NH4)2SO4 formed at the comparison point. Based on our analysis of the acid-base balance slopes, the atmospheric PM2.5 was alkaline. These results elucidate the characteristics of PM2.5-bound water-soluble inorganic ions formed during cooking.

Variations in the level of available phosphorus with changes in the status of water-soluble organic matter derived from different organic materials in soil

The combined application of organic material and phosphorus fertilizer is an effective method to enhance phosphorus use efficiency for plant growth. This is partly because the presence of water-soluble organic matter (WSOM) derived from different organic materials can enhance the level of available phosphorus in the soil; however, it is poorly understood how this level varies with changes in the WSOM status (i.e., decomposed, dissolved, and retained) in the soil depending on WSOM types. This study aimed to (i) understand how changes in the WSOM status enhances the available phosphorus level in the soil, and (ii) determine the WSOM type that contributes to such enhancement. The incubation test showed that fractions of 73%–92% and 8%–27% of WSOM-derived organic carbon were retained and dissolved, respectively, at the beginning of incubation, while 31%–45% was decomposed during the incubation period. The WSOM derived from cattle manure compost (CM) and sewage sludge compost (SSC) that was initially retained was maintained until the late stage of the incubation test, whereas that derived from hydrothermal decomposed liquid fertilizer (HDLF) was rapidly desorbed during the first 14 days of the incubation period. The available phosphorus level was higher under the combined application of CM- and SSC-derived WSOM than under the single phosphorus application throughout the incubation period, while it was high only during the first 3 days of incubation under the application of HDLF-derived WSOM. The amounts of retained organic carbon at each sampling point during the incubation period compared to those at the beginning were positively and linearly correlated to the available phosphorus levels that were enhanced by the WSOM present in the soil. This study for the first time provides quantitative experimental evidence that 1) the longer the WSOM continues to be retained, the higher the amount of available phosphorus remaining in the soil, and 2) the available phosphorus level decreases with WSOM sorption or decomposition. Furthermore, it was shown that highly humified WSOM has a great potential for the maintenance of higher available phosphorus levels. This study provides the insight that a combined application of highly humified organic materials with a chemical fertilizer is necessary for not only cost effective but also sustainable fertilization design.

Distribution of air quality data across stratified areas of Port Harcourt metropolis

Distribution of Air Quality Data across seven stratified areas of Port Harcourt Metropolis was carried out. The study utilized a total of 18 parameters from four different air quality datasets obtained from 24 sampling points during field data collection. The sampling points were structured and stratified into residential, industrial, commercial, recreational, transport, dumpsite and abattoir areas. Air quality data were acquired using a multi-digital potable tool, Aerosol Gas Monitor and TESTO Gas Analyser for meteorological data, particulate matters (PM), gaseous emissions and heavy metals. Results showed that the concentrations of air quality data were not evenly distributed across the seven stratified areas, an indication of the influence of location-based activities on the distribution of air quality parameters. The minimum and maximum values of wind speed, temperature and relative humidity were obtained within Residential and Transport Areas, Abattoir and Dumpsite Areas, and, Industrial and Recreational Areas with numerical values of 1.1ms-1 and 3.7ms-1, 27.20C and 350C, and, 67.8% and 923% for wind speed, temperature and relative humidity respectively. The values of TSP, PM2.5 and PM10 ranged from 53 to 255 μg/m3, 13 to 90 μg/m3 and 40 to 169μg/m3 for TSP, PM2.5 and PM10 respectively with the lowest and highest values obtained within Residential and Dumpsite Areas respectively. The concentrations of NO2 and SO2 exceeded the standards of WHO, NAAQS and FMEnv in Industrial, Dumpsite and Abattoir Areas. Lead ( Pb) showed very high values within Dumpsite Area (0.954mg/kg), Industrial Area (0.372mg/kg) and Transport Area (0.077mg/kg).

Analysis of Groundwater Salinity Distribution Based on Electrical Conductivity (EC) and Hydrochemical Approaches to Deep Wells in Sayung Subdistrict, Demak Central Java

In coastal areas, obtaining groundwater is not easy due to the lack of geological support conditions that cause water in the area to have poor quality such as groundwater that becomes salty. The coastal area of Sayung Subdistrict has geomorphological conditions whose surface tends to be flat with elevations 0-5 m higher than sea level and is dominated by alluvial material so that it can cause seawater infiltration to increase. The purpose of this research was to determine the distribution of salinity of groundwater in the coastal area of Sayung Subdistrict and to determine the cause of groundwater becoming salty. The method used in this research is the electrical conductivity (DHL) approach which is then classified based on the level of saltiness of groundwater. And then, well water from the sample points was tested using a hydrochemical test with the parameters used, namely the content of cations (Na, K, Mg, Ca) and anions (Cl, CO3, HCO3, SO4, NO3). This research was conducted at 33 well points spread across Sayung sub-district. The results of this study show that the electrical conductivity value obtained is between the range of 928-7,199 μS/cm which is divided into 3 classifications namely fresh water, fresh-brackish water, and brackish water. The analysis of the Trilinier Piper diagram shows that the saltiness of the groundwater is due to indications of seawater mixing characterized by a chloride (Cl) content that is increasingly greater towards the sea.

Quantification, characterization, and source identification of macro- and mesoplastics in the water column of Rivers Sabaki and Tana.

Five sampling campaigns were conducted in the water columns of River Sabaki and Tana in Kenya, Between October 2021 and January 2023, covering a 1-year cycle, at four sites in River Sabaki (2.5km, 3.05km, 3.51km, and 4.52km) and River Tana (1.5km, 1.8km, 2.0km, and 2.5km) distant from the river mouth. The ebb and flood tides were sampled to calculate net plastic litter fluxes. Two 6350-µm seine nets were deployed in two replicates per sampling point. Factor and cluster analysis were used to investigate plastic litter sources for both rivers. The influences of rainfall on plastic abundance and mass were explored using permutational linear models. A total of 15,318 plastic litter items weighing 1.37kg were recorded in River Sabaki, and 3741 plastic litter items weighing 0.95kg in River Tana. The top ten captured plastic litter types sorted by abundance and mass were mostly plastic fragments. The annual net plastic litter flux to the ocean through River Sabaki amounted to 1,277,120.63 items year-1 by abundance and 22.30kgyear-1 by mass. For River Tana, the same fluxes were 207,550.76 items year-1, and 28.09kgyear-1, respectively. In River Sabaki, significant impacts of rainfall on plastic abundance and mass were found. River Sabaki's pollution sources included upstream reaches, fishing activities, and littering by locals and tourists. River Tana's major pollution sources were illegal dumpsites, littering, fishing, and recreational activities. This research can guide combat plastic pollution in the rivers and ultimately the ocean.

Measurements and medication data are indispensable for population blood pressure monitoring

Abstract Background The National Study on Health of Older People in Germany “Gesundheit 65+” included blood pressure (BP) measurements at home by a study nurse and classification of all current medication. Methods Nationally representative examination survey of the population aged 65 years and older residing in Germany using two-stage population registry sampling from 128 sample points, with a standardized examination at home from June 2022 to April 2023. Three standardized blood pressure measurements at two-minute intervals were taken after 5 minutes rest with an oscillometric device (Mobil-O-Graph) using one of five cuffs according to upper arm circumference. The mean of the second and third measurement was used for analysis. Hypertension was defined as mean systolic BP (SBP) at or above 140 mmHg, or mean diastolic BP (DBP) at or above 90 mmHg, or the use of antihypertensives (antihypertensives, diuretics, beta-blockers, calcium-channel blockers, ACEI and ARB) in participants with self-reported hypertension diagnosis. Provisional weights account for unequal sampling and participation probabilities. Results The “Gesundheit 65+” examination part included 1,493 participants of whom 1,475 (98.8%) had successful BP measurements (763 men and 712 women, aged 66 to 101 years). Based on self-reported diagnoses alone, the prevalence of hypertension would be 58.3% [95% CI 53.2-63.3] in men and 59.5% [53.8-64.9] in women. However, overall hypertension prevalence was estimated at 70.4% [65.8-74.7] in men and 72.3% [66.9-77.1] in women based on the study definition which combines self-reported diagnoses, standardized BP measurements and medication. Conclusions The prevalence of hypertension is greatly underestimated in the absence of examination survey data with standardized measurements and recording of medication use. In addition, measurement and medication data are needed for monitoring the hypertension cascade, i.e. awareness, treatment and control of hypertension. Key messages • Hypertension prevalence is underestimated in the absence of health examination surveys with measurements and medication data. • The hypertension cascade, i.e. assessment of awareness, treatment and control of hypertension needs population surveys with measurements and medication assessment.

Examining the contribution of lithology and precipitation to the performance of earthquake-induced landslide hazard prediction

Earthquake-induced landslides (EQIL) are one of the most catastrophic geological hazards. Immediate and swift evaluation of EQIL hazard in the aftermath of an earthquake is critically important and of substantial practical value for disaster reduction. The selection of influencing factor layers is crucial when using machine learning methods to predict EQIL hazard. As important input factors for EQIL hazard models, lithology and precipitation are extensively employed in forecasting EQIL hazard. However, few work explored whether these layers can improve the accuracy of EQIL hazard predictions. With Random Forest (RF) models, we employed a traditional and a state-of-the-art sampling strategy to assess EQIL modelling with and without lithology and precipitation data for the 2022 Luding earthquake in China. First, by excluding both factors, we used eight other influencing factors (land use, slope aspect, slope, elevation, distance to faults, distance to rivers, NDVI, and peak ground acceleration) to generate a landslide hazard map. Second, lithology and precipitation were separately added to the original EQIL hazard models. The results indicate that neither lithology nor precipitation have positive effects on the prediction of EQIL for both sampling strategies. The high-risk areas (or low-risk areas) tend to cluster within certain lithology types or precipitation ranges, which significantly affects the accuracy of the hazard map. Additionally, the model with the state-of-the-art sampling strategy deteriorates more than the model with the traditional sampling strategy. We believe this is very likely due to the strong spatial clustering of negative sample points caused by the latest sampling strategy. Our findings will contribute to the assessment of post-earthquake landslide hazards and the advancement of emergency disaster mitigation efforts.

Flow Field Analysis and Development of a Prediction Model Based on Deep Learning

The velocity of ocean currents significantly affects the trajectory prediction of ocean drifters and the safe navigation of intelligent vessels. Currently, most ocean current predictions focus on time-based forecasts at specific fixed points. In this study, deep learning based on the flow field prediction model (CNNs–MHA–BiLSTMs) is proposed, which predicts the changes in ocean currents by learning from historical flow fields. Unlike conventional models that focus on single-point current velocity data, the CNNs–MHA–BiLSTMs model focuses on the ocean surface current information within a specific area. The CNNs–MHA–BiLSTMs model integrates multiple convolutional neural networks (CNNs) in parallel, multi-head attention (MHA), and bidirectional long short-term memory networks (BiLSTMs). The model demonstrated exceptional modelling capabilities in handling spatiotemporal features. The proposed model was validated by comparing its predictions with those predicted by the MIKE21 flow model of the ocean area within proximity to Dalian Port (which used a commercial numerical model), as well as those predicted by other deep learning algorithms. The results showed that the model offers significant advantages and efficiency in simulating and predicting ocean surface currents. Moreover, the accuracy of regional flow field prediction improved with an increase in the number of sampling points used for training. The proposed CNNs–MHA–BiLSTMs model can provide theoretical support for maritime search and rescue, the control or path planning of Unmanned Surface Vehicles (USVs), as well as protecting offshore structures in the future.

How far should adjacent pan traps be placed for bee sampling?

AbstractAmong several methods of active and passive sampling bees (Hymenoptera: Apoidea), pan traps, also known as bee bowls, are commonly utilized for their efficiency and as a means of avoiding collector bias. When comparing pan‐trap samples using traditional statistical methods, the assumption of independence among samples needs to be met. To determine the necessary distance between pan traps to obtain independent samples, we investigated spatial dependence in bee catches using pan traps at four sites. At each site, a regular grid of pan traps was laid out, with each sample having two colors of fluorescent trap (i.e. blue and yellow). These trap pairs were separated by 10 m, with 100–109 points in the grid. In addition, five points within the grid were chosen randomly, and an additional 40 traps were placed at 2 m and 4 m from the five sample points to sample bees at a minimum 2 m trap distance. Traps collected bees for five consecutive days and bees were counted for each point and each trap color. Bee count data were analyzed using geostatistics to determine the spatial dependency in trap catches for yellow traps, blue traps, and both colors combined. The highest value of the range parameter of the semivariogram found across the four sites and pan trap colors was 17 m for blue traps. From this, we recommend a conservative minimum distance of 17 m between adjacent pan traps to obtain spatially independent samples for optimizing sampling plans when independent samples are necessary.

HYDROCHEMICAL STUDY OF BALAK RIVER, ERBIL-IRAQ

This study was aimed to assess the water quality of different water resources (Surface water, subsurface in water, and groundwater) across the Balak river basin. Twenty two different water resource samples were collected and subjecting to a comprehensive physicochemical analysis. Balak river basin, situated in Erbil governorate, Iraq, it is located 10 km to the east from Bekhal Waterfall. Five attributers combine with Balak River within the basin areas which are: Rezanok Sub River, Balakiyan Sub River, Basan Sub River, Roste Sub River, and Alana Sub River. The hydro chemical study of all water resources during the investigated period revealed that the spring waters are relatively higher in electrical conductivity value compared to other sources. Some surface water samples were recorded high turbidity value. Values recorded for physicochemical parameters were within WHO and Iraqi standards guideline values except for some samples of turbidity. According to water classification, all samples have a very similar composition, and can be classified as Ca-HCO3 water. This indicated that they correspond to different sampling points along different rivers, and different samples along the same river.

Small Streams Support Beta Diversity in Neotropical Fish Assemblages in the Upper Paraná River Basin

ABSTRACTVariation in species composition along an environmental gradient (β‐diversity) can inform our understanding of biodiversity patterns and species composition. Beta diversity can be partitioned into species contributions to beta diversity (SCBD) and local contributions to beta diversity (LCBD). While SCBD describes the variations in species composition among samples, LCBD measures the uniqueness of a biological community within a larger ecosystem. Recently developed metrics enabled the estimation of species contribution to total beta diversity (SCBD) and the contribution of individual sites to total beta diversity (LCBD). Utilising these metrics, a one‐time survey of 275 sampling points within four sub‐basins of the Upper Paraná River Hydrographic Basin (low‐flow streams, < 5 m3/s) was conducted to assess fish community composition. Our results show that limnological influences at different scales contribute to higher LCBD. However, land use and species richness were not directly linked to this outcome, suggesting that sites with fewer fish species along sub‐basins contributed to higher beta diversity. It was concluded that beta diversity is more influenced by limnological characteristics and the position of sampling sites along the longitudinal gradient than that based on variations in species composition (SCBD). Moreover, high LCBD values were even found in portions of the gradient with lower species richness. This means that small streams in the sub‐basins of the upper Paraná River are unique and are comprised of varied habitats that contribute to increasing diversity. As such, they should be seen as a target in the development of environmental policies designed to conserve their pristine condition.

Coal mining activities driving the changes in bacterial community

The mechanism of the difference in bacterial community composition caused by environmental factors in the underground coal mine is unclear. In order to reveal the influence of coal mining activities on the characteristics of bacterial community structure in coal seam, 16S rRNA gene amplicon sequencing technology was used to determine the species abundance, biodiversity, and gene abundance of bacterial community in a coal mine in Shanxi Province, and the environmental factors such as metal elements, non-metal elements, pH value, and gas concentration of coal samples were determined. The results showed that environmental factors and bacterial communities had obvious regional characteristics. Mining activities greatly affected the α diversity of bacterial communities, mining working face > main airway > roadway roof > unexposed coal seam > tunneling roadway. The bacterial community composition of each sample point is also very different. The main airway, roadway roof, and unexposed coal seam are dominated by Actinobacteria while the mining working face and tunneling roadway are dominated by Proteobacteria. Among the gene abundances of metabolic pathways in each site, Citrate cycle had the greatest difference, followed by glycine, serine and threonine metabolism, and oxidative phosphorylation and methane metabolism had little difference. RDA analysis showed that the environmental factors affecting the bacterial community were mainly cadmium, oxygen, hydrogen, and gas content. CCA analysis divided the bacterial community into three categories. Degradation functional bacteria are located in mining working face, bacteria that tolerate poor environments are located in main airway and tunneling roadway, and human pathogens are mostly located in roadway roof and unexposed coal seam. The research results would provide support for realizing green and safe mining in coal mines.

Multivariate statistical analysis as a tool for monitoring drinking water sources in an Atlantic Rainforest Conservation Unit.

Water quality monitoring is paramount in identifying and mitigating pollution sources, protecting aquatic ecosystems, and ensuring safe water for human and wildlife consumption. This study is aimed at evaluating the quality of drinking water sources in three communities located in a Sustainable Use Conservation Unit in the municipality of Mangaratiba, Rio de Janeiro, Brazil, employing a multivariate statistical analysis. A total of 161 water samples were collected from January to December 2022, encompassing 32 surface water and 129 tap water samples. Physicochemical parameters were determined in situ employing a Horiba U50 multiparameter probe. The samples were stored and transported at 4°C to the laboratory for microbiological analyses concerning total coliforms and Escherichia coli using a commercial enzymatic test. All samples contained coliforms, while E. coli were detected in 87% of the samples. The multivariate analysis indicated that the microbiological water quality in sampling region R2 was influenced by rainy periods and that, in general, the water quality within R3 was the most affected by the transport of solids to the water sources. The statistical methods applied herein aided in characterizing the study areas and detecting points of attention regarding physicochemical and microbiological parameters that significantly influence the water quality of each sampling point. Representative points for each study region were identified and may be employed for future monitoring and prevention actions.

Evaluating the Patterns of Maize Development in the Hetao Irrigation Region Using the Sentinel-1 GRD SAR Bipolar Descriptor

Assessing maize yield is critical, as it is directly influenced by the crop’s growth conditions. Therefore, real-time monitoring of maize growth is necessary. Regular monitoring of maize growth indicators is essential for optimizing irrigation management and evaluating agricultural yield. However, quantifying the physical aspects of regional crop development using time-series data is a challenging task. This research was conducted at the Dengkou Experimental Station in the Hetao irrigation area, Northwest China, to develop a monitoring tool for regional maize growth parameters. The tool aimed to establish a correlation between satellite-based physical data and actual crop growth on the ground. This study utilized dual-polarization Sentinel-1A GRD SAR data, accessible via the Google Earth Engine (GEE) cloud platform. Three polarization descriptors were introduced: θc (pseudo-scattering type parameter), Hc (pseudo-scattering entropy parameter), and mc (co-polar purity parameter). Using an unsupervised clustering framework, the maize-growing area was classified into several scattering mechanism groups, and the growth characteristics of the maize crop were analyzed. The results showed that throughout the maize development cycle, the parameters θc, Hc, and mc varied within the ranges of 26.82° to 42.13°, 0.48 to 0.89, and 0.32 to 0.85, respectively. During the leaf development stage, approximately 80% of the maize sampling points were concentrated in the low-to-moderate entropy scattering zone. As the plants reached the big trumpet stage, the entire cluster shifted to the high-entropy vegetation scattering zone. Finally, at maturity, over 60% of the sampling points were located in the high-entropy distribution scattering zone. This study presents an advanced analytical tool for crop management and yield estimation by utilizing precise and high-resolution spatial and temporal data on crop growth dynamics. The tool enhances the accuracy of crop growth management across different spatial and temporal conditions.

Enhanced Assessment of Water Quality and Pollutant Source Apportionment Using APCS-MLR and PMF Models in the Upper Reaches of the Tarim River

To evaluate the pollution sources and dynamics of the upper reaches of the Tarim River, 10 typical sampling points were selected, and 23 water quality parameters from 2020 to 2022 were analyzed using one-way analysis of variance, the comprehensive Water Quality Identification Index (WQI), and Principal Component Analysis (PCA). The pollution status, sources, and contribution rates of water quality were investigated using the Absolute Principal Component-Multiple Linear Regression Model (APCS-MLR) and Positive Matrix Factorization (PMF). The results indicated that the water quality parameters of dissolved oxygen (DO), chemical oxygen demand (CODMn), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP), fluoride ions (F-), and ammonia-nitrogen (NH3-N) in the upper reaches of the Tarim River exceed standards, with noticeable spatial variations observed for each parameter. The water quality evaluation grades in the upper reaches of Tarim River primarily indicate “moderate” and “good” levels, with DO, TN, NH3-N, and electrical conductivity (EC) being the key parameters influencing variations in water quality. The source analysis results from APCS-MLR and PMF yielded similar outcomes, identifying six potential pollution sources. Among these, soil weathering, livestock and poultry breeding, and agricultural activities exhibited higher contribution rates. Specifically, the contribution rates for these sources according to APCS-MLR were 44.11%, 19.63%, and 11.67%, respectively; while according to PMF they are 24.08%, 17.88%, and 27.54%, respectively. Furthermore, industrial pollution sources contributed at a rate of 6.01% according to APCS-MLR, while urban living sources contributed at a rate of 2.13%. However, based on PMF analysis, the contribution rates for industrial pollution sources increased significantly to 16.71%. Additionally, APCS-MLR identified natural sources as contributing at a rate of 16.45%, whereas PMF suggested that a combination of agricultural activities and natural sources contributed at a lower rate of only 9.52%. In conclusion, the water quality within the upper reaches of the Tarim River is predominantly satisfactory. Nonetheless, localized pollution, primarily attributable to human activities, presents a substantial challenge. These observations provide critical insights into improving and protecting the fragile water quality of the Tarim River.

The influence of domestic dogs on the spatial and temporal distribution of tayra

The domestic dog Canis familiaris is an exotic species known to have impacts on native fauna and it may induce spatial and temporal segregation in other species as a mechanism to reduce agonistic interactions. The tayra Eira barbara, a medium‐sized, widely distributed mustelid, is one of the species that can be affected. To test this hypothesis, between 2020 and 2022, we installed camera traps in an agricultural landscape composed of a protected area, agro‐livestock areas, an urban area, and several rural households. We calculated the activity patterns, their overlapping coefficients and generated models to explain the species's occupancy and verify possible interferences. We obtained 32 records of tayras and 100 of domestic dogs. In only 8 of 73 sampling points, the two species occurred concomitantly. Activity overlap was 68%; however, at the points where both species occurred, the overlap was 43%. Tayra showed to be positively associated with forest areas, and negatively with agricultural exploitation areas. Dogs predominantly occupied agricultural areas and were negatively related to forest areas. In addition to dog interference, which seems to affect tayra's use of the landscape, environmental conditions are also essential in describing the occupancy of the subordinate species. It is possible that their scansorial habit is important to avoid agonistic events.

Enhanced Looped Lyapunov Functional for Sampled-Data Control for T-S Fuzzy Systems with Time Delay

This paper addresses the problem of sampled-data control in T-S fuzzy systems with time delays. Initially, a negative-definite criterion is developed for the matrix containing membership functions with the help of the idea of a switching mechanism. Building upon this criterion, a membership-function-based looped Lyapunov functional is introduced. Furthermore, a sampling-time-dependent looped Lyapunov functional is constructed, incorporating crucial information about the sampling instance. Notably, this functional matrix is not constrained to satisfy positive-definiteness, and it varies with each sampling point. By utilizing the enhanced looped Lyapunov functional, less conservative stability conditions and a new controller criterion for the T-S fuzzy system with time delays are derived. This demonstrates that our proposed stability conditions allow for a larger sampling interval compared to existing work. Finally, the proposed method’s effectiveness is demonstrated through the validation of two simulation examples.

FSH3D: 3D Representation via Fibonacci Spherical Harmonics

AbstractSpherical harmonics are a favorable technique for 3D representation, employing a frequency‐based approach through the spherical harmonic transform (SHT). Typically, SHT is performed using equiangular sampling grids. However, these grids are non‐uniform on spherical surfaces and exhibit local anisotropy, a common limitation in existing spherical harmonic decomposition methods. This paper proposes a 3D representation method using Fibonacci Spherical Harmonics (FSH3D). We introduce a spherical Fibonacci grid (SFG), which is more uniform than equiangular grids for SHT in the frequency domain. Our method employs analytical weights for SHT on SFG, effectively assigning sampling errors to spherical harmonic degrees higher than the recovered band‐limited function. This provides a novel solution for spherical harmonic transformation on non‐equiangular grids. The key advantages of our FSH3D method include: 1) With the same number of sampling points, SFG captures more features without bias compared to equiangular grids; 2) The root mean square error of 32‐degree spherical harmonic coefficients is reduced by approximately 34.6% for SFG compared to equiangular grids; and 3) FSH3D offers more stable frequency domain representations, especially for rotating functions. FSH3D enhances the stability of frequency domain representations under rotational transformations. Its application in 3D shape reconstruction and 3D shape classification results in more accurate and robust representations. Our code is publicly available at https://github.com/Miraclelzk/Fibonacci-Spherical-Harmonics.

An advancement in AdaSyn for imbalanced learning: An application to fraud detection in digital transactions

Imbalanced Learning is a significant issue in machine learning, affecting the performance and accuracy of binary or multi-classification algorithms, especially in large-scale data handling and classification. There are some popular techniques to covert this imbalanced data into a balanced one such as undersampling, under-sampling with tomek links, randomized oversampling, synthetic minority oversampling technique (SMOTE), and adaptive synthetic generation (ADASYN). Generally, the ADASYN algorithm could be used to propagate minority sample points to rise the imbalanced ratio between majority and minority sample points, but in some cases, it may conflict with decision boundary points and noisy points. This paper proposed a Refitted AdaSyn Algorithm (RAA) with Gaussian Distribution (GD). So that new minority samples are distributed much closer to the center of the minority sample to spotlight the conflicts. The classification accuracy has improved with RAA over formal ADASYN. For examining the proposed work the imbalanced benchmark datasets like European, Banksim, Paymentcard, and UCI credit card are considered. Vanilla Generative Adversarial Network (GAN) is a deep learning model used to classify fraud and non-fraud transactions, demonstrating significant differences between balanced and imbalanced learning approaches and achieving an accuracy of 97.5% on dataset DS4.

Seamless and Aligned Texture Optimization for 3D Reconstruction

AbstractRestoring the appearance of the model is a crucial step for achieving realistic 3D reconstruction. High‐fidelity textures can also conceal some geometric defects. Since the estimated camera parameters and reconstructed geometry usually contain errors, subsequent texture mapping often suffers from undesirable visual artifacts such as blurring, ghosting, and visual seams. In particular, significant misalignment between the reconstructed model and the registered images will lead to texturing the mesh with inconsistent image regions. However, eliminating various artifacts to generate high‐quality textures remains a challenge. In this paper, we address this issue by designing a texture optimization method to generate seamless and aligned textures for 3D reconstruction. The main idea is to detect misalignment regions between images and geometry and exclude them from texture mapping. To handle the texture holes caused by these excluded regions, a cross‐patch texture hole‐filling method is proposed, which can also synthesize plausible textures for invisible faces. Moreover, for better stitching of the textures from different views, an improved camera pose optimization is present by introducing color adjustment and boundary point sampling. Experimental results show that the proposed method can eliminate the artifacts caused by inaccurate input data robustly and produce high‐quality texture results compared with state‐of‐the‐art methods.