Distribution Map Research Articles

Geospatial evaluation and bio-remediation of heavy metal-contaminated soils in arid zones

Introduction: Soil pollution directly impacts food quality and the lives of both humans and animals. The concentration of heavy metals in Egypt’s drain-side soils is rising, which is detrimental to the quality of the soil and crops. The key to reducing the detrimental effects on the ecosystem is having accurate maps of the spatial distribution of heavy metals and the subsequent use of environmentally sustainable remediation approaches. The objective of this work is to assess soil contamination utilizing spatial mapping of heavy metals, determine contamination levels using Principal Component Analysis (PCA), and calculate both the contamination severity and the potential for bioremediation in the soils surrounding the main drain of Bahr El-Baqar. Furthermore, evaluating the capacity of microorganisms (bacteria, fungi, and “Actinomycetes) to degrade heavy elements in the soil.Methodology: 146 soil sample locations were randomly selected near the Bahr El-Baqar drain to examine the degree of soil pollution Ordinary Kriging (OK), method was used to map and analyze the spatial distribution of soil contamination by seven heavy metals (Cr, Fe, Zn, Cd, Pb, As, and Ni). Modified contamination degree (mCd) and PCA were used to assess the research area’s soil pollution levels. The process involved isolating, identifying, and classifying the microorganisms present in the soil of the study area. The study findings showed that variography suggested the Stable model effectively matched pH, SOM, and Cd values. Furthermore, the exponential model proved suitable for predicting Fe, Pb and Ni, while the spherical model was appropriate for Ni, Cr, and Zn.Results: The study revealed three levels of contamination, with an extremely high degree (EHDC) affecting approximately 97.49% of the area. The EHDC exhibited average concentrations of heavy metals: 79.23 ± 17.81 for Cr, 20,014.08 ± 4545.91 for Fe, 201.31 ± 112.97 for Zn, 1.33 ± 1.37 for Cd, 40.96 ± 26.36 for Pb, 211.47 ± 13.96 for As, and 46.15 ± 9.72 for Ni. Isolation and identification of microorganisms showed a significant influence on the breakdown of both organic and inorganic pollutants in the environment. The study demonstrated exceptionally high removal efficiency for As and Cr, with a removal efficiency reached 100%, achieved by Rhizopus oryzae, Pseudomonas aeruginosa, and Bacillus thuringiensis.Conclusion: This study has designated management zones for soil contamination by mapping soil pollutants, geo-identified them, and found potential microorganisms that could significantly reduce soil pollution levels.

A Study on the Correlation of Liquid Kinetic and Thermodynamic Distribution Mapping

A Study on the Correlation of Liquid Kinetic and Thermodynamic Distribution Mapping

Description of a new Psalmopoeus Pocock, 1895 species (Araneae, Theraphosidae, Psalmopoeinae) from the Chocó region of Ecuador

A new species of Theraphosidae, genus Psalmopoeus Pocock, 1895 is described from the Chocó region of Ecuador, Psalmopoeus pristirana n. sp. (male and female). The identity of the female of the recently described Psalmopoeus satanas Peñaherrera-R. & León-E., 2023 is discussed. Images of Psalmopoeus ecclesiasticus Pocock, 1903 are given for comparison and a distribution map of all Ecuadorian Psalmopoeus species is presented.

Artemisia Frigida Distribution Mapping in Grassland with Unmanned Aerial Vehicle Imagery and Deep Learning

Artemisia frigida, as an important indicator species of grassland degradation, holds significant guidance significance for understanding grassland degradation status and conducting grassland restoration. Therefore, conducting rapid surveys and monitoring it is crucial. In this study, to address the issue of insufficient identification accuracy due to the large density and small size of Artemisia frigida in UAV images, we improved the YOLOv7 object detection algorithm to enhance the performance of the YOLOv7 model in Artemisia frigida detection. We applied the improved model to the detection of Artemisia frigida across the entire experimental area, achieving spatial mapping of Artemisia frigida distribution. The results indicate: In comparison across different models, the improved YOLOv7 + Biformer + wise-iou model exhibited the most notable enhancement in precision metrics compared to the original YOLOv7, showing a 6% increase. The mean average precision at intersection over union (IoU) threshold of 0.5 (mAP@.5) also increased by 3%. In terms of inference speed, it ranked second among the four models, only trailing behind YOLOv7 + biformer. The YOLOv7 + biformer + wise-iou model achieved an overall detection precision of 96% and a recall of 94% across 10 plots. The model demonstrated superior overall detection performance. The enhanced YOLOv7 exhibited superior performance in Artemisia frigida detection, meeting the need for rapid mapping of Artemisia frigida distribution based on UAV images. This improvement is expected to contribute to enhancing the efficiency of UAV-based surveys and monitoring of grassland degradation. These findings emphasize the effectiveness of the improved YOLOv7 + Biformer + wise-iou model in enhancing precision metrics, overall detection performance, and its applicability to efficiently map the distribution of Artemisia frigida in UAV imagery for grassland degradation surveys and monitoring.

Экспериментальное и численное исследование нагрева стенок камеры сгорания малоразмерного газотурбинного двигателя

The combustion chamber is one of the main components that determine the energy and mass characteristics of the propulsion system. A detailed description of the combustion chambers of some model engines running on kerosene is given. The results of fire tests and numerical studies of combustion chambers of model small-sized gas turbine engines are considered. To qualitatively compare the results of calculations with the data of a physical experiment, annealed colors are used. Based on the results of the research, a map of tem perature distribution on the walls of the combustion chamber was compiled. The internal and external surfaces of the combustion chamber are unevenly colored with tarnished colors. This coloring is a consequence of non-uniform distributions of temperature and concentration of various components of combustion products, and also indicates the formation of a spatial flow in the combustion chamber.

Temperature imaging inside fluid devices using a ratiometric near infrared (NIR-II/III) fluorescent Y2O3: Nd3+, Yb3+, Er3+ nanothermometer.

Luminescence thermometry is a non-contact method that can measure surface temperatures and the temperature of the area where the fluorescent probe is located, allowing temperature distribution visualizations with a camera. Ratiometric fluorescence thermometry, which uses the intensity ratio of fluorescence peaks at two wavelengths with different fluorescence intensity dependencies, is an excellent method for visualizing temperature distributions independent of the fluorophore spatial concentration, excitation light intensity and absolute fluorescence intensity. Herein, Nd3+/Yb3+/Er3+-doped Y2O3 nanomaterials with a diameter of 200nm were prepared as phosphors for temperature distribution measurement of fluids at different temperatures. The advantages of this designed fluorescent material include non-aggregation in water and the fact that its near-infrared (NIR) fluorescence excitation (808nm) is not absorbed by water, thereby minimizing sample heating upon irradiation. Under optical excitation at 808nm, the ratio of the fluorescence intensities of Yb3+ (IYb; 975nm) and Er3+ (IEr; 1550nm), which exhibited different temperature responses, indicated the temperature distribution inside the fluid device. Thus, this technique using Nd3+/Yb3+/Er3+-doped Y2O3 is expected to be applied for temperature distribution mapping analysis inside fluidic devices as a ratiometric NIR fluorescence thermometer, which is unaffected by laser-induced heating.

Assessment of food safety risk using machine learning-assisted hyperspectral imaging: Classification of fungal contamination levels in rice grain

A rapid and nondestructive assessment of food safety risk using machine learning-assisted hyperspectral imaging was developed for classification of fungal contamination in brown rice grain. Brown rice was inoculated with Penicillium. The fungal infected rice was then mixed with healthy rice to obtain 0 %, 5 %, 25 %, 50 % and 100 % (w/w) contamination of infected rice. Volatile compounds including pentamethyl-heptane, decane, dodecane, 3-octanone, and 1-octen-3-ol were found in fungal infected rice, as analyzed using gas chromatography-mass spectrometry. The HSI system was used to collect spectral reflectance and spatial data of the samples covering the wavelength range of 400–1000 nm. The hypercubed data were analyzed using machine learning algorithms, including principal component analysis (PCA), discriminant factor analysis (DFA) and support vector machine (SVM). Using PCA for data reduction, 3 principal components were extracted with a cumulative variance of 90.53 %. DFA (linear and quadratic algorithms) and SVM (linear, quadratic, cubic, and Gaussian algorithms) were then used to classify the samples. HSI integrated with Gaussian SVM gave 93.4% accuracy which was best for classifying rice with different percentages of contamination. The image analysis gave a pseudo-color distribution map which facilitated the visualization of the contaminated rice by presenting data in an uncomplicated image. The machine learning-assisted HSI can be used as a rapid, nondestructive and chemical-free tool for an assessment of food safety risk for rice grain.

Moorish Viper, Daboia mauritanica (Gray, 1849) (Squamata, Viperidae), in Algeria: new provincial records, range extension, and an update on its distribution

 Daboia mauritanica (Gray, 1849), Moorish Viper, is native to North Africa. We update the distribution map for this species in Algeria, providing 19 new locality records, filling distribution gaps and significantly expanding the species’ known range. Our updated distribution map reveals a widespread presence of the species in northern Algeria, extending south into the northern Sahara. We note that some historical records in certain provinces remain unconfirmed and/or questionable, indicating the need for further investigation. Our findings underscore the importance of collaboration between researchers and citizen scientists in Algeria.

Research on Ancient academy Inscription Literature in Zhejiang Province

This paper takes ancient Zhejiang academy inscriptions as the main object of study, and will trace the cultural lineage of ancient academy culture from the inscriptions to create a new pattern of development of Zhejiang academy culture. At the same time, the author examines ancient academy inscriptions around Zhejiang, digs deeper into the current situation of collecting, collating and protecting academy inscriptions, rescues inscriptions abandoned in the wilderness, and uses new media technology to create a database of inscriptions and a distribution map of inscriptions, laying a solid foundation for the future protection and development of academy inscriptions.

In Situ Nondestructive Detection of Nitrogen Content in Soybean Leaves Based on Hyperspectral Imaging Technology

In this paper, hyperspectral imaging technology, combined with chemometrics methods, was used to detect the nitrogen content of soybean leaves, and to achieve the rapid, non-destructive and in situ detection of the nitrogen content in soybean leaves. Soybean leaves under different fertilization treatments were used as the research object, and the hyperspectral imaging data and the corresponding nitrogen content data of soybean leaves at different growth stages were obtained. Seven spectral preprocessing methods, such as Savitzky–Golay smoothing (SG), first derivative (1-Der), and direct orthogonal signal correction (DOSC), were used to establish the quantitative prediction models for soybean leaf nitrogen content, and the quantitative prediction models of different spectral preprocessing methods for soybean leaf nitrogen content were analyzed and compared. On this basis, successive projections algorithm (SPA), genetic algorithm (GA) and random frog (RF) were employed to select the characteristic wavelengths and compress the spectral data. The results showed the following: (1) The full-spectrum prediction model of soybean leaf nitrogen content based on DOSC pretreatment was the best. (2) The PLS model of soybean leaf nitrogen content based on the five characteristic wavelengths had the best prediction performance. (3) The spatial distribution map of soybean leaf nitrogen content was generated in a pixel manner using the extracted five characteristic wavelengths and the DOSC-RF-PLS model. The nitrogen content level of soybean leaves can be quantified in a simple way; this provides a foundation for rapid in situ non-destructive detection and the spatial distribution difference detection of soybean leaf nitrogen. (4) The overall results illustrated that hyperspectral imaging technology was a powerful tool for the spatial prediction of the nitrogen content in soybean leaves, which provided a new method for the spatial distribution of the soybean nutrient status and the dynamic monitoring of the growth status.

Hegnera obcordata (Fabaceae: Desmodieae), a newly recorded genus and species for the flora of China

Hegnera obcordata (Miq.) Schindl., is reported here as a newly recorded genus and species for the flora of China. It was collected from Xishuangbanna, Yunnan Province and Changjiang, Hainan Province. As a member of the tribe Desmodieae, H. obcordata can be easily distinguished from other genera by its climbing habit, depressed obovate terminal leaflet with an obcordate apex, and pods with U-shaped articles and deeply indented upper sutures. This study provides a detailed morphological description, phenology, distribution map and colored photographs of H. obcordata. Additionally, an updated dichotomous key to the Chinese Desmodieae genera is provided.

Taxonomic revision of the Neotropical skipper genus Incisus Grishin, 2019 (Lepidoptera, Hesperiidae, Pyrginae), with the description of a new species

The taxonomy of the genus Incisus Grishin, 2019 is reviewed, including redescriptions of known species, the description of a new species, Incisus mineirus sp. nov., identification keys, and an updated distribution map. Images of the genitalia for each species are included, with the female genitalia being illustrated for the first time. A lectotype and paralectotype for Antigonus incisus Mabille, 1878, and neotypes for Staphylus ordinarius Hayward, 1933 and Nisoniades aterea Plötz, 1884, are designated. Staphylus ordinarius Hayward, 1933 is a syn. nov. of Incisus fasciatus (Hayward, 1933). Antigonus variegatus Mabille, 1878 is a syn. nov. of Nisoniades macarius Herrich-Schäffer, 1870, and Staphylus dentivittata Hayward, 1933 is a syn. nov. of Staphylus musculus (Burmeister, 1875). Additionally, the subgeneric name Stolla Grishin, 2023 is a syn. nov. of Uniphylus Lemes, Siewert, Mielke, Casagrande & Warren, 2023.

An enigmatic Anthophorine bee from the south of France revealed as a new species: Anthophora (Paramegilla) ahlamae n. sp. (Hymenoptera: Apidae)

Summary A bee specimen identified as Anthophora planca Pérez, 1895, from France, was discovered in collections of Museum für Naturkunde, Berlin. This male specimen presents a morphology that does not conform to any of the Anthophora Latreille, 1803 species known from France, or indeed from Europe. After careful examination, it cannot be identified as Anthophora planca Pérez which is considered to be a nomen dubium (type lost), but furthermore it does not belong to any currently described species. Additional specimens of this species were collected in Morocco, Spain (Melilla) and Algeria. The new species is named Anthophora (Paramegilla) ahlamae Rasmont & Wood, n. sp. The new species is documented by numerous high-resolution photographs. Its diagnosis is compared with the near species Anthophora (Paramegilla) gallica (Dalla Torre & Friese, 1895) and A. (Paramegilla) podagra Lepeletier, 1841. DNA barcodes data are presented, allowing for confirmation of placement within the subgenus Paramegilla Friese, 1897. A detailed distribution map is provided along with ecological observations. Anthophora ahlamae n. sp. may be specialised on flowers of the genus Ballota L. (Lamiaceae).

Neea bradeana (Nyctaginaceae: Pisonieae), a new species with whorled leaf arrangement from the Atlantic Forest of Southeastern Brazil

Neea bradeana is here described for the Atlantic Rainforest of Southeastern Brazil. This new species had been confused for decades with N. verticillata, the type species of the genus which is described for Peru. N. bradeana is characterized by its unbranched shrubs with whorled sessile or short-petiolate leaves and erect-congested inflorescences. This species is here described and illustrated; remarks on taxonomic and morphologic affinities, distribution map, and conservation assessment are also provided.

Indigoferajintongpenensis (Fabaceae, Papilionoideae, Indigofereae), a new species from Yunnan, southwest China.

Indigoferajintongpenensis, a new species of the subfamily Papilionoideae of Fabaceae, is described and illustrated from Yunnan, southwest China. The new species is characterised by having a prostrate habit, flexible stems and branches, as well as spreading, sub-basifixed, asymmetrically 2-branched trichomes covering the entire plant, discoid calyx, and racemose inflorescences 6-8-flowered, short, 1-2 (-3.5) cm in length, apparently shorter than the leaf. A distribution map and comparison of morphological diagnostic characters with its morphologically similar species are provided. Additionally, a preliminary conservation assessment of I.jintongpenensis is proposed following IUCN criteria.

Analysis of changes in intrinsic neural timescales in male smoking addicts based on whole brain resting-state functional magnetic resonance imaging

Objective: To investigate the abnormal changes of intrinsic neural time scale (INT) in male smoking addicts based on whole brain resting state functional magnetic resonance imaging (rs-fMRI). Methods: A case-control study. The clinical data and whole brain rs-fMRI data of 139 male subjects, aged (34.1±8.8) years, recruited through the online platform from January 2019 to December 2021 were retrospectively analyzed. According to the existence of smoking addiction, they were divided into smoking addiction group (n=83) and healthy control group (n=56).INT was calculated to reflect the brain neural activity dynamics. Single sample t test was used to obtain the whole brain spatial distribution maps of INT in smoking addiction group and the control group. Then two-sample t test was conducted to explore the difference of INT between the smoking addition group and the healthy control group, with age and years of education as covariates. Finally, Spearman correlation analysis was used to explore the relationship between INT and nicotine dependence scale score and smoking index. Results: Subjects with smoking addiction and healthy control group showed a similar pattern of hierarchical neural timescales, namely shorter INT in sensorimotor areas and longer INT in parietal lobe, posterior cingulate cortex. In addition, in the smoking addiction group, the left medial occipital gyrus (peak t=-3.18), left suproccipital gyrus (peak t=-3.66), bilateral pericalar cleft cortex (left: peak t=-3.02, right: peak t=-3.22), bilateral lingual gyrus (left: peak t=-3.10, right: t peak=-3.04), left cuneus (peak t=-2.97), default network associated brain region [left anterior cuneus(peak t=-3.23), left angular gyrus (peak t=-3.07), and left posterior cingulate cortex (peak t=-3.54) were significantly lower than those of healthy controls (gaussian random field correction, voxel level all P<0.005, mass level all P<0.05). However, there was no significant correlation between INT and nicotine dependence scale score and smoking index (both P>0.05 after Bonferroni correction). Conclusion: Compared with healthy controls, smoking addicts showed abnormal changes in the dynamics of neural activity in the visual cortex and the default network.

Investigation of climate change by analysing the rainfall pattern in kuzhithuraiyar sub-basin of India using GIS-based spatial analysis

Global climate change may have a significant effect on several environmental components in many different nations worldwide. Variations in the pattern of precipitation have an immediate impact on the management of water resources. To improve water resource management techniques, it is critical to investigate differences in the temporal and geographical rainfall patterns. The current study looks at rainfall patterns in the Kuzhithuraiyar sub-basin of the Kodayar basin, Kanyakumari District. Utilizing the mean monthly data from four distinct rain gauge locations, we have comprehensively compiled and analysed rainfall statistics over an extensive timespan of 30 years (1991–2020). For precision in temporal categorization, this data was meticulously divided into four seasonal classifications: pre-monsoon, post-monsoon, southwest monsoon (SW), and northeast monsoon (NE). Using ArcGIS 10.8, the rainfall pattern was analysed, and spatial distribution maps for the four seasons listed above were created using the inverse distance weighting (IDW) approach. The region receives an average of 1456.78 mm of precipitation each year, with the post-monsoon, pre-monsoon, SW monsoon, and NE monsoon providing 32.87 mm, 335.28 mm, 538.67 mm, and 549.97 mm, respectively. The results reveal that the model correlates mean rainfall and rainfall variability over areas of Tamil Nadu's Kuzhithuraiyar sub-basin with some accuracy in terms of the number of rainfall extremes and their spatial distribution maps.

PECT Composite Defect Detection Algorithm Based on DualGAN

To address the problems of insufficient accuracy and slow reconstruction speed of Planar Electrical Capacitance Tomography (PECT) detection of damaged specimens, a Dual Generative Adversarial Networks (DualGAN)-based PECT image defect detection method is proposed in this paper. The improved particle swarm algorithm with adaptive particle number and L2-norm is used to optimize the sensitivity field, combined with the parallel Landweber algorithm to solve the PECT inverse problem to obtain the dielectric constant distribution map. In the DualGAN network, the Unet generator utilizes an Adam-based local attention mechanism to adjust module weights, facilitating feature extraction and the generation of high-quality transformation images of the Landweber dielectric constant distribution. A PatchGAN discriminator is employed to distinguish between transformation images and real images, using the generated transformation images as target images. Experimental results demonstrate that the sensitivity field, enhanced by the improved particle swarm algorithm and L2-norm normalization, achieves better balance. Furthermore, the addition of a network transformation using the Adam-based local attention weight mechanism on the DualGAN network reduces artifacts in the reconstructed images, resulting in more accurate PECT reconstructions. The PECT image defect detection method, integrating DualGAN, an improved particle swarm optimization algorithm, and a local attention mechanism, has made significant strides in addressing challenges related to image reconstruction accuracy and speed. This technological advancement has enhanced the precision and efficiency of defect detection in carbon fiber composite materials, thereby fostering the broader utilization of planar capacitance tomography technology in industrial damage detection and material defect analysis.

Diffusion calculations on reconstructed bentonite microstructures with anion exclusion effects.

Due to their prevalence in the lithosphere and their high capability of sorbing pollutants, smectite clays play a foreground role in environmental pollution studies, waste management, and soil science. In complementarity with existing approaches at the molecular or macroscopic scales, real microstructures have been employed to investigate ionic transport by diffusion through montmorillonite and water-saturated Wyoming bentonite at intermediate scales ranging between the nanometer and the micrometer. The coupled solute transport and electrostatic phenomena investigated at the nanopore scale are upscaled using the homogenization of porous media approach. Homogenization computations rely on a hierarchical description of bentonite that acknowledges the existence of pores networks at different scales. At the scale of montmorillonite layers, digitized TEM images have been employed to simulate the diffusion of ionic solutes by considering electrostatic interactions in the vicinity of the negatively charged clay platelets' surface. Finite element microstructures are created after extraction of the contours of the layers using dedicated image processing algorithms. Local electric potential distribution, anion exclusion, and cation inclusion are displayed by ion distribution maps. The effective diffusion tensor and the transport equation obtained through volume averaging are then used to simulate diffusion at the scale of a Wyoming bentonite sample composed of clay gels of variable density, solid grains, and micropores. Qualitative comparisons were made with existing diffusion data, and a particular attention is given to the anisotropy of the diffusion tensors at both the mesoscopic and macroscopic scales.

A novel framework for urban flood risk assessment: Multiple perspectives and causal analysis

Risk assessment and adaptation have become key focuses in the examination of urban flooding risk. In recent decades, global climate change has resulted in a high incidence of extreme weather events, notably flooding. This study introduces a spatial multi-indicator model developed for assessing flood risk at the urban agglomeration scale. A crucial addition to the model is the incorporation of an adaptive capacity within the IPCC risk framework. The model systematically considers various flood risk indicators related to the economic, social, and geographic environments of the central and southern Liaoning urban agglomeration (CSLN). It generates a spatial distribution map of integrated flood risk for multiple scenario combinations. Furthermore, the intricate relationship between different risk indicators and flood risk was analyzed using correlation analysis and the Light Gradient Boosting Machine model (Light GBM). The findings reveal notable variations in flood risk under different scenarios. The inclusion of vulnerability indicators increased flood risk by 33 %, while the subsequent inclusion of adaptive indicators decreased flood risk by 45 %. Dense populations and assets contribute to high flood risk, while adaptive capacity significantly mitigates urban flood risk. The framework adopted in this paper can be applied to other areas where urban agglomeration-scale flood risk assessment is needed, and can contribute to advancing scientific research on flood forecasting and mitigation.