Sampling Points Research Articles

Enhanced physics-informed neural networks without labeled data for weakly and fully coupled transient thermomechanical analysis

In this paper, a series of enhanced physics-informed neural networks (PINN) models without labeled data is proposed to solve the weakly and fully coupled thermomechanical problems. In these models, to better predict the thermal and mechanical responses, PINNs consisting of different deep neural networks (DNN) representing temperature, displacement, and stress are specifically constructed. Furthermore, to elevate the accuracy and avoid possible training failure, several advanced algorithms are developed to ensure the effectiveness of imposing boundary conditions, refining sampling distributions, and enhancing training strategy. A notable aspect of the enhanced PINNs is their independence from expensive, labeled data, relying solely on the temporal and spatial information embedded within the sampling points. The effectiveness and accuracy of the enhanced PINNs are validated through extensive numerical examples, including heat conduction and both weakly and fully coupled thermomechanical problems. The comparation between original PINN and enhanced PINN illustrates the necessity of involving these enhanced methods. The results demonstrate the significant potential of PINN methodologies in engineering areas involving complex thermomechanical processes.

Geographical distribution and molecular identification of cyst nematodes in Bajaur, Pakistan

Summary A survey conducted along the Panjkora river in Bajaur, Khyber Pakhtunkhwa (KPK), Pakistan, identified seven isolates of nematode populations belonging to the Heteroderidae family among the 25 soil and root samples examined. Within these isolates, three were classified as Heterodera avenae, H. filipjevi and H. zeae, while the remaining four were identified as Globodera pallida. The identification of these cyst nematodes involved a combination of morphological and morphometric analyses, complemented by molecular techniques employing newly designed primers to amplify diagnostic regions from any cyst nematode. This study marks the first molecular characterisation of H. avenae, H. zeae and G. pallida in Pakistan and the initial report of H. filipjevi in the country. The mapping of crop-growing regions with sampling points highlights areas at high risk for further pathogen spread.

Robust unsupervised 5D seismic data reconstruction on regular and irregular grids

Seismic data reconstruction has become a central focus in seismic data processing, addressing challenges posed by sparse sampling due to physical and budgetary constraints. The advent of 5D acquisition methodologies marks a significant advancement in the quality and completeness of seismic data sets. Most traditional 5D reconstruction methods commonly use the fast Fourier transform (FFT), requiring regular grids and preliminary 4D binning before 5D interpolation. Discrete Fourier transform and nonequidistant FFT can honor the original irregular coordinates. However, when using exact locations, these methods become computationally expensive. We introduce an unsupervised deep-learning methodology to learn a continuous function across the sampling points in seismic data, facilitating reconstruction on regular and irregular grids. The network comprises a multilayer perceptron with linear layers and element-wise periodic activation functions. It excels at mapping the input coordinates to the corresponding seismic data amplitudes without relying on external training sets. The network’s intrinsic low-frequency bias is crucial in prioritizing acquiring self-similar features over high-frequency and incoherent ones during training. This characteristic mitigates incoherent noise in seismic data, such as random and erratic components. To assess the robustness of the unsupervised reconstruction technique, we conduct comprehensive evaluations using synthetic data examples sampled regularly and irregularly, as well as field-data examples with and without binning. The findings demonstrate the efficacy of our deep-learning framework in achieving resilient and accurate seismic data reconstruction across diverse sampling scenarios.

High-Precision Photonics-Assisted Two-Step Microwave Frequency Measurement Combining Time and Power Mapping Method.

Photonics-assisted methods for microwave frequency measurement (MFM) show great potential for overcoming electronic bottlenecks and offer promising applications in radar and communication due to their wide bandwidth and immunity to electromagnetic interference. In common photonics-assisted MFM methods, the frequency-to-time mapping (FTTM) method has the capability to measure various types of signals, but with a trade-off between measurement error, measurement range, and real-time performance, while the frequency-to-power mapping (FTPM) method offers low measurement error but faces great difficulty in measuring signal types other than single-tone signals. In this paper, a two-step high-precision MFM method based on the combination of FTTM and FTPM is proposed, which balances real-time performance with measurement precision and resolution compared with other similar works based on the FTTM method. By utilizing high-speed optical sweeping and an optical filter based on stimulated Brillouin scattering (SBS), FTTM is accomplished, enabling the rough identification of multiple different signals. Next, based on the results from the previous step, more precise measurement results can be calculated from several additional sampling points according to the FTPM principle. The demonstration system can perform optical sweeping at a speed of 20 GHz/μs in the measurement range of 1-18 GHz, with a measurement error of less than 10 MHz and a frequency resolution of 40 MHz.

Seabed classification in Bahía de Navidad in the Mexican Central Pacific using a single-beam echosounder

This study proposes a methodology for characterizing subtidal habitats along rocky coastlines and adjacent seabeds in the epipelagic zone using a 120 kHz single-beam echo sounder (SBES). The data collected at depths of 15 to 80 m in Bahía de Navidad on the Mexican Central Pacific coast were postprocessed and classified using QTC Impact™ software to obtain three acoustic classes.Using the classified data and ArcMap™ software, a bathymetric map was built for the selection of 19 sampling points for ground truthing and granulometric analysis to create a matrix further subjected to two ordination analyses: principal component analysis (PCA) and nonmetric multivariate scaling analysis (MDS). Additionally, the matrix was adapted into a dummy matrix, which was subsequently used to conduct a Pearson correlation coefficient test.The analyses revealed a strong correlation between the medium-deep-zone acoustic class (20 m to 50 m) and the medium-grain-size sand substrate, as well as a high correlation between the deep-zone acoustic class (50 to 80 m) and finer sand substrates. This study demonstrated the potential of using affordable echosounders as a reliable and feasible alternative for characterizing marine habitats, especially for research institutions in developing countries with limited financial resources.

B-237 Engineered Reverse Transcriptases and Taq DNA Polymerases deliver robust yield in the presence of clinically relevant inhibitors

Abstract Background The past five years has seen explosive growth in molecular diagnostic assays that rely on sensitive and accurate detection of nucleic acids across a wide range of sample types. As the assays are required to become increasingly sensitive, the need for highly functional and purpose-built enzymatic tools has grown. Reverse transcriptase (RT) and Taq DNA Polymerase (Taq DNAP) are commonly used to detect RNA and/or DNA viruses in patient samples for point of care (POC) diagnostics. However, crude patient samples introduce inhibitors that decrease enzyme activity and increase false negative results in POC testing. In addition, there is a need for faster turn-around time for patient diagnosis which can be enabled by engineering more active and sensitive Taq variants. Herein, we present on our engineered RT and Taq DNAP enzymes that were developed to confer greater activity, thermostability, inhibitor tolerance and/or sensitivity. Methods We take a 3-pronged approach to protein engineering: rational design, computational design and directed evolution. Using these techniques, we identified and characterized many RT and Taq DNAP variants. We deeply characterized each RT variant by measuring processivity, inhibitor tolerance and thermostability. First strand synthesis was done at temperatures from 42-65°C in the presence and absence of various clinically relevant inhibitors followed by qPCR to assess cDNA yield. Next, we characterized the inhibitor tolerance of the Taq variants by performing qPCR reactions in the presence of inhibitors. To determine if the Taq variant would be an appropriate fit for fast PCR, the polymerization activity of the Taq DNAP variants were characterized using a primer extension assay. Results Reverse transcriptases termed; StellarScript, StellarScript HT and StellarScript HT+ exhibit high processivity at 42°C. StellarScript HT+ exhibited the highest inhibitor tolerance, processivity and thermostability at temperatures up to 65°C thus making StellarScript HT+ well suited to produce high yields with clinical samples. Taq Pol variants were identified that efficiently amplify DNA in the presence of inhibitors. Conclusions Based on the increased polymerase activity and inhibitor tolerance, WMG Taq variants are well suited to give robust DNA yields with clinical samples. We were successful in engineering RT and Taq DNAP variants that are highly thermostable, active and inhibitor tolerant and thus will enable breakthroughs in existing POC diagnostics.

Extraction of isotropic and anisotropic components for optical surface micro-metrology based on the two-dimensional power spectral density analysis

Extraction of surface characteristics is essential during surface processing and optical inspections. In this work, we propose a new extraction method by dividing the global feature within multiple directions into isotropic and anisotropic components and combining noise filtration, two-dimensional component extraction, and fast reconstruction. The mathematical descriptions of global and local features were derived. The noise by holes, scratches, and finite sampling points was restrained by Bearing Ratio analysis, Hough transform, and Welch window operation. The isotropic and anisotropic surface components were extracted in the two-dimensional power spectral density domain, reconstructed in the two-dimensional Fourier domain, and inversed in Cartesian coordinates. Five surfaces with anisotropic structural characteristics ranging from zero to two dimensions were analyzed. The general applicability was proved according to the consistency between surface processing methods and extracted results. A chemical mechanical polishing experiment was designed and accomplished to verify the sensitivity of the extraction method. The subtle variation in surface morphology was captured on the reconstructed surfaces near the polishing end-point, confirming its detectability on weak anisotropic components. This process-oriented surface extraction method can achieve qualified results without transcendental knowledge of surface conditions and offers openness to various surface evaluation criteria by statistical roughness indicators, which supports surface inspection for multiple surface processing techniques.

Environmental health risk assessment and acute effects of sulfur dioxide (SO2) inhalation exposure on traditional sulfur miners at Ijen Crater Volcano, Indonesia

The Ijen Crater volcano is one of the geological wonders recognized by UNESCO. Inside it is a blue lake with a high acidity level, and a blue fire phenomenon has formed due to the very high concentration of sulfur. This crater is also one of Indonesia's largest sources of sulfur and is used by locals as a traditional sulfur mine. This study aims to measure SO2 concentrations and assess the health risks of SO2 exposure in traditional sulfur mine workers. The SO2 measurements were taken using impingers at six sample points along the mine workers' path. In addition, anthropometric data, work activity patterns, and health complaints during work were collected through direct interviews with 30 respondents selected based on inclusion criteria. Short-Term Health Impact Method was carried out based on a comparison of threshold level values and acute effects obtained from interviews regarding health complaints. The Hazard Question Index (HQ Index) of SO2 exposure was calculated using the health risk assessment method. The SO2 concentrations between 3.14 and 18.24 mg/m3. All sample points were above the quality standard threshold set by the EPA of 1.97 mg/m3. The most common health complaints workers experienced were eye irritation and coughing while working, followed by headache, shortness of breath, and skin irritation. The HQ index of SO2 exposure in workers was 1.02 for real-time exposure and 2.15 for long-term exposure. An HQ index ≥ 1 indicates a potential health risk for workers. Therefore, it is important to control workers' SO2 exposure.

The hepatopancreas microbiome of velvet crab, Necora puber.

Crustaceans are a valuable resource globally, both ecologically and economically, and investigations into their health are becoming increasingly important as exploitation rises. The microbiome plays a crucial role in crustacean immunity, and understanding its composition and structure can provide insights into the health of an organism and its interactions with various factors. In this study, we investigated the hepatopancreas microbiome of the velvet swimming crab, Necora puber, and compared its composition and structure with several study factors, including two different sampling points and infection with a paramyxid parasite, Paramarteilia canceri. To our knowledge, we provide the first description of a velvet crab microbiome, highlighting the dominance of a single microorganism, Candidatus hepatoplasma. We identified variations in microbiome composition between sampling points and discussed the possible processes affecting microbiome assembly. We also outline a core microbiome for the velvet crab hepatopancreas, consisting of 12 core phyla. Our study adds to the growing literature on crustacean microbiomes and provides a baseline for future investigations into the velvet crab microbiome and the health of this crustacean species.

Changing water quality of a major rainfed river system of western India: insights from the Bhima river using water quality index

The Bhima River originating from the Western Ghats of India, a global biodiversity hotspot, is one of the most dammed rivers, used intensively for multifaceted purposes including agriculture and domestic usage. The present study was undertaken from early pre-monsoon to start of monsoon of 2023 encompassing 68 sampling points across Bhima River and its tributaries to develop the water quality index (WQI). Environmental parameters including SWT, pH, DO, TDS and EC were measured in situ during the time of sampling. Surface water samples (1 L) was collected for estimation of dissolved nutrients. The concentration of dissolved oxygen was found to be below 2 mg l−1 in several sampling points representing hypoxic conditions of the riverine system. TDS and EC ranged from 55.12 ppm to 2983 ppm and 108.3 μS cm−1 to 5939 μS cm−1 respectively, indicating significant spatial and temporal variations. The concentration of dissolved ammonia (0.05 mg l−1–14.09 mg l−1), nitrate (5.88 mg l−1 to 1596.16 mg l−1), o-phosphate (0.21 mg l−1–35.47 mg l−1) and reactive silicate (36.70 mg l−1 to 1455.15 mg l−1) indicates possible influx of agricultural run-off and untreated municipal discharges. Calculations of water quality index (WQI) using the measured parameters showed ‘bad to very bad’ and ‘medium to good’ water quality along the Bhima River and its tributaries. In-depth analyses of WQI indicates pollution ‘hot-spots’ and hints toward urgent implementation of basin-level mitigation strategies for improvement of the ecological health of Upper Bhima basin.

Fluid-Solid coupling analysis of PTFE impeller in hydraulic turbines for sewage resource utilization

Abstract Hydraulic turbines, vital energy-saving devices, convert pressure energy from liquid fluids into mechanical energy. As wastewater resource utilization and zero discharge industries proliferate, relevant research is of great interest. In order to design a suitable medium and low pressure hydraulic turbine for industrial water treatment, we conducted a study analyzing the stress and strain characteristics of a PTFE turbine impeller by means of bi-directional fluid-structure coupling numerical simulation. Our findings suggest that the impeller’s maximum equivalent stress and maximum deformation fall within acceptable material limits, with stress concentration at the blade-cover plate interface. The blade experiences maximum deformation at its midpoint due to high liquid pressure on the volute periphery. Fluid solid coupling reduces the maximum equivalent stress of the impeller by about 1.65MPa and the maximum deformation of the blade by about 0.05mm. The equivalent stress at the sampling point fluctuates periodically, with a significant change in stress amplitude at the sampling point of the cover plate on one side of the support surface, increasing susceptibility to fatigue damage.

Environmental Radioactivity Monitoring and Radiological Impact Assessment of Agbara Industrial Area, Ogun State, Nigeria

This study assessed the naturally occurring radioactivity of 40K, 238U, and 232Th, which pose a significant threat to human health, particularly when their concentrations exceed the threshold. Background radiation levels were measured at two specific locations, Access Bank and Market areas, across a total of forty (40) sample points. The measurements were taken using a calibrated RS125 Gamma Spectrometer (a portable NaI [Tl] detector) designed in Canada, in conjunction with a global positioning system (GPS) to accurately record the research coordinates within the Agbara industrial area, Ogun State, Nigeria. The mean activity concentrations of the primordial radionuclides were 177.87 Bqkg-1, 20.01 Bqkg-1, and 52.90 Bqkg-1 for 40K, 238U, and 232Th, respectively. More so, the in-situ measured dose rate (DR) ranges between 12.18 nGyh-1 (Access Bank area) and 97.95 nGyh-1 (Market area), with an average value of 47.22 nGyh-1. The average measured and estimated absorbed dose rates were within the safe limit of 57 nGyh-1 provided by UNSCEAR. However, the measured dose rates exceeded the recommended limit in ten locations, while measured activity for thorium exceeded the world average value for over half of the study locations. Although all estimated radiological parameters were within recommended threshold values, suggesting the low risk of exposure to higher levels of ionising radiation in most locations in the Agbara industrial area, there is a potential cancer risk for individuals who have resided in the area for 70 years or more due to long-term exposure to ionising radiation.

Assessment of water quality and emerging pollutants in two fish species from the mallorquin swamp in the Colombian Caribbean

The Mallorquín Swamp, an important ecosystem in Atlántico, Colombian Caribbean, underwent environmental monitoring at eight points during rainy, transition, and dry seasons. This was to assess water quality, seasonal variation, and the bioaccumulation of metals, emerging pollutants, and organic compounds in the fish Ariopsis canteri and Mugil incilis. Water parameters were analyzed using descriptive statistics and multifactorial ANOVA with the Tukey HSD test for seasonal differences. Normality and variance of the fish results were verified, and differences between groups were evaluated using ANOVA or Kruskal-Walli's method when data transformation failed. Spearman correlation was used to relate the results. Water sampling revealed variations in temperature, dissolved oxygen, salinity, and nutrient levels. Significant differences in alkalinity and hardness were observed across seasons and sample points. The most probable number (MPN) levels of Total coliform and E. coli peaked near areas with domestic wastewater inputs, reaching 5.4x106 and 4.0x106 MPN, respectively, indicating potential microbiological contamination of water. Fish samples revealed high concentrations of persistent substances such as methylmercury, polycyclic aromatic hydrocarbons (PAHs), and emerging pollutants. Heavy metal analysis showed elevated iron levels (5.28 ± 0.657 mg/L), while emerging pollutants, including ibuprofen (218 μg/L) and naproxen (343.89 μg/L), exhibited high concentrations near human settlements. Ariopsis canteri showed higher bioconcentration tendencies for methylmercury (238.5 ± 100 μg/kg), and acenaphthene (7782 ± 4123.8 μg/kg), possibly influenced by its feeding habits and habitat preferences. In contrast, Mugil incilis exhibited higher bioaccumulation trends of PAH (2376.23 ± 599.63 μg/kg acenaphthene) and emerging pollutants like galaxolide (139.49 ± 34.98 μg/kg), possibly due to its mobility and exposure to various contaminants in their environment. These findings emphasize the need to monitor and manage aquatic ecosystems' health to mitigate anthropogenic influences on water quality and biodiversity. This research serves as a reference for global conservation efforts, emphasizing the need for comprehensive monitoring and regulatory frameworks to protect aquatic environments and ensure their sustainability for future generations.

An effective farmer-centred mobile intelligence solution using lightweight deep learning for integrated wheat pest management

Integrated Pest Management (IPM) techniques have been widely used in agriculture to manage pest damage in the most economical way and to minimise harm to people, property and the environment. However, current research and products on the market cannot consolidate this process. Most existing solutions either require experts to visually identify pests or cannot automatically assess pest levels and make decisions based on detection results. To make the process from pest identification to pest management decision making more automated and intelligent, we propose an end-to-end integrated pest management solution that uses deep learning for semi-automated pest detection and an expert system for pest management decision making. Specifically, a low computational cost sampling point generation algorithm is proposed to enable mobile devices to generate uniformly distributed sampling points in irregularly shaped fields. We build a pest detection model based on YoloX and use Pytorch Mobile to deploy it on mobile phones, allowing users to detect pests offline. We develop a standardised sampling specification and a mobile application to guide users to take photos that allow pest population density to be calculated. A rule-based expert system is established to derive pest management thresholds from prior agricultural knowledge and make decisions based on pest detection results. We also propose a human-in-the-loop algorithm to continuously track and update the validity of the thresholds in the expert system. The mean average precision of the pest detection model is 58.17% for 97 classes, 75.29% for 2 classes, and 57.33% for 11 classes on three pest datasets, respectively. The usability of the pest management system is assessed by the User Experience Surveys and achieves a System Usability Scale (SUS) score of 76. The usability of the proposed solution is validated by qualitative field experiments.

Distribution of mercury and methylmercury in ice-water-sediments in lakes during the freezing period under the influence of ice cover

The presence of lake ice cover alters the subglacial water environment, thereby influencing the migration and transformation of mercury (Hg) and methylmercury (MeHg) within the ice-water-sediment media of lakes. This study investigated the occurrence characteristics of mercury and methylmercury in various environmental compartments within lakes located at high latitudes in cold regions during the freezing period. To this end, Wuliangsuhai Lake, the largest freshwater lake situated at 40°N in China, was selected as the study site. The contents of mercury and methylmercury in lake ice were determined for the first time. The percentage of methylmercury (MeHg%) and ice-water partition coefficient were analyzed. The pollution situation and health risk were evaluated by single factor pollution index. The results show that the ice body and water body of Wuliangsuhai are not polluted by mercury and methylmercury, but some sampling points in the sediment are slightly polluted. The mercury content in sediment is negatively correlated with the ice thickness, and the methylmercury content in water is positively correlated with the methylmercury content in sediment, but negatively correlated with the ice thickness. The migration ability of methylmercury in ice-water system is stronger than that of mercury. The MeHg% of water in ice period is higher than that in non-freezing period, which is different from other lakes without ice sheet. The results show that in the dynamic equilibrium of methylation and demethylation in the high-latitude lake water, the methylation is higher in the ice period than in the non-freezing period due to the influence of light intensity, while the mercury in the non-freezing period is more susceptible to the demethylation.

Effects of alpine meadow degradation on the soil physical and chemical properties in Maqu, China

While alpine meadow degradation presents a significant ecological challenge, research on the complex patterns of soil property changes induced by degradation has been somewhat limited. In this study, we investigated the Maqu alpine meadow, meticulously categorizing it into different grassland types exhibiting varying degrees of degradation based on factors such as vegetation coverage and community, soil characteristics, and landscape features. These classifications included typical grassland, degraded grassland, desertified grassland, and sandy land. In August 2018, we established three quadrats at each sample point and collected soil samples at five depths (surface [0–2 ​cm], 2–5 ​cm, 5–10 ​cm, 10–20 ​cm, and 20–40 ​cm) to analyze soil particle size distribution (PSD) and nutrients content. The results revealed a discernible trend: alpine meadow degradation led to selective loss of nutrient-rich soil fine particles, resulting in significant alterations in soil PSD and nutrients, particularly pronounced in grasslands with low degrees of degradation. Moreover, within a specific range of degradation degree, the clay content in the shallow soil of alpine meadow increased with the degradation degree, but it declined when the degradation degree exceeded a certain threshold. Degradation also disrupted the intricate relationship between soil nutrients, with notable variations in their coupling. This difference was also reflected in the influence of soil physical and chemical properties on soil nutrients, with the explanatory power of each environmental indicator on soil nutrients decreasing significantly with increasing degradation. This study systematically analyzed the variation in soil physical and chemical properties throughout the degradation process, revealing the mechanism of soil nutrient imbalance and decline caused by the degradation process. It provides crucial theoretical foundations and reference points for the preservation and rejuvenation of alpine meadows, enriching the methodology for assessing the impact of grassland degradation.

Density clustering method based on k-nearest neighbor propagation

Abstract Density clustering, an effective data analysis tool, performs well on arbitrary shapes and non-convex datasets. However, it still has some limitations in identifying the cluster structures of datasets with irregular shapes and uneven density distribution. Aiming at the above problem, this paper proposes a density clustering method based on k-nearest neighbor propagation. Firstly, based on the theory of k-nearest neighbor, the nearest hub points are defined to replace the sample points within their k-nearest neighbors, and the cluster boundary is ascertained relying on the propagation of the nearest hub points with density gradient trend so as to divide the nearest hub points into multiple groups; secondly, a group merging method of the nearest hub points based on the set similarity is given to obtain the set of nearest hub points; and finally, the remaining sample points are assigned to the set of their nearest hub points to obtain the final clusters. The correctness and effectiveness of the algorithm are verified by experiments on synthetic and UCI datasets, and the results show that the algorithm can better cope with the clustering needs of multiple complex data distributions compared with other comparative algorithms.

Point cloud-based feature extraction and surface reconstruction for parts paring in high-precision assembly

Abstract Assembly plays a crucial role in industrial manufacturing in industrial manufacturing, but the efficiency of conventional manual methods for parts pairing is limited. Previous research has demonstrated the feasibility of deep learning for point cloud feature extraction and 3D reconstruction. An innovative method utilizing deep learning for high-precision feature extraction and surface reconstruction is introduced to optimize parts pairing in this paper. Geometric dimensions and surface topography data are obtained by defining key assembly features and utilizing the Random Sample Consensus method. Deep learning is then used to directly regress the Surface Distance Function from point samples, facilitating comprehensive part surface modeling and supporting assembly simulation in the digital twin. To validate this approach, a case study demonstrates successful matching of 30 shaft parts and 30 hole parts after optimization, with an increase in average uniformity by 0.024. This highlights the proposed method’s superior effectiveness and accuracy in feature extraction and surface reconstruction.

Variability of soil chemical properties and rice productivity in salt-affected soil in the north coastal rice field of Central Java, Indonesia

A coastal rice field is generally characterized by salt-affected soil and low soil quality for rice cultivation. Identifying soil chemical properties in these areas is necessary to determine soil management options for rice production. Therefore, soil samples were collected from 33 sampling points in the Wedung Sub-district of Demak Regency in the late dry season of 2021 to evaluate the variation among soil chemical characteristics in a coastline rice field. Soil samples were obtained beneath the topsoil (0-20 cm soil depth) and observed for electrical conductivity, exchangeable potassium, sodium, calcium, magnesium, and cation exchange capacity. Soils in the research field were categorized as slightly salty (0.75-2 dS m-1) to lightly salty (2.0-4.0 dS m-1) with very high sodium (>2 cmol(+) kg-1). Exchangeable potassium was dominated by moderate (0.3-0.7 cmol(+) kg-1) and low categories (0.2-0.3 cmol(+) kg-1). Based on soil calcium-to-magnesium ratios, around 6% of all samples were classified as calcium-deficient. The range of soil cation exchange capacity was 22-30 cmol(+) kg-1 and classified as high soil cation exchange capacity. Rice productivity in the salt-affected soil was around 4 t ha-1. Strategies for soil and controlling plants, such as soil amelioration and salt-tolerant rice cultivars, should be pursued to support plant growth and enhance rice productivity in the salt-affected soil, particularly in the coastal area.

Assessing soil fungal diversity under different sampling schemes in conjunction with remote sensing technologies in a subtropical forest

Fungi, serving as real-time bioindicators to environmental changes and stressors, are crucial for effective forest conservation and management practices under ongoing global change. However, the large-scale assessment of soil fungi still encounters challenges in striking a balance between the extensive sampling costs and the limited accuracy of minimal sampling. In this study, we analyzed 1,606 soil samples collected from 625 quadrats (20 m × 20 m) within a 25-ha subtropical forest dynamic plot in East China. Our primary objective was to explore the impact of different sampling schemes, in conjunction with remote sensing (RS) technologies, on the interpolation of soil fungal diversity using Ordinary Kriging (OK) and Co-kriging (CoK) models. Our findings suggested that a sampling scheme including points at 0 m (the base points) and 8 m within each quadrat, totaling to 26 points/ha, would be a sufficient scheme. This scheme with OK model yielded comparable results to those of more intensive schemes (at 0, 2, 5 and 8 m), but required the fewest sampling points. Upon incorporating each RS variable separately into the CoK models, including two vegetation indices (normalized difference vegetation index and transformed chlorophyll absorption ratio index 2), three terrain attributes (Elevation, Aspect and Slope), and the synthesis of these RS variables, the accuracy of the predicted results was further improved for each sampling scheme. By leveraging high-precision soil DNA sequencing in conjunction with cost-effective RS technologies, this study proposes a rapid and affordable approach for monitoring soil fungal diversity on a large scale. This will facilitate data collection for understanding responses of forest soil fungi to ongoing global change.