Automatic Sampling of Geographic objects

Forest inventories are undergoing rapid changes due to an increasingly complex set of economic, environmental, and social policy objectives. Therefore, the objective is to identify, analyse, and discuss the main forest inventory methods at global, regional, and local levels, with an analytical perspective on the goals they seek to achieve in various forest ecosystems. For this review, information from 79 relevant studies related to the objectives and methods used in sampling forest resources in tropical, boreal, temperate, and arid ecosystems was considered. According to the analysed studies, forest inventories in different ecosystems face challenges and apply varied methods to assess forests. In the tropics, the focus is on monitoring biomass and carbon, but they show limitations in data quality and quantity limitations. To improve accuracy, robust sampling methods are suggested. In boreal ecosystems, LiDAR and data-driven models offer detailed biomass estimates. In temperate forests, diversified sampling techniques are employed to balance accuracy and efficiency. In arid ecosystems, non-plot methods are useful for mapping density and diversity of the forests. To board the specific challenges of each region, innovative approaches are needed. Inventories have been influenced by changes in environmental policies and technology; therefore, the need to estimate key forest variables and monitor their dynamics requires robust and technologically advanced sampling methods.

DNA-based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the methods used to collect samples upon which these later steps are based. This represents a critical knowledge gap, as methodologically sound field sampling is the foundation for subsequent analyses. We reviewed field sampling methods used for metabarcoding studies of both terrestrial and freshwater ecosystem biodiversity over a nearly three-year period (n=75). We found that 95% (n=71) of these studies used subjective sampling methods and inappropriate field methods and/or failed to provide critical methodological information. It would be possible for researchers to replicate only 5% of the metabarcoding studies in our sample, a poorer level of reproducibility than for ecological studies in general. Our findings suggest greater attention to field sampling methods, and reporting is necessary in eDNA-based studies of biodiversity to ensure robust outcomes and future reproducibility. Methods must be fully and accurately reported, and protocols developed that minimize subjectivity. Standardization of sampling protocols would be one way to help to improve reproducibility and have additional benefits in allowing compilation and comparison of data from across studies.

Cluster analysis (Jain & Dubes, 1988) provides insight into the data by dividing the objects into groups (clusters), such that objects in a cluster are more similar to each other than objects in other clusters. Cluster analysis has long played an important role in a wide variety of fields, such as psychology, bioinformatics, pattern recognition, information retrieval, machine learning, and data mining. Many clustering algorithms, such as K-means and Unweighted Pair Group Method with Arithmetic Mean (UPGMA), have been wellestablished. A recent research focus on clustering analysis is to understand the strength and weakness of various clustering algorithms with respect to data factors. Indeed, people have identified some data characteristics that may strongly affect clustering analysis including high dimensionality and sparseness, the large size, noise, types of attributes and data sets, and scales of attributes (Tan, Steinbach, & Kumar, 2005). However, further investigation is expected to reveal whether and how the data distributions can have the impact on the performance of clustering algorithms. Along this line, we study clustering algorithms by answering three questions: 1. What are the systematic differences between the distributions of the resultant clusters by different clustering algorithms? 2. How can the distribution of the “true” cluster sizes make impact on the performances of clustering algorithms? 3. How to choose an appropriate clustering algorithm in practice? The answers to these questions can guide us for the better understanding and the use of clustering methods. This is noteworthy, since 1) in theory, people seldom realized that there are strong relationships between the clustering algorithms and the cluster size distributions, and 2) in practice, how to choose an appropriate clustering algorithm is still a challenging task, especially after an algorithm boom in data mining area. This chapter thus tries to fill this void initially. To this end, we carefully select two widely used categories of clustering algorithms, i.e., K-means and Agglomerative Hierarchical Clustering (AHC), as the representative algorithms for illustration. In the chapter, we first show that K-means tends to generate the clusters with a relatively uniform distribution on the cluster sizes. Then we demonstrate that UPGMA, one of the robust AHC methods, acts in an opposite way to K-means; that is, UPGMA tends to generate the clusters with high variation on the cluster sizes. Indeed, the experimental results indicate that the variations of the resultant cluster sizes by K-means and UPGMA, measured by the Coefficient of Variation (CV), are in the specific intervals, say [0.3, 1.0] and [1.0, 2.5] respectively. Finally, we put together K-means and UPGMA for a further comparison, and propose some rules for the better choice of the clustering schemes from the data distribution point of view.

While Chap. 2 presented the broadness of the spectrum of clustering methods, this chapter focusses on clustering of objects that are embedded in some metric space and gives an insight into the potential variations of clustering algorithms driven by peculiarities of data at hand. It concentrates essentially around k-means clustering algorithm and its variants, which serve as an illustration on the kinds of issues the algorithms need to accommodate to. After presenting the basic variants of k-means, implementational issues are discussed, including initialisation and efficiency (e.g. k-means++). Then accommodations to special forms of data, e.g. text data (spherical k-means), or data with clusters separated by complicated boundary lines (kernel k-means), or ones residing in non-Euclidean space (k-medoids, k-modes) are presented. Subsequently non-sharp separation areas between clusters are discussed. First the EM algorithm is presented, then fuzzy k-means, with fuzzified variants of the aforementioned algorithms. k-means application presumes apriorical knowledge of the number of clusters. To overcome this limitation, so-called affinity propagation algorithm is presented. Finally approaches to handling data of non-typical relation between the number of objects and the number of dimensions, that is residing (predominantly) in a narrow section of the feature space are discussed (projective clustering, random projection, k q-flats, manifold clustering), or with too large data sets (subsampling), or clustering changing over time. The latter issue is closely connected to clustering under partial pre-labelling of objects. Also methods for simultaneous clustering of both objects and their features (co-clustering) are briefly outlined.

Active learning with fairness-aware clustering for fair classification considering multiple sensitive attributes

Background: Cervical spondylitis, a degenerative condition of the cervical spine, is increasingly prevalent due to aging populations and sedentary lifestyles. It significantly impacts individuals' quality of life through chronic pain and neurological symptoms, posing both physical and economic challenges. The primary objective of this study was to assess the awareness and knowledge of cervical spondylitis among the general public in District Kangra, Himachal Pradesh. Methods: A descriptive cross-sectional survey design was employed. The study involved 400 adults from District Kangra, selected using a robust sampling method. Data was collected through a pre-tested questionnaire distributed via digital platforms. The questionnaire covered socio-demographic information and 20 structured questions related to cervical spondylitis symptoms, causes, risk factors, prevention, and management. Data analysis was performed using Epi Info V7 Software, and appropriate statistical tests were applied. Results: The study revealed that while 78.8% of participants had heard of cervical spondylitis, detailed knowledge of its symptoms, causes, and management was moderate to low. Only 72.3% knew it could cause neck pain, and 64.5% were aware of its neurological implications. Awareness of preventive measures was limited, with only 53.3% knowing that maintaining good posture could help prevent the condition. Knowledge of treatment options was also lacking, with less than half of the participants aware of the benefits of physical therapy and medications. The overall knowledge scores showed a wide distribution, with 17.8% of participants falling into the poor knowledge category. Conclusion: The study highlights significant deficiencies in the knowledge and awareness of cervical spondylitis in District Kangra. There is a pressing need for focused educational interventions that address the full spectrum of cervical spondylitis, from symptoms to prevention and treatment. Enhancing public health campaigns, promoting early diagnosis, and leveraging digital platforms for broader outreach are crucial steps in reducing the burden of cervical spondylitis and improving the quality of life for those affected.

The surrogate model-based sampling method has been an active area of research due to its evident efficiency over direct sampling method. The fusion of the adaptive kriging (AK) model with the adaptive radial-based importance sampling (ARBIS) method is developed as an improvement of the AK model combined with Monte Carlo simulation (MCS) method especially for estimating the small failure probabilities. In the AK–ARBIS method, the number of candidate samples in each updating process of the kriging model is reduced through partitioning the whole MCS sample domain into different subdomains and removing the samples inside the last subdomain from the participating candidate sampling pool, which can avoid the time-consuming matrix operation involved in kriging predictions in order to find the best training sample in each updating step. In the existing AK–ARBIS method, kriging model stops updating according to the learning function-based stopping criterion, which cannot directly reflect the error of the estimated failure probability. In this regard, this paper proposes the error-based stopping criterion (ESC) enhanced AK–ARBIS method, where the processes of finding the optimal hypersphere and accurately identifying the sign of candidate samples are decoupled in order to successfully embed the ESC. In addition to embedding the ESC into the AK–ARBIS method, two target-oriented candidate sampling reduction strategies are investigated, respectively, to improve the efficiency of finding the optimal hypersphere and accurately identifying the sign of the participating candidate samples. Based on the candidate sampling pool reduction strategies and ESC, both the computational time and the required number of calls to the real limit state function are reduced over the existing AK–ARBIS method. Three case studies are analyzed to demonstrate the accuracy and efficiency of the proposed enhanced AK–ARBIS method, especially for estimating the small failure probabilities.

Techniques of data mining and its applications have become significant in almost all domains. Each technique has its own significance in a given problem context. Likewise, clustering technique is in use to group elements with similar properties together. Objective of this research work is to apply multithreading approach on grouping the elements into various clusters. This multithreading technique determines the target cluster for multiple elements simultaneously. Thus, this approach improves overall performance of clustering technique which involves many iterations required for finding out target cluster. Implementation approach in this work involves, partitioning the input data elements and associating each of these partitions to multiple threads. Each thread is responsible for picking up the input element from the respective partition and determining the destination cluster. This implementation technique uses master–slave design. Master component makes various partitions of input data elements, creates instances of slave components, and invoke the services offered by slaves. Each slave runs in separate thread of computation and performs the task of determining target cluster of the given input element from the corresponding partition. Slaves will terminate its task once it finishes the determination of target cluster for the data elements present in the respective partition. We have seen that the above-designed technique does well when it comes to consumption of computational time. Partitioning of input data elements and number of data points in a partition is decided based on number of cores available for multithreading. It consumes less computational time as each partition is processed simultaneously on different available cores. Thereby, it eliminates the large computational time needed for existing techniques. We did an experiment sample dataset from the data mining open-source library source spfm. The size of the datasets we have used is closed to 50 MB. Our test environment created maximum of four simultaneous threads for the processing of entire input data points. When observed with the current approaches, experimental results tell that our approach provides a performance gain of 30% on an average.

K-harmonic means data clustering with simulated annealing heuristic

The paper considers new techniques for clustering and sorting objects that are described with many quantitative and/or qualitative attributes and may exist in several copies with inconsistent and contradictory attributes. Methods are based on the theory of multiset metric spaces. New options for the objectspsila aggregation and features of classes generated are discussed.

An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images

Investigation and application of diffusive gradients in thin-films technique for measuring endocrine disrupting chemicals in seawaters

The uniform bandpass sampling theorem has been modified to cope with sampling frequency instability and carrier frequency variations. Minimum sampling rates for given sampling and carrier frequency variations are derived. A robust bandpass sampling method is proposed which requires the sampling frequency to be such that the carrier frequency is on the 1/4 or –1/4 sampling frequency grid.

The article considers the task of fuzzy clustering in a multi-objective optimization (MO) framework. It compares the relative performance of four recently developedmulti-objective variants of Differential Evolution (DE) on over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation for the candidates is used for DE. A comparative study of four DE variants with two most well-known MO clustering techniques, namely the NSGA II (Non Dominated Sorting GA) and MOCK (Multi- Objective Clustering with an unknown number of clusters K) is also undertaken. Experimental results reported for six artificial and four real life datasets (including a microarray dataset of budding yeast) of varying range of complexities indicates that DE can serve as a promising algorithm for devising MO clustering techniques.

Sampling of radiation source particles is important for obtaining a correct result in the thermal radiative transfer simulation with implicit Monte Carlo. When conducting the implicit Monte Carlo simulation of spherical geometry, temperature in a cell (a spherical shell) is generally treated as a spatially independent value. That means that the particles of radiative source are uniformly distributed in a spherical shell. In some cases where the gradient of temperature inside a cell is relatively small, the treatment does not cause too many errors. However, when the opacity of material becomes large enough or the spherical shell becomes thick enough, the temperature of thermal wave head will change sharply and there will be a great temperature gradient even in a single spherical shell. The treatment will make the thermal radiation propagate much faster than the practical one, which is unacceptable in physics. We investigate the physical and numerical reasons for this violation, finding that the simulation results strongly rely on the separation of cell and that the thermal wave propagates faster with the cell number decreasing. In order to yield an accurate result, the cell number has to increase up to a large enough value. Unfortunately, more cells need more particles to reduce the numerical variance, and more particles will cost more computation time and thus causing the simulation efficiency to lower. In our work, temperature is not treated as a constant in space any more. Instead, it is treated as a linear function in a cell. Based on a new temperature function and radiative energy density distribution, a probability density distribution function of emitting position of radiation source particle in spherical geometry is obtained. Then two new spatial sampling methods are proposed and the sampling procedures of radiation source particle are designed. To verify our new sampling methods, we test several typical thermal radiative problems and compare the result with a reference solution. Numerical experiments show that both two new sampling methods can correct the errors of thermal radiative propagation speed and overcome the difficulty that simulation result is strongly dependent on cell number. In addition, both new sampling methods can obtain an accurate result even with less cells and less particles, which can saves plenty of computation time and improves the simulation efficiency.