Classification Scheme Research Articles

A concept for the global assessment of tomographic resolution and uncertainty

SUMMARY A major challenge in seismic tomography consists in quantifying and representing model resolution and uncertainty, particularly at global scales. This information is crucial for interpretations of tomographic images and their technical application in geodynamics. However, due to large computational costs, there have been only few attempts so far to coherently analyse the spatially varying resolving power for a complete set of model parameters. Here, we present a concept for an effective evaluation and global representation of the 3-D resolution information contained in a full set of averaging kernels. In our case, these kernels are constructed using the ‘Subtractive Optimally Localized Averages’ (SOLA) method, a variant of classic Backus-Gilbert inversion suitable for global tomography. Our assessment strategy incorporates the following steps: (1) a 3-D Gaussian function is fitted to each averaging kernel to measure resolution lengths in different directions and (2) we define a classification scheme for the quality of the averaging kernels based on their focus with respect to the estimated 3-D Gaussian, allowing us to reliably identify whether the inferred resolution lengths are robust. This strategy is not restricted to SOLA inversions, but can, for example, be applied in all cases where point-spread functions are computed in other tomographic frameworks. Together with model uncertainty estimates that are derived from error propagation in the SOLA method, our concept reveals at which locations, resolution lengths and interpretations of model values are actually meaningful. We finally illustrate how the complete information from our analysis can be used to calibrate the SOLA inversion parameters—locally tunable target resolution kernels and trade-off parameters—without the need for visual inspection of the individual resulting averaging kernels. Instead, our global representations provide a tool for designing tomographic models with specific resolution-uncertainty properties that are useful in geodynamic applications, especially for linking seismic inversions to models of mantle flow.

Diagnosis coding of neurocognitive disorders.

Objective: Diagnosis coding is a core clinical competency. A basic understanding of the structure of the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), the conventions and rules for diagnosis coding, and what constitutes accurate coding, is fundamental to the clinician's knowledge base. This commentary seeks to provide a practical framework for clinicians to perform accurate diagnosis coding of neurocognitive disorders. Method: This paper: (1) summarizes the structure of the ICD-10-CM, (2) describes the rules and conventions of diagnosis coding for diagnostic categories relevant to neurocognitive disorders, (3) presents clinical examples and pragmatic recommendations to help readers improve their day-to-day use of diagnosis codes, and (4) describes limitations and discrepancies in the diagnosis coding advice for neurocognitive disorders presented within the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) and the DSM-5-TR Neurocognitive Disorders Supplement. Its content originates from the ICD-10-CM itself and its companion document, the ICD-10-CM Official Guidelines for Coding and Reporting. Conclusion: The ICD-10-CM classification scheme is logically organized and easy to navigate for users who understand its structure and rules. Many neuropsychologists rely on the DSM-5-TR diagnosis coding advice, however that advice is limited with respect to the range of diagnosis codes relevant to neurocognitive disorders and their underlying causes. Relying on the ICD-10-CM directly for diagnosis coding of neurocognitive disorders, rather than the DSM-5-TR or other secondary sources, is therefore preferable and aids clinicians in accurate diagnosis coding.

Differences in LCZ composition according to urban planning and impacts on urban thermal environment

As a climate-aware and standardized classification scheme, the Local Climate Zone (LCZ) can be a research framework for analysis of effects of urban development on urban forms and Urban Heat Island (UHI) phenomenon in relation to urban planning. In this study, two new towns built with different urban planning in South Korea are selected as the study areas. In respect of the growing popularity of deep learning and advancements deep learning-based LCZ classification, we build a deep convolutional neural network architecture using a modified SE-ResNext50 backbone. As input data, we designed six schemes using different combinations of Sentinel-1 SAR and Sentinel-2 MSI imagery and thermal band from Landsat 9 is used for SUHI magnitude estimation and heat vulnerability. In addition, robust and quantitative data sampling using building surface fraction data and building height data was performed. The results indicate that combining SAR and multispectral data could increase LCZ classification accuracy and outline the capacity of polarimetric decomposition components. In addition, it was suggested that urban planning causes differences in the LCZ distribution of each new town, resulting in differences in SUHI magnitude and heat vulnerability. The research findings can help guide future urban development by considering the urban form and thermal environment according to the LCZ composition within the new plannedcity.

Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples

With the increasing prevalence of cloud computing platforms, ensuring data privacy during the cloud-based image-related services such as classification has become crucial. In this study, we propose a novel privacy-preserving image classification scheme that enables the direct application of classifiers trained in the plaintext domain to classify encrypted images without the need of retraining a dedicated classifier. Moreover, encrypted images can be decrypted back into their original form with high fidelity (recoverable) using a secret key. Specifically, our proposed scheme involves utilizing a feature extractor and an encoder to mask the plaintext image through a newly designed Noise-like Adversarial Example (NAE). Such an NAE not only introduces a noise-like visual appearance to the encrypted image but also compels the target classifier to predict the ciphertext as the same label as the original plaintext image. At the decoding phase, we adopt a Symmetric Residual Learning (SRL) framework for restoring the plaintext image with minimal degradation. Extensive experiments demonstrate that (1) the classification accuracy of the classifier trained in the plaintext domain remains the same in both the ciphertext and plaintext domains; (2) the encrypted images can be recovered into their original form with an average PSNR of up to 51+ dB for the SVHN dataset and 48+ dB for the VGGFace2 dataset; (3) our system exhibits satisfactory generalization capability on the encryption, decryption, and classification tasks across datasets that are different from the training one; and (4) a high-level of security is achieved against three potential threat models. The code is available at https://github.com/csjunjun/RIC.git .

The anatomy of internal interference in public service media: How do journalists interpret whether editorial interference constitutes unacceptable encroachment on their autonomy?

This study examines the suspected transmission of political pressure through management to journalists in public service media, where independence is crucial for fulfilling its democratic mission. Such interference is often covert and performed without hard evidence, making it difficult to prove. To provide insight into these practices, this study explores the case of RTVS, the public service broadcaster in Slovakia, during a conflict that arose after the appointment of a new director general in 2017. Using semi-structured interviews with journalists and managers ( N = 16), it introduces an inductively developed classification scheme for internal interference and examines the context and conditions under which the journalists considered it to be politically motivated and as an illegitimate encroachment on their autonomy. The study shows that perceived internal interference by management takes two forms: interference in editorial content at various levels (and through multiple tools and means) and the use of disciplinary mechanisms. When journalists experience internal interference with a possible political background, trust in their superiors and the perception of their motives are crucial factors influencing their reactions and interpretation of the situation. However, for a variety of reasons that the study analyzes in detail, the RTVS journalists distrusted their superiors' moral integrity, professional skills, and political independence, which resulted in the departure of a large part of the TV newsroom. The article concludes with suggestions for methodological implications for further research on perceived interference.

Updates from the International League Against Epilepsy Classification of Epilepsy (2017) and Focal Cortical Dysplasias (2022): Imaging Phenotype and Genetic Characterization.

The International League Against Epilepsy (ILAE) is an organization of 120 national chapters providing the most widely accepted and updated guidelines on epilepsy. In 2022, the ILAE Task Force revised the prior (2011) classification of focal cortical dysplasias to incorporate and update clinicopathologic and genetic information, with the aim to provide an objective classification scheme. New molecular-genetic information has led to the concept of "integrated diagnosis" on the same lines as brain tumors, with a multilayered diagnostic model providing a phenotype-genotype integration. Major changes in the new update were made to type II focal cortical dysplasias, apart from identification of new entities, such as mild malformations of cortical development and cortical malformation with oligodendroglial hyperplasia. No major changes were made to type I and III focal cortical dysplasias, given the lack of significant new genetic information. This review provides the latest update on changes to the classification of focal cortical dysplasias with discussion about the new entities. The ILAE in 2017 updated the classification of seizure and epilepsy with 3 levels of diagnosis, including seizure type, epilepsy type, and epilepsy syndrome, which are also briefly discussed here.

Rheological behavior of 3D printed concrete: Influential factors and printability prediction scheme

The rheological properties of cementitious materials play a crucial role in determining the printability for extrusion-based 3D concrete printing. This study develops data-driven machine learning (ML) models to predict two key rheological parameters - plastic viscosity (PV) and yield stress (YS) of 3D printable cementitious composites based on the mixture composition and time after water addition. A systematic experimental study is conducted by varying the contents of cement, fly ash, silica fume, sulfoaluminate cement, superplasticizer, and water-to-binder ratio, and time after water addition. The measured rheological data is used to construct a database for training predictive models including linear regression, support vector regression, random forest, extreme gradient boosting, and multi-layer perceptron neural network. The extreme gradient boosting model achieves the highest prediction accuracy with low root mean square error and all coefficients of determination exceeding 0.9 for both plastic viscosity and yield stress. Importance analysis identifies the most influential parameters affecting the rheological properties. A printability classification scheme is proposed using the model predictions by defining a printable zone of PV and YS. The data-driven framework is validated to effectively predict printability of new mixtures without trial-and-error. This study demonstrates the potential of ML models to accelerate the design and optimization of 3D printable cementitious materials.

A survey on social network’s anomalous behavior detection

The onset of Web 3.0 has catalyzed the rapid advancement of social networking, transforming platforms into essential elements deeply embedded within the fabric of daily life. Researchers have proposed several methods for detecting anomalous behaviors in various scenarios. This article provides a comprehensive review of current research and the latest developments in anomalous behavior detection within social networks. We present a hierarchical three-layer categorization scheme based on the distinct characteristics of base-level detection technologies and various datasets. First, anomaly detection based on user behavioral characteristics can intuitively reflect deviations in individual behavior. However, it may overlook the overall network structure’s impact. Second, detecting anomalies within a network’s topological structure highlights structural significance, but may overlook the subtle nuances of individual behavior. Finally, the coordinated fusion method, which blends individual behavioral characteristics and the network’s topological structure, addresses the multifaceted nature of anomalies, yielding a more thorough and accurate anomaly detection strategy. This paper provides an overview and assesses the performance of three anomaly detection methods. Furthermore, we explore the challenges associated with social network anomaly detection and the potential pathways for further research.

A comparative assessment of machine learning algorithms in the IoT-based network intrusion detection systems

The rapid increase in online risks is a reflection of the exponential growth of Internet of Things (IoT) networks. Researchers have proposed numerous intrusion detection techniques to mitigate the harm caused by these threats. Enterprises use intrusion detection systems (IDSs) and intrusion prevention systems (IPSs) to keep their networks safe, stable, and accessible. Network intrusion detection solutions have lately integrated powerful Machine Learning (ML) techniques to safeguard IoT networks. Selecting the proper data features for effectively training such ML models is critical to maximizing detection accuracy and computational efficiency. However, the efficiency of these systems degrades in high-dimensional data spaces, and it is crucial to have a suitable feature extraction method to eliminate extraneous data from the classification procedure. The detection accuracy and false positive rate of many ML-based IDSs also rise when the samples used to train the models are unbalanced. This study provides a detailed overview of the UNSW-NB15(DS-1) and NF-UNSWNB15(DS-2) datasets for intrusion detection, which will be utilized to develop and evaluate our models. In addition, this model uses the MaxAbsScaler algorithm to implement a filter-based feature scaling strategy . Then, use the condensed feature set to perform several ML techniques, including Support Vector Machines (SVM), K-nearest neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF), considering multiclass classification. Accuracy tests for the multiclass classification scheme were improved from 60% to 94% using the MaxAbsScaler-based feature scaling method.

Endoluminal Biopsy for Vein of Galen Malformation.

Vein of Galen malformation (VOGM), the result of arteriovenous shunting between choroidal and/or subependymal arteries and the embryologic prosencephalic vein, is among the most severe cerebrovascular disorders of childhood. We hypothesized that in situ analysis of the VOGM lesion using endoluminal tissue sampling (ETS) is feasible and may advance our understanding of VOGM genetics, pathogenesis, and maintenance. We collected germline DNA (cheek swab) from patients and their families for genetic analysis. In situ VOGM "endothelial" cells (ECs), defined as CD31+ and CD45-, were obtained from coils through ETS during routine endovascular treatment. Autologous peripheral femoral ECs were also collected from the access sheath. Single-cell RNA sequencing of both VOGM and peripheral ECs was performed to demonstrate feasibility to define the transcriptional architecture. Comparison was also made with a published normative cerebrovascular transcriptome atlas. A subset of VOGM ECs was reserved for future DNA sequencing to assess for somatic and second-hit mutations. Our cohort contains 6 patients who underwent 10 ETS procedures from arterial and/or venous access during routine VOGM treatment (aged 12 days to ∼6 years). No periprocedural complications attributable to ETS occurred. Six unique coil types were used. ETS captured 98 ± 88 (mean ± SD; range 17-256) experimental ECs (CD31+ and CD45-). There was no discernible correlation between cell yield and coil type or route of access. Single-cell RNA sequencing demonstrated hierarchical clustering and unique cell populations within the VOGM EC compartment compared with peripheral EC controls when annotated using a publicly available cerebrovascular cell atlas. ETS may supplement investigations aimed at development of a molecular-genetic taxonomic classification scheme for VOGM. Moreover, results may eventually inform the selection of personalized pharmacologic or genetic therapies for VOGM and cerebrovascular disorders more broadly.

Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors

Revolutionary advances in technology have been seen in many industries, with the IIoT being a prime example. The IIoT creates a network of interconnected devices, allowing smooth communication and interoperability in industrial settings. This not only boosts efficiency, productivity, and safety but also provides transformative solutions for various sectors. This research looks into open-source IIoT and edge platforms that are applicable to a range of applications with the aim of finding and developing high-potential solutions. It highlights the effect of open-source IIoT and edge computing platforms on traditional IIoT applications, showing how these platforms make development and deployment processes easier. Popular open-source IIoT platforms include DeviceHive and Thingsboard, while EdgeX Foundry is a key platform for edge computing, allowing IIoT applications to be deployed closer to data sources, thus reducing latency and conserving bandwidth. This study seeks to identify potential future domains for the implementation of IIoT solutions using these open-source platforms. Additionally, each sector is evaluated based on various criteria, such as development requirement analyses, market demand projections, the examination of leading companies and emerging startups in each domain, and the application of the International Patent Classification (IPC) scheme for in-depth sector analysis.

MF-LIO: integrating multi-feature LiDAR inertial odometry with FPFH loop closure in SLAM

Abstract Simultaneous localization and mapping (SLAM) is a pivotal technology in autonomous vehicle navigation and a significant research area in robotics. Addressing the inaccuracies in point cloud registration of traditional LiDAR SLAM, which lead to localization and mapping errors, we propose a novel LiDAR inertial odometry approach integrating inertial measurement units and a multi-feature joint registration strategy. Initially, we introduce an innovative ground segmentation method and feature categorization strategy, enhancing ground detection mechanisms and optimizing the feature extraction process. Subsequently, our multi-feature joint registration method computes the pose transformations between current frames and the local map. Finally, we employ a global registration method based on fast point feature histograms feature descriptors for coarse alignment, providing initial estimates for the generalized iterative closest point algorithm, thus efficiently and accurately mitigating cumulative errors. Extensive evaluations on the KITTI dataset and real-world campus environment demonstrate that our approach significantly surpasses existing advanced LiDAR SLAM solutions, achieving over a 24% improvement in pose estimation accuracy.

A comprehensive petrochemical study of coals of Jaintia Hills: unveiling their industrial applicability and potential as hydrocarbon source rocks

ABSTRACT The current investigation aims to classify Jaintia Hill coals into different types, ranks, and grades based on their geochemical and petrographic characteristics and to evaluate the potential utilization of these coals under the framework of different coal classification schemes, essential for various industrial, commercial, and residential applications. During the present study, representative coal samples were collected from the working coal mines of Khliehriat and Lad Rymbai areas of the East Jaintia hills of Meghalaya, India, and subjected to proximate analysis, ultimate analysis, and micro petrographic study. Using international and Indian classification schemes, an attempt has been made to propose the appropriate utilization of these coals. It has been observed that these coals have high vitrinite content and low ash content but have high sulfur content. The petrographic and geochemical data suggest that these coals can be utilized for gasification, hydrogenation, and as a coal blend for metallurgical coke-making in the steel industries. The coal samples were also investigated from the perspective of hydrocarbon source rock potential, and it was concluded that these coals have good source rock potential for gas and liquid hydrocarbon.

Quantitative MRI imaging of parenchyma and venation networks in Brassica napus leaves: effects of development and dehydration

BackgroundCharacterisation of the structure and water status of leaf tissues is essential to the understanding of leaf hydraulic functioning under optimal and stressed conditions. Magnetic Resonance Imaging is unique in its capacity to access this information in a spatially resolved, non-invasive and non-destructive way. The purpose of this study was to develop an original approach based on transverse relaxation mapping by Magnetic Resonance Imaging for the detection of changes in water status and distribution at cell and tissue levels in Brassica napus leaves during blade development and dehydration.ResultsBy combining transverse relaxation maps with a classification scheme, we were able to distinguish specific zones of areoles and veins. The tissue heterogeneity observed in young leaves still occurred in mature and senescent leaves, but with different distributions of T2 values in accordance with the basipetal progression of leaf blade development, revealing changes in tissue structure. When subjected to severe water stress, all blade zones showed similar behaviours.ConclusionThis study demonstrates the great potential of Magnetic Resonance Imaging in assessing information on the structure and water status of leaves. The feasibility of in planta leaf measurements was demonstrated, opening up many opportunities for the investigation of leaf structure and hydraulic functioning during development and/or in response to abiotic stresses.

Toward a classification of PT-symmetric quantum systems: From dissipative dynamics to topology and wormholes

Studies of many-body non-Hermitian parity-time (PT)-symmetric quantum systems are attracting a lot of interest due to their relevance in research areas ranging from quantum optics and continuously monitored dynamics to Euclidean wormholes in quantum gravity and dissipative quantum chaos. While a symmetry classification of non-Hermitian systems leads to 38 universality classes, we show that, under certain conditions, PT-symmetric systems are grouped into 24 universality classes. We identify 14 of them in a coupled two-site Sachdev-Ye-Kitaev (SYK) model and confirm the classification by spectral analysis using exact diagonalization techniques. Intriguingly, in 4 of these 14 universality classes, AIIIν, BDIν†, BDI++ν, and CI−−ν, we identify a basis in which the SYK Hamiltonian has a block structure in which some blocks are rectangular, with ν∈N the difference between the number of rows and columns. We show analytically that this feature leads to the existence of ν robust purely eigenvalues, whose level statistics follow the predictions of Hermitian random matrix theory for classes A, AI, BDI, and CI, respectively. We have recently found that this ν is a topological invariant, so these classes are topological. By contrast, nontopological real eigenvalues display a crossover between Hermitian and non-Hermitian level statistics. Similarly to the case of Lindbladian dynamics, the reduction of universality classes leads to unexpected results, such as the absence of Kramers degeneracy in a given sector of the theory. Another novel feature of the classification scheme is that different sectors of the PT-symmetric Hamiltonian may have different symmetries. Published by the American Physical Society 2024

Reductitherus cryptostomus n. gen., n. sp. (Ciliophora: Armophorea: Clevelandellida), a remarkable new nyctotherid from an Australian cockroach, Parapanesthia gigantea (Blaberidae: Panesthiinae)

Ciliates of the family Nyctotheridae (Armophorea: Clevelandellida) are frequent intestinal symbionts of various invertebrates and some poikilotherm vertebrates. Depending on the classification scheme, there are between 15 and 18 recognized genera of Nyctotheridae, the majority of which exhibit a rather uniform morphology. They have round to ellipsoidal cells with an adoral zone of membranelles that begins anteriorly in an adoral groove and continues posteriorly into the buccal cavity where it extends deep into the cell in the peristomial funnel. The taxonomy of the Nyctotheridae is primarily based on the number and location of kinetal sutures. The only known divergence from the relatively conservative nyctortherid body plan are the bizarre symbionts of Panesthiinae cockroaches, ciliates of the family Clevelandellidae, which forms a clade nested within the Nyctotheridae genus Nyctotherus. In this study we report another ciliate that diverges morphologically from the canonical Nyctotheridae body plan, and which is also found in Panesthiinae hosts. The novel ciliate Reductitherus cryptostomus n. gen., n. sp. differs from the rest of Nyctotheridae by absence of the anterior adoral groove, a shortened adoral zone completely enclosed in a notably small buccal cavity, and two strongly reduced kinetal sutures, one left anterodorsal and the other right posterodorsal.

Improvements on Phase Classification Using Atmospheric Melting and Refreezing Energy Based on Soundings

AbstractDetermining precipitation as solid (snow) or liquid (rain) phase is crucial for remote sensing of precipitation. Most phase classification methods rely on near‐surface temperatures. Further attempts to incorporate atmospheric information aloft only achieved mixed level of success, particularly for precipitation with temperature inversion. In our study, we developed a phase classification scheme based on the upper‐level melting energy (ME) and refreezing energy (RE), which is proportional to the area enclosed by the temperature profile and the 0°C isotherm. We performed least squares fitting and linear discriminant analysis to derive phase separation functions using observed surface and sounding data in North America. We provided separation functions for snow conditional probabilities ranging from 30% to 80% for various applications. Compared to a previously published (Probsnow) method, our energy method achieved comparable performance for Type 1 soundings with one near‐surface melting layer, and significantly improves the phase classification scores for Type 2 soundings with an aloft melting layer and a near‐surface refreezing layer. We innovatively combined surface ice‐bulb temperature with the ratio between the ME and RE to represent Type 2 profiles. For Type 2 soundings, our energy method improves the Heidke skill score (HSS) from 0.25 to 0.47 and reduces false alarm rate (FAR) by 0.47 compared to the Probsnow method for 50% threshold. By applying the physically based method, we improved accuracy and HSS, and reduced FAR for more than two thirds of the evaluation stations across the North America. Finally, we tested the application of the new method in satellite snowfall retrievals.

Classification of classical spin liquids: Detailed formalism and suite of examples

The hallmark of highly frustrated systems is the presence of many states close in energy to the ground state. Fluctuations between these states can preclude the emergence of any form of order and lead to the appearance of spin liquids. Even on the classical level, spin liquids are not all alike: they may have algebraic or exponential correlation decay, and various forms of long wavelength description, including vector or tensor gauge theories. Here, we introduce a classification scheme, allowing us to fit the diversity of classical spin liquids (CSLs) into a general framework as well as predict and construct new kinds. CSLs with either algebraic or exponential correlation-decay can be classified via the properties of the bottom flat band(s) in their soft-spin Hamiltonians. The classification of the former is based on the algebraic structures of gapless points in the spectra, which relate directly to the emergent generalized Gauss's laws that control the low-temperature physics. The second category of CSLs, meanwhile, are classified by the fragile topology of the gapped bottom band(s). Utilizing the classification scheme we construct new models realizing exotic CSLs, including one with anisotropic generalized Gauss's laws and charges with subdimensional mobility, one with a network of pinch-line singularities in its correlation functions and a series of fragile topological CSLs connected by zero-temperature transitions. Published by the American Physical Society 2024

Land Use-Land Cover Changes and Associated Drivers: A Case of Salooni Block of Chamba

Land Use-Land Cover (LULC) changes are the anthropogenic alteration of the natural landscape. The LULC changes have been generated by a variety of factors, including natural, demographic, and economic ones, which put pressure on the land and had a variety of environmental effects. The transformation in Landscape is affecting the livelihoods of people in numerous ways. Therefore, it is crucial to understand the patterns in land use in order to determine how land should be developed, managed, and planned in order to understand the way it will be utilised in the future. In the present study, Salooni Block of Chamba district, Himachal Pradesh was subjected to a first-ever attempt at land-use change detection for the years 2002 and 2022. Satellite imageries of Landsat-7 Enhanced Thematic Mapper (ETM+) Landsat-8 Operational Land Imager (OLI) were used for landuse maps of 2002 and 2022 respectively. The classification was completed using a supervised classification technique and the USGS Land use Classification Scheme at Level I classes, including Agricultural Land, Barren Land, Built-up, Forest, Pasture Land, Snow Cover and Waterbody. Furthermore, fifty people aged 50 and above were interviewed to understand the local factors responsible for LU/LC change. The result indicates a rise in the forest cover, which now accounts for 48.2% of the total area. Since 2002, there has been a 2.3-fold rise in built-up land, which is a glaring indication of the pressure on land. The total area of agricultural and pasture land is decreasing, while the percentage of barren land has increased by 2%. To preserve the sustainability and food security of the ecologically vulnerable environment, stringent land use planning needs to be enforced in the mountainous region. The results of the LU/LC dynamics discussed above can be used to establish policies and strategies for restoring and enhancing the sustainability of the environment as well as the carrying capacity of the land.

Fault detection and classification scheme for power islands with inverter interfaced distributed generators

The low fault current contribution from inverter-interfaced distributed generators (IIDGs) and bidirectional flow of current in the power networks introduces protection challenges when islanded. The existing fault detection scheme based on the transient monitoring function (TMF) fails under such scenarios. A new decentralised machine learning (ML) based fault detection and classification scheme is proposed in this paper using terminal voltage information of IIDG (vo−abc). TMF of vo−abc, along with the zero sequence component of vo−abc, is utilised for training and testing of ensemble bagged trees ML algorithm. The proposed scheme is implemented inside each IIDG, and hence, it does not require communication. It is validated on a modified CIGRE test benchmark microgrid for all shunt faults at various locations with different loading, fault resistances, and inception angles. The accuracy of detection and classification of fault (DCF) during fault cases with unbalanced loads, noise, harmonic loads, and no-fault scenarios such as load and DG switching proves the effectiveness of the proposed method with varying scenarios. The performance of the proposed scheme is found to be superior when compared with the existing threshold based TMF and Hilbert–Huang transform (HHT) based techniques considering accuracy, dependability, security, and response time.