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Abstract Islanding detection is a challenging issue in modern grid‐connected distributed generation networks (GCDGN). Generally, islanding detection has two categories local and remote, local schemes can be categorized into active, passive, and hybrid schemes. This article proposes a triple‐indexed passive islanding detection (TIPID) scheme using an extended Kalman filter (EKF). Initially, the EKF algorithm is applied on voltage signal at the point of the common coupling to estimate the desired fundamental and non‐fundamental features. The first index, known as the cumulative voltage logarithmic index, is computed by taking the natural logarithm of the fundamental voltage features to detect any variations in the GCDGN. The second index, known as the voltage differentiation index (VDI), is calculated from the fundamental voltage features, while the third index, known as the odd‐order harmonic distortion index (OOHDI), is obtained from the non‐fundamental odd‐order harmonics of the PCC voltage. Then, the VDI and OOHDI are compared to pre‐defined threshold to detect/distinguish islanding events. The proposed TIPID method is validated through extensive simulations on the IEEE 13‐bus test bed via MATLAB/Simulink 2022b. Results show that under both balanced/unbalanced load & generation, the proposed TIPID approach detects islanding occurrences with reduced non‐detection zone (NDZ) in less than 5 ms.

In this study, we explore the dynamic nature of metallocene catalysts during ethylene/isoprene E/IP) copolymerization, with a focus on the influence of various reaction parameters. Challenges, why do olefinic catalysts show lower activity and molecular weight (Mw) at higher temperatures and low activity with higher diene content? Firstly, we investigate the observed phenomena of decreased catalyst activity and Mw at elevated temperatures using 1.25 μmole of the metallocene. Notably, a substantial increase in active sites from 30 °C to 40 °C, reaching a peak of 89 %. Surprisingly, further temperature increments lead to a noticeable decline in active sites. At 30 °C, the active site primarily engages in the insertion of IP through 3,4 connections, with no detectable cis-1,4 or trans-1,4 connections, which suggests a lack of stereoselectivity at this temperature. At 40–50°C, cis/trans-1,4 connections and active sites are approaching a higher level. This implies a reduction in chain transfer reactions, making catalytic active sites more favorable to cis/trans-1,4 connections. Secondly, we observe a remarkable impact of IP concentration in the E/IP copolymers, active centers, and activity show stability when the amount of IP was 0.116–0.48 mol/L and then started to decline when the amount of IP 0.96 mol/L, indicating a threshold beyond which deactivation occurs. Increasing IP concentration not only reduces activity and active sites but also fails to reactivate dormant polyethylene sites. The Mw and active centers of copolymers progressively decrease with elevated IP concentration, suggesting a faster chain transfer reaction with IP compared to E.

Surface material identification using hyperspectral imaging (HSI) analysis is a crucial and challenging issue in remote sensing. Researchers widely recognize that exploiting spectral-spatial data outperforms using spectral pixel-wise methods. Most of the work focuses on machine learning techniques to address issues with HSI categorization. The semi-supervised learning model, for example, was utilized in early research to address the issue of the expenses associated with training samples during HSI classification. Nevertheless, these approaches and techniques have certain drawbacks. The suggested framework must be improved in terms of hardware and computational capacity to enhance security. While integrated patch-based convolutional neural network (CNN) can help overcome these issues, due to the complicated geographical distribution of many targets, there are some downsides, such as using spectra as odorless vectors. The supervised learning techniques categorize the inputs by using a collection of appropriate examples for each class, referred to as training samples. For this purpose, samples must be obtained, however, the process is expensive and time-consuming. Deep learning techniques for evaluating spatial spectrum data have significantly advanced, yet security challenges remain in remote sensing with HSI. In this article, we introduce a novel HSI classification method, termed Deep-Detect, which leverages deep learning models with attention mechanisms to tackle these challenges. We use two weighting factors that may be obtained flexibly during the training phase to integrate both spectral and spatial properties, whose importance differs for various objects and scenarios. Additionally, we proposed a spatial and spectral fully convolutional networks (Spe-FCNs) attention mechanism that focuses attention on the input patches’ most informative regions. To verify performance, we used the northern Italy dataset from Pavia University and compared our proposed model with state-of-the-art HSI classification models. Our findings show that the proposed Deep-Detect model enhances classification accuracy while addressing key security concerns in HSIs.

This study introduces a novel method for classifying sets of images, called Riemannian geodesic discriminant analysis–minimum Riemannian mean distance (RGDA-MRMD). This method first converts image data into symmetric positive definite (SPD) matrices, which capture important features related to the variability within the data. These SPD matrices are then mapped onto simpler, flat spaces (tangent spaces) using a mathematical tool called the logarithm operator, which helps to reduce their complexity and dimensionality. Subsequently, regularized local Fisher discriminant analysis (RLFDA) is employed to refine these simplified data points on the tangent plane, focusing on local data structures to optimize the distances between the points and prevent overfitting. The optimized points are then transformed back into a complex, curved space (SPD manifold) using the exponential operator to enhance robustness. Finally, classification is performed using the minimum Riemannian mean distance (MRMD) algorithm, which assigns each data point to the class with the closest mean in the Riemannian space. Through experiments on the ETH-80 (Eidgenössische Technische Hochschule Zürich-80 object category), AFEW (acted facial expressions in the wild), and FPHA (first-person hand action) datasets, the proposed method demonstrates superior performance, with accuracy scores of 97.50%, 37.27%, and 88.47%, respectively. It outperforms all the comparison methods, effectively preserving the unique topological structure of the SPD matrices and significantly boosting image set classification accuracy.

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Quantitative phase microscopy (QPM) is a powerful tool for label-free and noninvasive imaging of transparent specimens. In this paper, we propose a novel QPM approach that utilizes deep learning to reconstruct accurately the phase image of transparent specimens from a defocus bright-field image. A U-net based model is used to learn the mapping relation from the defocus intensity image to the phase distribution of a sample. Both the off-axis hologram and defocused bright-field image are recorded in pair for thousands of virtual samples generated by using a spatial light modulator. After the network is trained with the above data set, the network can fast and accurately reconstruct the phase information through a defocus bright-field intensity image. We envisage that this method will be widely applied in life science and industrial detection.

In recent advancements within wireless sensor networks (WSN), the deployment of unmanned aerial vehicles (UAVs) has emerged as a groundbreaking strategy for enhancing routing efficiency and overall network functionality. This research introduces a sophisticated framework, driven by computational intelligence, that merges clustering techniques with UAV mobility to refine routing strategies in WSNs. The proposed approach divides the sensor field into distinct sectors and implements a novel weighting system for the selection of cluster heads (CHs). This system is primarily aimed at reducing energy consumption through meticulously planned routing and path determination. Employing a greedy algorithm for inter-cluster dialogue, our framework orchestrates CHs into an efficient communication chain. Through comparative analysis, the proposed model demonstrates a marked improvement over traditional methods such as the cluster chain mobile agent routing (CCMAR) and the energy-efficient cluster-based dynamic algorithms (ECCRA). Specifically, it showcases an impressive 15% increase in energy conservation and a 20% reduction in data transmission time, highlighting its advanced performance. Furthermore, this paper investigates the impact of various network parameters on the efficiency and robustness of the WSN, emphasizing the vital role of sophisticated computational strategies in optimizing network operations. [ABSTRACT FROM AUTHOR]

A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new “DR-Insight” dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system’s goal is to augment optometrists’ expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR).

Distributed Energy Resources (DER) offer significant advantages by means of raising reliability of distribution system, providing clean source of electricity and reduction in fossil fuel consumption. However, penetration of DER in distribution system poses severe challenges for control procedures and maintain protection coordination for secure operation. Unintentional islanding is a threat to the safety of equipment and line worker. Different islanding detection approaches are broadly classified in remote type methods which employ communication infrastructure and local type techniques based on monitoring parameters at Distributed Generator (DG) side. In this paper broad assessment about Islanding Detection Methods (IDMs) with their limitations and benefits are explained with respect to accuracy, detection time and non-detection zone. Comparative assessments of different IDMs are listed in tabulation for conveniently judging the most economical and efficient approach.

Magnetic molecularly imprinted polymers (MMIPs) contain the predesigned specialized recognition capability that can be chosen to build credible functional materials, that are easy to handle and have a good degree of specificity. Hence, the given piece of work is intended to design a novel electrochemical sensor incorporating magnetite-based molecularly imprinted polymers. The building materials consisted of a cross-linker (EGDMA), reaction-initiator (AIBN), monomer (methylene succinic acid-MSA), and template molecule (Sunset Yellow-SY dye). MMIPs exhibited a diameter of 57 nm with an irregular shape due to the presence of cavities based on SEM analysis. XRD patterns exhibited crystallinity, as well as amorphous peaks that are attributed to polymeric and non-polymeric frameworks of MMIPs. The crystallite size of the MMIPs from XRD analysis was found to be 16.28 nm based on the Debye-Scherrer’s equation. Meanwhile, the FTIR bands showed the synthesis of MMIPs using monomer and methylene succinic acid. The sorption data at the optimized operating conditions (pH 2, sorbent dosage 3 mg, time 18 min) showed the highest sorption capacity of 40 mg/g. The obtained data best fitted to the Langmuir sorption isotherm and followed the pseudo-second-order kinetics. The magneto-sensors were applied for ultrasensitive, rapid, and simple sensing of SY dye. The electrochemical experiments were run at the operating condition range of (scan rate 10–50 mV/s, tads 0–120 s, pH 5–9, potential range 1–1.5 V for CV and 1–1.3 V for SWAdASV). The linear range of detection was set to 1.51 × 10−6 M to 1.51 × 10−6 M posing LOD and LOQ values of 8.6242 × 10−5 M and 0.0002874 M, respectively. The regression analysis value for the calibration was found to be 0.950. Additionally, high adsorption efficiency, selectivity, reusability, and strong structural stability of the magneto-sensors showed potential use for SY detection in real samples. These characteristics make MMIPs a viable electrochemical substrate for the detection of chemical contaminants in the environment and in health-related products.

This study introduces a novel method for classifying sets of images, called Riemannian geodesic discriminant analysis–minimum Riemannian mean distance (RGDA-MRMD). This method first converts image data into symmetric positive definite (SPD) matrices, which capture important features related to the variability within the data. These SPD matrices are then mapped onto simpler, flat spaces (tangent spaces) using a mathematical tool called the logarithm operator, which helps to reduce their complexity and dimensionality. Subsequently, regularized local Fisher discriminant analysis (RLFDA) is employed to refine these simplified data points on the tangent plane, focusing on local data structures to optimize the distances between the points and prevent overfitting. The optimized points are then transformed back into a complex, curved space (SPD manifold) using the exponential operator to enhance robustness. Finally, classification is performed using the minimum Riemannian mean distance (MRMD) algorithm, which assigns each data point to the class with the closest mean in the Riemannian space. Through experiments on the ETH-80 (Eidgenössische Technische Hochschule Zürich-80 object category), AFEW (acted facial expressions in the wild), and FPHA (first-person hand action) datasets, the proposed method demonstrates superior performance, with accuracy scores of 97.50%, 37.27%, and 88.47%, respectively. It outperforms all the comparison methods, effectively preserving the unique topological structure of the SPD matrices and significantly boosting image set classification accuracy. [ABSTRACT FROM AUTHOR]

In clinical research, it is crucial to segment the magnetic resonance (MR) brain image for studying the internal tissues of the brain. To address this challenge in a sustainable manner, a novel approach has been proposed leveraging the power of unsupervised clustering while integrating conditional spatial properties of the image into intuitionistic clustering technique for segmenting MRI images of brain scans. In the proposed technique, an Intuitionistic-based clustering approach incorporates a nuanced understanding of uncertainty inherent in the image data. The measure of uncertainty is achieved through calculation of hesitation degree. The approach introduces a conditional spatial function alongside the intuitionistic membership matrix, enabling the consideration of spatial relationships within the image. Furthermore, by calculating weighted intuitionistic membership matrix, the algorithm gains the ability to adapt its smoothing behavior based on the local context. The main advantages are enhanced robustness with homogenous segments, lower sensitivity to noise, intensity inhomogeneity and accommodation of degree of hesitation or uncertainty that may exist in the real-world datasets. A comparative analysis of synthetic and real datasets of MR brain images proves the efficiency of the suggested approach over different algorithms. The paper investigates how the suggested research methodology performs in medical industry under different circumstances including both qualitative and quantitative parameters such as segmentation accuracy, similarity index, true positive ratio, false positive ratio. The experimental outcomes demonstrate that the suggested algorithm outperforms in retaining image details and achieving segmentation accuracy. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) refer to the communication network, consisting of different types of nodes connected through communication networks deploying diverse radio access technologies like LTE, Wi‐Fi, Zigbee, and Z‐wave, and using different communication protocols and operating frequencies. Vertical handover, is the process of switching a mobile device from one network type to another, such as from a cellular network to a Wi‐Fi network, and is critical for ensuring a seamless user experience and optimal network performance, within the handover process handover triggering estimation is one of the crucial step affecting the overall performance. A mathematical analysis is presented for the handover triggering estimation. The performance evaluation shows significant improvement in the probability of successful handover using the proposed handover triggering condition based on speed, distance, and different mobility models. The handover triggering condition is optimised based on the speed of the mobile node, handover completion time, and the coverage range of the current and the target networks of the HetNet node, with due consideration of the mobility model. [ABSTRACT FROM AUTHOR]