Regular Distribution Research Articles

Fault Handling and Localization Strategy Based on Waveform Characteristics Recognition with Coordination of Peterson Coil and Resistance Grounding Method

To address challenges in locating high-impedance grounding faults (HIGFs) and isolating fault areas in resonant grounding systems, this paper proposes a novel fault identification method based on coordinating a Peterson coil and a resistance grounding system. This method ensures power supply reliability by extinguishing the fault arc during transient faults with the Peterson coil. When a fault is determined to be permanent, the neutral point switches to a resistance grounding mode, ensuring regular distribution of zero-sequence currents in the network, thereby addressing the challenges of HIGF localization and fault area isolation. Fault calibration and nature determination rely on recognizing neutral point displacement voltage waveforms and dynamic characteristics, eliminating interference from asymmetric phase voltage variations. Fault area identification involves assessing the polarity of zero-sequence current waveforms attenuation during grounding mode switching, preventing misjudgments in grounding protection due to random initial fault angles and Peterson coil compensation states. Field experiments validate the feasibility of this fault location method and its control strategy.

Analytical realization of complex thermal meta-devices

Fourier’s law dictates that heat flows from warm to cold. Nevertheless, devices can be tailored to cloak obstacles or even reverse the heat flow. Mathematical transformation yields closed-form equations for graded, highly anisotropic thermal metamaterial distributions needed for obtaining such functionalities. For simple geometries, devices can be realized by regular conductor distributions; however, for complex geometries, physical realizations have so far been challenging, and sub-optimal solutions have been obtained by expensive numerical approaches. Here we suggest a straightforward and highly efficient analytical de-homogenization approach that uses optimal multi-rank laminates to provide closed-form solutions for any imaginable thermal manipulation device. We create thermal cloaks, rotators, and concentrators in complex domains with close-to-optimal performance and esthetic elegance. The devices are fabricated using metal 3D printing, and their omnidirectional thermal functionalities are investigated numerically and validated experimentally. The analytical approach enables next-generation free-form thermal meta-devices with efficient synthesis, near-optimal performance, and concise patterns.

Distribution of trace elements in growth zones and sectors of spodumene crystals (Pashki deposit, Nuristan, Afghanistan)

Regular distribution of several trace elements in spodumene crystals of the Pashki pegmatite Li deposits in Afghanistan (Nuristan province) is analyzed using secondary ion mass spectrometry (SIMS). Twenty seven local analyses of 19 elements were carried out in spodumene samples of different generations taken from pegmatites during feld works of 2023. We studied an early generation spodumene crystal from blocky spodumene-quartz-albite pegmatite and two late generation spodumene crystals including an euhedral crystal from a quartz core of pegmatite and a crystal extracted from a mineralized cavity together with quartz crystal, albite, and muscovite. It is found that the Fe, B, and Be content is signifcantly higher in the growth pyramid of the pinacoid face (010) than in the growth pyramid of the prism face (021). From early to late growth zones, the Be, B, Fe, and Mn content increases and the K, Rb and Ti content decreases. The heterogeneity of trace element composition of spodumene crystals thus mostly depends on their sectoriality, which is caused by different crystal chemical conditions of the incorporation of isomorphic trace elements into the mineral structure on faces parallel and perpendicular to the direction of pyroxene chains of Si–O tetrahedra along axis [001] rather than by their zonation. From early to late generations of spodumene, the content of most analyzed trace elements signifcantly decreases corresponding to the ideas about the change in the crystallization fractionation of elements during the evolution of the pegmatite process transiting from the closed to open physicochemical system.

A dispersion function for the regularized kappa distribution function

In this work, we define the plasma dispersion function for a suprathermal plasma described with a regularized kappa distribution. As is known from Maxwellian as well as (standard) kappa plasmas, the respective Fried–Conte and the modified plasma dispersion functions are valuable tools for various analytical studies. For the latter it has been proven very useful to know about the mathematical properties, as analytical continuation, series expansions as well as asymptotic expressions. Given the growing popularity of the regularized kappa distribution, as indicated by its increasing number of applications to various problems related to suprathermal plasmas, we extend its theoretical treatment here by providing the corresponding plasma dispersion function along with various properties.

A magnetic phantom technique for investigating structural effects on quantitative ultrasound parameters.

Media that contain ultrasound scatterers arranged in a regular spatial distribution can be considered as structured. Structural effects affect quantitative ultrasound parameters that reflect the microstructure properties. Prior studies examined structural effects using simulations or phantoms with fixed microarchitecture, focusing on a limited set of ultrasound parameters, with limited attention given to their underlying physical significance. This study aims to investigate the concordance of the physical interpretations of multiple quantitative ultrasound parameters experimentally by introducing a phantom type with an adjustable microarchitecture. The phantom consists of an aqueous solution containing superparamagnetic microspheres, acting as scatterers. The spatial arrangement of the magnetic particles is modified by applying an external magnetic field, therefore changing the degree of structure of the phantom. Quantitative ultrasound parameters are estimated in three different configurations: the magnetic field intensity is varied over time, strength, and orientation. In each experiment, the backscatter coefficient and the envelope quantitative ultrasound parameters are successfully extracted (R2 ≈ 0.94). Their physical interpretations are supported by microphotographs and geometrical considerations through concordant hypotheses. This study paves the way for the use of magnetic phantoms. This methodology could be followed to validate theoretical scattering models and the physical meanings of quantitative ultrasound parameters.

A local meshless method for the numerical solution of multi-term time-fractional generalized Oldroyd-B fluid model

This work presents an accurate and efficient method, for solving a two dimensional time-fractional Oldroyd-B fluid model. The proposed method couples the Laplace transform (LT) with a radial basis functions based local meshless method (LRBFM). The suggested numerical scheme first uses the LT which transform the given equation to an elliptic equation in LT space, and then it utilizes the LRBFM to solve transformed equation in LT space, and then the solution is converted back into the time domain via the improved Talbot's scheme. The local meshless methods are widely recognized for scattered data interpolation and for solving PDEs in complex shaped domains. The adaptability, simplicity, and ease of use are features that have led to the popularity of local meshless methods. The local meshless methods are easy and straightforward, they only requires to solve linear system of equations. The main objective of using the LT is to avoid the computation of costly convolution integral in time-fractional derivative and the effect of time stepping on accuracy and stability of numerical solution. The stability and the convergence of the proposed numerical scheme are discussed. Further, the Ulam-Hyers (UH) stability of the proposed model is discussed. The accuracy and efficiency of the suggested numerical approach have been demonstrated using numerical experiments on five different domains with regular nodes distribution.

ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs.

Electrocardiogram (ECG) delineation to identify the fiducial points of ECG segments, plays an important role in cardiovascular diagnosis and care. Whilst deep delineation frameworks have been deployed within the literature, several factors still hinder their development: (a) data availability: the capacity of deep learning models to generalise is limited by the amount of available data; (b) morphology variations: ECG complexes vary, even within the same person, which degrades the performance of conventional deep learning models. To address these concerns, we present a large-scale 12-leads ECG dataset, ICDIRS, to train and evaluate a novel deep delineation model-ECGVEDNET. ICDIRS is a large-scale ECG dataset with 156,145 QRS onset annotations and 156,145 T peak annotations. ECGVEDNET is a novel variational encoder-decoder network designed to address morphology variations. In ECGVEDNET, we construct a well-regularized latent space, in which the latent features of ECG follow a regular distribution and present smaller morphology variations than in the raw data space. Finally, a transfer learning framework is proposed to transfer the knowledge learned on ICDIRS to smaller datasets. On ICDIRS, ECGVEDNET achieves accuracy of 86.28%/88.31% within 5/10 ms tolerance for QRS onset and accuracy of 89.94%/91.16% within 5/10 ms tolerance for T peak. On QTDB, the average time errors computed for QRS onset and T peak are -1.86 ± 8.02 ms and -0.50 ± 12.96 ms, respectively, achieving state-of-the-art performances on both large and small-scale datasets. We will release the source code and the pre-trained model on ICDIRS once accepted.

A multi-feature spatial–temporal fusion network for traffic flow prediction

The traffic flow prediction is the key to alleviate traffic congestion, yet very challenging due to the complex influence factors. Currently, the most of deep learning models are designed to dig out the intricate dependency in continuous standardized sequences, which are dependent to high requirements for data continuity and regularized distribution. However, the data discontinuity and irregular distribution are inevitable in the real-world practical application, then we need find a way to utilize the powerful effect of the multi-feature fusion rather than continuous relation in standardized sequences. To this end, we conduct the prediction based on the multiple traffic features reflecting the complex influence factors. Firstly, we propose the ATFEM, an adaptive traffic features extraction mechanism, which can select important influence factors to construct joint temporal features matrix and global spatial features matrix according to the traffic condition. In this way, the feature’s representation ability can be improved. Secondly, we propose the MFSTN, a multi-feature spatial–temporal fusion network, which include the temporal transformer encoder and graph attention network to obtain the latent representation of spatial–temporal features. Especially, we design the scaled spatial–temporal fusion module, which can automatically learn optimal fusion weights, further adapt to inconsistent spatial–temporal dimensions. Finally, the multi-layer perceptron gets the mapping function between these comprehensive features and traffic flow. This method helps to improve the interpretability of the prediction. Experimental results show that the proposed model outperforms a variety of baselines, and it can accurately predict the traffic flow when the data missing rate is high.

Feature-Based Molecular Network-Assisted Cannabinoid and Flavonoid Profiling of Cannabis sativa Leaves and Their Antioxidant Properties.

Cannabis sativa (C. sativa) leaves are rich in cannabinoids and flavonoids, which play important antioxidant roles. Since the environmental factors may influence the accumulation of antioxidants in herbal medicines, which affects their activity, this study aimed to investigate the correlation between the chemical composition of C. sativa leaves and their geographical origin and antioxidant activity. Firstly, a high-resolution mass spectrometry method assisted by semi-quantitative feature-based molecular networking (SQFBMN) was established for the characterization and quantitative analysis of C. sativa leaves from various regions. Subsequently, antioxidant activity analysis was conducted on 73 batches of C. sativa leaves, and a partial least squares regression (PLS) model was employed to assess the correlation between the content of cannabinoids and flavonoids in the leaves and their antioxidant activity. A total of 16 cannabinoids and 57 flavonoids were annotated from C. sativa, showing a significant regular geographical distribution. The content of flavonoid-C glycosides in Sichuan leaves is relatively high, and their antioxidant activity is also correspondingly high. However, the leaves in Shaanxi and Xinjiang were primarily composed of flavonoid-O glycosides, and exhibited slightly lower antioxidant activity. A significant positive correlation (p < 0.001) was found between the total flavonoids and cannabinoids and the antioxidant activity of the leaves, and two flavonoids and one cannabinoid were identified as significant contributors.

An immersed boundary fast meshfree integration methodology with consistent weight learning

An immersed boundary fast integration methodology featured by a consistent weight learning is proposed to accelerate Galerkin meshfree computation. In the proposed approach, the problem domain is embedded in a rectangular spatial domain discretized by regular distributions of meshfree nodes and integration sampling points with virtual integration cells. A trimming operation of the rectangular spatial domain by the physical problem boundary with nodal discretization yields the discrete model for meshfree computation, where the integration sampling points are grouped into the interior sampling points and the near boundary sampling points which form a training set. For the interior sampling points, a natural alignment between the influence domains of meshfree shape functions and virtual integration cells can be easily realized through employing integer support sizes, which ensures a satisfactory accuracy for the basis degree correspondent normal order Gauss integration. The background cells for the domain integration are completely avoided, which greatly simplifies the preprocessing for numerical integration. Meanwhile, a machine learning module is devised to optimize the weights of near boundary integration sampling points. The key step to construct this machine learning module for weight optimization is the selection of the variational integration consistency condition as the loss function, which guarantees the convergence of Galerkin meshfree formulation. The accuracy and efficiency of the proposed weight learning immersed boundary fast integration methodology is thoroughly validated through numerical results.

A study on partial pivot ACA boundary element method for elasticity problems

The boundary element method (BEM) employing the partial pivot adaptive cross approximation (PACA) algorithm has been observed to experience convergence failures and reduced solution accuracy when solving elasticity problems, especially at large scales. To address this issue, this paper proposes an improved algorithm for both 2D and 3D elasticity problems.Our investigations revealed that when the normal of an element is parallel to the coordinate axes and the element is in the same plane as the source point, zero entries with regular distributions will appear in the coefficient matrix. This results in the premature satisfaction of the convergence criterion of the PACA algorithm, leading to the omission of some rows and columns of the coefficient matrix. To address this issue, this paper proposes improvements to the PACA algorithm through the Coordinate Rotation Method (CRM) and the Component Traversal Method (CTM), and validates the effectiveness of these two improved methods.Furthermore, we investigate reasons behind the decrease in accuracy when employing the CTM to solve large-scale problems. We attribute this to the oscillatory nature of the estimated relative error and propose the modified PACA (M-PACA) algorithm. M-PACA not only maintains the advantages of the PACA algorithm in reducing storage space and accelerating coefficient matrix generation but also addresses its limitations, ensuring more accurate and reliable solutions for elasticity problems.

The human dermal white adipose tissue (dWAT) morphology: A multimodal imaging approach

BackgroundDermal white adipose tissue (dWAT) in humans can be characterized as a relaxed dermal skin compartment consisting of functionally interlinked adipocytes. dWAT is typically discerned both in terms of morphology and function from subcutaneous white adipose tissue (sWAT). In particular in human thigh, the dWAT appears as thin extensions from the adipose panniculus to the dermis, and it is primarily associated with pilosebaceous units, hair follicles, sebaceous glands, and erector pili muscles. In this work, human fat tissue samples obtained post-mortem from the gluteo-femoral region were analyzed focusing on the thin extensions of dWAT named dermal cones. This anatomical region was chosen to deepen the dWAT morphological features of this site which is interesting both for clinical applications and genetical studies. The purpose of this exploratory methodological study was to gain deeper insights into the morphological features of human dWAT through a multimodal imaging approach. MethodsOptical microscopy, Magnetic Resonance Imaging (MRI) and Scanning Electron Microscopy (SEM), have been employed in this study. The cones’ length and their distances were measured on the acquired images for optical microscopy and SEM. The cone’s apparent regular distribution in MRI images was evaluated using a mathematical criterion, the conformity ratio, which is the ratio of the mean nearest-neighbor distance to its standard deviation. ResultsThe imaging techniques revealed white adipocytes forming a layer, referred to as sWAT, with cones measuring nearly 2 mm in size measured on SEM and Optical images (2.1 ± 0.4 mm), with the lower part embedded in the sWAT and the upper part extending into the dermis. The distance between the cones results about 1 mm measured on MRI images and they show an overall semiregular distribution. ConclusionsMRI images demonstrated an orderly arrangement of cones, and their 3D reconstruction allowed to elucidate the dermal cones’ disposition in the tissue sample and a more general comprehensive visualization of the entire fat structure within the dermis.

Word frequency and cognitive effort in turns-at-talk: turn structure affects processing load in natural conversation.

Frequency distributions are known to widely affect psycholinguistic processes. The effects of word frequency in turns-at-talk, the nucleus of social action in conversation, have, by contrast, been largely neglected. This study probes into this gap by applying corpus-linguistic methods on the conversational component of the British National Corpus (BNC) and the Freiburg Multimodal Interaction Corpus (FreMIC). The latter includes continuous pupil size measures of participants of the recorded conversations, allowing for a systematic investigation of patterns in the contained speech and language on the one hand and their relation to concurrent processing costs they may incur in speakers and recipients on the other hand. We test a first hypothesis in this vein, analyzing whether word frequency distributions within turns-at-talk are correlated with interlocutors' processing effort during the production and reception of these turns. Turns are found to generally show a regular distribution pattern of word frequency, with highly frequent words in turn-initial positions, mid-range frequency words in turn-medial positions, and low-frequency words in turn-final positions. Speakers' pupil size is found to tend to increase during the course of a turn at talk, reaching a climax toward the turn end. Notably, the observed decrease in word frequency within turns is inversely correlated with the observed increase in pupil size in speakers, but not in recipients, with steeper decreases in word frequency going along with steeper increases in pupil size in speakers. We discuss the implications of these findings for theories of speech processing, turn structure, and information packaging. Crucially, we propose that the intensification of processing effort in speakers during a turn at talk is owed to an informational climax, which entails a progression from high-frequency, low-information words through intermediate levels to low-frequency, high-information words. At least in English conversation, interlocutors seem to make use of this pattern as one way to achieve efficiency in conversational interaction, creating a regularly recurring distribution of processing load across speaking turns, which aids smooth turn transitions, content prediction, and effective information transfer.

Expanding the clinical application of OPM-MEG using an effective automatic suppression method for the dental brace metal artifact

Optically pumped magnetometer magnetoencephalography (OPM-MEG) holds significant promise for clinical functional brain imaging due to its superior spatiotemporal resolution. However, effectively suppressing metallic artifacts, particularly from devices such as orthodontic braces and vagal nerve stimulators remains a major challenge, hindering the wider clinical application of wearable OPM-MEG devices.A comprehensive analysis of metal artifact characteristics from time, frequency, and time–frequency perspectives was conducted for the first time using an OPM-MEG device in clinical medicine. This study focused on patients with metal orthodontics, examining the modulation of metal artifacts by breath and head movement, the incomplete regular sub-Gaussian distribution, and the high absolute power ratio in the 0.5–8 Hz band. The existing metal artifact suppression algorithms applied to SQUID-MEG, such as fast independent component analysis (FastICA), information maximization (Infomax), and algorithms for multiple unknown signal extraction (AMUSE), exhibit limited efficacy. Consequently, this study introduced the second-order blind identification (SOBI) algorithm, which utilized multiple time delays for the component separation of OPM-MEG measurement signals. We modified the time delays of the SOBI method to improve its efficacy in separating artifact components, particularly those in the ultralow frequency range. This approach employs the frequency-domain absolute power ratio, root mean square (RMS) value, and mutual information methods to automate the artifact component screening process.The effectiveness of this method was validated through simulation experiments involving four subjects in both resting and evoked experiments. In addition, the proposed method was also validated by the actual OPM-MEG evoked experiments of three subjects. Comparative analyses were conducted against the FastICA, Infomax, and AMUSE algorithms. Evaluation metrics included normalized mean square error, normalized delta band power error, RMS error, and signal-to-noise ratio, demonstrating that the proposed method provides optimal suppression of metal artifacts. This advancement holds promise for enhancing data quality and expanding the clinical applications of OPM-MEG.

High Performance CNN Accelerators based on Hardware and Algorithm Co-Optimization

The efficacy of convolutional neural networks (CNNs) has led to their extensive application in picture recognition and categorization. Nevertheless, embedded systems face challenges when it comes to storing the massive volumes of weight data required by CNNs in their on-chip memory. Pruning a convolutional neural network (CNN) model can reduce its size with no impact on accuracy; however, when used with a parallel architecture, the resulting model will be slower to execute. A CNN compression method that is hardware-centric is introduced in this paper. A model of a deep neural network (DNN) will include "pruning layers (P-layers)" and "no-pruning layers (NP-layers)" attached to it. For effective and parallel processing, an NP-layer employs a regular distribution for its weights. Pruning causes a P-layer to have an irregular form, yet the high compression ratio it yields makes the shape undesirable. Using uniform and gradual quantization methods, we can achieve a processing efficiency/compression ratio trade-off with a small loss of precision. Integrating multiple parallel finite impulse response (FIR) filters into an NP-layer distributed convolutional architecture, we further improve the regular model. The P-layer irregular sparse model is suggested to use an ADF-based processing element. The suggested compression strategy and hardware architecture can be utilised by a hardware/algorithm co-optimization (HACO) method to run an NP→P hybrid compressed CNN model on FPGAs. The VGG-16 network achieves a compression ratio of 27.5× with a top-5 accuracy loss of 0.44% by using a hardware accelerator on a single FPGA chip without off-chip memory. A result of 83.0 FPS, or 1.8 times quicker, was achieved when the compressed VGG-16 model was implemented on a Xilinx VCU118 evaluation board for image applications. Index Terms - CNN, Accelerators, Co-Optimization.

Dissolution behaviour of WC particles and evolution of precipitated phases in the plasma transfer arc Ni-based composite coating reinforced by 30 wt% WC particles

Ni-WC composite coating was prepared on Q235 steel substrate by plasma transfer arc (PTA) with welding current ranging from 70 A to 85 A. The results show that the dissolution behaviour of WC particles and the elemental diffusion at the interface promoted by the increased welding current promote the formation and evolution of precipitates, including M7(B, C)3 and M6C. The increased current reduced the volume fraction of WC particles in the coating, but increased that of the precipitates. At welding currents of 75 A and 80 A, dendritic-like M7(B, C)3 presented a regular distribution at the bottom of the coating. At a welding current of 80 A, M6C phase began to form at the bottom of the coating. When the welding current reached 85 A, the dendritic structure was broken, and the M6C phase aggregated and grew. The Ni-WC coating had visible delamination. WC particles at the upper part of the coating dissolved more severely compared to those at the middle and bottom of the coating. Ni3Si compound was formed at the upper and middle of the coating, and it had a crystal orientation relationship of [011]Ni3Si∥[011]γ-Ni, (11-1)Ni3Si∥(11-1)γ-Ni, (−200)Ni3Si∥(-200)γ-Ni, (-11-1)Ni3Si 1.3° from (-11-1)γ-Ni with γ-Ni. The average hardness of the coating decreased slightly due to the dissolution of WC, but the hardness at the interface increased, showing an improved bonding of the interface. The newly formed high hardness precipitates can share the load coming from the friction paired ball, thus reducing CoF and wear loss of the coating.

Optimization, characterization and evaluation of sodium alginate nanoparticles for Ganoderic acid DM encapsulation: Inhibitory activity on tyrosinase activity and melanin formation

The efficacy of nanoencapsulation as a technology for enhancing the solubility of active substances has been demonstrated. In this particular investigation, Ganoderic acid DM (GA-DM) was encapsulated within sodium alginate nanoparticles (NPs) using the ionic crosslinking method. The confirmation of the successful loading of GA-DM was ascertained through the analysis of Fourier transform infrared spectrum (FTIR). Empirical evidence derived from the examination of scanning electron microscope (SEM) images, transmission electron microscope (TEM) images, atomic force microscope (AFM) images, and dynamic light scattering (DLS) demonstrated a regular distribution and spherical morphology, with an average particle size of approximately 133 nm. The investigation yielded an encapsulation efficiency of 95.27 ± 0.11 % and a drug loading efficiency of 21.17 ± 0.02 % for the prepared sample. The release kinetics of SGPN was fitted with the Korsmeyer-Peppas kinetic model corresponding to diffusion-controlled release. The incorporation of GA-DM into sodium alginate nanocarriers exhibited a mitigating effect on the cytotoxicity of HaCat and B16, while also demonstrating inhibitory properties against tyrosinase activity and melanin formation.

Graph convolutional network for lithological classification and mapping using stream sediment geochemical data and geophysical data

Lithological mapping plays a vital role in acquiring a comprehensive understanding of subsurface rocks and geological structures. Traditional lithological mapping relies heavily on manual field surveys, with the accuracy of the mapping greatly constrained by the expertise of mappers, and thus this process is commonly slow and costly. In the present study, the graph convolutional network (GCN) method was proposed for efficient and automatic lithological mapping, using various geological survey data (e.g. geochemical and geophysical data). The GCN is not limited by the spatial distribution of sampling data, and hence it can be directly applied to the integrated resampling data that do not adhere to the regular spatial distribution patterns of Euclidean space. To evaluate the potential application of the GCN method in lithological classification and mapping, a case study was conducted in the Maqin region (China). Geochemical and geophysical data were utilized as the basis for lithological classification, as they can provide valuable information on geochemical compositions and physical properties of geological bodies, respectively. The study process was primarily divided into three parts. First, basic pre-processing was conducted on the geochemical data and the geophysical data, including centred log-ratio transformation and resampling. Subsequently, the resampled points were divided into training and testing sets in the proportion of 5.5:1. In the training set, 20% of the points are randomly selected as the validation set. Ultimately, using each resampled point as a node and the integrated geochemical and geophysical data as features, a GCN model and an undirected graph were constructed based on an adjacency distance of 1 km. The graph was employed by the constructed GCN model for lithological classification. The results reveal that the majority of lithological units can be accurately classified with an overall accuracy of 86%, indicating that the GCN model exhibits good applicability in lithological classification.

Fitting of Different Intraradicular Composite Posts to Oval Tooth Root Canals: A Preliminary Assessment.

The purpose of the present study was to perform a preliminary analysis of the fitting of different fiber-reinforced composite (GFRC) posts to tooth root canals and determine the resin cement layer thickness. The following GFRC posts were assessed: bundle posts (Rebilda GTTM, VOCO, Germany), sleeve system (SAPTM, Angelus Ind, Brazil), and accessory posts (ReforpinTM, Angelus, Brazil). Twenty-four freshly extracted mandibular single-rooted pre-molars were endodontically treated and divided into six groups, according to the type of GFRC post and resin cement (self-adhesive or conventional dual-cured). Then, specimens were cross-sectioned and inspected by optical microscopy regarding the cement layer thickness and presence of defects such as pores, voids, or fissures were assessed. Bundle and accessory posts revealed a regular distribution of resin cement with a lower number of voids than found with sleeve systems. The sleeve system posts showed poor fitting at the apical portion of the root canals. The type of resin cement did not affect the thickness of the interface, although both bundle and accessory posts allow a better distribution of resin cement and fibers. The present preliminary study reveals interesting insights on the fitting of bundle and accessory posts to root dentin and resin cement layer thickness in oval-shape root canals. The sleeve system posts showed adequate fitting only at the coronal portion of the canals.

Diversity and Distribution Patterns of Dragonflies in The Region Bagek Kembar Ecotourism, Sekotong

Dragonflies have an important role as environmental bioindicators, especially as indicators of the success of mangrove ecosystem restoration projects and their distinctive color has becomes an ecotourism attraction, so this research is important to do. This research aims to determine the diversity and distribution patterns of dragonflies in the Bagek Kembar Sekotong Mangrove Ecotourism area. This is an explorative descriptive study and was carried out between December 2023-January 2024. The research method used was catching dragonflies with insect nets following three transect paths along the river flow in the Bagek Kembar Sekotong Mangrove Ecotourism area. Analysis of species diversity data using the Shannon Wiener diversity index and calculation of the dragonfly distribution pattern index using the variance test. Based on the research results, 9 species of dragonflies were obtained, including 8 species of dragonflies (Anisoptera) and 1 species of dragonflies (Zygoptera). The diversity index (H') of dragonflies at the observation location is 1,866. The conclusion is that diversity of dragonflies at the research location is in the medium category. The distribution pattern of dragonflies at the research location shows that one Potamarcha congener species has a regular distribution pattern and the other 8 species that had groups distribution pattern.