Search Results (79)

Andean grassland ecosystems in Peru are characterized by diverse plant species adapted to environmental factors including weather, soil type, elevation, slope orientation, and soil moisture. This study evaluated the floristic composition, alpha diversity, indicator species, and suitable species for Andean livestock in the Sillapata micro-watershed, Junín region, Peru, across rainy and dry seasons. Data collection involved 100 m linear transects, and analyses included floristic composition and dissimilarity, Shannon-Wiener (H′) and Simpson (D) diversity indices, and the identification of indicator and suitable species using QGIS vs 3.28.14 and R software vs 4.3.2. Results revealed a total of 130 species classified into 74 genera and 23 families, with Asteraceae and Poaceae as the dominant families, exhibiting variations in richness and dissimilarity between control points and seasonal periods. Alpha diversity (H′) ranged from 2.07 to 3.1867, while Simpson’s index (D) ranged from 0.7644 to 0.9234. Six indicator species were identified, along with 11 families containing suitable species, predominantly Poaceae (38–60%), Cyperaceae (11–15%), and Asteraceae (3–9%). The findings indicate that the studied ecosystem exhibits a heterogeneous floristic composition with medium to low and variable diversity, influenced by seasonal climatic changes and the current grassland management regime, which involves rotational grazing with cattle adapted to high-altitude conditions. Full article

In this study, a novel improved ultra-wideband (UWB) antipodal Vivaldi antenna suitable for breast cancer detection via microwave imaging was designed. The antenna was made more directional by adding three pairs of nestings to the antenna fins by adding elliptical patches. The frequency operating range of the proposed antenna is UWB 3.6–13 GHz, its directivity is 11 dB, and its gain is 9.27 dB. The antenna is designed with FR4 dielectric material and dimensions of 34.6 mm × 33 mm × 1.6 mm. It was demonstrated that the bandwidth, gain, and directivity of the proposed antenna meet the requirements for UWB radar applications. The Vivaldi antenna was tested on an imaging system developed using the CST Microwave Studio (CST MWS) program. In CST MWS, a hemispherical heterogeneous breast model with a radius of 50 mm was created and a spherical tumor with a diameter of 0.9 mm was placed inside. A Gaussian pulse was sent through Vivaldi antennas and the scattered signals were collected. Then, adaptive Wiener filter and image formation algorithm delay-multiply-sum (DMAS) steps were applied to the reflected signals. Using these steps, the tumor in the breast model was scanned at high resolution. In the simulation application, the tumor in the heterogeneous phantom was detected and imaged in the correct position. A monostatic radar-based system was implemented for scanning a breast phantom in the prone position in an experimental setting. For experimental measurements, homogeneous (fat and tumor) and heterogeneous (skin, fat, glandular, and tumor) breast phantoms were produced according to the electrical properties of the tissues. The phantoms were designed as hemispherical with a diameter of 100 mm. A spherical tumor tissue with a diameter of 16 mm was placed in the phantoms produced in the experimental environment. The dynamic range of the VNA device used allowed us to image a 16 mm diameter tumor in the experimental setting. The developed microwave imaging system shows that it is suitable for the early-stage detection of breast cancer by scanning the tumor in the correct location in breast phantoms. Full article

Denoising has long been a challenge in image processing. Noise appears in various forms, such as additive white Gaussian noise (AWGN) and Poisson noise across different frequencies. This study aims to denoise images without prior knowledge of the noise distribution. First, we estimate the noise power in the frequency domain to approximate the local signal-to-noise ratio (SNR) and guide an adaptive Wiener filter. The initial denoised result is obtained by assembling the locally filtered patches. However, since the Wiener filter is a low-pass filter, it can remove fine details along with the noise. To overcome this limitation, we post-process the noise and interpolate it between the denoised and original noisy patches to enhance the denoised image. We also mask the frequency domain to avoid grid-like artifacts. Additionally, we introduce a convolutional neural network-based refinement technique to the spatial domain to recover latent textures lost during denoising. The method presents the effectiveness of masking and feature extraction. Full article

Global coral reef ecosystems face various levels of disturbance pressure. Understanding the depth-structured variation in coral reef fish communities can help us to better grasp and predict the adaptive changes of the ecosystem under different stressors. This study applied eDNA metabarcoding technology to analyze the spatial distribution of the coral reef fish at various depths (0 m, 5 m, 10 m, 15 m, 20 m, 30 m, 40 m, 50 m, and 60 m) within the Xisha Islands of China. The results indicated that the eDNA technology detected a total of 213 amplicon sequence variants (ASVs), including 33 species that were not identified using traditional methods. Herbivorous fish generally dominated in relative abundance across different depths. Moreover, the similarity among depth groups was largely absent, and significant differences existed in fish assemblages across depth gradients, consistent with the unique depth preferences of fish microhabitats. Importantly, our findings revealed distinct depth-structured variation among different functional groups of coral reef fish. Large carnivorous fish initially increased and then decreased along the depth gradient from 0 to 60 m, with a turning point around 20 m, while large herbivorous fish displayed the opposite trend. Small carnivorous and small herbivorous fish consistently declined along the same depth gradient. Additionally, the Margalef index (D) and Function richness (FRic) both displayed a consistent downward trend with increasing depth, while the Shannon–Wiener index (H′), Pielou index (J′), Quadratic entropy (RaoQ), Functional dispersion (FDis), and Functional evenness (FEve) initially increased and then decreased, peaking around 20 m. This study revealed that eDNA metabarcoding is an effective tool for evaluating coral reef fish biodiversity, community composition, and spatial distribution. It enhances our understanding of distribution dynamics and offers valuable insights for coral reef conservation and restoration efforts. Full article

This study presents the first scientific evaluation of food web integrity in the Black Sea under the Marine Strategy Framework Directive (MSFD) Descriptor 4 (Food Webs), utilizing zooplankton indicators to assess the environmental status during the warm season (May to October) from 2018 to 2023. The research aims to analyze trophic interactions, biodiversity patterns, and ecological stability by examining three key indicators: the Shannon–Wiener diversity index, Copepoda biomass, and Mnemiopsis leidyi biomass. The findings reveal that the Shannon–Wiener diversity index failed to achieve Good Environmental Status (GES) in any of the four Marine Reporting Units (MRUs), suggesting insufficient community diversity and potential ecological imbalances. Copepoda biomass met GES criteria only in marine and offshore waters, indicating spatial variability in copepod population stability across the Black Sea. Meanwhile, M. leidyi biomass remained within GES thresholds in all MRUs, suggesting that its population levels do not currently threaten the food web, despite its known invasive impact. This study provides critical baseline data on food web structure and dynamics in the Black Sea, offering a scientifically grounded framework for future ecological monitoring and management strategies. The results emphasize the need for targeted conservation efforts and adaptive management approaches to enhance the ecological health of the Black Sea in alignment with MSFD objectives. Full article

Variational Mode Decomposition (VMD) serves as an effective method for simultaneously decomposing signals into a series of narrowband components. However, its theoretical foundation, the classical Wiener filter, exhibits limited adaptability when applied to broadband signals. This paper proposes a novel Variable Filtered-Waveform Variational Mode Decomposition (VFW-VMD) method to address critical limitations in VMD, particularly in handling broadband and chirp signals. By incorporating fractional-order constraints and dynamically adjusting filter waveforms, the proposed algorithm effectively mitigates mode mixing and over-smoothing issues. The mathematical framework of VFW-VMD is formulated, and its decomposition performance is validated through simulations involving both synthetic and real-world signals. The results demonstrate that VFW-VMD exhibits superior adaptability in extracting broadband signals and effectively captures more rolling bearing fault features. This work advances signal processing techniques, enhancing capability and significantly improving the performance of practical bearing fault diagnostic applications. Full article

Sabkha (inland and coastal—saline beds or saline lands) are widespread in Saudi Arabia and are distinguished by their hypersaline nature. These hypersaline habitats are commonly covered by halophytic vegetation. Moreover, Arbuscular mycorrhizal fungi (AMF) are an essential component of these habitats and exhibit a unique adaptation and contribute significantly to ecosystem variability, diversity, and function. Additionally, AMF from saline habitats are an essential component for the successful rehabilitation of salinity-affected areas. Despite their importance, little is known about the distribution and abundance of AMF along inland and coastal sabkhat of Saudi Arabia. Therefore, the main objective of this study was to investigate the abundance and diversity of AMF in the coastal and inland sabkhat of Saudi Arabia. Five soil samples, each from five randomly selected spots (considering the presence of dominant and co-dominant halophytic species), were collected from every location and were used to assess the AMF abundance and diversity. The study indicated that the highest number of AMF spores was recorded from Jouf, averaging ≈ 346 spores 100 g⁻¹ dry soil, and the lowest from Uqair, averaging ≈ 96 spores 100 g⁻¹ dry soil. A total of 25 AMF species were identified, belonging to eight identified genera viz., Acaulospora, Diversispora, Gigaspora, Scutellospora, Claroideoglomus, Funneliformis, Glomus, and Rhizophagus and five families. Of the total identified species, 52% belonged to the family Glomeraceae. Moreover, the highest number of species was isolated from the sabkha in Qasab. Additionally, Glomeraceae was abundant in all the studied locations with the highest relative abundance in Uqair (48.34%). AMF species Claroideoglomus etunicatum, Funneliformis mosseae, Glomus ambisporum, and Rhizophagus intraradices were the most frequently isolated species from all the Sabkha locations with isolation frequency (IF) ≥ 60%, and Claroideoglomus etunicatum (Ivi ≥ 50%) was the dominant species in all the studied locations. Furthermore, data on the Shannon–Wiener diversity index showed that the highest AMF species diversity was in Qaseem and Qasab habitats. The highest Pielou’s evenness index was recorded in Jouf. Moreover, the soil parameters that positively affected the diversity of identified species included Clay%, Silt%, HCO₃¹⁻, OM, MC, N, and P, while some soil parameters such as EC, Na⁺, SO₄²⁻, and Sand% had a significant negative correlation with the isolated AMF species. This study revealed that AMF can adapt and survive the harshest environments, such as hypersaline sabkhas, and thus can prove to be a vital component in the potential restoration of salinity-inflicted/degraded ecosystems. Full article

This study investigates the impacts of climate variability, particularly La Niña events, on the fish community on the continental shelf of the northern South China Sea, a region highly sensitive to environmental fluctuations. Historical fishery survey data, collected from autumn 2019 to autumn 2022, were used to analyze changes in species composition, diversity indices, and community structure during La Niña and non-La Niña periods. The results show that La Niña significantly altered the fish community dynamics. During La Niña, cold-water conditions expanded the range of suitable habitats for cold-water species, leading to increased dominance of the Japanese scad (Decapterus maruadsi), with its index of relative importance (IRI) reaching 1795.9 and 1320.2 in autumn 2021 and 2022, respectively. In contrast, warm-water species experienced a reduction in suitable habitats. During La Niña, Margalef’s richness index (D’) peaked at 23.18 in autumn 2021 but decreased to 20.69 by spring 2022. The Shannon–Wiener diversity index (H’) dropped from 2.597 during a non-La Niña period (spring 2020) to 2.406 during La Niña (spring 2022); similarly, Pielou’s evenness index fell from 0.4749 to 0.4396, indicating an increase in ecological imbalance. As La Niña conditions weakened, the fish community began to recover. By autumn 2022, D’ had risen to 22.73 and H’ to 2.573, reflecting a gradual return to fish community conditions before the La Niña event. Species distribution models incorporating key environmental variables (i.e., sea surface temperature, salinity, and dissolved oxygen) demonstrated that the habitat of D. maruadsi expanded significantly during La Niña and contracted during post-event periods. Our findings highlight the ecological sensitivity of fish communities to climate variability and underscore the importance of adaptive resource management strategies to mitigate the impacts of climate change on marine ecosystems. This research provides valuable insights for sustaining regional fishery resources under changing environmental conditions. Full article

Medical ultrasound imaging using coherent plane-wave compounding (CPWC) for higher frame-rate applications has generated considerable interest in the research community. The adaptive Eigenspace Beamformer technique combined with a Generalized Sidelobe Canceler (GSC) provides noise and interference reduction in images, improving resolution and contrast compared to basic methods: Delay and Sum (DAS) and Minimum Variance (MV). Different filtering approaches are applied in ultrasound image processing to reduce speckle signals. This work introduces the combination of beamformer Eigenspace Based on Minimum Variance (ESBMV) associated with GSC (EGSC) and the Kuan (EGSCK), Lee (EGSCL), and Wiener (EGSCW) filters and their enhanced versions to obtain better quality of plane-wave ultrasound images. The EGSCK technique did not present significant improvements compared to other methods. However, the EGSC with enhanced Kuan (EGSCKe) showed a remarkable reduction in geometric distortion, i.e., 0.13 mm (35%) and 0.49 mm (67%) compared to the EGSC and DAS techniques, respectively. The EGSC with Enhanced Wiener (EGSCWe) showed the best improvements in contrast radio (CR) aspects, i.e., 74% compared to the DAS technique and 60% to the EGSC technique. Furthermore, our proposed method reduces geometric distortion, making it a good option for plane-wave ultrasound imaging. Full article

Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance strategy. However, among the numerous studies that conducted maintenance and replacement activities based on the results of RUL prediction, little attention has been paid to the impact of preventive maintenance on sensor-based monitoring data, which further affects the RUL for repairable degrading devices. In this paper, an adaptive RUL prediction method is proposed for repairable degrading devices in order to improve the accuracy of prediction results and achieve adaptability to future degradation processes. Firstly, a phased degradation model based on an adaptive Wiener process is established, taking into account the impact of imperfect maintenance. Meanwhile, integrating the impact of maintenance activities on the degradation rate and state, the probability distribution of RUL can be derived based on the concept of first hitting time (FHT). Secondly, a method is proposed for model parameter identification and updating that incorporates the individual variation among devices, integrating maximum likelihood estimation and Bayesian inference. Finally, the effectiveness of the RUL prediction method is ultimately validated through numerical simulation and its application to repairable gyroscope degradation data. Full article

Understanding the correlation between soil pollution, environmental indices, humic substances, and soil biota diversity is critical for assessing ecological health, particularly in areas with prolonged contamination. In this study, 90 soil samples were collected from ten locations in El-Mahla El-Kobra area, Egypt, affected by industrial pollution and unsustainable agricultural practices. Significant variations in organic matter, humic substances, microbial biomass carbon, and microbial populations were observed. Heavy metal contamination was highest in site S3, with a contamination degree (CD) of 29.45 and a pollution load index (PLI) of 1.67. Self-organizing maps showed the possible need for targeted remediation to mitigate ecological risk. Biodiversity analysis identified Oribatida as the dominant species, with shifts in diversity indices indicating species adaptation to pollution levels. Positive correlations between soil contamination (CD, PLI) and both Shannon–Wiener and Simpson indices, alongside negative correlations between MCD, PLI, and the Berger–Parker dominance index, suggest a complex shift toward species dominance in polluted environments. The findings highlight the complex interplay between soil contamination and biodiversity, offering critical insights for ecological risk assessment and sustainable soil management in contaminated regions. Full article

Infrared (IR) magneto-optical (MO) bi-imaging is an innovative method for detecting weld defects, and it is important to process both IR thermography and MO imaging characteristics of weld defects. IR thermography and MO imaging can not only run simultaneously but can also run separately in special welding processes. This paper studies the sensing processing of eddy current IR thermography and MO imaging for detecting weld defects of laser spot welding and butt joint laser welding, respectively. To address the issues of high-level noise and low contrast in eddy current IR detection thermal images interfering with defect detection and recognition, a method based on least squares and Gaussian-adaptive bilateral filtering is proposed for denoising eddy current IR detection thermal images of laser spot welding cracks and improving the quality of eddy current IR detection thermal images. Meanwhile, the image gradient is processed by Gaussian-adaptive bilateral filtering, and then the filter is embedded in the least squares model to smooth and denoise the image while preserving defect information. Additionally, MO imaging for butt joint laser welding defects is researched. For the acquired MO images of welding cracks, pits, incomplete fusions, burn-outs, and weld bumps, the MO image processing method that includes median filtering, histogram equalization, and Wiener filtering was used, which could eliminate the noise in an image, enhance its contrast, and highlight the weld defect features. The experimental results show that the proposed image processing method can eliminate most of the noise while retaining the weld defect features, and the contrast between the welding defect area and the normal area is greatly improved. The denoising effect using the Natural Image Quality Evaluator (NIQE) and the Blind Image Quality Index (BIQI) has been evaluated, further demonstrating the effectiveness of the proposed method. The differences among weld defects could be obtained by analyzing the gray values of the weld defect MO images, which reflect the weld defect information. The MO imaging method can be used to investigate the magnetic distribution characteristics of welding defects, and its effectiveness has been verified by detecting various butt joint laser welding weldments. Full article

The Fokas method (also known as the unified transform method) is used to investigate acoustic scattering by thin, infinite grating by extending the methodology to apply to spatially periodic domains. Infinite grating is used to model a perforated screen, a material of interest in aeroacoustics and noise reduction. Once the method is established, its numerical results are verified against the Wiener–Hopf (WH) technique, which has solved the problem only for a special case. A key benefit of the novel approach is that the scatterer, modelled as an infinitely repeating unit cell consisting of a thin, rigid plate, can take any length. This is in contrast to the WH method, where the plate length is restricted to half the width of the unit cell (for this method, no such restriction exists). The numerical method is an over-sampled collocation method of the integral equation resulting from applying the Fokas method: the global relation. The only increase in complexity in adapting the Fokas method to more complicated cell geometries is a higher number of terms in the global relation. The proportion of energy transmitted and reflected by the grating structure is assessed for varying incident wave angles, frequencies, and plate lengths. Full article

Contact-free concealed object detection using passive millimeter-wave imaging (PMMWI) sensors is a challenging task due to a low signal-to-noise ratio (SNR) and nonuniform illumination affecting the captured image’s quality. The nonuniform illumination also generates a higher false positive rate due to the limited ability to differentiate small hidden objects from the background of images. Several concealed object detection models have demonstrated outstanding performance but failed to combat the above-mentioned challenges concurrently. This paper proposes a novel three-stage cascaded framework named BWFER-YOLOv8, which implements a new alpha-reshuffled bootstrap random sampling method in the first stage, followed by image reconstruction using an adaptive Wiener filter in the second stage. The third stage uses a novel FER-YOLOv8 architecture with a custom-designed feature extraction and regularization (FER) module and multiple regularized convolution (Conv_Reg) modules for better generalization capability. The comprehensive quantitative and qualitative analysis reveals that the proposed framework outperforms the state-of-the-art tiny YOLOv3 and YOLOv8 models by achieving 98.1% precision and recall in detecting concealed weapons. The proposed framework significantly reduces the false positive rate, by up to 1.8%, in the detection of hidden small guns. Full article

Indonesia is known for its incredible diversity of insects. Being ectothermic, insects are influenced by environmental factors. The relationship between insect diversity and the environment can be understood using multivariate analysis. The Paseh District in Sumedang Regency has various land uses, including gardens, rice fields, and plantations. Changes in land use due to the construction of the Cisumdawu Toll Road can impact environmental factors, such as soil quality, microclimate, and water availability, which are critical for sustaining diverse insect communities. Similarly, changes in vegetation cover can alter temperature and humidity levels, impacting terrestrial insects adapted to specific climatic conditions. This study aims to gather information on the relationship between insect diversity and environmental factors in different land use types in the Paseh District. A preliminary survey was carried out to record land use types and determine sampling locations. An intensive survey was done to collect and identify flying insect samples, as well as to measure the environmental factors. The results were analyzed using the Shannon–Wiener Diversity Index (H’), Evenness Index (E’), Simpson’s Diversity Index (C), and Canonical Correspondence Analysis (CCA). The study found 115 species of flying insects, with mixed gardens having the highest diversity. The CCA results showed that temperature strongly and positively correlated with insect diversity across all land uses, while wind speed correlated positively with insect diversity in gardens. Altitude correlated negatively with insect diversity in mixed gardens but positively in rice fields. Humidity had a strong positive correlation with insect diversity in other land uses. This research is important for understanding how land use types and environmental factors influence flying insect diversity, which is crucial for conserving biodiversity and maintaining essential ecosystem services such as pollination and pest control. Its impact lies in providing scientific data to guide sustainable land management practices, support agricultural productivity, and inform policies for biodiversity conservation in the Paseh District and similar regions. Full article