Frequency Patterns Research Articles

A Method for Identifying Warm Fronts in Eurasia

ABSTRACTWarm fronts often trigger significant weather changes, which also play a role in many extreme weather incidents. Therefore, it is crucial to understand the location and characteristics of warm fronts to accurately forecast weather changes. However, warm fronts are more difficult to identify than cold fronts on average, since the gradients can be weaker and shallower. This paper proposes a new objective method for identifying warm fronts in the Eurasia region using ERA‐5 hourly reanalysis data. The method uses the appropriate thermal front parameter and warm advection threshold to identify the potential warm frontal zone, then determines the corresponding warm boundary according to the predominant wind direction within the frontal zone, and finally locates the warm front line along the warm boundary. In various weather processes, the location and shape of the objective warm front correspond well with the distribution of weather systems and meteorological elements, indicating the effectiveness of the method. Meanwhile, the high agreement between objective and manual warm fronts further supports the reliability of the method. Furthermore, this method is applied to the long‐term datasets covering Eurasia, enabling an exploration of the climatological characteristics of warm fronts in the region. The study reveals distinct seasonal patterns in warm front frequency, with the highest frequency occurring during winter and the lowest during summer. Warm fronts are notably active in Europe, the Siberian Plain, the Northeast China Plain extending to the Western Pacific during winter, spring, and autumn. The dataset of warm fronts produced by this method proves valuable for climate change research.

Effect of tube dimensions on resonant frequency of stepped acoustic resonator

An investigation of the effects of tube dimensions on resonant frequencies of stepped acoustic resonators is conducted. A linear acoustic theory is employed to model the plane wave propagation in the stepped resonator. The linear eigenvalue problem obtained by the method of separation of variables is solved to derive a frequency equation. Then, an experimental apparatus is established, in which the tube dimensions can be changed to adjust frequency. The variation pattern of resonant frequency with different lengths and diameters of the sub-tubes is analyzed theoretically and experimentally. The results suggest that the overall length determines the approximate scope of the resonant frequencies while the exact values of resonant frequencies and the relationship between resonant frequencies of each mode are determined by the ratio of sub-tube lengths and the ratio of sub-tube diameters. The calculated results are compared with the measured data. The comparative research declares that the derived frequency equation is an effective tool to calculate the resonant frequency of stepped resonator with adjustable size used in wide-band microphone calibration.

Text-independent speaker identification using modified SincNet with robust features from suitable acoustic region and appropriate optimizer for raw audio analysis

Speaker identification is a method of identifying an individual from a set of speakers, and text-independent speaker identification systems allow speakers to utter any phrase without any constraints. This study is focused on raw audio analysis as phase, fine-grained frequency patterns, timing cues, and other minute characteristics are preserved when raw waveforms are processed as compared to handcrafted features like Mel-Frequency Cepstral Coefficients (MFCC) and visual representation of audio-like spectrogram. Due to the depth of information, which includes variations in speech rhythm, pitch, and vocal tract shape, it is beneficial for identifying speakers. The deep learning architecture known as SincNet has gained popularity in speaker identification because of its parametric Sinc functions that allow it to operate directly on the raw audio input. In this paper, we have considered SincNet as the baseline model for speaker identification. The effect of proper speech boundary detection, including high-level features and effective optimizer selection are analysed. The precise identification of the signal start and terminus point is important for eliminating the redundant non-speech regions. We have included endpoint detection module as a pre-processing step in the system. Proper feature extraction and selection are crucial to the model's success. To extract more abstract features from the data, we have added more convolution layers to the original SincNet model. Further, we investigated the hyperparameter tuning protocol's sensitivity to the optimizer and selected the suitable optimizer for raw audio analysis. With all the modifications in the system architecture, we are able to archive improvements of 12.76 %, 13.33 %, and 13.39 % respectively for training, validation, and testing over the original SincNet model. In terms of validation loss, our proposed approach attains 0.35 in comparison to the original SincNet loss of 1.02. With this significant improvement, the total training time is marginally increased by 20 minutes for our proposed model. We have performed our investigation on the LibriSpeech dataset to check the effectiveness of our proposed system in comparison to the other model..

Pulsation in Hot Main-Sequence Stars: Comparison of Observations with Models

Pulsation in Hot Main-Sequence Stars: Comparison of Observations with Models

Insolation Cycles Control the Timing and Pattern of Resonance Frequency Drifts at a Natural Rock Tower, Utah, USA

Resonance frequency monitoring can detect structural changes during progressive rock slope failure; however, reversible environmentally-driven frequency drifts may inhibit identification of permanent changes. Frequency drifts are commonly correlated with air temperature, lagging temperature changes by zero to 35–60 days. Here we report observations from two years of monitoring at a rock tower in Utah, USA where annual resonance frequency changes appear to precede air temperature cycles by ~35 days. Using correlations with meteorological data supplemented by numerical modeling, we identify changes in insolation as the primary driver of annual frequency drifts, giving rise to the negative lag time. Sparse in-situ insolation data show that the daily frequency increase lags sunrise by several hours, while frequencies decrease at sunset, responses we attribute to the west facing aspect of the tower. Modeled daily insolation patterns match frequency data for months when in-situ measurements are not available. Numerical models offer the advantage of predicting insolation patterns for different aspects of the rock tower, such as the west facing cliff where measurements would be challenging. Our study highlights the value of long-term datasets in identifying mechanisms driving environmentally associated frequency drifts, understanding that is crucial to facilitate detection of permanent changes during progressive failure.

Comprehensive Molecular Epidemiology of Influenza Viruses in Brazil: Insights from a Nationwide Analysis

Abstract Influenza A and B viruses represent significant global health threats, contributing substantially to morbidity and mortality rates. However, a comprehensive understanding of the molecular epidemiology of these viruses in Brazil, a continental-size country and a crucial hub for the entry, circulation, and dissemination of Influenza viruses within South America, still needs to be improved. This study addresses this gap by consolidating data and samples across all Brazilian macroregions, as part of the Center for Viral Surveillance and Serological Assessment (CeVIVAS) project, together with an extensive number of other Brazilian sequences provided by a public database during the epidemic seasons spanning 2021-2023. Phylogenetic analysis of the Hemagglutinin segment (HA) of Influenza A/H1N1pdm09, A/H3N2, and Influenza B/Victoria-lineage revealed that in 2021 and in the first semester of 2022 the A/H3N2 2a.3 strain was the predominant circulating strain. Subsequently, the A/H3N2 2b became the prevalent strain until October, when it was substituted by A/H1N1pdm09 5a.2a and 5a.2a.1 lineages. This scenario was maintained during the year of 2023. B/Victoria emerged and circulated at low levels between December 2021 and September 2022, then became co-prevalent with A/H1N1pdm09 5a.2a and 5a.2a.1 lineages. The comparison between the vaccine strain A/Darwin/9/2021 and circulating viruses revealed shared mutations to aspartic acid at residues 186 and 225 across all A/H3N2 lineages from 2021 to 2023, altering the charge in the RBD domain. For A/H1N1pdm09, the 2022 consensus of 5a.2a.1 and the vaccine strain A/Victoria/2570/2019 showed 14 amino acid substitutions. Key residues H180, D187, K219, R223, E224, and T133 are involved in hydrogen interactions with sialic acids, while N130, K142, and D222 may contribute to distance interactions based on docking analyses. Importantly, distinct Influenza A lineage frequency patterns were observed across Brazil’s macroregions, underscoring the regional variations in virus circulation. This study characterizes Influenza A and B viruses circulating in Brazil, providing insights into their circulation patterns and dynamics across Brazilian macroregions. These findings hold significant implications for public health interventions, informing strategies to mitigate transmission risks, optimize vaccination efforts, and enhance outbreak control measures.

Sequential introgression of a carotenoid processing gene underlies sexual ornament diversity in a genus of manakins.

In a hybrid zone between two tropical lekking birds, yellow male plumage of one species has introgressed asymmetrically replacing white plumage of another via sexual selection. Here, we present a detailed analysis of the plumage trait to uncover its physical and genetic bases and trace its evolutionary history. We determine that the carotenoid lutein underlies the yellow phenotype and describe microstructural feather features likely to enhance color appearance. These same features reduce predicted water shedding capacity of feathers, a potential liability in the tropics. Through genome-scale DNA sequencing of hybrids and each species in the genus, we identify BCO2 as the major gene responsible for the color polymorphism. The BCO2 gene tree and genome-wide allele frequency patterns suggest that carotenoid-pigmented collars initially arose in a third species and reached the hybrid zone through historical gene flow. Complex interplay between sexual selection and hybridization has thus shaped phenotypes of these species, where conspicuous sexual traits are key to male reproductive success.

PBAN regulates sex pheromone biosynthesis by Ca2+/CaN/ACC and Ca2+/PKC/HK2 signal pathways in Spodoptera litura.

Sex pheromones emitted by female moths play important roles in mate attraction. The molecular mechanism underlying pheromone biosynthesis activating neuropeptide (PBAN)-regulated sex pheromone biosynthesis has been well elucidated in many moth species, although this mechanism is species-dependent. Spodoptera litura, an important pest, has caused serious economic losses to agricultural production, yet the mechanism for its sex pheromone biosynthesis has not been fully identified. The present study investigates in detail mechanism underlying PBAN-regulated sex pheromone biosynthesis in S. litura. The transcriptome sequencing of S. litura pheromone glands (PGs) was analysed to identify a serial of candidate genes potentially involved in sex pheromone biosynthesis. Further investigation revealed a bimodal pattern in both sex pheromone release and mating frequency. PBAN was found to regulate sex pheromone biosynthesis via its receptor by using Ca2+ as a secondary messenger, as demonstrated by RNA interference and the application of pharmacological inhibitors. Furthermore, PBAN/Ca2+ signalling activated calcineurin (CaN) and acetyl-CoA carboxylase (ACC), which mediated sex pheromone biosynthesis in response to PBAN stimulation. Mostly importantly, hexokinase 2 (HK2) was confirmed to be activated by PBAN/PBANR /Ca2+/PKC signalling via phosphorylation at two specific sites (ser423 and ser434 sites of HK2). Overall, our findings shed light on the intricate processes involved in sex pheromone production in S. litura, in which PBAN regulates sex pheromone biosynthesis through PBAN/PBANR/Ca2+/CaN/ACC and PBAN/PBANR/Ca2+/PKC/HK2 signalling pathways. These insights significantly contribute to our comprehension of the specific mechanisms underlying sex pheromone biosynthesis in this moth species.

Assessing Appropriation of Space in Urban Green Spaces: Three Case Studies in Downtown Shanghai

This study investigated patterns of activities in urban green spaces (UGSs) in downtown Shanghai. UGSs are essential public infrastructure, contributing to urban sustainability, quality of life, and social cohesion. Although widely studied, there is a gap in the literature regarding Chinese UGSs when the object of study is the nature the activities. In this sense, we aimed to investigate the activities from the perspective of appropriation of the space, considered here as different from the use of space. This study addressed this by analyzing user demographics, frequency, and spatial activity patterns to assess how these activities could be classified and scored according to a varying levels of appropriation. Through a mixed-methods design based on non-participant observation and behavior mapping, the study was conducted across three comprehensive parks in Shanghai, divided into nine observation zones. The data were analyzed through descriptive statistics, IBM SPSS, and qualitative coding, revealing, as the main findings, sixty distinct activity types, a soft to moderate level of appropriation, and notable variations in demographic presence and temporal trends. This research underscores the effectiveness of observational methods, validates appropriation as an analytical category, and emphasizes the importance of structured classification systems for improving the understanding of UGSs’ socio-spatial performance and their societal role.

Empowering primary care physicians in child and adolescent psychiatry: a needs assessment on collaborative care in Dubai.

Child and adolescent psychiatric disorders pose significant public health concerns necessitating prompt intervention. Primary care physicians (PCPs) play a critical role as initial points of contact, facilitating early detection, management, and referral of these conditions. In Dubai, integrating mental health services into primary care faces unique challenges, highlighting the need for systemic reforms and enhanced PCP training. This study investigated perceptions, barriers, and systemic challenges encountered by PCPs in managing child and adolescent psychiatric conditions in Dubai's primary care setting. It also assessed family physicians' involvement and preparedness in this domain. Using a mixed-methods approach, we conducted a survey among family physicians in Dubai Health facilities and analyzed patient data from family medicine clinics. The survey evaluated formal training, preparedness, encounter frequency, and referral patterns for psychiatric care in young patients. Qualitative insights from open-ended survey questions provided additional understanding of specific challenges. Findings revealed significant gaps in formal training, with only 33.3% of respondents trained in child and adolescent psychiatry during medical education, and 21.1% participating in Continuing Medical Education (CME). A majority (54.4%) of PCPs felt unprepared to manage psychiatric care in young patients. Patient data analysis showed a predominance of male patients (59.9%) and identified Autism Spectrum Disorder (43.1%) as the most common condition, emphasizing the reliance on specialized care through a high referral rate (63.1%). Major barriers included time constraints, limited psychiatric knowledge, resistance to mental health services, systemic and structural issues, communication challenges, and resource shortages. Enhanced training and systemic reforms are urgently needed to integrate mental health services effectively into Dubai's primary care. Implementing structured collaborative care models, fostering interdisciplinary collaboration, and addressing systemic barriers are crucial for improving child and adolescent psychiatric condition management. These initiatives promise better patient outcomes and more efficient healthcare resource utilization.

Modified Multiresolution Convolutional Neural Network for Quasi-Periodic Noise Reduction in Phase Shifting Profilometry for 3D Reconstruction

Fringe profilometry is a method that obtains the 3D information of objects by projecting a pattern of fringes. The three-step technique uses only three images to acquire the 3D information from an object, and many studies have been conducted to improve this technique. However, there is a problem that is inherent to this technique, and that is the quasi-periodic noise that appears due to this technique and considerably affects the final 3D object reconstructed. Many studies have been carried out to tackle this problem to obtain a 3D object close to the original one. The application of deep learning in many areas of research presents a great opportunity to to reduce or eliminate the quasi-periodic noise that affects images. Therefore, a model of convolutional neural network along with four different patterns of frequencies projected in the three-step technique is researched in this work. The inferences produced by models trained with different frequencies are compared with the original ones both qualitatively and quantitatively.

Characterizing the Wildfire Patterns in the Marromeu Complex Using Remote Sensing Data

Objective: To caraterize the wildfire regime in the Marromeu Complex between 2002 and 2019. Method: Data from the MOD14 Collection 6 sensor, pertaining to thermal anomalies (MCD14ML, MCD64A1) corresponding to hot spots and burned areas, were used on a monthly basis. Only pixels with a reliability of 80% or higher were considered. Patterns of hot spots, burned area, density, intensity, frequency, and seasonality of fires were analyzed. Data processing was conducted using ArcGIS 10.8. Results and Discussion: A total of 12,227 hot spots were identified, covering 73% of the burned area. The annual average burned area was 24%, with densities ranging from moderate to very high. The fires showed low intensity across most of the Complex, with an average fire frequency of 2 to 3 times per year, and the highest fire records were observed in September. These findings are analyzed within the theoretical framework, emphasizing the implications of temporal and spatial variations in fires and their potential causes and environmental impacts. Implications of the Research: This study offers insights into wildfire management and emphasizes the necessity of fire management strategies in the Marromeu Complex. The implications include enhancing management plans and mitigating environmental impacts associated with fires. Originality/Value: This study contributes to the literature by providing a detailed characterization of the fire regime in the Marromeu Complex. The research is relevant as it offers new information on fire dynamics and their variations over time, which could impact management practices and environmental policies.

Contribution of blowing-snow sublimation to the surface mass balance of Antarctica

Abstract. Blowing-snow sublimation is a key boundary layer process in polar regions and is the major ablation term in the surface mass balance (SMB) of the Antarctic ice sheet. This study updates the blowing-snow model in the Regional Atmospheric Climate Model (RACMO), version 2.3p3, incorporating blowing-snow sublimation into the prognostic equations for temperature and water vapour. These updates address numerical artefacts in the previous model version by replacing the uniformly discretised ice particle radius distribution, which limited the maximum ice particle radius to ≤ 50 µm, with a non-uniform distribution covering radii from 2 to 300 µm without additional computational overhead. The improved model is validated against meteorological observations from site D47 in Adélie Land, East Antarctica. The updates fix the numerical artefacts, successfully predicting the power-law variation in the blowing-snow flux with wind speed while improving the prediction of its magnitude. Additionally, a qualitative comparison with CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite data shows that RACMO accurately forecasts the spatial pattern of monthly blowing-snow frequencies. The model also yields an average blowing-snow layer depth of 230±116 m at D47, matching typical satellite observation values. Results reveal that, without blowing snow, sublimation in Antarctica mainly occurs in summer (October–March), with minimal surface sublimation in winter (April–September). Introducing the blowing-snow model creates an additional sublimation mechanism primarily contributing in winter. From 2000–2012, model-integrated blowing-snow sublimation averaged 175±7 Gt yr−1, a 52 % increase from the previous version. Total sublimation, summing blowing-snow and surface sublimation, reached 234±10 Gt yr−1, 47 % higher than in simulations without the blowing-snow model. This increase leads to a 1.2 % reduction in the Antarctic ice sheet's integrated SMB. Additionally, changes in sublimation in coastal and lower escarpment zones underscore the importance of the model updates for Antarctic climatology.

Predicting the intelligibility of Mandarin Chinese with manipulated and intact tonal information for normal-hearing listeners.

Objective indices for predicting speech intelligibility offer a quick and convenient alternative to behavioral measures of speech intelligibility. However, most such indices are designed for a specific language, such as English, and they do not take adequate account of tonal information in speech when applied to languages like Mandarin Chinese (hereafter called Mandarin) for which the patterns of fundamental frequency (F0) variation play an important role in distinguishing speech sounds with similar phonetic content. To address this, two experiments with normal-hearing listeners were conducted examining: (1) The impact of manipulations of tonal information on the intelligibility of Mandarin sentences presented in speech-shaped noise (SSN) at several signal-to-noise ratios (SNRs); (2) The intelligibility of Mandarin sentences with intact tonal information presented in SSN, pink noise, and babble at several SNRs. The outcomes were not correctly predicted by the Hearing Aid Speech Perception Index (HASPI-V1). A new intelligibility metric was developed that used one acoustic feature from HASPI-V1 plus Hilbert time envelope and temporal fine structure information from multiple frequency bands. For the new metric, the Pearson correlation between obtained and predicted intelligibility was 0.923 and the root mean square error was 0.119. The new metric provides a potential tool for evaluating Mandarin intelligibility.

Spatio-temporal variation of meteorological, hydrological and agricultural drought vulnerability: Insights from statistical, machine learning and wavelet analysis

The study of how agricultural drought (AD) is responsible for meteorological drought (MD) and hydrological drought (HD) is crucial for drought prevention and the socio-economic development of a nation. This is due to AD constitutes a significant threat to the nation's food productivity and security. In depth comprehension and mitigation of drought incidents depend on understanding their frequency and propagation patterns. In this study, spatio-temporal variation of three types of droughts have been assessed in the sub-tropical environment of eastern India. In this perspective, seasonal i.e., pre-monsoon, monsoon, post-monsoon, and winter MD, HD, and AD were assessed considering Standardized Precipitation Index (SPI), Standardized Water Level Index (SWI), and Standardized Soil Moisture Index (SSMI) statistical tool respectively in sub-tropical agro-climatic zone of eastern India. In addition to this, spatial drought vulnerability of MD, HD and AD was assessed using Analytic Hierarchy Process (AHP) considering suitable factors for each drought type, and overall drought vulnerability was assessed using “Random Forest (RF)” and “Artificial Neural Network (ANN)” methods. Furthermore, drought periodicity has been measured using a wavelet power spectrum analysis. The result of seasonal drought revealed that pre-monsoon season has more frequent drought occurrences than other seasons among the applied three types of droughts. The outcomes of overall drought vulnerability revealed that RF gives the optimum result followed by ANN i.e., 0.841 and 0.828, respectively, for validation purposes. The periodicity of drought ranges from 0.25 to 4 as obtained from wavelet analysis. In general, this research on how AD spreads from MD and HD is crucial for drought resilience, drought management, and food security among the stakeholders and policymakers for achieving the SDGs.

Radar gait recognition using Dual-branch Swin Transformer with Asymmetric Attention Fusion

Video-based gait recognition suffers from potential privacy issues and performance degradation due to dim environments, partial occlusions, or camera view changes. Radar has recently become increasingly popular and overcome various challenges presented by vision sensors. To capture tiny differences in radar gait signatures of different people, a dual-branch Swin Transformer is proposed, where one branch captures the time variations of the radar micro-Doppler signature and the other captures the repetitive frequency patterns in the spectrogram. Unlike natural images where objects can be translated, rotated, or scaled, the spatial coordinates of spectrograms and CVDs have unique physical meanings, and there is no affine transformation for radar targets in these synthetic images. The patch splitting mechanism in Vision Transformer makes it ideal to extract discriminant information from patches, and learn the attentive information across patches, as each patch carries some unique physical properties of radar targets. Swin Transformer consists of a set of cascaded Swin blocks to extract semantic features from shallow to deep representations, further improving the classification performance. Lastly, to highlight the branch with larger discriminant power, an Asymmetric Attention Fusion is proposed to optimally fuse the discriminant features from the two branches. To enrich the research on radar gait recognition, a large-scale NTU-RGR dataset is constructed, containing 45,768 radar frames of 98 subjects. The proposed method is evaluated on the NTU-RGR dataset and the MMRGait-1.0 database. It consistently and significantly outperforms all the compared methods on both datasets. The codes are available at:https://github.com/wentaoheunnc/NTU-RGR.

Reconstruction of intra- and extra-neurite conductivity tensors via conductivity at Larmor frequency and DWI data patterns

The developed magnetic resonance electrical properties tomography (MREPT) techniques visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data. In biological tissues, electrical conductivity is influenced by ion concentrations and mobility. To visualize the anisotropic conductivity tensor of biological tissues, we use the Einstein–Smoluchowski equation, which links the diffusion coefficient to particle mobility. By assuming a correlation between ion mobility and water diffusivity, we aim to decompose the internal isotropic conductivity at Larmor frequency into anisotropic conductivity tensors within the intra- and extra-neurite compartments. The multi-compartment spherical mean technique (MC-SMT), utilizing both high and low b-value diffusion-weighted imaging (DWI) data, characterizes the diffusion of water molecules within and across the intra- and extra-neurite compartments by analyzing the microstructural intricacies and the foundational architectural arrangement of the brain’s tissues. By analyzing the relationships between the measured DWI data, the microscopic diffusion signal, and the fiber orientation distribution function, we predict the DWI data for the intra- and extra-neurite compartments using spherical harmonic decomposition. Using the predicted DWI data for the intra- and extra-neurite compartments, we develop a conductivity tensor imaging method that operates specifically within the separated compartments. Human brain experiments, involving four healthy volunteers and a brain tumor patient, were performed to assess and confirm the reliability of the proposed method.

A Synoptic Scale Perspective of Solar Forcing on Extreme Precipitation and Floods Over Europe During Summer

AbstractThe relationship between total solar irradiance (TSI) forcing and summer extreme precipitation and flood frequency over western Europe is investigated from a synoptic‐scale perspective, with a focus on the role of Rossby wave packets (RWPs). Utilizing observational, model, and proxy data, we reveal a significant increase in RWP frequency along a zonal band centered around 50°N, extending from North America to western Europe, during periods of low TSI. This anomaly in RWP frequency is consistent with a significant increase in the frequency of extreme precipitation events recorded over western Europe. Sensitivity experiments conducted with a state‐of‐the‐art chemistry‐climate model corroborate our findings based on observational data. Additionally, a flood record from western Europe demonstrates a significant increase in flood frequency during low TSI years, a relationship that persists across timescales. We argue that the frequency patterns associated with TSI forcing presented in this study are robust and, therefore, valuable for estimating the frequency of extreme precipitation events over western Europe under various solar irradiance scenarios. Moreover, our findings indicate that the North Atlantic sector is more responsive to changes in solar forcing during the boreal summer than previously thought, with this effect manifesting primarily on synoptic timescales rather than the long‐term climatological mean.

Quadratic Frequency Dispersion in the Oscillations of Intermediate-mass Stars

Asteroseismology, the study of stellar vibration, has met with great success, shedding light on stellar interior structure, rotation, and magnetism. Prominently known as δ Scutis, the intermediate-mass main-sequence oscillators that often exhibit rapid rotation and possess complex internal stratification are important targets of asteroseismic study. δ Scuti pulsations are driven by the κ (opacity) mechanism, resulting in a set of acoustic modes that can be challenging to interpret. Here, we apply machine learning to identify new patterns in the pulsation frequencies of δ Scuti stars, discovering resonances spaced according to quadratic functions of integer mode indices. This unusual connection between mode frequencies and indices suggests that rotational influence may play an important role in determining the frequencies of these acoustic oscillations.

Macrophyte Diversity and Ecosystem Services in Wetlands of Eastern Ranchi: A Multi-site Analysis of Abundance, Frequency, and Importance Value Index Patterns

Macrophytes, visible aquatic and semi-aquatic plants, are integral components of wetland ecosystems, playing crucial roles in maintaining biodiversity, providing ecosystem services, and ensuring ecological stability. This research examines the distribution, abundance, frequency, diversity, and ecosystem services of macrophytes in several wetlands (W1-W7) located in the eastern part of Ranchi district, Jharkhand, India. The study sites include Bundu Lake, Hindalco Pond, Choga Bada Talab, Raja Bandh, Kita Uparbandh, Tamar Bada Talab, and Rukka Dam. A quantitative assessment using quadrat sampling and phytosociological methods was conducted from September 2022 to March 2023 to ensure accurate data collection and analysis. The study documented 78 macrophyte species belonging to 33 families and 58 genera. Emergent macrophytes were found to be the dominant life form, constituting 69% of the recorded species. Several species demonstrated high frequency and abundance across multiple study sites, including Pontederia crassipes Mart., Alternanthera philoxeroides (Mart) Griseb., Hydrilla verticillata (L.f.) Royle, Ipomoea aquaticaForssk., and Nymphoides hydrophyllum (Lour.) kuntze. To assess the diversity and ecological characteristics of the macrophyte communities, various diversity indices were calculated. These included the Shannon-Wiener index, Simpson's index, Margalef index, and Pielou's Evenness index. The results of these analyses revealed high levels of macrophyte diversity and evenness within the studied wetlands. Twenty-one macrophytes were identified as key species and nine species as rare species based on their IVI (Importance Value Index) scores. The key species were found to provide essential ecosystem services, including erosion control, water quality improvement, nutrient cycling, carbon sequestration, and habitat provision for other organisms. This research contributes significantly to our understanding of macrophyte diversity patterns and traits-based ecosystem services in wetland ecosystems. The findings of this study have important implications for the development and implementation of effective conservation strategies for these vital wetland ecosystems in the eastern Ranchi (W1- W7).