Frequency Distribution Research Articles

The Contribution of Subsistence Agriculture to the Livelihoods of the Smallholder Farmers in South Kivu Province, Democratic Republic of Congo

Subsistence agriculture in most cases is practiced on a limited space of land by farmers and it is dependent on human power that results into production of food consumed at household level, with little or no surplus for sale. A study about the contribution of subsistence agriculture to the livelihoods of the smallholder farmers in south Kivu province was carried out. A total of 303 smallholder farmers were randomly selected and interviewed to examine the effects of practicing subsistence agriculture on the socioeconomic status of the farmers. How the socioeconomic status attained by the farmers influenced their sustainable livelihoods was also assessed. Data were analyzed using means, frequency distribution, correlation, ANOVA and Chi square test. Thematic analysis was done on qualitative data. The farmers practiced shifting and primitive agriculture, intensive subsistence, and nomadic herding. There was a significant difference in the yields of crops grown (F = 0.0088; P = 0.05), while education and income status significantly affected the farmer’s socioeconomic status by practicing subsistence agriculture (P < 0.05). An average mean value of 3.12 for the components of the farmers’ livelihoods were exhibited, with the human capital mean value being highest (4.16) compared to natural, social and financial capital. The socioeconomic status attained had a significant effect on sustainable livelihoods of the farmers at P < 0.05. This implies that the smallholder farmers need to be trained by the agricultural extension workers better and sustainable methods of farming for higher crop yields. In addition, the Central Government though the Ministry of Agriculture, should avail improved seed inputs to the local community for better crop yields. All these will enable the smallholder farmers improve their socioeconomic status and at the same time attain higher sustainable livelihoods.

Gastrointestinal Myoelectrical Activity (GIMA) Biomarker for Noninvasive Diagnosis of Endometriosis

Background/Objectives: Endometriosis represents substantial direct and indirect healthcare costs impacted by an absence of uniformly accurate, non-invasive diagnostic tools. We endeavored to demonstrate gastrointestinal myoelectrical activity (GIMA) biomarkers, unique to endometriosis, will allow non-invasive, uniformly accurate diagnosis or exclusion of endometriosis. Methods: Prospective open-label comparative study of 154 patients, age ≥ 18, with or without diagnosed endometriosis. Population included 62 non-endometriosis controls (Cohort 1), 43 subjects with surgically/histologically confirmed endometriosis (Cohort 2), and 49 subjects with abdominal pain and negative imaging (Cohort 3). Non-invasive electroviscerography (EVG) recorded GIMA biomarkers from three abdominal electrodes before and 30 min post water load protocol. Cohort 2 had postoperative EVG and Cohort 3 had preoperative EVG. Calculated specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV), and predictive probability or C-statistic used univariate, multivariate, linear, and logistical regression analyses of the area under the curve (AUC) at all frequency and time points, including age and pain covariants. Results: The non-endometriosis cohort differed significantly from the endometriosis cohorts (p < 0.001) for median (IQR) and AUC percent frequency distribution of power at baseline, 10 min, 20 min, and 30 min post water load at all frequency ranges: 15–20 cpm, 30–40 cpm, and 40–50 cpm. The endometriosis cohorts were statistically similar (p > 0.05). GIMA biomarker threshold scoring demonstrated 95%/91% sensitivity and PPV, 96%/95% specificity and NPV, and a C-statistic of >99%/98%, respectively, for age subsets. GIMA biomarkers in Cohort 3 predicted 47/49 subjects positive and 2/49 negative for endometriosis, confirmed surgically. Hormonal therapy, surgical stage, nor pain score affected diagnostic accuracy. Conclusions: EVG with GIMA biomarker detection distinguished participants with and without endometriosis based upon endometriosis-specific GIMA biomarkers threshold scoring.

Long-term stellar activity of M dwarfs. A combined K2 and TESS study of two early M-type stars

Studies of the rotation and activity of M type stars are essential in order to enhance our understanding of stellar dynamos and angular momentum evolution. Using the outstanding photometric capabilities of space telescopes, even those rotation signals with low amplitudes can now be investigated in previously unrivaled detail. By combining data of K2 and the TESS prime mission, the star spot activity of M dwarfs can be monitored over a timescale of half a decade. In the framework of our study on the rotation--activity relation for bright and nearby M dwarfs, we also aim to investigate their long-term activity. While K2 was observing fields distributed around the ecliptic plane, the TESS prime mission was oriented along a line of ecliptic longitude, with one camera centered on an ecliptic pole. Due to these different observing strategies, the overlap between K2 and the TESS prime mission is marginal. However, 45 stars from our sample were observed with both missions, and of these, two early M-type stars that fulfill our selection criteria, EPIC\,202059229 and EPIC\,245919787, were analyzed in more detail. We find that, for both stars, the rotation period did not change, while the rotational phase did change for EPIC\,245919787 by sim 0.2. The amplitude of the spot-induced variability changed for both stars but more significantly for EPIC\,245919787. By comparing the cumulative flare frequency distributions, we find that the flare activity for EPIC\,202059229 is unchanged, while it slightly changes for EPIC\,245919787 between the K2 and TESS epochs. Using a combination of light curves from K2 and TESS that span a baseline of up to 4.5 years, we are able to measure significant differential rotation for EPIC\,245919787. Furthermore, we show that combining missions like K2 and TESS is a promising method for detecting stellar activity cycles.

Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders.

Electronic Health Records (EHRs) can contain vast amounts of clinical information that could be reused in modelling outcomes of work-related musculoskeletal disorders (WMSDs). Determining the generalizability of an EHR dataset is an important step in determining the appropriateness of its reuse. The study aims to describe the EHR dataset used by occupational musculoskeletal therapists and determine whether the EHR dataset is generalizable to the Australian workers' population and injury characteristics seen in workers' compensation claims. Variables were considered if they were associated with outcomes of WMSDs and variables data were available. Completeness and external validity assessment analysed frequency distributions, percentage of records and confidence intervals. There were 48,434 patient care plans across 10 industries from 2014 to 2021. The EHR collects information related to clinical interventions, health and psychosocial factors, job demands, work accommodations as well as workplace culture, which have all been shown to be valuable variables in determining outcomes to WMSDs. Distributions of age, duration of employment, gender and region of birth were mostly similar to the Australian workforce. Upper limb WMSDs were higher in the EHR compared to workers' compensation claims and diagnoses were similar. The study shows the EHR has strong potential to be used for further research into WMSDs as it has a similar population to the Australian workforce, manufacturing industry and workers' compensation claims. It contains many variables that may be relevant in modelling outcomes to WMSDs that are not typically available in existing datasets.

Examining Color Recognition in the Kurdish Language: A Sociolinguistic Perspective

This study employs a cross-sectional research design to explore color recognition among Kurdish participants, encompassing individuals from various age and education groups. The research aims to investigate patterns of color perception across demographic segments within the Kurdish population. Participants were selected from four distinct groups: elementary school graduates (elderly), high school students, university students, and university graduates. The study involved presenting participants with a list of color names in Kurdish and recording their responses to assess color recognition. Descriptive statistics, including frequency distributions and percentages, were utilized to analyze the data and identify trends in color perception. The total number of participants in the study was 274. Findings indicate a high level of familiarity with basic colors such as black, white, red, green, yellow, and blue across all participant groups, with recognition rates ranging from 85% to 96%. However, less common colors, including gray, orange, pink, and purple, showed varying levels of recognition among different participant groups. Gray was relatively better recognized compared to other less common colors, with recognition rates ranging from 8.93% to 15%, suggesting potential differences in exposure to color terminology among different age and education groups.

Financial Accounting Controls and Financial Reporting Quality: An Empirical Analysis of Public Educational Agencies in Nigeria

This study examined financial accounting controls and financial reporting quality: an empirical analysis of public educational agencies in Nigeria. This study adopted a cross-sectional descriptive survey design because it provides a clear outcome and the characteristics associated with it at a specific point in time. The target population for this study constituted the total number of employees in the accounting/finance, administration, and operations departments in the four educational examination agencies. The number of staff in the accounts/finance, administration, and operations departments in the four regulatory agencies totaled one hundred and thirty-seven (137). The sample size derived with the Cochran sample size derivation statistic yielded one hundred and one (101). For this study, primary data was employed. The data were collected with the aid of a questionnaire that was properly drafted using the 5-point Likert scale for the questionnaire. The study adopted the use of frequency tables and percentages to analyze the questions and employed linear regression with the application of the ordinary least squares to test the hypotheses of the study. The major findings that were extracted from the analysis reveal that detective accounting controls have a significant effect on the financial reporting quality of selected educational regulatory agencies in Nigeria and preventive accounting controls have no significant effect on the financial reporting quality of selected educational regulatory agencies in Nigeria. It is therefore the recommendation of the study that regulatory agencies should sustain the use of detective accounting control systems through proper optimization and regulatory agencies should ensure they make consistent periodic financial reconciliations as a complement to preventive accounting control.

Random and time-persistent depositional processes in turbidite successions: an example from the marine deep-water Aoshima Formation (Neogene, Kyushu Island, southwest Japan)

ABSTRACT The deposits of flood- and earthquake-derived subaqueous sediment gravity flows represent a significant fraction of lacustrine and deep-sea sedimentary successions, thus providing a valuable record of such natural disasters. The magnitude of these events and the thickness of the associated deposits are considered to follow a lognormal or power-law frequency distribution, whilst that of time intervals between subsequent events appear to be best approximated by a Poisson model, indicative of a random, time-independent phenomenon. However, the debate on whether the sedimentary record of these natural disasters is governed by randomness alone or whether there is some underlying stratigraphic ordering is still unsettled and requires detailed time-series analysis. This study consists of a time-series analysis of mudstone- and sandstone-dominated turbidite successions offshore a fan-delta system in the Neogene Aoshima Formation that belongs to the sedimentary fill of the forearc basin of southwest Japan. The formation consists of a monotonous alternation of very fine- to medium-grained sandstones capped by hemipelagic mudstones and, more rarely, by turbidite mudstones. The results show that the autocorrelation function of the time series suggests quasi-periodic variability in the upper sandstone-dominated part, whereas the lower mudstone-dominated part shows a white-noise-like pattern. Rescaled range analysis shows that the number of events per unit time in the lower part is characterized by a random time series, such as Brownian noise with a Hurst exponent of 0.5. In contrast, the thickness of event beds of the lower part and the thickness and the number of events of the upper part are persistent time series with a Hurst exponent > 0.5. These results suggest that the number of turbidite depositional events in the mudstone-dominated part indicates random timing, whereas its thickness time series and the sandstone-dominant part are not governed by simple stochastic processes but are affected by sea-level changes, sediment transport dynamics, and other factors such as, for example, seafloor topography.

Program VUE: analysing distributions of cryo-EM projections using uniform spherical grids

Three-dimensional cryo electron microscopy reconstructions are obtained by extracting information from a large number of projections of the object. These projections correspond to different `views' or `orientations', i.e. directions in which these projections show the reconstructed object. Uneven distribution of these views and the presence of dominating preferred orientations may distort the reconstructed spatial images. This work describes the program VUE (views on uniform grids for cryo electron microscopy), designed to study such distributions. Its algorithms, based on uniform virtual grids on a sphere, allow an easy calculation and accurate quantitative analysis of the frequency distribution of the views. The key computational element is the Lambert azimuthal equal-area projection of a spherical uniform grid onto a disc. This projection keeps the surface area constant and represents the frequency distribution with no visual bias. Since it has multiple tunable parameters, the program is easily adaptable to individual needs, and to the features of a particular project or of the figure to be produced. It can help identify problems related to an uneven distribution of views. Optionally, it can modify the list of projections, distributing the views more uniformly. The program can also be used as a teaching tool.

Time distribution of stimulated transition probabilities

Abstract In recent years, an increasing number of experimental results have been published for a wide variety of systems in which high-precision, ultra-fast, and ultra-short laser pulses techniques have been used to study time-resolved dynamical processes. In the absence of a theoretical prediction of the time distribution of the transition probabilities, the lifetimes $\tau$ are generally extracted by fitting the decay time with an exponential, bi-exponential or three-exponential function. In fitting the data, the short-time behavior is generally neglected. The purpose of this study is to show that an explicit formula for the time distribution of the transition probability can be determined rigorously using time-dependent perturbation theory. A formula that perfectly fits an important subset of the experimental results from $t=0$ to $t=\infty$. We show that by following a route different from the usual procedure for deriving transition amplitudes and Einstein coefficients in time-dependent perturbation theory, one ends up with a time-dependent factor, that is, a temporal distribution function of the transition probabilities, and for the Einstein coefficients. The time distribution reported here looks in the time domain similar to the Planck distribution in the frequency domain. In fact, the behavior of the time distribution, with a maximum at $2\tau \ln 2$, and with $\tau$ playing the role of temperature, resembles the behavior of the Planck frequency distribution. We present several examples to demonstrate that the theoretical formula allows for easier adjustment of the experimental results reported in the literature. We also show that the time distribution explains the difference in resonance heights between the experimental and theoretical blue laser spectra.

Harnessing Text and Web Analytics to Enhance Decision-Making in Job Opportunity Categorization

Text analytics is defined as a method of analyzing compilations of structured text such as dates, times, locations, semi structured text, such as HTML and JSON as well as unstructured text, such as word documents, videos, and images, to extract and discover trends and relationships without requiring the exact words or terms to convey those concepts. Web analytics on the other hand is the technology that collects, measures, analyses, and provides reports of data on how users use websites and web applications. It is used to track a number of aspects of direct user-website interactions, such as the number of visits, time spent on the site, and click pathway. It also aids in the identification of user interest areas and the enhancement of web application features. We used clustering techniques to categorize the job opportunities that are available for the job seekers. By implementing text analytics, text data may be grouped with the goal of providing outcomes in the form of word frequency distribution, pattern identification, and predictive analytics. Text analytics may create one-of-a-kind values to use in the improvement of decision-making and business processes, as well as the development of new business models.

Managing the E-commerce Data Deluge through Text Analytics and Web Management (Overview of Amazon.com)

Today, more than 80% of the big data handled in the e-commerce industry is text and unstructured data. Text analytics is an automated process for analyzing text and extracting useful information from it. It can discover trends and relationships in data. Web analytics is the collection, processing, and analysis of data in order to draw conclusions to optimize usability on a website. Web analytics can be used to improve the usability of a site by analyzing user behavior patterns such as time spent on the site, abandonment rates, most frequently accessed products, click-through rates, etc. It can also help analyze the interests of different user demographics, as it tracks granular details such as user demographics, age and gender, geography, and devices used as data. In order to obtain UpToDate information, the business can utilize business intelligence for real-time data processing, then they can practice stream analysis to analyse continuous flow of data. For instance, the business can collect instant information in Twitter or other social media and analyse it by using social media analysis. For website management, business can practice web analysis to analyse the customer’s behaviours. Tracking the customer’s activity, page view and conversion rate is important for business to analyse how to improve the website performance. Text analytics of comments received on Amazon can be used to group text data and produce results in terms of word frequency distribution and sentiment analysis. Text analytics could be used for decision making, improving service quality, and developing new business models.

Tuberculosis in Wild Pigs from Argentina.

Mycobacterium bovis, a member of the Mycobacterium tuberculosis complex (MTC) and non-tuberculous Mycobacteria (NTM), may infect wild and domestic mammals, including humans. Although cattle are the main hosts and spreaders of M. bovis, many wildlife hosts play an important role worldwide. In Argentina, wild boar and domestic pigs are considered important links in mammalian tuberculosis (mTB) transmission. The aim of this work was to investigate the presence of M. bovis in wild pigs from different regions of Argentina, to characterize isolates of M. bovis obtained, and to compare those with other previously found in vertebrate hosts. A total of 311 samples from wild pigs were obtained, and bacteriological culture, molecular identification and genotyping were performed, obtaining 63 isolates (34 MTC and 29 NTM). Twelve M. bovis spoligotypes were detected. Our findings suggest that wild pigs have a prominent role as reservoirs of mTB in Argentina, based on an estimated prevalence of 11.2 ± 1.8% (95% CI 8.0-14.8) for MTC and the frequency distribution of spoligotypes shared by cattle (75%), domestic pigs (58%) and wildlife (50%). Argentina has a typical scenario where cattle and pigs are farm-raised extensively, sharing the environment with wildlife, creating conditions for effective transmission of mTB in the wildlife-livestock-human interface.

Interspecific and intraspecific difference in egg size of two mackerel (Scomber spp.) species in relation to sea surface temperature in the western North Pacific: A new approach to species identification

AbstractChub mackerel (Scomber japonicus, Scombridae) and blue mackerel (Scomber australasicus) are two important fishery resources in the western North Pacific that spawn eggs during the same season. Although estimating the total egg production (TEP) is important for evaluating the spawning stock biomass of mackerel species, it is difficult to accurately identify formalin‐preserved eggs collected during ichthyoplankton surveys. Hence, a new identification criterion that incorporates the effects of water temperature on egg size was developed in this study. The diameter of 37,304 mackerel eggs collected over 16 years (2006–2021) was measured, and frequency distributions of egg size across sea surface temperature (SST) ranges at 1°C interval were constructed. The frequency distributions were classified into two groups using a Gaussian mixture model; based on the results of DNA analysis, the small group was chub mackerel, while the large group was blue mackerel. The SST at the sampling stations and the mean egg size of both groups were negatively correlated. The new identification criterion, incorporating the relationship between SST and egg size, provided reasonable estimates of the TEP of the two mackerel species compared with the conventional criterion. The new species identification approach is applicable to other fish taxa in the western North Pacific.

Occupational therapy assessments and interventions for patients with ankylosing spondylitis: a scoping review protocol.

The proposed scoping review aims to explore the literature on the occupational therapy (OT) scope of practice for patients with ankylosing spondylitis (AS), including assessment methods and intervention approaches used by OT practitioners, areas of impairment addressed, and practice settings where OT practitioners provide services. AS is a type of spondyloarthritis primarily involving inflammation of the spine. Studies have begun to examine the role of OT in addressing pain, function, and disability among AS patients. Given the increased recognition of OT services for this population, a comprehensive understanding of the assessment methods and intervention approaches used by OT practitioners when working with AS would benefit clinicians, providers, and patients and support future research efforts. The review will consider studies that include participants of any age diagnosed with AS and any form of OT assessment and intervention. All relevant published and unpublished studies will be considered, without date or language limitations, including all primary studies, gray literature, text and opinion papers, and clinical guidelines. The review will follow the JBI methodology for scoping reviews. Searches will be conducted in MEDLINE (PubMed), Embase, CINAHL (EBSCOhost), Scopus (EBSCOhost), PsycINFO, ProQuest Dissertations and Theses Global, SportDiscus (EBSCOhost), OTDBase, OTSeeker, and Google Scholar. Two independent reviewers will extract data from selected papers using a standardized tool modified for the review. The results will be presented using frequency tables and will be accompanied by a narrative summary. https://doi.org/10.17605/OSF.IO/VPY56.

Large eddy simulations of sheet-to-cloud cavitation transitions with special emphasis on the simultaneous existence of the re-entrant jet and shock waves

Sheet-to-cloud cavitation transitions are very complex owing to the simultaneous appearance of the re-entrant jet and shock waves. The objective of this paper is to investigate the physics of compressible cavitating flows with emphasis on the simultaneous existence of the re-entrant jet and shock waves. A compressible cavitating solver, which considers the compressibility effects of both the liquid and the vapour, was used to account for the different shedding characteristics induced by the re-entrant jet mechanism (RJM), the shock wave mechanism (SWM), and both the re-entrant jet and the shock waves (RJM-SMW). The solver used Large Eddy Simulations (LES) and the Schnerr-Sauer cavitation model. The numerical results are first compared with available experimental data [1] which showed satisfactory agreement for various aspects of the flow, including the pressure distribution, cavity shapes, condensation front speed, and shedding frequency. The results first show three different cavity shedding processes induced by the re-entrant jet and the shock waves. Frequency analyses show that the shock waves produce a wider frequency distribution which indicates that shock waves induce more shedding than the re-entrant jet. The quasi-periodic characteristics of the transitions between the various cavity shedding characteristics are mainly induced by the variations of the pressure, residual cavity volume and medium compressibility. Further analyses show that the different pressure, residual cavity volume and medium compressibility amplitudes determine the cavity shedding characteristics in the subsequent shedding cycle. When the conditions favour both the re-entrant jets and the shock wave effects, both the re-entrant jet and the shock waves impact the cavity shedding.

Streamlining social media information extraction for public health research with deep learning.

Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a UMLS-colloquial symptom dictionary from COVID-19-related tweets as proof of concept. COVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity sample were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research. We identified 498,480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18,226. The final dictionary contains 38,175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons. This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes.

Investigation of model forecast biases and skilful prediction for Assam heavy rainfall 2022

Extreme rainfall events (ERE) during the summer monsoon season have been occurring over most parts of India resulting in flooding and immense socio-economic loss. These extremes are becoming a frequent norm in the hilly and mountainous regions of the country such as Assam. Assam received one of the most historical EREs from 14–June 17, 2022. The present study analyses the performance of a suite of high-resolution ensemble model forecasts for this extreme event in terms of its intensity, and distribution with a lead time of up to 96 h. Furthermore, the 36 numerical experiments are carried out using two different land use and land cover (LULC) data sets (i.e. ISRO and USGS) and three different sets of parameterization schemes (i.e. planetary boundary layer, cumulus, and microphysics).Rainfall distributions in the case of USGS LULC are relatively less coherent and underestimated (60–260 mm/day) against IMD (80–300 mm/day) including the rainfall categories heavy (HR), very heavy (VHR), and extremely heavy (EHR) rainfall throughout the day-1 to day-4. Among all the ensembles (E1-E10), USGS (E6 - E10) has underestimated rainfall (140–260 mm/day) compared to ISRO (150–280 mm/day), specifically in MR and HR categories over the upper Assam (UAD) and lower Assam (LAD) divisions. Further, the Bias Correction Ensemble (BCE) technique is applied to minimize the forecast errors. A rigorous statistical analysis in terms of frequency distribution, Taylor diagram, and benchmark skill scores is carried out to elucidate the model biases. The set of the model ensembles using ISRO (E1- E5) and USGS (E6- E10) reasonably captured the HR, VHR, and EHR. In addition, throughout the forecast hour, BCE E5 (E10) is noted with the distinct realistic (underestimated) representation of model bias (5–20 %) (10–30 %) over all the subdivisions of Assam. Our results suggest that the combined efforts of ensembles of physical parameterization schemes, along with proper LULC, and the BCE approach are required to overcome challenges to improve the skills of rainfall events, particularly over complex terrains such as Assam.

Analyzing the Active Site and Predicting the Overall Activity of Alloy Catalysts.

As one of the potential catalysts, disordered solid solution alloys can offer a wealth of catalytic sites. However, accurately evaluating their activity localization structure and overall activity from each individual site remains a formidable challenge. Herein, an approach based on density functional theory and machine learning was used to obtain a large number of sites of the Pt-Ru alloy as the model multisite catalyst for the hydrogen evolution reaction. Subsequently, a series of statistical approaches were employed to unveil the relationship between the geometric structure and overall activity. Based on the radial frequency distribution of metal elements and the distribution of ΔGH, we have identified the surface and subsurface sites occupied by Pt and Ru, respectively, as the most active sites. Particularly, the concept of equivalent site ratio predicts that the overall activity is highest when the Ru content is 20-30%. Furthermore, a series of Pt-Ru alloys were synthesized to validate the proposed theory. This provides crucial insights into understanding the origin of catalytic activity in alloys and thus will better guide the rational development of targeted multisite catalysts.

Intra-decadal increase in globally-spread Magallana gigas in southern California estuaries.

Introduction and establishment of non-indigenous species (NIS) has been accelerated on a global scale by climate change. NIS Magallana gigas' (formerly Crassostrea gigas') global spread over the past several decades has been linked to warming waters, specifically during summer months, raising the specter of more spread due to predicted warming. We tracked changes in density and size distribution of M. gigas in two southern California, USA bays over the decade spanning 2010-2020 using randomly placed quadrats across multiple intertidal habitats (e.g., cobble, seawalls, riprap) and documented density increases by 2.2 to 32.8 times at 7 of the 8 sites surveyed across the two bays. These increases in density were coincident with 2-4° C increases in median monthly seawater temperature during summer months, consistent with global spread of M. gigas elsewhere. Size frequency distribution data, with all size classes represented across sites, suggest now-regular recruitment of M. gigas. Our data provide a baseline against which to compare future changes in density and abundance of a globally-spread NIS of significant concern.

Comprehensive Signal Analysis: Unraveling Mean Frequency, Power, and Bandwidth

Objective: The power spectral density (PSD) serves as a fundamental tool in signal analysis, revealing valuable insights into the distribution of power across frequencies. This paper explores the concept of PSD, which emerges from the Fourier transform of a signal’s autocorrelation function. By examining power distribution, PSD provides a comprehensive understanding of a signal’s spectral characteristics. Methods: We generate a chirp signal with specific parameters, including initial and final frequencies, and introduce white Gaussian noise. The combination of these signals forms a composite signal, enabling the calculation of its PSD. Results: Visualizing the PSD allows for discernment of the frequency distribution, with the median frequency providing insights into the central tendency of the distribution. Conclusion: Furthermore, the customization of plots enhances the visual representation of data, tailoring it to the requirements of the specific application. Adjustments to color schemes, line styles, and annotations improve clarity and aid in conveying complex information effectively.