Intensive Sampling Research Articles

Assessment of dynamics and variability of organic substances in river bank filtration for prioritisation in analytical workflows

Bank filtration supports the growing global demand for drinking water amidst concerns over organic micropollutants (OMPs). Efforts to investigate, regulate and manage OMPs have intensified due to their documented impacts on ecosystems and human health. Non-targeted analysis (NTA) is critical for addressing the challenge of numerous OMPs. While identification in NTA typically prioritises compounds based on properties like toxicity, considering substance quantity, occurrence frequency and exposure duration is essential for comprehensive risk management. A prioritisation scheme, drawing from intensive sampling and NTA of bank filtrate, is presented and reveals significant variability in OMP occurrence. Quasi-omnipresent substances, though only 7% of compounds, accounted for 44% of cumulative detections. Moderately common substances, constituting 31% of compounds, accounted for 50% of cumulative detections. Rare compounds, comprising 61%, contributed only 6% to cumulative detections. The application of suspect screening for 31 substances to the dataset yielded results akin to NTA, underscoring NTA’s value. Correlation between both methods demonstrates the efficacy of high-resolution mass spectrometry-based NTA in assessing temporal and quantitative OMP dynamics.

Effect of the irregular shelterwood system on soil organic carbon stock and soil quality of Shorea robusta Gaertn. f. forest in Nepal

The effective management of forests relies on the crucial role played bysilvicultural systems. However there exist a significant knowledge gap regarding impact of these systems in Nepalese forests. Therefore, this research was conducted to assess the effects of the forest management activities under irregular shelterwood system on soil organic carbon (SOC) stock and the overall soil quality of Sal (Shorea robusta Gaertn. f.) forests in Terai region of Nepal. Stratified random sampling method with 1.67 % sampling intensity was adopted in this study where management of stands was used as basis of strata. A total of 30 composite soil samples (15 each from managed and unmanaged forest stands) were collected from a depth of 0–30 cm, taken from the four corners and the center of each plot. Soil quality index (SQI) method was used for soil quality assessment using indicators on the basis of prior studies conducted in Nepal. Our study found significant difference in soil parameters except organic carbon, pH, silt, and clay among the managed and unmanaged forest stands (p < 0.05). SOC stock of unmanaged forest stands (48.87 ± 1.34 ton ha−1) was significantly greater than managed forest stands (27.76 ± 1.27 ton ha−1). Similarly, unmanaged forest stands demonstrated better soil quality with higher SQI value (0.66) than managed forest stands (0.50). This negative impact of irregular shelterwood silviculture system highlights the necessity for management interventions to enhance SOC stock and overall soil quality. To establish a robust conclusion, further replication of similar studies at different soil depths and in other management regimes, along with longitudinal studies, is essential.

Integrating dynamic psychophysiological indices across time and contexts: Elucidating mechanisms, risk markers, and intervention targets.

Why should researchers measure psychophysiological processes repeatedly over time? The study of psychophysiology inherently involves sampling biological processes as they manifest over time. The most common approach is to use a brief sample to make conclusions about how individuals or groups differ. Although these types of between-subject comparisons have utility for understanding individual and group differences, many of the important conceptual questions in the field involve processes that are dynamic, varying within individuals over time. Using examples from the literature on affect regulation, this conceptual review contrasts three types of study designs: the classic single-observation design and the aggregated and temporally linked repeated observation designs, which have great promise for measuring variables that fluctuate dynamically over time. Importantly, these designs can be integrated to elucidate research questions about risk (when and for whom will the likelihood of an unwanted outcome occurring increase?), mechanisms (how and why does a change in psychophysiology contribute to a change in another process of interest?), and interventions (how and when should interventions take place to modify an outcome?). Researchers are encouraged to implement intensive sampling in their research, which can be conducted in traditional laboratory settings (e.g., fMRI, event-related brain potentials, and heart rate variability) and in ecologically valid contexts in everyday life using ambulatory assessment.

Blue carbon assessment in Avicennia marina sediments and vegetation along the Red Sea Coast of Egypt: Improving methods and insights

Mangrove forests play a crucial role in the coastal environment. A plethora of research is focusing on mangroves’ role in climate mitigation through sequestering and storing CO2. This study aims to assess the organic carbon (OC) stocks and carbon sequestration rate (CSR) of the largest stands of mangroves in the Red Sea Coast of Egypt, to evaluate their significance in climate mitigation strategies. Intensive sampling was conducted in mangrove stands of Safaga, Qulaan, and Hamata to investigate OC and sediment properties along depth intervals to 1 m deep. Additionally, OC stored in the above- and below-ground biomass was estimated using Landsat 9 OLI/TIRS satellite images. TOC content (∼0.50%) and stocks in the sediments (54.49 ± 31.71–86.00 ± 29.00 MgC/ha) were very low compared to the global average for mangrove stands, and comparable to the mangrove forest in the Middle East region. This can be attributed to the desert and semi-desert conditions, limited riverine input, and low precipitation rates. Moreover, the above-ground biomass, estimated from Normalized Difference Vegetation Index, exhibited extremely low OC storage. Therefore, the sediment acted as the main pool of organic carbon in these mangrove ecosystems. The research findings revealed that mangrove stands in Egypt exhibit low CSR, ranging from 11.94 to 18.02 g C m−2 year−1. Additionally, the study determined that the stable nature of mangrove stands in Egypt makes their annual CO2 emissions negligible. Despite local studies suggesting otherwise, it is highly unlikely that mangroves in Egypt contribute to the climate mitigation related to CO2 sequestration. Therefore, it is important to implement restoration plans to ensure the preservation of other ecological services provided by mangroves along the Red Sea Coast.

Cysteine as an Innovative Biomarker for Kidney Injury.

Kidney transplantation is a widely used treatment for end-stage kidney disease. Nevertheless, the incidence of acute kidney injury (AKI) in deceased donors poses a potential hazard because it significantly increases the risk of delayed graft function and potentially exerts an influence on the kidney allograft outcome. It is crucial to develop a diagnostic model capable of assessing the existence and severity of AKI in renal grafts. However, no suitable kidney injury markers have been developed thus far. We evaluated the efficacy of the molecular probe NPO-B, which selectively responds to cysteine, as a new diagnostic tool for kidney injury. We used an in vitro model using ischemia/reperfusion injury human kidney-2 cells and an in vivo ischemia/reperfusion injury mouse model. Additionally, cysteine was investigated using urine samples from deceased donors and living donors to assess the applicability of detection techniques to humans. This study confirmed that the NPO-B probe effectively identified and visualized the severity of kidney injury by detecting cysteine in both in vitro and in vivo models. We observed that the fluorescence intensity of urine samples measured using NPO-B from the deceased donors who are at a high risk of renal injury was significantly stronger than that of the living donors. If implemented in clinical practice, this new diagnostic tool using NPO-B can potentially enhance the success rate of kidney transplantation by accurately determining the extent of AKI in renal grafts.

Limited Sampling Strategies Fail to Accurately Predict Mycophenolic Acid Area Under the Curve in Kidney Transplant Recipients and the Impact of Enterohepatic Recirculation.

Therapeutic drug monitoring for mycophenolic acid (MPA) is challenging due to difficulties in measuring the area under the curve (AUC). Limited sampling strategies (LSSs) have been developed for MPA therapeutic drug monitoring but come with risk of unacceptable performance. The authors hypothesized that the poor predictive performance of LSSs were due to the variability in MPA enterohepatic recirculation (EHR). This study is the first to evaluate LSSs models performance in the context of EHR. Adult kidney transplant recipients (n = 84) receiving oral mycophenolate mofetil underwent intensive MPA pharmacokinetic sampling. MPA AUC0-12hr and EHR were determined. Published MPA LSSs in kidney transplant recipients receiving tacrolimus were evaluated for their predictive performance in estimating AUC0-12hr in our full cohort and separately in individuals with high and low EHR. None of the evaluated LSS models (n = 12) showed good precision or accuracy in predicting MPA AUC0-12hr in the full cohort. In the high EHR group, models with late timepoints had better accuracy but low precision, except for 1 model with late timepoints at 6 and 10 hours postdose, which had marginally acceptable precision. For all models, the good guess of predicted AUC0-12hr (±15% of observed AUC0-12hr) was highly variable (range, full cohort = 19%-61.9%; high EHR = 4.5%-65.9%; low EHR = 27.5%-62.5%). The predictive performance of the LSS models varied according to EHR status. Timepoints ≥5 hours postdose in LSS models are essential to capture EHR. Models and strategies that incorporate EHR during development are required to accurately ascertain MPA exposure.

Identifying factors impacting missingness within smartphone-based research: Implications for intensive longitudinal studies of adolescent suicidal thoughts and behaviors.

Intensive longitudinal research-including experience sampling and smartphone sensor monitoring-has potential for identifying proximal risk factors for psychopathology, including suicidal thoughts and behaviors (STB). Yet, missing data can complicate analysis and interpretation. This study aimed to address whether clinical and study design factors are associated with missing data and whether missingness predicts changes in symptom severity or STB. Adolescents ages 13- to 18 years old (N = 179) reporting depressive, anxiety, and/or substance use disorders were enrolled; 65% reported current suicidal ideation and 29% indicated a past-year attempt. Passively acquired smartphone sensor data (e.g., global positioning system, accelerometer, and keyboard inputs), daily mood surveys, and weekly suicidal ideation surveys were collected during the 6-month study period using the effortless assessment research system smartphone app. First, acquisition of passive smartphone sensor data (with data on ∼80% of days across the whole sample) was strongly associated with survey data acquisition on the same day (∼44% of days). Second, STB and psychiatric symptoms were largely not associated with missing data. Rather, temporal features (e.g., length of time in study, weekends, and summer) explained more missingness of survey and passive smartphone sensor data. Last, within-participant changes in missing data over time neither followed nor predicted subsequent change in suicidal ideation and psychiatric symptoms. Findings indicate that considering technical and study design factors impacting missingness is critical and highlight several factors that should be addressed to maximize the validity of clinical interpretations in intensive longitudinal research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Morphological and molecular characterization of the European dagger nematode Xiphinema diversicaudatum on peach (Prunus persica) in Canada

Plant-parasitic nematodes (PPNs) pose a significant threat to global agriculture, leading to substantial yield losses. Managing nematode populations remains a challenging option due to financial, technical and environmental constraints. In Ontario, Canada, the presence of Xiphinema nematodes, a virus vector species, is of particular concern. This study reports the discovery of X. diversicaudatum in a peach field in Southern Ontario, Canada. The description includes morphological and molecular characterization using the 18S and 28S rDNA sequences. Morphological and molecular characterization of Ontario’s population matched those from Europe. While X. diversicaudatum was presumed eradicated from Canada, the present finding showed that the nematode could have spread beyond greenhouses. The study highlights the importance of intensive soil sampling for early nematode detection, even as strict reporting requirements may deter such efforts. We report for the first time the presence of X. diversicaudatum on peaches in Canada and in North America.

KEANEKARAGAMAN VEGETASI ULTRABASA DI HUTAN LINDUNG NANGA-NANGA PAPALIA KOTA KENDARI

This research aimed to determine the ultra-basic vegetation diversity in the Nanga-Nanga Papalia Protected Forest, Kendari City, Southeast Sulawesi. The study was conducted in May to June 2023, covering an area of 42 hectares within the Nanga-Nanga Papalia Protected Forest. The sampling intensity was set at 5% over a study area of 2.1 hectares, resulting in 52 sampling plots arranged using the Line Transect method. The findings revealed that the ultra-basic vegetation diversity across all vegetation levels in the protected forest was classified as low, with values of 1.25 for trees, 1.22 for saplings, 1.27 for poles, and 1.13 for seedlings

Assessment of Species Composition, Diversity and Carbon Stock in a Community Managed Forest of Udaypur District of Nepal

Species composition, diversity, and carbon sock of forests are all critical factors that affect the ability of forests to provide various important ecosystem services. However, there is a notable dearth of research regarding these factors in the community forests of Udaypur district. Therefore, this research was undertaken to assess species composition, and biodiversity and quantify the carbon stock potential of the Sringar community forest (CF) of the Udaypur district of Eastern Nepal. A total of 57 circular plots of 500m2 were inventoried using a systematic random sampling method with 0.5 % sampling intensity. In the CF, 17 tree species from 15 genera and 11 families were identified. The dominant tree species, Shorea robusta observed with a maximum importance value index (IVI) (176.15). According to our study, the total biomass and carbon stock in Sringar CF were 276.98 ton ha-1 and 138.18 ton ha-1, respectively. Accurate estimation of soil carbon stocks is crucial for long-term forest management and climate change mitigation, and the integration of advanced monitoring techniques and predictive models to enhance accuracy and account for future climate projections is needed.

Population Pharmacokinetic Modeling of Abacavir/Dolutegravir/Lamivudine to Support a Fixed-Dose Combination in Children with HIV-1.

Once-daily fixed-dose combinations (FDC) containing abacavir (ABC), dolutegravir (DTG), and lamivudine (3TC) have been approved in the US for adults and children with HIV weighing ≥ 6kg. This analysis assessed the ability of previously developed ABC, DTG, and 3TC pediatric population pharmacokinetic (PopPK) models using multiple formulations to describe and predict PK data in young children using dispersible tablet (DT) and tablet formulations of ABC/DTG/3TC FDC in the IMPAACT 2019 study. IMPAACT 2019 was a Phase I/II study assessing the PK, safety, tolerability, and efficacy of ABC/DTG/3TC FDC in children with HIV-1. Intensive and sparse PK samples were collected over 48weeks. Existing drug-specific pediatric PopPK models for ABC (2-compartment), DTG (1-compartment), and 3TC (1-compartment) were applied to the IMPAACT 2019 drug concentration data without re-estimation (external validation) of PopPK parameters. Drug exposures were then simulated across World Health Organization weight bands for children weighing ≥ 6 to < 40kg for each drug and compared with pre-defined exposure target ranges. Goodness-of-fit and visual predictive check plots demonstrated that the previously developed pediatric PopPK models sufficiently described and predicted the data. Thus, new PopPK models describing the IMPAACT 2019 data were unnecessary. Across weight bands, the predicted geometric mean (GM) for ABC AUC0-24 ranged from 14.89 to 18.50μg*h/ml, DTG C24 ranged from 0.74 to 0.95μg/ml, and 3TC AUC0-24 ranged from 10.50 to 13.20μg*h/ml. These exposures were well within the pre-defined target ranges set for each drug. This model-based approach leveraged existing pediatric data and models to confirm dosing of ABC/DTG/3TC FDC formulations in children with HIV-1. This analysis supports ABC/DTG/3TC FDC dosing in children weighing ≥ 6kg.

Freshwater fishes of the Northeastern Mata Atlântica ecoregion, Brazil: an updated checklist with distributional patterns of a highly endemic ichthyofauna.

We present an annotated and updated checklist of freshwater fish species occurring in the Northeastern Mata Atlântica ecoregion (NMAF), Eastern Brazil. A total of 305 native freshwater fish species are documented for this ecoregion, of which 210 are endemic. Distribution maps of 298 species are provided. There is a general pattern of increased sampling intensity in coastal zones, gradually diminishing towards the west as elevation increases. A bioregionalization algorithm identified four biogeographic units in the NMAF ecoregion: Northern Bioregion, Central Bioregion, Coastal Tablelands Bioregion and Southern Bioregion. The heterogeneous nature of the ichthyofauna in this area, along with the significant faunal overlap observed between NMAF and adjacent ecoregions, suggests that the approach of ecoregion delimitation may not be fully congruent with the current faunal compositions. This underscores the need for novel regionalization approaches that reinforce outcomes consistent with the diversity patterns evident within the ecoregion.

Application of Environmental Monitoring Programs and Root Cause Analysis to Identify and Implement Interventions to Reduce or Eliminate Listeria Populations in Apple Packinghouses

Controlling Listeria in produce packinghouses can be challenging due to the large number of potential contamination routes. For example, repeated isolation of the same Listeria subtype in a packinghouse could indicate persistence in the packinghouse or reintroduction of the same Listeria from an upstream source. To improve understanding of Listeria transmission patterns in packinghouses, we performed a longitudinal study in four apple packinghouses, including testing of 1,339 environmental sponges and whole genome sequencing (WGS)-based characterization of 280 isolates. Root cause analysis and subsequent intervention implementation were also performed and assessed for effectiveness. Listeria prevalence among environmental sponges collected from the four packinghouses was 20% (range of 5–31% for individual packinghouses). Sites that showed high Listeria prevalence included drains, forklift tires and forks, forklift stops, and waxing area equipment frames. A total of 240/280 WGS-characterized isolates were represented in 41 clusters, each containing two or more isolates that differed by ≤50 high-quality single nucleotide polymorphisms (hqSNPs); 21 clusters were isolated from one packinghouse over ≥2 samplings (suggesting persistence or possibly reintroduction), while 11 clusters included isolates from >2 packinghouses, suggesting common upstream sources. Some interventions successfully (i) reduced Listeria detection on forklift tires and forks (across packinghouses) and (ii) mitigated packinghouse-specific Listeria issues (e.g., in catch pans). However, interventions that lacked enhanced equipment disassembly when persistence was suspected typically appeared to be unsuccessful. Overall, while our data suggest a combination of intensive environmental sampling with subtyping and root cause analysis can help identify effective interventions, implementation of effective interventions continues to be a challenge in packinghouses.

A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example.

Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.

Spatial ecology of the Endangered and endemic Sagalla caecilian Boulengerula niedeni in the Eastern Arc Mountains of Kenya

Caecilians (Order Gymnophiona) are generalist predators of soil invertebrates, and may therefore play an important role in tropical soil ecosystems. However, their fossorial lifestyle and the associated difficulties in surveying them have caused a deficit in data for the majority of species. We applied a systematic approach and an intensive sampling strategy to an Endangered and evolutionarily distinct caecilian from the Eastern Arc Mountains, the Sagalla caecilian Boulengerula niedeni. We investigated the association between habitat type and caecilian occupancy across its entire range, the Sagalla Hill, Kenya, and explored the relationship between several variables (land use type, surface soil temperature, soil compactness and landowner prediction of caecilian presence) and its presence in different habitats. We found no significant effects of any of the investigated variables in predicting caecilian presence across the Sagalla landscape. Instead, our findings suggest that the species survives at least as well in agricultural landscapes as it does in areas with indigenous vegetation, with an estimated density of around 900 caecilians per hectare. A bimodal distribution of sizes and weights of captured specimens suggests ongoing successful breeding and recruitment. This suggests that there is a case for cautious optimism with regard to the status of B. niedeni. Our work could act as a useful pilot for further, improved caecilian surveys in the Eastern Arc Mountains and beyond, to improve our understanding and conservation of these overlooked fossorial amphibians. Keywords: amphibian, Gymnophiona, Herpelidae, soil ecology, amphibian conservation, Taita Hills

Estimation of maximum body size in fossil species: A case study using Tyrannosaurus rex.

Among extant species, the ability to sample the extremes of body size-one of the most useful predictors of an individual's ecology-is highly unlikely. This improbability is further exaggerated when sampling the already incomplete fossil record. We quantify the likelihood of sampling the uppermost limits of body size in the fossil record using Tyrannosaurus rex Osborn, 1905 as a model, selected for its comparatively well-understood life history parameters. We computationally generate a population of 140 million T. rex (based on prior estimates), modelling variation about the growth curve both with and without sexual dimorphism (the former modelled after Alligator mississippiensis), and building in sampling limitations related to species survivorship and taphonomic bias, derived from fossil data. The 99th percentile of body mass in T. rex has likely already been sampled, but it will probably be millennia before much larger giants (99.99th percentile) are sampled at present collecting rates. Biomechanical and ecological limitations notwithstanding, we estimate that the absolute largest T. rex may have been 70% more massive than the currently largest known specimen (~15,000 vs. ~8800 kg). Body size comparisons of fossil species should be based on ontogenetically controlled statistical parameters, rather than simply comparing the largest known individuals whose recovery is highly subject to sampling intensity.

Examining missingness at the momentary level in clinical research using ecological momentary assessment: Implications for suicide research.

The use of intensive time sampling methods, such as ecological momentary assessment (EMA), has increased in clinical, and specifically suicide, research during the past decade. While EMA can capture dynamic intraindividual processes, repeated assessments increase participant burden, potentially resulting in low compliance. This study aimed to shed light on study-level and psychological variables, including suicidal ideation (SI), that may predict momentary prompt (i.e., prompt-to-prompt) completion. We combined data from three EMA studies examining mental health difficulties (N = 103; 10,656 prompts; 7144 completed), using multilevel models and machine learning to determine how well we can predict prompt-to-prompt completion and which variables are most important. The two most important variables in prompt-to-prompt completion were hours since the last prompt and time in study. Psychological variables added little predictive validity; similarly, trait-level SI demonstrated a small effect on prompt-to-prompt completion. Our study showed how study-level characteristics can be used to explain prompt-to-prompt compliance rates in EMA research, highlighting the potential for developing adaptive assessment schedules to improve compliance.

Medical image registration via neural fields

Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between two images, run much faster, but suffer from performance deficiencies due to domain shift. Here we present a new neural network based image registration framework, called NIR (Neural Image Registration), which is based on optimization but utilizes deep neural networks to model deformations between image pairs. NIR represents the transformation between two images with a continuous function implemented via neural fields, receiving a 3D coordinate as input and outputting the corresponding deformation vector. NIR provides two ways of generating deformation field: directly output a displacement vector field for general deformable registration, or output a velocity vector field and integrate the velocity field to derive the deformation field for diffeomorphic image registration. The optimal registration is discovered by updating the parameters of the neural field via stochastic mini-batch gradient descent. We describe several design choices that facilitate model optimization, including coordinate encoding, sinusoidal activation, coordinate sampling, and intensity sampling. NIR is evaluated on two 3D MR brain scan datasets, demonstrating highly competitive performance in terms of both registration accuracy and regularity. Compared to traditional optimization-based methods, our approach achieves better results in shorter computation times. In addition, our methods exhibit performance on a cross-dataset registration task, compared to the pre-trained learning-based methods.

National-scale 10-m maps of cropland use intensity in China during 2018–2023

The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018–2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.

The measurement of complex modal coefficients of a superposed vortex beam based on intensity sampling methods

In recent years, extracting information from superposed vortex beams has been a topic of intense study. In this paper, complex coefficients of various superpositions are measured in both simulation and experiment by proposing and implementing four different sampling methods. Superposed vortex beams are experimentally generated using a digital micromirror device, and recorded on a 2 f optical imaging setup. To extract both amplitude and phase values of modal coefficients, a single intensity frame of the beam is sampled in the form of concentric circles, sectors, random circles, and random squares. Considering just specified parts of the intensity instead of the whole to sample the pattern increases the speed of the modal coefficient extraction. Besides, a linear set of coherent equations is solved, and achievements are compared together. As a consequence, measuring both the amplitude and phase values of coefficients simultaneously can pave the way to enable high-capacity optical communication which is carried out in this research with better than 99% and 96% accuracy, respectively.