Pollution on the surface of an insulator, combined with conditions such as fog, dew, or moisture, directly influences partial discharge (PD) activity, a key indicator of an insulator’s aging process. Using an artificial pollution chamber, three classes of insulators were subjected to saline dew across multiple test runs, during which radiofrequency emissions were recorded by an antenna. The contamination process was monitored and characterized using three indicators derived from the Cumulative Energy per Interval, the Cumulative Average Time Between Signals, and the Cumulative Distributed Energy. By employing segmented regression analysis on these variables, it was possible to detect and quantify changes in PD activity as measured in the ultra-high-frequency (UHF) band by a nearby antenna. Compared with galvanic, leakage-current based approaches and artificial intelligence (AI)-based models, the proposed scheme has lower computational cost and provides operator tunable alert states for training-free, non-contact, trend-based early warning. In every test run, the developed algorithm identified at least one trend change by 80% of the test duration. Analyzing each trend change with the three indicators enabled graded alert states, demonstrating flexibility for assessing flashover (FO) risk. In all experiments conducted on the different types of insulators, FO consistently occurred during the highest alarm state.

When ingested as part of a blood meal, the antiparasitic drug ivermectin kills mosquitoes, making it a candidate for mass drug administration (MDA) in humans and livestock to reduce malaria transmission. When administered to livestock, most ivermectin is excreted unmetabolized in the dung within 5 days post administration. Presence of ivermectin, has been shown to adversely affect dung colonizers and dung degradation in temperate settings; however, those findings may not apply to, tropical environment, where ivermectin MDA against malaria would occur. Here we report results of a randomized field experiment conducted with dung from ivermectin-treated and control cattle to determine the effect of ivermectin on dung degradation in tropical Tanzania. For intact pats, we measured termite colonization, larval numbers and pat wet and dry weights. Pat organic matter was interpolated from a subsample of the pat (10 g wet weight). Additionally, we counted larvae growing in the treated and untreated pats in a semi-field setting. We found that termites colonized ivermectin pats more readily than controls. Despite this, wet weight decreased significantly slower in the ivermectin-treated pats in the first two weeks. As water was lost, sub-sample dry weight increased, and organic matter decreased similarly over time for the treatment and control. Interpolated for whole pats, total organic matter was higher, and larval counts were lower in the ivermectin-treated pats after the first month. Our results demonstrate an effect of ivermectin and its metabolites on dung degradation and fauna in a tropical savanna setting. Because slow dung degradation and low insect abundance negatively impact pastureland, these non-target, environmental effects must be further investigated within the context of real-world implementation of ivermectin MDA in cattle and weighed against the potential benefits for malaria control.

In eusocial insects, individual variation and its influence on emergent outcomes, like communication success between foragers, remain poorly understood1. The honey bee waggle dance is a celebrated communication behavior that conveys to nestmates a distance and direction from the hive to a valuable resource, usually nectar or pollen2. Intriguingly, each forager possesses an individual calibration to communicate the resource's distance3, but the effect of this individuality on recruitment success is unknown. Here we tested whether the magnitude and/or direction of calibration mismatch in dancer-follower dyads affects their ability to communicate. We created fully-marked observation colonies and trained bees to forage from artificial feeders at known distances. Concurrently, we filmed dances inside the colony to identify successful dancer-follower dyads. We then compared the distribution of calibration mismatch values among these successful dyads (n = 30) to a simulated expected distribution based on a null hypothesis of random assortment of calibration values. Surprisingly, mismatch magnitude did not affect recruitment (p = 0.74), but mismatch direction did: followers predicted to overshoot the resource were over-represented among successful dyads compared to the null distribution (p = 0.03). Overall, our data demonstrate that the calibration relationship in dancer-follower dyads, created by individual differences, can shape communication outcomes.

Social network analysis is increasingly and fruitfully applied to study the collective structure and function of animal societies across space and time. Honey bees (Apis mellifera L.) are a particularly tractable model system that is rich in social relationships and dynamics. Despite the rich body of literature describing the social life of the honey bee, including the famous waggle dance by which foragers recruit nestmates to profitable resources, relatively little is known about the networks that arise from waggle dance communication. Here we conducted a field experiment with fully-marked experimental colonies (N = 2 colonies, 3,000 bees each) to characterize the honey bee waggle dance recruitment network structure and function. Particularly, we studied network density, burstiness in waggle dance bouts, and the effect of individuality in waggle dance communication behavior on network structure. We simulated a maximally-efficient honey bee recruitment network using a deterministic susceptible-infected model. Then we used this simulated network as an upper bound for network density to calculate the proportion of successful recruitment events in observed networks compared to the simulated maximal network. Next, we characterized the burstiness, or temporal distribution, of waggle dance bouts. Finally, we tested whether inter-bee differences, or individuality, in waggle dance communication affected the recruitment network structure. We found that (1) real recruitment networks are sparse, with each individual recruiting up to 3.5% as many nestmates as predicted by the simulated maximal network; (2) individual bees danced steadily, not in bursts, and (3) that individuality in waggle dance calibrations was positively associated with successful recruitment and thus the propagation of the recruitment network (p = 0.008). Our results offer the first empirical and biologically-informed descriptive statistics for honey bee waggle dance networks and may be informative in the parameterization of bio-inspired computing models.

A new study shows that, as floral resources decline over the season, honey bees gradually increase their tolerance to attacks when foraging, a shift that may enable them to exploit other colonies' honey stores during robbing season.

Oil-filled transformers are critical assets in electrical power systems, both economically and operationally. Their condition is assessed through insulation system, which is greatly affected by various degradation mechanisms. Hence, effective fault diagnosis is essential to prolong their lifespan. Early detection and correction of incipient faults through Dissolved Gas Analysis (DGA) are crucial to prevent irreversible damage. Current measurement systems have significant limitations that impede their use in routine monitoring and underscore the need for new, accessible technologies that are both technically and economically viable to efficiently detect incipient faults. This study evaluates the performance of various Machine Learning (ML) techniques to predict the concentrations of hydrogen (H2), methane (CH4), acetylene (C2H2), ethylene (C2H4), and ethane (C2H6) in oil samples subjected to different types of electrical faults, using data from a novel electronic nose (E-Nose) equipped with eleven MOS-type gas sensors. The evaluated ML techniques include Linear Regression (LR), Multivariate Linear Regression (MLR), Principal Component Regression (PCR), Multilayer Perceptron (MLP), Partial Least Squares Regression (PLS), Support Vector Regression (SVR), and Random Forest Regression (RFR). Experimental results from 218 measurement processes revealed that RFR and MLP models exhibited superior performance, with RFR achieving the highest accuracy for predicting H2, C2H2, and C2H6, while MLP excelled for CH4 and C2H4.A comparison with a commercial DGA system using the Duval Pentagon Method confirmed the effectiveness of these models in diagnosing transformer faults. These findings underscore the potential of combining E-Noses with ML techniques as an innovative and efficient solution for early fault diagnosis.

Optimal foraging theory (OFT) predicts that animals employ foraging strategies that maximize a particular currency, such as net energetic efficiency, to meet their nutritional demands. Two nonexclusive patterns that arise from OFT are convergence on high-quality resources and resource partitioning. Honey bees make collective decisions by integrating their individual foraging with social recruitment behaviors: returning foragers communicate the approximate vector to high-quality resources using waggle dances. Because we can eavesdrop on their communications, waggle dance decoding is a valuable tool for exploring OFT predictions as it allows us to map how honey bees use landscapes. In this study, we analyzed 8049 dances from colocalized colonies across three landscapes to investigate whether neighboring colonies forage by not partitioning patches (i.e., converging their food collection on the same patches), by partitioning at the landscape level, or by partitioning at the local level. To differentiate between these three possible scenarios, we examined three metrics: (1) interdance distances between and within colonies; (2) k-nearest neighbors; and (3) k-means clustering. We observed no difference in the distances between dances performed by bees from the same colony compared to those from different colonies. Also, we found at each of the three field sites that dances from the same colony were not more likely to appear as close neighbors to each other. Finally, k-means cluster analysis demonstrates that dance locations advertised by the same colony aggregated nonrandomly in the three sites, where dances from the same colony comprised a significant majority of dances within k-means clusters and 62% of clusters consisted entirely of dances from a single colony. Together, these results support a foraging scenario where honey bees partition their foraging, but at the local level. This strategy may help limit intercolony foraging competition.

Glyphosate is a broad-spectrum herbicide that inhibits the shikimate pathway, which honey bees (Apis mellifera), a non-target beneficial pollinator, do not endogenously express. Nonetheless, sublethal glyphosate exposure in honey bees has been correlated to impairments in gustation, learning, memory and navigation. While these impacted physiologies underpin honey bee foraging and recruitment, the effects of sublethal glyphosate exposure on these important behaviors remain unclear, and any proximate mechanism of action in the honey bee is poorly understood. We trained cohorts of honey bees from the same hives to forage at one of two artificial feeders offering 1 mol l-1 sucrose solution, either unaltered (N=40) or containing glyphosate at 5 mg acid equivalent (a.e.) l-1 (N=46). We then compared key foraging behaviors and, on a smaller subset of bees, recruitment behaviors. Next, we quantified protein levels of octopamine, tyramine and dopamine, and levels of the amino acid precursor tyrosine in the brains of experimental bees collected 3 days after the exposure. We found that glyphosate treatment bees reduced their foraging by 13.4% (P=0.022), and the brain content of tyramine was modulated by a crossover interaction between glyphosate treatment and the number of feeder visits (P=0.004). Levels of octopamine were significantly correlated with its precursors tyramine (P=0.011) and tyrosine (P=0.018) in glyphosate treatment bees, but not in control bees. Our findings emphasize the critical need to investigate impacts of the world's most-applied herbicide and to elucidate its non-target mechanism of action in insects to create better-informed pollinator protection strategies.

Human-induced land-use change is a well-documented driver of species decline, including bees, but its true cost may be underestimated. The effects of habitat conversion on honey bee foraging metabolic costs are not well documented. Here, we quantify the impact of land use change on the foraging of freely flying honey bees (Apis mellifera) before (2018-2019, n=382) and after (2022, n=502) their historical foraging habitat is developed. We decoded and analyzed honey bee waggle dances, through which returning foragers communicate the vector of forage. We found that bees increased (from 2.4% to 8.4%) their use of undisturbed microhabitat within the development. The small-scale developments, covering just 1% of the foraging range, nearly doubled flight distance and energy expenditure. Average distance increased from 0.69 to 1.28 kilometers (from 7 to 13 Joules). Our study updates our understanding of land development costs on local bees, revealing concrete consequences to changing land upon which pollinators depend.

Outdoor spatial mosquito repellents, such as mosquito coils or heating devices, release pyrethroid insecticides into the air to provide protection from mosquitoes within a defined area. This broadcast discharge of pyrethroids into the environment raises concern about the effect on non-target organisms. A previous study found that prallethrin discharged from a heating device did not affect honey bee (Apis mellifera L.) [Hymenoptera: Apidae] foraging or recruitment. In this second study, there was no significant difference in foraging frequency (our primary outcome), waggle dance propensity, or persistency in honey bees collecting sucrose solution between those exposed to metofluthrin from a different heating device and bees exposed to a non-metofluthrin control. One measure, waggle dance frequency, was higher in the metofluthrin treatment than the control but this outcome was likely a spurious result due to the small sample size. The small particle size of the emissions, averaging 4.43 mu m, from the heated spatial repellent products, which remain airborne with little settling, may play an important role in the lack of effect found on honey bee foraging.

Partial discharges are one of the main aging mechanisms of electrical components. Some specific types of partial discharges, such as surface discharges and treeing, involve the interaction of a plasma with a polymeric surface. Our aim is to build a multi-scale model that describes the ageing of dielectric materials exposed to these types of partial discharges and can, possibly, shed new light on the treeing progression.

Electric treeing is a mechanism of failure in solid polymeric insulations. Under some conditions, trees grow through filamentary trees, which have a small diameter and do not cause breakdown when they reach the counter-electrode. In this case, reverse trees grow opposite to the forward-filamentary tree. Several methods are available to model electric trees and their phenomenology. Among them is the kinetic model, which proposes that electric trees grow due to microfractures present in the material. This model had previously been considered to model tree growth, but we extended it to include the widening of tree branches in a prior study. In this work, we included the phenomenology of reverse trees and their relationship with filamentary trees. The simulation showed that filamentary trees grow until they are close to the counter-electrode, and when reverse trees begin to form, their branches widen. From this point, filamentary trees do not grow, but branches widen until the current density is high enough for final dielectric breakdown.

Advances in predicting thunderstorms have been made possible through the use of artificial intelligence. Convolution neural networks, inspired by the processes of the human brain, are particularly effective in image classification. In particular, one-dimensional convolution neural networks have played a significant role in time series analysis, including thunderstorm forecasting. Unfortunately, these models face challenges when used with unbalanced datasets, where the proportion of events of interest, such as thunderstorms, is significantly lower than other phenomena. To overcome this limitation, several over-sampling and under-sampling strategies have emerged. In this paper, we propose a method for forecasting thunderstorm occurrences in the northern region of Chile using a one-dimensional convolutional neural network, combined with a balanced batch generator and attention models. The algorithm developed to predict thunderstorm days achieved a performance metric of approximately 79.6%, a promising result due to the minimal failure rates observed.

The detection of partial discharges (PDs) is vital to evaluate the condition of the insulation systems in high-voltage equipment. Although various methods exist to measure the presence of PD, ultrahigh frequency (UHF) approaches have been widely adopted due to their ease of application and their ability to perform measurements without the need for direct galvanic contact with the equipment under test. One of the main challenges with these measurement methods is having sensors with adequate dimensions, bandwidth, and gain, to efficiently measure, in any environment, the electromagnetic (EM) emissions that propagate from the PD pulses even if they are of very low magnitude. This article presents the development of a new bioinspired antenna based on the structural characteristics of the antennae of the Antheraea Polyphemus moth. The bioinspired parameters of the proposed antenna were carefully adjusted to obtain a portable antenna (27.4 x 14.4 cm), with an average gain of 3.07 dBi (between 1.36 and 4.45 dBi) and a bandwidth that covers the entire frequency spectrum, where the most important spectral power contents of PD are emitted (1750 MHz below -10 dB). The results obtained confirm that the proposed antenna is more sensitive than others, such as the Vivaldi antenna or the monopole antenna, commonly used in UHF PD measurements, since it allows accurate temporal and spectral differentiation of the PD pulses, even when the PD is of low magnitude.

Knowledge of foraging currencies and costs is important for understanding honeybee food collection economics and to parameterize their foraging behaviors as indicators of habitat quality, which is important in the identification of management targets in human-altered landscapes. Previous research has yielded inconsistent results regarding the relationship between honey bees and important agroecosystems, such as agricultural grasslands. Waggle dance decoding provides a method for resolving these inconsistencies by mapping and quantifying bee recruitment to agricultural grasslands using statistical methods that appropriately account for foraging distance, or cost. Here we decoded 3881 dances across two foraging years to investigate when and where honey bees forage in a mixed-use landscape in Virginia, with a particular interest in honey bee use of agricultural grasslands (pastures and haylands). We initially observe that bees recruited heavily to agricultural grasslands compared to croplands, developed lands and forests, where the percent foraging to that land type was at 30.7% (CI: 29.4-31.8%), and thus significantly higher than its representation in the landscape (c. 23%). Honey bees also recruited heavily to agricultural grasslands across months, with percent foraging ranging from 26.9% (23.5-30.1%) in August to 38.8% (31.3-46.9%) in October. However, when we examined distance-corrected foraging rates, which allowed us to compare land type attractiveness when flight cost is removed, we found that the agricultural grasslands were not more attractive than the broader landscape and were significantly less attractive than, for example, croplands. We additionally identify potential forage gaps in agricultural grasslands during June and August, while also distinguishing them as a possible source of forage in October before colony overwintering in this landscape. Furthermore, we qualitatively observe a hot spot, demonstrating high foraging interest that is composed of agricultural grasslands, developed lands, and croplands and is itself a mixed-use area. Together, these results demonstrate that honey bees utilize heterogeneous land areas and underscore the importance of statistical analyses that incorporate biological knowledge. Lastly, these data will be important in informing future management aimed at pollinators in agricultural grasslands.

Polymeric insulation employed in electrical power industry undergo irreversible and unpredictable ageing due to partial discharges (PDs). In particular, the dielectric properties of polyethylene (PE) used in high voltage cables are often compromised by the formation of electrical trees. In this work we assume that the propagation of treeing channels involves the injection of carbonic material into the gas interacting with the surface of the defect. Experimental characterizations proved that, in certain conditions, disordered graphitic carbon can form in some areas of the electrical trees, thus increasing surface conductivity and inhibiting PDs. The chemical mechanism involved in this process is yet to be clarified. Here we propose a model for this process. By means of a series of molecular dynamics simulations, we show how the chemisorption of gaseous molecules on a PE surface can lead to a bidimensional carbonic structure. The characterization of the density of states of such systems suggests that the presence of pure carbon adsorbed on the polymer causes an increase in surface conductivity.

Electrical treeing is one of the main degradation mechanisms in high-voltage polymeric insulation. Epoxy resin is used as insulating material in power equipment such as rotating machines, power transformers, gas-insulated switchgears, and insulators, among others. Electrical trees grow under the effect of partial discharges (PDs) that progressively degrade the polymer until the tree crosses the bulk insulation, then causing the failure of power equipment and the outage of the energy supply. This work studies electrical trees in epoxy resin through different PD analysis techniques, evaluating and comparing their ability to identify tree bulk-insulation crossing, the precursor of failure. Two PD measurement systems were used simultaneously—one to capture the sequence of PD pulses and another to acquire PD pulse waveforms—and four PD analysis techniques were deployed. Phase-resolved PD (PRPD) and pulse sequence analysis (PSA) identified tree crossing; however, they were more sensible to the AC excitation voltage amplitude and frequency. Nonlinear time series analysis (NLTSA) characteristics were evaluated through the correlation dimension, showing a reduction from pre- to post-crossing, and thus representing a change to a less complex dynamical system. The PD pulse waveform parameters had the best performance; they could identify tree crossing in epoxy resin material independently of the applied AC voltage amplitude and frequency, making them more robust for a broader range of situations, and thus, they can be exploited as a diagnostic tool for the asset management of high-voltage polymeric insulation.

Volatile organic compounds (VOCs) contribute to air pollution both directly, as hazardous gases, and through their reactions with common atmospheric oxidants to produce ozone, particulate matter, and other hazardous air pollutants. There are enormous ranges of structures and reaction rates among VOCs, and there is consequently a need to accurately characterize the spatial and temporal distribution of individual identified compounds. Current VOC measurements are often made with complex, expensive instrumentation that provides high chemical detail but is limited in its portability and requires high expense (e.g., mobile labs) for spatially resolved measurements. Alternatively, periodic collection of samples on cartridges is inexpensive but demands significant operator interaction that can limit possibilities for time-resolved measurements or distributed measurements across a spatial area. Thus, there is a need for simple, portable devices that can sample with limited operator presence to enable temporally and/or spatially resolved measurements. In this work, we describe new portable and programmable VOC samplers that enable simultaneous collection of samples across a spatially distributed network, validate their reproducibility, and demonstrate their utility. Validation experiments confirmed high precision between samplers as well as the ability of miniature ozone scrubbers to preserve reactive analytes collected on commercially available adsorbent gas sampling cartridges, supporting simultaneous field deployment across multiple locations. In indoor environments, 24 h integrated samples demonstrate observable day-to-day variability, as well as variability across very short spatial scales (meters). The utility of the samplers was further demonstrated by locating outdoor point sources of analytes through the development of a new mapping approach that employs a group of the portable samplers and back-projection techniques to assess a sampling area with higher resolution than stationary sampling. As with all gas sampling, the limits of detection depend on sampling times and the properties of sorbents and analytes. The limit of detection of the analytical system used in this work is on the order of nanograms, corresponding to mixing ratios of 1–10 pptv after 1 h of sampling at the programmable flow rate of 50–250 sccm enabled by the developed system. The portable VOC samplers described and validated here provide a simple, low-cost sampling solution for spatially and/or temporally variable measurements of any organic gases that are collectable on currently available sampling media.

The measurement and subsequent analysis of partial discharges (PDs) have become a fundamental tool for diagnosing insulation systems, thereby assessing the reliability of power electrical equipment. PDs are used to characterize the evolution of progressive defects such as electrical trees, considered one of the main mechanisms of degradation in polymeric insulation, that leads to failure. In this work, a new methodology for the identification of the aging stage of electrical trees is proposed, based on the measurement and analysis of each PD pulse waveform by means of the discrete wavelet transform (DWT) and principal component analysis (PCA). A total of 24 treeing samples of epoxy resin were analyzed and tested under ac voltages of 10, 12, and 14 kV with frequencies of 50–550 Hz. The results showed that it is possible to characterize the development of electrical trees into three stages: initial, valley, and postcrossing, showing different behaviors in terms of discharge and tree structure. It was found that PD can be classified into two groups, in which one group (“Group (b)”) served as an identifier of the postcrossing stage, and therefore an alert of the imminent insulation failure. Regarding the structure of trees, it was observed that growth in the valley stage can be associated with the development of filamentary branches with low PD activity, while growth in the postcrossing stage can be associated with the development of dark branches with higher PD activity. In some cases, reverse trees grew in this last stage. Additionally, it was observed that using the methodology proposed, the development of dark branches was associated with the appearance of Group (b) PD. The ability of the presented measurement and evaluation technique to identify growth-stage of electrical trees at different applied voltages and frequencies shows its potential for the implementation of insulation assessment of real power equipment in service, such as power cables.

This paper investigates the characteristics and behaviors of electrical tree growth of treeing samples of epoxy resin with metal filings inclusions. The location of the filing varied among the samples, with one being near the needle tip, another in the middle of the insulation gap, and the third in contact with the counter-electrode. The study utilizes Phase-Resolved Partial Discharge (PRPD), Pulse Sequence analysis (PSA) and analysis based on partial discharge (PD) waveform parameters to analyze experimental data from the three samples. The findings reveal significant differences in partial discharge patterns and waveform parameters depending on the position of the inclusion. Additionally, our findings revealed that the technique used for sample preparation significantly influences both the behavior of PD and the structure of electrical trees. The observed impact of imperfections on PD activity suggests their potential use in monitoring and assessing tree growth. However, further investigations involving different types of imperfections are required to provide a comprehensive analysis and broaden our understanding in this field.