Environmental niche and global potential distribution of the giant resin bee Megachile sculpturalis, a rapidly spreading invasive pollinator

Abstract Since alien species may threaten native ecosystems when becoming invasive, one of the main challenges is try to predict their potential spread. Despite bees are essential pollinators and provide important ecosystem services in their native areas, outside these areas they could represent a risk for the local bee fauna, e.g. by competing for resources or by transmitting pathogens, as it was observed for species of Megachile, the bee genus with the highest number of recorded alien species. Here, using two complementary methods (Multidimensional Envelope procedure (MDE) and the Maximum Entropy algorithm (MaxEnt)), we aim to explore environmental niche as well as to identify potential worldwide distribution of the giant resin bee Megachile sculpturalis, native to Asia and recently introduced in North America and Europe. The two methodological approaches predict an important expansion for the species and reveal a preference for areas of Palearctic and Nearctic regions with reduced temperature fluctuations and moderate precipitation regimes. The Southern hemisphere seems not having good conditions for this species. Estimations for the future (2070) predict a further, though limited expansion to northern areas in the North hemisphere. However, during roughly 25 years of spreading outside its native range, M. sculpturalis clearly expanded the range of inhabitable environmental conditions, which may increase its potential invasiveness in a pattern difficult to predict using only correlative methods. Physiological and ecological data are necessary to better assess the potential niche of this bee species and in consequence to better predict its future spreading dynamics

Development of a Network of Accurate Ozone Sensing Nodes for Parallel Monitoring in a Site Relocation Study

Recent technological advances in both air sensing technology and Internet of Things (IoT) connectivity have enabled the development and deployment of remote monitoring networks of air quality sensors. The compact size and low power requirements of both sensors and IoT data loggers allow for the development of remote sensing nodes with power and connectivity versatility. With these technological advancements, sensor networks can be developed and deployed for various ambient air monitoring applications. This paper describes the development and deployment of a monitoring network of accurate ozone (O3) sensor nodes to provide parallel monitoring in an air monitoring site relocation study. The reference O3 analyzer at the station along with a network of three O3 sensing nodes was used to evaluate the spatial and temporal variability of O3 across four Southern California communities in the San Bernardino Mountains which are currently represented by a single reference station in Crestline, CA. The motivation for developing and deploying the sensor network in the region was that the single reference station potentially needed to be relocated due to uncertainty that the lease agreement would be renewed. With the implication of siting a new reference station that is also a high O3 site, the project required the development of an accurate and precise sensing node for establishing a parallel monitoring network at potential relocation sites. The deployment methodology included a pre-deployment co-location calibration to the reference analyzer at the air monitoring station with post-deployment co-location results indicating a mean absolute error (MAE) < 2 ppb for 1-h mean O3 concentrations. Ordinary least squares regression statistics between reference and sensor nodes during post-deployment co-location testing indicate that the nodes are accurate and highly correlated to reference instrumentation with R2 values > 0.98, slope offsets < 0.02, and intercept offsets < 0.6 for hourly O3 concentrations with a mean concentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for diurnal O3 trends was found between locations within 5 km of each other with spatial variability between sites more pronounced during nighttime hours. The parallel monitoring was successful in providing the data to develop a relocation strategy with only one relocation site providing a 95% confidence that concentrations would be higher there than at the current site

A probabilistic treatment of uncertainty in nonlinear dynamical systems

In this work, computationally efficient approximate methods are developed for analyzing uncertain dynamical systems. Uncertainties in both the excitation and the modeling are considered and examples are presented illustrating the accuracy of the proposed approximations. For nonlinear systems under uncertain excitation, methods are developed to approximate the stationary probability density function and statistical quantities of interest. The methods are based on approximating solutions to the Fokker-Planck equation for the system and differ from traditional methods in which approximate solutions to stochastic differential equations are found. The new methods require little computational effort and examples are presented for which the accuracy of the proposed approximations compare favorably to results obtained by existing methods. The most significant improvements are made in approximating quantities related to the extreme values of the response, such as expected outcrossing rates, which are crucial for evaluating the reliability of the system. Laplace's method of asymptotic approximation is applied to approximate the probability integrals which arise when analyzing systems with modeling uncertainty. The asymptotic approximation reduces the problem of evaluating a multidimensional integral to solving a minimization problem and the results become asymptotically exact as the uncertainty in the modeling goes to zero. The method is found to provide good approximations for the moments and outcrossing rates for systems with uncertain parameters under stochastic excitation, even when there is a large amount of uncertainty in the parameters. The method is also applied to classical reliability integrals, providing approximations in both the transformed (independently, normally distributed) variables and the original variables. In the transformed variables, the asymptotic approximation yields a very simple formula for approximating the value of SORM integrals. In many cases, it may be computationally expensive to transform the variables, and an approximation is also developed in the original variables. Examples are presented illustrating the accuracy of the approximations and results are compared with existing approximations

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector

Nanosecond-Level Resilient GNSS-Based Time Synchronization in Telecommunication Networks Through WR-PTP HA

In recent years, the push for accurate and reliable time synchronization has gained momentum in critical infrastructures, especially in telecommunication networks, driven by the demands of 5G new radio and next-generation technologies that rely on submicrosecond timing accuracy for radio access network (RAN) nodes. Traditionally, atomic clocks paired with global navigation satellite systems (GNSS) timing receivers have served as grand master clocks, supported by dedicated network timing protocols. However, this approach struggles to scale with the increasing numbers of RAN intermediate nodes. To address scalability and high-accuracy synchronization, a more cost-effective and capillary solution is needed. Standalone GNSS timing receivers leverage ubiquitous satellite signals to offer stable timing signals but can expose networks to radio-frequency attacks due to the consequent proliferation of GNSS antennas. Our research introduces a solution by combining the white rabbit precise time protocol with a backup timing source logic acting in case of timing disruptive attacks against GNSS for resilient GNSS-based network synchronization. It has been rigorously tested against common jamming, meaconing, and spoofing attacks, consistently maintaining 2 ns relative synchronization accuracy between nodes, all without the need for an atomic clock

Influence of adiposity, insulin resistance, and intrahepatic triglyceride content on insulin kinetics

BACKGROUNDInsulin is a key regulator of metabolic function. The effects of excess adiposity, insulin resistance, and hepatic steatosis on the complex integration of insulin secretion and hepatic and extrahepatic tissue extraction are not clear.METHODSA hyperinsulinemic-euglycemic clamp and a 3-hour oral glucose tolerance test were performed to evaluate insulin sensitivity and insulin kinetics after glucose ingestion in 3 groups: (a) lean subjects with normal intrahepatic triglyceride (IHTG) and glucose tolerance (lean-NL; n = 14), (b) obese subjects with normal IHTG and glucose tolerance (obese-NL; n = 24), and (c) obese subjects with nonalcoholic fatty liver disease (NAFLD) and prediabetes (obese-NAFLD; n = 22).RESULTSInsulin sensitivity progressively decreased and insulin secretion progressively increased from the lean-NL to the obese-NL to the obese-NAFLD groups. Fractional hepatic insulin extraction progressively decreased from the lean-NL to the obese-NL to the obese-NAFLD groups, whereas total hepatic insulin extraction (molar amount removed) was greater in the obese-NL and obese-NAFLD subjects than in the lean-NL subjects. Insulin appearance in the systemic circulation and extrahepatic insulin extraction progressively increased from the lean-NL to the obese-NL to the obese-NAFLD groups. Total hepatic insulin extraction plateaued at high rates of insulin delivery, whereas the relationship between systemic insulin appearance and total extrahepatic extraction was linear.CONCLUSIONHyperinsulinemia after glucose ingestion in obese-NL and obese-NAFLD is due to an increase in insulin secretion, without a decrease in total hepatic or extrahepatic insulin extraction. However, the liver\u27s maximum capacity to remove insulin is limited because of a saturable extraction process. The increase in insulin delivery to the liver and extrahepatic tissues in obese-NAFLD is unable to compensate for the increase in insulin resistance, resulting in impaired glucose homeostasis.TRIAL REGISTRATIONClinicalTrials.gov NCT02706262.FUNDINGNIH grants DK56341 (Nutrition Obesity Research Center), DK052574 (Digestive Disease Research Center), RR024992 (Clinical and Translational Science Award), and T32 DK007120 (a T32 Ruth L. Kirschstein National Research Service Award); the American Diabetes Foundation (1-18-ICTS-119); Janssen Research & Development; and the Pershing Square Foundation

Ambient vibration studies of three steel-frame buildings strongly shaken by the 1994 Northridge earthquake

Ambient vibration surveys (AVS) can be used efficiently, cheaply and unobtrusively to identify the small-amplitude periods and modeshapes of lower modes of vibration of structures. Under Task 3.2 of Phase I of the SAC Steel Building Program, AVS were performed on three steel-frame buildings which experienced strong shaking during the January 17, 1994 Northridge earthquake. The primary objective of this study was to identify the small-amplitude modal parameters of the building for assessment of the analytical models constructed by others under SAC Task 3.1. A preliminary comparison of the modal periods obtained from the AVS with those calculated form the analytical models is presented. Although the periods identified from the AVS were considerably shorter than the model periods, the ratios of the identified to model periods for each mode were quite similar. The differences in periods are thought to be primarily because the analytical models treat only the structural frame, ignoring the stiffness of nonstructural components such as architectural partitions and building cladding. This study also serves as a first step in the testing of proposed structural health monitoring methodologies. Since one of the tested steel-frame buildings was damaged and has not yet been repaired, its data represent an "after" damage state. The tests on this steel-frame building can be followed later by an AVS if the structure is repaired. These "before" and "after" AVS results would provide valuable data to test proposed global structural health monitoring methodologies whose goal is to detect, locate and assess damage by monitoring ambient vibrations. A successful structural health monitoring method would allow "hidden" damage to be detected almost immediately rather than weeks after an earthquake