Aedes aegypti and Ae. albopictus are the main vectors transmitting dengue and chikungunya viruses. Despite being pathogens of global public health importance, knowledge of their vectors’ global distribution remains patchy and sparse. A global geographic database of known occurrences of Ae. aegypti and Ae. albopictus between 1960 and 2014 was compiled. Herein we present the database, which comprises occurrence data linked to point or polygon locations, derived from peer-reviewed literature and unpublished studies including national entomological surveys and expert networks. We describe all data collection processes, as well as geo-positioning methods, database management and quality-control procedures. This is the first comprehensive global database of Ae. aegypti and Ae. albopictus occurrence, consisting of 19,930 and 22,137 geo-positioned occurrence records respectively. Both datasets can be used for a variety of mapping and spatial analyses of the vectors and, by inference, the diseases they transmit.

Assessment of Solicited Adverse Event (AE) Intensity.

Dataset Card for "ae-signal_processing_attacks_assembly_commonvoice"

More Information needed

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Historical Dataset of A E Angier is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (1991-2023),Total Classroom Teachers Trends Over Years (2005-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2005-2023),Asian Student Percentage Comparison Over Years (1991-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1991-2023),White Student Percentage Comparison Over Years (1991-2023),Two or More Races Student Percentage Comparison Over Years (2009-2023),Diversity Score Comparison Over Years (1991-2023),Free Lunch Eligibility Comparison Over Years (2000-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2001-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2022)

COVID-19 Vaccine Adverse Event Detection (Synthetic)

This dataset contains synthetic examples generated to train and evaluate large language models (LLMs) to detect whether a passage of text describes an adverse event (AE) following COVID-19 vaccination.

  Dataset Structure

The dataset is split into:

train.jsonl: 500 examples for training test.jsonl: 100 examples for testing

Each entry follows the Alpaca-style instruction format with the following fields:

instruction:… See the full description on the dataset page: https://huggingface.co/datasets/podiche/covid19-vaccine-ae-detection-synthetic.

This is a data set containing LoRa traces used in the evaluation of TnB.

The AE index is derived from geomagnetic variations in the horizontal component observed at selected (10-13) observatories along the auroral zone in the northern hemisphere. To normalize the data a base value for each station is first calculated for each month by averaging all the data from the station on the five international quietest days. This base value is subtracted from each value of one-minute data obtained at the station during that month. Then among the data from all the stations at each given time (UT), the largest and smallest values are selected. The AU and AL indices are respectively defined by the largest and the smallest values so selected. The symbols, AU and AL, derive from the fact that these values form the upper and lower envelopes of the superposed plots of all the data from these stations as functions of UT. The difference, AU minus AL, defines the AE index, and the mean value of the AU and AL, i.e. (AU+AL)/2, defines the AO index. The term "AE indices" is usually used to represent these four indices (AU, AL, AE and AO). The AU and AL indices are intended to express the strongest current intensity of the eastward and westward auroral electrojets, respectively. The AE index represents the overall activity of the electrojets, and the AO index provides a measure of the equivalent zonal current.

This dataset tracks annual total students amount from 1991 to 2023 for A E Angier

TeamSODA/ae-awgn_commonvoice dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset tracks annual reading and language arts proficiency from 2011 to 2022 for A E Angier vs. Massachusetts and Newton School District

AE data (.seg2 files) contain AE recordings from all 12 sensors (recloc.mat) for individual events. These events have been picked (pktimes_ml.mat) and located using a Time Difference of Arrival methodology (sourceloc_ml.mat).

Darley dale sandstone (DDS) is a brown-yellow, feldspathic sandstone with a modal composition of quartz (69%), feldspars (26%), clay (3%) and mica (2%) (Heap et al., 2009). Previous studies report a connected porosity of 13.3% ± 0.8% with grain sizes varying from 100-800 µm (Zhu & Wong, 1997). The unconfined compressive strength is 160 MPa (Baud & Meredith, 1997). At the scale analysed here, no distinct layering or laminations were present. A cylindrical rock sample was cored using a diamond tipped hollow coring drill to prepare a 4 cm diameter sample that was then trimmed to 10 cm length with a diamond saw. End faces are accurately ground using a lathe fitted with a cross-cutting diamond grinding disk with surfaces flat and parallel to within 0.01 mm.

Deformation was performed using a conventional triaxial deformation cell installed at the Rock Mechanics Laboratory, University of Portsmouth (Fazio, 2017). The sample presented here was deformed until brittle failure at a confining pressure of 20 MPa at a constant deformation rate of 3.6 mm/hr. Experimentation was performed under fully drained conditions to avoid any fluid-driven effects on AE frequency content (Benson et al., 2010). These environmental conditions ensure that a high number of AE are obtained and any time-dependent variations in the signal waveform are predominantly due to the scattering effects of microfractures, thus allowing for the sampling of a diverse range of deformation structure. Axial displacement is measured with a non-contact Eddy Displacement system mounted to the apparatus. It comprises of three sensors that accurately (sub-micron) measure the distance to a target steel plate attached to the driving piston. These readings are averaged and are used to set the target deformation rate via feedback to an axial stress intensifier. Differential stress (MPa) and sample strain (%) are in attached .txt files.

For AE data acquisition the protocol of Benson et al. (2007) was followed. The dry sample was positioned inside an engineered rubber jacket fitted with ports for an array of twelve 1 MHz single-component Piezo-Electric Transducers (PZTs, model PAC Nano30) were embedded. These sensors have a relatively flat frequency response between 125-750 KHz. Sensor output is connected to preamplifiers set to 40 dB, focusing on data quality over quantity. An ITASCA-Image “Milne” recorder operate in a standard ‘trigger’ model, downloading all twelve channels when any single channel passes a set 100 mV threshold (e.g. Gehne, 2018).

Heap, M. J., Baud, P., Meredith, P. G., Bell, A. F., & Main, I. G. (2009). Time‐dependent brittle creep in Darley Dale sandstone. Journal of Geophysical Research: Solid Earth, 114(B7).

Zhu, W., & Wong, T. (1997). The transition from brittle faulting to cataclastic flow: Permeability evolution. Journal of Geophysical Research: Solid Earth, 102(B2), 3027–3041.

Baud, P., & Meredith, P. (1997). Damage accumulation during triaxial creep of Darley Dale sandstone from pore volumometry and acoustic emission. International Journal of Rock Mechanics and Mining Sciences, 34(3–4), 24-e1.

Fazio, M. (2017, January). Dynamic Laboratory Simulations of Fluid-Rock Coupling with Application to Volcano Seismicity and Unrest (PhD Thesis). University of Portsmouth, School of Earth and Environmental Sciences.

Benson, P. M., Vinciguerra, S., Meredith, P. G., & Young, R. P. (2010). Spatio-temporal evolution of volcano seismicity: A laboratory study. Earth and Planetary Science Letters, 297(1–2), 315–323.

Benson, P. M., Thompson, B. D., Meredith, P. G., Vinciguerra, S., & Young, R. P. (2007). Imaging slow failure in triaxially deformed Etna basalt using 3D acoustic-emission location and X-ray computed tomography. Geophysical Research Letters, 34(3). https://doi.org/10.1029/2006gl028721

Gehne, S. (2018). A laboratory study of fluid-driven tensile fracturing in anisotropic rocks. University of Portsmouth.

Dataset for paper published in Advancing Earth and Space Science. July 2021Acoustic emission data during freeze–thaw experiment on limestone in Sussex Permafrost LaboratoryThe dataset contains the continuous acoustic emission (AE) activity measured during the course of 16 bidirectional (bottom up and top down) freezing and thawing cycles in 300 mm cubic block of Tuffeau limestone. The physical experiment under the dynamic thermal boundary conditions aims to understand the mechanistic development of micro- to macroscale fracturing over the course of 470 days by sensing the location, timing, duration and amplitude of acoustic waves during any events of cracking. Eight R15-alpha piezoceramic transducers (PZT) (manufactured by Mistras Group) were used to detect the AE waveforms mounted on the surfaces of the limestone by silicon grease epoxy (Pro silicone grease 494-124, from RS Components). The signals were further amplified by 40 dB using Physical Acoustic Corporation (PAC) preamplifiers (IL40S with 32 to 1100 kHz). A threshold of 40 dB was set to separate noise induced in the laboratory from the signals of microcracking events. The signals were acquired by an 8-channel PCI Express-8 data card (Mistras Group) and the entire operation was controlled by AEWin software (Mistras Group). The first column in the datasheet represents the days since the beginning of the experiment, followed by 3D locations (X, Y and Z, measured in millimetres from the top left corner in the front face of the block) of the AE (i.e., microcracking) events in columns two to four. Signals from at least four out of eight transducers were considered to indicate the location of released energy (AE hit). Column five and six illustrates rise time and duration of the AE waveforms in microseconds. The number of times a signal crossing the preset threshold (i.e., count) and the maximum amplitude (in dB) were presented in column seven and eight, respectively. The dataset is analyzed and interpreted in the following draft research paper, currently in preparation: Maji V, Murton JB. Experimental observations and statistical modelling of crack propagation dynamics in limestone by acoustic emission analysis during freezing and thawing.AbstractThe timing and location of microcracking events, their propagation and coalescence to form macrocracks, and their development by tension, shearing or mixed modes are little known but essential to understanding the fracture of intact rock by freezing and thawing. The aims of the present study are to investigate the mechanisms and transition of microcracking and macrocracking during repeated freeze-thaw, and to develop a statistical model of crack propagation that assesses the distance and angular relationship of neighboring cracking events arranged in their temporal order of occurrence. Eight acoustic emission (AE) sensors mounted on a 300 mm cubic block of chalk captured the three-dimensional locations of microcracking events in their temporal order of occurrence during 16 seasonal freeze-thaw cycles simulating an active layer above permafrost. AE events occurred mostly during thawing periods (45%) and freeze-to-thaw transitions (37%) rather than during freezing periods (9%) and thaw-to-freeze transitions (8%), suggesting that most AE (microcrack) events were driven by the process of ice segregation rather than volumetric expansion. The outcomes of a novel statistical model of crack propagation based on two boundary conditions—inside-out and outside-in modes of cracking—were assessed based on Bayes’ theorem by testing the hypothesis that the inside-out mode of cracking was favored by tensional activity, whereas the outside-in mode was supported by shearing events. In both situations, the hypothesis accounted for 54%–73% confidence level. The microcrack propagation model can distinguish reasonably between cracks formed by volumetric expansion and ice segregation.Plain Language SummaryIt is well known that repeated freezing and thawing of water within some porous and fine-grained rocks can form large cracks visible to the unaided eye. But the initiation and growth of precursor tiny cracks too small to see without a microscope remain enigmatic in terms of their timing, location, growth, and coalescence to form eventually large cracks. Thus, prediction of rock fracture by frost is difficult. Here we present results from a laboratory experiment that measured the location and timing of tiny sound (acoustic) waves within a block of limestone subject to 16 cycles of freezing and thawing. The waves indicated the occurrence of tiny cracking events. Measurement of rock temperature suggested that most cracking events resulted from water migrating through the rock toward lenses of ice rather than expansion of water freezing in place within empty spaces in rock. In addition, cracks propagating outward from the block center tended to form as the rock was being pulled apart, whereas those propagating inward tended to form by scissor-like tearing of rock. A new statistical model of rock cracking can distinguish reasonably well between cracks formed by growing ice lenses and those formed by expansion of freezing water.

Dataset Card for "ae-Kenansville_attack"

More Information needed

Directional Cooling-Induced Fracturing (DCIF) experiments were conducted on rectangular Westerly granite blocks (width=depth=4.0", height=2.0"). Liquid nitrogen was poured in a small, 1"-diameter copper cup attached to the top of the sample, and the resulting acoustic emissions (AEs) and temperature changes on the surface of the sample were monitored. Several confining stresses were applied bi-axially to the sides of the samples so that the onset of AE activity and the stress applied to the sample were correlated. The obtained AEs were used to determine the microcracking source locations and amplitude, and the associated moment tensors. Included in this submission are the animations of the AE locations and graphics displaying the measured temperature-AE activity changes for different stresses.

Data supports the publication "Mapping Aedes aegypti (Diptera: Culicidae) and Aedes albopictus Vector Mosquito Distribution in Brownsville, TX". We investigated the spatiotemporal dynamics of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) mosquito trap captures in Brownsville, TX, using high-resolution land cover, socioeconomic, and meteorological data. We modeled mosquito trap counts using a Bayesian hierarchical mixed-effects model with spatially correlated residuals. The models indicated an inverse relationship between temperature and mosquito trap counts for both species, which may be due to the hot and arid climate of southern Texas. The temporal trend in mosquito populations indicated Ae. aegypti populations peaking in the late spring and Ae. albopictus reaching a maximum in winter. Our results indicated that seasonal weather variation, vegetation height, human population, and land cover determine which of the two Aedes species will predominate. This dataset is associated with the following publication: Myer, M., C. Fizer, K. McPherson, A. Neale, A. Pilant, A. Rodriguez, P. Whung, and J. Johnston. Mapping Aedes aegypti (Diptera: Culicidae) and Aedes albopictus Vector Mosquito Distribution in Brownsville, TX. JOURNAL OF MEDICAL ENTOMOLOGY. Entomological Society of America, Lantham, MD, USA, 57(1): 231–240, (2020).

Dataset

This dataset was created by Younes Boukacem

Contents

A list of the top 50 AE Wealth Management LLC holdings showing which stocks are owned by AE Wealth Management LLC's hedge fund.

Experiments were designed to reproduce the loosening phenomenon observed in aeronautics, automotive or civil engineering structures where parts are assembled together by means of bolted joints. The bolts can indeed be subject to self-loosening under vibrations. Therefore, it is of paramount importance to develop sensing strategies and algorithms for early loosening estimation. The test rig was specifically designed to make the vibration tests as repeatable as possible. The dataset ORION-AE is made of a set of time-series measurements obtained by untightening a bolt with seven different levels. The data have been sampled at 5 MHz on four different sensors, including three permanently attached acoustic emission sensors in contact with the structure, and one laser (contactless) measurement apparatus. This dataset can thus be used for performance benchmarking of supervised, semi-supervised or unsupervised learning algorithms, including deep and transfer learning for time-series data, with possibly seven classes. This dataset may also be useful to challenge denoising methods or wave-picking algorithms, for which the vibrometer measurements can be used for validation. ORION is a jointed structure made of two plates manufactured in a 2024 aluminium alloy, linked together by three bolts. The contact between the plates is done through machined overlays. The contact patches has an area of 12x12 mm^2 and is 1 mm thick. The structure was submitted to a 100 Hz harmonic excitation force during about 10 seconds. The load was applied using a Tyra electromagnetic shaker, which can deliver a 200 N force. The force was measured using a PCB piezoelectric load cell and the vibration level was determined next to the end of the specimen using a Polytec laser vibrometer. The ORION-AE dataset is composed of five directories collected in five campaigns denoted as B, C, D, E and F in the sequel. Seven tightening levels were applied on the upper bolt. The tightening was first set to 60 cNm with a torque screwdriver. After a 10 seconds vibration test, the shaker was stopped and this vibration test was repeated after a torque modification at 50 cNm. Then torque modifications at 40, 30, 20, 10 and 5 cNm were applied. Note that, for campaign C, the level 40 cNm is missing. During each cycle of the vibration test for a given tightening level, different AE sources can generate signals and those sources may be activated or not, depending on the tribological conditions within the contact between the beams which are not controlled. The tightening levels can be used to represent a reference against which clustering or classification results can be compared with. In that case, the main assumption is that the torque remained close to the level which was set at the beginning of every period of 10 s. This assumption can not be checked in the current configuration of the tests. For each campaign, four sensors were used: a laser vibrometer and three different AE sensors (micro-200-HF, micro-80 and the F50A from Euro-Physical Acoustics) with various frequency bands were attached onto the lower plate (5 cm above the end of the plate). All data were sampled at 5 MHz using a Picoscope 4824 and a preamplifier (from Euro-Physical Acoustics) set to 60 dB. The velocimeter is used for different purposes, in particular to control the amplitude of the displacement of the top of the upper beam so that it remains constant whatever the tightening level. The sensors are expected to detect the stick-slip transitions or shocks in the interface that are known to generate small AE events during vibrations. The acoustic waves generated by these events are highly dependent on bolt tightening. These sources of AE signals have to be detected and identified from the data stream which constitute the challenge. Details of the folders and files There is 1 folder per campaign, each composed of 7 subfolders corresponding to 7 tightening levels: 5 cNm, 10 cNm, 20 cNm, 30 cNm, 40 cNm, 50 cNm, 60 cNm. So, 7 levels are available per campaign, except for campaign C for which 40 cNm is missing. There is about 10 seconds of continuous recording of data per level (the exact value can be found according to the number of files in each subfolder). The sampling frequency was set to 5 MHZ on all channels of a picoscope 4824 and a preamplifer of 60 dB (model 2/4/6 preamplifier made by Europhysical acoustics). The characteristics of both the picoscope and preamplifier are provided in the enclosed documentation. Each subfolder is made of .mat files. There is about 1 file per second (depending on the buffering, it can vary a little). The files in a subfolder are named according to the timestamps (time of recording). Each file is composed of vectors of data named: A = micro80 sensor. B = F50A sensor. C = micro200HF sensor. D = velocimeter. Note ... Visit https://dataone.org/datasets/sha256%3A1448d7e6ddf29be42ecf7a171aae8a54a9d9ee5fd29055dfbe282f0cd5519f1e for complete metadata about this dataset.

Acoustic Emission (AE) Sensors Market Outlook

The global Acoustic Emission (AE) Sensors Market is seeing a robust growth trajectory, with a market size valued at approximately USD 180 million in 2023 and projected to reach USD 375 million by 2032, demonstrating an impressive compound annual growth rate (CAGR) of 8.5% during the forecast period. The growth of this market is largely fueled by the increasing demand for real-time monitoring systems across various industries, where AE sensors play a crucial role in offering advanced diagnostic and monitoring capabilities. This growth is further catalyzed by the rising need for predictive maintenance and condition monitoring in industrial applications, leading to heightened adoption of AE sensors due to their effective non-destructive testing features.

One of the primary growth factors for the AE Sensors Market is the expanding adoption of structural health monitoring systems across sectors such as aerospace, automotive, and civil infrastructure. These systems utilize AE sensors to continuously assess the integrity of structures, leading to enhanced safety and reduced maintenance costs. With the increasing focus on sustainability and safety compliance, industries are heavily investing in technologies that ensure operational efficiency and prolong asset life. Additionally, technological advancements in AE sensor capabilities, such as improved sensitivity and real-time data processing, have significantly enhanced their appeal across various high-stakes applications.

Another key driver is the growing application of AE sensors in leak detection systems, particularly in the energy and oil & gas sectors. As these industries strive to adhere to stringent safety regulations and environmental standards, the need for precise and reliable leak detection solutions has become imperative. AE sensors are increasingly utilized in these applications for their ability to detect minute changes in pressure or flow rates, thereby identifying leaks swiftly and accurately. This capability is crucial in preventing environmental damage and reducing costly downtime, which is further bolstering the demand for AE sensors in these sectors.

Moreover, the increasing integration of AE sensors in machine condition monitoring systems is a significant growth factor. Industries such as manufacturing and energy are progressively adopting condition monitoring solutions to enhance machinery efficiency and prevent unexpected failures. AE sensors provide valuable insights into the health of machines by identifying characteristics like crack formation or material degradation. This enables predictive maintenance strategies that not only optimize operational efficiency but also extend the lifespan of critical equipment. The widespread emphasis on maximizing productivity and minimizing maintenance costs is expected to sustain the demand for AE sensors in the coming years.

Regionally, North America dominates the AE Sensors Market, attributed to the extensive deployment of advanced monitoring technologies across its well-established industrial landscape. The region's focus on innovation and technological advancements in sectors like aerospace and automotive further accelerates market growth. The Asia Pacific region is also witnessing substantial growth, driven by rapid industrialization, urbanization, and infrastructure development. As countries like China and India continue to expand their industrial bases, the demand for AE sensors in monitoring applications is set to increase. Europe remains a significant player, with its strong emphasis on regulatory compliance and sustainability, enhancing the adoption of AE sensors in various applications.

Sensor Type Analysis

The AE Sensors Market is segmented by sensor type, including Piezoelectric, Capacitive, Fiber Optic, and Others, each offering distinctive advantages and catering to varied application needs. Piezoelectric sensors hold a significant share in the market due to their inherent sensitivity and ability to operate over a wide frequency range, making them an ideal choice for numerous monitoring applications. These sensors are extensively used in industries such as aerospace and automotive, where detecting minute acoustic emissions is critical for safety and reliability. The robustness and long operational life of piezoelectric sensors further enhance their popularity, contributing to their widespread adoption across different sectors.

Capacitive sensors, although a smaller segment, are gaining traction due to their high precision and ability to function effectively in challenging environments. These se