University of Southern Maine

Plain Language Summary Solar flares are large emissions of energy from the Sun that hit the Earth with X‐rays and ultraviolet emissions. A solar flare has significant effects on the upper atmosphere of the Earth, known as the ionosphere. The solar flare X‐ray radiation leads to an increase in free electron density in the lower ionosphere by ionizing more of the neutral atmosphere. This increase in electron density can in turn have profound effects on long range radio wave propagation. In this study we focus on observations of Extremely Low Frequency (ELF: 3–3,000 Hz) waves and how they are affected by a solar flare. ELF waves are challenging to observe and generate. The primary source of ELF radiation on Earth is thunderstorm lighting. We use a new analysis technique and improved observation hardware to show that ELF waves propagate more effectively and faster under the influence of a solar flare. The unique response of ELF waves to solar flares can be leveraged in global monitoring of the lower ionosphere which is important for a wide range of communication technologies.

This research paper delves into Twitter data analysis through hashtag searches associated with smartwatches, offering a framework for extracting product attributes and sentiments from a time series perspective. A sample of 133,000 tweets was collected from Twitter in two distinct periods (t1 and t2) to scrutinize the prevailing sentiments and product attributes evident in online chats about smartwatches. This study aims to uncover valuable insights into brand sentiment and product attributes by comparatively analyzing brand sentiment and word clouds, as well as employing Latent Dirichlet Allocation (LDA) to identify topic evolution between time periods t1 and t2. The outcomes of this investigation highlight the significance of employing text analytics as a potent method for gauging consumers' opinions concerning emerging product attributes from a time series perspective. The study also provides procedures and actionable recommendations for businesses, elucidating how they can harness text data to gain a deeper understanding of consumer perceptions pertaining to their products and those of their competitors from a time series perspective.

Bioturbation (sediment disturbance by animal actions) effects on nutrient cycling and nutrient levels in surface waters are difficult to quantify, in part because the diversity and magnitude of species‐specific influences are poorly understood. These influences may have consequences for the management of the trophic state of freshwater ecosystems. Fish cause bioturbation in freshwater and marine ecosystems by digging in benthic sediments, manipulating periphyton mats while searching for prey and scraping hard substrates while feeding. We used experimental enclosures (2.25 m ² ) to quantify bioturbation‐mediated phosphorus (P) and nitrogen (N) regeneration from sediment by three species of fish that differ in interactions with the benthos (largemouth bass, Micropterus salmoides ; tilapia, Oreochromis spp.; and sailfin catfish, Pterogoplichthys spp.) in shallow eutrophic wetlands in Southern Florida. Tilapia are omnivores that include detritus in their diet (winnowing or ingesting sediments) and dig nests in soft sediments year round, sailfin catfish actively burrow into substrate and consume detritus (digging and ingesting sediments), and largemouth bass are piscivores that do not routinely interact with the benthos when feeding but may dig nests in soft sediment in spawning season (January–April). We quantified the amount of suspended flocculent organic matter and changes in water column nutrients (total phosphorus [TP] and total nitrogen [TN]) in 2‐week trials for each species and estimated the portion of nutrient increases relative to fishless controls that could be attributed to bioturbation‐mediated internal nutrient loading through suspension of organic matter (as opposed to excretion or other sources of nutrient loading). Water column nutrient concentrations increased with increasing biomass for all species, but the bioturbation contribution differed by species. Largemouth bass increased water column nutrient concentrations (TP: 86% and TN: 5% relative to controls) but did not influence water column suspended particulate matter through bioturbation of sediment. Tilapia increased water column nutrients a modest amount (TP: 8%; TN: 15%), of which a small portion was attributed to bioturbation ( c . 18% of TP). Sailfin catfish raised water column nutrients substantially (TP: 105%; TN: 46%) and up to 100% of the increased TP was attributed to bioturbation. Sailfin catfish also suppressed algal growth and TP accumulation on the sides of the enclosures and reduced nutrient concentrations of the flocculent sediments. Our results were consistent with our hypothesis that behaviour and foraging traits affect bioturbation contributions to nutrient loading. The results also demonstrated that species with similar net effects like largemouth bass and sailfin catfish, added nutrients via different mechanisms (i.e. excretion vs. bioturbation). Considering the feeding strategies and interactions with the substrate of common fish species may assist managers in meeting nutrient reduction goals for eutrophic wetlands and managed freshwater systems.

Eddy currents are linked to the Faraday’s Law. Although eddy currents are a by-product they have a wide range of applications. This chapter discusses the applications of eddy currents.

In this book, you will learn about 100 applications of Maxwell's equations—or correctly named Maxwell–Heaviside equations. James Clerk Maxwell (1831–1879) was a Scottish physicist considered the father of electromagnetism. Electromagnetism indicates the interaction between electric and magnetic fields. Maxwell's equations comprise Gauss's Law for electric fields, Gauss's Law for magnetic fields, Faraday's Law, and Ampere's Law with Maxwell's contribution of the displacement current. Maxwell originally expressed the theory of electromagnetic fields as 20 equations. It was English mathematician and physicist Oliver Heaviside (1850–1925) who condensed those to the current four equation form. Hence, in full, we call those Maxwell–Heaviside equations.

This chapter discusses the applications of magnetic forces, magnetic energy stored in components as well as magnetic circuits. The majority of the applications discussed in this section are based on Faraday's Law and the electromotive force. Faraday's Law is important in physics since it shows that a time-varying magnetic field can induce an electric field. In addition to Faraday's Law, another equation that is heavily used is Ampere's Law. This section discusses the displacement current term which was introduced by Maxwell to the Ampere's Law.

Lorentz force is not a direct derivation of Maxwell's equations although it could be derived using Maxwell's equations. The applications of the Lorentz force and the Maxwell’s equations are linked. This chapter discusses some of the applications of the Lorentz force.

This chapter presents the applications of Maxwell’s equations in testing materials. Material properties play an important role in the semiconductor industry as well as in the aerospace and space industries.

Electrostatic applications use Gauss's Law for electric fields or Maxwell's first equation as their primary operating principle. This chapter shows some of the most common applications of Gauss's Law for electric fields and how to apply Gauss's Law in different coordinate systems.

This chapter discusses the applications of Maxwell’s equations in electromagnetic waves and electromagnetic power. Electromagnetic waves are one of the reasons why Maxwell’s equations are considered revolutionary.

This paper demonstrates the successful dispersion of up to 8 wt% nickel-coated graphite (NiGr) particles in A206 aluminum alloys using stir mixing followed by casting. The microstructure, as well as selected mechanical and tribological properties, were thoroughly characterized. The nickel coating improves the dispersion of graphite, overcoming the challenges faced with uncoated graphite in aluminum melts and ensuring uniform distribution of NiGr particles. The A206/NiGr composites show potential as replacements for aluminum-tin alloys, particularly in bearing applications. Wear and friction performance were evaluated using a pin-on-disc tribometer with a 440 stainless steel counterface. Both as-cast and worn surfaces were examined via optical microscopy, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). Additionally, a supervised machine learning algorithm was developed to model the relationship between NiGr content, heat treatment, load, and wear rate. The results indicate that the coefficient of friction (COF) decreases with NiGr additions up to 4 wt%, demonstrating self-lubricating behaviour. However, the wear rate increased with higher NiGr content due to reduced hardness. The machine learning model effectively predicts wear rates based on NiGr percentage, heat treatment, and load, highlighting NiGr content as the most significant factor influencing wear, followed by load and heat treatment.

A significant challenge in measurements of neutrino oscillations is reconstructing the incoming neutrino energies. While modern fully-active tracking calorimeters such as liquid argon time projection chambers in principle allow the measurement of all final state particles above some detection threshold, undetected neutrons remain a considerable source of missing energy with little to no data constraining their production rates and kinematics. We present the first demonstration of tagging neutrino-induced neutrons in liquid argon time projection chambers using secondary protons emitted from neutron-argon interactions in the MicroBooNE detector. We describe the method developed to identify neutrino-induced neutrons and demonstrate its performance using neutrons produced in muon-neutrino charged current interactions. The method is validated using a small subset of MicroBooNE’s total dataset. The selection yields a sample with $60\%$ 60 % of selected tracks corresponding to neutron-induced secondary protons. At this purity, the integrated efficiency is 8.4% for neutrons that produce a detectable proton.

Accelerated development and engagement of artificial intelligence are among the most significant global challenges in transforming the social and economic environment, resulting in the heightened emphasis on inclusive, collaborative, ethical decision‐making and responsible leadership. Higher education is an integral part of the global landscape of society and is influenced by its changing context. Accordingly, leadership educators must respond to the changes in the global and institutional environments and the new leadership paradigm in designing and implementing their leadership education programs. Here, we consider the macro level of the “place” within which higher education institutions are situated and reflect upon the impact of Artificial Intelligence on the design and delivery of leadership education programs.

Digital storytelling is an innovative approach that evaluators can adopt to expand their dissemination efforts. The stories use brief audio and video recordings, and they can be used to provide reflections on the perceived value, experiences, or impact of public health efforts. We offer tips for evaluators to add this tool to their portfolio using several traditional evaluation data collection techniques. We also discuss a series of planning considerations and lessons learned based on the experiences of an evaluation of a research capacity-building initiative.