California State Polytechnic University

Background Reporting quality of aromatherapy-focused research in humans is inconsistent and often incomplete yet there are no (North American or American) nationally or internationally agreed upon core criteria for aromatherapy-focused research. The Aromatic Research Quality Appraisal Task Force developed the Transparent Reporting for Essential oil and Aroma Therapeutic Studies (TREATS) checklist as initial steps toward developing a reporting guideline. The purpose of this Delphi study is to engage with an international community of aromatherapy researchers to reach consensus on which items should be included in reports of aromatherapy-focused studies in humans. The result of the consensus process will be to publish an aromatherapy research reporting guideline that can be used as an extension to existing research reporting guidelines for various studies such as randomized controlled trials, observational studies, and case reports. Methods A modified Delphi consensus study will be used. The consensus study, approved by the West Virginia University Institutional Review Board, will consist of up to four rounds of an online survey. To improve understanding and buy-in, experts attending a large international aromatherapy-focused conference will take part in a four-hour in-person/virtual hybrid introductory meeting where they can learn the study process and ask questions. The 48-item survey is divided into categories covering study products, processes, aromatherapy intervention, safety, sustainability, and olfactory ability and aroma preference. Participants will be asked to rate each checklist item for relevance on a 5-point Likert scale ranging from “of little importance” to “extremely important”. During the Delphi study, participants can provide comments and, in the first and second rounds, may suggest additional items or modifications to existing items. An item will be automatically included in the final guidelines if it is rated as "very important" or "extremely important" by at least ≥80% of the participants in Rounds 1–3, and automatically excluded if > 50% of participants rate the item as “not important” or “of little importance”. Aggregated ratings will be statistically analyzed for response rates, level of agreement, medians, and interquartile ranges. Discussion This protocol supports conducting a Delphi consensus that will add to the current knowledge of items considered necessary for complete and consistent reporting of aromatherapy-focused research in humans. This is of international significance as world-wide use and research of aromatherapy and essential oils in humans has continued to increase, currently without consistent and clear reporting. The Delphi method is appropriate for developing consensus between diverse experts, researchers, and practitioners as it offers anonymity and minimizes bias. Findings will contribute to creating an extension to primary reporting guidelines.

This chapter presents fundamentals of Gaussian Process (GP) modeling, starting from the definitions of the GP mean and covariance kernel functions, introducing GP regression equations and then discussing the role and interpretation of GP hyperparameters. Section 2.4 then explores the kernel smoothing perspective, presenting a distilled theory of Reproducing Kernel Hilbert Spaces (RKHS) and connecting GPs to RKHS and to Kernel Ridge Regression. The Chapter includes an online supplement—a Python Jupyter notebook that reproduces two case studies of GP regression on a synthetic univariate response function.

This Chapter discusses several extensions and modifications of the core GP model to make it better suited for financial applications. Topics covered include heteroskedastic GPs (Sect. 3.1) to capture input-dependent noise and alternative likelihoods to tackle non-Gaussian noise (Sect. 3.2). We also survey multi-output GPs (Sect. 3.3) that provide a framework to capture correlation across multiple outputs, localization methods to combat nonstationarity or reduce runtime for large datasets (Sect. 3.4), and GP updating equations for streaming training data (Sect. 3.5).

This Chapter discusses the use of GPs as emulators for more general stochastic control problems, extending the emulation of the continuation values in Chap. 5 to other settings. We outline some of the extant applications of GPs in switching control, continuous control and impulse control, showcasing GPs as a tool for implementing dynamic programming value function approximation.

This Chapter investigates GP models for financial market structures, including one-dimensional term structures, two-dimensional volatility surfaces (both implied and local), three-dimensional swaption cubes, and valuation of variable annuities. Additionally, the discourse extends into the realms of mortality modeling and actuarial mathematics (linking to variable annuity pricing), illustrating a wider applicability of GP models. The Chapter is accompanied by a R Markdown notebook that illustrates the basics of fitting a mortality surface using a GP surrogate with a separable kernel and a Python Jupyter notebook illustrating fitting a GP with an additive kernel to a forward curve of natural gas futures quotes.

This chapter gives a thorough examination of kernel functions $k(\mathbf {x}, \mathbf {x}')$ which are the primary driver of a GP model. We review the most common examples of stationary and non-stationary kernel families, discuss kernel composition and survey kernel selection approaches. The latter half of the chapter considers convergence and universal approximation properties of GP surrogates as the training set grows and then reviews links between GPs and stochastic differential equations. The Chapter is accompanied by a Python Jupyter notebook illustrating fitting of different GP kernels and prior mean functions to a synthetic one-dimensional dataset.

In this Chapter we discuss applications of GPs to optimal stopping problems, especially pricing of American-style options. We consider the use of GPs to learn continuation values connecting to the Regression Monte Carlo framework for simulation-based solvers of optimal stopping. We adopt the discrete-time paradigm of Bermudan options that allow for exercise at a predetermined collection of K (discrete) times $\mathcal {T}=\{t_k: k=0,1,2,\ldots ,K,\ t_K=T\}$ up to T. The Chapter highlights the specific features of Optimal Stopping Problems to pinpoint what aspects of the GP model are critical for successful implementation, such as the recursive nature of the function approximation tasks and the importance of noise modeling. Section 5.2 considers active learning and adaptive batching approaches to construct more efficient GP surrogates. This Chapter is accompanied by an R notebook illustrating a construction of GP surrogates for one- and two-dimensional Bermudan option valuation.

In this Chapter we discuss employing GPs as pricing surrogates, providing a fast prediction of contracts’ values as a function of their parameters. Thus, we consider the use of GPs to map a state $\mathbf {x}$ (which might include underlying asset price, but also deterministic contract parameters like its strike) into a option price $P(\mathbf {x})$. The different sections cover learning derivative valuations within a probabilistic framework (Sect. 4.1); ingredients of GP surrogates for option pricing (Sect. 4.2); using GPs to learn option Greeks and other sensitivities (Sect. 4.3); imposing no-arbitrage constraints in GP models (Sect. 4.4); and applications to portfolio modeling and credit value adjustment computation (Sect. 4.5). The Chapter includes an R-based notebook that illustrates the learning of option prices and Deltas within the Black-Scholes and Heston models.

Understanding how community resilience changes over time and the drivers of those changes requires longitudinal studies to test components of resilience within (1) spatio-temporal contexts as well as (2) a conceptual framework of what is changeable. In this study, first, we look at the temporal and spatial patterns of change in community resilience scores, using the established Baseline Resilience Indicators for Communities (BRIC) through three snapshots in 2010, 2015, and a newly calculated 2020 for the United States. Second, we examine the actionable and contextual drivers of improved resilience which can set goals for communities. While areas with the highest and lowest resilience scores remain consistent, on average, resilience scores improved between 2010 and 2015 and 2010–2020, though they decreased between 2015 and 2020. Our findings suggest infrastructural capital is more explanatory in terms of changes in resilience scores. Finally, health coverage and internet access were the highest individual actionable predictors of BRIC in 2010 and 2015 but less important in 2020, given the reduced variability in access from place to place. The observed changes are derived from the BRIC construction, variable constraints, and conceptual framework, thus further validations and qualitative studies are required to complement or contradict our findings.

Diet-driven ecological radiation has been proposed as a key factor in the diversification of Nudibranchia. Members of Doridina, one of the two major clades of nudibranchs, have a remarkably wide range of dietary preferences. The morphology of the feeding apparatus is related to prey preferences and feeding mechanisms. Therefore, the investigation of the evolutionary changes in the morphology of the feeding apparatus can provide valuable insights into the evolution of Doridina. Recent significant changes in our understanding of the phylogeny of Doridina have highlighted the need to re-evaluate current hypotheses on the evolution of the buccal armature morphology and correlated dietary shifts in this group. To address this, we compiled and analysed a comprehensive dataset that combined phylogenetic and morphological data to reconstruct the evolution of the buccal armature in Doridina. We also review the feeding biology of various groups of dorids to provide a deeper view of the evolution of the morphology of the feeding apparatus. We hypothesised the plesiomorphic conditions of the buccal armature for each large clade of Doridina and for the entire group. Within Doridina, there is a strong phylogenetic correlation with prey preference as major changes in the diet preferences of several clades led to significant transformations in radular morphology. We also discovered several cases of retention of plesiomorphic radular morphology and feeding mechanisms in different phylogenetic lineages of Doridina.

Nanotechnology has revolutionized circuit design by enabling highly efficient and compact components. Central to this innovation is the two-input NOR logic gate, a universal element in logic circuits that facilitates the construction of diverse logic configurations. Its versatility plays a pivotal role in digital logic design, particularly within the realm of molecular transistor technology, where miniaturization and efficiency are paramount. In this paper, a novel device is presented based on the Oligo (phenylene ethynylene) (OPE) molecule. OPE molecules offers significant advantages in digital circuits due to their superior electronic properties, nanoscale size, self-assembling capabilities, and tunable characteristics. By leveraging this intriguing feature of the proposed dual-gate molecular transistor, a two-input NOR logic gate is realized. The study employs advanced simulation techniques, including Non-Equilibrium Green’s Function formalism and density functional theory, to model quantum transport properties. Insights gained from these simulations elucidate the performance and reliability of molecular transistors under varying operational conditions, advancing our understanding of their potential in future nanoelectronics applications.

This article examines digital signature as a critical security measure for authentication and verification in electronic transactions, distinguishing them from simpler e-signatures by their use of advanced cryptographic techniques. Digital signature leverage asymmetric cryptography to provide higher security, with various standardised algorithms, such as the Digital Signature Algorithm (DSA) and Rivest- Shamir-Adleman (RSA), forming the foundation of secure systems today. The paper outlines the evolution of digital and electronic signature, from early telegraph-based approvals to the modern applications that facilitate secure digital contracts and communications. Key digital signature algorithms are analysed in detail, highlighting their strengths and weaknesses, including RSA, DSA, Elliptic Curve Digital Signature Algorithm (ECDSA), and Edwards-Curve Digital Signature Algorithm (EdDSA). Additionally, the article addresses the technical and implementation challenges of digital signature algorithms, such as high computational demands, complexities in key management, and difficulties in secure implementation. It concludes with an exploration of future trends, such as the development of efficient and quantum-resistant algorithms, improvements in cryptographic hardware, and new strategies to simplify secure key management. This comprehensive overview provides insights into both current digital signature practices and emerging solutions poised to enhance security and efficiency in an evolving digital landscape.

Recent genomic studies on Dendrodoris evolution revealed two distinct mitochondrial lineages corresponding to tuberculated and smooth mantle morphotypes, suggesting habitat transitions as a key driver of cladogenesis. However, limited taxon sampling and the absence of deep-sea specimens prevented more definitive conclusions. Here, we sequenced the molecular markers cytochrome oxidase 1 (cox1), large ribosomal subunit 16S (16S), and Histone 3 (H3) for the tuberculated D. warta and an unidentified deep-sea Dendrodoris species from the Gulf of Mexico. After incorporating these undersampled taxa into phylogenetic and species delimitation analyses, we recovered D. warta within the smooth Dendrodoris clade, and identified the deep-sea Dendrodoris species as the earliest diverging lineage within the smooth Dendrodoris clade. Interestingly, based on nuclear H3 data, the deep-sea species is more closely related to the tuberculated species D. atromaculata. Supported by ancestral reconstruction state analyses, we hypothesize that Dendrodoris species experienced significant habitat transitions, from medium depths to intertidal environments, with a subsequent colonization of the deep-sea, accompanied by molecular and morphological evolution. Moreover, the species delimitation analyses conducted herein also indicate the presence of multiple species complexes, and provide molecular support for the description of the deep sea Dendrodoris sinusensis sp. nov. This study offers new insights into the evolutionary history of the genus Dendrodoris, highlighting ecological transitions and their impact on mitochondrial evolution. Future research on this group should focus on the underlying molecular mechanisms driving ecological transitions and their evolutionary consequences.

State beaches and parks provide access to coastal environments for recreational activities that rely on access to the ocean, coastal climate, and scenic amenities. Approximately 46 million people visit state beaches in California annually, and another 20 million people visit other types of state park units located in the Coastal Zone, which together constitute 72% of overall visitation to the state parks system. We utilized monthly attendance estimates available between 2001 and 2020 to assess the influence of extreme drought or wet conditions on visitation to state beaches and coastal parks for day use and overnight use. State beaches include direct access to the ocean for water-dependent recreation activities from swimming to scenery, while coastal park types range from coastal forests to historical sites and may include some ocean access but are not directly dependent on water. State park unit climate conditions were analyzed by coastal region according to seasonal variability between moderate and extreme drought and wet categories using the Palmer Drought Severity Index. We found that visitation to state beaches is more sensitive to climate than coastal parks, particularly during times of extreme drought, and that overall day use visitation is more sensitive to climate than overnight use.

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

Leptospirosis is a bacterial zoonotic disease that spreads through contaminated soil and water or directly from infected animals through urine. Although animal-to-human transmission is low, humans are most susceptible to contracting leptospirosis from these contaminated sources. This makes leptospirosis a public health concern, and therefore it is important to control these bacteria from spreading into the environment. A survey targeting Los Angeles County communities, in which a 2021 leptospirosis outbreak occurred, was sent out via groups on the online platforms Instagram and Facebook to gather dog and owner demographics. With 92 (90.2%) respondents having a primary veterinarian, it could not be determined what caused certain owners to have a greater vaccination rate than those who did not (n = 10; 9.8%). Overall, 69 respondents (68%), regardless of whether they had a primary veterinarian or not, reported not knowing of canine leptospirosis and 79 (77%) not knowing the signs to look for or that it is zoonotic. These data help provide a basis in terms of the status of dog owners’ knowledge of leptospirosis and how to begin to inform dog owners better about preventatives for this disease.

Coronal holes (CHs) are large-scale, low-density regions in the solar atmosphere that may expel high-speed solar wind streams that incite hazardous, geomagnetic storms. Coronal and solar wind models can predict these high-speed streams, and the performance of the coronal model can be validated against segmented CH boundaries. We present a novel method named Sub-Transition Region Identification of Ensemble Coronal Holes (STRIDE-CH) to address prominent challenges in segmenting CHs using extreme-ultraviolet (EUV) imagery. Ground-based, chromospheric He i 10,830 Å line imagery and underlying Fe i photospheric magnetograms are revisited to disambiguate CHs from filaments and quiet Sun, overcome obscuration by coronal loops, and complement established methods in the community which use space-borne coronal EUV observations. Classical computer vision techniques are applied to constrain the radiative and magnetic properties of detected CHs, produce an ensemble of boundaries, and compile these boundaries in a confidence map that quantifies the likelihood of the CH presence throughout the solar disk. This method is a science-enabling one towards future studies of CH formation and variability from a mid-atmospheric perspective.

The Surface Dust Analyser (SUDA) is a mass spectrometer onboard the Europa Clipper mission for investigating the surface composition of the Galilean moon Europa. Atmosphereless planetary moons such as the Galilean satellites are wrapped into a ballistic dust exosphere populated by tiny samples from the moon’s surface produced by impacts of fast micrometeoroids. SUDA will measure the composition of such surface ejecta during close flybys of Europa to obtain key chemical signatures for revealing the satellite’s composition such as organic molecules and salts, history, and geological evolution. Because of their ballistic orbits, detected ejecta can be traced back to the surface with a spatial resolution roughly equal to the instantaneous altitude of the spacecraft. SUDA is a Time-Of-Flight (TOF), reflectron-type impact mass spectrometer, optimized for a high mass resolution which only weakly depends on the impact location. The instrument will measure the mass, speed, charge, elemental, molecular, and isotopic composition of impacting grains. The instrument’s small size of $268 ~\mathrm {mm} \times 250 ~\mathrm {mm} \times 171$$~\mathrm {mm}$, radiation-hard design, and rather large sensitive area of 220 cm² matches well the challenging demands of the Clipper mission.

Stress is necessary for survival. However, chronic unnecessary stress exposure leads to cardiovascular, gastrointestinal and neuropsychiatric disorders. Thus, understanding the mechanisms involved in the initiation and maintenance of the stress response is essential since it may reveal the underpinning pathophysiology of these disorders and may aid in the development of medication to treat stress-mediated diseases. Pituitary adenylyl cyclase activating polypeptide (PACAP) and its receptors (PAC1, VPAC1 and VPAC2) are expressed in the hypothalamus and other brain areas as well as in the adrenal gland. Previous research has shown that this peptide/receptor system serves as a modulator of the stress response. In addition to modulating the stress response, this system may also be connected to its emerging role as neuroprotective against hypoxia, ischemia, and neurodegeneration. This article aims to review the literature regarding the role of PACAP and its receptors in the stress response, the involvement of different brain regions and microglia in PACAP-mediated modulation of the stress response, and the long-term adaptation to stress recognizable clinically as survival with resilience while manifested in anxiety, depression and other neurobehavioral disorders.