The Relationship Between Self-Efficacy, Optimism, and Sensation Seeking in Predicting Self-reported Adherence to Health Behaviors

The relationship between health behaviors and three psychological variables that included optimism, self-efficacy, and sensation seeking was investigated in this study. A demographic form, the Health Adherence Behavior Inventory (HABIT), the General Self-Efficacy Scale (GSE), the Life Orientation Test-Revised (LOT-R), and the Brief Sensation Seeking Scale (BSSS-8) were administered to 258 participants. The data were analyzed for two separate and independent samples based on gender. Results indicated that self-efficacy predicted male health behaviors while optimism predicted female health behaviors. In addition, men scored higher than women on self-reported sensation-seeking behaviors, as predicted. Limitations of this research and directions for further research are discussed. These findings may have indications for primary-care physicians, as they may better understand factors related to patient adherence

XRSpotlight: Example-based Programming of XR Interactions using a Rule-based Approach

Research on enabling novice AR/VR developers has emphasized the need to lower the technical barriers to entry. This is often achieved by providing new authoring tools that provide simpler means to implement XR interactions through abstraction. However, novices are then bound by the ceiling of each tool and may not form the correct mental model of how interactions are implemented. We present XRSpotlight, a system that supports novices by curating a list of the XR interactions defined in a Unity scene and presenting them as rules in natural language. Our approach is based on a model abstraction that unifies existing XR toolkit implementations. Using our model, XRSpotlight can find incomplete specifications of interactions, suggest similar interactions, and copy-paste interactions from examples using different toolkits. We assess the validity of our model with professional VR developers and demonstrate that XRSpotlight helps novices understand how XR interactions are implemented in examples and apply this knowledge in their projects

Statistics of low-energy levels of a one-dimensional weakly localized Frenkel exciton: A numerical study

Numerical study of the one-dimensional Frenkel Hamiltonian with on-site randomness is carried out. We focus on the statistics of the energy levels near the lower exciton band edge, i. e. those determining optical response. We found that the distribution of the energy spacing between the states that are well localized at the same segment is characterized by non-zero mean, i.e. these states undergo repulsion. This repulsion results in a local discrete energy structure of a localized Frenkel exciton. On the contrary, the energy spacing distribution for weakly overlapping local ground states (the states with no nodes within their localization segments) that are localized at different segments has zero mean and shows almost no repulsion. The typical width of the latter distribution is of the same order as the typical spacing in the local discrete energy structure, so that this local structure is hidden; it does not reveal itself neither in the density of states nor in the linear absorption spectra. However, this structure affects the two-exciton transitions involving the states of the same segment and can be observed by the pump-probe spectroscopy. We analyze also the disorder degree scaling of the first and second momenta of the distributions.Comment: 10 pages, 6 figure

From Microbial Ecology to Innovative Applications in Food Quality Improvements: the Case of Sourdough as a Model Matrix.

Since millennia, humankind has exploited microbial diversity associated to give foodmatrices in order to obtain fermented foods and beverages, resulting in products with improvedquality and extended shelf life. This topic has received deserved and continuous interest in thescientific community, for the reason of its significance as a driver of innovation in the food and beveragesector. In this review paper, using sourdough as a model matrix, we provide some insights into thefield, testifying the relevance as a transdisciplinary subject. Firstly, we encompassed the prokaryoticand eukaryotic microbial diversity associated with the sourdough ecosystems. The importance ofthis micro-biodiversity in the light of flour-related chemical diversity was examined. Finally, wehighlighted the increasing interest in microbial-based applications oriented toward biocontrol solutionin the field of sourdough-based products (i.e., bread)

Microbial Resources and Innovation in the Wine Production Sector

Microbial starter cultures represent a fundamental level of innovation in the wine sector. Selected yeast strains are routinely used to achieve the needed biomass preparation to accelerate and steer alcoholic fermentation in grape must. The use of starter cultures to induce malolactic fermentation in wine relies on the characterisation and propagation of suitable strains of lactic acid bacteria. Furthermore, the selection of new strains, the renewal of management of microbial resources and new technologies allow continuous improvements in oenology, which may increase the beneficial aspects of wine. In this review, with the aim to stimulate microbial-driven, consumer-oriented advances in the oenological sector, we propose an overview of recent trends in this field that are reported by following the classical separation into 'product innovation' and 'process innovation'. Hence, we shall highlight i) the possible positive innovative impacts of microbial resources on the safety and the sensorial and functional properties of wine (product innovation) and ii) the potential microbial-based improvements allowing the reduction of time/costs and the environmental impacts associated with winemaking (process innovation)

AR TutorialKit: an Augmented Reality Toolkit to Create Tutorials

Augmented Reality (AR) is a widely used technology in fields such as medicine, engineering, and architecture, and is also prevalent in social media platforms like Snapchat, Instagram, and TikTok. In recent years, the availability of AR applications and improvements in hardware have made it affordable for educational training in various disciplines. However, limited options are available for the general construction of AR tutorials in the literature. Most solutions are specific for particular contexts, such as medical procedures or industry-specific tasks. This paper proposes an AR toolkit that enables novice programmers to create tutorials without topic restrictions. Our aim is to keep improving TutorialKit in such a way that it can be used flexibly and effectively in a variety of different contexts, enabling it to meet the diverse needs and requirements of users

Non-Saccharomyces Commercial Starter Cultures: Scientific Trends, Recent Patents and Innovation in the Wine Sector.

For 15 years, non-Saccharomyces starter cultures represent a new interesting segment in the dynamic field of multinationals and national companies that develop and sell microbial-based biotechnological solutions for the wine sector. Although the diversity and the properties of non-Saccharomyces species/strains have been recently fully reviewed, less attention has been deserved to the commercial starter cultures in term of scientific findings, patents, and their innovative appli-cations. Considering the potential reservoir of biotechnological innovation, these issues represent an under-estimated possible driver of coordination and harmonization of research and development activities in the field of wine microbiology. After a wide survey, we encompassed 26 different commercial yeasts starter cultures formulated in combination with at least one non-Saccharomyces strain. The most recent scientific advances have been explored delving into the oenological significance of these commercial starter cultures. Finally, we propose an examination of patent literature for the main yeasts species commercialised in non-Saccharomyces based products. We highlight the presence of asymmetries among scientific findings and the number of patents concerning non-Saccharomyces-based commercial products for oenological purposes. Further in-vestigations on these microbial resources might open new perspectives and stimulate attractive in-novations in the field of wine-making biotechnologies

Machine learning techniques for fine dead fuel load estimation using multi‐source remote sensing data

Fine dead fuel load is one of the most significant components of wildfires without which ignition would fail. Several studies have previously investigated 1‐h fuel load using standard fuel parameters or site‐specific fuel parameters estimated ad hoc for the landscape. On the one hand, these methods have a large margin of error, while on the other their production times and costs are high. In response to this gap, a set of models was developed combining multi‐source remote sensing data, field data and machine learning techniques to quantitatively estimate fine dead fuel load and understand its determining factors. Therefore, the objectives of the study were to: (1) estimate 1‐h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1‐h fuel load in a pilot area of the Apulia region (southern Italy). In pursuit of the above, fine dead fuel load estimation was performed by the integration of field inventory data (251 plots), Synthetic Aperture Radar (SAR, Sentinel‐1), optical (Sentinel‐2), and Light Detection and Ranging (LIDAR) data applying three different algorithms: Multiple Linear regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). Model performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the coefficient of determination (R2) and Pearson’s correlation coefficient (r). The results showed that RF (RMSE: 0.09; MSE: 0.01; r: 0.71; R2: 0.50) had more predictive power compared to the other models, while SVM (RMSE: 0.10; MSE: 0.01; r: 0.63; R2: 0.39) and MLR (RMSE: 0.11; MSE: 0.01; r: 0.63; R2: 0.40) showed similar performances. LIDAR variables (Canopy Height Model and Canopy cover) were more important in fuel estimation than optical and radar variables. In fact, the results highlighted a positive relationship between 1‐h fuel load and the presence of the tree component. Conversely, the geomorphological variables appeared to have lower predictive power. Overall, the 1‐h fuel load map developed by the RF model can be a valuable tool to support decision making and can be used in regional wildfire risk management

Evaluation of Fused Pyrrolothiazole Systems as Correctors of Mutant CFTR Protein

Cystic fibrosis (CF) is a genetic disease caused by mutations that impair the function of the CFTR chloride channel. The most frequent mutation, F508del, causes misfolding and premature degradation of CFTR protein. This defect can be overcome with pharmacological agents named "correctors". So far, at least three different classes of correctors have been identified based on the additive/synergistic effects that are obtained when compounds of different classes are combined together. The development of class 2 correctors has lagged behind that of compounds belonging to the other classes. It was shown that the efficacy of the prototypical class 2 corrector, the bithiazole corr-4a, could be improved by generating conformationally-locked bithiazoles. In the present study, we investigated the effect of tricyclic pyrrolothiazoles as analogues of constrained bithiazoles. Thirty-five compounds were tested using the functional assay based on the halide-sensitive yellow fluorescent protein (HS-YFP) that measured CFTR activity. One compound, having a six atom carbocyle central ring in the tricyclic pyrrolothiazole system and bearing a pivalamide group at the thiazole moiety and a 5-chloro-2-methoxyphenyl carboxamide at the pyrrole ring, significantly increased F508del-CFTR activity. This compound could lead to the synthesis of a novel class of CFTR correctors