Science Talks最新文献

Loss of taste and smell is one of the most troubling symptoms of long COVID and may be permanent for some. Correlation between subjectively and objectively assessed olfactory and gustatory impairment is low, leading to uncertainty about how many people are affected, how many recover, and to what extent. We prospectively investigated the effects of COVID-19 on long-term chemosensory function in a university and hospital-based cohort in NJ. We followed 856 participants from March 2020 through April 2022, of which 58 were diagnosed with COVID-19 and completed the NHANES 2013–2014 taste and smell protocol, including a chemosensory questionnaire, whole-mouth taste tests, and an 8-item odor identification test at and/or before acute COVID-19 infection. Of these, 29 repeated taste and smell assessments at 6 months (183.0 ± 54.6) follow-up. Total overall smell score significantly improved from baseline to 6-month follow up (6.9 ± 1.4 vs 7.6 ± 0.8; p = .01). Taste intensity also improved across 6 months, but not significantly. Our study is the first to show psychophysically-assessed and self-reported long-term recovery of olfactory and gustatory function in the same population after acute COVID-19.

The present study aimed to address the potential of ground-based food and sensory research in representing the isolated and confined environment of a spacecraft. Virtual Reality (VR) technology was employed to simulate the experience of perceived isolation and confinement within space. The VR simulation emulates the International Space Station in low Earth orbit, comprising interconnected space modules equipped with integrated sensory analysis tools for evaluating food odor cues within the VR environment (‘Food in Space’, Supplementary A). In our first experiment, 44 healthy participants were asked to rate the intensity of three commercially available food odor samples (vanilla, lemon, almond) and a control on a 5-point Likert scale, in the neutral sitting posture, a NASA-Neutral sitting posture (mimicking a ‘microgravity’ posture using a commercial ‘Zero-gravity’ outdoor chair set at 122–124°), and within the VR simulation. This first phase revealed large individual variations across odors. Importantly, there were no significant differences for most odors when the odor perceptions of the three odors between the neutral and ‘microgravity’ neutral postures were compared. However, there were significant differences for select odors between VR and both the postures (Supplementary B) indicating that the VR ‘Food in Space’ environment may impact odor perception differently across odors. A second pilot study with 16 participants evaluated four food odor samples (vanilla, lemon, almond, eucalyptus) and a control across different contexts (baseline control, virtual reality) and time points during virtual reality. The emotional responses during the experiences were also evaluated explicitly using validated scales such as the Self-Assessment Manikin (SAM) and the short-formed Positive and Negative Affect Schedule (PANAS-SF). This second phase revealed that participants' descriptions of their emotional responses underwent changes before and after their virtual reality experiences. Terms used were generally more neutral and positive before VR (e.g., ‘interested’, ‘attentive’) and more negative after spending an average of 9 min 35 s in virtual reality (e.g., ‘nervous’, ‘guilty’). There was also variation across participants in terms of emotional responses and odor intensity perception (blank control) especially after spending a longer time in the virtual reality environment (Supplementary C). This exploratory study underscores the potential of using VR technology as a space analog to simulate context for studying sensory responses in relation to food as the current data matches anecdotal eating behavior of space travelers. Personal variation in odor perception should also be taken into consideration, especially in creating personalized meal plans for space applications.

In ultraprecision manufacturing of freeform surfaced optics and devices, the ultraprecision diamond turning process holds a significant importance. However, high costs, quality assurance and long machining times are inevitable challenges in ultraprecision manufacturing. This scientific talk presents the concept of a virtual lens model based on the requirement of ‘deterministic manufacturing’ in the ultraprecision machining process, while enabled by scientific understanding of micro cutting mechanics and its applicational affect. By analysis of the freeform surface modelling and machining toolpaths and underlying micro cutting mechanics, this research aims to define surface quality and its optical performance prior to the machining process. The research further delves into cutting force modelling and 3D surface parameters to analyze the machining toolpath, and virtual simulations and experiments are conducted. The simulations and experiments are focused on verifying the correlations between the surface characteristics, such as surface roughness, peak valley distance and most importantly, surface texture aspect ratio, and the optical performance of the freeform surface. The analysis of surface texture formation and cutting forces modelling are essential for the simulation development and experimental design. The cutting forces modelling integrates the Akins' model with the influence of continuously varying shear angles on the freeform surface. Toolpath data from the cutting process is used to meticulously analyze depth-of-cut (DoC), curvature variations, and shear angle variations throughout the process, and thus to enable the consistent surface texture aspect ratio at the surface generation as desired.

The development of sustainable and renewable gasoline additives is necessary to provide green engine fuels in response to environmental issues and the energy crisis. From this angle, the current study aims to assess the impact of environmentally friendly and renewable gasoline additives on generating low-carbon high-octane gasoline biofuel using multiple renewable and sustainable additives. Renewable and sustainable gasoline biofuel additives include ethanol, methanol, ethyl tertiary butyl ether (ETBE), isopropanol, and so on, which have exceptional anti-detonation properties and excellent chemical and physical characteristics. Furthermore, the base commercial gasoline components involve straight-run naphtha (SRN), naphtha made from natural gas condensate, heavy hydrocracked naphtha (HHN), gasoline Fisher Tropsch, and so on, which have poor chemical and physical attributes and low octane rating. Physicochemical characteristics and operational properties of various gasoline biofuel components are studied. Research findings indicated that these encouraging constituents have yielded favorable synergistic chemical, physical, mechanical, and environmental properties when combined with low-octane hydrocarbon fractions. Besides, the results reported that by combining with gasoline components to create high-octane gasoline, the amount and quality of low-octane gasoline fractions were optimized. The primary issues that affect the entire planet are the scarcity of water, energy, and the environment. Two of the above energy and environmental issues might be resolved by utilizing these motor gasoline components. Finally, environmental gasoline offers refining firms favorable prospects due to its low overhead, improved product grade, and significant environmental impact.

Beach wrack plays an important role as an ecosystem engineer with its function to beach fauna in and off the water and its role to dune formation and preventing coastal erosion at land.

The seasonality and species composition of beach wrack at the micro-tidal coast of the Baltic Sea was evaluated at the island of Poel and litterbag experiments were conducted, both at land and in the water column of the shallow coast. Special interest was given to the seasonal decay of seagrass of the species Zostera marina in the water until its complete disintegration. The decomposition experiments were divided into a light group in translucent mesh bags, and a dark group in black mesh bags: The influence of abiotic parameters on the microbial community during decay was the objective of these experiments.

The results provide an important insight into the decomposition processes of seagrass at a micro-tidal coast, its lasting effects on beach management processes at recreational beaches and coasts and also into a balanced co-existence between humanity and nature.

Poverty is as old as human civilization, hard to eradicate, multidimensional, and difficult to measure. The methods used to measure poverty today are costly, labor-intensive, and time-consuming. Therefore, policymakers find it difficult to target policies when putting poverty reduction initiatives into action. Indigenous communities are among the most disadvantaged and vulnerable populations in society. Their socioeconomic situations are complex and multifaceted. While research on poverty is usually generic, prone to large sampling errors, and intended to guide national policy, research on indigenous people is qualitative. Thus, to measure multidimensional poverty with disaggregated techniques, this work blends machine learning and econometrics. Researchers who have been studying poverty worldwide can replicate all of the approaches, strategies, and resources used in this study. With the best R-square and accuracy, random forest models perform better than all regressors and classifiers combined. It also confirms the causal relationship and current econometric association between multidimensional characteristics and poverty consequences. This study demonstrates the viability of using machine learning to predict poverty in a way that can save costs, cut labor, and maximize time to empower indigenous communities and alleviate the poverty of impoverished societies in the poorest region of Luzon, Philippines.