A multiple regression assessment of the biomineral urease activity from urine drainpipes of California public restrooms

Clogging and odor is strongly associated with ureolytic biomineralization in waterless and low-flow urinal drainage systems in high usage settings. These blockages continue to hinder widespread waterless and low-flow urinal adoption due to subsequent high maintenance requirements and hygiene concerns. Through field observations, hypothesis testing, and multiple regression analysis, this study attempts to characterize, for the first time, the ureolytic activity of the biomineralization found in alternative technologies located at 9 State-owned restrooms. Multiple regression analysis (n = 55, df = 4, R2 = 0.665) suggests that intrasystem sampling location ($$ \hat{\upbeta} $$ β ̂ = 1.23, p < 0.001), annual users per rest area ($$ \hat{\upbeta} $$ β ̂ = 0.5, p = 0.004), and the volatile solids to total solids mass fraction ($$ \hat{\upbeta} $$ β ̂ = 0.59, p = 0.003), are statistically significant influencers of the ureolytic activity of biomineral samples (p < 0.05). Conversely, ureC gene abundance (p = 0.551), urinal type (p = 0.521) and sampling season (p = 0.956) are not significant predictors of biomineral ureolytic activity. We conclude that high concentrations of the urease alpha subunit, ureC, which can be interpreted as proxy measure of a strong, potentially ureolytic community, does not necessarily mean that the gene is being expressed. Future studies should assess ureC transcriptional activity to measure gene expression rather than gene abundance to assess the relationship between environmental conditions, their role in transcription, and urease activities. In sum, this study presents a method to characterize biomineral ureolysis. This study establishes baseline values for future ureolytic inhibition treatment studies that seek to improve the usability of urine collection and related source separation technologies.

Applying Eigenstructure Assignment to Inner-Loop Flight Control Laws for a Multibody Aircraft

Unmanned aircraft used as high-altitude platform system has been studied in research and industry as alternative technologies to satellites. Regarding actual operation and flight performance of such systems, multibody aircraft seems to be a promising aircraft configuration. In terms of flight dynamics, this aircraft strongly differs from classical rigid-body and flexible aircraft, because a strong interference between flight mechanic and formation modes occurs. For unmanned operation in the stratosphere, flight control laws are required. While control theory generally provides a number of approaches, the specific flight physics characteristics can be only partially considered. This paper addresses a flight control law approach based on a physically exact target model of the multibody aircraft dynamics rather than conventionally considering the system dynamics only. In the target model, hypothetical spring and damping elements at the joints are included into the equations of motion to transfer the configuration of a highly flexible multibody aircraft into one similar to a classical rigid-body aircraft. The differences between both types of aircraft are reflected in the eigenvalues and eigenvectors. Using the eigenstructure assignment, the desired damping and stiffness are established by the inner-loop flight control law. In contrast to other methods, this procedure allows a straightforward control law design for a multibody aircraft based on a physical reference model.

A Quality Control Check to Ensure Comparability of Stereophotogrammetric Data between Session and Systems

Optoelectronic stereophotogrammetric (SP) systems are widely used in human movement research for clinical diagnostics, interventional applications, and as a reference system for validating alternative technologies. Regardless of the application, SP systems exhibit different random and systematic errors depending on camera specifications, system setup and laboratory environment, which hinders comparing SP data between sessions and across different systems. While many methods have been proposed to quantify and report the errors of SP systems, they are rarely utilized due to their complexity and need for additional equipment. In response, an easy-to-use quality control (QC) check has been designed that can be completed immediately prior to a data collection. This QC check requires minimal training for the operator and no additional equipment. In addition, a custom graphical user interface ensures automatic processing of the errors in an easy-to-read format for immediate interpretation. On initial deployment in a multicentric study, the check (i) proved to be feasible to perform in a short timeframe with minimal burden to the operator, and (ii) quantified the level of random and systematic errors between sessions and systems, ensuring comparability of data in a variety of protocol setups, including repeated measures, longitudinal studies and multicentric studies.

geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq

AbstractscRNA-seq datasets are increasingly used to identify gene panels that can be probed using alternative technologies, such as spatial transcriptomics, where choosing the best subset of genes is vital. Existing methods are limited by a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cells. We introduce an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. Our approach outperforms existing strategies and can resolve cell types and subtle cell state differences.

Accessible data curation and analytics for international-scale citizen science datasets

The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. As of May 23rd, 2021, over 5 million participants have collectively logged over 360 million self-assessment reports since its introduction in March 2020. The success of the Covid Symptom Study creates significant technical challenges around effective data curation. The primary issue is scale. The size of the dataset means that it can no longer be readily processed using standard Python-based data analytics software such as Pandas on commodity hardware. Alternative technologies exist but carry a higher technical complexity and are less accessible to many researchers. We present ExeTera, a Python-based open source software package designed to provide Pandas-like data analytics on datasets that approach terabyte scales. We present its design and capabilities, and show how it is a critical component of a data curation pipeline that enables reproducible research across an international research group for the Covid Symptom Study.

Alternative technologies of composite high-loaded aircraft constructions: a qualitative method of making multicriterial decisions. Part I. Initial stages in the problem of decision-making

Comparison of efficiency based on the qualitative decision-making method of autoclave and non-autoclave technologies for the manufacture of carbon-fiber aircraft structures such as aircraft wing planes B787, A350, MC-21, Cseries. The differences and advantages in the serial production of prepreg-autoclave and VARTM technological process. The comparison was carried out on sufficient presentational basis of criteria of two levels using combinatorial and decomposition research method. Each variant of the process is characterized by evaluations according to selection criteria. Their scores are vector assessment of the criteria based on expert judgment and opinion of the decision maker. Noted that each option has own boundaries of compatibility and practical application.

Refrigerated Transport: State of the Art, Technical Issues, Innovations and Challenges for Sustainability

The cold chain is responsible for perishable products preservation and transportation, maintaining a proper temperature to slow biological decay processes. Often the efficiency of the cold chain is less than ideal, significantly increasing food waste and energy consumption. Refrigerated transport is a critical phase of the cold chain because of its negative impact on energy consumption and greenhouse gas emissions. It is estimated that around 15% of global fossil fuel energy is used in the refrigerated transport sector, so there has been a growing interest in the last decades in the optimization of these systems in order to reduce their environmental impact. Vapor compression refrigeration units, usually powered by means of a diesel engine, are the most commonly used systems in road refrigerated transport. This paper provides a review of (a) currently used systems and alternative technologies that could reduce the environmental impacts of road refrigerated transport and (b) optimization models and methods used to minimize fuel/energy consumption and greenhouse gas emissions, focusing both on reducing the thermal loads and solving the refrigerated vehicle routing problem.

Insight on Current Advances in Food Science and Technology for Feeding the World Population

While the world population is steadily increasing, the capacity of Earth to renew its resources is continuously declining. Consequently, the bioresources required for food production are diminishing and new approaches are needed to feed the current and future global population. In the last decades, scientists have developed novel strategies to reduce food loss and waste, improve food production, and find new ingredients, design and build new food structures, and introduce digitalization in the food system. In this work, we provide a general overview on circular economy, alternative technologies for food production such as cellular agriculture, and new sources of ingredients like microalgae, insects, and wood-derived fibers. We present a summary of the whole process of food design using creative problem-solving that fosters food innovation, and digitalization in the food sector such as artificial intelligence, augmented and virtual reality, and blockchain technology. Finally, we briefly discuss the effect of COVID-19 on the food system. This review has been written for a broad audience, covering a wide spectrum and giving insights on the most recent advances in the food science and technology area, presenting examples from both academic and industrial sides, in terms of concepts, technologies, and tools which will possibly help the world to achieve food security in the next 30 years.