Experimental evidence for excess entropy discontinuities in glass-forming solutions

Glass transition temperatures T_g are investigated in aqueous binary and multi-component solutions consisting of citric acid, calcium nitrate (Ca(NO_3)_2), malonic acid, raffinose, and ammonium bisulfate (NH_4HSO_4) using a differential scanning calorimeter. Based on measured glass transition temperatures of binary aqueous mixtures and fitted binary coefficients, the T_g of multi-component systems can be predicted using mixing rules. However, the experimentally observed T_g in multi-component solutions show considerable deviations from two theoretical approaches considered. The deviations from these predictions are explained in terms of the molar excess mixing entropy difference between the supercooled liquid and glassy state at T_g. The multi-component mixtures involve contributions to these excess mixing entropies that the mixing rules do not take into account

A Numerical Solution Algorithm for a Heat and Mass Transfer Model of a Desalination System Based on Packed-Bed Humidification and Bubble Column Dehumidification

The humidification-dehumidification (HDH) desalination system can be advantageous in small-scale, off-grid applications. The main drawback of this technology has been its low energy efficiency, which results in high water production costs. Previous studies have approached this issue through thermodynamic balancing of the system; however, most theoretical work on the balancing of HDH has followed a fixed-effectiveness approach that does not explicitly consider transport processes in the components. Fixing the effectiveness of the heat and mass exchangers allows them to be modeled without explicitly sizing the components and gives insight on how the cycle design can be improved. However, linking the findings of fixed-effectiveness models to actual systems can be challenging, as the performance of the components depends mainly on the available surface areas and the flow rates of the air and water streams. In this study, we present a robust numerical solution algorithm for a heat and mass tranfer model of a complete humidification-dehumidification system consisting of a packed-bed humidifier and a multi-tray bubble column dehumidifier. We look at the effect of varying the water-to-air mass flow rate ratio on the energy efficiency of the system, and we compare the results to those reached following a fixed-effectiveness approach. In addition, we study the effect of the top and bottom temperatures on the performance of the system. We recommended the implementation a control system that varies the mass flow rate ratio in order to keep the system balanced in off-design conditions, especially with varying top temperature.Center for Clean Water and Clean Energy at MIT and KFUPM (Project R4-CW-08

Principles in the Design of Mobile Medical Apps: Guidance for Those who Care

The promises of mobile technology in healthcare have led to a great many mobile apps in public app stores that target patients with specific illnesses. Medical experts have criticized the status quo of mobile medical apps owing to the low level of professional medical involvement in mobile app design, leading to weak clinical performance and a poor integration of these tools into clinical practice. Grounded in an action design research study, we build and evaluate a mobile app for elderly patients with age-related macular degeneration. We formalize our learnings and provide a set of design principles to guide the effective and feasible construction of mobile medical apps. Our study systematically develops design knowledge that helps to bridge the current gap between the rapid advances in mobile technology and the specific needs of the healthcare sector

Entropy Generation Analysis of Desalination Technologies

Increasing global demand for fresh water is driving the development and implementation of a wide variety of seawater desalination technologies. Entropy generation analysis, and specifically, Second Law efficiency, is an important tool for illustrating the influence of irreversibilities within a system on the required energy input. When defining Second Law efficiency, the useful exergy output of the system must be properly defined. For desalination systems, this is the minimum least work of separation required to extract a unit of water from a feed stream of a given salinity. In order to evaluate the Second Law efficiency, entropy generation mechanisms present in a wide range of desalination processes are analyzed. In particular, entropy generated in the run down to equilibrium of discharge streams must be considered. Physical models are applied to estimate the magnitude of entropy generation by component and individual processes. These formulations are applied to calculate the total entropy generation in several desalination systems including multiple effect distillation, multistage flash, membrane distillation, mechanical vapor compression, reverse osmosis, and humidification-dehumidification. Within each technology, the relative importance of each source of entropy generation is discussed in order to determine which should be the target of entropy generation minimization. As given here, the correct application of Second Law efficiency shows which systems operate closest to the reversible limit and helps to indicate which systems have the greatest potential for improvement.King Fahd University of Petroleum and MineralsCenter for Clean Water and Clean Energy at MI

New and extended parameterization of the thermodynamic model AIOMFAC: calculation of activity coefficients for organic-inorganic mixtures containing carboxyl, hydroxyl, carbonyl, ether, ester, alkenyl, alkyl, and aromatic functional groups

We present a new and considerably extended parameterization of the thermodynamic activity coefficient model AIOMFAC (Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients) at room temperature. AIOMFAC combines a Pitzer-like electrolyte solution model with a UNIFAC-based group-contribution approach and explicitly accounts for interactions between organic functional groups and inorganic ions. Such interactions constitute the salt-effect, may cause liquid-liquid phase separation, and affect the gas-particle partitioning of aerosols. The previous AIOMFAC version was parameterized for alkyl and hydroxyl functional groups of alcohols and polyols. With the goal to describe a wide variety of organic compounds found in atmospheric aerosols, we extend here the parameterization of AIOMFAC to include the functional groups carboxyl, hydroxyl, ketone, aldehyde, ether, ester, alkenyl, alkyl, aromatic carbon-alcohol, and aromatic hydrocarbon. Thermodynamic equilibrium data of organic-inorganic systems from the literature are critically assessed and complemented with new measurements to establish a comprehensive database. The database is used to determine simultaneously the AIOMFAC parameters describing interactions of organic functional groups with the ions H^+, Li^+, Na^+, K^+, NH_(4)^+, Mg^(2+), Ca^(2+), Cl^−, Br^−, NO_(3)^−, HSO_(4)^−, and SO_(4)^(2−). Detailed descriptions of different types of thermodynamic data, such as vapor-liquid, solid-liquid, and liquid-liquid equilibria, and their use for the model parameterization are provided. Issues regarding deficiencies of the database, types and uncertainties of experimental data, and limitations of the model, are discussed. The challenging parameter optimization problem is solved with a novel combination of powerful global minimization algorithms. A number of exemplary calculations for systems containing atmospherically relevant aerosol components are shown. Amongst others, we discuss aqueous mixtures of ammonium sulfate with dicarboxylic acids and with levoglucosan. Overall, the new parameterization of AIOMFAC agrees well with a large number of experimental datasets. However, due to various reasons, for certain mixtures important deviations can occur. The new parameterization makes AIOMFAC a versatile thermodynamic tool. It enables the calculation of activity coefficients of thousands of different organic compounds in organic-inorganic mixtures of numerous components. Models based on AIOMFAC can be used to compute deliquescence relative humidities, liquid-liquid phase separations, and gas-particle partitioning of multicomponent mixtures of relevance for atmospheric chemistry or in other scientific fields

Thermal characteristics of a classical solar telescope primary mirror

We present a detailed thermal and structural analysis of a 2m class solar telescope mirror which is subjected to a varying heat load at an observatory site. A 3-dimensional heat transfer model of the mirror takes into account the heating caused by a smooth and gradual increase of the solar flux during the day-time observations and cooling resulting from the exponentially decaying ambient temperature at night. The thermal and structural response of two competing materials for optical telescopes, namely Silicon Carbide -best known for excellent heat conductivity and Zerodur -preferred for its extremely low coefficient of thermal expansion, is investigated in detail. The insight gained from these simulations will provide a valuable input for devising an efficient and stable thermal control system for the primary mirror.Comment: 14 pages, 8 figures, Accepted for publication in New Astronom

Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction

Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases

Entering the World of Individual Routines: The Affordances of Mobile Applications

The IS discipline has a long tradition in investigating how new technologies affect work practices, but has mostly focused on the organizational level. With mobile applications, we are facing a new technology wave that is centered on the individual users. Despite their popularity, mobile applications' possibilities to enhance an individual's knowledge, skills, and competence in daily work practices have not been studied in a systematic way. Building on the concept of routines from organizational theory and insights from two field studies, we investigate mobile applications acting as material artifacts and their possibilities of goal-oriented actions in individual routines. Our main contributions are the extension of Pentland & Feldman's generative system model and a set of affordances that mobile applications bring to individual routines. Our findings complement recent studies on routines at the organizational level and contribute to enhance artifact design knowledge for mobile applications beyond "interaction design"

Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data