Modelling and simulation techniques for forced convection heat transfer in heat sinks with rectangular fins

The official published version of this article can be found at the link below.This paper provides a comprehensive description of the thermal conditions within a heat sink with rectangular fins under conditions of cooling by laminar forced convection. The analysis, in which increasing complexity is progressively introduced, uses both classical heat transfer theory and a computational approach to model the increase in air temperature through the channels formed by adjacent fins and the results agree well with published experimental data. The calculations show how key heat transfer parameters vary with axial distance, in particular the rapid changes in heat transfer coefficient and fin efficiency near the leading edges of the cooling fins. Despite these rapid changes and the somewhat ill-defined flow conditions which would exist in practice at the entry to the heat sink, the results clearly show that, compared with the most complex case of a full numerical simulation, accurate predictions of heat sink performance are attainable using analytical methods which incorporate average values of heat transfer coefficient and fin efficiency. The mathematical modelling and solution techniques for each method are described in detail.This work was part of a project funded by Solas Technology Limited, Ireland

An Analysis of Likely Scalants in the Treatment of Produced Water from Nova Scotia

A significant barrier to further use of hydraulic fracturing to recover shale oil and/or gas is the treatment and/or disposal of hypersaline produced water. This work is an analysis of produced water from Nova Scotia, with the aim of understanding how scale impacts the choice of desalination system used in its treatment. Four water samples are presented, and for a representative case, the supersaturation of some likely scalants is estimated as a function of temperature, recovery ratio, and pH. This supersaturation map is then compared to conditions representative of common desalination systems, allowing the identification of limitations imposed by the water's composition. In contrast to many natural waters, it is found that sodium chloride is the most likely first solid to form at high recovery ratios, and that the top temperature of thermal desalination systems is unlikely to be scale-limited in the treatment of these waters.Center for Clean Water and Clean Energy at MIT and KFUPM (Project R4-CW-08

Anti-Treponema pallidum IgA response as a potential diagnostic marker of syphilis

Objectives: Serological tests for syphilis detect mainly total Ig, IgM or IgG antibodies. We aimed to evaluate the specific IgA response in syphilis patients according to disease stage. Methods: A serum IgA-enzyme immunoassay was developed using commercially available microplates coated with recombinant treponemal antigens and an anti-IgA-conjugate. To define a cut-off, we used 91 syphilis positive and 136 negative sera previously defined by the rapid plasma reagin and the Treponema pallidum particle agglutination results. Then we determined the intra- and inter-assay precisions, diagnostic sensitivity according to the clinical stage (in 66, 55 and 42 sera from primary, secondary and latent syphilis patients, respectively) and specificity (in 211 sera from people with conditions different to syphilis). IgA values were further measured in 71 sera from patients with previously treated syphilis. Results: The newly developed IgA-enzyme immunoassay showed a good discrimination between negative and positive samples with intra- and inter-assay variation coefficients <20%. The sensitivity was 80.3% (95% CI, 70.0-90.6), 100.0% (95% CI, 99.1-100.0) and 95.2% (95% CI, 87.6-100.0) in primary, secondary and latent syphilis, respectively, and the specificity was 98.1% (95% CI, 96.0-100.0). Further, IgA values were negative in 61.3% (38/62) of patients with previously treated syphilis. Discussion: Our findings suggest serum IgA as a sensitive and specific marker of syphilis and its detection could be used as a screening assay for active infection. Further evaluation is needed in prospective longitudinal field studies

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

The effect of increased top brine temperature on the performance and design of OT-MSF using a case study

A mathematical model of a Once-Through Multi-Stage-Flash (OT-MSF) desalination system is developed. This study shows the impact of top brine temperature (TBT) of up to 160 °C on both the design and performance characteristics of MSF systems. Such a high TBT can be achieved by nanofiltration pretreatment to remove scale-forming compounds. System performance is evaluated by the thermal performance ratio (PR) and the required specific area (sA). For a fixed brine reject temperature (Tend) and inter-stage temperature drop (ΔT), adding stages results in the TBT increasing by ΔT for each stage added and the PR increases monotonically with the TBT. On the other hand, the required sA decreases and then increases again beyond a certain TBT. The Sirte desalination plant in Libya is taken as a case study. It is found that by increasing the TBT to 161 °C from a typical value of 118 °C keeping Tend and ΔT fixed; the PR can be increased by 41.5%, reaching a value of 14.6 while the required sA increases by 0.9%. Although there is a penalty in terms of the increased number of stages required to achieve this arrangement, there is a clear advantage in terms of PR, with a relatively small compromise in sA.King Fahd University of Petroleum and MineralsMassachusetts Institute of Technology. Center of Excellence for Research Collaboratio

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

Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types

Background:There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually.Methods:In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data.Results:We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: https://knowledgebase.microdiscovery.de/heatmap.Conclusions:Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the inter-active knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs

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

ENERGY REQUIREMENT OF ALTERNATIVE TECHNOLOGIES FOR DESALINATING GROUNDWATER FOR IRRIGATION

Increased global water demand coupled with limited water resources has led to acute water shortage in many regions, significantly affecting a griculture, which is the world’s largest consumer of water. Groundwater resources are thus increasingly being used to meet irrigation requirements. However, groundwater resources around the world tend to be saline ( 0.5 ≤ S ≤ 5 g/kg ) rquiring desalination before use. Furthermore, with decreasing water availability, demands for producing permeate from the feed at higher recoveries (>85%) is also increasing. In this work, a thermodynamic least work analysis for desalination and pumping ground water is developed first. Then, the actual energy required by high recovery desalination technologies such as brackish water reverse osmosis (RO), closed circuit reverse osmosis (CCRO) and electrodialysis reversal (EDR) are compared with the thermodynamic least work of desalination from 50-95% recovery. CCRO consumed the least energy until a recovery of 92% after which EDR consumed the least energy. While the energy required for RO and CCRO changed with recovery, EDR energy consumption remained approximately constant at 0.85 kWh/m³. Water table depth was also found to significantly contribute to the total energy consumed, with the power required to pump groundwater being comparable to the desalination power requirements at water table depths greater than 50 m. Thus, the choice of selection of desalination technologies is particularly crucial for water table depths less than 50 m