Performance Analysis of a Solar-powered Ejector Air-conditioning Cycle with Heavier Hydrocarbons as Refrigerants

AbstractIn this paper the overall performance of the solar-powered ejector air-conditioning system, using pentane (R601) and hexane (R602) as a refrigerants, is presented. The modeling process and the efficiency of solar vapor generator (SVG) were shown. The effects of condenser and generator temperatures were examined. Simulation results indicated that condensing temperature had a strong influence on the ejector's performance. Maximum overall systems’ value of COP, obtained for refrigerants R601 and R602, were 0.1 and 0.5, respectively, at Te=12°C, Tc=30°C, and G=800 W·m-2. Generator temperatures were 100–190°C for pentane and 100–200°C for hexane

The influence of Si/Al ratio on the distribution of OH groups in zeolites with MWW topology

A series of MWW-type zeolites of increasing Si/Al ratio were investigated with respect to their acidic properties. Concentration of the Brønsted acid centers located at the external crystal surface was invariant for the entire series. Ethanol conversion to ethyl-tert-butyl ether, proceeding only at the external surface, was also constant. The OH groups in MWW zeolites were found to be homogeneous with proton affinity value equal to 1142.7 kJ/mol

Analysis of Distributed Systems Dynamics with Erlang Performance Lab

Modern, highly concurrent and large-scale systems require new methods for design, testing and monitoring. Their dynamics and scale require real-time tools, providing a holistic view of the whole system and the ability of showing a more detailed view when needed. Such tools can help identifying the causes of unwanted states, which is hardly possible with static analysis or metrics-based approach. In this paper a new tool for analysis of distributed systems in Erlang is presented. It provides real-time monitoring of system dynamics on different levels of abstraction. The tool has been used for analyzing a large-scale urban traffic simulation system running on a cluster of 20 computing nodes

Role of transplantation in treatment of multiple myeloma in era of novel agents

Multiple myeloma (MM) is a B-cell malignancy characterized by clonal proliferation of plasma cells. Despite the introduction of novel agents such as immunomodulatory drugs, proteasome inhibitors, and monoclonal antibodies, high-dose chemotherapy with autologous transplantation remains the primary treatment for patients with newly diagnosed multiple myeloma. This review presents the results of clinical trials assessing the effectiveness and safety of various kinds of transplantation such as single, allogeneic, tandem and salvage. Nowadays, in the era of access to new therapies, the following questions should be asked: when is the best time to perform autologous transplantation? What is the significance of allogeneic or tandem transplantation? Is the use of a second or third salvage transplant justified? Will chimeric antigen receptor T-cell (CAR-T) therapy become a valuable therapeutic method in MM? In this article, we will try to answer these questions

Could the kinetin riboside be used to inhibit human prostate cell epithelial-mesenchymal transition?

The epithelial-mesenchymal transition (EMT) is a molecular process connected to higher expression of vimentin and increased activity of transcription factors (Snail, Twist) which restrains E-cadherin. EMT has been linked to prostate cancer metastatic potential, therapy resistance, and poor outcomes. Kinetin riboside (9-(b-dribofuranosyl)-6-furfurylaminopurine, KR) is a naturally occurring cytokinin, which induces apoptosis and shows strong antiproliferative activity against various human cancer cell lines. To establish the effect of KR on human prostate cell lines, expression of, e.g. AR, E-, N-cadherins, Vimentin, Snail, Twist, and MMPs, was analysed at mRNA and protein levels using Western Blot and RT-PCR and/or RQPCR techniques. KR inhibited the growth of human prostate cancer cells, but also, to a small extent, of normal cells. This effect depended on the type of the cells and their androgen sensitivity. KR also decreased the level of p-Akt, which takes part in androgen signalling modulation. The antiapoptotic Bcl-2 protein was down-regulated in cancer cell lines, while that of Bax is up-regulated upon KR exposure. KR contributed to re-expression of the E-cadherin as well as to significant changes in cell migration. Taken together, our results indicate for the first time that KR can be proposed as a factor for signalling pathways regulation that participates in the inhibition of development of aggressive forms of prostate cancer, and may alter the approach to therapeutic interventions. We propose KR as a potent inhibitor of EMT in human prostate cells

A SPC strategy for decision making in manufacturing processes

Tapping is an extensively employed manufacturing process by which a multi-teeth tool, known as tap, cuts a mating thread when driven into a hole. When taps are new or slightly worn the process is usually in control and the geometry of the resulting threads on the work piece is correct. But as the tap wear increases the thread geometry deviates progressively from the correct one and eventually the screw threads become unacceptable.The aim of this paper consists on a development of statistical process control strategy for decision making according to data coming from the current signal of the tap spindle for assessing thread quality. It could operate on line and indicates when the tap wear is so critical that, if the process were continued, it would result in unacceptable screw threads. The system would be very cost-effective since the tapping process could be run without any operator intervention

Design of an Active Vision System for High-Level Isolation Units through Q-Learning

This article belongs to the Special Issue Applied Intelligent Control and Perception in Robotics and Automation.The inspection of Personal Protective Equipment (PPE) is one of the most necessary measures when treating patients affected by infectious diseases, such as Ebola or COVID-19. Assuring the integrity of health personnel in contact with infected patients has become an important concern in developed countries. This work focuses on the study of Reinforcement Learning (RL) techniques for controlling a scanner prototype in the presence of blood traces on the PPE that could arise after contact with pathological patients. A preliminary study on the design of an agent-environment system able to simulate the required task is presented. The task has been adapted to an environment for the OpenAI Gym toolkit. The evaluation of the agent’s performance has considered the effects of different topological designs and tuning hyperparameters of the Q-Learning model-free algorithm. Results have been evaluated on the basis of average reward and timesteps per episode. The sample-average method applied to the learning rate parameter, as well as a specific epsilon decaying method worked best for the trained agents. The obtained results report promising outcomes of an inspection system able to center and magnify contaminants in the real scanner system.The research leading to these results received funding from: Inspección robotizada de los trajes de protección del personal sanitario de pacientes en aislamiento de alto nivel, incluido el ébola, Programa Explora Ciencia, Ministerio de Ciencia, Innovación y Universidades (DPI2015-72015-EXP); the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub ("Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV"; S2018/NMT-4331), funded by "Programas de Actividades I+D en la Comunidad de Madrid" and cofunded by Structural Funds of the EU; and ROBOESPAS: Active rehabilitation of patients with upper limb spasticity using collaborative robots, Ministerio de Economía, Industria y Competitividad, Programa Estatal de I+D+i Orientada a los Retos de la Sociedad (DPI2017-87562-C2-1-R)

Ensemble of classifiers based on deep learning for medical image recognition

The paper presents special forms of an ensemble of classifiers for analysis of medical images based on application of deep learning. The study analyzes different structures of convolutional neural networks applied in the recognition of two types of medical images: dermoscopic images for melanoma and mammograms for breast cancer. Two approaches to ensemble creation are proposed. In the first approach, the images are processed by a convolutional neural network and the flattened vector of image descriptors is subjected to feature selection by applying different selection methods. As a result, different sets of a limited number of diagnostic features are generated. In the next stage, these sets of features represent input attributes for the classical classifiers: support vector machine, a random forest of decision trees, and softmax. By combining different selection methods with these classifiers an ensemble classification system is created and integrated by majority voting. In the second approach, different structures of convolutional neural networks are directly applied as the members of the ensemble. The efficiency of the proposed classification systems is investigated and compared to medical data representing dermoscopic images of melanoma and breast cancer mammogram images. Thanks to fusion of the results of many classifiers forming an ensemble, accuracy and all other quality measures have been significantly increased for both types of medical images

New method to increase pesticide deposition: Copper microencapsulation

Copper plant protection products have been severely restricted in the EU according to soil and groundwater contamination due to the traditional use as a fungicide, especially in vineyards. This limitation, together with the dependence on copper use for mildew control, places winegrowers in a great disadvantage, especially in organic production. Therefore, EURECAT together with the UPC, have developed a new copper product, more efficient in terms of deposition, in order to reduce the amount of active ingredient necessary for good disease control. Preliminary trials have been carried out by the UPC, in order to select the best formulation and to compare it with a conventional copper-based product in terms of deposition using filter paper as a collector in an artificial vineyard. The obtained results show that deposition of different developed products is statistically different from the control product, even doubling the amount of copper deposited in the collectors, which would be a promising solution to solve the problem outlined above.The presented research is part of the COPPEREPLACE project (INTERREG SUDOE, Ref. SOE4/ P1/E100) in the frame of the activity 3.3 in the GT3.Postprint (published version