Response of human Leukemia cell upon Treatment with bioactive extracts from tropical medical mushrooms

In der vorliegenden Arbeit wurde ein tropischer, holzzerstörender Pilz Phellinus pachyphloeus (Pat.) Pat. unter Verwendung klassischer und moderner molekularer Analytik auf seine Wirkung auf die Leukämiezelllinie HL-60 untersucht. Zunächst wurde festgestellt, dass die Bioakkumulation von Schwermetallen durch Phellinus pachyphloeus keinen negativen Einfluss auf die menschliche Zelllinie hat. Im nächsten Schritt wurden die antiproliferativen Effekte der bioaktiven Komponenten aus Wasserextrakten dieses Pilzes untersucht. Dosis-Wirkungsversuche bestätigten eine antiproliferative Wirkung der bioaktiven Pilzextrakte. Diese wurde als apoptotischer Effekt eingegrenzt. Unsere Ergebnisse zeigten, dass die Wasserextrakte aus Phellinus pachyphloeus keinen cytotoxischen sondern einen apoptotischen Effekt in humanen Leukämiezellen induzieren könne. Die induzierte Apoptose erfolgte sowohl über den äußeren, Rezeptorvermittelten, als auch den mitochondrialen Signalweg

Fixed-bed column recirculation system for investigation of sorption and biodegradation of organic pollutants in saturated sediment: a detailed design and development

Background: Sorption and biodegradation are the primary processes of organic pollution remediation in aquatic and soil/sediment environments. While researchers have substantially reported their findings regarding these processes, little attention has been given to description of experimental apparatus. This technical paper aims to present the development and detailed design of a fixed-bed column recirculation (FBCR) system which has been widely applied to investigate sorption and biodegradation of organic pollutants in aquatic and/or sediment environments. Findings: The FBCR system was developed and tested by three experiments investigating sorption and biodegradation of two herbicides (isoproturon and mecoprop) in different saturated materials (hydrofilt and river sediment). Efficiency of the FBCR system was assessed according to criteria i.e. reliability, leaking inhibition, reproducibility, practical of use and cost. The results indicated that the latest version (Version 4) of the FBCR system has been significantly improved and ready to extend to similar studies. Conclusions: This system is therefore recommended to researchers who intend to investigate the remediation of organic pollutants in aquatic, soil and sediment environments

Dynamic structure identification of Bayesian network model for fault diagnosis of FMS

International audienceThis paper proposes an approach to accurately localize the origin of product quality drifts, in a flexible manufacturing system (FMS). The logical diagnosis model is used to reduce the search space of suspected equipment in the production flow; however, it does not help in accurately localizing the faulty equipment. In the proposed approach, we model this reduced search space as a Bayesian network that uses historical data to compute conditional probabilities for each suspected equipment. This approach helps in making accurate decisions on localizing the cause for product quality drifts as either one of the equipment in production flow or product itself

Diagnosis in complex system with multiple failure sources

International audienceThis paper proposes an approach to accurately localize the origin of product quality drifts, in a flexible manufacturing system (FMS). The failure propagation mechanism in a production process is proposed based on the relationships between failure sources to explain the failure propagation following production flow. The logical diagnosis model is used to reduce the search space of suspected equipment in the production flow; however, it does not help in accurately localizing the faulty equipment. In the proposed approach, we model this reduced search space as a Bayesian network that uses historical data to compute conditional probabilities for each suspected equipment. This approach helps in making accurate decisions on localizing the cause for product quality drifts as either one of the equipment in production flow or product itself

A deep reinforcement learning-based offloading scheme for multi-access edge computing-supported eXtended reality systems

In recent years, eXtended Reality (XR) applications have been widely employed in various scenarios, e.g., health care, education, manufacturing, etc. Such application are now easily accessible via mobile phones, tablets, or wearable devices. However, such devices normally suffer from constraints in terms of battery capacity and processing power, limiting the range of applications supported or lowering Quality of Experience. One effective way to address these issues is to offload the computation tasks to the edge servers that are deployed at the network edges, e.g., base stations or WiFi access point, etc. This communication fashion, also named as Multi-access Edge Computing (MEC), is proposed to overcome the limitations in terms of long latency due to long propagation distance of traditional cloud computing approach. XR devices, that are limited in computation resources and energy, can then benefit from offloading the computation intensive tasks to MEC servers. However, as XR applications are comprised of multiple tasks with variety of requirements in terms of latency and energy consumption, it is important to make decision whether one task should be offloaded to MEC server or not. This paper proposes a Deep Reinforcement Learning-based offloading scheme for XR devices (DRLXR). The proposed scheme is used to train and derive the close-to optimal offloading decision whereas optimizing a utility function optimization equation that considers both energy consumption and execution delay at XR devices. The simulation results show how our proposed scheme outperforms the other counterparts in terms of total execution latency and energy consumption

A deep reinforcement learning-based resource management scheme for SDN-MEC-supported XR applications

The Multi-Access Edge Computing (MEC) paradigm provides a promising solution for efficient computing services at edge nodes, such as base stations (BS), access points (AP), etc. By offloading highly intensive computational tasks to MEC servers, critical benefits in terms of reducing energy consumption at mobile devices and lowering processing latency can be achieved to support high Quality of Service (QoS) to many applications. Among the services which would benefit from MEC deployments are eXtended Reality (XR) applications which are receiving increasing attention from both academia and industry. XR applications have high resource requirements, mostly in terms of network bandwidth, computation and storage. Often these resources are not available in classic network architectures and especially not when XR applications are run by mobile devices. This paper leverages the concepts of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to propose an innovative resource management scheme considering heterogeneous QoS requirements at the MEC server level. The resource assignment is formulated by employing a Deep Reinforcement Learning (DRL) technique to support high quality of XR services. The simulation results show how our proposed solution outperforms other state-of-the-art resource management-based schemes

Factors of Purchase Intentions toward Foreign Products: Empirical Evidence from Vietnamese Consumers’ Perspective

This study empirically explores factors affecting consumers’ purchase intentions toward foreign products, and their impact magnitudes in six major cities of Vietnam (including Ha Noi, Hai Phong, Da Nang, Ho Chi Minh, Binh Duong, and Can Tho). Our results illustrate that Vietnamese consumers’ purchase intentions toward foreign products are positively affected by Perceived quality, Perceived prestige, Perceived value, and Influence of others. Notably, Perceived prestige has the strongest impact on consumers’ purchase intentions. The findings of this study enrich the international marketing literature on the consumer evaluation of foreign products in developing countries like Vietnam as well as assist practitioners to build more appropriate marketing strategies for targeting emerging markets

Beyond First/Last Mile Active Transportation - BikeShare@UH

Bike sharing is a new green transportation solution that has been developed and adopted at various cities around the world. In this paper, we present the process and results of the design and prototypes that a group of undergraduate students developed for a BikeShare@UH program during Summer 2017. After presenting the detailed results of four project teams focusing on customer discovery, bike share station (BSS) location identification, cloud-based mobile computing platform for user engagement and bike share program operation and management, smart lock, and alternative energy source based on PV panel. With the phase one implementation at the University planned in Spring 2018, we anticipate gathering real time data and feedback to improve the system.Cockrell School of Engineerin

The perspective of psychology students on the areas of psychology

Perception is defined as the result of an awareness process about phenomena, things (living animals, plants, or humans), connections between objects by activities including noticing, observing, differentiating, and acknowledging. A recent study was conducted to investigate the undergraduate’s perception of areas of psychology. This research used the Vietnamese version of the Scale of Interests by Areas of Psychology (EIAPsi), including ten subscales to survey 252 psychology students (57 males and 195 females) from four universities in Vietnam. The findings showed significant effects of university and major on psychology undergraduate’s perception of areas of psychology. Students majoring in Counseling and Clinical Psychology had more general knowledge about the functions and roles of Clinical and Health Psychology and Neuropsychology than other undergraduates. Industrial and Organizational Psychology students had more general knowledge about Organizational Psychology than students in other majors