Distributed Supervisory Control of Discrete-Event Systems with Communication Delay

This paper identifies a property of delay-robustness in distributed supervisory control of discrete-event systems (DES) with communication delays. In previous work a distributed supervisory control problem has been investigated on the assumption that inter-agent communications take place with negligible delay. From an applications viewpoint it is desirable to relax this constraint and identify communicating distributed controllers which are delay-robust, namely logically equivalent to their delay-free counterparts. For this we introduce inter-agent channels modeled as 2-state automata, compute the overall system behavior, and present an effective computational test for delay-robustness. From the test it typically results that the given delay-free distributed control is delay-robust with respect to certain communicated events, but not for all, thus distinguishing events which are not delay-critical from those that are. The approach is illustrated by a workcell model with three communicating agents

Intrapersonal and interpersonal dimensions of cancer perception: a confirmatory factor analysis of the cancer experience and efficacy scale (CEES)

Purpose Sociocultural factors influence psychological adjustment to cancer in Asian patients in two major ways: Prioritization of relationships over individual orientations and belief in the efficacy of interpersonal cooperation. We derived and validated among Chinese colorectal cancer (CRC) patients an instrument assessing cancer perceptions to enable the study of the sociocultural processes. Patients and methods Qualitative interviews (n=16) derived 15 items addressing interpersonal experience in Chinese CRC patients' adjustment. These 15 items and 18 corresponding self-referent items were administered to 166 Chinese CRC survivors and subjected to exploratory factor analysis (EFA) to establish the initial scale structure and reliability. The final 29 items, together with other psychometric measures, were administered to a second cohort of 215 CRC patients and subjected to confirmatory factor analysis (CFA). Results EFA (63.35% of the total variance) extracted six factors: Personal strain, socioeconomic strain, emotional strain, personal efficacy, collective efficacy, and proxy efficacy. CFA confirmed the psychometric structure [?2(df)=702.91 (368); Comparative Fit Index=0.95; Nonnormed Fit Index= 0.94; Incremental Fit Index=0.95; standardized root mean square residual=0.08] of the six factors by using a model with two latent factors: Experience and efficacy. All subscales were reliable (a=0.76-0.92). Appropriate correlations with adjustment outcomes (symptom distress, psychological morbidity, and subjective well-being), optimistic personalities, and social relational quality indicated its convergent and divergent validity. Known group comparisons (i.e., age, active treatment, and colostomy) showed its clinical utility. Conclusion The cancer experience and efficacy scale is a valid multidimensional instrument for assessing intrapersonal and interpersonal dimensions of cancer experience in Asian patients, potentiating existing patient-reported outcome measures. © Springer-Verlag 2009.published_or_final_versionSpringer Open Choice, 01 Dec 201

Nutrition and cancer: A review of the evidence for an anti-cancer diet

It has been estimated that 30–40 percent of all cancers can be prevented by lifestyle and dietary measures alone. Obesity, nutrient sparse foods such as concentrated sugars and refined flour products that contribute to impaired glucose metabolism (which leads to diabetes), low fiber intake, consumption of red meat, and imbalance of omega 3 and omega 6 fats all contribute to excess cancer risk. Intake of flax seed, especially its lignan fraction, and abundant portions of fruits and vegetables will lower cancer risk. Allium and cruciferous vegetables are especially beneficial, with broccoli sprouts being the densest source of sulforophane. Protective elements in a cancer prevention diet include selenium, folic acid, vitamin B-12, vitamin D, chlorophyll, and antioxidants such as the carotenoids (α-carotene, β-carotene, lycopene, lutein, cryptoxanthin). Ascorbic acid has limited benefits orally, but could be very beneficial intravenously. Supplementary use of oral digestive enzymes and probiotics also has merit as anticancer dietary measures. When a diet is compiled according to the guidelines here it is likely that there would be at least a 60–70 percent decrease in breast, colorectal, and prostate cancers, and even a 40–50 percent decrease in lung cancer, along with similar reductions in cancers at other sites. Such a diet would be conducive to preventing cancer and would favor recovery from cancer as well

Towards intelligent datacenter traffic management: Using automated fuzzy inferencing for elephant flow detection

© 2014, IJICIC Editorial Office, Inc. All rights reserved.Effective traffic management has always been one of the key considerations in datacenter design. It plays an even more important role today in the face of increasingly widespread deployment of communication intensive applications and cloud- based services, as well as the adoption of multipath datacenter topologies to cope with the enormous bandwidth requirements arising from those applications and services. Of central importance in traffic management for multipath datacenters is the problem of timely detection of elephant flows flows that carry huge amount of data so that the best paths can be selected for these flows, which otherwise might cause serious network congestion. In this paper, we propose FuzzyDetec, a novel control architecture for the adaptive detection of elephant flows in multipath datacenters based on fuzzy logic. We develop, perhaps for the first time, a close loop elephant flow detection framework with an automated fuzzy inference module that can continually compute an appropriate threshold for elephant flow detection based on current information feedback from the network. The novelty and practical significance of the idea lie in allowing multiple imprecise and possibly conflicting criteria to be incorporated into the elephant flow detection process, through simple fuzzy rules emulating human expertise in elephant flow threshold classification. The proposed approach is simple, intuitive and easily extensible, providing a promising direction towards intelligent datacenter traffic management for autonomous high performance datacenter networks. Simulation results show that, in comparison with an existing state-of-the-art elephant flow detection framework, our proposed approach can provide considerable throughput improvements in datacenter network routing

Towards intelligent datacenter traffic management: Using automated fuzzy inferencing for elephant flow detection

© 2014, IJICIC Editorial Office, Inc. All rights reserved.Effective traffic management has always been one of the key considerations in datacenter design. It plays an even more important role today in the face of increasingly widespread deployment of communication intensive applications and cloud- based services, as well as the adoption of multipath datacenter topologies to cope with the enormous bandwidth requirements arising from those applications and services. Of central importance in traffic management for multipath datacenters is the problem of timely detection of elephant flows flows that carry huge amount of data so that the best paths can be selected for these flows, which otherwise might cause serious network congestion. In this paper, we propose FuzzyDetec, a novel control architecture for the adaptive detection of elephant flows in multipath datacenters based on fuzzy logic. We develop, perhaps for the first time, a close loop elephant flow detection framework with an automated fuzzy inference module that can continually compute an appropriate threshold for elephant flow detection based on current information feedback from the network. The novelty and practical significance of the idea lie in allowing multiple imprecise and possibly conflicting criteria to be incorporated into the elephant flow detection process, through simple fuzzy rules emulating human expertise in elephant flow threshold classification. The proposed approach is simple, intuitive and easily extensible, providing a promising direction towards intelligent datacenter traffic management for autonomous high performance datacenter networks. Simulation results show that, in comparison with an existing state-of-the-art elephant flow detection framework, our proposed approach can provide considerable throughput improvements in datacenter network routing