Capture of Organic Vapors Using Adsorption and Electrothermal Regeneration

Activated-carbon-fiber cloth (ACFC) is an alternative adsorbent to granular activated carbon (GAC) for removing and recovering organic vapors from gas streams. Electrothermal desorption (ED) of ACFC provides rapid regeneration while requiring less energy compared to traditional regeneration techniques used with GAC. This paper provides proof-of-concept results from a bench-scale ACFC adsorption system. The automated system captured 1,000 ppmv of hazardous air pollutants/volatile organic compounds (HAPs/VOCs) from air streams and demonstrated the use of ED, using ac voltage, to recover the HAP/VOC as a pure liquid. The desorbed HAP/VOC condensed onto the inner walls of the adsorber and was collected at the bottom of the vessel, without the use of ancillary cooling. Seventy percent of the HAP/VOC was collected per cycle as condensate, with the balance being retained in the regenerated adsorber or recycled to the second adsorber. ED with in-vessel condensation results in minimal N2 consumption and short regeneration cycle times allowing the process to be cost competitive with conventional GAC-based adsorption processes. This technology extends the application of carbon adsorption systems to situations that were previously economically and physically impractical.     

过程链管理--用于制定和管理转型组织的工具

在当今企业环境日益复杂而活跃的背景条件下，企业需要不断进行重组和迅速转型。这要求企业要具备更强的适应性和更高的灵活性。为了确保实现这些动态的要求，企业必须制定和实施新型的组织形式。本文介绍过程链管理——一种面向过程进行转型的工具。本文首先简要说明了过程链管理应用的工具、过程链管理的任务和构造。它的出发点是由行动、结构、资源、和指挥所组成的过程链管理。在第二步骤中，重点阐述了客户取向这个过程链管理中最重要的取向量。总体来说，整个价值创造链及与此相关的内部交换关系都具有重要意义。在对过程链管理的方法进行了说明以后，提出了一个应用实例，该实例不仅描述了垂直重构，也对水平重构进行了描述。     

Adverse effects of dental local anaesthesia

This paper considers the adverse effects that a patient may suffer as a result of anticipating an injection of dental local anaesthetic. Although most of these are extremely rare (a testimony to good technique), the dental practitioner should be aware of the possibility of their occurrence and of ways to deal with them.     

Cost effective second generation AC-modules: Development and testing aspects

In the framework of the European research project PV2GO, a new AC-module inverter was developed, taking into account all relevant aspects from a European market's point of view (standards, market, application, and research and development goals). The project goal was to achieve the overall system costs of 3 Euro per Wp for a modular plug-and-play photovoltaic system. For the photovoltaic-module, a standard 130-Wp Eurosolare module was chosen. The research and development (R&D) goal was to develop an advanced DC-control system consisting of a state-of-the-art programmable digital device and an Application Specific Integrated Circuit (ASIC) for the AC-control of the inverter. According to the topology concept, thermal and magnetic designs were optimized with regard to production technology and packaging for large-scale production. The new AC-modules were tested in a number of field-test sites in various parts of Europe and their reliability was assessed through Highly Accelerated Stress Tests. Efficiency and power quality have been tested in the laboratory. Further in the PV2GO project an optimization study of the manufacturing process of the new generation of AC-modules for high volume output was done. Another task was the pre-certification procedure to assure compliance with the European guidelines and standards.     

Biomic river restoration: A new focus for river management

River management based solely on physical science has proven to be unsustainable and unsuccessful, evidenced by the fact that the problems this approach intended to solve (e.g., flood hazards, water scarcity, and channel instability) have not been solved and long‐term deterioration in river environments has reduced the capacity of rivers to continue meeting the needs of society. In response, there has been a paradigm shift in management over the past few decades, towards river restoration. But the ecological, morphological, and societal benefits of river restoration have, on the whole, been disappointing. We believe that this stems from the fact that restoration overrelies on the same physical analyses and approaches, with flowing water still regarded as the universally predominant driver of channel form and structural intervention seen as essential to influencing fluvial processes. We argue that if river restoration is to reverse long‐standing declines in river functions, it is necessary to recognize the influence of biology on river forms and processes and re‐envisage what it means to restore a river. This entails shifting the focus of river restoration from designing and constructing stable channels that mimic natural forms to reconnecting streams within balanced and healthy biomes, and so levering the power of biology to influence river processes. We define this new approach as biomic river restoration.     

Grid Resource Availability Prediction-Based Scheduling and Task Replication

The frequent and volatile unavailability of volunteer-based Grid computing resources challenges Grid schedulers to make effective job placements. The manner in which host resources become unavailable will have different effects on different jobs, depending on their runtime and their ability to be checkpointed or replicated. A multi-state availability model can help improve scheduling performance by capturing the various ways a resource may be available or unavailable to the Grid. This paper uses a multi-state model and analyzes a machine availability trace in terms of that model. Several prediction techniques then forecast resource transitions into the model’s states. We analyze the accuracy of our predictors, which outperform existing approaches. We also propose and study several classes of schedulers that utilize the predictions, and a method for combining scheduling factors. We characterize the inherent tradeoff between job makespan and the number of evictions due to failure, and demonstrate how our schedulers can navigate this tradeoff under various scenarios. Lastly, we propose job replication techniques, which our schedulers utilize to replicate those jobs that are most likely to fail. Our replication strategies outperform others, as measured by improved makespan and fewer redundant operations. In particular, we define a new metric for replication efficiency, and demonstrate that our multi-state availability predictor can provide information that allows our schedulers to be more efficient than others that blindly replicate all jobs or some static percentage of jobs.     

Statistical relational learning of trust

The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentally-opaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn context-sensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic two-player negotiation scenario.