浅谈变电站开展安全活动和技术培训的经验

变电站安全活动开展得好坏，直接关系到本站职工能否善于吸取他人事故教训，为我所用，自觉地增强安全生产意识，从而保持一个长期稳定的安全局面；变电站技术培训工作搞的是否扎实、有效，关系到该站职工素质的高低，关系到该站运行人员是否能高质量地完成设备巡视、倒闸操作、设备维护和事故处理等工作，也必将影响到安全生产。文章总结了变电站开展安全活动和技术培训的一些经验，并阐述了在变电站开展安全活动、技术培训的重要性及意义。     

A circular visualization of people׳s activities in distributed teams

When working in distributed teams, it is very important to be aware of the activities of all members, since it provides hints about when they might be available for collaboration. We propose a novel visualization technique that combines several representations to show the daily patterns of team members׳ activities. It uses a 24 h circular display to facilitate international collaboration across time zones. Current calendar information can be compared to the typical patterns and reveal likely availability. User studies evaluating the tool that implements the proposed technique are reported and discussed.     

Enhancing Online Community Activity: Development and validation of the CA framework

Factors contributing to development of active communities are identified and combined into the Community Activity framework, which is useful in setting up new, or revitalizing inactive, communities. Found factors include: notifying members of new messages by e-mail, having a news section, and ability to add pictures to member profiles. During application of the framework to an inactive community, changes have been made to privacy options, polls, activity notifications, and other areas. Significant positive effects have been found in the number of visits, volume of posted messages, and number of topics. Interest of community members in both user profiles and the message board increased significantly. We conclude that the Community Activity framework is able to contribute in developing active online communities.     

Performance comparison of two newly developed bimetallic (X-Mo/Al2O3, X=Fe or Co) catalysts for reverse water gas shift reaction

The performance of the two newly developed bimetallic catalysts based on the precursor, Mo/Al_2O_3, was compared for reverse water gas shift(RWGS) reaction. The structures of the precursor and the catalysts were studied using X-ray diffraction(XRD), Brunauer–Emmett–Teller(BET) analysis, inductively coupled plasma-atomic emission spectrometry(ICP-AES), CO chemisorption, temperature programmed reduction of hydrogen(H_2-TPR) and scanning electron microscopy(SEM) techniques. The activity of Fe-Mo and Co-Mo catalysts was compared in a fixed bed reactor at different temperatures. It is shown that the Co-Mo catalyst has higher CO_2 conversion at all temperature level. The time-on-stream(TOS) analysis of the activity of catalysts for the RWGS reaction was carried out over a continuous period of 60h for both catalysts. The Fe-Mo/Al_2O_3 catalyst exhibits good stability within a period of 60h, however, the Co-Mo/Al_2O_3 is gradually deactivated after 50h of reaction time. Existence of(Fe_2(MoO4_))_3 phase in Fe-Mo/Al_2O_3 catalyst makes this catalyst more stable for RWGS reaction.     

Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers

Video recordings of earthmoving construction operations provide understandable data that can be used for benchmarking and analyzing their performance. These recordings further support project managers to take corrective actions on performance deviations and in turn improve operational efficiency. Despite these benefits, manual stopwatch studies of previously recorded videos can be labor-intensive, may suffer from biases of the observers, and are impractical after substantial period of observations. This paper presents a new computer vision based algorithm for recognizing single actions of earthmoving construction equipment. This is particularly a challenging task as equipment can be partially occluded in site video streams and usually come in wide variety of sizes and appearances. The scale and pose of the equipment actions can also significantly vary based on the camera configurations. In the proposed method, a video is initially represented as a collection of spatio-temporal visual features by extracting space–time interest points and describing each feature with a Histogram of Oriented Gradients (HOG). The algorithm automatically learns the distributions of the spatio-temporal features and action categories using a multi-class Support Vector Machine (SVM) classifier. This strategy handles noisy feature points arisen from typical dynamic backgrounds. Given a video sequence captured from a fixed camera, the multi-class SVM classifier recognizes and localizes equipment actions. For the purpose of evaluation, a new video dataset is introduced which contains 859 sequences from excavator and truck actions. This dataset contains large variations of equipment pose and scale, and has varied backgrounds and levels of occlusion. The experimental results with average accuracies of 86.33% and 98.33% show that our supervised method outperforms previous algorithms for excavator and truck action recognition. The results hold the promise for applicability of the proposed method for construction activity analysis.     

Tuning the alloy degree for Pd-M/Al2O3 (M=Co/ Ni /Cu) bimetallic catalysts to enhance the activity and selectivity of dodecahydro-N-ethylcarbazole dehydrogenation

Hydrogen is a promising candidate to substitute the fossil fuels. However, the efficient hydrogen storage technologies restrict the commercial applications. Developing new catalysts with high activity and selectivity is important for the dehydrogenation reaction in N-ethylcarbazole/dodecahydro-N-ethylcarbazole (NECZ/12H-NECZ) hydrogen storage system. In this work, a series of Pd-M/Al₂O₃ (M = Co, Ni and Cu) bimetallic catalysts are synthesized successfully and show good performance in the dehydrogenation reaction of 12H-NECZ than the commercial Pd/Al₂O₃ catalyst. The Pd₁Co₁/Al₂O₃ catalyst (Practical Pd content = 2.4136 wt%) showed the highest catalytic performance with 95.34% H₂ release amount, TOF of 230.5 min⁻¹ and 85.4% selectivity of NECZ. Combined with the characterization analysis, it can be proposed that the dehydrogenation performance of 12H-NECZ is dependent on the alloy phases, reasonable electronic structures and nanoparticle size of catalysts. The fine-tuned alloy degree and appropriate nanoparticle size of Pd₁Co₁/Al₂O₃ bring the 17.7% increase of H₂ release amount and 99.5% increase of NECZ selectivity than those of Pd/Al₂O₃. For the bimetallic catalysts, the enhancement of selectivity of NECZ is mainly from the increase of the kinetic constant of rate-limiting step.     

Nanostructured active chitosan-based films for food packaging applications: Effect of graphene stacks on mechanical properties

Bioactive food-preserving materials are based on the use of a natural antimicrobial compound loaded in a carrier material, which is able to trigger its release when requested and to modulate the rate of release, thus using either toxic or inhibitory properties against pathogens or bacteria due to food decomposition. In this study, the Schiff base formation for chitosan functionalization was achieved by the reaction of chitosan with cinnamaldehyde at different concentrations. Cinnamaldehyde is an aromatic α,β-unsaturated aldehyde, and the major component in essential oils from some cinnamon species. It has been shown to exert antimicrobial action against a large number of microorganisms including bacteria, yeasts, and mould. The formation of the Schiff base is reversible under suitable conditions, and this might allow the release of the active cinnamaldehyde from chitosan, used as the carrier. The reaction kinetics was investigated by means of rheological measurements, while infrared spectroscopy was used to assess the efficacy of the functionalization. The addition of nanometric graphene stacks to the cinnamaldehyde-functionalized chitosan films was evaluated with the aim to increase the mechanical properties of the film. Finally, the films were tested for antifungal properties with bread slices against a selected mould line.     

Co-operation at work: a process-oriented perspective on joint activity in inter-organizational relations

In this paper the authors present a conceptual framework for analysing cooperation between organizations as a situated process, highlighting co-operation, co-ordination and co-construction as different modes of joint activity. Within this framework, the analysis of unexpected events becomes a crucial issue. The analytic power of reconstructing the (psycho-) logics of unexpected events in inter-organizational relations will be demonstrated in the presentation of selected outcomes of a research project on the dynamics of producer-supplier relationships in the German automobile industry. Also in this empirical part, the significance of trust and confidence in inter-organizational relations will be reflected.     

Modeling patterns of activities using activity curves

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual’s normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.     

One-step synthesis of Cu@FeNi core–shell nanoparticles: Highly active catalyst for hydrolytic dehydrogenation of ammonia borane

This paper investigates a facile and one-step synthesis of trimetallic magnetic Cu@FeNi core–shell nanoparticles, which are composed of crystalline Cu cores and amorphous FeNi shells, at room temperature under ambient atmosphere within 2 min. It is found that among the Cu@FeNi system, Cu_0.4@Fe_0.1Ni_0.5 shows the best synergistic performance for catalyzing the hydrolytic dehydrogenation of ammonia borane with the activation energy of 32.9 kJ/mol, being lower than most of the reported data, and the catalytic activity of Cu_0.4@Fe_0.1Ni_0.5 is much better than its monometallic, bimetallic and trimetallic counterparts whether in states of pure metals, alloys or physical mixtures. Further, the present catalyst has a good recycle stability with an easy magnetic separation method.