Radio frequency identification (RFID) is a form of automatic identification and data capture (AIDC) technology applicable to various areas. The integration of RFID equipments and enterprises applications (e.g. ERP, SCM) is one of the key problems in the development of RFID technology. RFID middleware turns out to be a good solution, which in practice has accelerated the adoption of RFID technology. A SOA-based RFID application integration project is described. In the project, enterprise applications can invoke the services provided by RFID middleware across services interface layer. In particular, for code reuse, some common functions, such as SOAP communication, data exchange mechanism, are built into a component called service proxy. By inheriting the proxy, other system components (e.g. inbound and outbound management) can have these functions. The project is an effort of integrating an RFID middleware with a warehouse management system (WMS).
In recent years, winter water treatment plant effluent quality in Baotou is abnormal. In order to investigate the causes of affecting the water quality. After a year water quality monitored parameters of the waterworks source and combined with algae lab laboratory testing, counting. Analyzeed water quality status and the relationship between the parameters. Obtained water had been polluted. In the annual end of winter the water in eutrophication, algal blooms. At last, analyzed the water occurred eutrophication's causes and mechanism at low temperature.r the engineering, design and application of bedpan.
Duplicate Question Identification (DQI) improves the processing efficiency and accuracy of large-scale community question answering and automatic QA system. The purpose of DQI task is to identify whether the paired questions are semantically equivalent. However, how to distinguish the synonyms or homonyms in paired questions is still challenging. Most previous works focus on the word-level or phrase-level semantic differences. We firstly propose to explore the asking emphasis of a question as a key factor in DQI. Asking emphasis bridges semantic equivalence between two questions. In this paper, we propose an attention model with multi-fusion asking emphasis (MFAE) for DQI. At first, BERT is used to obtain the dynamic pre-trained word embeddings. Then we get inter- and intra-asking emphasis by summing inter-attention and self-attention, respectively; the idea is that, the more a word interacts with others, the more important the word is. Finally, we use eight-way combinations to generate multi-fusion asking emphasis and multi-fusion word representation. Experimental results demonstrate that our model achieves state-of-the-art performance on both Quora Question Pairs and CQADupStack data. In addition, our model can also improve the results for natural language inference task on SNLI and MultiNLI datasets. The code is available at https://github.com/rzhangpku/MFAE.
After describing the principle and features of service-oriented architecture, we introduce an SOA based distributed architecture of spacecraft control software system. Comparing with the current spacecraft control software system architecture, the proposed architecture has advantages in coupling, conformity, maintainability etc. Last a concrete implementation approach of spacecraft fault simulation system is investigated, which satisfies the simulation and dynamic configuration of on-orbit faults for various platforms and spacecraft.
Mulching to conserve moisture has become an important agronomic practice in saline soil cultivation, and the effects of the dual stress of salinity and microplastics on soil microbes are receiving increasing attention. In order to investigate the effect of polyethylene microplastics on the microbial community of salinized soils, this study investigated the effects of different types (chloride and sulphate) and concentrations (weak, medium, and strong) of polyethylene (PE) microplastics (1% and 4% of the dry weight mass of the soil sample) on the soil microbial community by simulating microplastic contamination in salinized soil environments indoors. The results showed that:PE microplastics reduced the diversity and abundance of microbial communities in salinized soils and were more strongly affected by sulphate saline soil treatments. The relative abundance of each group of bacteria was more strongly changed in the sulphate saline soil treatment than in the chloride saline soil treatment. At the phylum level, the relative abundance of Proteobacteria was positively correlated with the abundance of fugitive PE microplastics, whereas the relative abundances of Bacteroidota, Actinobacteriota, and Acidobacteria were negatively correlated with the abundance of fugitive PE microplastics. At the family level, the relative abundances of Flavobacteriaceae, Alcanivoracaceae, Halomonadaceae, and Sphingomonasceae increased with increasing abundance of PE microplastics. The KEGG metabolic pathway prediction showed that the relative abundance of microbial metabolism and genetic information functions were reduced by the presence of PE microplastics, and the inhibition of metabolic functions was stronger in sulphate saline soils than in chloride saline soils, whereas the inhibition of genetic information functions was weaker than that in chloride saline soils. The secondary metabolic pathways of amino acid metabolism, carbohydrate metabolism, and energy metabolism were inhibited. It was hypothesized that the reduction in metabolic functions may have been caused by the reduced relative abundance of the above-mentioned secondary metabolic pathways. This study may provide a theoretical basis for the study of the effects of microplastics and salinization on the soil environment under the dual pollution conditions.
Large Language Models (LLMs) are widely applied to downstream domains. However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically generate brief answers without reasoning processes and explanations. This limits users' confidence in making decisions based on their responses. While original CoT shows promise, it lacks self-correction mechanisms during reasoning. This work introduces Domain$o1$s, which enhances LLMs' reasoning capabilities on domain tasks through supervised fine-tuning and tree search. We construct CoT-stock-2k and CoT-legal-2k datasets for fine-tuning models that activate domain-specific reasoning steps based on their judgment. Additionally, we propose Selective Tree Exploration to spontaneously explore solution spaces and sample optimal reasoning paths to improve performance. We also introduce PROOF-Score, a new metric for evaluating domain models' explainability, complementing traditional accuracy metrics with richer assessment dimensions. Extensive experiments on stock investment recommendation and legal reasoning QA tasks demonstrate Domaino1s's leading performance and explainability. Our code is available at https://anonymous.4open.science/r/Domaino1s-006F/.
A BATS-SAST model was employed to simulate the snow deposition processes in the four snow deposition cases in Canada,i.e.,Sk_OJP 2001/02,2002/03,2003/04 and Sk_HarvestJP 2003/04. At Sk_OJP site the long wave radiation scheme and precipitation scheme were modified. Considering different interceptions between rain and snowfall and effect of wind and canopy temperature on snow download,the canopy interception model was improved. At Sk_HarvestJP site the snow cover fraction scheme was modified. Results show that the model is able to simulate the basic processes of snow cover reasonably. The modified model,which considers the part of the transmitted through the canopy in computing long wave radiation and precipitation at Sk_OJP site,can make the simulation of snow depth more and closer to the observation. The improved canopy interception model,which influences the variation of snow depth under canopy by changing canopy interception,has a great improvement on simulation of snow depth,especially on the ablation period of snow cover. At Sk_HarvestJP site,the snow depth was lessened simulated by the improved model.
Abstract. Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called âImpact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Predictionâ (LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of landâatmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect of the Tibetan Plateau, discusses the LST/SUBT initialization, and presents the preliminary results. Multi-model ensemble experiments and analyses of observational data have revealed that the hydroclimatic effect of the spring LST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond East Asia and its S2S prediction. Preliminary studies and analysis have also shown that LS4P models are unable to preserve the initialized LST anomalies in producing the observed anomalies largely for two main reasons: (i)Â inadequacies in the land models arising from total soil depths which are too shallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii)Â reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state and anomalies of LST over the Tibetan Plateau. Innovative approaches have been developed to largely overcome these problems.
The observation data collected by Atmospheric and Environmental Comprehensive Observation and Research Station on Mt.Qomolangma,Chinese Academy of Sciences(AECORSQ,CAS) and the AIRS(Atmospheric Infrared Sounder) satellite data obtained during the period of from March to May of 2008 are employed to analyze the diurnal changes and the vertical features of atmosphere over the northern region of the Himalayas in spring.The results show that the diurnal mean variation of surface air temperature has one-peak-one-vale pattern.The highest temperature occurred around 18:00 Beijing local time and the lowest was between 7:00 to 9:00 Beijing local time.The diurnal mean variation of wind speed has one-peak pattern,and the air pressure has two-peaks-two-vales pattern while the lowest occurred at 19:00 Beijing local time.The diurnal mean variations of sensible heat flux and latent heat flux were consistent with the diurnal change of air temperature.The pattern looks like the net radiation diurnal pattern,but the peak time appears about 2 hours later.The sensible heating flux is stronger than latent heating flux over the Tibetan Plateau in spring.There are two main reasons which caused the remarkable diurnal variation of air temperature over the Tibetan Plateau while the mass of the atmosphere over the plateau is much less than the mass of atmosphere over other regions,and it can gain more solar shortwave radiation and less shortwave radiation while the optical depth over this region is smaller.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
