无线、智能、安全是智能家居的关键词

1 智能家居的应用 最近,我们看到了家庭自动化和安全解决方案的广泛采用,其中包括照明、温度控制、安防、娱乐以及无处不在的传感器.短期内我们将看到的一大趋势是远距离应用,其将延伸到家庭之外的范围,例 如 Z-Wave Long?Range和Sidewalk,它们将开启一系列新的应用场景.     

Development and validation of a new LC–MS/MS method for the simultaneous determination of six major ergot alkaloids and their corresponding epimers. Application to some food and feed commodities

A liquid chromatography–tandem mass spectrometry (LC–MS/MS) method was developed and validated for the simultaneous determination of six major ergot alkaloids, ergometrine, ergosine, ergotamine, ergocornine, ergokryptine and ergocristine, as well as their corresponding epimers in food and feed samples. The method involves extraction under alkaline conditions and subsequent clean-up by applying a simple and rapid liquid–liquid partitioning procedure prior to LC–MS/MS analysis. Evaluation of the method revealed good linearity, accuracy and precision. The limits of quantification varied from 0.1 to 1 μg/kg depending on the analyte and matrix. The average extraction and clean-up recoveries in different matrices were between 45 (only for ergometrine in biscuit) and 90%. The uncertainty associated with the analytical method was not higher than 51% and 30%, at concentration levels of 2.5 and 150 μg/kg respectively. Analyte epimerization proved to be minimal during the analytical procedure. The method has been successfully applied to the determination of ergot alkaloids in some Belgian food and feed commodities. Ergot alkaloids were found in 104 out of 122 samples investigated. Ergosine was the most frequently occurring alkaloid, while the highest levels were observed for ergotamine, ergocristine or ergosine, depending on the product type. The total alkaloid content in positive samples varied from 1 to 1145 μg/kg.     

Model selection, identification and validation in anaerobic digestion: a review

Anaerobic digestion enables waste (water) treatment and energy production in the form of biogas. The successful implementation of this process has lead to an increasing interest worldwide. However, anaerobic digestion is a complex biological process, where hundreds of microbial populations are involved, and whose start-up and operation are delicate issues. In order to better understand the process dynamics and to optimize the operating conditions, the availability of dynamic models is of paramount importance. Such models have to be inferred from prior knowledge and experimental data collected from real plants. Modeling and parameter identification are vast subjects, offering a realm of approaches and methods, which can be difficult to fully understand by scientists and engineers dedicated to the plant operation and improvements. This review article discusses existing modeling frameworks and methodologies for parameter estimation and model validation in the field of anaerobic digestion processes. The point of view is pragmatic, intentionally focusing on simple but efficient methods.     

Tracking the Molecular Fingerprint of Head and Neck Cancer for Recurrence Detection in Liquid Biopsies

The 5-year relative survival for patients with head and neck cancer, the seventh most common form of cancer worldwide, was reported as 67% in developed countries in the second decade of the new millennium. Although surgery, radiotherapy, chemotherapy, or combined treatment often elicits an initial satisfactory response, relapses are frequently observed within two years. Current surveillance methods, including clinical exams and imaging evaluations, have not unambiguously demonstrated a survival benefit, most probably due to a lack of sensitivity in detecting very early recurrence. Recently, liquid biopsy monitoring of the molecular fingerprint of head and neck squamous cell carcinoma has been proposed and investigated as a strategy for longitudinal patient care. These innovative methods offer rapid, safe, and highly informative genetic analysis that can identify small tumors not yet visible by advanced imaging techniques, thus potentially shortening the time to treatment and improving survival outcomes. In this review, we provide insights into the available evidence that the molecular tumor fingerprint can be used in the surveillance of head and neck squamous cell carcinoma. Challenges to overcome, prior to clinical implementation, are also discussed.     

Ash-forming elements in four Scandinavian wood species. Part 1: Summer harvest

Woody biomass in Finland and Sweden comprises mainly four wood species: spruce, pine, birch and aspen. To study the ash, which may cause problems for the combustion device, one tree of each species were cut down and prepared for comparisons with fuel samples. Well-defined samples of wood, bark and foliage were analyzed on 11 ash-forming elements: Si, Al, Fe, Ca, Mg, Mn, Na, K, P, S and Cl. The ash content in the wood tissues (0.2–0.7%) was low compared to the ash content in the bark tissues (1.9–6.4%) and the foliage (2.4–7.7%). The woods’ content of ash-forming elements was consequently low; the highest contents were of Ca (410–1340 ppm) and K (200–1310), followed by Mg (70–290), Mn (15–240) and P (0–350). Present in the wood was also Si (50–190), S (50–200) and Cl (30–110). The bark tissues showed much higher element contents; Ca (4800–19,100 ppm) and K (1600–6400) were the dominating elements, followed by Mg (210–2400), P (210–1200), Mn (110–1100) and S (310–750), but the Cl contents (40–330) were only moderately higher in the bark than in the wood. The young foliage (shoots and deciduous leaves) had the highest K (7100–25,000 ppm), P (1600–5300) and S (1100–2600) contents of all tissues, while the shoots of spruce had the highest Cl contents (820–1360) and its needles the highest Si content (5000–11,300). This paper presented a new approach in fuel characterization: the method excludes the presence of impurities, and focus on different categories of plant tissues. This made it possible to discuss the contents of ash element in a wide spectrum of fuel-types, which are of large importance for the energy production in Finland and Sweden.     

Bark as feedstock for dual fluidized bed gasifiers—Operability,efficiency, and economics

The demand for biofuels and biochemicals is expected to increase in the future, which will in turn increase the demand for biomass feedstock. Large gasification plants fueled with biomass feedstock are likely to be a key enabling technology in a resource‐efficient, bio‐based economy. Furthermore, the costs for producing biofuels and biochemicals in such plants could potentially be decreased by utilizing inexpensive low‐grade residual biomass as feedstock. This study investigates the usage of shredded tree bark as a feedstock for the production of biomethane in the GoBiGas demonstration plant in Gothenburg, Sweden, based on a 32 MW_th industrial dual fluidized bed gasification unit. The plant was operated with bark feedstock for 12 000 hours during the period 2014 to 2018. Data from the measurement campaign were processed using a stochastic approach to establish the plant's mass and energy balances, which were then compared with operation of the plant with wood pellets. For this comparison, an extrapolation algorithm was developed to predict plant performance using bark dried to the same moisture content as wood pellets, ie, 8%_w.b. Plant operation with bark feedstock was evaluated for operability, efficiency, and feedstock‐related cost. The gas quality achieved during the test period was similar to that obtained for operation with wood pellets. Furthermore, no significant ash sintering or agglomeration problems were observed more than 750 hours of operation. The calculated biomass‐to‐biomethane efficiency is 43% to 47% (lower heating value basis) for operation with wet bark. However, the predicted biomass‐to‐biomethane efficiency can be increased to 55%–65% for operation with bark feedstock dried to 8% moisture content, with corresponding feedstock costs in the range of 24.2 to 32.7 EUR/MWh; ie, a cost reduction of about 40% compared with wood pellets.     

Peaks Over Thresholds Modeling With Multivariate Generalized Pareto Distributions

When assessing the impact of extreme events, it is often not just a single component, but the combined behavior of several components which is important. Statistical modeling using multivariate generalized Pareto (GP) distributions constitutes the multivariate analogue of univariate peaks over thresholds modeling, which is widely used in finance and engineering. We develop general methods for construction of multivariate GP distributions and use them to create a variety of new statistical models. A censored likelihood procedure is proposed to make inference on these models, together with a threshold selection procedure, goodness-of-fit diagnostics, and a computationally tractable strategy for model selection. The models are fitted to returns of stock prices of four UK-based banks and to rainfall data in the context of landslide risk estimation. Supplementary materials and codes are available online.