Temporal Bayesian Knowledge Bases – Reasoning about uncertainty with temporal constraints

Time is ubiquitous. Accounting for time and its interaction with change is crucial to modeling the dynamic world, especially in domains whose study of data is sensitive to time such as in medical diagnosis, financial investment, and natural language processing, to name a few. We present a framework that incorporates both uncertainty and time in its reasoning scheme. It is based on an existing knowledge representation called Bayesian Knowledge Bases. It provides a graphical representation of knowledge, time and uncertainty, and enables probabilistic and temporal inferencing. The reasoning scheme is probabilistically sound and the fusion of temporal fragments is well defined. We will discuss some properties of this framework and introduce algorithms to ensure groundedness during the construction of the model. The framework has been applied to both artificial and real world scenarios.     

Detection of the residues of nineteen pesticides in fresh vegetable samples using gas chromatography–mass spectrometry

In this study, we investigated the presence of 19 different agricultural pesticides in 210 samples of eight types of domestic vegetables collected from several vegetable-growing regions in Bangladesh. A multiresidue method was developed to detect the pesticide levels in the collected samples using gas chromatography with mass spectrophotometry (GC–MS). Pesticide residues were detected in 51.30% of the total samples, and among the positive samples, 38.89% contained levels above the maximum residue levels (MRLs). The most frequently detected pesticides were chlorpyrifos (34) followed by carbofuran (17), diazinon (16), carbaryl (14), malathion (11), endosulfan (8), cypermethrin (7) and dimethoate (6). Some (10.47%) of the samples contained multiple residues. It is concluded that the continuous monitoring and strict regulation of pesticide use on food crops, especially vegetables, are necessary.     

Physicochemical and thermo physical characterization of CaO–CaF2–SiO2 and CaO–TiO2–SiO2 based electrode coating for offshore welds

This paper investigates the physicochemical and thermo-physical properties of CaO–CaF₂–SiO₂ and CaO–TiO₂–SiO₂ based electrode coating for welding offshore structures. Twenty-one electrode coating compositions have been formulated using extreme vertices design method. The coating was crushed to powder form. The powder was characterized for weight loss, density, specific heat, enthalpy, thermal conductivity, diffusivity, and specific heat. Coating's structural analysis was done using X-Ray Diffraction and Fourier transformation. X-Ray Fluorescence, Thermogravimetric Analyzer, and Hot disc have been used to characterize the coating mixture. The regression analysis has been used to study the effect of individual constituents and their binary, tertiary interactions on the properties. The obtained output of properties has been optimized using multi-response optimization.     

Hard pure carbon coatings from a voltage-assisted arc

A simple apparatus is described that produces hard carbon coatings by accelerating ions produced by a carbon arc operating in vacuum. Coatings up to 1.1 μm thick have been produced on a variety of metals and semiconductors.     

Annular denuder method for sampling reactive gases and aerosols in the atmosphere

The annular denuder method (ADM), consisting of a denuder-filter pack assembly, has been developed to measure several gaseous and aerosol species without sampling artifacts. Its application to the determination of gaseous acids and ammonia in the atmosphere indicates that this aim is achieved, even though the ratio of analytes in the gas phase and particulate matter is extremely low. The procedure is particularly suitable for the measurement of trace levels (0.1 μg m⁻³) of HNO₃ and SO₂ (even in the presence of high levels of sulfate and nitrate) as well as of HNO₂ (by making correction for the positive interference of NO₂ and PAN).The ADM sampling and analysis precision for the examined gases does not exceed ±10% in the 0.1–10 μg m⁻³ range. By sampling at an air flow rate of 151 min⁻¹, the minimum detectable atmospheric concentrations is ∼0.1 μgm⁻³ for each species over a 3 h interval.     

环氧树脂/丙烯酸树脂自分层影响因素研究

对丙烯酸树脂和环氧树脂间的自分层行为进行了研究。研究了不同种类固化剂、湿膜厚度、底材对自分层的影响。试验结果表明:聚酰胺类固化剂利于体系自分层;表面能高的马口铁和玻璃板底材出现自分层,表面能低的聚四氟乙烯底材没有出现自分层;湿膜厚度对自分层影响不大。     

Self-terminated electrodeposition of platinum on titanium nitride for methanol oxidation reaction in acidic electrolyte

The development of high-performance electrocatalysts for methanol oxidation is an urgent task to enhance the efficiency of direct methanol fuel cells. We report a simple and controllable method to fabricate Pt-decorated TiN electrocatalysts using self-terminated electrodeposition at room temperature and ambient pressure. Under optimized deposition parameters such as electrolyte pH, TiN substrate pretreatment, and pulsed deposition potential, quenching of the Pt electrodeposition facilitates obtaining an extremely low Pt mass loading (0.93 μg/cm²) on the TiN substrate. Repeated deposition potential pulses enable a gradual increase in Pt loading, with a precise control of the loaded Pt mass. Maximum intrinsic and mass activities for the methanol oxidation reaction are achieved for the catalyst with a Pt loading mass of 55.0 μg/cm², prepared by 20 deposition pulses. The maximum intrinsic activity achieved with the Pt-decorated TiN electrocatalyst is five times higher than that obtained with bulk Pt. The present results thus provide a facile method for the fabrication of cost-effective electrocatalysts.     

Stable auto-tuning of hybrid adaptive fuzzy/neural controllers for nonlinear systems

In direct adaptive control, the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. In the indirect case, however, we approximate parts of the plant dynamics that are used by a feedback controller to cancel the system nonlinearities. In both cases, “approximators” such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as either the parameterized nonlinear controller or identifier model. In this paper, we present an algorithm to tune the adaptation gain for a gradient-based hybrid update law used for a class of nonlinear continuous-time systems in both direct and indirect cases. In our proposed algorithm, the adaptation gain is obtained by minimizing the instantaneous control energy. Finally, we will demonstrate the performance of the algorithm via a wing rock regulation example.