COD and NH4-N Estimation in the Inflow of Wastewater Treatment Plants using Machine Learning Techniques

The in-line measurement of COD and NH4-N in the WWTP inflow is crucial for the timely monitoring of biological wastewater treatment processes and for the development of advanced control strategies for optimized WWTP operation. As a direct measurement of COD and NH4-N requires expensive and high maintenance in-line probes or analyzers, an approach estimating COD and NH4-N based on standard and spectroscopic in-line inflow measurement systems using Machine Learning Techniques is presented in this paper. The results show that COD estimation using Radom Forest Regression with a normalized MSE of 0.3, which is sufficiently accurate for practical applications, can be achieved using only standard in-line measurements. In the case of NH4-N, a good estimation using Partial Least Squares Regression with a normalized MSE of 0.16 is only possible based on a combination of standard and spectroscopic in-line measurements. Furthermore, the comparison of regression and classification methods shows that both methods perform equally well in most cases.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

Thermography-based virtual MPPT scheme for improving PV energy efficiency under partial shading conditions

This paper proposes a new thermography-based maximum power point tracking (MPPT) scheme to address photovoltaic (PV) partial shading faults. Solar power generation utilizes a large number of PV cells connected in series and in parallel in an array, and that are physically distributed across a large field. When a PV module is faulted or partial shading occurs, the PV system sees a nonuniform distribution of generated electrical power and thermal profile, and the generation of multiple maximum power points (MPPs). If left untreated, this reduces the overall power generation and severe faults may propagate, resulting in damage to the system. In this paper, a thermal camera is employed for fault detection and a new MPPT scheme is developed to alter the operating point to match an optimized MPP. Extensive data mining is conducted on the images from the thermal camera in order to locate global MPPs. Based on this, a virtual MPPT is set out to find the global MPP. This can reduce MPPT time and be used to calculate the MPP reference voltage. Finally, the proposed methodology is experimentally implemented and validated by tests on a 600-W PV array. 

Effects of Elodea nuttallii on temperate freshwater plants, microalgae and invertebrates: small differences between invaded and uninvaded areas

The invasive aquatic plant species Elodea nuttallii could pose a considerable risk to European freshwater ecosystems based on its current distribution, rate of spread and potential for high biomass. However, little research has been conducted on the impacts of this species on native biota. This study takes an ecosystem-wide approach and examines the impact of E. nuttallii on selected physicochemical parameters (dissolved oxygen and pH), algae, invertebrate and macrophyte communities. Elodea nuttallii had small but significant impacts on plant, invertebrate and algal species. The richness of algal periphyton was lower on E. nuttallii than on native macrophytes. The taxonomic composition of invertebrate communities associated with E. nuttallii differed from that associated with similar native plant species, but did not differ in terms of total biomass or species richness. Macrophyte species richness and total cover were positively correlated with percentage cover of E. nuttallii. Not all macrophyte species responded in the same way to E. nuttallii invasion; cover of the low-growing species, Elodea canadensis and charophytes were negatively correlated with E. nuttallii cover, whilst floating-rooted plants were positively correlated with E. nuttallii cover. All observed differences in the macrophyte community were small relative to other factors such as nutrient levels, inter-annual variation and differences between sites. Despite this, the observed negative association between E. nuttallii and charophytes is a key concern due to the rarity and endangered status of many charophyte species.

Plants Fagonia cretica L. and Hedera nepalensis K. Koch contain natural compounds with potent dipeptidyl peptidase-4 (DPP-4) inhibitory activity

Ethnopharmacological relevance
The two plants investigated here (Fagonia cretica L. and Hedera nepalensis K. Koch) have been previously reported as natural folk medicines for the treatment of diabetes but until now no scientific investigation of potential anti-diabetic effects has been reported. 

Materials and methods
In vitro inhibitory effect of the two tested plants and their five isolated compounds on the dipeptidyl peptidase 4 (DPP-4) was studied for the assessment of anti-diabetic activity. 

Results
A crude extract of Fagonia cretica possessed good inhibitory activity (IC₅₀value: 38.1 μg/ml) which was also present in its n-hexane (FCN), ethyl acetate (FCE) or aqueous (FCA) fractions. A crude extract of Hedera nepalensis (HNC) possessed even higher inhibitory activity (IC₅₀value: 17.2 μg/ml) and this activity was largely retained when further fractionated in either ethyl acetate (HNE; IC₅₀: 34.4 μg/ml) or n-hexane (HNN; 34.2 μg/ml). Bioactivity guided isolation led to the identification of four known compounds (isolated for the first time) from Fagonia cretica: quinovic acid (1), quinovic acid-3β-O-β-d-glycopyranoside (2), quinovic acid-3β-O-β-d-glucopyranosyl-(28→1)-β-d-glucopyranosyl ester (3), and stigmasterol (4) all of which inhibited DPP-4 activity (IC₅₀: 30.7, 57.9, 23.5 and >100 μM, respectively). The fifth DPP-4 inhibitor, the triterpenoid lupeol (5) was identified in Hedera nepalensis (IC₅₀: 31.6 μM). 

Conclusion
The experimental study revealed that Fagonia cretica and Hedera nepalensis contain compounds with significant DPP-4 inhibitory activity which should be further investigated for their anti-diabetic potential.

Virtual Reality: immersed in the structural world

Virtual reality is a rapidly emerging technology, driven by the computer gaming industry. The maturity of the concept, combined with modern hardware, is delivering an experience which offers a useful commercial tool for industry and educators. This article discusses the uses of virtual reality within structural engineering and provides an understanding of how it can be incorporated easily and efficiently for design purposes and beyond.

Movement and Perceptual Strategies to Intercept Virtual Sound Sources

To intercept a moving object, one needs to be in the right place at the right time. In order to do this, it is necessary to pick up and use perceptual information that specifies the time to arrival of an object at an interception point. In the present study, we examined the ability to intercept a laterally moving virtual sound object by controlling the displacement of a sliding handle and tested whether and how the interaural time difference (ITD) could be the main source of perceptual information for successfully intercepting the virtual object. The results revealed that in order to accomplish the task, one might need to vary the duration of the movement, control the hand velocity and time to reach the peak velocity (speed coupling), while the adjustment of movement initiation did not facilitate performance. Furthermore, the overall performance was more successful when subjects employed a time-to-contact (tau) coupling strategy. This result shows that prospective information is available in sound for guiding goal-directed actions.