The effect of the scale of horizontal subsurface flow constructed wetlands on flow and transport parameters.

Horizontal subsurface flow constructed wetlands have proven their efficiency in treating wastewater and removing the pollutants of concern. Treatment efficiency depends on the wastewater residence time, which is a function of the hydraulic loading and the physical conditions of the constructed filter system, which can be described with effective parameters such as: hydraulic conductivity, porosity, dispersivity etc. Because spatial variability is often scale dependent, these effective parameters may be affected by the scale of the system being studied. In this paper the results of tracer experiments in constructed filters using saturated horizontal flow at three scales (small and medium lab scales and full-scale system) using the same filter media is reported. Light-weight aggregate (filter media termed Filtralite-P) was used at all scales. Increasing the scale was associated with increasing dispersivity, meanwhile hydraulic conductivity experienced dramatic reduction and variation by increasing the examined scale. Observed changes in the hydraulic parameters indicate that heterogeneity at different scales should be taken into account when the performance of LWA filters are evaluated from small-scale experiments.     

Predictive and content-aware load balancing algorithm for peer-service area based IPTV networks

Load balancing is a crucial factor in IPTV delivery networks. Load balancing aims at utilizing the resources efficiently, maximizing the throughput, and minimizing the request rejection rate. The peer-service area is the recent architecture for IPTV delivery networks that overcomes the flaws of the previous architectures. However, it still suffers from the load imbalance problem. This paper investigates the load imbalance problem, and tries to augment the peer-service area architecture to overcome this problem. To achieve the load balancing over the proposed architecture, we suggest a new load-balancing algorithm that considers both the expected and the current load of both contents and servers. The proposed load-balancing algorithm consists of two stages. The first stage is the contents replication according to their expected load, while the second stage is the content-aware request distribution. To test the effectiveness of the proposed algorithm, we have compared it with both the traditional Round Robin algorithm and Cho algorithm. The experimental results depict that the proposed algorithm outperforms the two other algorithms in terms of load balance, throughput, and request rejection rate.     

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian’s is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian’s behavior detection. Kalman filter has been used to detect pedestrian’s using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans. Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes. Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier. Performance has been evaluated using pedestrian’s data from avenue and UBI-Fight datasets, where both have different environment. The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively. These results are more accurate as compared to other existing methods.     

Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework

The deterioration of surface water quality occurs due to the presence of various types of pollutants generated from human, agricultural, and industrial activities. Thus, mapping concentrations of different surface water quality parameters (SWQPs), such as turbidity, total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), and dissolved oxygen (DO), is indeed critical for providing the appropriate treatment to the affected waterbodies. Traditionally, concentrations of SWQPs have been measured through intensive field work. Additionally, quite a lot of studies have attempted to retrieve concentrations of SWQPs from satellite images using regression-based methods. However, the relationship between SWQPs and satellite data is complex to be modelled accurately by using regression-based methods. Therefore, our study attempts to develop an artificial intelligence modelling method for mapping concentrations of both optical and non-optical SWQPs. In this context, a remote-sensing framework based on the back-propagation neural network (BPNN) is developed for the first time to quantify concentrations of different SWQPs from the Landsat8 satellite imagery. Compared to other methods, such as Support Vector Machine, significant coefficients of determination (R²) between the Landsat8 surface reflectance and concentrations of SWQPs were obtained using the developed Landsat8-based-BPNN models. The resulting R² values were 0.991, 0.933, 0.937, 0.930, and 0.934 for turbidity, TSS, COD, BOD, and DO, respectively. Indeed, these findings indicate that the developed Landsat8-based-BPNN framework is capable of developing highly accurate models for retrieving concentrations of different SWQPs from the Landsat8 imagery.     

Mechanical Properties and Microstructure of a Metakaolin-Based Inorganic Polymer Mortar Reinforced with Quartz Sand

Silicon - The synthesis, mechanical behaviour, and microstructure of metakaolin-based geopolymer mortar reinforced with quartz sand are presented in this investigation. Fine sand (quartz sand)...     

High temperature bias-stress-induced instability in power trench-gated MOSFETs

We report on the high-temperature reverse-bias (HTRB) stress reliability of trench-gated n-channel metal-oxide-silicon field-effect transistors (n-UMOSFETs). The degradation induced by the HTRB is examined using changes in transistor parameters, optical microscopy, and scanning electron microscopy. The HTRB causes degradations in the threshold voltage and drain leakage of the n-UMOSFET and these degradations are particularly large when the stress is applied in a humid ambient. The observations were interpreted in terms of water molecule diffusion into the gate oxide through passivation cracks in the edge termination of the n-UMOSFET during HTRB in a humid ambient. The water molecules catalyze proton (H⁺) generation through electric-field assisted interactions and hole injection into the gate oxide at the bottom of the trench. Also, H⁺ is observed to be very stable in the gate oxide and to migrate between the gate-oxide and oxide–Si interfaces driven by an applied gate-voltage. It is proposed that the employed HTRB configuration and level give rise to negative-bias temperature instability (NBTI) in a parasitic p-channel MOSFET structure occurring in the trench base of the n-UMOSFET, and that NBTI is a serious reliability concern in power UMOSFETs subjected to stress in a moist ambient.     

Quality control of equipment used in digital and interventional radiology

Digital and interventional radiology are increasingly important areas of radiology. Quality control (QC) of such equipment is of particular importance to avoid unnecessary high doses and to help to achieve good image quality. Within the DIMOND III project, equipment requirements and specifications for digital and interventional radiology have been formulated. A protocol for QC tests has been drafted based on various national and international recommendations. Tests are included for various parts of the imaging chain, i.e. X-ray tube and generator, X-ray tube control system, laser printer and display station, and image quality and patient dose. Preliminary tolerance levels have been set for the various tests, after initial measurements. To check the suitability of QC tests and stated tolerance levels, measurements were made at the University Hospital Gasthuisberg in Leuven for equipment used for paediatric radiology and a unit used for chest examinations. The results of the various tests are reported.     

Bioactive-Tissue-Derived Nanocomposite Hydrogel for Permanent Arterial Embolization and Enhanced Vascular Healing

Transcatheter embolization is a minimally invasive procedure that uses embolic agents to intentionally block diseased or injured blood vessels for therapeutic purposes. Embolic agents in clinical practice are limited by recanalization, risk of non-target embolization, failure in coagulopathic patients, high cost, and toxicity. Here, a decellularized cardiac extracellular matrix (ECM)-based nanocomposite hydrogel is developed to provide superior mechanical stability, catheter injectability, retrievability, antibacterial properties, and biological activity to prevent recanalization. The embolic efficacy of the shear-thinning ECM-based hydrogel is shown in a porcine survival model of embolization in the iliac artery and the renal artery. The ECM-based hydrogel promotes arterial vessel wall remodeling and a fibroinflammatory response while undergoing significant biodegradation such that only 25% of the embolic material remains at 14 days. With its unprecedented proregenerative, antibacterial properties coupled with favorable mechanical properties, and its superior performance in anticoagulated blood, the ECM-based hydrogel has the potential to be a next-generation biofunctional embolic agent that can successfully treat a wide range of vascular diseases.     

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

Statistical Analysis with Dingo Optimizer Enabled Routing for Wireless Sensor Networks

Security is a vital parameter to conserve energy in wireless sensor networks (WSN). Trust management in the WSN is a crucial process as trust is utilized when collaboration is important for accomplishing trustworthy data transmission. But the available routing techniques do not involve security in the design of routing techniques. This study develops a novel statistical analysis with dingo optimizer enabled reliable routing scheme (SADO-RRS) for WSN. The proposed SADO-RRS technique aims to detect the existence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based linear discriminant analysis (LDA) for attack detection, Moreover, a trust based dingo optimizer (TBDO) algorithm is applied for optimal route selection in the WSN and accomplishes secure data transmission in WSN. Besides, the TBDO algorithm involves the derivation of the fitness function involving different input variables of WSN. For demonstrating the enhanced outcomes of the SADO-RRS technique, a wide range of simulations was carried out and the outcomes demonstrated the enhanced outcomes of the SADO-RRS technique.