Multimodal emotion recognition based on peak frame selection from video

We present a fully automatic multimodal emotion recognition system based on three novel peak frame selection approaches using the video channel. Selection of peak frames (i.e., apex frames) is an important preprocessing step for facial expression recognition as they contain the most relevant information for classification. Two of the three proposed peak frame selection methods (i.e., MAXDIST and DEND-CLUSTER) do not employ any training or prior learning. The third method proposed for peak frame selection (i.e., EIFS) is based on measuring the “distance” of the expressive face from the subspace of neutral facial expression, which requires a prior learning step to model the subspace of neutral face shapes. The audio and video modalities are fused at the decision level. The subject-independent audio-visual emotion recognition system has shown promising results on two databases in two different languages (eNTERFACE and BAUM-1a).     

Electro-sorption of ammonium by a modified membrane capacitive deionization unit

A modified membrane capacitive deionization (MCDI) unit with additional flow channels in the anode and cathode chambers was used to study the performance of ammonium ion removal. The influences of operating parameters including initial ammonium concentration, flow rate and applied potential and the effect of flow in the anode and cathode chambers were investigated. Flow through the anode and cathode chambers significantly enhanced the electro-sorption efficiency. The electro-sorption of ammonium at 5 mL/min was about 65% greater than without flow.     

Conductive heating and microwave hydrolysis under identical heating profiles for advanced anaerobic digestion of municipal sludge

Microwave (2.45 GHz, 1200 W) and conventional heating (custom pressure vessel) pretreatments were applied to dewatered municipal waste sludge (18% total solids) using identical heating profiles that span a wide range of temperatures (80–160 °C). Fourteen lab-scale semi-continuous digesters were set up to optimize the energy (methane) output and sludge retention time (SRT) requirements of untreated (control) and thermally pretreated anaerobic digesters operated under mesophilic and thermophilic temperatures. Both pretreatment methods indicated that in the pretreatment range of 80–160 °C, temperature was a statistically significant factor (p-value < 0.05) for increasing solubilization of chemical oxygen demand and biopolymers (proteins, sugars, humic acids) of the waste sludge. However, the type of pretreatment method, i.e. microwave versus conventional heating, had no statistically significant effect (p-value >0.05) on sludge solubilization. With the exception of the control digesters at a 5-d SRT, all control and pretreated digesters achieved steady state at all three SRTs, corresponding to volumetric organic loading rates of 1.74–6.96 g chemical oxygen demand/L/d. At an SRT of 5 d, both mesophilic and thermophilic controls stopped producing biogas after 20 d of operation with total volatile fatty acids concentrations exceeding 1818 mg/L at pH <5.64 for mesophilic and 2853 mg/L at pH <7.02 for thermophilic controls, while the pretreated digesters continued producing biogas. Furthermore, relative (to control) organic removal efficiencies dramatically increased as SRT was shortened from 20 to 10 and then 5 d, indicating that the control digesters were challenged as the organic loading rate was increased. Energy analysis showed that, at an elevated temperature of 160 °C, the amount of methane recovered was not enough to compensate for the energy input. Among the digesters with positive net energy productions, control and pretreated digesters at 80 °C were more favorable at an SRT of 10 d.     

Synergetic pretreatment of sewage sludge by microwave irradiation in presence of H2O2 for enhanced anaerobic digestion

A microwave-enhanced advanced hydrogen peroxide oxidation process (MW/H₂O₂-AOP) was studied in order to investigate the synergetic effects of MW irradiation on H₂O₂ treated waste activated sludges (WAS) in terms of mineralization (permanent stabilization), sludge disintegration/solubilization, and subsequent anaerobic biodegradation as well as dewaterability after digestion. Thickened WAS sample pretreated with 1 g H₂O₂/g total solids (TS) lost 11-34% of its TS, total chemical oxygen demand (COD) and total biopolymers (humic acids, proteins and sugars) via advanced oxidation. In a temperature range of 60-120 °C, elevated MW temperatures (>80 °C) further increased the decomposition of H₂O₂ into OH^• radicals and enhanced both oxidation of COD and solubilization of particulate COD (>0.45 micron) of WAS indicating that a synergetic effect was observed when both H₂O₂ and MW treatments were combined. However, at all temperatures tested, MW/H₂O₂ treated samples had lower first-order mesophilic (33 ± 2 °C) biodegradation rate constants and ultimate (after 32 days of digestion) methane yields (mL per gram sample) compared to control and MW irradiated WAS samples, indicating that synergistically (MW/H₂O₂-AOP) generated soluble organics were slower to biodegrade or more refractory than those generated during MW irradiation.     

A resampling-based Markovian model for automated colon cancer diagnosis

In recent years, there has been a great effort in the research of implementing automated diagnostic systems for tissue images. One major challenge in this implementation is to design systems that are robust to image variations. In order to meet this challenge, it is important to learn the systems on a large number of labeled images from a different range of variation. However, acquiring labeled images is quite difficult in this domain, and hence, the labeled training data are typically very limited. Although the issue of having limited labeled data is acknowledged by many researchers, it has rarely been considered in the system design. This paper successfully addresses this issue, introducing a new resampling framework to simulate variations in tissue images. This framework generates multiple sequences from an image for its representation and models them using a Markov process. Working with colon tissue images, our experiments show that this framework increases the generalization capacity of a learner by increasing the size and variation of the training data and improves the classification performance of a given image by combining the decisions obtained on its sequences.     

Novel structured gadolinium doped ceria based electrolytes for intermediate temperature solid oxide fuel cells

Novel three-layered intermediate temperature solid oxide fuel cell (SOFC) electrolytes based on gadolinium doped ceria (GDC) are developed to suppress the electronic conductivity of GDC, to improve the mechanical properties of the cell and to minimize power loss due to mixed conductive nature of GDC. Three different electrolytes are fabricated by sandwiching thin YSZ, ScSZ and ScCeSZ between two relatively thick GDC layers. An electrolyte composed of pure GDC is also manufactured for comparison. NiO/GDC and LSCF/GDC electrodes are then coated on the electrolytes by a screen printing route. SEM results show that it is possible to obtain dense and crack free thin layers of YSZ, ScSZ and ScCeSZ between two GDC layers without delamination. Performance measurements indicate that interlayered thin electrolytes act as an electronic conduction barrier and improve open circuit voltages (OCVs) of GDC based cells.     

A sinkhole resilient protocol for wireless sensor networks: Performance and security analysis

This work focuses on: (1) understanding the impact of selective forwarding attacks on tree-based routing topologies in wireless sensor networks (WSNs), and (2) investigating cryptography-based strategies to limit network degradation caused by sinkhole attacks. The main motivation of our research stems from the following observations. First, WSN protocols that construct a fixed routing topology may be significantly affected by malicious attacks. Second, considering networks deployed in a difficult to access geographical region, building up resilience against such attacks rather than detection is expected to be more beneficial. We thus first provide a simulation study on the impact of malicious attacks based on a diverse set of parameters, such as the network scale and the position and number of malicious nodes. Based on this study, we propose a single but very representative metric for describing this impact. Second, we present the novel design and evaluation of two simple and resilient topology-based reconfiguration protocols that broadcast cryptographic values. The results of our simulation study together with a detailed analysis of the cryptographic overhead (communication, memory, and computational costs) show that our reconfiguration protocols are practical and effective in improving resilience against sinkhole attacks, even in the presence of collusion.     

Efficient Hydrogen Generation with Co3O4@TiO2-g-C3N4 Composite Catalyst via Catalytic NaBH4 Hydrolysis

In this study, g-C₃N₄-TiO₂ nanocomposite structure has been loaded with Co₃O₄ via electroless plating and thermal annealing to form Co₃O₄@g-C₃N₄-TiO₂ catalyst material for H₂ generation from NaBH₄ hydrolysis. The material characterizations of the fabricated catalyst have been performed before and after exposure to an aqueous NaBH₄ solution to understand the changes in catalytic performance and material properties. The Arrhenius activation energies have been determined to be 58 kJ mol^?1. The hydrogen generation rates have been observed as 180 and 1200 mL min^?1 g_cat^?1 for the catalyst hydrolysis of NaBH₄ at 30 °C and 60 °C, respectively. The catalytic activity performed in NaBH₄ solution exhibited good reusability.

Graphical Abstract