University of Alberta

The initial concept of Negative Hesitation Fuzzy Sets (NHFSs) has been introduced recently. NHFSs are applied to decision-making problems accompanied by soft set theory. In this paper, a detailed clarification of NHFSs is proposed. Meanwhile, we introduced the way to construct membership, non-membership, and negative hesitation degrees by studying the overlap area between the projections of the element and classes in a two-dimensional space. This unified construction has concluded the relationship between NHFSs and Intuitionistic Fuzzy Sets (IFSs). A corollary of cosine similarity satisfying the NHFSs is employed for the pattern recognition problems. Classification of both synthetic numerical examples and the EEG signals are evaluated for the effectiveness of NHFSs in this paper.

Improving the power density of multilevel converters is a crucial task, particularly in high-power applications where the volume of passive components is significant. To achieve a high number of voltage levels with minimal device count, this article presents a novel and generalized derivation method for a new family of tree-type active neutral-point-clamped (TANPC) multilevel topologies. By utilizing the two-level bridge and three-level neutral-point pilot bridge as fundamental cells, the proposed method enables the extension of output voltage levels in a simple and straightforward way. Each derived TANPC topology is equivalent to a single-pole multi-throw switch without the need for flying capacitors, where n+1 voltage levels can be generated with only 2n active switches. The related pulsewidth modulation and neutral-point voltage balancing strategies for the derived TANPC multilevel converter family are also introduced. Experimental results are presented, demonstrating the effectiveness of the proposed topology derivation method through the implementation of a novel fivelevel TANPC converter.

Both efficiency and operation cost are the key to offshore microgrids operation. At the same time, due to the lack of external energy support, the paralleled converters in offshore microgrids often needs to handle the operation in a wide load range. In that case, this paper proposes a dual-level optimal control framework to improve overall operation performance of offshore microgrid with paralleled converters within a wide load range. First, a normalized nonlinear relationship between power loss and operation cost of paralleled converters is established. Based on it, a multi-objective optimal function is established. Then, the optimal operation condition is derived by Lagrange Multiply method with the established converter performance index. Furthermore, optimal power sharing considering both efficiency and operation cost is proposed at first level. Then, second level control is proposed to improve system performance in a wide load range. The proposed decentralized dispatch strategy is realized by a consensus protocol mechanism with the mealy machine based on the established performance index. Experiment results in a scaled-down prototype are given to validate the effectiveness of the proposed dual-level control strategy. The proposed strategy is able to optimize performance of paralleled converters under different power profiles.

Recently, the dual-active-bridge (DAB) DC/DC converters are widely utilized in renewable energy systems. Lots of research on the optimization of DAB converters has been conducted, including novel topologies, modulation schemes, control schemes, and fault-tolerant schemes for active switches or riding-through, etc. However, there is a limited fault-tolerant scheme developed for capacitor failure in DAB converters so far, due to the low-device redundancy. To address this important issue, a capacitor fault-tolerant scheme is proposed in this paper. With the proposed scheme, the DAB converter can maintain power transfer sustainability even if the port capacitors break down. Besides, potential voltage spikes can also be suppressed by the proposed scheme when the snubber function of port capacitors is lost. Subsequently, the modulation scheme, topology characters, and control scheme are elaborated in this paper. Finally, the experimental results based on a scale-down laboratory prototype are presented to verify the feasibility of the proposed fault-tolerant scheme.

Algae biorefinery requires the use of specific parameters that allow the control and monitoring of unit operations. A current challenge in microalgal biorefineries is the real-time monitoring of cell disruption processes. The cell disruption is essential for the development of efficient processes of biomass biorefining. Herein, different parameters were evaluated as potential indicators of cell disruption based on a wet route of biomass biorefining, and the parameters were present either in the aqueous phase and the recovered biomass. It was possible to plot precise models that correlate the levels of some parameters with the degree of cell disruption. Parameters present in the aqueous phase (proteins and ultraviolet absorbance) and the recovered biomass (lipids, chlorophylls a and b, and carotenoids) showed great potential for use in algae biorefineries. Ultraviolet absorbance and pigments were the most practical indicators of cell disruption since no colorimetric reaction is required to measure them.

The direct position determination (DPD) technique utilizes raw received signals to localize agents in a single step, eliminating the need for intermediary measurements. The DPD is recognized for its accuracy superiority over the two-step approach, especially under low signal-noise-ratio (SNR) condition. However, few existing DPD research has focused on scenarios involving moving or unsynchronized agents. In this paper, we develop a novel and extended problem, Direct Localization and Synchronization (DLAS) for highly mobile agents with unsynchronized frequency shifts in collocated multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The base stations (BSs) sequentially broadcast signals in a time-division multiple access (TDMA) manner, and both Doppler effect and oscillator’s nondeterminism lead to frequency shifts at the agent side. In order to compensate for the position variation of the fast-moving agent, we construct a motion model with uniform acceleration. Next, we propose a computationally efficient DLAS method based on the maximum likelihood (ML) principle. Specifically, we first decouple the frequency shifts from other unknowns by exploiting the periodicity of block-type pilots and determine a nonlinear optimization problem. We then develop an iterative solution using the frequency shifts to optimally extract real DLAS parameters from complex signal observables. Moreover, we present the closed-form Cramér-Rao lower bound (CRLB) for our estimators determined from the derived general bounding result in complex field. We theoretically analyze the performance gain owing to prior information, and compare the computational complexity among different algorithms. Finally, we provide extensive numerical results to establish the superiority of our proposed method.

In educational assessment, behavioral engagement refers to the extent to which learners actively participate in the assessment process and are motivated to perform well. Behavioral engagement plays a crucial role in the design, use, and interpretation of educational assessments because learners who are engaged in the assessment process are likely to perform better and retain the information being assessed than those who fail to make enough effort when responding to test items. To date, researchers have identified various indicators of behavioral engagement in digital assessments, such as spending sufficient time on each item or task, attempting to answer all questions, and interacting with the assessment environment. Assessment analytics can enable researchers to use these indicators to track, analyze, and interpret learners’ behavioral engagement in digital assessments. This chapter discusses the role of behavioral engagement in digital assessments, including computer-based tests and intelligent tutoring systems. We examine research on behavioral engagement and explain how it can be utilized in assessment analytics. As we describe state-of-the-art methods for modeling and interpreting behavioral engagement, we also present real-life case studies to illustrate these methods.

Microservices offer the benefits of scalable flexibility and rapid deployment, making them a preferred architecture in today's IT industry. However, their dynamic nature increases their susceptibility to failures, highlighting the need for effective troubleshooting strategies. Current methods for pinpointing issues in microservices often depend on impractical supervision or rest on unrealistic assumptions. We propose a novel approach using graph unsuper-vised neural networks and critical path analysis to address these limitations. Our experiments on four open-source microservice benchmarks show significant results, with top-1 accuracy ranging from 86.4% to 96%, over 6% enhancement compared to existing methods. Moreover, our approach reduces training time by 5.6 times compared to similar works on the same datasets. CCS CONCEPTS • Software and its engineering → Software defect analysis; • Computer systems organization → Reliability.

The complexity of microservices and their distributed nature necessitates constant monitoring and tracing of their execution to identify performance problems and underlying root causes. However, the large volume of collected data and the complexity of distributed communications pose challenges in identifying and locating abnormal services. In this paper, we propose a novel approach that takes into consideration the importance of execution contexts in propagating and localizing performance root causes. We achieve this by integrating social network analysis techniques with spectrum analysis. To evaluate our proposed approach, we conducted an experiment using a real-world benchmark, and we observed promising preliminary results, with a success rate of 91.3% in correctly identifying the primary root cause (top-1), and a perfect 100% success rate in finding the root cause within the top three candidates (top-3). CCS CONCEPTS • Software and its engineering → Software reliability; Software performance.

Electrocatalytic nitrogen (N2) reduction reaction (NRR) presents a sustainable alternative to the Haber–Bosch process for ammonia (NH3) synthesis. Iron phthalocyanine (FePc) is demonstrated as a promising catalyst for the electrocatalytic NRR. However, FePc with planar symmetric Fe‐N4 sites exhibits poor N2 adsorption and activation capabilities, resulting in an unsatisfactory NRR performance. Herein, an axial oxygen coordination strategy is developed to optimize the local electron distribution on FePc for improving N2 adsorption and activation. The as‐obtained FePc‐O‐CP shows a superior NH3 yield rate (59.72 µg h⁻¹ mg⁻¹cat.) and a considerable Faradaic efficiency (13.76%) in 0.1 m HCl. Density functional theory (DFT) calculations verify that the axial oxygen ligand on FePc inhibits the adsorption of H⁺ and enhances the N2 adsorption and activation, thereby greatly promoting NH3 generation. This work reveals the significance of regulating the local coordination environment of single‐atom catalysts for improving electrocatalytic NRR performance and provides a feasible strategy for the rational design of atomic‐scale active sites.

This current study provides a comprehensive examination of a novel method for studying the dynamics of a fractionalized Maxwell flow near an inclined plate, considering non-uniform mass transfer through a permeable media. Through the use of partial differential equations, incorporating heat and mass movement effects, the study employs a combination of generalized Fick’s and Fourier’s law with the Caputo operator. Transforming the fractionalized model into dimensionless form using appropriate dimensionless values, semi-analytical solutions for the non-dimensional transmitted fractional model are obtained via the Laplace transformation method. Through graphical analysis, the precise contributions of key parameters such as heat generation, radiation, and chemical reactions are elucidated, including their impacts on the calculated heat generation parameter (Qo), radiation parameter (Nr), and others. The study’s significance lies in its implications for the design of efficient heat exchangers, fluid flow systems, and cooling components in complex engineering systems, including nuclear reactors and power generation plants. Furthermore, the fractional derivative approach offers a more accurate representation of the viscoelastic behavior of materials like polymers, crucial for optimizing fabrication processes such as extrusion and molding. The insights gained from this study extend to the realm of miniaturized fluidic devices, including bio-analysis tools, lab-on-a-chip technology, and microfluidic drug delivery systems, where improved performance and control need a grasp of Maxwell fluid dynamics. The physical outcome of this research lays the groundwork for future investigations that will maximize heat transfer efficiency in real-world systems and give insightful information on the behavior of complicated fluids. We compute and display the skin friction, mass and heat transfer rate in tabular form.

Using different fibres and ratios can considerably enhance the mechanical properties of thermoplastic composites, and the fibre-matrix interface plays a crucial role in realizing the effects of reinforcements. This research aims to enhance the fibre-matrix interface using sustainable resources to increase the mechanical properties of composites produced using additive manufacturing. To do this, cellulose nanofibrils (CNF) were used for surface modification of carbon, glass, and hybrid (carbon + glass) fibres used in reinforcements in the PA6 matrix. Samples were produced by 3D printing done through material extrusion (MEX). and the effects of fibre types and ratios, print layer thickness, and interface enhancement between fibre-matrix on mechanical properties were investigated experimentally. Results reveal a 5 to 11% increase in the tensile strength of the carbon fibre-reinforced samples, whereas a 72 to 88% increase was observed for the glass fibre-reinforced samples. Furthermore, the tensile modulus value has been increased 4 times in carbon fibre reinforcement samples that used modified fibre compared to PA6 pure. Finally, different types and ratios of fibres had an impact on the glass transition temperature, but there was little to no change in the melting and crystallization temperatures. Our work highlights the potential of the proposed CNF modification made to the fibres for MEX production to produce parts with higher mechanical properties.

The nonlinear stiffness of a structure results in complex nonlinear dynamic behaviors and bifurcations of rotor systems. However, there still lacks of comprehensive studies on the bifurcation-induced motion to chaos of the nonlinear system. This study investigated the energy transfer during the motion evolution to chaos around bifurcations. In this paper, a flexible rotor system with nonlinear stiffness is established and the nonlinear responses under different parameter excitations are studied. We construct the energy trajectory in energy space and propose a bifurcation detection method based on generalized energy transfer for studying the evolution of motion cascades to chaos. The induction of period-doubling and period-halving bifurcation is revealed through the energy trajectory. The stability domains of the rotor system in different parameter planes are determined based on the Lyapunov stability criterion. A nonlinear rotor test platform is built and speed-up experiments are carried out to verify the proposed bifurcation detection method based on generalized energy transfer. These results indicate that the energy transfer is consistent with the switching of bifurcations. The sudden shift and fluctuation in the generalized energy amplitude correspond to period-doubling bifurcation and chaos, respectively. The generalized energy curves reveal the period-halving bifurcation, which cannot be observed in the speed-up test. This research and proposed method have potential for application in condition monitoring and bifurcation recognition during the operation of rotating machinery.

Background In humans, two ubiquitously expressed N-myristoyltransferases, NMT1 and NMT2, catalyze myristate transfer to proteins to facilitate membrane targeting and signaling. We investigated the expression of NMTs in numerous cancers and found that NMT2 levels are dysregulated by epigenetic suppression, particularly so in hematologic malignancies. This suggests that pharmacological inhibition of the remaining NMT1 could allow for the selective killing of these cells, sparing normal cells with both NMTs. Methods and results Transcriptomic analysis of 1200 NMT inhibitor (NMTI)-treated cancer cell lines revealed that NMTI sensitivity relates not only to NMT2 loss or NMT1 dependency, but also correlates with a myristoylation inhibition sensitivity signature comprising 54 genes (MISS-54) enriched in hematologic cancers as well as testis, brain, lung, ovary, and colon cancers. Because non-myristoylated proteins are degraded by a glycine-specific N-degron, differential proteomics revealed the major impact of abrogating NMT1 genetically using CRISPR/Cas9 in cancer cells was surprisingly to reduce mitochondrial respiratory complex I proteins rather than cell signaling proteins, some of which were also reduced, albeit to a lesser extent. Cancer cell treatments with the first-in-class NMTI PCLX-001 (zelenirstat), which is undergoing human phase 1/2a trials in advanced lymphoma and solid tumors, recapitulated these effects. The most downregulated myristoylated mitochondrial protein was NDUFAF4, a complex I assembly factor. Knockout of NDUFAF4 or in vitro cell treatment with zelenirstat resulted in loss of complex I, oxidative phosphorylation and respiration, which impacted metabolomes. Conclusions Targeting of both, oxidative phosphorylation and cell signaling partly explains the lethal effects of zelenirstat in select cancer types. While the prognostic value of the sensitivity score MISS-54 remains to be validated in patients, our findings continue to warrant the clinical development of zelenirstat as cancer treatment.