scispace - formally typeset
Search or ask a question

Showing papers by "University of Windsor published in 2022"


Journal ArticleDOI
01 Jun 2022
TL;DR: Bonnassieux et al. as mentioned in this paper presented a roadmap for the key areas of flexible and printable electronics. And they highlighted the current status and future challenges in the areas covered by the roadmap and highlighted the breadth and wide-ranging opportunities made available by flexible electronics technologies.
Abstract: Author(s): Bonnassieux, Y; Brabec, CJ; Cao, Y; Carmichael, TB; Chabinyc, ML; Cheng, KT; Cho, G; Chung, A; Cobb, CL; Distler, A; Egelhaaf, HJ; Grau, G; Guo, X; Haghiashtiani, G; Huang, TC; Hussain, MM; Iniguez, B; Lee, TM; Li, L; Ma, Y; Ma, D; McAlpine, MC; Ng, TN; Osterbacka, R; Patel, SN; Peng, J; Peng, H; Rivnay, J; Shao, L; Steingart, D; Street, RA; Subramanian, V; Torsi, L; Wu, Y | Abstract: This roadmap includes the perspectives and visions of leading researchers in the key areas of flexible and printable electronics. The covered topics are broadly organized by the device technologies (sections 1–9), fabrication techniques (sections 10–12), and design and modeling approaches (sections 13 and 14) essential to the future development of new applications leveraging flexible electronics (FE). The interdisciplinary nature of this field involves everything from fundamental scientific discoveries to engineering challenges; from design and synthesis of new materials via novel device design to modelling and digital manufacturing of integrated systems. As such, this roadmap aims to serve as a resource on the current status and future challenges in the areas covered by the roadmap and to highlight the breadth and wide-ranging opportunities made available by FE technologies.

68 citations


Journal ArticleDOI
TL;DR: An edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids and a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution.
Abstract: With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy data analysis applications and brings intelligent and personalized energy services for users. In conventional AI of Things (AIoT) paradigms, a wealth of individual energy data distributed across users’ IoT devices needs to be migrated to a central storage (e.g., cloud or edge device) for knowledge extraction, which may impose severe privacy violation and data misuse risks. Federated learning, as an appealing privacy-preserving AI paradigm, enables energy data owners (EDOs) to cooperatively train a shared AI model without revealing the local energy data. Nevertheless, potential security and efficiency concerns still impede the deployment of federated-learning-based AIoT services in smart grids due to the low-quality shared local models, non-independently and identically distributed (non-IID) data distributions, and unpredictable communication delays. In this article, we propose a secure and efficient federated-learning-enabled AIoT scheme for private energy data sharing in smart grids with edge-cloud collaboration. Specifically, we first introduce an edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids. Then, by considering non-IID effects, we design a local data evaluation mechanism in federated learning and formulate two optimization problems for EDOs and energy service providers. Furthermore, due to the lack of knowledge of multidimensional user private information in practical scenarios, a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution. Extensive simulation results show that the proposed scheme can effectively stimulate EDOs to share high-quality local model updates and improve the communication efficiency.

36 citations


Journal ArticleDOI
TL;DR: In this article, a semi-supervised learning technique, namely pseudo-label stacked auto-encoder (PLSAE), was used to detect Android malware, which involves training using a set of labeled and unlabeled instances.
Abstract: Android has become the target of attackers because of its popularity. The detection of Android mobile malware has become increasingly important due to its significant threat. Supervised machine learning, which has been used to detect Android malware is far from perfect because it requires a significant amount of labeled data. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique, namely pseudo-label stacked auto-encoder (PLSAE), which involves training using a set of labeled and unlabeled instances. We use a hybrid approach of dynamic analysis and static analysis to craft feature vectors. We evaluate our proposed model on CICMalDroid2020, which includes 17,341 most recent samples of five different Android apps categories. After that, we compare the results with state-of-the-art techniques in terms of accuracy and efficiency. Experimental results show that our proposed framework outperforms other semi-supervised approaches and common machine learning algorithms.

21 citations


Journal ArticleDOI
TL;DR: A novel side-channel-based passwords cracking system, namely MAGLeak, is proposed to recognize the victim's passwords by leveraging accelerometer, gyroscope, and magnetometer of IIoT touch-screen smart device.
Abstract: As an emerging technology, industrial Internet of Things (IIoT) connects massive sensors and actuators to empower industrial sectors being smart, autonomous, efficient, and safety. However, due the large number of build-in sensors of IIoT smart devices, the IIoT systems are vulnerable to side-channel attack. In this article, a novel side-channel-based passwords cracking system, namely MAGLeak, is proposed to recognize the victim's passwords by leveraging accelerometer, gyroscope, and magnetometer of IIoT touch-screen smart device. Specifically, an event-driven data collection method is proposed to ensure that the user's keystroke behavior can be reflected accurately by the obtained measurements of three sensors. Moreover, random forest algorithm is leveraged for the recognition module, followed by a data preprocessing process. Extensive experimental results demonstrate that MAGLeak achieves a high recognition accuracy under small training dataset, e.g., achieving recognition accuracy 98% of each single key for 2000 training samples.

19 citations


Journal ArticleDOI
TL;DR: In this article, a polynomial-based objective model was proposed to find the MTPA angle to maximize the control objective (the ratio of output torque to stator current), which can avoid the timeconsuming search process resulting in fast detection speed in comparison to existing search-based methods.
Abstract: For interior permanent magnet synchronous machines (IPMSMs), maximum torque per ampere (MTPA) control aims to find the MTPA angle to maximize the control objective (the ratio of output torque to stator current). This article proposes a novel online polynomial curve fitting technique for fast and accurate MTPA angle detection, which is motivated by the fact that the objective increases before MTPA angle and decreases after MTPA angle. This article proposes a polynomial-based objective model and identifies the polynomial parameters from a few test data for direct MTPA angle calculation. The proposed approach can avoid the time-consuming search process resulting in fast detection speed in comparison to existing search-based methods. In implementation, the current angle is set to a few test values to obtain the data for online curve fitting and MTPA angle calculation, in which there is no need of machine inductances and PM flux linkage. Moreover, the proposed polynomial model is analyzed to obtain the number of test data required for fast and accurate MTPA angle detection. The proposed approach is validated with extensive experiments and comparisons with existing methods on a laboratory IPMSM.

15 citations


Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors investigated the risk return relationship of China carbon market by evaluating the risk compensation coefficients, especially to consider the structural breaks caused by the policy uncertainty and vital events would impact the investment and carbon market risk-return relations.

13 citations


Journal ArticleDOI
TL;DR: In this article , a feasibility analysis of stand-alone HES on Western side of Pelee Island, Canada, whose load is 2426 kWh/day, was performed for several hybridization cases, including diesel (DG), wind (WT), and solar (PV) energy generation, coupled with converters (CNV) and four different battery-electric storage technologies, for technical and economic suitability.

12 citations


Journal ArticleDOI
TL;DR: In this article, the authors evaluated studies using ML algorithms based on ECG characteristics to assess people suffering from sleep apnea, and found that the most common features used in the studies were frequency, time series, and statistical features.
Abstract: Sleep apnea (SA) is a common sleep disorder that is not easy to detect. Recent studies have highlighted ECG analysis as an effective method of diagnosing SA. Because the changes caused by SA on the ECG are imperceptible, the need for new methods in diagnosing this disease is required more than ever. Machine Learning (ML) is recognized as one of the most successful methods of computer aided diagnosis. ML uses new methods to diagnose diseases using past clinical results. The purpose of this study is to evaluate studies using ML algorithms based on ECG characteristics to assess people suffering from SA. In this study, systematically-reviewed articles written in English before October 2020 and indexed in PubMed, Scopus, Web of Science, and IEEE databases were searched with no lower time limit. From these articles, 48 were selected for further review. The selected articles adopteddifferent ML methods for classification. All of these studies were binary where SA was detected from the normal state based on a full ECG stripe (per record), or based on one-minute segments (per segment). Our analysis show that the most common features used in the studies were frequency, time series, and statistical features. Support-Vector Machine (SVM) and deep learning-based neural network (i.e. CNN, DNN) performed best in full record data detection. The highest accuracy, sensitivity, and specificity reported among the selected studies were 100%, which was obtained by an SVM. In another study, the classification was conducted based on ECG segments, and accordingly, the highest classification accuracy was observed in the residual neural network algorithm (RNN). The accuracy, sensitivity, and specificity of this algorithm were reported to be 99%. In general, it can be stated that ML techniques based on ECG characteristics have a high capability in diagnosing SA. These techniques can increase the diagnosis of patients with SA or the detection of SA episodes on ECG record, and can potentially prevent complications of the disease at later stages.

11 citations


Journal ArticleDOI
G. Patteau1
01 Feb 2022
TL;DR: In this paper , the authors showed that incorporating titanium atoms into the DLC structure would reduce the running-in coefficient of friction (COF) and wear resistance against aluminum at elevated temperatures.
Abstract: Diamond-like carbon (DLC) coatings show a low coefficient of friction (COF) and high wear resistance against aluminum at elevated temperatures, yet they exhibit a high running-in COF (μR) prior to reaching a low and stable steady-state COF (μs). This study shows that incorporating titanium (Ti) atoms into the DLC structure would reduce the μR. During pin-on-disk tests conducted on Ti incorporating hydrogenated-DLC (Ti-H-DLC) with 6.2 at.% Ti subjected to dry sliding against 319 Al (Al-6.5% Si), μs values decreased from 0.27 at 25 °C to 0.11 at 200 °C. The specific wear rate of Ti-H-DLC decreased from 2.44 × 10−5 mm3/Nm at 25 °C to 0.71 × 10−5 m3/Nm at 200 °C. A typical DLC with 40 at.% H (H-DLC) tested at 200 °C showed a low μs of 0.08 and a wear rate of 1.11 × 10−5 mm3/Nm. However, at 200 °C, Ti-H-DLC showed a lower μR of 0.16 compared to μR = 0.78 of H-DLC, and the duration of the running-in period for Ti-H-DLC, tR = 3 revolutions, was shorter than H-DLC with tR of 200 revolutions. Comparisons made with other DLCs, including, NH-DLC, W-DLC, ta-C, and Si-O-H-DLC, in addition to H-DLC, all tested using the same method, revealed that, in the temperature range of 100–250 °C, Ti-H-DLC showed a better running-in behavior making Ti-H-DLC a suitable tool coating for manufacturing processes where high-temperature running-in sliding friction is important, including warm forming and (single-point) turning of aluminum alloys.

10 citations


Journal ArticleDOI
TL;DR: A model of attention allocation is proposed that indicates that user attention is most likely to be allocated to applications that have been used both recently and persistently as well as by applications that are popular and current and highlights the negative impact that a large application portfolio has on the allocation of user attention to individual applications.

10 citations


Journal ArticleDOI
TL;DR: In this paper , a clinical classification system for the Boston Naming Test-Short Form (BNT-15) was introduced as a measure of object-naming skills and the proposed clinical classification ranges provide useful guidelines for practitioners.
Abstract: Background: Abbreviated neurocognitive tests offer a practical alternative to full-length versions but often lack clear interpretive guidelines, thereby limiting their clinical utility. Objective: To replicate validity cutoffs for the Boston Naming Test—Short Form (BNT–15) and to introduce a clinical classification system for the BNT–15 as a measure of object-naming skills. Method: We collected data from 43 university students and 46 clinical patients. Classification accuracy was computed against psychometrically defined criterion groups. Clinical classification ranges were developed using a z-score transformation. Results: Previously suggested validity cutoffs (≤11 and ≤12) produced comparable classification accuracy among the university students. However, a more conservative cutoff (≤10) was needed with the clinical patients to contain the false-positive rate (0.20–0.38 sensitivity at 0.92–0.96 specificity). As a measure of cognitive ability, a perfect BNT–15 score suggests above average performance; ≤11 suggests clinically significant deficits. Demographically adjusted prorated BNT–15 T-scores correlated strongly (0.86) with the newly developed z-scores. Conclusion: Given its brevity (<5 minutes), ease of administration and scoring, the BNT–15 can function as a useful and cost-effective screening measure for both object-naming/English proficiency and performance validity. The proposed clinical classification ranges provide useful guidelines for practitioners.

Journal ArticleDOI
TL;DR: In this article , the demographics of pulmonary ACE2 expression, Mendelian randomization (MR) of ACE2 and COVID-19, and comparative tropism of SARS-CoV-2 were analyzed.
Abstract: Sick, male, and older populations are more vulnerable to COVID-19. However, it remains unclear whether a common mechanism exists across different demographic characteristics. SARS-CoV-2 infection is initiated by the specific binding of the viral spike protein to angiotensin-converting enzyme 2 (ACE2). This study analyzed the demographics of pulmonary ACE2 expression, Mendelian randomization (MR) of ACE2 and COVID-19, and comparative tropism of SARS-CoV-2. The key features of SARS-CoV-2 tropism, including pulmonary ACE2 expression and ACE2-expressing cell types, showed distinct subphenotypes associated with the demographics of vulnerable COVID-19 populations, suggesting a hypothesis centered on "ACE2" to explain their interplay. Next, by integrating multiple COVID-19 cohorts of genome-wide association studies (GWASs) and cis-expression quantitative trait loci (cis-eQTLs) of ACE2, MR analysis demonstrated that ACE2 played a causal role in COVID-19 susceptibility and severity, suggesting ACE2 as a promising target for early COVID-19 treatment. Next, by analyzing the expression of host cell receptors using single-cell RNA sequencing (scRNA-seq) data of human lung tissues, comparative tropism analysis showed that SARS-CoV-2 and other respiratory viruses, but not non-respiratory viruses, had remarkably overlapping and enriched cellular tropism in alveolar type 2 (AT2) cells. This finding indicates the possibility of coinfection with SARS-CoV-2 and other respiratory viruses, perhaps implying sociovirology at the cellular level. Moreover, the binding of viral entry proteins to the compatible host cell receptors is under strong natural selection pressure. Therefore, comparative tropism might reveal the footprint of natural selection that shapes the virus population, which provides a novel perspective for understanding zoonotic spillover events.

Journal ArticleDOI
TL;DR: In this article , a network-based integration approach was proposed to best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data, which was applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes.
Abstract: 'De novo' drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called 'in silico' drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.

Journal ArticleDOI
TL;DR: In this article , human satellite II (HSATII) satellite repeat expression was associated with epithelial-mesenchymal transition (EMT) and anticorrelated with IFN-response genes indicative of a more aggressive phenotype.
Abstract: Aberrant expression of viral-like repeat elements is a common feature of epithelial cancers, and the substantial diversity of repeat species provides a distinct view of the cancer transcriptome. Repeatome profiling across ovarian, pancreatic, and colorectal cell lines identifies distinct clustering independent of tissue origin that is seen with coding gene analysis. Deeper analysis of ovarian cancer cell lines demonstrated that human satellite II (HSATII) satellite repeat expression was highly associated with epithelial-mesenchymal transition (EMT) and anticorrelated with IFN-response genes indicative of a more aggressive phenotype. SATII expression — and its correlation with EMT and anticorrelation with IFN-response genes — was also found in ovarian cancer RNA-Seq data and was associated with significantly shorter survival in a second independent cohort of patients with ovarian cancer. Repeat RNAs were enriched in tumor-derived extracellular vesicles capable of stimulating monocyte-derived macrophages, demonstrating a mechanism that alters the tumor microenvironment with these viral-like sequences. Targeting of HSATII with antisense locked nucleic acids stimulated IFN response and induced MHC I expression in ovarian cancer cell lines, highlighting a potential strategy of modulating the repeatome to reestablish antitumor cell immune surveillance.

Journal ArticleDOI
TL;DR: In this article, the authors proposed a Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available computations.
Abstract: The Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available comput...

Journal ArticleDOI
TL;DR: In this paper , the abundance of tumor-infiltrating T cells (TIL-T) and TIL-B cells was analyzed across heterogeneous human malignancies.
Abstract: Recently, immunotherapy targeting tumor-infiltrating lymphocytes (TILs) has emerged as a critical and promising treatment in several types of cancer. However, not all cancer types have been tested in immunotherapeutic trials, and different patients and cancer types may have unpredictable clinical outcomes. This situation has created a particular exigency for analyzing the prognostic significance of tumor-infiltrating T cells (TIL-T) and B cells (TIL-B) across different cancer types. To address the critical role of TILs, the abundances of TIL-T and TIL-B cells, as determined by the protein levels of LCK and CD20, were analyzed across heterogeneous human malignancies. TIL-T and TIL-B cells showed varying prognostic significances across heterogeneous cancer types. Additionally, distinct distributions of TIL-T and TIL-B cells were observed in different cancer and tumor microenvironment (TME) subtypes. Next, we analyzed the cellular context for the TME communication network involving the well-acknowledgeable chemokine receptors of TIL-T and TIL-B cells, implying the functional interactions with TME. Additionally, these chemokine receptors, expressed by TIL-T and TIL-B cells, were remarkably correlated with the levels of TIL-T or TIL-B cell infiltrations across nearly all the cancer types, indicating these chemokine receptors as universal targets for up- and down-regulating the TIL-T and TIL-B cells. Lastly, we provide the prognostic landscape of TIL-T and TIL-B cells across 30 cancer types and the subgroups defined by gender, histopathology, histological grade, therapeutic approach, drug, and TME subtype, which are intended to be a resource to fuel the investigations of TILs, with important implications for cancer immunotherapy.

Journal ArticleDOI
15 Jan 2022-Talanta
TL;DR: In this paper, the authors showed that the fluorescence of fluorescein isothiocyanate (FITC) is not altered by its reaction with primary amines.

Journal ArticleDOI
TL;DR: In this article, idealized low-rise buildings with small, medium, and long periods were investigated, considering the presence of axial force in columns, and the results obtained from the current study makes an attempt to exhibit the trends of post elastic range response for the structures.
Abstract: During an earthquake, structures are generally excited to multiple ground motion components: two orthogonal horizontal components and one vertical component. However, the interaction of the deformations along two principal orthogonal directions in the presence of various levels of axial force in columns in post elastic range can be successfully captured if nonlinear dynamic analysis can be carried out using the bidirectional hysteresis model. On the other hand, the seismic codes prescribed simple combination rules to capture this effect in an equivalent sense even without consideration of axial force in columns. Though these rules may be valid in the elastic range, they may not be applicable in the post-elastic range as mentioned in a few seismic codes. Surprisingly, these codes did not specify any user-friendly provision needed by design offices. Hence, the applicability and efficacy of these rules in the post-elastic range is needed to be compared with that obtained from detailed nonlinear tine history analysis under orthogonal pair of bidirectional ground motions obtained from a number of chosen seismic acceleration data for performance-based design. In this study, idealized low-rise buildings with small, medium, and long periods were investigated, considering the presence of axial force in columns. Most of the current design codes and standards suggest that the addition of response of 30% of ground motion in other direction if added to the response of unidirectional ground motion in the considered direction of analysis or the resultant responses obtained by square root of sum of square from the unidirectional analyses carried out separately in both principal directions (referred as SRSS response) may predict the response with reasonable accuracy. The results obtained from the current study makes an attempt to exhibit the trends of post elastic range response for the structures. Further, it shows that seismic codes options significantly underestimate the post-elastic range seismic demand though they are proved to be adequate to predict elastic range response. This paper may prove helpful in improving code provisions.

Journal ArticleDOI
TL;DR: In this article, a spring-based anti-vibration handle that can be attached to vibrating equipment (blueberry hand harvester) was proposed to reduce HAV by 61.1%, which is within the exposure limit values (ELV) defined by the European Union.

Journal ArticleDOI
TL;DR: In this paper, an experimental program is conducted to evaluate the effect of lateral coupling in rectangular unbonded fiber-reinforced elastomeric isolators (UFREIs), where an apparatus with six degrees of freedom was used to apply a vertical load with simultaneous displacement in both primary lateral directions, whereas previous experimental programs are mostly based on a two degree of freedom analysis (vertical and lateral).

Journal ArticleDOI
TL;DR: In this article, the authors investigated the effects of projectile shape on the ballistic performance of a single-stage UAV using a honeycomb-core sandwich structure as orbital debris shielding in unmanned satellites.
Abstract: Honeycomb-core sandwich structures are commonly utilized as orbital debris shielding in unmanned satellites. This study investigated the effects of projectile shape on the ballistic perform...

Journal ArticleDOI
TL;DR: In this article , the authors synthesize spherical diketopyrrolopyrrole-based Conjugated Polymer Nanoparticles (CPNs) with an average diameter of 109 nm, which include fluorescein-conjugated Hyaluronic Acid (HA), a ligand for the CD44 receptor present on one population of TICs.
Abstract: Glioblastoma is one of the most aggressive types of cancer with success of therapy being hampered by the existence of treatment resistant populations of stem-like Tumour Initiating Cells (TICs) and poor blood-brain barrier drug penetration. Therapies capable of effectively targeting the TIC population are in high demand. Here, we synthesize spherical diketopyrrolopyrrole-based Conjugated Polymer Nanoparticles (CPNs) with an average diameter of 109 nm. CPNs were designed to include fluorescein-conjugated Hyaluronic Acid (HA), a ligand for the CD44 receptor present on one population of TICs. We demonstrate blood-brain barrier permeability of this system and concentration and cell cycle phase-dependent selective uptake of HA-CPNs in CD44 positive GBM-patient derived cultures. Interestingly, we found that uptake alone regulated the levels and signaling activity of the CD44 receptor, decreasing stemness, invasive properties and proliferation of the CD44-TIC populations in vitro and in a patient-derived xenograft zebrafish model. This work proposes a novel, CPN- based, and surface moiety-driven selective way of targeting of TIC populations in brain cancer.

Journal ArticleDOI
TL;DR: In this article, the interactive effects of environmental conditions and mercury contamination on laying phenology and incubation behavior were examined in female common eiders nesting at Canada's largest Arctic breeding colony.

Journal ArticleDOI
TL;DR: In this paper , an experimental program is conducted to evaluate the effect of lateral coupling in rectangular unbonded fiber-reinforced elastomeric isolators (UFREIs), where an apparatus with six degrees of freedom was used to apply a vertical load with simultaneous displacement in both primary lateral directions, whereas previous experimental programs are mostly based on a two degree of freedom analysis (vertical and lateral).

Journal ArticleDOI
TL;DR: The authors conducted a scoping review of feasibility studies reporting on preferred supervised consumption services (SCS) design characteristics, staffing models and ancillary services, and found that participants generally preferred aligning design elements with the goal of harm reduction for clients while other stakeholders valued treatment as a goal.

Journal ArticleDOI
TL;DR: In this article, a backward Monte Carlo method is proposed to determine the temperature distribution of heaters to achieve desirable prescribed uniform heat flux on the design surfaces, and the proposed approach is highly efficient and simple to implement with appropriate results.
Abstract: The maintenance of uniform temperature distribution affects the efficiency in the most industrial applications. In the current study, a novel strategy has been developed for inverse radiative boundary design problems in radiant enclosures. This study presents the Backward Monte Carlo method to investigate the inverse boundary design of an enclosure composed of specular and diffuse surfaces. A new optimized Monte Carlo method is proposed to determine the temperature distribution of heaters to achieve desirable prescribed uniform heat flux on the design surfaces. The proposed approach is highly efficient and simple to implement with appropriate results. The evaluated heat fluxes on design surfaces and temperature distribution of heaters are compared with the case where the reradiating walls are assumed to be perfectly diffuse. In the proposed approach, for a specific range of specularity, the absorptivity of the reradiating surfaces does not affect the temperature distribution of heaters. Compared to the diffuse walls, the specular walls have more uniform temperature distribution and heat flux of heaters. This finding will provide insight into solar furnaces design to enhance temperature uniformity, making specular surfaces suitable in many industrial applications.

Journal ArticleDOI
TL;DR: In this paper, an experimental investigation assessed the effectiveness of carbon and polyparaphenylene benzobisoxazole (PBO) fabric-reinforced cementitious matrix (C-FRCM and PBO-FCM) systems in rehabi...
Abstract: An experimental investigation assessed the effectiveness of carbon and polyparaphenylene benzobisoxazole (PBO) fabric-reinforced cementitious matrix (C-FRCM and PBO-FRCM) systems in rehabi...

Journal ArticleDOI
TL;DR: ZnGa2O4 was used as a photocatalyst degradation of three organic dyes rhodamine-B, methylene blue, and methyl orange, under ultraviolet (UV) light irradiation as discussed by the authors.

Journal ArticleDOI
Talal Sati1
TL;DR: In this article , a virtual impedance-fault current limiter (VI-FCL) is proposed to enable modeling droop-based IIDGs as a voltage source behind an impedance.
Abstract: Fault currents of inverter-interfaced distributed generators (IIDGs) depend on inverter controllers. Thus, IIDGs fault currents are different than those of synchronous-based DGs, both from the magnitude and waveshape perspectives. In the event of short-circuit faults, droop-based IIDGs switch between a voltage source and a current source, which increases the complexity and non-linearity of short-circuit current calculation (SCC). This paper proposes a new SCC algorithm that incorporates virtual impedance-fault current limiters (VI-FCLs) to enable modelling droop-based IIDGs as a voltage source behind an impedance. The VI-FCL was implemented as an additional control loop in the inverter control scheme to limit IIDG fault currents and achieve optimal protection coordination (OPC). Further, the VI-FCL is adaptively adjusted to enhance overcurrent protection sensitivity. A two-stage OPC algorithm for directional overcurrent relays (DOCRs) is developed. In Stage I, an optimal value for the adaptive VI-FCLs and relay currents are calculated. Stage II aims at obtaining optimal DOCRs settings. Time-domain simulations are used to demonstrate the effectiveness of the proposed adaptive VI-FCL and the accuracy of the proposed SCC algorithm. The proposed SCC algorithm and the OPC program are successfully validated using an islanded microgrid that is part of a Canadian distribution system.