Showing papers by "Florida Polytechnic University published in 2020"

Electrochemical SARS-CoV-2 Sensing at Point-of-Care and Artificial Intelligence for Intelligent COVID-19 Management

Abstract: To manage the COVID-19 pandemic, development of rapid, selective, sensitive diagnostic systems for early stage β-coronavirus severe acute respiratory syndrome (SARS-CoV-2) virus protein detection i...

Short-Term Photovoltaic Power Forecasting Using an LSTM Neural Network and Synthetic Weather Forecast

Abstract: In this paper, a forecasting algorithm is proposed to predict photovoltaic (PV) power generation using a long short term memory (LSTM) neural network (NN). A synthetic weather forecast is created for the targeted PV plant location by integrating the statistical knowledge of historical solar irradiance data with the publicly available type of sky forecast of the host city. To achieve this, a ${K}$ -means algorithm is used to classify the historical irradiance data into dynamic type of sky groups that vary from hour to hour in the same season. In other words, the types of sky are defined for each hour uniquely using different levels of irradiance based on the hour of the day and the season. This can mitigate the performance limitations of using fixed type of sky categories by translating them into dynamic and numerical irradiance forecast using historical irradiance data. The proposed synthetic weather forecast is proved to embed the statistical features of the historical weather data, which results in a significant improvement in the forecasting accuracy. The performance of the proposed model is investigated using different intraday horizon lengths in different seasons. It is shown that using the synthetic irradiance forecast can achieve up to 33% improvement in accuracy in comparison to that when an hourly categorical type of sky forecast is used, and up to 44.6% in comparison to that when a daily type of sky forecast is used. This highlights the significance of utilizing the proposed synthetic forecast, and promote a more efficient utilization of the publicly available type of sky forecast to achieve a more reliable PV generation prediction. Moreover, the superiority of the LSTM NN with the proposed features is verified by investigating other machine learning engines, namely the recurrent neural network (RNN), the generalized regression neural network (GRNN) and the extreme learning machine (ELM).

Resilient Control Design for Load Frequency Control System Under False Data Injection Attacks

Abstract: Smart power grids are being enhanced by adding a communication infrastructure to improve their reliability, sustainability, and efficiency. Despite all of these significant advantages, their open communication architecture and connectivity renders the power systems’ vulnerability to a range of cyberattacks. This article proposes a novel resilient control system for load frequency control (LFC) system under false data injection (FDI) attacks. It is common to use encryption in data transfer links as the first layer of defending mechanism; here, we propose a second defense layer that can jointly detect and mitigate FDI attacks on power systems. In this article, we propose a new anomaly detection technique that consists of a Luenberger observer and an artificial neural network (ANN). Since FDI attacks can happen rapidly, the observer structure is enhanced by the extended Kalman filter to improve the ANN ability for online detection and estimation. The resilient controller is designed based on the attack estimation, which can eliminate the need for control reconfiguration. The resiliency of the proposed design against FDI attacks is tested on the LFC system. The simulation results clearly show that the proposed control system can successfully detect anomalies and compensate for their adverse effects.

Core–shell nanostructures: perspectives towards drug delivery applications

Abstract: Nanosystems have shown encouraging outcomes and substantial progress in the areas of drug delivery and biomedical applications. However, the controlled and targeted delivery of drugs or genes can be limited due to their physicochemical and functional properties. In this regard, core-shell type nanoparticles are promising nanocarrier systems for controlled and targeted drug delivery applications. These functional nanoparticles are emerging as a particular class of nanosystems because of their unique advantages, including high surface area, and easy surface modification and functionalization. Such unique advantages can facilitate the use of core-shell nanoparticles for the selective mingling of two or more different functional properties in a single nanosystem to achieve the desired physicochemical properties that are essential for effective targeted drug delivery. Several types of core-shell nanoparticles, such as metallic, magnetic, silica-based, upconversion, and carbon-based core-shell nanoparticles, have been designed and developed for drug delivery applications. Keeping the scope, demand, and challenges in view, the present review explores state-of-the-art developments and advances in core-shell nanoparticle systems, the desired structure-property relationships, newly generated properties, the effects of parameter control, surface modification, and functionalization, and, last but not least, their promising applications in the fields of drug delivery, biomedical applications, and tissue engineering. This review also supports significant future research for developing multi-core and shell-based functional nanosystems to investigate nano-therapies that are needed for advanced, precise, and personalized healthcare systems.

A Neural Adaptive Approach for Active Fault-Tolerant Control Design in UAV

Abstract: Faults in aircraft actuators can cause serious issues in safety. Due to the autonomous nature of the unmanned aerial vehicles (UAVs), faults can lead to more serious problems in these systems. In this paper, a new active fault tolerant control (FTC) system design for an UAV is presented. The proposed design uses a neural network adaptive structure for fault detection and isolation (FDI), then, the FDI signal combined with a nonlinear dynamic inversion technique is used to compensate for the faults in the actuators. The proposed scheme detects and isolates faults in the actuators of the system in real-time without the need of controller reconfiguration in presence of faults in the actuators. The simulation results of the designed FTC system when it is applied to WVU YF-22 UAV show that the proposed design can successfully detect and isolate the faults and compensate for their effect.

COVID-19: Review of a 21st Century Pandemic from Etiology to Neuro-psychiatric Implications.

Abstract: COVID-19 is a severe infectious disease that has claimed >150,000 lives and infected millions in the United States thus far, especially the elderly population. Emerging evidence has shown the virus to cause hemorrhagic and immunologic responses, which impact all organs, including lungs, kidneys, and the brain, as well as extremities. SARS-CoV-2 also affects patients', families', and society's mental health at large. There is growing evidence of re-infection in some patients. The goal of this paper is to provide a comprehensive review of SARS-CoV-2-induced disease, its mechanism of infection, diagnostics, therapeutics, and treatment strategies, while also focusing on less attended aspects by previous studies, including nutritional support, psychological, and rehabilitation of the pandemic and its management. We performed a systematic review of >1,000 articles and included 425 references from online databases, including, PubMed, Google Scholar, and California Baptist University's library. COVID-19 patients go through acute respiratory distress syndrome, cytokine storm, acute hypercoagulable state, and autonomic dysfunction, which must be managed by a multidisciplinary team including nursing, nutrition, and rehabilitation. The elderly population and those who are suffering from Alzheimer's disease and dementia related illnesses seem to be at the higher risk. There are 28 vaccines under development, and new treatment strategies/protocols are being investigated. The future management for COVID-19 should include B-cell and T-cell immunotherapy in combination with emerging prophylaxis. The mental health and illness aspect of COVID-19 are among the most important side effects of this pandemic which requires a national plan for prevention, diagnosis and treatment.

Grand Challenges in Bio-Nanotechnology to Manage the COVID-19 Pandemic

Abstract: Recent COVID-19 pandemic outbreak, a human beta coronavirus severe acute respiratory syndrome (SARS-CoV-2) virus infection, has severely affected the world and is not yet in full control due to lacking rapid diagnostics and therapeutics. This viral infection is steadily increasing life loss and emerged as a significant socio-economic burden for every class of the world. As a result, it has brought many countries united for exploring molecular biology, biomedical science, and nanotechnology aspects to manage COVID-19 successfully. As of now, the current priority is to investigate novel therapies of high efficacy and smart diagnostics tools for early-stage disease diagnostics along with monitoring. Keeping these advancements and challenges into consideration, this perspective article mainly highlights the contribution and possibilities of bio-nanotechnology to manage the COVID-19 pandemic, even in a personalized manner. Authors also pinpoint barriers in the utilization of current bio-nanotechnology to facilitate a more accurate understanding of COVID-19 and to lead in the way of personalized health and wellness. Further, we follow discussion towards aspects and challenges in upcoming bio-nanotechnology approaches for COVID-19 management. In this progressive option report, bio-nanotechnologies enriched with the power of artificial intelligence and optimized at the personalized level will lead to a sustainable treatment and cure strategy at the global population scale.

Tailored Biofunctionalized Biosensor for the Label-Free Sensing of Prostate-Specific Antigen

Abstract: The increase in the demand and popularity of smart biosensors has brought a novel and innovative concept to develop a diverse range of semen mutual biomarker (i.e., prostate-specific antigen, PSA)-...

Proficient Photocatalytic and Sonocatalytic Degradation of Organic Pollutants Using CuO Nanoparticles

Abstract: In recent years, due to the advancement in nanotechnology, advanced oxidation processes (AOPs), especially sonocatalysis and photocatalysis, have become a topic of interest for the elimination of pollutants from contaminated water. In the research work reported here, an attempt has been made to study and establish a physicochemical mechanism for the catalytic activity of copper oxide nanoparticles (CuO NPs) in AOPs using the degradation of dyes as model contaminants. CuO NPs exhibited brilliant sonocatalytic and photocatalytic activities for the degradation of a cationic dye (Victoria Blue) as well as an anionic dye (Direct Red 81). The degradation efficiency of CuO NPs was calculated by analysing the variation in the absorbance of dye under a UV-Vis spectrophotometer. The influence of different operating parameters on the catalytic activity of CuO NPs, such as the amount of catalysts dose, pH of the solution, and the initial dye concentration, was thoroughly investigated. In addition, the kinetic process for the degradation was also examined. It was observed that both dyes exhibited and followed the pseudo-first-order kinetics relation. The rate constant for sonocatalysis was high as compared to photocatalysis. The rate constant for both sonocatalysis and photocatalysis was successfully established, and reusability tests were done to ensure the stability of the used catalysts. To get an insight into the degradation mechanism, experiments were performed by using ⋅OH radical scavengers. The efficacy of CuO NPs for dye decolorization was found to be superior for the sonocatalyst than the photocatalyst.

Inhibition of Amyloid-Beta Production, Associated Neuroinflammation, and Histone Deacetylase 2-Mediated Epigenetic Modifications Prevent Neuropathology in Alzheimer's Disease in vitro Model.

Abstract: Alzheimer's disease (AD) is a growing global threat to healthcare in the aging population. In the USA alone, it is estimated that one in nine persons over the age of 65 years is living with AD. The pathology is marked by the accumulation of amyloid-beta (Aβ) deposition in the brain, which is further enhanced by the neuroinflammatory process. Nucleotide-binding oligomerization domain, leucine rich repeat and pyrin domain containing 3 (NLRP3) and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) are the major neuroinflammatory pathways that intensify AD pathogenesis. Histone deacetylase 2 (HDAC2)-mediated epigenetic mechanisms play a major role in the genesis and neuropathology of AD. Therefore, therapeutic drugs, which can target Aβ production, NLRP3 activation, and HDAC2 levels, may play a major role in reducing Aβ levels and the prevention of associated neuropathology of AD. In this study, we demonstrate that withaferin A (WA), an extract from Withania somnifera plant, significantly inhibits the Aβ production and NF-κB associated neuroinflammatory molecules' gene expression. Furthermore, we demonstrate that cytokine release inhibitory drug 3 (CRID3), an inhibitor of NLRP3, significantly prevents inflammasome-mediated gene expression in our in vitro AD model system. We have also observed that mithramycin A (MTM), an HDAC2 inhibitor, significantly upregulated the synaptic plasticity gene expression and downregulated HDAC2 in SH-SY5Y cells overexpressing amyloid precursor protein (SH-APP cells). Therefore, the introduction of these agents targeting Aβ production, NLRP3-mediated neuroinflammation, and HDAC2 levels will have a translational significance in the prevention of neuroinflammation and associated neurodegeneration in AD patients.

Exploring Low Loss and Single Mode in Antiresonant Tube Lattice Terahertz Fibers

Abstract: We propose and numerically analyze various hollow-core antiresonant fiber (HC-ARF) for operation at terahertz frequencies We compare typical HC-ARF designs with nested and adjacent nested designs while analyzing performance in terms of loss and single-mode guidance of terahertz waves With optimized fiber dimensions, the fundamental core mode, cladding mode, core higher-order modes (HOMs), and the angle dependence of adjacent tubes are analyzed to find the best design for low loss terahertz transmission Analysis of the fiber designs shows that the nested tube-based antiresonant fiber exhibits lower transmission loss and superior HOM suppression, exceeding 140 The nested HC-ARF is feasible for fabrication using existing fabrication technologies and opening up the possibility of efficient transmission of terahertz waves

A flower-like ZnO–Ag2O nanocomposite for label and mediator free direct sensing of dinitrotoluene

Abstract: 2,4-Dinitrotoluene (2,4-DNT) is a nitro aromatic compound used as a raw material for trinitrotoluene (TNT) explosive synthesis along with several other industrial applications. Easy, rapid, cost-effective, and selective detection of 2,4-DNT is becoming essential due to its hepato carcinogenic nature and presence in surface as well as ground water as a contaminant. Keeping this in view, this research, for the first-time, reports the synthesis of novel ZnO–Ag2O composite nanoflowers on a gold (Au) substrate, to fabricate an electrochemical sensor for label-free, direct sensing of 2,4-DNT selectively. The proposed ZnO–Ag2O/Au sensor exhibits a sensitivity of 5 μA μM−1 cm−2 with a low limit of detection (LOD) of 13 nM, in a linear dynamic range (LDR) of 0.4 μM to 40 μM. The sensor showed reasonably high re-usability and reproducibility, with reliable results for laboratory and real-world samples.

SHIELDeNN: Online Accelerated Framework for Fault-Tolerant Deep Neural Network Architectures

Abstract: We propose SHIELDeNN, an end-to-end inference accelerator frame-work that synergizes the mitigation approach and computational resources to realize a low-overhead error-resilient Neural Network (NN) overlay. We develop a rigorous fault assessment paradigm to delineate a ground-truth fault-skeleton map for revealing the most vulnerable parameters in NN. The error-susceptible parameters and resource constraints are given to a function to find superior design. The error-resiliency magnitude offered by SHIELDeNN can be adjusted based on the given boundaries. SHIELDeNN methodology improves the error-resiliency magnitude of cnvW1A1 by 17.19% and 96.15% for 100 MBUs that target weight and activation layers, respectively.

Short-Term Load Forecasting Using an LSTM Neural Network

Abstract: In this paper, two forecasting models using long short term memory neural network (LSTM NN) are developed to predict short-term electrical load. The first model predicts a single step ahead load, while the other predicts multi-step intraday rolling horizons. The time series of the load is utilized in addition to weather data of the considered geographic area. A rolling time-index series including a time of the day index, a holiday flag and a day of the week index, is also embedded as a categorical feature vector, which is shown to increase the forecasting accuracy significantly. Moreover, to evaluate the performance of the LSTM NN, the performance of other machines, namely a generalized regression neural network (GRNN) and an extreme learning machine (ELM) is also shown. Hourly load data from the electrical reliability council of Texas (ERCOT) is used as benchmark data to evaluate the proposed algorithms.

Identifying Mobile Inter-App Communication Risks

Abstract: Malware collusion is a technique utilized by attackers to evade standard detection. It is a new threat where two or more applications, appearing benign, communicate to perform a malicious task. Most proposed approaches aim at detecting stand-alone malicious applications. We point out the need for analyzing data flows across multiple Android apps, a problem referred to as end-to-end flow analysis . In this work, we present a flow analysis for app pairs that computes the risk level associated with their potential communications. Our approach statically analyzes the sensitivity and context of each inter-app flow based on inter-component communication (ICC) between communicating apps, and defines fine-grained security policies for inter-app ICC risk classification. We perform an empirical study on 7,251 apps from the Google Play store to identify the apps that communicate with each other via ICC channels. Our results report four times fewer warnings on our dataset of 197 real app pairs communicating via explicit external ICCs than the state-of-the-art permission-based collusion detection.

Development of Multifunctional Biopolymeric Auto-Fluorescent Micro- and Nanogels as a Platform for Biomedical Applications.

Abstract: The emerging field of theranostics for advanced healthcare has raised the demand for effective and safe delivery systems consisting of therapeutics and diagnostics agents in a single monarchy. This requires the development of multi-functional bio-polymeric systems for efficient image-guided therapeutics. This study reports the development of size-controlled (micro-to-nano) auto-fluorescent biopolymeric hydrogel particles of chitosan and hydroxyethyl cellulose (HEC) synthesized using water-in-oil emulsion polymerization technique. Sustainable resource linseed oil-based polyol is introduced as an element of hydrophobicity with an aim to facilitate their ability to traverse the blood-brain barrier (BBB). These nanogels are demonstrated to have salient features such as biocompatibility, stability, high cellular uptake by a variety of host cells, and ability to transmigrate across an in vitro BBB model. Interestingly, these unique nanogel particles exhibited auto-fluorescence at a wide range of wavelengths 450-780 nm on excitation at 405 nm whereas excitation at 710 nm gives emission at 810 nm. In conclusion, this study proposes the developed bio-polymeric fluorescent micro- and nano- gels as a potential theranostic tool for central nervous system (CNS) drug delivery and image-guided therapy.

Novel hollow-core asymmetric conjoined-tube anti-resonant fiber for low-loss THz wave guidance

Abstract: We report a novel hollow-core asymmetric conjoined-tube anti-resonant (HC-ACTAR) fiber for efficient and low-loss THz wave guidance. The cladding tubes of the proposed HC-ACTAR fiber is formed by conjoining a half circle and a half elliptical tube and is placed in the radial direction. We observe that the proposed fiber is superior in terms of achieving low-loss and low dispersion in a wide range of frequencies than the previously reported designs. We show that our proposed HC-ACTAR fiber ensures lowest loss of 0.034 dB/m at 1 THz and marinates a low-loss window of 0.5 THz. Moreover, the proposed fiber has promising optical properties in the THz regime such as low bending loss, broadband flattened dispersion, and effective single-mode guidance, which are essential for efficient THz wave guidance.

Mathematical Framework of Tetramorphic MWCNT Configuration for VLSI Interconnect

Abstract: Having a 1D material like Multiwall Carbon Nanotube (MWCNT) as a potential candidate for high speed Very Large Scale Integration (VLSI) interconnect creates a good scope to reduce the delay by estimating the parasitic elements i.e. Resistance ( $R$ ), Inductance ( $L$ ) and Capacitance ( $C$ ) properly. We have contrived an innovative configuration namely Tetramorphic (TM) for the bundle of MWCNTs with four different diameters. We have focused on 45 nm, 22 nm, 11 nm and 7 nm technology nodes to justify the novelty of our proposed configuration over the existing MWCNT bundle configurations. Having the parasitic $RLC$ elements for a specific technology node, the diameter optimization took place in this work. Subsequently, we obtain the propagation delay results for local, semi-global and global level interconnect. Finally, we compare the results with the other existing configuration to show the supremacy of our introduced configuration for MWCNT bundle to explore high speed VLSI interconnect and represent crosstalk delay and power dissipation. Moreover, this configuration is highly dense which will offer the size shrinkage feature in a substantial manner.

LISA: Language-Independent Method for Aspect-Based Sentiment Analysis

Abstract: Understanding “what others think” is one of the most eminent pieces of knowledge in the decision-making process required in a wide spectrum of applications. The procedure of obtaining knowledge from each aspect (property) of users' opinions is called aspect-based sentiment analysis which consists of three core sub-tasks: aspect extraction, aspect and opinion-words separation, and aspect-level polarity classification. Most successful approaches proposed in this area require a set of primary training or extensive linguistic resources, which makes them relatively costly and time consuming in different languages. To overcome the aforementioned challenges, we propose an unsupervised paradigm for aspect-based sentiment analysis, which is not only simple to use in different languages, but also holistically performs the subtasks for aspect-based sentiment analysis. Our methodology relies on three coarse-grained phases which are partitioned to manifold fine-grained operations. The first phase extracts the prior domain knowledge from dataset through selecting the preliminary polarity lexicon and aspect word sets, as representative of aspects. These two resources, as primitive knowledge, are assigned to an expectation-maximization algorithm to identify the probability of any word based on the aspect and sentiment. To determine the polarity of any aspect in the final phase, the document is firstly broken down to its constituting aspects and the probability of each aspect/polarity based on the document is calculated. To evaluate this method, two datasets in the English and Persian languages are used and the results are compared with various baselines. The experimental results show that the proposed method outperforms the baselines in terms of aspect, opinion-word extraction and aspect-level polarity classification.

mmWave Radar Based Approach for Pedestrian Identification in Autonomous Vehicles

Abstract: In this paper, a mmWave radar algorithm will be presented to detect moving pedestrians and differentiate them from other vehicles on the road. This is an important problem for advanced driver assistance systems (ADAS), and autonomous vehicles (AV) applications, especially for pedestrians safety. This approach will leverage radar ability to operate under different lighting and environmental conditions (e.g. day, night, fog, etc.) without being computationally extensive. To test the proposed approach, a setup that involves Texas Instruments (TI) AWR1642 77 GHz radar kit and DCA1000 FPGA board was used. In addition, a recently developed Python based testbed, developed by the authors, was used for control, data acquisition, and real-time processing of radar data (25 radar images per second without any dropped frames). Results show that the proposed approach can distinguish between moving pedestrians and vehicles with accuracy comparable to results obtained from cameras and lidars. This is especially important for hardware in the loop (HIL) type testing of high level algorithms in ADAS and AV systems.