Multiphase permanent-magnet synchronous machines (PMSMs) with nonsinusoidal back-electromotive force (back-EMF) offer high fault tolerance and torque density for electric vehicles. Most current-reference generation methods either minimize stator copper loss (SCL) or maximize achievable torque. Optimization of both goals is accomplished by full-torque-range minimum-loss (FRML) strategies, but so far just for sinusoidal back-EMF. Thus, FRML for nonsinusoidal back-EMF should be sought. Moreover, many methods are only suitable for healthy conditions or specific machines, harmonics, or open-phase-fault (OPF) scenarios. Additionally, the torque range may be extended by permitting torque ripple or (transiently) greater rms current, but this approach is not general nor FRML yet. This paper proposes online FRML current-reference generation for multiphase PMSMs with nonsinusoidal back-EMF: nonsinusoidal-back-EMF FRML (NSBE-FRML). When the torque reference is feasible, minimum SCL is attained while maximizing the achievable torque (i.e., FRML). For higher torque references, the instantaneous torque deviation is minimized, and the torque reference is saturated in consecutive samples limiting the torque ripple to a pre-specified threshold. Furthermore, the rms current is limited after transient overload by automatically decreasing the torque reference. The NSBE-FRML is suitable for any harmonics, healthy/OPF conditions, and multiphase PMSMs with negligible saliency ratio. Experiments are performed with a six-phase PMSM.
Six-phase induction motor (6PIM) drives offer enhanced fault tolerance and reduced per-phase ratings. Hysteresis current control (HCC) is attractive for 6PIMs because it is simple, robust and fast. HCC is conventionally implemented so that each leg voltage is directly set based on the respective phase-current error. However, this approach does not consider that, in multiphase drives, phase voltages and currents are related through a combination of equivalent impedances corresponding to various subspaces. In general, there is a notable dissimilarity between these impedances, being typically small for secondary (xy) subspaces. This can cause large current distortion and poor reference tracking. This article proposes an improved HCC for 6PIM drives. Instead of directly inputting the per-phase current error to the hysteresis comparator and directly applying the switching states chosen by it, the input and output components associated with different subspaces are segregated. The input and output xy components are nullified in open loop so that the xy impedance no longer affects the HCC behavior, even if low. This prevents the disrupting xy currents, ensures effective tracking of the torque/flux-producing alpha-beta reference current, and enables reconfiguration-less fault tolerance. Experiments using 6PIMs with different winding configurations corroborate the significant advantages of the proposal.
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) predisposed to the emergence of worldwide catastrophe that impels the evolution of safe and effective therapeutic system. Polyphenols as resveratrol (RSV) exhibit a well evidenced antiviral activity. Unfortunately, like most phenolic nutraceuticals, RSV suffers from restrained solubility and massive degradation in GIT and liver which in turn prohibit its clinical use. Herein, PEGylated bilosomes (PBs) contain PEGylated edge activator along with the traditional components as (Span 60, cholesterol and bile salts) were proposed to boost both permeability and bioavailability of RSV. The investigation of the prominent effect of the diverse variables on the characteristics of the vesicles and picking of the optimum formula were conducted via construction of 2³ factorial experiment. The appraisal of the formulae was conducted on the basis of entrapment efficiency percent (EE%), particle size (PS) and zeta potential (ZP). In addition, the spherical shaped optimal formula (F5) exhibited EE% of 86.1 ± 2.9%, PS of 228.9 ± 8.5 nm, and ZP of −39.8 ± 1.3 mV. The sorted optimum formula (F5) exhibited superior dissolution behaviors, and boosted Caco-2 cells cellular uptake by a round 4.7 folds relative to RSV dispersion. In addition, F5 demonstrated a complete in vitro suppression of SARS-CoV-2 at a concentration 0.48 μg/ml with 6.6 times enhancement in antiviral activity relative to RSV dispersion. The accomplished molecular modeling heavily provided proof for the possible interactions of resveratrol with the key residues of the SARS-CoV2 Mpro enzyme. Finally, F5 could be proposed as a promising oral panel of RSV for curation from SARS-CoV-2 infection.
Due to high penetration of renewable generation in power systems, and the need to provide the interface between distributed energy resources, the split-source inverter (SSI) provides both the boosting and the conversion capabilities in one single-stage. Also the need for converter-based artificial inertia has become more important. In this paper a model-predictive control (MPC) based on virtual synchronous generator (VSG) algorithm for a parallel-connected three-phase SSI is proposed for conceiving regulation of local voltage and realizing power-sharing of an islanded AC microgrid (MG). A virtual synchronous generator (VSG) is deployed to ensure active-power-sharing and provide inertia-emulation and hence reducing the rate of change of frequency (RoCoF) that results from sudden load change. To accomplish a simple control construction, quick dynamic performance, high stability, and enhanced current limitation, a finite-set MPC (FS-MPC) is used. The analysis and modeling of the proposed technique are presented in detail. A simulation model is used to investigate the proposed system performance.
Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications.
Background and objective Type 2 diabetes mellitus (T2DM) is caused by insulin resistance or tissue insensitivity to insulin, as well as relative insulin insufficiency. Diabetes that is uncontrolled for an extended period of time is linked to substantial comorbidities and organ damage. The purpose of the current study is to assess the effect of coadministration of omega-3 fatty acids with glimepiride on blood glucose, lipid profile, serum irisin, and sirtuin-1 levels in T2DM patients. Methods This clinical trial involved 70 type 2 diabetic patients randomly assigned to glimepiride 3 mg with either omega-3 capsules contained fish oil 1000 mg, 13% of eicosapentaenoic acid (EPA) and 9% docosahexaenoic acid (DHA) (omega-3 group, n = 35) or placebo capsules contained corn oil and linoleic acid (control group, n = 35) daily for three months. Blood samples were obtained at the start of the study and 12 weeks later for biochemical examination of HbA1c%, FBG, fasting insulin, and lipid profile. In addition, the atherogenic index of plasma (AIP) was calculated. Human enzyme-linked immunosorbent assay (ELISA) kits were utilized for assessing serum irisin and sirtuin-1 levels before and after the intervention. Results Compared to the control group, omega-3 fatty acids decreased serum fasting blood glucose (FBG, p < 0.001), glycated hemoglobin percent (HbA1C%, p < 0.001), total cholesterol (TC, p < 0.001), triglycerides (TGs, p = 0.006), low density lipoprotein (LDL, p = 0.089), and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR, p = 0.021) after three months of intervention. However, a significant increase was reported in serum irisin and high density lipoprotein (HDL) between both groups after intervention (p = 0.026 and p = 0.007, respectively). The atherogenic index of plasma (AIP) increased in the control group but decreased in the omega-3 group, with significant differences between the two groups (p < 0.001). Conclusion The present study found that supplementing with omega-3 fatty acids might dramatically enhance blood irisin levels, as well as improve glycemic control and lipid profile in type 2 diabetes mellitus patients using glimepiride. Trial Registration This study is registered on ClinicalTrials.gov under identifier NCT03917940. (The registration date: April 17, 2019).
Cardiac image segmentation is a critical step in the early detection of cardiovascular disease. The segmentation of the biventricular is a prerequisite for evaluating cardiac function in cardiac magnetic resonance imaging (CMRI). In this paper, a cascaded model CAT-Seg is proposed for segmentation of 3D-CMRI volumes. CAT-Seg addresses the problem of biventricular confusion with other regions and localized the region of interest (ROI) to reduce the scope of processing. A modified DeepLabv3+ variant integrating SqueezeNet (SqueezeDeepLabv3+) is proposed as a part of CAT-Seg. SqueezeDeepLabv3+ handles the different shapes of the biventricular through the different cardiac phases, as the biventricular only accounts for small portion of the volume slices. Also, CAT-Seg presents a segmentation approach that integrates attention mechanisms into 3D Residual UNet architecture (3D-ResUNet) called 3D-ARU to improve the segmentation results of the three major structures (left ventricle (LV), Myocardium (Myo), and right ventricle (RV)). The integration of the spatial attention mechanism into ResUNet handles the fuzzy edges of the three structures. The proposed model achieves promising results in training and testing with the Automatic Cardiac Diagnosis Challenge (ACDC 2017) dataset and the external validation using MyoPs. CAT-Seg demonstrates competitive performance with state-of-the-art models. On ACDC 2017, CAT-Seg is able to segment LV, Myo, and RV with an average minimum dice symmetry coefficient (DSC) performance gap of 1.165%, 4.36%, and 3.115% respectively. The average maximum improvement in terms of DSC in segmenting LV, Myo and RV is 4.395%, 6.84% and 7.315% respectively. On MyoPs external validation, CAT-Seg outperformed the state-of-the-art in segmenting LV, Myo, and RV with an average minimum performance gap of 6.13%, 5.44%, and 2.912% respectively.
Role of V2O5 as a strong network glass modifier on the optical, elastic, physical, and radiation protection performance of V2O5-based glasses in form 10CaF2-(20-x)Bi2O3-60P2O5-10B2O3:xV2O5, x = 0.0, 0.5, 1.0, 1.2, 0.2, and 2.5 mol% was investigated in this article. The studied glasses have been synthesized using the melt-quenching technique. The investigated samples coded as V0, V1, V2, V3, V4, and V5 according to x values. Difference theoretical approaches have been utilized to achieve these goals including: radiation shielding calculations, optical, and elasto-mechanical properties. The optical basicity (Λ(Eg)) was found to decrease from 0.5630 to 0.5593 with increasing the mol% of [V4+] in the glass samples. The density was decreased from 4.134 to 3.817 g cm−3 associated with a simultaneous decrease in the oxygen molar volume (OMV) from 12.5 to 12.25 cm3/mol as the mol% of [V4+] increased in the samples. The Vicker’s hardness was found to be diminished with increasing V2O5 content in the investigated glasses. All elastic moduli were reduced as V2O5 increased. Refractive index increased from 2.20 to 2.63, while metallization criterion decreased from 0.341 to 0252. The observed trend of mass attenuation coefficient (MAC) values in the tested energy spectrum followed the sequence: (MAC)v0 > (MAC)v1 > (MAC)v2 > (MAC)v3 > (MAC)v4 > (MAC)v5. Also, we found that linear attenuation coefficient (LAC) increased, while the half value layer (HVL), and transmission coefficient (TF%) were also decreased. The HVL values of the current samples were compared with standard glasses, and it found that V2O5-based samples possess a comparable photon protection capacity as commercial RS-360 and RS-520 glasses.
Increasing water consumption and climate change are putting countries globally at risk of running out of water supplies. The provision of groundwater helped reduce global food scarcity, but overexploitation resulted in the depletion of groundwater resources in many regions worldwide. This study applies the concepts of reliability, resiliency, and vulnerability to determine the spatial distribution of sustainability in groundwater resources over the intensive groundwater-irrigated regions of South Asia and China. The satellite-based Global Land Data Assimilation System (GLDAS) data of groundwater storage for 2003-2020 was acquired and used for this reason. The findings showed a decrease in groundwater storage in Northern China, Western India, and Eastern Pakistan, with the highest declination rate in Western India by − 50 to − 200 mm per decade. Groundwater reliability, resiliency, and vulnerability decreased in some regions of the study area, with the highest decrease in west India by − 0.2 to − 0.3 per decade. This caused a decrease in groundwater sustainability in the area at a rate of − 0.1 to − 0.3 per decade. The reduction in groundwater sustainability in western India may be due to intensive groundwater abstraction in those regions. It is important to prevent excessive groundwater pumping in the study area for sustainable development. It also is imperative to adopt sustainable groundwater management strategies, such as reducing groundwater extraction during drought years and growing less water-consuming crops in less sustainable regions.
Glasses constructed, (1 − x) (0.6595P2O5–0.0958ZnO–0.2447PbO) · xSm2O3 with x = 0.00, 0.0045, 0.0089, 0.0132, and 0.0261 mol%, had been created to investigate the attenuation of longitudinal ultrasonic waves at 2, 4, 6, and 14 MHz frequencies between 120 and 300 K. At a variety of temperatures, clear peaks of a large absorption curve have been seen. These peaks are dependent on the structure of the glass as well as the switching frequency. Maximum peaks have been shown to shift to higher temperatures, and the increase in overall frequency points to the presence of some kind of relaxation process. A thermally induced relaxation process is responsible for producing a calm approach, which has been identified as a result of this mechanism. A quiet approach has been defined as a consequence of a thermally triggered relaxation mechanism. The variance of the mean energy of activation of the mechanism counts on primarily the amount of Sm2O3 mol%. Such dependency has been evaluated based on the loss of normal linear solid form, attaining low dispersion, and a large allocation of Arrhenius kind relaxation through temperature-autonomous relaxation power. The measured acoustical energy of activation values have been quantifiably represented based on the number of loss centers (amount of oxygen atoms that now move at a double-well potential).
In this paper, for standalone and grid‐connected PV systems, a three‐phase simplified split‐source inverter (SSI) is proposed and controlled using a model‐predictive control (MPC). The maximum power point tracking (MPPT) approach used is an incremental conductance method based on a PI controller for both systems. The standalone system is composed of PV modules, a three‐phase SSI, and a bidirectional power DC–DC converter that connects a battery bank and a DC‐side capacitor. The output AC voltages of SSI are controlled using MPC. The bidirectional power DC–DC converter regulates the DC‐link voltage (DCLV). The grid‐connected system consists of PV modules, a three‐phase SSI, and an AC‐side L‐filter. The DC‐link PI controller generates reference currents for the MPC algorithm. The MPC uses these reference currents to adjust and deliver the PV power to the grid while regulating the DCLV. The PI controllers' parameters are selected for both systems using the Harris Hawks optimization method. Both PV systems simulation results show that under various operating conditions, they have succeeded in fixing a DCLV and producing a high‐quality AC output voltage and current at low THD. Experimental results for the three‐phase standalone PV system used to verify the system's performance.
The article utilizes the fractional bioheat model in spherical coordinates to explain the transfer of heat in living tissues during magnetic hyperthermia treatment for tumors. Maintaining therapeutic temperature is crucial in magnetic fluid hyperthermia, which requires accurate estimations of power dissipation to determine the appropriate number of magnetic particles required for treatment. To address this problem, a hybrid numerical approach that combines Laplace transforms, change of variables, and modified discretization techniques is proposed in this paper. The study investigates the impact of the fractional parameter and differences in thermophysical properties between diseased and healthy tissue. The numerical temperature results are presented in a graph, and their validity is demonstrated by comparing them with previous literature.
The goal of this research article is to investigate the effects of using two-dimensional functionally graded materials on the performance of piezoelectric sensors/actuators when subjected to simultaneous complex loading conditions. The considered disc-shaped sensors/actuators have nonuniform thicknesses and undergo asymmetric hygro-thermo-electro-mechanical loading. A power-law model is used to grade the materials radially, whereas the cosine function, which includes two independent parameters, describes the pattern along the circumferential direction. Comparing the results obtained by using the finite element method with those of one-dimensional graded structures leads to promising outcomes. For example, the radial displacement exhibits vital changes that varied between −13 and 31%. This is beneficial for such structures in terms of enhancing their sensing/actuating abilities. Also, the tangential stress can be reduced substantially by about 39.5% through the proper selection of the corresponding material parameters. In addition, this reduction of the tangential stress has a positive effect on the von Mises stress that can be decreased by nearly 33%. Accordingly, the structure would have improved durability and sustain higher loads. These findings would revolutionize the manufacturing of smart structures and enhance their behaviors under severe conditions.
Lexical Normalization (LN) aims to normalize a nonstandard text to a standard text. This problem is of extreme importance in natural language processing (NLP) when applying existing trained models to user-generated text on social media. Users of social media tend to use non-standard language. They heavily use abbreviations, phonetic substitutions, and colloquial language. Nevertheless, most existing NLP-based systems are often designed with the standard language in mind. However, they suffer from significant performance drops due to the many out-of-vocabulary words found in social media text. In this paper, we present a new (LN) technique by utilizing a transformer-based sequence-to-sequence (Seq2Seq) to build a multilingual characters-to-words machine translation model. Unlike the majority of current methods, the proposed model is capable of recognizing and generating previously unseen words. Also, it greatly reduces the difficulties involved in tokenizing and preprocessing the nonstandard text input and the standard text output. The proposed model outperforms the winning entry to the Multilingual Lexical Normalization (MultiLexNorm) shared task at W-NUT 2021 on both intrinsic and extrinsic evaluations.
The present study seeks to untangle the courtroom trial’s sequential categorization of the topical device of Amber Heard’s ‘lies’ as YouTubed by the Daily Mail . Towards this end, the study develops and utilizes a synergetic approach of the ethnomethodological method of membership categorization analysis (MCA), the reconsidered model, the forensic-linguistics model of analysing courtroom trials, and translanguaging emoji pattern analysis. The following hypothesis has been tested: YouTube-mediated courtroom trials can publicly bring out an emergent digital genre with a special kind of translocal participatory engagement of trial participants, YouTube creators, and YouTube users. Towards proving the foregoing hypothesis, three sets of YouTube-video data have been investigated. The analysis has proven the study’s hypothesis with three findings ensuing. First, a new digital genre of trial emerged with the shift from the local setting of courtroom to the translocal/global setting of YouTube. Second, the YouTube translocal affordances have enabled a situated membership categorization of Heard as a lying defendant and Vasquez as a heroic lawyer. Third, a set of morally contrastive devices have been detected through the metadiscursive practices of enabling the internet/video and video-moment reporting and quoting as well as the translanguaging practice of emoji assignment by YouTube users.
This paper presents the performance of an Orthogonal Frequency Division Multiplexing (OFDM) system using intensity modulation with the modern equalizer in Wavelength–Division Multiplexing–Radio Over Fiber–Passive Optical Network (WDM–ROF–PON). The WDM–ROF–PON is considered for its high capacity and more extended optical reach, where the multiplexing and de-multiplexing are used at optical line terminal and optical network unit. First, the performance of the OFDM-based system with wired and wireless access is analyzed. We introduce an advanced equalizer namely, Deep Neural Network-Nonlinear Equalizer (DNN-NLE). The analysis is performed in the downlink by introducing a fiber Bragg grating and an equalizer with various fiber lengths. The system with DNN-NLE provides a better bit error rate of 10⁻⁹ and optical signal to noise ratio (OSNR) of 14.7 dB.
On the basis of the observed biological activity of coumarin and acrylamide derivatives, a new set of coumarin-acrylamide-CA-4 hybrids was designed and synthesized. These compounds were investigated for their cytotoxic activity against cancerous human liver cell line HepG2 cells using 5-fluorouracil (5-FU) as a reference drug. Compound 6e had promising antiproliferative activity with an IC50 value of 1.88 μM against HepG2 cells compared to 5-FU (IC50 = 7.18 μM). The results of β-tubulin polymerization inhibition indicated that coumarin-acrylamide derivative 6e was the most active, with a percentage inhibition value of 84.34% compared to podophyllotoxin (88.19% β-tubulin inhibition). Moreover, the active coumarin-acrylamide molecule 6e exerted cell cycle cession at the G2/M phase stage of HepG2 cells. In addition, this compound produced a 15.24-fold increase in apoptotic cell induction compared to no-treatment control. These observations were supported by histopathological studies of liver sections. The conducted docking studies illustrated that 6e is perfectly positioned within the tubulin colchicine binding site, indicating a significant interaction that may underlie its potent tubulin inhibitory activity. The main objective of the study was to develop new potent anticancer compounds that might be further optimized to prevent the progression of cancer disease.
Solar energy, a prominent renewable resource, relies on photovoltaic systems (PVS) to capture energy efficiently. The challenge lies in maximizing power generation, which fluctuates due to changing environmental conditions like irradiance and temperature. Maximum Power Point Tracking (MPPT) techniques have been developed to optimize PVS output. Among these, the incremental conductance (INC) method is widely recognized. However, adapting INC to varying environmental conditions remains a challenge. This study introduces an innovative approach to adaptive MPPT for grid-connected PVS, enhancing classical INC by integrating a PID controller updated through a fuzzy self-tuning controller (INC-FST). INC-FST dynamically regulates the boost converter signal, connecting the PVS's DC output to the grid-connected inverter. A comprehensive evaluation, comparing the proposed adaptive MPPT technique (INC-FST) with conventional MPPT methods such as INC, Perturb & Observe (P&O), and INC Fuzzy Logic (INC-FL), was conducted. Metrics assessed include current, voltage, efficiency, power, and DC bus voltage under different climate scenarios. The proposed MPPT-INC-FST algorithm demonstrated superior efficiency, achieving 99.80%, 99.76%, and 99.73% for three distinct climate scenarios. Furthermore, the comparative analysis highlighted its precision in terms of control indices, minimizing overshoot, reducing rise time, and maximizing PVS power output.
Bringing together narrative elements, virtual affordances, and participants’ embodied interactions, virtual reality (VR) movies instantiate new narrative techniques by offering an immersive experience. This study examines virtual narrative beyond mere interactional engagement and extends the phenomenon to include worlding, metaleptic embodiment, and instantiated possible selves. It aims at exploring VR narrative as idiosyncratic cognitive processes, with a special focus on the notions of empathy and emotional involvement as significant elements contributing to this peculiar interactional and cognitive experience. A cognitive stylistic approach is adopted to explain the functional ability of VR technology in transporting participants to alternate worlds and in making them experience a kind of self-transformation. The immersively metaleptic discourse of Baba Yaga is examined as engaging participants in a quest of how to act as morally and socially empathetic and responsible citizens—global citizens. Baba Yaga narrative deploys the narrative discourses of flashbacks, facework, doubly deictic ‘you’, performatives, and imperatives along with material processes to situate participants in a virtual space of actions and doings and hence encourage them to configure their desired self(ves) across different immersive interactions. The global citizen is embodied in the interactive narrative of Baba Yaga, through invoking various storyworld possible selves (SPSs): the feeling self, the responsible self, and the moral self, which encompasses climate activist self and interculturally aware self who manages to get rid of its own cultural biases as the narrative proceeds. Embodied in these selves, participants transform the virtual world into possible worlds of their own passion, agency, choices, hopes, and desires.
This study evaluates the validity of forecasting air temperature ranges in 2100 using the SimCLIM climate projection model at spatial and temporal scales within the Southern Levantine basin. The model utilized historical air temperature data from 2000 to 2016, collected at seven southeastern Mediterranean stations, as well as 74 climate pattern ensembles integrated within SimCLIM. A combination of 40 global climate models (GCMs) and IPCC AR5 greenhouse gas emissions scenarios embedded in SimCLIM was employed to forecast mean, minimum, and maximum temperatures for 2100.The findings reveal that the average temperature increase in 2100, relative to the representative concentration pathways 2.6, 4.5, 6.0, and 8.5, will range between 0.8–1.17 °C, 1.48–2.0 °C, 2.1–3.8 °C, and 3.9–4.6 °C, respectively. Due to its acceptable accuracy, the SimCLIM model, incorporating 40 GCMs and 74 climate pattern ensembles, is highly recommended for forecasting future climate conditions. The model was evaluated using available temperature records in the study area, yielding a prediction percentage error of 2%, which strongly supports the use of SimCLIM.
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