In this paper, we propose two different control strategies for the position control of the ball of the ball and beam system (BBS). The first control strategy uses the proportional integral derivative-second derivative with a proportional integrator PIDD2-PI. The second control strategy uses the tilt integral derivative with filter (TID-F). The designed controllers employ two distinct metaheuristic computation techniques: grey wolf optimization (GWO) and whale optimization algorithm (WOA) for the parameter tuning. We evaluated the dynamic and steady-state performance of the proposed control strategies using four performance indices. In addition, to analyze the robustness of proposed control strategies, a comprehensive comparison has been performed with a variety of controllers, including tilt integral-derivative (TID), fractional order proportional integral derivative (FOPID), integral-proportional derivative (I-PD), proportional integral-derivative (PI-D), and proportional integral proportional derivative (PI-PD). By comparing different test cases, including the variation in the parameters of the BBS with disturbance, we examine step response, set point tracking, disturbance rejection analysis, and robustness of proposed control strategies. The comprehensive comparison of results shows that WOA-PIDD2-PI-ISE and GWO-TID-F- ISE perform superior. Moreover, the proposed control strategies yield oscillation-free, stable, and quick response, which confirms the robustness of the proposed control strategies to the disturbance, parameter variation of BBS, and tracking performance. The practical implementation of the proposed controllers can be in the field of under actuated mechanical systems (UMS), robotics and industrial automation. The proposed control strategies are successfully tested in MATLAB simulation.
The fungal transformations of medroxyrogesterone (1) were investigated for the first time using Cunninghamella elegans, Trichothecium roseum, and Mucor plumbeus. The metabolites obtained are as following: 6β, 20-dihydroxymedroxyprogesterone (2), 12β-hydroxymedroxyprogesterone (3), 6β, 11β-dihydroxymedroxyprogesterone (4), 16β-hydroxymedroxyprogesterone (5), 11α, 17-dihydroxy-6α-methylpregn-4-ene-3, 20-dione (6), 11-oxo-medroxyprogesterone (7), 6α-methyl-17α-hydroxypregn-1,4-diene-3,20-dione (8), and 6β-hydroxymedroxyprogesterone (9), 15β-hydroxymedroxyprogesterone (10), 6α-methyl-17α, 11β-dihydroxy-5α-pregnan-3, 20-dione (11), 11β-hydroxymedroxyprogesterone (12), and 11α, 20-dihydroxymedroxyprogesterone (13). Among all the microbial transformed products, the newly isolated biotransformed product 13 showed the most potent activity against proliferation of SH-SY5Y cells. Compounds 12, 5, 6, 9, 11, and 3 (in descending order of activity) also showed some extent of activity against SH-SY5Y tumour cell line. The never been reported biotransformed product, 2, showed the most potent inhibitory activity against acetylcholinesterase. Molecular modelling studies were carried out to understand the observed experimental activities, and also to obtain more information on the binding mode and the interactions between the biotransformed products, and enzyme.
Zeolites P in sodium (NaP) and potassium (KP) forms were used as adsorbents for the removal of calcium (Ca2+) and zinc (Zn2+) cations from aqueous solutions. Zeolite KP was prepared by ion exchange of K+ with Na+ which neutralizes the negative charge of the zeolite P framework structure. The ion exchange capacity of K+ on zeolite NaP was determined through the Freundlich isotherm equilibrium study. Characterization of zeolite KP was determined using infrared spectroscopy and X-ray diffraction (XRD) techniques. From the characterization, the structure of zeolite KP was found to remain stable after the ion exchange process. Zeolites KP and NaP were used for the removal of Ca and Zn from solution. The amount of Ca2+ and Zn2+ in aqueous solution before and after the adsorption by zeolites was analysed using the flame atomic absorption spectroscopy method. The removal of Ca2+ and Zn2+ followed the Freundlich isotherm rather than the Langmuir isotherm model. This result also revealed that zeolite KP adsorbs Ca2+ and Zn2+ more than zeolite NaP and proved that modification of zeolite NaP with potassium leads to an increase in the adsorption efficiency of the zeolite. Therefore, the zeolites NaP and KP can be used for water softening (Ca removal) and reducing water pollution/toxicity (Zn removal).
In this paper, we establish a ZIKV model and investigate the transmission dynamics of ZIKV with two types of harvesting: proportional harvesting and constant harvesting, and give the stability of the steady states of both disease-free and endemic equilibrium, analyze the effect of harvesting on ZIKV transmission dynamics via numerical simulation. We find that proportional harvesting strategy can eliminate the virus when the basic reproduction number $R_0$ is less than 1, but the constant harvesting strategy may control the virus whether the basic reproduction number is less than 1 or not. Epidemiologically, we find that increasing harvesting may stimulate an increase in the number of virus infections at some point, and harvesting can postpone the peak of disease transmission with the mortality of mosquito increasing. The results can provide us with some useful control strategies to regulate ZIKV dynamics.
Since December 2019, a novel coronavirus (COVID-19) has spread all over the world, causing unpredictable economic losses and public fear. Although vaccines against this virus have been developed and administered for months, many countries still suffer from secondary COVID-19 infections, including the United Kingdom, France, and Malaysia. Observations of COVID-19 infections in the United Kingdom and France and their governance measures showed a certain number of similarities. A further investigation of these countries' COVID-19 transmission patterns suggested that when a turning point appeared, the values of their stringency indices per population density (PSI) were nearly proportional to their absolute infection rate (AIR). To justify our assumptions, we developed a mathematical model named VSHR to predict the COVID-19 turning point for Malaysia. VSHR was first trained on 30-day infection records prior to the United Kingdom, Germany, France, and Belgium's known turning points. It was then transferred to Malaysian COVID-19 data to predict this nation's turning point. Given the estimated AIR parameter values in 5 days, we were now able to locate the turning point's appearance on June 2nd, 2021. VSHR offered two improvements: (1) gathered countries into groups based on their SI patterns and (2) generated a model to identify the turning point for a target country within 5 days with 90% CI. Our research on COVID-19's turning point for a country is beneficial for governments and clinical systems against future COVID-19 infections.
This paper provides the details of a study on the effects of electron radiation on the Performance of Inters-satellite Optical Wireless Communication (IsOWC). Academia and industry focus on solutions that can improve performance and reduce the cost of IsWOC systems. Spacecraft, space stations, satellites, and astronauts are exposed to an increased level of radiation when in space, so it is essential to evaluate the risks and performance effects associated with extended radiation exposures in missions and space travel in general. This investigation focuses on LEO, especially in the near-equatorial radiation environment. Radiation experiments supported with simulations have made it possible to obtain and evaluate the electron radiation impact on optoelectronics at the device level and system level performances. The electron radiation has induced a system degradation of 70%. This result demonstrates the importance of such an investigation to predict and take necessary and suitable reliable quality service for future space missions.
Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility.
Detection of buried improvised explosive devices (IEDs) is a delicate task, leading to a need to develop sensitive stand-off detection technology. The shape, composition and size of the IEDs can be expected to be revised over time in an effort to overcome increasingly sophisticated detection methods. As an example, for the most part, landmines are found through metal detection which has led to increasing use of non-ferrous materials such as wood or plastic containers for chemical based explosives being developed.
To segment an image using the random walks algorithm; users are often required to initialize the approximate locations of the objects and background in the image. Due to its segmenting model that is mainly reflected by the relationship among the neighborhood pixels and its boundary conditions, random walks algorithm has made itself sensitive to the inputs of the seeds. Instead of considering the relationship between the neighborhood pixels solely, an attempt has been made to modify the weighting function that accounts for the intensity changes between the neighborhood nodes. Local affiliation within the defined neighborhood region of the two nodes is taken into consideration by incorporating an extra penalty term into the weighting function. Besides that, to better segment images, particularly medical images with texture features, GLCM variance is incorporated into the weighting function through kernel density estimation (KDE). The probability density of each pixel belonging to the initialized seeds is estimated and integrated into the weighting function. To test the performance of the proposed weighting model, several medical images that mainly made up of 174-brain tumor images are experimented. These experiments establish that the proposed method produces better segmentation results than the original random walks.
Radionuclide imaging using (111)In, (99m)Tc and (153)Sm is commonly undertaken for the clinical investigation of gastric emptying, intestinal motility and whole gut transit. However the documented evidence concerning internal radiation dosimetry for such studies is not readily available. This communication documents the internal radiation dosimetry for whole gastrointestinal transit studies using (111)In, (99m)Tc and (153)Sm labeled formulations. The findings were compared to the diagnostic reference levels recommended by the United Kingdom Administration of Radioactive Substances Advisory Committee, for gastrointestinal transit studies.
Brain segmentation in magnetic resonance images (MRI) is an important stage in clinical studies for different issues such as diagnosis, analysis, 3-D visualizations for treatment and surgical planning. MR Image segmentation remains a challenging problem in spite of different existing artifacts such as noise, bias field, partial volume effects and complexity of the images. Some of the automatic brain segmentation techniques are complex and some of them are not sufficiently accurate for certain applications. The goal of this paper is proposing an algorithm that is more accurate and less complex).
Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.
Thermal characteristics of turbulent nanofluid flow in a rectangular pipe have been investigated numerically. The continuity, momentum, and energy equations were solved by means of a finite volume method (FVM). The symmetrical rectangular channel is heated at the top and bottom at a constant heat flux while the sides walls are insulated. Four different types of nanoparticles Al2O3, ZnO, CuO, and SiO2 at different volume fractions of nanofluids in the range of 1% to 5% are considered in the present investigation. In this paper, effect of different Reynolds numbers in the range of 5000 < Re < 25000 on heat transfer characteristics of nanofluids flowing through the channel is investigated. The numerical results indicate that SiO2-water has the highest Nusselt number compared to other nanofluids while it has the lowest heat transfer coefficient due to low thermal conductivity. The Nusselt number increases with the increase of the Reynolds number and the volume fraction of nanoparticles. The results of simulation show a good agreement with the existing experimental correlations.
In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.
Traditional robotic work cell design and programming are considered inefficient and outdated in current industrial and market demands. In this research, virtual reality (VR) technology is used to improve human-robot interface, whereby complicated commands or programming knowledge is not required. The proposed solution, known as VR-based Programming of a Robotic Work Cell (VR-Rocell), consists of two sub-programmes, which are VR-Robotic Work Cell Layout (VR-RoWL) and VR-based Robot Teaching System (VR-RoT). VR-RoWL is developed to assign the layout design for an industrial robotic work cell, whereby VR-RoT is developed to overcome safety issues and lack of trained personnel in robot programming. Simple and user-friendly interfaces are designed for inexperienced users to generate robot commands without damaging the robot or interrupting the production line. The user is able to attempt numerous times to attain an optimum solution. A case study is conducted in the Robotics Laboratory to assemble an electronics casing and it is found that the output models are compatible with commercial software without loss of information. Furthermore, the generated KUKA commands are workable when loaded into a commercial simulator. The operation of the actual robotic work cell shows that the errors may be due to the dynamics of the KUKA robot rather than the accuracy of the generated programme. Therefore, it is concluded that the virtual reality based solution approach can be implemented in an industrial robotic work cell.
Chronic myeloid leukemia (CML) patients who do not achieve landmark responses following treatment with imatinib mesylate (IM) are considered IM-resistant. Although IM-resistance can be due to BCR-ABL kinase domain (KD) mutations, many IM-resistant patients do not have detectable BCR-ABL KD mutations. MicroRNAs (miRNAs) are short non-coding RNAs that control gene expression. To investigate the role of miRNAs in IM-resistance, we recruited 8 chronic phase CML patients with IM-resistance who tested negative for BCR-ABL KD mutations and 2 healthy normal controls. Using miRNA sequencing, we identified 54 differentially expressed miRNAs; 43 of them downregulated. The 3 most differentially downregulated miRNAs were miR-146a-5p, miR-99b-5p and miR-151a-5p. Using real-time quantitative reverse transcriptase-polymerase chain reaction, the expression patterns of the 3 miRNAs were validated on the same cohort of 8 patients in addition to 3 other IM-resistant CML patients. In-silico analysis showed that the predicted gene targets are ATRIP, ATR, WDR48, RAD51C and FANCA genes which are involved in the Fanconi Anemia/BRCA pathway. This pathway regulates DNA damage response (DDR) and influences disease response to chemotherapy. Thus it is conceivable that DDR constitutes a key component in IM-resistance. Further research is needed to elucidate miRNA modulation of the predicted gene targets.
We present a new theoretical model for the broadband reflection spectra of etched FBGs which includes the effects of axial contraction and stress-induced index change. The reflection spectra of the etched FBGs with several different taper profiles are simulated based on the proposed model. In our observation, decaying exponential profile produces a broadband reflection spectrum with good uniformity over the range of 1540-1560 nm. An etched FBG with similar taper profile is fabricated and the experimental result shows good agreement with the theoretical model.
In this study, a novel reconfigurable triple-band monopole antenna for LoRa IoT applications is fabricated on an FR-4 substrate. The proposed antenna is designed to function at three distinct LoRa frequency bands: 433 MHz, 868 MHz, and 915 MHz covering the LoRa bands in Europe, America, and Asia. The antenna is reconfigurable by using a PIN diode switching mechanism, which allows for the selection of the desired operating frequency band based on the state of the diodes. The antenna is designed using CST MWS® software 2019 and optimized for maximum gain, good radiation pattern and efficiency. The antenna with a total dimension of 80 mm × 50 mm × 0.6 mm (0.12λ0×0.07λ0 × 0.001λ0 at 433 MHz) has a gain of 2 dBi, 1.9 dBi, and 1.9 dBi at 433 MHz, 868 MHz, and 915 MHz, respectively, with an omnidirectional H-plane radiation pattern and a radiation efficiency above 90% across the three frequency bands. The fabrication and measurement of the antenna have been carried out, and the results of simulation and measurements are compared. The agreement among the simulation and measurement results confirms the design's accuracy and the antenna's suitability for LoRa IoT applications, particularly in providing a compact, flexible, and energy efficient communication solution for different LoRa frequency bands.
Shaanxi is one of China's most important cradles of civilization. The main vein of Chinese culture is rich history and culture, and brilliant red culture embodies the essence of socialist core values. It is still relatively weak to deeply analyze the related research of Shaanxi Province's cultural province construction on the basis of studying the achievements of cultural development in foreign countries and China and combining with the reality of Shaanxi Province. In this paper, a BPNN (BP neural network) model is selected to study the comprehensive evaluation of tourism competitiveness of smart tourism cities, and the software is used to realize the simulation of the comprehensive evaluation system of tourism competitiveness of smart tourism cities, which more comprehensively and objectively reflects the level of comprehensive competitiveness of each city. It is believed that there are some problems in Shaanxi regional cultural communication, such as insufficient exploration of content resources, insufficient communication channels, and low audience awareness, hoping to provide ideas and reference for further exploring the promotion of cultural communication power.
This study characterises and evaluates an Al2O3:C-based optically stimulated luminescent dosemeter (OSLD) system, commercially known as the nanoDot™ dosemeter and the InLight® microStar reader, for personal and in vivo dose measurements in diagnostic radiology. The system characteristics, such as dose linearity, reader accuracy, reproducibility, batch homogeneity, energy dependence and signal stability, were explored. The suitability of the nanoDot™ dosemeters was evaluated by measuring the depth dose curve, in vivo dose measurement and image perturbation. The nanoDot™ dosemeters were observed to produce a linear dose with ±2.8% coefficient variation. Significant batch inhomogeneity (8.3%) was observed. A slight energy dependence (±6.1%) was observed between 60 and 140 kVp. The InLight® microStar reader demonstrated good accuracy and a reproducibility of ±2%. The depth dose curve measured using nanoDot™ dosemeters showed slightly lower responses than Monte Carlo simulation results. The total uncertainty for a single dose measurement using this system was 11%, but it could be reduced to 9.2% when energy dependence correction was applied.