Expert opinion on the pharmacological management of multiple sclerosis in women of childbearing age in Iraq.

Background: Multiple sclerosis (MS) is often diagnosed in women of childbearing age (WCBA), with a mean age of onset of 30 years. Women with MS have long been cautioned to carefully plan their pregnancies and, traditionally, disease-modifying therapies (DMTs) have not been recommended for use in patients engaged in family planning. In 2020, the United States Food and Drug Administration (FDA) approved a label update for interferon beta (IFN ß) by adding new safety data on pregnancy and breastfeeding. Because current management guidelines do not yet reflect the recent label update, a panel of neurology experts from Iraq decided to discuss the potential need for changes in treatment strategies in Iraq. Methods: A panel of experts consisting of 8 neurologists from Iraq and one international neurology expert from Germany convened to develop an expert opinion that would provide practical guidance for the pharmacological management of WCBA with MS in Iraq. They considered the latest label update and relevant published literature, along with local clinical practice and available resources. Results: Interferon and Glatiramer acetate have no evidence of harm during pregnancy. IFN ß can be continued safely through pregnancy. Switching treatment during pregnancy is generally not recommended. Short-term intravenous methylprednisolone can be used to treat disabling relapses. Conclusion: Given the complexity of managing MS in pregnant women, it is the opinion of the expert panel that family planning should be discussed early in the disease course, planned pregnancy should be encouraged, and open communication with patient for her treatment decisions is paramount. Patients who are engaged in family planning are no longer discouraged from treatment with some of the currently available DMTs.

Public Perceptions Regarding the Preparedness of Government to Combat the Third Wave of COVID-19 (SARS-CoV-2) Infection Across Various States of India.

India has already passed through 2 waves of the coronavirus disease (COVID-19) pandemic losing many lives. The reason for losing lives may be due to the unpreparedness of the health care system of India for this unprecedented pandemic. To assess the government's preparedness, an institutional-based cross-sectional prospective survey was conducted among the adult population of selected states in India. A self-administered 30-item questionnaire divided into 5 sections (demography of the participants, steps to create awareness, prevent spread of infection, handle the emergency, and prognosis) was distributed online through Google Forms. The responses were collected in an Excel file. SPSS software was used to perform the descriptive statistics and analysis of variance (ANOVA). Nearly a quarter of the participants "strongly disagree"/"disagree" about the government's preparedness for the third wave. Considering their perception, it cannot be assured that the government is well prepared to handle the emergency. So, the government must maintain emergency funding and develop a health infrastructure. The government should take steps to reduce social stigma, prevent spreading of unscientific propagation, and make people aware of the World Health Organization (WHO) as the reliable source of information for health emergencies to avoid a human crisis in the future.

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models.

Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120-400 bar, 308-338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E-06, 1.95E-06, and 3.45E-06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E-06, 2.34E-06, and 5.26E-06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e-05).

Cellulose paper sorptive extraction (CPSE): A simple and affordable microextraction method for analysis of basic drugs in blood as a proof of concept.

Aiming towards simplifying sample preparation procedure, the present work explores use of unmodified laboratory filter paper as sorbent for extraction of nine basic drugs (five antidepressants, four benzodiazepines, and ketamine) from human blood samples and their analysis by gas chromatography-mass spectrometry (GC-MS). The procedure termed as cellulose paper sorptive extraction (CPSE) is straightforward. It involves adsorption of target analytes from deproteinized diluted blood samples on the unmodified cellulose paper followed by elution into 2 mL of methanol. Multivariate optimization, consisting of Placket-Burman design (PBD) and central composite design (CCD), was used to screen and optimize significant factors for CPSE. The proposed method follows the principles of green analytical chemistry (GAC), as the unmodified filter paper used as the sorbent is inexpensive and biodegradable. The technique is easy to perform and requires only 2 mL of MeOH during the entire extraction procedure. Under the optimized conditions, the limit of detection and quantification for the target analytes were estimated to be in the range of 0.003-0.035 and 0.010-0.117 µg mL-1, respectively. In contrast, the relative standard deviations were consistently below 10 %. The calibration curves were linear in the range of 0.015-2 µg mL-1 with a coefficient of determination (R2) in the range of 0.995-0.999.Satisfactory recoveries ranging from 87 to 99 % was achieved. As proof of concept, the analysis of nine drugs in blood samples from the patients was performed to demonstrate the potential application of the proposed method.

Alginate as a Promising Biopolymer in Drug Delivery and Wound Healing: A Review of the State-of-the-Art.

Biopolymeric nanoparticulate systems hold favorable carrier properties for active delivery. The enhancement in the research interest in alginate formulations in biomedical and pharmaceutical research, owing to its biodegradable, biocompatible, and bioadhesive characteristics, reiterates its future use as an efficient drug delivery matrix. Alginates, obtained from natural sources, are the colloidal polysaccharide group, which are water-soluble, non-toxic, and non-irritant. These are linear copolymeric blocks of α-(1→4)-linked l-guluronic acid (G) and ß-(1→4)-linked d-mannuronic acid (M) residues. Owing to the monosaccharide sequencing and the enzymatically governed reactions, alginates are well-known as an essential bio-polymer group for multifarious biomedical implementations. Additionally, alginate's bio-adhesive property makes it significant in the pharmaceutical industry. Alginate has shown immense potential in wound healing and drug delivery applications to date because its gel-forming ability maintains the structural resemblance to the extracellular matrices in tissues and can be altered to perform numerous crucial functions. The initial section of this review will deliver a perception of the extraction source and alginate's remarkable properties. Furthermore, we have aspired to discuss the current literature on alginate utilization as a biopolymeric carrier for drug delivery through numerous administration routes. Finally, the latest investigations on alginate composite utilization in wound healing are addressed.

Zein-Stabilized Nanospheres as Nanocarriers for Boosting the Aphrodisiac Activity of Icariin: Response Surface Optimization and In Vivo Assessment.

Icariin (ICA), a main active compound of the Epimedium genus, is used as an aphrodisiac in traditional Chinese herbal medicine. Despite its therapeutic efficacy, ICA displays reduced oral absorption, and therefore, low bioavailability hindered its clinical application. Implementing nanotechnology in the field of formulation has been a focus to improve the efficacy of ICA. In this regard, polymeric nanoparticles find a potential application as drug delivery systems. A nanosphere formula was designed, aiming to improve the drug's efficacy. The proposed ICA nanosphere formula (tocozeinolate) was optimized using D-optimal response surface design. The concentrations of ICA (X1), D-α-tocopherol polyethylene glycol 1000 succinate (TPGS, X2), zein (X3), and sodium deoxycholate (SDC, X4) expressed as percentages were investigated as quantitative independent variables. As per the experimental design, 23 formulations were developed, which were investigated for particle size (PS, nm), zeta potential (ZP, mV), and entrapment efficiency (EE, %) as response parameters. Numerical optimization and desirability approach were employed to predict the optimized variable levels that, upon combination, could result in minimized size and maximized zeta potential and ICA entrapment. The optimized ICA-tocozeinolate nanospheres showed a particle size of 224.45 nm, zeta potential of 0.961 mV, and drug entrapment of 65.29% that coincide well with the predicted values. The optimized ICA-tocozeinolate nanospheres were evaluated for sexual behavior in Wistar male rats compared to raw ICA at equivalent doses (20 mg/kg). In vivo assessment results showed significant sexual behavior enhancement by the optimized formulation, as evidenced by decreased average time of both mount latency (ML) and ejaculation latency (EL) to almost half those of raw ICA. Additionally, intromission latency (IL) time was reduced by 41% compared to the raw ICA. These results highlighted the potential of the proposed ICA-tocozeinolate nanospheres as a promising platform for improving the delivery and efficacy of therapeutic agents.

Biological Effect of Quercetin in Repairing Brain Damage and Cerebral Changes in Rats: Molecular Docking and In Vivo Studies.

This study examined the protective effect of quercetin against high-altitude-induced brain damage in rats. A molecular docking study was performed to investigate the potential effect of quercetin in reducing brain damages through its ability to target the oxidative stress enzymes. Biomarker assessment screening assays were also performed then followed by in vivo studies. Three groups of rats were divided into the control group, an untreated animal model group with induced brain damage, and finally, the quercetin treated group that received quercetin dose equal to 20 mg/kg of their body weights. Molecular docking studies and biomarker assessment screening assays proved the potential effect of quercetin to affect the level of representative biomarkers glutathione (GSH), glutathione reductase (GR), glutathione-S-transferase (GST), glutathione peroxidase (GPx), superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA). Additionally, the protective effect of quercetin against high altitude, low pressure, and low oxygen was also investigated by exploring the brain histopathology of experimental rats. Brain damage was observed in the untreated animal model group. After treatment with quercetin, the cerebral edema in the brain tissues was improved significantly, confirming the protective effects of quercetin. Therefore, quercetin can be used as a natural food additive to protect from the highaltitude-induced brain damage.

Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features.

The multichannel electrode array used for electromyogram (EMG) pattern recognition provides good performance, but it has a high cost, is computationally expensive, and is inconvenient to wear. Therefore, researchers try to use as few channels as possible while maintaining improved pattern recognition performance. However, minimizing the number of channels affects the performance due to the least separable margin among the movements possessing weak signal strengths. To meet these challenges, two time-domain features based on nonlinear scaling, the log of the mean absolute value (LMAV) and the nonlinear scaled value (NSV), are proposed. In this study, we validate the proposed features on two datasets, the existing four feature extraction methods, variable window size, and various signal-to-noise ratios (SNR). In addition, we also propose a feature extraction method where the LMAV and NSV are grouped with the existing 11 time-domain features. The proposed feature extraction method enhances accuracy, sensitivity, specificity, precision, and F1 score by 1.00%, 5.01%, 0.55%, 4.71%, and 5.06% for dataset 1, and 1.18%, 5.90%, 0.66%, 5.63%, and 6.04% for dataset 2, respectively. Therefore, the experimental results strongly suggest the proposed feature extraction method, for taking a step forward with regard to improved myoelectric pattern recognition performance.

Hyaluronic acid functionalization improves dermal targeting of polymeric nanoparticles for management of burn wounds: In vitro, ex vivo and in vivo evaluations.

Owing to its intricate pathophysiology, impaired wound healing is one of the substantial challenges in the treatment of burn wounds (BWs). Despite the variety of conventional therapies available, morbidities associated with BWs have not subsided. Therefore, aim of the present study was to design an advanced nanotechnology-composited therapy for effectual management of BWs. Hyaluronic acid (HA)-functionalized curcumin (CUR) and quercetin (QUE) co-loaded nanoparticle (HA-CUR-QUE-CSNPs) were fabricated, optimized, characterized and evaluated for successful co-encapsulation of drugs, morphology, stability, drug release, cell proliferation, penetration across the skin, localization in the epidermis and dermis, and in vivo wound healing efficacy. Fabricated HA-functionalized CSNPs exhibited ultra-small size (177 ± 11 nm), good zeta potential (+37.0 ± 3.2 mV), high encapsulation efficiency (EE) (QUE ∼84% and CUR ∼64%) and loading capacity (LC) (QUE ∼38% and CUR ∼43%), and spherical shape with uniformly rough surface. HA-functionalized CSNPs showed a triphasic release pattern with Fickian diffusion kinetics, a time-mannered progression in MC3T3-E1 cells proliferation, improved penetration of CUR (2414 µg/cm2) and QUE (1984 µg/cm2) through stratum corneum, and good localization of drugs in the epidermis and dermis. A superior wound healing efficacy (98% wound closure rate at day 28) with marked histological signs of minimal infiltration of inflammatory cells, re-epithelization, ECM formation, fibroblast infiltration at wound site, granulation tissue formation, angiogenesis, and collagen deposition were also evidenced. This study concludes that HA-functionalization of polymeric NPs could be a promising approach to maximize skin penetration efficiency, localization of drugs in skin tissues, tissue regeneration and BWs healing.

Development and Characterization of Eudragit® EPO-Based Solid Dispersion of Rosuvastatin Calcium to Foresee the Impact on Solubility, Dissolution and Antihyperlipidemic Activity.

Poor solubility is the major challenge involved in the formulation development of new chemical entities (NCEs), as more than 40% of NCEs are practically insoluble in water. Solid dispersion (SD) is a promising technology for improving dissolution and, thereby, the bioavailability of poorly soluble drugs. This study investigates the influence of a pH-sensitive acrylate polymer, EPO, on the physicochemical properties of rosuvastatin calcium, an antihyperlipidemic drug. In silico docking was conducted with numerous polymers to predict drug polymer miscibility. The screened-out polymer was used to fabricate the binary SD of RoC in variable ratios using the co-grinding and solvent evaporation methods. The prepared formulations were assessed for physiochemical parameters such as saturation solubility, drug content and in vitro drug release. The optimized formulations were further ruled out using solid-state characterization (FTIR, DSC, XRD and SEM) and in vitro cytotoxicity. The results revealed that all SDs profoundly increased solubility as well as drug release. However, the formulation RSE-2, with a remarkable 71.88-fold increase in solubility, presented 92% of drug release in the initial 5 min. The molecular interaction studied using FTIR, XRD, DSC and SEM analysis evidenced the improvement of in vitro dissolution. The enhancement in solubility of RoC may be important for the modulation of the dyslipidemia response. Therefore, pharmacodynamic activity was conducted for optimized formulations. Our findings suggested an ameliorative effect of RSE-2 in dyslipidemia and its associated complications. Moreover, RSE-2 exhibited nonexistence of cytotoxicity against human liver cell lines. Convincingly, this study demonstrates that SD of RoC can be successfully fabricated by EPO, and have all the characteristics that are favourable for superior dissolution and better therapeutic response to the drug.

Computational Identification of Druggable Bioactive Compounds from Catharanthus roseus and Avicennia marina against Colorectal Cancer by Targeting Thymidylate Synthase.

Colorectal cancer (CRC) is the second most common cause of death worldwide, affecting approximately 1.9 million individuals in 2020. Therapeutics of the disease are not yet available and discovering a novel anticancer drug candidate against the disease is an urgent need. Thymidylate synthase (TS) is an important enzyme and prime precursor for DNA biosynthesis that catalyzes the methylation of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP) that has emerged as a novel drug target against the disease. Elevated expression of TS in proliferating cells promotes oncogenesis as well as CRC. Therefore, this study aimed to identify potential natural anticancer agents that can inhibit the activity of the TS protein, subsequently blocking the progression of colorectal cancer. Initially, molecular docking was implied on 63 natural compounds identified from Catharanthus roseus and Avicennia marina to evaluate their binding affinity to the desired protein. Subsequently, molecular dynamics (MD) simulation, ADME (Absorption, Distribution, Metabolism, and Excretion), toxicity, and quantum chemical-based DFT (density-functional theory) approaches were applied to evaluate the efficacy of the selected compounds. Molecular docking analysis initially identified four compounds (PubChem CID: 5281349, CID: 102004710, CID: 11969465, CID: 198912) that have better binding affinity to the target protein. The ADME and toxicity properties indicated good pharmacokinetics (PK) and toxicity ability of the selected compounds. Additionally, the quantum chemical calculation of the selected molecules found low chemical reactivity indicating the bioactivity of the drug candidate. The global descriptor and HOMO-LUMO energy gap values indicated a satisfactory and remarkable profile of the selected molecules. Furthermore, MD simulations of the compounds identified better binding stability of the compounds to the desired protein. To sum up, the phytoconstituents from two plants showed better anticancer activity against TS protein that can be further developed as an anti-CRC drug.

Antibacterial and Wound-Healing Activities of Statistically Optimized Nitrofurazone- and Lidocaine-Loaded Silica Microspheres by the Box-Behnken Design.

In the current study, nitrofurazone- (NFZ) and lidocaine-loaded (LD) silica microspheres were fabricated to address pathological indications of skin infections. The microspheres were prepared by the sol-gel method applying the Box-Behnken design and evaluated for size distribution, morphology, zeta potential, physico-chemical compatibility, XRD, thermogravimetric analysis, antibacterial and cytotoxicity activities. The comparative in vitro drug release study of microspheres revealed a 30% release of NFZ and 33% of LD after 8 h. The microspheres showed 81% percentage yield (PY) and 71.9% entrapment efficiency. XRD patterns confirmed the entrapment of NFZ-LD in silica microspheres with a significant reduction in crystallinity of the drugs. Thermal and FTIR studies proved the absence of any profound interactions of the formulation ingredients. The smooth spherical microspheres had a -28 mV zeta potential and a 10-100 µm size distribution. In vitro antibacterial activities of the NFZ-LD microspheres showed an increased zone of inhibition compared to pure drug suspensions. The in vivo efficacy tested on rabbits showed a comparatively rapid wound healing with complete lack of skin irritation impact. The cytotoxicity studies revealed more acceptability of silica microspheres with negligible harm to cells. The study suggests that the NFZ- and LD-loaded silica microspheres would be an ideal system for accelerating and promoting rapid healing of various acute and chronic wounds.

Enhancement of the Solubility and Bioavailability of Pitavastatin through a Self-Nanoemulsifying Drug Delivery System (SNEDDS).

The purpose of the study was to develop an SNEDDS to improve the solubility and bioavailability of pitavastatin. The solubility of pitavastatin in different oils, surfactants, and co-surfactants was determined and a pseudo-ternary phase diagram was constructed. The SNEDDS was characterized by zeta-sizer, zeta-potential, FTIR, DSC, and TGA. Release and permeation of pitavastatin from the SNEDDS was studied for 12 and 24 h, respectively. The lipolysis test, RBC lysis, effect on lipid profile, and pharmacokinetics were studied. The SPC3 formulation showed a 104 ± 1.50 nm particle size, a 0.198 polydispersity index (PDI), and a -29 zeta potential. FTIR, DSC, and TGA showed the chemical compatibility and thermal stability. The release and permeation of pitavastatin from SPC3 was 88.5 ± 2.5% and 96%, respectively. In the lipolysis test, the digestion of SPC3 yielded a high amount of pitavastatin and showed little RBC lysis. The lipid profile suggested that after 35 days of administration of the SNEDDS, there was a marked decrease in TC, LDL, and triglyceride levels. The SNEDDS of SPC3 showed an 86% viability of Caco-2 cells. Pharmacokinetics of SPC3 showed improved values of Cmax, Tmax, half-life, MRT, AUC, and AUMC compared to the reference formulation. Our study demonstrated that the SNEDDS effectively enhanced the solubility and bioavailability of a BCS class II drug.

Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study.

Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model's performance using weighted kappa and Cohen's kappa statistical analyses. Results: The model's validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model's validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions: The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

Deep learning for the classification of cervical maturation degree and pubertal growth spurts:A pilot study / 대한치과교정학회지

Objective@#This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. @*Methods@#The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model’s performance using weighted kappa and Cohen’s kappa statistical analyses. @*Results@#The model’s validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model’s validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. @*Conclusions@#The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review.

COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R2 coefficient of determination (R2), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.

Active inductor based fully integrated CMOS transmit/ receive switch for 2.4 GHz RF transceiver.

Modern Radio Frequency (RF) transceivers cannot be imagined without high-performance (Transmit/Receive) T/R switch. Available T/R switches suffer mainly due to the lack of good trade-off among the performance parameters, where high isolation and low insertion loss are very essential. In this study, a T/R switch with high isolation and low insertion loss performance has been designed by using Silterra 0.13µm CMOS process for 2.4GHz ISM band RF transceivers. Transistor aspect ratio optimization, proper gate bias resistance, resistive body floating and active inductor-based parallel resonance techniques have been implemented to achieve better trade-off. The proposed T/R switch exhibits 0.85dB insertion loss and 45.17dB isolation in both transmit and receive modes. Moreover, it shows very competitive values of power handling capability (P1dB) and linearity (IIP3) which are 11.35dBm and 19.60dBm, respectively. Due to avoiding bulky inductor and capacitor, the proposed active inductor-based T/R switch became highly compact occupying only 0.003mm2 of silicon space; which will further trim down the total cost of the transceiver. Therefore, the proposed active inductor-based T/R switch in 0.13µm CMOS process will be highly useful for the electronic industries where low-power, high-performance and compactness of devices are the crucial concerns.

A new adaptive configuration of PID type fuzzy logic controller.

In this paper, an adaptive configuration for PID type fuzzy logic controller (FLC) is proposed to improve the performances of both conventional PID (C-PID) controller and conventional PID type FLC (C-PID-FLC). The proposed configuration is called adaptive because its output scaling factors (SFs) are dynamically tuned while the controller is functioning. The initial values of SFs are calculated based on its well-tuned counterpart while the proceeding values are generated using a proposed stochastic hybrid bacterial foraging particle swarm optimization (h-BF-PSO) algorithm. The performance of the proposed configuration is evaluated through extensive simulations for different operating conditions (changes in reference, load disturbance and noise signals). The results reveal that the proposed scheme performs significantly better over the C-PID controller and the C-PID-FLC in terms of several performance indices (integral absolute error (IAE), integral-of-time-multiplied absolute error (ITAE) and integral-of-time-multiplied squared error (ITSE)), overshoot and settling time for plants with and without dead time.