A new compact and low phase imbalance microstrip coupler for 5G wireless communication systems.

Microstrip couplers play a crucial role in signal processing and transmission in various applications, including RF and wireless communication, radar systems, and satellites. In this work, a novel microstrip 180° coupler is designed, fabricated and measured. The layout configuration of this coupler is completely new and different from the previously reported Rat-race, branch-line and directional couplers. To obtain the proposed coupler, the meandrous coupled lines are used and analyzed mathematically. To improve the performance of our coupler, an optimization method is used. The designed coupler is very compact with an overall size of 0.014λg2. The obtained values of S21 and S31 are -3.45 dB and -3.75 dB, respectively at the operating frequency, while the fractional bandwidth (FBW) is 56.2%. It operates at fo = 1.61 GHz (suitable for 5G applications) and can suppress harmonics up to 2.17fo. Another advantage of this coupler is its low phase imbalance, while the phase difference between S21 and S31 is 180°± 0.023°. Therefore, our device is a balanced coupler with ±0.3 dB magnitude unbalance at its operating frequency. It is important to note that it is very difficult to find a coupler that has all these advantages at the same time. The proposed 180° coupler is fabricated and measured. The comparison shows that the measurement and simulation results are in good agreement. Therefore, the proposed coupler can be easily used in designing high-performance 5G communication systems.

Parametric study and process modeling for metronidazole removal by rhombic dodecahedron ZIF-67 crystals.

Metronidazole (MNZ) is an extensively used antibiotic against bacterial infections for humans and farm animals. Prevention of antibiotics discharge is essential to prevent adverse environmental and health impacts. A member of metal-organic frameworks, zeolite imidazole framework-67 with cobalt sulfate precursor (ZIF-67-SO4) and exceptional physio-chemical properties was prepared via room temperature precipitation to adsorb MNZ. The study framework was designed by Box-Behnken Design to evaluate the effect of pH, ZIF-67-SO4 dose, and contact time on adsorption efficiency. The polynomial model fitted the adsorption system indicated the optimal condition for 97% MNZ removal occurs at pH = 7, adsorbent dosage = 1 g/L, and mixing time = 60 min. The model also revealed that the removal increased with contact time and decreased at strong pH. Equilibrium and kinetic study also indicated the adsorption of MNZ followed the intra-particle diffusion model and the Langmuir isotherm model with a qmax = 63.03 mg/g. The insignificant loss in removal efficacy in use-reuse adsorption cycles reflected the practical viability of ZIF-67-SO4.

Boosting the adsorptive and photocatalytic performance of MIL-101(Fe) against methylene blue dye through a thermal post-synthesis modification.

Photocatalytic degradation under ultra-low powered light is a viable advanced oxidation process technique against extensive emerging contaminants. As a new and remarkable class of nanoporous materials, metal-organic frameworks (MOFs), attract interest for the supreme adsorptive and photocatalytic functionalities. An outstanding MOF, MIL-101(Fe) chosen as a photocatalyst template for the synthesis of α-Fe2O3 by a simple thermal modification to improve the structural properties toward methylene blue (MB) eradication. Octahedron-like α-Fe2O3 photocatalyst (Modified MIL-101(Fe), M-MIL-101(Fe)) was superior in dispersion and separation properties in aqueous medium. Moreover, the adsorptive and catalytic performance was increased for modified form by ~ 7.3% and ~ 17.1% compared to pristine MIL-101(Fe), respectively. Synergistic improvement of MB removal achieved by simultaneous adsorption/degradation under 5-W LED irradiation. Parametric study indicated an 18.1% and 44.5% improvement in MB removal was observed by increasing pH from 4 to 10, and M-MIL-101(Fe) dose from 0.2 to 1 g L-1, respectively. MB removal followed the pseudo-second-order kinetics model and the process efficiency dropped by 38% as MB concentration increased from 5 to 20 mg L-1. Radical trapping tests revealed the significant role of [Formula: see text] and electron radicals as the major participants in dye degradation. A significant loss in the efficiency of M-MIL-101(Fe) was observed in the reusability tests that is good to study further. In conclusion, a simple thermal post-synthesis modification on MIL-101(Fe) improved its structural, catalytic, and adsorptive properties against MB.

Fabrication of new composite NCuTiO2/CQD for photocatalytic degradation of ciprofloxacin and pharmaceutical wastewater treatment: degradation pathway, toxicity assessment.

In this research, the photocatalytic degradation of CIP from aqueous solutions using CQD decorated on N-Cu co-doped titania (NCuTCQD) was made during two synthesis steps by sol-gel and hydrothermal methods. The fabricated catalysts were analyzed using various techniques, including XRD, FT-IR, BET, FESEM, EDX, and DRS. The results showed that N and Cu atoms were doped on TiO2 and CQD was well deposited on NCuT. The investigation of effective operational parameters demonstrated that the complete removal of ciprofloxacin (CIP: 20 mg/L) could be achieved at pH 7.0, NCuTCQD4wt%: 0.8 g/L, and light intensity: 100 mW/cm2 over 60 min reaction time. The O2•- and OH˙ radicals were identified as the primary reactive species during the decontamination process. The synthesized photocatalyst could be recycled after six consecutive cycles of CIP decomposition with an insignificant decrease in performance. Pharmaceutical wastewater was treated through the optimum degradation conditions which showed the photocatalytic degradation eliminated 89% of COD and 75% of TOC within 180 min. In the effluent toxicity evaluation, the EC50 values for treated and untreated pharmaceutical wastewater increased from 62.50% to 140%, indicating that the NCuTCQD4wt%/Vis system can effectively reduce the toxic effects of pharmaceutical wastewater on aquatic environments.

Nanocatalysts as fast and powerful medical intervention: Bridging cochlear implant therapies and advanced modelling using Hidden Markov Models (HMMs) for effective treatment of inner ear infections.

As human population growth and waste from technologically advanced industries threaten to destabilise our delicate ecological equilibrium, the global spotlight intensifies on environmental contamination and climate-related changes. These challenges extend beyond our external environment and have significant effects on our internal ecosystems. The inner ear, which is responsible for balance and auditory perception, is a prime example. When these sensory mechanisms are impaired, disorders such as deafness can develop. Traditional treatment methods, including systemic antibiotics, are frequently ineffective due to inadequate inner ear penetration. Conventional techniques for administering substances to the inner ear fail to obtain adequate concentrations as well. In this context, cochlear implants laden with nanocatalysts emerge as a promising strategy for the targeted treatment of inner ear infections. Coated with biocompatible nanoparticles containing specific nanocatalysts, these implants can degrade or neutralise contaminants linked to inner ear infections. This method enables the controlled release of nanocatalysts directly at the infection site, thereby maximising therapeutic efficacy and minimising adverse effects. In vivo and in vitro studies have demonstrated that these implants are effective at eliminating infections, reducing inflammation, and fostering tissue regeneration in the ear. This study investigates the application of hidden Markov models (HMMs) to nanocatalyst-loaded cochlear implants. The HMM is trained on surgical phases in order to accurately identify the various phases associated with implant utilisation. This facilitates the precision placement of surgical instruments within the ear, with a location accuracy between 91% and 95% and a standard deviation between 1% and 5% for both sites. In conclusion, nanocatalysts serve as potent medicinal instruments, bridging cochlear implant therapies and advanced modelling utilising hidden Markov models for the effective treatment of inner ear infections. Cochlear implants loaded with nanocatalysts offer a promising method to combat inner ear infections and enhance patient outcomes by addressing the limitations of conventional treatments.

Evaluation of waste management and energy saving for sustainable green building through analytic hierarchy process and artificial neural network model.

A significant portion of the solid waste filling landfills worldwide is debris from construction and demolition projects. Across the world, a significant portion of the solid waste filling landfills is made up of construction and demolition waste. Recycling construction waste may help cut down on the quantity of waste sent to landfills and the requirement for energy and other natural resources. To help with construction waste reduction, a management hierarchy that begins with rethink, reduce, redesign, refurbish, reuse, incineration, composting, recycle, and eventually disposal is likely to be effective. The objective of this research is to investigate the viability of the Analytic Hierarchy Process (AHP) as a data gathering instrument for the development of a solid waste management assessment tool, followed by an examination of an artificial neural network (ANN). Using a standardized questionnaire, all data was gathered from waste management practitioners in three industry sectors. The survey data was subsequently analyzed using ANN and later AHP. The suggested framework consisted of four components: (1) the development of different level structures for fluffy AHP, (2) the calculation of weights, (3) the collection of data, and (4) the making of decisions. An ANN feedforward with error back propagation (EBP) learning computation is coupled to identify the association between the items and the store execution. It was found that the combination of AHP and ANN has emerged as a key decision support tool for landfilling, incineration, and composting waste management strategies, taking into account the environmental profile and economic and social characteristics of each choice. Composting has the highest sustainable performance when a balanced weight distribution of criteria is assumed, especially if the environmental component is considered in comparison to the other criteria. However, if social and economic features are addressed, incineration or landfilling have more favorable characteristics, respectively.

Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field data.

Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R 2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.

Stabilizing control for a class of delay unstable processes.

The stabilization of unstable first-order plus time-delay processes with a zero by means of simple controllers is investigated in detail. Explicit stabilizability conditions are established. And the computational methods for determining stabilizing controller parameters are also presented with illustrative examples.