Array Design Research Articles

On the Difficulty Predicting Word Recognition Performance After Cochlear Implantation.

Preimplantation word scores cannot reliably predict postimplantation outcomes. To date, there is no model based on preoperative data that can reliably predict the postoperative outcomes of cochlear implantation in the postlingually deafened adult patient. In a group of 228 patients who received a cochlear implant between 2002 and 2021, we tested the predictive power of nine variables (age, etiology, sex, laterality of implantation, preimplantation thresholds and word scores, as well as the design, insertion approach, and angular insertion depth of the electrode array) on postimplantation outcomes. Results of multivariable linear regression analyses were then interpreted in light of data obtained from histopathological analyses of human temporal bones. Age and etiology were the only significant predictors of postimplantation outcomes. In agreement with many investigations, preimplantation word scores failed to significantly predict postimplantation outcomes. Analysis of temporal bone histopathology suggests that neuronal survival must fall below 40% before word scores in quiet begin to drop. Scores fall steeply with further neurodegeneration, such that only 20% survival can support acoustically driven word scores of 50%. Because almost all cochlear implant implantees have at least 20% of their spiral ganglion neurons (SGNs) surviving, it is expected that most cochlear implant users on average should improve to at least 50% word recognition score, as we observed, even if their preimplantation score was near zero as a result of widespread hair cell damage and the fact that ~50% of their SGNs have likely lost their peripheral axons. These "disconnected" SGNs would not contribute to acoustic hearing but likely remain electrically excitable. The relationship between preimplantation word scores and data describing the survival of SGNs in humans can explain why preimplantation word scores obtained in unaided conditions fail to predict postimplantation outcomes.

Enhancing 5G Sub-3.5 GHz Networks through Optimization of Microstrip Antenna Arrays with Sequential Rotation-Based MIMO using Machine Learning Techniques

The research concerns the design and optimization of microstrip antenna arrays with sequential rotation-based MIMO configurations for 5G sub-3.5 GHz networks. In total, the use of five optimization approaches was accessed, namely, Bayesian optimization, sparse learning, genetic algorithms, particle swarm optimization, and convolutional neural networks, to evaluate their efficiency in improving antenna properties. In general, Bayesian optimization outperformed the other methodologies most consistently, with the value of the average antenna gain ranging from 0.84 to 0.88, SNR between 14.8 and 15.3, and radiation efficiency ranging between 0.90 and 0.93. The use of sparse learning also ensured a high level of performance, with the average gain ranging between 0.80 and 0.85, and the values of SNR between 15.2 and 15.7. Thus, this approach showed the highest resistance to noise. Genetic algorithms, particle swarm optimization , and CNN are also quite close to each other in the efficiency of properties, with the average antenna gain value ranging between 0.85 and 0.89 and the value of SNR between 14.7 and 15.1. In overall, the results of the research show that there is a variety of available approaches to the optimization of antenna design, and each of them has a number of advantages that can be of use for the improvement of the properties and makes it possible to derive a solution that outweighs others in terms of several properties and the efficiency of its application.

Optical design of a tunable microneedle array for photodynamic therapy of metastatic melanomas

The need for photodynamic therapy is increasing with the rise of skin cancer melanoma detection. However, low penetration depth of light in the UV range and higher scattering of skin tissues cause harsh clinical practices and reduced irradiance for the activation of chemicals such as photosensitizers to facilitate cell necrosis of malignant tumors. In this paper, a tunable microlens integrated on a microneedle array is designed for variable focusing to reduce melanomas at multiple sites with an improved intensity for reactive oxygen species production. A light distribution of 36% percent of the input intensity is achieved at the targeted distance of 4.35 mm for blue light. Red light attained a light distribution of 13% of the input intensity at a targeted distance of 6.5 mm. Designing such a light, delivery-based in-vivo biomedical device is of extreme importance for photodynamic therapy of skin cancer on and beyond the epidermal tissue layer.

Design and analysis of MIMO antenna array for TeraHertz communication

Design and analysis of MIMO antenna array for TeraHertz communication

Toward Onboard Proportional Control of Multi-Chamber Soft Pneumatic Robots: A Magnetorheological Elastomer Valve Array.

Soft pneumatic actuators (SPAs) are commonly used in various applications because of their structural compliance, low cost, ease of manufacture, high adaptability, and safe human-robot interaction. The traditional approach for achieving proportional control of soft pneumatic robots requires the use of industrial proportional valves or syringe drivers, which are not only rigid and bulky but also hard to be integrated into the body of soft robots. In our previous research, we developed a Magnetorheological elastomer (MRE)-based soft valve that showed advantages for controlling SPAs due to its compliance, compactness, robustness, and compatibility for continuous pressure modulation. Modern soft robots with multiple chambers require more MRE valves onboard for their control. However, merely packing more MRE valves for soft robots can cause problems like magnetic interference, flow rate deviation, and overheating. Therefore, in this study, we proposed a two-dimensional MRE valve array design to solve issues of magnetic interference and overheating when expanding from a single MRE proportional valve into an integrated array. The magnetic interference and the overheating problem were investigated through multiphysics simulation, bringing the optimal choice of valve spacing (1.2 times the single valve diameter), magnetic coil pole arrangement (same pole), and the cooling system design (internal cooling chamber with flowing water). Physical experiments showed that our MRE valve array maintained its original flowrate performance with low magnetic interference (0.89 mT) and low coil temperature (under 73.9°C for 5 min).

Tie failure detection technique for total cross tied solar photovoltaic system

Partial Shading in the solar photovoltaic (PV) system leads to more power loss due to mismatch among cells or module’s power generation. The Total Cross Tied (TCT) array design approach is employed for Partial Shading Conditions (PSC) to reduce this issue and boost output power. A tie is used to connect the strings and modules in a solar-powered photovoltaicarray. However, external environmental conditions like strong winds and storms may influence this tie to break. The conventional protection and fault identification technique in the PV system failed to detect this fault. The Solar PV system's entire power generation is drastically impacted by this TCT tie connector problem. Furthermore, during PSC, there is a notable fall in power generation too. To address this, a new technique has been proposed in this work which is capable of precisely identifying the tie interconnection fault in the PV array. A line inspector circuit is used to identify and classify the partial shading and tie interconnection fault. This proposed technique is experimentally verified in the 4 × 4 PV array of 160 W. In all the cases, the proposed algorithm exactly finds the tie interconnection failure. When the diagnosed fault is fixed, the PV array's power output is found to rise by up to 4.5 times.

Joint Array Design and Pattern Synthesis Optimization for Flexible Conformal Arrays

Joint Array Design and Pattern Synthesis Optimization for Flexible Conformal Arrays

Hydroporphyrin-BODIPY arrays

Arrays composed of BODIPY and hydroporphyrin (chlorin or bacteriochlorin) components feature multiple strong absorption bands across visible and near-IR regions, efficient energy transfer from BODIPY to hydroporphyrin moiety, and strong deep-red or near-IR emission. These properties make such arrays attractive candidates for a variety of applications, particularly in solar energy conversion and biomedicine. In this review, the molecular design, synthesis, and photophysical properties of BODIPY-hydroporphyrin arrays are described. Their applications as fluorescent probes for in vivo imaging are also discussed.

Hydrogen Epoch of Reionization Array (HERA) Phase II Deployment and Commissioning

This paper presents the design and deployment of the Hydrogen Epoch of Reionization Array (HERA) phase II system. HERA is designed as a staged experiment targeting 21 cm emission measurements of the Epoch of Reionization. First results from the phase I array are published as of early 2022, and deployment of the phase II system is nearing completion. We describe the design of the phase II system and discuss progress on commissioning and future upgrades. As HERA is a designated Square Kilometre Array pathfinder instrument, we also show a number of “case studies” that investigate systematics seen while commissioning the phase II system, which may be of use in the design and operation of future arrays. Common pathologies are likely to manifest in similar ways across instruments, and many of these sources of contamination can be mitigated once the source is identified.

Withdrawal Notice: Design and Synthesis of Circular Antenna Array for Low Side Lobe Level and High Directivity Using Artificial Hummingbird Optimization Algorithm

The article has been withdrawn at the request of the authors of the journal Recent Advances in Electrical and Electronic Engineering. Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policiesmain. php. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Antenna Array and Waveform Design for 4-D-Imaging mmWave MIMO Radar Sensors

Antenna Array and Waveform Design for 4-D-Imaging mmWave MIMO Radar Sensors

Sparse RF Lens Antenna Array Design for AoA Estimation in Wideband Systems: Placement Optimization and Performance Analysis

Sparse RF Lens Antenna Array Design for AoA Estimation in Wideband Systems: Placement Optimization and Performance Analysis

Ion Transport in Multi-Nanochannels Regulated by pH and Ion Concentration.

Nanochannels are a powerful technique for detecting a wide range of biomolecules without labeling. The ion transport phenomena in nanochannel arrays differ from those in single nanochannels and are caused by interchannel communication. This study uses a fully coupled Poisson-Nernst-Planck (PNP) and Navier-Stokes model to investigate ion transport in nanochannel arrays. Instead of being set at a constant value, the surface charge density used in this study is established by the protonation and deprotonation of the silanol groups that are present on the walls of the silicon-based nanochannels. The surface charge density of the nanochannel walls varies with the number of nanochannels, the channel lateral distance, and the background solution properties, which consequently influence the ionic concentration distribution, flow velocity, and electric field strength. For example, in different numbers of nanochannel systems, the ion concentration in nanochannels is not much different, but it is different in reservoirs, especially near the openings of nanochannels. The number of nanochannels and the distance between nanochannels can also affect the formation of electro-convective vortex zones under certain conditions. These findings can aid in optimizing the nanochannel array design by regulating the number and distance of nanochannels and facilitating the construction of solid-state nanochannel arrays with any desired nanochannel dimensions.

Design and Simulation of a High Performance 5G mm-Wave MIMO Antenna Array for Mobile Applications

This paper presents the design and simulation of an efficient multiple input multiple output (MIMO) antenna array for 5G millimeter-wave (mm-wave) mobile applications. With a dielectric constant of 2.2 and a loss tangent of 0.0009, the substrate employed is a Rogers RT5880 that is 0.254 mm thick. The 37 GHz frequency spectrum, reserved for millimeter-wave mobile applications for 5G, is covered by the proposed MIMO antenna arrays. The single antenna element has a gain of 6.44 dBi, which is increased to 7.89 dBi with a two-element array configuration and 10.88 dBi with a four-element array configuration. The proposed MIMO antenna array performance metrics—including reflection coefficient, VSWR, radiation efficiency, and gain—are seen and discovered to be below the accepted threshold. In the desired operating frequency band, it is noticed that more than 85% of the proposed MIMO antenna array's radiation efficiency is achieved. According to simulation findings, the proposed design may be potentially feasible for mobile applications using millimeter waves in the 5G network.

Tribological impact on borosilicate glass powder reinforced Al-MMC by variation of sliding velocity and the study of its machinability

Now-a-days, metal matrix composites based on pure aluminium are widely used as a replacement for a wide range of applications thanks to their high strength-to-weight ratio, ductility, and thermal conductivity. Micron-sized (10−3 mm) borosilicate (mixer of Silica (SiO2) and Boron Oxide (B2O3)) glass particles were used to create a dispersion in an aluminium matrix composite (Al7075- borosilicate glass MMC) utilizing a stir casting technique while in a liquid condition. The present study examines the effect of borosilicate glass particle content (0, 3, 6, and 9 wt%) and changes in sliding velocity (0.5, 1, 1.5, and 2 m s−1) on the wear behaviour of a composite rubbing against an EN31 steel disc at a constant load of 20N and a sliding distance of 1000 m. Analysis of wear showed that the presence of borosilicate glass increased the wear rate and countered the trend shown in the coefficient of friction. In a similar vein, the sliding velocity of the counter plate rotated faster resulted in a higher coefficient of friction and a higher wear rate. A reliable mathematical model is developed to find the best combination of machining parameters for Al7075-borosilicate glass metal matrix composite (MMC). This model will consider important performance measures like surface smoothness and material removal rate. The authors will also use a sophisticated statistical method called the Taguchi L9 orthogonal array design of experiments (DOE) to understand how different machining settings affect how easily the composite can be machined. By analyzing the results from the DOE, it can optimize the machining parameters for efficient and cost-effective processing. This work can lead to manufacturing innovation in the future.

Small planar antenna array design using length and spacing through Matlab‐HFSS interfacing

SummaryThis study presents a novel method for reducing return loss and achieving lower side lobe levels in small planar antenna arrays (PAAs). Quantum particle swarm optimisation (QPSO) findings are utilised for antenna design modelling and performance assessment utilising the MATLAB‐based moment's code approach and MATLAB‐HFSS interface. The mutual coupling between antenna components is considered and computed using the method of moments (MoM). Changing the array elements' length and spacing will accomplish the abovementioned objectives. The Matlab‐based moments code calculates the antenna components' length and spacing, and the antennas' length and spacing are used to produce the radiation pattern via the Matlab‐HFSS interface. Four examples of small PAA designs are given to illustrate how well the applied methodology works. The method discussed in this paper is generic and can be employed in the future for different sizes of the array elements and the different antenna array structure designs.

Study on the surface topography changes of photoresist in the design and fabrication of microlens array

Study on the surface topography changes of photoresist in the design and fabrication of microlens array

Modes of the Dark Ages 21 cm field accessible to a lunar radio interferometer.

At redshifts beyond [Formula: see text], the 21 cm line from neutral hydrogen is expected to be essentially the only viable probe of the three-dimensional matter distribution. The lunar far-side is an extremely appealing site for future radio arrays that target this signal, as it is protected from terrestrial radio frequency interference, and has no ionosphere to attenuate and absorb radio emission at low frequencies (tens of MHz and below). We forecast the sensitivity of low-frequency lunar radio arrays to the bispectrum of the 21 cm brightness temperature field, which can in turn be used to probe primordial non-Gaussianity generated by particular early universe models. We account for the loss of particular regions of Fourier space due to instrumental limitations and systematic effects, and predict the sensitivity of different representative array designs to local-type non-Gaussianity in the bispectrum, parametrized by [Formula: see text]. Under the most optimistic assumption of sample variance-limited observations, we find that [Formula: see text] could be achieved for several broad redshift bins at [Formula: see text] if foregrounds can be removed effectively. These values degrade to between [Formula: see text] and 0.7 for [Formula: see text] to [Formula: see text], respectively, when a large foreground wedge region is excluded. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades (part 2)'.

Implementation of Taguchi method, ANOVA and regression analyses to enhance char yield by carbonization in lignite-biomass blended

ABSTRACT Energy is a basic need for modern societies. Renewable energy refers to producing and using energy without harming the environment and without consuming resources. The transition to renewable and sustainable energy can provide many environmental, economic and social benefits. Academic research plays an important role in achieving this transition. Sustainable energy is critical to protecting the environment, promoting economic development and supporting social improvements. The transition to sustainable energy is a necessity for the future of humanity. For this purpose, in this study, lignite and biomass (apricot kernel shell) carbonization experiments were carried out under different conditions. Mixing ratio (Lignite/Biomass (w/w 1:1, 1:2, 1:3)), temperature (400°C, 500°C, 600°C) and heating rate (10°C/min, 30°C/min, 50°C/min) were determined as variable parameters. Taguchi’s experimental design was used to optimize the parameters. The orthogonal array design plan was determined as L9 according to the available variables (33 x 33). Since the study aimed to obtain clean solid fuel, char yields were taken into account from the results. Signal-to-noise (S/N) ratios were calculated based on the larger the better condition. As a result of the carbonization experiments performed under Taguchi optimization conditions, it was determined that the S/N ratio was 31.73 and the char yield was 40.19%. Since the estimated char yield with the Taguchi method was 38.85%, it was concluded that the optimization was achieved with 96.67% accuracy. The test parameters satisfying these conditions were determined as 1:1 lignite/biomass mixing ratio, 400°C temperature and 30°C/min heating rate. As a result of the ANOVA analysis, it was seen that the heating rate did not have a significant effect on these parameters. In contrast, the temperature and mixing ratio were important variables. Also, regression analysis (linear and quadratic) was used in this study to calculate the equations for the prediction of char yield. It was concluded that the linear regression model was more successful in estimating the char yield.

Integrated optimization of common rail direct injection diesel engine input parameters with linseed biodiesel: A hybrid approach using grey relational analysis and genetic algorithm techniques

In the pursuit of improved productivity and efficiency, the search for alternative fuels that reduce dependence on fossil fuels is of utmost importance. This research study aims to investigate the influence of process parameters, namely blend percentage, fuel injection pressure, exhaust gas recirculation (EGR) rate, and engine load, on various engine performance parameters such as torque, brake thermal efficiency (BTE), brake mean effective pressure (BMEP), hydrocarbon emissions (HC), and mechanical efficiency. The experiments are conducted following the Taguchi orthogonal array (L25) design of experiments. Additionally, grey relational analysis, a multi-objective optimization technique, is applied to identify the optimal levels of the process variables that optimize all the response parameters. The optimal values obtained through grey relational analysis are determined as 0% blend, 600 bar fuel injection pressure, 12% EGR rate, and 12 kg engine load. Moreover, the Genetic Algorithm-based Multi-Objective Genetic Algorithm (MOGA) is employed for multi-objective optimization of the engine input variables, yielding optimal levels of 19.45% blend, 594.35 bar fuel injection pressure, 14.12% EGR rate, and 12 kg engine load. Furthermore, multivariable regression models are developed to predict the response variables within the experimental domain. These models are validated through a confirmation test. The findings of this study provide insights into the optimization of process variables for enhanced engine performance, with the developed models serving as valuable tools for future predictions and optimization.