Search Results (8)

Magnetic induction (MI)-operated wireless sensor networks (WSNs), due to their similar performance in air, underwater, and underground mediums, are rapidly emerging networks that offer a wide range of applications, including mine prevention, power grid maintenance, underground pipeline monitoring, and upstream oil monitoring. MI-based wireless underground sensor networks (WUSNs), utilizing small antenna coils, offer a viable solution by providing consistent channel conditions. The cross-layer protocols address the specific challenges of WUSNs, leading to improved network performance and enhanced operational capabilities in real-world applications. This work proposes a distributed cross-layer solution, leveraging the hybrid marine predator naked mole rat algorithm (MPNMRA) for MI-operated WUSNs. The solution, called DECMN (distributed energy-throughput efficient cross-layer network using MPNMRA), is designed to optimize the MI communication channels, MI relay coils (MI waveguide), and MI waveguide with 3D coils to fulfill quality of service (QoS) parameters, while achieving energy savings and throughput gains. DECMN utilizes the interactions between various layers to develop cross-layer protocols based on MPNMRA. Simulation results demonstrate the effectiveness of DECMN, offering energy savings, increased throughput, and reliable transmissions within the performance limits. Full article

The slime mould algorithm may not be enough and tends to trap into local optima, low population diversity, and suffers insufficient exploitation when real-world optimization problems become more complex. To overcome the limitations of SMA, the Gaussian mutation (GM) with a novel strategy is proposed to enhance SMA and it is named as SMA-GM. The GM is used to increase population diversity, which helps SMA come out of local optima and retain a robust local search capability. Additionally, the oscillatory parameter is updated and incorporated with GM to set the balance between exploration and exploitation. By using a greedy selection technique, this study retains an optimal slime mould position while ensuring the algorithm’s rapid convergence. The SMA-GM performance was evaluated by using unconstrained, constrained, and CEC2022 benchmark functions. The results show that the proposed SMA-GM has a more robust capacity for global search, improved stability, a faster rate of convergence, and the ability to solve constrained optimization problems. Additionally, the Wilcoxon rank sum test illustrates that there is a significant difference between the optimization outcomes of SMA-GM and each compared algorithm. Furthermore, the engineering problem such as industrial refrigeration system (IRS), optimal operation of the alkylation unit problem, welded beam and tension/compression spring design problem are solved, and results prove that the proposed algorithm has a better optimization efficiency to reach the optimum value. Full article

This paper introduces a parallel meta-heuristic algorithm called Cuckoo Flower Search (CFS). This algorithm combines the Flower Pollination Algorithm (FPA) and Cuckoo Search (CS) to train Multi-Layer Perceptron (MLP) models. The algorithm is evaluated on standard benchmark problems and its competitiveness is demonstrated against other state-of-the-art algorithms. Multiple datasets are utilized to assess the performance of CFS for MLP training. The experimental results are compared with various algorithms such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Evolutionary Search (ES), Ant Colony Optimization (ACO), and Population-based Incremental Learning (PBIL). Statistical tests are conducted to validate the superiority of the CFS algorithm in finding global optimum solutions. The results indicate that CFS achieves significantly better outcomes with a higher convergence rate when compared to the other algorithms tested. This highlights the effectiveness of CFS in solving MLP optimization problems and its potential as a competitive algorithm in the field. Full article

Wireless Sensor Networks (WSNs) have a wonderful potential to interconnect with the physical world and collect data. Data estimation, long lifespan, deployment, routing, task scheduling, safety, and localization are the primary performance difficulties for WSNs. WSNs are made up of sensor nodes set up with minimal battery power to monitor and reveal the occurrences in the sensor field. Detecting the location is a difficult task, but it is a crucial characteristic in many WSN applications. Locating all of the sensor nodes efficiently to obtain the precise location of an occurrence is a critical challenge. Surveillance, animal monitoring, tracking of moving objects, and forest fire detection are just a few of the applications that demand precise location determination. To cope with localization challenges in WSNs, there is a variety of localization algorithms accessible in the literature. The goal of this research is to use various optimization strategies to solve the localization problem. In this work, a modified learning enthusiasm-based teaching–learning-based optimization (mLebTLBO) algorithm is used to cope with a 2D localization problem applying the notion of an exclusive anchor node and movable target nodes. A modified LebTLBO algorithm seeks to increase overall efficiency by assessing the exploration and exploitation abilities. The computational results reveal that this technique outperforms others with respect to localization errors in a 2D environment of WSN. Full article

The fast growth of wireless technology for mobile communication devices requires broad bandwidth, high data rate facilities and compact device size. The solution to the next generation user equipment is high data rate 4G/5G technologies. In this research, wideband antenna design was analyzed and evaluated for 4G, 5G and NB-IoT applications. CST microwave studio was used for simulations and investigations of the performance parameters. The antenna was designed as a folded dipole with a tunable bandwidth and resonates for 5G NR n78, NR-IoT bands B1, B2, and B25, and eleven TDD LTE frequency bands with a bandwidth percentage and minimum scattering loss of 69.02% and −42 dB respectively. Additionally, the designed antenna is small (35 × 48 × 1.62 mm³) and planar in structure and can be easily integrated with radio equipment. The antenna design was also investigated for SAR minimization and gain enhancement using metamaterial integration. For all operating frequency bands, the antenna design results in a considerable gain improvement. The metamaterial was shown to be an excellent absorber of radiation, particularly in high frequency regions. This research also included a SAR examination with and without metamaterial integration. SAR values were found to be significantly reduced throughout all operating bands. The results were validated by fabricating the design prototype on FR-4 substrate for 4G, 5G and IoT bands. The antenna will be possibly used for communication in high data rate applications. Full article

Fused filament fabrication (FFF) has numerous process parameters that influence the mechanical strength of parts. Hence, many optimization studies are performed using conventional tools and algorithms. Although studies have also been performed using advanced algorithms, limited research has been reported in which variants of the naked mole-rat algorithm (NMRA) are implemented for solving the optimization issues of manufacturing processes. This study was performed to scrutinize optimum parameters and their levels to attain maximum impact strength, flexural strength and tensile strength based on five different FFF process parameters. The algorithm yielded better results than other studies and successfully achieved a maximum response, which may be helpful to enhance the mechanical strength of FFF parts. The study opens a plethora of research prospects for implementing NMRA in manufacturing. Moreover, the findings may help identify critical parametric levels for the fabrication of customized products at the commercial level and help to attain the objectives of Industry 4.0. Full article

Fused filament fabrication (FFF), a portable, clean, low cost and flexible 3D printing technique, finds enormous applications in different sectors. The process has the ability to create ready to use tailor-made products within a few hours, and acrylonitrile butadiene styrene (ABS) is extensively employed in FFF due to high impact resistance and toughness. However, this technology has certain inherent process limitations, such as poor mechanical strength and surface finish, which can be improved by optimizing the process parameters. As the results of optimization studies primarily depend upon the efficiency of the mathematical tools, in this work, an attempt is made to investigate a novel optimization tool. This paper illustrates an optimization study of process parameters of FFF using neural network algorithm (NNA) based optimization to determine the tensile strength, flexural strength and impact strength of ABS parts. The study also compares the efficacy of NNA over conventional optimization tools. The advanced optimization successfully optimizes the process parameters of FFF and predicts maximum mechanical properties at the suggested parameter settings. Full article

Invasive central nervous system (CNS) aspergillosis is acquired by either hematogenous dissemination or direct spread from a sinus infection. We describe a series of nine patients with CNS aspergillosis from a tertiary care teaching institute in North India who were treated with voriconazole alone or in combination with surgery. All patients who had clinical and radiological features consistent with fungal CNS infection, showed the presence of septate hyphae on histopathology/microscopy and were either culture positive for Aspergillus spp. or had serum galactomannan positivity were diagnosed as CNS aspergillosis. Clinical features, risk factors, diagnostic modalities, treatment details and outcome at last follow-up were recorded for all patients diagnosed with CNS aspergillosis. A total of nine patients were diagnosed with CNS aspergillosis. The median duration of presentation at our hospital was six months (IQR-2-9 months). Six patients had concomitant sinus involvement, while two patients had skull-base involvement as well. All patients were treated with voriconazole therapy, and three of these patients underwent surgery. All but one patient survived at the last follow-up (median duration was 14 months (IQR- 8-21.5). Two patients had complete resolution, and voriconazole was stopped at the last follow-up, and the rest of the patients were continued on voriconazole. Of the six patients who were continued on voriconazole, all but one had more than 50% radiological resolution on follow-up imaging. Invasive CNS aspergillosis is an important cause of CNS fungal infection that is often diagnosed late and requires long-term voriconazole-based therapy. Full article