Resistance Fluctuations Research Articles

Low-frequency resistance fluctuations in an ionic-liquid-gated channel probed by cross-correlation noise spectroscopy

Low-frequency resistance fluctuations in an ionic-liquid-gated channel probed by cross-correlation noise spectroscopy

Dielectric, low‐frequency noise and mechanical properties of hybrid carbon nanotubes/graphene nanoplatelets epoxy composites

AbstractThis study analyzes the electrical, microwave (26–37 GHz), low‐frequency (20 Hz–10 kHz) noise, and mechanical properties of epoxy hybrid composites filled with multi‐walled carbon nanotubes (MWCNT) and graphene nanoplatelets (GNP). The system's percolation threshold was around 0.25 vol.% MWCNT, while the electromagnetic properties were almost independent of GNP concentration. The composites are shown to be suitable for electromagnetic shielding in the 26–37 GHz range, with microwave absorption increasing as the total nanocarbon concentration rises. The electrical transport in these composites is governed by one‐dimensional Mott hopping at low temperatures (below 245 K) and redistribution effects above room temperature. The low‐frequency voltage noise spectra of the composites are predominant of the 1/f component, with noise spectral density proportional to Ub, where the exponent b is close to 2, indicating noise caused by resistance fluctuations. The optimal electromagnetic properties are near 0.5 vol.% MWCNT and 0.25 vol.% GNP concentrations. The tensile elastic modulus and yield strength showed the highest increases at 0.25/0.25 loading, with gains of 40.43% and 16.77%, respectively, compared to neat epoxy. Fracture toughness and energy release rate increased with adding 0.25 vol.% GNP, while Charpy impact strength improved with higher MWCNT loading.Highlights Percolation threshold in hybrid MWCNT/GNP composites is close to 0.25 vol.% MWCNT Low‐frequency voltage noise spectra of the composites are 1/f type

Glucose-fueled cationic nanomotors for promoting the healing of infected diabetic wounds

Hyperglycemia-promoted bacterial infection will seriously exacerbate diabetic wounds, and its current clinical treatments are suffering from the adverse effects associated with off-target, bacterial resistance, and glycemic fluctuation. Herein, we present a kind of glucose-fueled cationic nanomotors capable of remarkably enhancing antibacterial efficacy, and thus expediting diabetic wound healing. The nanomotors have positively charged surfaces, and consist of mesoporous bowl-shaped polydopamine nanoparticles grafted with quaternized polymer brushes and coupled with glucose oxidase (GOx) and catalase (CAT). Stemming from the GOx-CAT cascade reaction in diabetic wound microenvironment, they can perform robust chemotactic motion towards both high glucose regions, where bacteria proliferation predominantly occurs, and elevated H2O2 levels, which bacterial metabolism produced. This enables the nanomotors to facilitate targeted migration towards bacteria-rich regions and simultaneous downregulation of glycemic levels, as well as to significantly enhance the electrostatic interaction between antibacterial components and bacteria. Consequently, the nanomotors exhibit amplified contact-killing effects of their attached cationic molecules, leading to an almost 10-fold enhancement in antibacterial efficacy compared to previous counterparts. The in vivo experiments approved that the nanomotors demonstrated the accelerated healing of infected diabetic wounds by S. aureus and biosafety. The results herein provide an insight into the clinical treatment of infected diabetic wounds.

Oxygen Vacancy Compensation-Induced Analog Resistive Switching in the SrFeO3-δ/Nb:SrTiO3 Epitaxial Heterojunction for Noise-Tolerant High-Precision Image Recognition.

Neuromorphic computing, inspired by the brain's architecture, promises to surpass the limitations of von Neumann computing. In this paradigm, synaptic devices play a crucial role, with resistive switching memory (memristors) emerging as promising candidates due to their low power consumption and scalability advantages. This study focuses on the development of metal/oxide-semiconductor heterojunctions, which offer several technological advantages and have broad potential for applications in artificial neural synapses. However, constructing high-quality epitaxial interfaces between metal and oxide semiconductors and designing modifiable contact barriers are challenging. Herein, we construct high-quality epitaxial metal/semiconductor interfaces based on the metallicity of the perovskite phase SrFeO3-δ (PV-SFO) and a small Schottky barrier in contact with Nb-doped SrTiO3 (NSTO). X-ray diffraction patterns, reciprocal space mapping results, and cross-sectional transmission electron microscopy images reveal that the prepared PV-SFO film exhibits a perfect single-crystal structure and an excellent epitaxial interface with the NSTO (111) substrate. The corresponding memristor exhibits analog-type resistive-variable characteristics with an ON/OFF ratio of ∼1000, stable data retention after 10,000 s, and no noticeable fluctuation in resistance after 10,000 pulse cycles. Electron energy loss spectroscopy, first-principles calculations, and electrical measurements reveal that compensating or restoring oxygen vacancies at the NSTO surface decreases or increases the contact barrier between PV-SFO and NSTO, respectively, thereby gradually regulating the resistance value. Furthermore, high-quality epitaxial PV-SFO/NSTO devices achieve up to 98.21% recognition accuracy for handwriting recognition tasks using LeNet-5-based network structures and 92.21% accuracy for color images using visual geometry group (VGG) network structures. This work contributes to the advancement of interface-type memristors and provides valuable insights into enhancing synaptic functionality in neuromorphic computing systems.

Increasing trends of antibiotic resistance in Uganda: analysis of the national antimicrobial resistance surveillance data, 2018–2021

BackgroundContinuous monitoring of antimicrobial resistance (AMR) in Uganda involves testing bacterial isolates from clinical samples at national and regional hospitals. Although the National Microbiology Reference Laboratory (NMRL) analyzes these isolates for official AMR surveillance data, there's limited integration into public health planning. To enhance the utilization of NMRL data to better inform drug selection and public health strategies in combating antibiotic resistance, we evaluated the trends and spatial distribution of AMR to common antibiotics used in Uganda.MethodsWe analyzed data from pathogenic bacterial isolates from blood, cerebrospinal, peritoneal, and pleural fluid from AMR surveillance data for 2018–2021. We calculated the proportions of isolates that were resistant to common antimicrobial classes. We used the chi-square test for trends to evaluate changes in AMR resistance over the study period.ResultsOut of 537 isolates with 15 pathogenic bacteria, 478 (89%) were from blood, 34 (6.3%) were from pleural fluid, 21 (4%) were from cerebrospinal fluid, and 4 (0.7%) were from peritoneal fluid. The most common pathogen was Staphylococcus aureus (20.1%), followed by Salmonella species (18.8%). The overall change in resistance over the four years was 63–84% for sulfonamides, fluoroquinolones macrolides (46–76%), phenicols (48–71%), penicillins (42–97%), β-lactamase inhibitors (20–92%), aminoglycosides (17–53%), cephalosporins (8.3–90%), carbapenems (5.3–26%), and glycopeptides (0–20%). There was a fluctuation in resistance of Staphylococcus aureus to methicillin (60%-45%) (using cefoxitin resistance as a surrogate for oxacillin resistance) Among gram-negative organisms, there were increases in resistance to tetracycline (29–78% p < 0.001), ciprofloxacin (17–43%, p = 0.004), ceftriaxone (8–72%, p = 0.003), imipenem (6–26%, p = 0.004), and meropenem (7–18%, p = 0.03).ConclusionThe study highlights a concerning increase in antibiotic resistance rates over four years, with significant increase in resistance observed across different classes of antibiotics for both gram-positive and gram-negative organisms. This increased antibiotic resistance, particularly to commonly used antibiotics like ceftriaxone and ciprofloxacin, makes adhering to the WHO's Access, Watch, and Reserve (AWaRe) category even more critical. It also emphasizes how important it is to guard against the growing threat of antibiotic resistance by appropriately using medicines, especially those that are marked for "Watch" or "Reserve."

Numerical investigation of bucket wear and excavation performance with non-spherical materials

Wear significantly affects equipment performance at mining sites, especially when excavating materials with diverse shapes, leading to increased bucket wear and reduced service life. This work combines the co-simulation of multibody dynamics and discrete element method with the Archard wear model to investigate the dynamic interactions between the bucket and non-spherical materials, and the impact of bucket wear on excavation performance. The results indicate that wear is evident on the bottom surface, side surface, and teeth of the bucket throughout the working cycle. The wear patterns observed for different teeth vary, highlighting the importance of maintenance for the middle and side teeth. Compared to spherical particles, ellipsoidal ones cause greater excavating resistance, bucket wear, and hydraulic fluctuations. As wear intensifies, the teeth become blunt, and the bottom surface exhibits unevenness, resulting in reduced payload and increased resistance and hydraulic power. Proposed wear mitigation strategies can help uphold satisfactory operating performance.

Experimental study on evaluating fracture processes of different rocks using multiple physical parameters

To improve the accuracy of rock fracture evaluation, three types of rocks with different lithologies, namely, granite, coal, and sandstone, were processed into cuboid samples with unilateral notches for three-point bending tests. Real-time data were obtained via load, resistance, and acoustic emission (AE) monitoring. Various fracture patterns of the fractured rocks were collected, and the correlations between multiple physical parameters were determined. The results indicated that granite had the highest fracture toughness and initial fracture energy required for macroscopic fracturing, followed by sandstone, which had the lowest fracture toughness and initial fracture energy. The primary crack participated in the fracture process of coal and produced the most complex crack morphology, exhibiting the characteristics of “Progressive fracture” different from the “Instantaneous fracture” of granite and sandstone. The crack length reached 90.5 mm, which was much longer than that of sandstone. The temporal characteristics obtained from multiple physical quantities were synchronized with the fracture behavior. The resistivity increased gradually with the fracture, and the resistivity fluctuation caused by crack propagation increased. The resistivity change rate of granite was the highest when macrofractures occurred, whereas that of coal after complete fracture was the highest. Rock fracture produced a large number of AE events with small amplitude and low peak frequency, concentrated in the 100 ± 50 kHz band. The AE count corresponded to the crack propagation process, and the AE location indicated the crack complexity. The peak change rate of the resistivity was used to evaluate the fracture performance and exhibited a significant linear relationship with the peak load, fracture toughness, and initial fracture energy. The relationship between the accumulated AE energy and the fracture length was a quadratic function, which was used to evaluate the fracture complexity. The multiple physical quantity monitoring method is promising for predicting the fracture behavior of rocks.

Anaesthetic considerations in congenital single ventricle cyanotic heart disease patients undergoing non – cardiac surgery

Congenital heart disease (CHD) is one of the most prevalent congenital disorders, with an incidence rate of 9.1 per 1000 live births. Lesions featuring single ventricle physiology make up about 20% of these cases. This condition involves an abnormal parallel circulation, leading to complications such as ventricular dysfunction, chronic hypoxia, polycythaemia, and infective endocarditis, which collectively increase mortality risk. Patients with a single functioning ventricle have limited tolerance for changes in preload, afterload, myocardial depression, and fluctuations in pulmonary and systemic vascular resistance. Factors like reduced tidal volume, atelectasis, interstitial lung water, and hypoxic pulmonary vasoconstriction contribute to a balanced circulation, but these factors can be significantly altered during induction and intubation. Here, we report the successful management of a male child with single ventricle physiology who underwent an emergency burr hole procedure and tapping of a brain abscess under general anesthesia.

Age-related differences in fluctuations in insulin resistance evaluated with HOMA-IR and triglyceride and glucose-based indices during the menstrual cycle, as determined using the NHANES cross-sectional dataset

ObjectivesTo determine how age affects insulin resistance during the menstrual cycle and insulin resistance-associated indices: the Triglyceride-glucose and Triglyceride-glucose-BMI indexes. MethodsThis prospective observational study used fasting plasma glucose, fasting insulin, triglycerides, body mass index (BMI), and days since the start of the menstrual period collected from the NHANES dataset (1999–2006). Insulin resistance was determined using the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR). The participants were categorized as young (16–34 years) or older (>35 years). Rhythmicity during the menstrual cycle was analyzed using the Cosinor and Cosinor2 packages for R. Main outcome measuresCosine fit curves for insulin resistance during the menstrual cycle and age-associated effects on rhythmicity. ResultsUsing 1256 participants, rhythmicity was observed for fasting insulin and HOMA-IR (p < 0.05) but not for fasting plasma glucose, the Triglyceride-glucose index, or the Triglyceride-glucose-BMI index. Significant amplitudes for fasting insulin and HOMA-IR were observed when age was considered. Acrophases for fasting insulin and HOMA-IR were significant only for the younger group, and the differences between these groups were significant, suggesting that the changes in scores for insulin resistance for the younger and older groups occur at different times of their menstrual cycle. ConclusionsInsulin resistance does fluctuate during the menstrual cycle, and it is at a maximum at different times for younger and older women. Since these results are unadjusted, this study is preliminary and further investigation is required.

Improvement of switching uniformity in TiO2-based resistive random access memory with graphene oxide embedded film

TiO2-based resistive random access memory (RRAM) devices has the potential to fabricating nonvolatile memory applications. However, the memories based on TiO2 have severe problems like large resistive switching parameter fluctuations, poor cycling endurance and so on, which can be a limitation of the device in practical applications. Graphene oxide (GO) is a novel material that has attracted much attention and is often applied to enhance the performance of memories due to its own special properties. In this work, two types of bilayer structure based on TiO2 for RRAM devices were fabricated. The significant improvement in uniformity of operating voltages and high HRS/LRS ratio of the TiO2-based RRAM were realized by inserting a two-dimensional GO film between TiO2 and FTO. The presence of the GO film would prevent the ion exchange between the electrodes and the switching layer as well as its insulating properties. The conduction mechanisms of all the devices in low and high resistance states were dominant by the Ohmic conduction mechanism and the space charge limited conduction mechanism, respectively. This research manifests the promising potential of two-dimensional GO embedded layer for improving resistive switching performance of RRAMs.

Plasma plume enhancement of a dual-anode vacuum arc thruster with magnetic nozzle

Vacuum arc thruster (VAT) is a type of pulsed electric propulsion device that generates thrust based on vacuum arc discharges, it has great candidate for micro-newton force applications in orbit. To improve both the thrust and longevity of the VAT, a novel dual-anode structure, comprising a central anode and a ring anode, was developed. We conducted an investigation into the plasma discharge and acceleration process within the influence of a magnetic nozzle. The dual-anode architecture resulted in a reduction in the initial plasma impedance, thereby enhancing ion current and velocity. Analysis of surface parameters during discharge revealed a synergistic mechanism between the two insulator-conducting films, enabling a co-cyclic distribution of energy and resistance fluctuations within the discharge. Consequently, the dual-anode setup demonstrated a lifespan extension of at least twofold. Comparative analyses of arc energy, plasma velocity, ion current, and thrust variations with magnetic field strength were conducted between the dual-anode and single-anode configurations under magnetic nozzle influence. Results showed that the dual-anode structure increased ion current and velocity when subjected to magnetic nozzle influence, resulting in a thrust increase of up to 303%. Additionally, we developed a theoretical model for the diffusion coefficient to elucidate the adaptive splitting phenomenon of the arc within the dual-anode structure under magnetic field influence. This model suggests that the dual-anode structure can achieve a more significant enhancement in beam current from the magnetic nozzle compared to the single-anode configuration.

Optimization of garnet-type solid-state lithium batteries via synergistic integration of an advanced composite interface for elevated performance

Solid-state batteries (SSBs) represent a pivotal avenue of development owing to their superior energy density and enhanced safety profile. However, the widespread utilization about SSBs confronts challenges such as inadequate interfacial connectivity resulting in high resistance, dendrite formation, and volumetric fluctuations in the lithium metal anode during plating and stripping. In this study, we introduce an innovative and remarkably efficient approach, leveraging the transformative potential of TiN-induced conversion. This method yields a lithium-ion-conductive TiN material concurrently addressing pre-existing porosity. The LiTiN| LLZTO| LiTiN symmetric cell is particularly noteworthy for its remarkable long-term cycle stability, which exceeds 1000 h at 0.2 mA cm−2, and its remarkable critical current density of 1.4 mA cm−2 at 25 °C. By subjecting TiN, the formation of the Li-Ti-N phase is induced, thereby establishing an additional Li3N-conductive layer that significantly enhances battery performance. Of paramount significance is the validation of the exceptional attributes of the composite through the deployment of LiFePO4 (LFP) full cells. In this configuration, the LFP coupled full cell manifests a remarkable discharge rate capacity of about 147 mAh g−1 at 1C, along with a noteworthy discharge capacity retention rate of 90 % even following 1000 charge and discharge cycles. These outcomes underscore the material’s robust lithium affinity and uniform lithium-ion distribution, which together mitigate dendrite growth and enhance the cycle stability of lithium-metal batteries.

An olfactory figure-ground segregation: The resistance fluctuation analysis of acetone gas for acetone/random gas mixtures recognition

Recognizing the disease-specific odor in human breath is a feasible non-invasive disease detection strategy. The most critical issue of olfactory diagnosis lies in recognizing the target gas among complex respiration compounds accurately. This paper demonstrated a novel approach to binary gas mixture recognition, exploring nature-related features of the target “odor object” (acetone gas) regardless of its “noisy background” (ethanol or methanol gases). Those features were extracted from time-series resistance fluctuations collected with one conventional SnO2 thin film gas sensor (TF) or our sensitivity-controllable lines gas sensor (TL). 6 features (variance, root mean square, band power, relative band power, entropy, and the number of values crossing mean value) were selected and analyzed with k-Nearest Neighbor regressor (KNN) and Support Vector Regressor (SVR). 18 types of acetone/ethanol mixtures and 3 acetone/methanol mixtures were tested with the proposed models and further explained with SHAP (SHapley Additive exPlanations). The model of TL and KNN achieved the optimal performance of regressing acetone concentrations with the coefficient of determination (R2) of 0.97 and 0.95 in individual gas and binary gas mixtures, respectively. Entropy was found to be the key feature that was linked to sensor properties. Finally, acetone gas data mixed with simulated gas and random noise data were experimented to evaluate our models’ potential for acetone/random gas mixture recognition and robustness.

Temporal resistance fluctuations during the initial filtration period of colloidal matter filtration

Membrane filtration processes with flux reversal for backwashing are employed to manage fouling during colloidal fluid treatment. This flux reversal accompanies filtration cycles where the total resistance increases over time. However, little is known about the initial phase of filtration. We focus on early filtration phase and present a microfluidic filtration system that allows precise observation of filtration resistances under constant flux or constant pressure operation for soft matter dead-end filtration with subsequent percolation of pure solvent. We identified temporal hydraulic resistance fluctuations during the start-up phase, which significantly differed from the steady-state condition. We focus on the experimental filtration of soft core–shell poly(N-isopropylacrylamide)-co-acrylic-acid microgels and observed a distinct peak in filter cake resistance with a subsequent reduction down to 50% of the initial peak resistance. To comprehend the phenomenon, we complement the experiments with temporal and spatial confocal microscopy studies linking cake compression and cake density gradients to its resistance. By comparing filtration, dialysis, and centrifugation as different microgel purification methods, we discover microgel shell degradation leading to detached polymer chains permeating in the downstream which causes the observed resistance reduction. This result demonstrates the permeation-induced degradation of macromolecular colloids during membrane filtration.

An investigation of the electrical contact failure of JPT electric connectors used in automobiles under multiple stresses

The primary mode of failure in electric connectors is contact failure, one of the reasons is which occurs as a result of external vibrations in the environment. This failure arises due to the reduction in positive pressure on the electric connector terminal caused by the vibration, leading to fluctuations in the electrical contact resistance (ECR). When both vibrational stress and plug/pull stress is simultaneously present, the contact area of electric connectors experiences fretting wear and shear stress, resulting in a more intricate failure mechanism for the electrical connector. Thus, this study presents an accelerated degradation testing method designed to investigate the reliability of the junior power timer (JPT) electric connector employed in automobiles. A temperature resistance test, plug/pull test (sliding wear) and vibration test (fretting wear) were conducted for the JPT electric connector. The electrical contact failure mechanism of the JPT electrical connector was analyzed, with specific focus on its correlation with the concentration of oxides in the contact area.

Silver Nanowire-Based Flexible Strain Sensor for Human Motion Detection.

Accurately capturing human movements is a crucial element of health status monitoring and a necessary precondition for realizing future virtual reality/augmented reality applications. Flexible motion sensors with exceptional sensitivity are capable of detecting physical activities by converting them into resistance fluctuations. Silver nanowires (AgNWs) have become a preferred choice for the development of various types of sensors due to their outstanding electrical conductivity, transparency, and flexibility within polymer composites. Herein, we present the design and fabrication of a flexible strain sensor based on silver nanowires. Suitable substrate materials were selected, and the sensor's sensitivity and fatigue properties were characterized and tested, with the sensor maintaining reliability after 5000 deformation cycles. Different sensors were prepared by controlling the concentration of silver nanowires to achieve the collection of motion signals from various parts of the human body. Additionally, we explored potential applications of these sensors in fields such as health monitoring and virtual reality. In summary, this work integrated the acquisition of different human motion signals, demonstrating great potential for future multifunctional wearable electronic devices.

JTC online comprehensive fault diagnosis method based on angle feature and linear trend feature

Aiming at the problem of incompatibility of the fault diagnosis method between JTC (Jointless Track Circuit) compensation capacitor and tuning zone, based on the amplitude envelope mathematical model of induced voltage of locomotive signal, an online comprehensive fault diagnosis algorithm for multi-compensation capacitor and tuning equipment fault was proposed. It is characterized by the angle of the tangent vector of the amplitude envelope at the compensation capacitor and the linear trend within the three compensation capacitor sections close to the transmitter when the tuning device fails, the angle feature and linear trend feature are extracted to determine the fault type. Experimental results show that the proposed algorithm can accurately detect multi-compensation capacitor faults and tuning equipment faults with high accuracy and high efficiency, especially the sudden multi-compensation capacitor faults and tuning zone unit breaking faults. The accuracy of the algorithm for JTC fault diagnosis is verified under different signal-to-noise ratio and ballast resistance fluctuation, which shows that the algorithm has strong applicability.

Modelling the impact of wastewater flows and management practices on antimicrobial resistance in dairy farms

Dairy slurry is a major source of environmental contamination with antimicrobial resistant genes and bacteria. We developed mathematical models and conducted on-farm research to explore the impact of wastewater flows and management practices on antimicrobial resistance (AMR) in slurry. Temporal fluctuations in cephalosporin-resistant Escherichia coli were observed and attributed to farm activities, specifically the disposal of spent copper and zinc footbath into the slurry system. Our model revealed that resistance should be more frequently observed with relevant determinants encoded chromosomally rather than on plasmids, which was supported by reanalysis of sequenced genomes from the farm. Additionally, lower resistance levels were predicted in conditions with lower growth and higher death rates. The use of muck heap effluent for washing dirty channels did not explain the fluctuations in cephalosporin resistance. These results highlight farm-specific opportunities to reduce AMR pollution, beyond antibiotic use reduction, including careful disposal or recycling of waste antimicrobial metals.

An industrial IoT-based deformation resistance prediction and thickness control method of cold-rolled strip in steel production systems

Different from traditional analytical and numerical simulation methods, data-driven methods can more effectively describe the effects of nonlinear and coupling factors. Although the fluctuations of deformation resistance and thickness for hot-rolled strips significantly influence the thickness accuracy in strip cold rolling, the traditional deformation resistance prediction model and thickness control mode don't involve that. Thus, based on the Industrial Internet of Things (IIOT) platform and data-driven technology, this work proposed a new method for deformation resistance prediction and a new mode of feed-forward control for strip thickness. IIOT platform can collect production data from the various levels to provide a complete data set. The deformation resistance prediction model is established by adopting the differential evolutionary algorithm to optimize the bi-directional long and short-term memory network. In addition, the feed-forward control (DFFC) strategy for strip thickness is proposed based on deformation resistance prediction. An application results of a 1420 mm production line of CR indicated that the established prediction model could provide a high accuracy prediction for deformation resistance, and compared to traditional thickness gauges-based automatic gauge control methods, the DDFC can capture the effect of the fluctuations of deformation resistance and thickness for the hot-rolled strip to improve thickness accuracy effectively.

Improved Incremental Conductance MPPT Technique Designed to Addressing Drift Problem in a Photovoltaic System

Many researchers have concentrated on improving the efficiency of photovoltaic (PV) systems by optimizing control mechanisms aimed at extracting the maximum power from PV panels. The variable step size incremental conductance control (VSS-INC) technique has been the primary focus of the majority of these studies. However, this strategy faces challenges in the form of drift when confronted with swift changes in solar irradiation, temperature variations, and resistive load fluctuations. In addressing this issue, the present study proposes an innovative VSS-INC method. It is suggested to utilize a buck-boost converter as an impedance adaptor in achieving maximum power point tracking (MPPT). This involves controlling the duty cycle and aligning its input with that of the PV module. The efficiency of the suggested approach was evaluated through MATLAB software, and the outcomes were compared with traditional algorithms across various operational scenarios. Simulation results demonstrate the notably satisfactory and efficient performance of the proposed method when compared to conventional approaches.