Reduce Noise Levels Research Articles

Exploring the role of nanoparticle additives in reducing emissions in compression ignition engine

The paper discusses the growing environmental issues caused by vehicle emissions, leading many countries to tighten their emission standards to reduce the harmful gases released by motor vehicles. This is especially important as urbanization has increased, leading to more consumption of petroleum products. As a result, there is a growing need for cleaner, more sustainable fuel alternatives. Biodiesel has been recognized as a potential solution since it is renewable, non-toxic, and less environmentally harmful than traditional diesel. The study in question explores how adding biodiesel and nanoparticles affects diesel engine performance, particularly in terms of noise and particulate matter emissions (small particles of pollution). The research was carried out using a compression ignition engine (like those found in diesel vehicles) under constant RPM (revolutions per minute) and variable loads (changes in engine stress or power requirements). The results showed that when biodiesel was blended with nanoparticles and used as fuel, there was a noticeable reduction in noise levels, carbon dioxide (CO2), and particulate matter emissions compared to regular diesel. This suggests that biodiesel, especially when enhanced with nanoparticles, can effectively lower the harmful effects of diesel engines on the environment, offering a greener and quieter alternative.

Reduction of Noise Levels During Caesarean Births Through Audiovisual Feedback is Associated With Lower Stress Levels for Patients.

Noise reduction during surgical procedures leads to improved surgical performance and results. The caesarean birth (CB) is an exceptional operation and a life changing experience. Through the introduction of staff education and implementation of audiovisual feedback, we intended to reduce noise, and subsequently reduce surgical complications and increase the well-being of patients and staff. During Phase I, blinded baseline measurements of noise were conducted. Phase II started after staff education and structured questionnaires on subjective noise and stress were added, and in Phase III audiovisual feedback was introduced. Mean and peak noise levels over the time of the procedure were obtained in A-weighted decibels (dB(A)). Kruskal-Wallis H tests were performed to evaluate the impact of interventions on noise levels. Questionnaires were evaluated using descriptive statistics; stress-scores were compared using independent sample t-tests. Ninety planned CBs were included. Median noise levels were 62.85 dB(A) at baseline. They decreased significantly to 60.60 dB(A) (Phase II) and 59.25 dB(A) (Phase III), respectively. This reduction of 3.6 dB(A) leads to a subjective noise reduction of around 20%. Significant differences for A-weighted and peak noise levels during actual surgery were found after combining staff education with audiovisual feedback. In Phase III, staff reported less stressful noise. Stress also decreased significantly in the patient group. Beeping machines and telephones were identified as the most stressful sources of noise. We show that noise reduction during CB is both necessary and possible. Diminished subjective perception of noise and stress are positive impacts of this intervention. Staff education and audiovisual feedback can help to provide a calm and lower stress environment for patients and staff during caesarean births.

Automatic Noise Monitor and Control in Library

Abstract: This paper introduces imperative to sustain a study-friendly atmosphere in libraries, as noise levels can greatly influence patron satisfaction. In order to reduce noise levels in libraries, this project suggests designing and implementing an autonomous noise monitoring and control system. The device constantly measures ambient noise levels by comparing them to predetermined thresholds using sound sensors and an Arduino. The technology reminds users to keep the environment quiet by sounding alerts and displaying visual cautions when acceptable limits are exceeded. To further reinforce silent norms, automatic audio announcements can be incorporated.

Smart noise pollution monitoring system

Increasing pollution poses serious health risks, necessitating urgent control measures for a healthier future for all. Among various pollution types, sound pollution significantly impacts quality of life and well-being, especially in densely populated urban areas. This proposal describes the creation and deployment of an Internet of Things (IoT)-based noise pollution monitoring system intended to track sound levels in certain regions in real time. The core of this system comprises advanced sensors strategically placed to measure sound intensity accurately. These sensors can detect even minor variations in noise levels, providing precise and reliable data. After being gathered, the data is sent to a microcontroller, which effectively processes it. This microcontroller ensures the data is accurately captured and sent over the internet to an online server. Once the data reaches the online server, it becomes accessible for analysis and monitoring by authorities. The system's real-time data capability is crucial for timely intervention. Authorities can continuously monitor noise levels and identify trends or spikes in sound pollution, allowing them to address issues promptly. This feature is particularly beneficial for sensitive areas such as schools, hospitals, and residential zones, areas with heavy traffic, library, Where excessive noise can have severe health effects, including stress, sleep disturbances, and hearing loss. The system includes an alert mechanism that notifies relevant authorities when noise levels exceed predefined thresholds. These alerts enable quick responses to mitigate noise pollution. For instance, in a school, reducing noise levels can enhance the learning atmosphere and improve students' concentration. In hospitals, maintaining a quiet environment is crucial for patient recovery. The integration of IoT technology in this noise pollution monitoring system not only enhances efficiency but also provides a scalable solution adaptable to various urban and rural settings. This scalability ensures the system can be expanded to cover larger areas or multiple locations as needed.

Single-Shot Fringe Projection Profilometry Based on LC-SLM Modulation and Polarization Multiplexing

Fringe projection profilometry (FPP) is extensively utilized for the 3D measurement of various specimens. However, traditional FPP typically requires at least three phase-shifted fringe patterns to achieve a high-quality phase map. In this study, we introduce a single-shot FPP method based on common path polarization interferometry. In our method, the projected fringe pattern is created through the interference of two orthogonal circularly polarized light beams modulated by a liquid crystal spatial light modulator (LC-SLM). A polarization camera is employed to capture the reflected fringe pattern, enabling the simultaneous acquisition of four-step phase-shifting fringe patterns. The system benefits from advanced anti-vibration capabilities attributable to the common path self-interference optical path design. Furthermore, the utilization of a low-coherence LED light source results in reduced noise levels compared to a laser light source. The experimental results demonstrate that our proposed method can yield 3D measurement outcomes with high accuracy and efficiency.

Effect of Bio-Inspired Cutout Shapes at the Leading Edge of Propellers on Noise and Flight Efficiency

In recent years, the application of bio-inspired structures has garnered attention for enhancing the performance of fluid machinery. In this study, we experimentally investigated the effects of introducing a bio-inspired cutout structure to the propellers of drones, aiming to improve thrust efficiency and reduce noise levels. Our results demonstrated reductions in noise levels compared to conventional propellers. Parametric studies revealed that the roundness of the structure significantly influenced both flight efficiency and noise levels, suggesting its importance for replicating the inherent fluid characteristics found in nature. Additionally, optimal parameters for noise reduction, such as the length of the cutout, angle of incision relative to the flow direction, and the distance between the gap were identified. Although no improvements in flight efficiency were observed, most of the models investigated exhibited only around a 5% reduction in efficiency compared to the standard propellers, suggesting practical applicability for scenarios such as nighttime drone operations in urban areas. The noteworthy reduction in sound pressure levels in the mid- to high-frequency range achieved by the bio-inspired propellers in this study holds the potential to address the issue of drone noise pollution and encourage drone operations in urban areas. Moreover, the confirmed decrease in sound pressure at specific frequencies and the suggested controllability hint at the possibility of enhancing sound source localization performance using drones.

Computational Exploration of Variable-Pitch Fans as Thrust Reversers and Their Influence on Noise Abatement in Turbofan Propulsion Systems

Variable pitch propellers, once confined to turboprop engines, are now revolutionizing turbofan applications. Recent breakthroughs in materials and technology, exemplified by Pratt & Whitney’s geared turbofan engine, underscore the practicality of variable pitch systems. Ongoing research promises to extend their adoption across diverse engine types, significantly enhancing safety and performance. This study investigates a novel approach to enhance reverse thrust using dual-row radial fans with adjustable pitch angles. These fan blades exhibit geometry variations, combining features from both turbofan motor fan blades and turbo- propeller motor blades. The results are promising: this configuration nearly triples the thrust force, producing approximately 292.917 kilo-newtons. Moreover, it enables the generation of reverse thrust equivalent to 25.077 kilo- newtons. These enhancements are achieved while reducing blade rotational speed from 5200 revolutions per minute to 3200 revolutions per minute and inlet airspeed from 660 km/h (at maximum power) to 220 km/h. Additionally, a notable 11% reduction in noise level at the blade tips has been observed. This research sheds light on the potential of innovative fan blade designs to revolutionize reverse thrust capabilities in turbofan engines, contributing to safer and more efficient aircraft landings.

Effectiveness of noise barriers in sensitive urban areas: a comprehensive study on noise reduction strategies and public health implications.

This study investigates the effectiveness of noise management strategies, focusing on installing barriers to reduce urban noise pollution, particularly around hospitals. The research in Sakarya, Turkey, employs comprehensive noise measurements and modeling techniques to evaluate barrier interventions. The study included 24-h noise averages and specific measures for daytime, evening, and nighttime noise levels, using SoundPlan software for modeling and mapping. Results reveal that noise levels frequently exceed international thresholds, especially in high-traffic areas. However, introducing noise barriers significantly reduces noise levels, particularly at specific measurement points, demonstrating their effectiveness. This underscores the crucial role of noise management strategies in sensitive urban areas and highlights the urgency for proactive interventions to manage noise pollution. The study identifies the need for future research, including long-term studies to assess noise barriers' durability and sustained effectiveness, and the integration of other noise reduction measures such as green infrastructure. Practical applications of this research include informing urban planners and policymakers about effective strategies for mitigating noise pollution and improving public health. In conclusion, this study provides valuable insights into urban noise management by empirically demonstrating the effectiveness of barrier interventions in reducing noise pollution. It underscores the importance of proactive measures to protect human health and preserve environmental balance in sensitive urban areas. The findings advocate for the broader implementation of noise barriers and other complementary strategies in urban planning to enhance the quality of urban life.

ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING

The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteristics and significant noise interference. The proposed method of multilevel recurrent time-frequency segmentation allows for the modification of time segment durations and the use of segments of varying lengths, providing flexibility in signal processing and adaptation to current conditions. This adaptability enables optimal signal processing for each individual case, taking into account short-term impulses, long-term fluctuations, and various types of noise and distortions. This approach effectively separates frequency components and reduces interference between them, which is crucial for maintaining high signal quality and communication stability in cognitive networks. It has been proven that the use of adaptive filters such as LMS (Least Mean Squares) and RLS (Recursive Least Squares), as well as fast Fourier Transform (STFT), wavelet, and Hilbert transforms at different stages of multilevel time-frequency segmentation, significantly enhances signal interference resistance and energy efficiency. Comparative analysis of signal metrics before and after filtering and transformation shows an increase in signal quality by 14,3–24,5% and a reduction in noise levels by 21,7–29,6%. The wavelet transform, in particular, proved to be highly effective, allowing for precise extraction of useful frequency components from the noise background and improving signal parameters through dynamic adjustment to specific radio environment conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its ability to ensure consistently high-quality processing of complex signal ensembles even in dynamic cognitive radio environments.

Development of hybrid active noise cancellation system for the children with autism spectrum disorder

Children with autism spectrum disorder often experience auditory hyperreactivity, where they find certain sounds intolerable and may exhibit negative behaviours in response to noise. While noise-cancelling headphones are commonly used to address these behavioural issues, they primarily focus on reducing noise levels without considering the diverse auditory perceptions of children with autism. Aural perception and electroencephalography tests were conducted to design a noise-control function in headphones that cater to their individual needs. Autistic children with auditory hyperreactivity listened to sounds of various frequencies and amplitudes and their subjective responses were analyzed. The results were utilized to determine suitable noise-attenuation targets based on a function of mean aural perception ratings and noise levels. Using these findings, a hybrid active noise cancellation system was developed and validated. The results showed that frequencies of 250 Hz and 8 kHz were rated by the majority of the children with autism as most unpleasant. The aural perception response improved when children used headsets tailored to their specific aural perception characteristics.

Attention-based convolutional neural network for multi-channel active noise control

Multi-channel active noise control systems utilize multiple control units to cancel unwanted noise across different channels simultaneously. By employing adaptive filtering techniques, these systems can effectively reduce noise levels in complex environments, offering improved noise mitigation compared to single-channel active noise control approaches. The disadvantage of adaptive filtering in active noise control is its limited ability to accurately model nonlinearities present in real-world noise environments. In this work, we propose an attention-based convolutional neural network comprising convolutional, deconvolutional, and attention mechanism to approach multi-channel active noise control. The system simultaneously calculates multiple canceling signals to eliminate primary noises captured by error microphones. To evaluate the effectiveness of our proposed model, we conducted testing utilizing filtered white noise spanning various frequency bands. Experimental evaluations across various setups, including different loudspeaker and microphone configurations, as well as variations in secondary source positioning, are used to assess the performance of the proposed approach. Results demonstrate that the proposed attention-based convolutional neural network for multi-channel active noise control yields a noise reduction of 22.65 dB on wideband noise, while the conventional Filtered-x least mean square approach yields a noise reduction of 4.23 dB, indicating the effectiveness of the proposed approach.

An innovative approach for enclosure design of power generators to meet noise regulation targets

The use of diesel-powered engine generators as a power source is very common for various industrial and residential applications. There are strict regulatory requirements and legislations to limit the noise levels from these generators below statutory limits. The purpose of the paper is to present an approach using statistical energy analysis (SEA) augmented by experimental inputs for the prediction of noise levels for the generators and propose strategies for optimized enclosure design that effectively mitigate noise to achieve regulatory requirements. The study begins by developing an SEA model that represents the acoustic behavior of the generator and enclosure. The airborne sources such as engine, alternator, radiator fan and exhaust are modelled explicitly using experimental noise source characterization. Based on the established SEA model and excitation sources, noise levels are predicted through the calculation of energy flow between subsystems. The predicted sound levels for an existing Diesel Generator with enclosure were validated with its test data to ensure correctness of SEA model. Parametric studies were carried out using simulations on key variables, including critical noise paths, panel thickness, and noise control treatments. These investigations helped identify optimal process parameters that reduce noise levels emitted by genset in adherence to noise regulations.

Design of a Cancer Infusion Center: Results from a Pre- and Post-Occupancy Evaluation.

The current study performed a post-occupancy evaluation on a new cancer infusion center with pod-like layout and compared results to a pre-occupancy evaluation to investigate the impact of different cancer infusion center designs on staff efficiency and patient and staff satisfaction. The new cancer infusion center opened in October 2020 and replaced two previously existing infusion centers, in the same healthcare system. The study used a similar mixed-method approach as the pre-occupancy research, which included staff shadowing, medication delivery shadowing, and staff and patient questionnaires. The new infusion center improved staff efficiencies by reducing nurse travel time compared to pre-occupancy infusion centers. Results also showed an increase in satisfaction with different aspects of the new infusion center including patient privacy, by both patients and nurses. The pod design allowed for better audio and visual privacy for patients, provided a higher amount of worksurface and availability of workstations, reduced noise levels, and enhanced nurse concentration at workstations. Findings indicated that nurses who had prior experience working in the pre-occupancy infusion centers expressed significantly lower levels of satisfaction in the new infusion center, especially in the ability to connect with nurses in other pods. Although the new pod design had limitations in terms of collaborative opportunities across pods, it showed to provide a more efficient work environment for the staff and increase staff and patient satisfactions. The results also highlight the importance of effective change management strategies when nurses transition to a new work environment.

Secondary Skin Facade for Urban Noise Reduction: A Case Study of Bandung Creative Hub Co-working Space

Abstract In recent years, the proliferation of co-working spaces has offered a collaborative environment for entrepreneurs, freelancers, and small businesses in urban areas. However, the issue of environmental noise within these spaces presents a challenge to comfort and productivity. This study focuses on the Bandung Creative Hub (BCH), a prominent co-working space in Bandung, Indonesia, and investigates the efficacy of facade engineering solutions, particularly the installation of secondary skin, in mitigating urban noise pollution. Through a comprehensive analysis utilizing both empirical measurements and computational simulations, the study aims to understand the intricacies of noise dynamics within the BCH co-working space. Empirical measurements using the Noise Tool software capture ambient noise levels, while computational simulations with IBANA provide predictive modeling of acoustic conditions. The results reveal a significant reduction in noise levels, with the sound level around the workspace corridor decreasing by approximately 23.3%, from 62 dB to 47 dB, indicating the effectiveness of facade engineering interventions. The limitations of prior research, which concentrated solely on barrier intervention methods and spacing ratio. Nevertheless, this study introduces a fresh perspective by examining the efficacy of secondary skin facade engineering in achieving noise standards and enhancing the quality of urban environments.

Super-resolution deep learning image reconstruction: image quality and myocardial homogeneity in coronary computed tomography angiography

BackgroundThe recently introduced super-resolution (SR) deep learning image reconstruction (DLR) is potentially effective in reducing noise level and enhancing the spatial resolution. We aimed to investigate whether SR-DLR has advantages in the overall image quality and intensity homogeneity on coronary computed tomography (CT) angiography with four different approaches: filtered-back projection (FBP), hybrid iterative reconstruction (IR), DLR, and SR-DLR.MethodsSixty-three patients (mean age, 61 ± 11 years; range, 18–81 years; 40 men) who had undergone coronary CT angiography between June and October 2022 were retrospectively included. Image noise, signal to noise ratio, and contrast to noise ratio were quantified in both proximal and distal segments of the major coronary arteries. The left ventricle myocardium contrast homogeneity was analyzed. Two independent reviewers scored overall image quality, image noise, image sharpness, and myocardial homogeneity.ResultsImage noise in Hounsfield units (HU) was significantly lower (P < 0.001) for the SR-DLR (11.2 ± 2.0 HU) compared to those associated with other image reconstruction methods including FBP (30.5 ± 10.5 HU), hybrid IR (20.0 ± 5.4 HU), and DLR (14.2 ± 2.5 HU) in both proximal and distal segments. SR-DLR significantly improved signal to noise ratio and contrast to noise ratio in both the proximal and distal segments of the major coronary arteries. No significant difference was observed in the myocardial CT attenuation with SR-DLR among different segments of the left ventricle myocardium (P = 0.345). Conversely, FBP and hybrid IR resulted in inhomogeneous myocardial CT attenuation (P < 0.001). Two reviewers graded subjective image quality with SR-DLR higher than other image reconstruction techniques (P < 0.001).ConclusionsSR-DLR improved image quality, demonstrated clearer delineation of distal segments of coronary arteries, and was seemingly accurate for quantifying CT attenuation in the myocardium.

INNOVATIVE MEANS OF NORMALIZING PHYSICAL FACTORS THE ENVIRONMENT IN THE PROCESSES OF RECONSTRUCTION AND RESTORATION HISTORICAL HERITAGE OBJECTS

The problems of reconstruction and restoration of historical buildings and structures are relevant all over the world. It is especially important for Ukraine, where a large number of historical buildings and buildings of architectural value have been destroyed or damaged by military operations. The developed composite materials allow, due to their small thickness, to reduce the levels of electromagnetic fields and noise to values that meet modern international standards. A particularly important result is the reduction of low-frequency noise levels (by 12–30dB), which is practically not absorbed by building materials and structures. LED sources ultraviolet radiation can be used in the presence of people for at least 8 hours without reaching the maximum permissible exposure level of 30J/m2 according to SBM-2015. The use of LED ultraviolet radiation sources allows to increase the concentration of ions in deionized air to the standard concentration (500 cm-3) within 10 minutes according to SBM-2015 and disinfect the environment.

Quieting the neonatal intensive care unit: A quality improvement initiative.

The neonatal intensive care unit (NICU) is vital for preterm infants but is often plagued by harmful noise levels. Excessive noise, ranging from medical equipment to conversations, poses significant health risks, including hearing impairment and neurodevelopmental issues. The American Academy of Pediatrics recommends strict sound limits to safeguard neonatal well-being. Strategies such as education, environmental modifications, and quiet hours have shown to reduce noise levels. However, up to 60% of the noises remain avoidable. High noise exposure exacerbates physiological disturbances, impacting vital functions and long-term neurological outcomes. Effective noise reduction in the NICU is crucial for promoting optimal neonatal development. To measure the sound levels in a NICU and reduce ambient sound levels by at least 10% from baseline. A quasi-experimental quality improvement project was conducted over 4 mo in a 20-bed level 3 NICU in a tertiary care medical college. Baseline noise levels were recorded continuously using a sound level meter. The interventions included targeted education, environmental modifications, and organizational changes, and were implemented through three rapid Plan-Do-Study-Act (PDSA) cycles. Weekly feedback and monitoring were conducted, and statistical process control charts were used for analysis. The mean noise values were compared using the paired t-test. The baseline mean ambient noise level in the NICU was 67.8 dB, which decreased to 50.5 dB after the first cycle, and further decreased to 47.4 dB and 51.2 dB after subsequent cycles. The reduction in noise levels was 21% during the day and 28% at night, with an overall decrease of 25% from baseline. The most significant reduction occurred after the first PDSA cycle (mean difference of -17.3 dB, P < 0.01). Peak noise levels decreased from 110 dB to 88.24 dB after the intervention. A multifaceted intervention strategy reduced noise in the NICU by 25% over 4 months. The success of this initiative emphasizes the significance of comprehensive interventions for noise reduction.

An efficient acoustic energy harvester by using deep learning-based traffic prediction

With rapid urbanisation, traffic noise pollution has become a growing community concern. However, traffic noise can be valuable due to its ubiquity and cleanliness, which can be exploited for acoustic energy harvesting systems. This paper aims to develop an efficient acoustic energy harvester to improve energy utilisation efficiency and reduce noise levels by using deep-learning methods. We propose a temporal graph attention convolutional network (TGACN) to predict noise frequency, providing guidance for the acoustic energy harvester design. Power efficiency can be maximised by adjusting the harvester's neck size to the frequency predicted by the TGACN model. The relationship between neck size and the optimum frequency range is obtained through simulations and experiments. We analysed the performance differences among the various cavities in multiple-cavity acoustic energy harvesters and discussed the circuit connection methods between each cavity to identify the optimal configurations. Additionally, we evaluated the superiority of TGACN over the state-of-the-art methods in terms of prediction accuracy by conducting extensive experiments with two real-world datasets.

Hearing Aid apps: are they safe, practical and beneficial for children and teens in challenging situations?

An adult version of an app giving users the control over the level of the volume, microphone directionality and noise reduction was adapted for children. The main purpose of this study was to evaluate the effect of changes made to microphone directionality and noise reduction in the myPhonak Junior (the app) on Speech intelligibility in challenging listening environments in children and teens. The randomized, non-blinded interventional study with a single group of subjects involved two study visits with a home trial in-between. In the final study session speech assessment in noise was conducted in three different, randomly assigned conditions: default mode (Autosense Sky OS), preffered (self-adjusted) and the extreme condition. Questionnaire based assessment was conducted to assess the subjective benefit of using the app in different daily situations. The best scores (speech results in noise) were achieved with the preferred setting and the default Autosense Sky OS setting was significantly better than the extreme setting. The self-reported benefit through the questionnaire indicates significantly better result when adjusting the hearing aids through the app. The app is an easy-to-use way of controlling the level of noise reduction and the beam forming for children 11 years and older. It has the potential to help customizing the hearing aids beyond the default setting and helping to improve speech understanding in noise.

Tunnel oxide passivating contact enabled by polysilicon on ultra-thin SiO2 for advanced silicon radiation detectors

Conventional silicon junction detectors encounter significant carrier recombination within the heavily doped p+ and n+ layers, as well as beneath the metal contact regions, creating the so-called “dead layers”, especially on the detector side. In this study, we present the tunnel oxide passivating contact with doped polysilicon on oxide, which demonstrates exceptional surface passivation and carrier selectivity. The key innovation lies in an ultra-thin (~ 1.5 nm) interfacial oxide layer that facilitates efficient majority carrier transportation via tunneling while effectively block minority carriers. Remarkably low saturation current densities, ranging from 5 to 10 fA/cm2 even with the metal contact, underscore the superiority of both n-type and p-type tunnel oxide passivating contacts. In contrast, conventional p–n junction or high-low junction exhibit saturation current densities ranging from 10 to 90 fA/cm2 in the studied p+ and n+ layers with surface passivation schemes due to Auger recombination and surface recombination, and 1000–6000 fA/cm2 with metal contacts due to intense metal-induced recombination at the interface. These findings indicate the potential and superiority of implementing n-type tunnel oxide passivating contact on the detector side and p-type contact on the back side for advanced silicon radiation detectors. This approach would enable thorough collection of generated charge carriers along the track of incident ionizing radiation particles, leading to improved energy resolution and reduced noise levels.