Adjustable Parameters Research Articles

Proton minibeam (pMBRT) radiation therapy: experimental validation of Monte Carlo dose calculation in the RayStation TPS.

Proton minibeam radiation therapy (pMBRT) dose profile is characterized by highly heterogeneous dose in the plane perpendicular to the beam direction and rapidly changing depth dose profiles. Typically, dose measurements are benchmarked against in-house Monte Carlo simulation tools. It is essential to have a treatment planning system (TPS) that can accurately predict pMBRT doses in tissue and be available via commercial platform for preclinical and clinical use. 
Methods: The pMBRT beam model was implemented in RayStation for the IBA Proteus®ONE single-room compact proton machine. The RayStation pMBRT beam model is an add-on to the clinically used beam model. The adjustable parameters include air gap, slit thickness, slit pitch, number of slits, slits direction and slit thickness.
The pMBRT TPS is validated experimentally against measurements. Six different collimators with various slit widths and center-to-center slit distance are used. The slit width varies from 0.4 mm to 1.4 mm, and the center to center (c-t-c) distance varies from 2.8 mm to 4.0 mm. The slits are non-divergent with a total of 5 slits. 
Results: When comparing the average depth dose measurements against the RayStation dose MC calculation, the agreement is better than a 95% gamma passing rate using 3mm/3% criteria except the 0.4 mm slit width. However, after we adjusted the slit width by 40 - 60 μm to account for machining uncertainty, the agreement again exceeds a 95% gamma passing rate using 3mm/3% criteria. When comparing the PDDs of the peaks and valleys between RayStation and film measurements, the agreement is above 90% using 2mm/5% criteria. When comparing later profiles at various depths, the agreement is above 90% for all curves using 0.2mm/5%. 
Conclusions: The pMBRT beam modeling has been successfully established for our Proteus®ONE-based pMBRT system using the RayStation TPS, with demonstrated accuracy through experimental validation.

"Synthetic" DSC Perfusion MRI with Adjustable Acquisition Parameters in Brain Tumors Using Dynamic Spin-and-Gradient-Echo Echoplanar Imaging.

Normalized relative cerebral blood volume (nrCBV) and percentage of signal recovery (PSR) computed from dynamic susceptibility contrast (DSC) perfusion imaging are useful biomarkers for differential diagnosis and treatment response assessment in brain tumors. However, their measurements are dependent on DSC acquisition factors, and CBV-optimized protocols technically differ from PSR-optimized protocols. This study aimed to generate "synthetic" DSC data with adjustable synthetic acquisition parameters using dual-echo gradient-echo (GE) DSC datasets extracted from dynamic spin-and-gradient-echo echoplanar imaging (dynamic SAGE-EPI). Synthetic DSC was aimed at: 1) simultaneously create nrCBV and PSR maps using optimal sequence parameters, 2) compare DSC datasets with heterogeneous external cohorts, and 3) assess the impact of acquisition factors on DSC metrics. Thirty-eight patients with contrast-enhancing brain tumors were prospectively imaged with dynamic SAGE-EPI during a non-preloaded single-dose contrast injection and included in this cross-sectional study. Multiple synthetic DSC curves with desired pulse sequence parameters were generated using the Bloch equations applied to the dual-echo GE data extracted from dynamic SAGE-EPI datasets, with or without optional preload simulation. Dynamic SAGE-EPI allowed for simultaneous generation of CBV-optimized and PSR-optimized DSC datasets with a single contrast injection, while PSR computation from guideline-compliant CBV-optimized protocols resulted in rank variations within the cohort (Spearman's ρ = 0.83-0.89, i.e. 31%-21% rank variation). Treatment-naïve glioblastoma exhibited lower parameter-matched PSR compared to the external cohorts of treatment-naïve primary CNS lymphomas (PCNSL) (p<0.0001), supporting a role of synthetic DSC for multicenter comparisons. Acquisition factors highly impacted PSR, and nrCBV without leakage correction also showed parameter-dependence, although less pronounced. However, this dependence was remarkably mitigated by post-hoc leakage correction. Dynamic SAGE-EPI allows for simultaneous generation of CBV-optimized and PSR-optimized DSC data with one acquisition and a single contrast injection, facilitating the use of a single perfusion protocol for all DSC applications. This approach may also be useful for comparisons of perfusion metrics across heterogeneous multicenter datasets, as it facilitates post-hoc harmonization.

Finite element-based nonlinear dynamic optimization of nanomechanical resonators

Nonlinear dynamic simulations of mechanical resonators have been facilitated by the advent of computational techniques that generate nonlinear reduced order models (ROMs) using the finite element (FE) method. However, designing devices with specific nonlinear characteristics remains inefficient since it requires manual adjustment of the design parameters and can result in suboptimal designs. Here, we integrate an FE-based nonlinear ROM technique with a derivative-free optimization algorithm to enable the design of nonlinear mechanical resonators. The resulting methodology is used to optimize the support design of high-stress nanomechanical Si3N4 string resonators, in the presence of conflicting objectives such as simultaneous enhancement of Q-factor and nonlinear Duffing constant. To that end, we generate Pareto frontiers that highlight the trade-offs between optimization objectives and validate the results both numerically and experimentally. To further demonstrate the capability of multi-objective optimization for practical design challenges, we simultaneously optimize the design of nanoresonators for three key figure-of-merits in resonant sensing: power consumption, sensitivity and response time. The presented methodology can facilitate and accelerate designing (nano) mechanical resonators with optimized performance for a wide variety of applications.

Hyperelastic constitutive modeling of healthy and enzymatically mediated degraded articular cartilage.

This research demonstrates a systematic curve fitting approach for acquiring parametric values of hyperelastic constitutive models for both healthy and enzymatically mediated degenerated cartilage to facilitate finite element modeling of cartilage. Several widely used phenomenological hyperelastic constitutive models were tested to adequately capture the changes in cartilage mechanics that vary with the differential/unequal abundance of matrix metalloproteinases (MMPs). Trauma and physiological conditions result in an increased production of collagenases (MMP-1) and gelatinases (MMP-9), which impacts the load-bearing ability of cartilage by significantly deteriorating its extracellular matrix (ECM). The material parameters in the constitutive equation of each hyperelastic model are significant for developing a comprehensive computational interpretation of MMP mediated degenerated cartilage. Stress-strain responses obtained from indentation test were fitted with selected Ogden, polynomial, reduced polynomial, and van der Waals hyperelastic constitutive models by optimizing their adjustable parameters (material constants). The goodness of fit of the 2nd order reduced polynomial and van der Waals model exhibited the closest data fitting with the experimental stress-strain distributions of healthy and degraded articular cartilage. The coefficient of the shear modulus for the 2nd order reduced polynomial decreased gradually by 21.9% to 80.1% with more enzymatic degradation of collagen fibril due to the relative abundance of MMP-1 (collagenases), and 28.5% to 69.2% for the van der Waals model. Our findings showed that the major materials coefficients of the models were reduced in the degenerated cartilages, and the reduction varied differentially with the relative abundance of MMPs-1 and 9, correlating the severity of degeneration. This work advances the understanding of cartilage mechanics and offers insights into the impact of biochemical (enzymatic) effects on cartilage degradation.

Endolift® is a non-surgical treatment for skin tissue conditions. Is there evidence for its application?

The Endolift® technique, introduced in 2005, gained popularity among medical and non-medical professionals as a non-surgical approach using subdermal laser devices. However, its widespread adoption lacked a thorough understanding of its physiological interaction, resulting in controversies over its effectiveness and safety. This study aimed to assess the evidence of Endolift® efficacy, parametrization, and safety by analyzing adverse events. A systematic literature review was conducted by searching the following databases: NCBI/PubMed, Medline, Embase, CINAHL, and the Cochrane Central Library. These searches resulted in 111 articles. Seven articles were selected after removing duplicates and screening titles, abstracts, and full texts. These articles exhibited a high risk of bias, a lack of standardization in treatment parameters, and reports of adverse events that did not align with clinical reality, often occurring with off-label use.In conclusion, due to insufficient high-quality research and inconsistent indications and parameter adjustments, asserting the efficacy and safety of Endolift® is challenging. Randomized studies are recommended to curb indiscriminate use, which may compromise patient safety. This analysis underscores the importance of evidence-based clinical practices for patient safety and ethical treatment.

Influence of Process Parameter and Build Rate Variations on Defect Formation in Laser Powder Bed Fusion SS316L.

Laser powder bed fusion (LPBF) is an additive manufacturing process that has gained interest for its material fabrication due to multiple advantages, such as the ability to print parts with small feature sizes, good mechanical properties, reduced material waste, etc. However, variations in the key process parameters in LPBF may result in the instantiation of porosity defects and variation in build rate. Particularly, volumetric energy density (VED) is a variable that encapsulates a number of those parameters and represents the amount of energy input from the laser source to the feedstock. VED has been traditionally used to inform the quality of the printed part but different values of VED are presented as optimal values for certain material systems. An optimal VED value can be maintained by changing the key process parameters so that various combinations yield a constant value. In this study, an optimal constant VED value is maintained while printing SS316L with variable key processing parameters. Porosity analysis is performed using optical microscopy, as well as X-ray computed tomography, to reveal the volume density and distribution of those pores. Two primary defect categories are identified, namely lack of fusion and porosity induced by balling defects. The findings indicate that, even at optimal VED, variations in process parameters can significantly influence defect type, underscoring the sensitivity of defect formation to the variation of these parameters. Furthermore, a minor change in the build rate, driven by adjustments in process parameters, was found to influence defect categories. These findings emphasize that fine tuning the process parameters and build rate is essential to minimize defects. Finally, fiducial marks have been identified as a source of unintentional porosity defects. These results enable the refinement of process parameters, ultimately optimizing LPBF to achieve enhanced material density and expedite the printing.

Dispersionless Nonhybrid Density Functional.

A dispersion-corrected density functional theory (DFT+D) method has been developed. It includes a nonhybrid dispersionless generalized gradient approximation (GGA) functional paired with a literature-parametrized dispersion function. The functional's 9 adjustable parameters were optimized using a training set of 589 benchmark interaction energies. The resulting method performs better than other GGA-based DFT+D methods, giving a mean unsigned error of 0.33 kcal/mol. It even performs better than some more expensive meta-GGA or hybrid dispersion-corrected functionals. An important advantage of using the new functional is that its dispersion energy given by the D component is very close to the true dispersion energy at all intermolecular separations, whereas in other similarly accurate DFT+D approaches, such a dispersion contribution in the van der Waals minimum region is only a small fraction of the true value.

A study on the influence of laser hardening on microstructure, and microhardness of additively manufactured H13

ABSTRACT Laser surface treatment was employed as a promising approach to enhance the fatigue life of additively manufactured samples by increasing the surface hardness of the material. In this study, H13 tool steel was manufactured using the selective laser melting method, and lines of laser treatment were applied to the surface. Experimental and statistical approaches were utilised to investigate the microstructural changes, microhardness variations, and weld geometry resulting from different laser treatment processes. The aim was to control the laser parameters and analyse the behaviour of the microstructure and hardness profile of the laser-treated zone. The results revealed an amelioration in the hardness of the laser-treated surface, except for the heat-affected zone, which exhibited a lower hardness compared to the substrate. Statistical approaches were employed to study the effect of laser parameters on the weld geometry including width and depth of the laser-treated area using ANOVA method, elucidating the impact of each factor on these values. Finally, a predictive method for estimating the width and depth was proposed, to facilitate the adjustment of laser parameters for achieving specific outcomes, such as desired hardness profiles or geometrical characteristics, in the laser surface treatment process.

Spatio-Temporal Joint Trajectory Planning for Autonomous Vehicles Based on Improved Constrained Iterative LQR.

With advancements in autonomous driving technology, the coupling of spatial paths and temporal speeds in complex scenarios becomes increasingly significant. Traditional sequential decoupling methods for trajectory planning are no longer sufficient, emphasizing the need for spatio-temporal joint trajectory planning. The Constrained Iterative LQR (CILQR), based on the Iterative LQR (ILQR) method, shows obvious potential but faces challenges in computational efficiency and scenario adaptability. This paper introduces three key improvements: a segmented barrier function truncation strategy with dynamic relaxation factors to enhance stability, an adaptive weight parameter adjustment method for acceleration and curvature planning, and the integration of the hybrid A* algorithm to optimize the initial reference trajectory and improve iterative efficiency. The improved CILQR method is validated through simulations and real-vehicle tests, demonstrating substantial improvements in human-like driving performance, traffic efficiency improvement, and real-time performance while maintaining comfortable driving. The experiment's results demonstrate a significant increase in human-like driving indicators by 16.35% and a 12.65% average increase in traffic efficiency, reducing computation time by 39.29%.

A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security

Adversarial attacks were commonly considered in computer vision (CV), but their effect on network security apps rests in the field of open investigation. As IoT, AI, and 5G endure to unite and understand the potential of Industry 4.0, security events and incidents on IoT systems have been enlarged. While IoT networks efficiently deliver intellectual services, the vast amount of data processed and collected in IoT networks also creates severe security concerns. Numerous research works were keen to project intelligent network intrusion detection systems (NIDS) to avert the exploitation of IoT data through smart applications. Deep learning (DL) models are applied to perceive and alleviate numerous security attacks against IoT networks. DL has a considerable reputation in NIDS, owing to its robust ability to identify delicate differences between malicious and normal network activities. While a diversity of models are aimed at influencing DL techniques for security protection, whether these methods are exposed to adversarial examples is unidentified. This study introduces a Two-Tier Optimization Strategy for Robust Adversarial Attack Mitigation in (TTOS-RAAM) model for IoT network security. The major aim of the TTOS-RAAM technique is to recognize the presence of adversarial attack behaviour in the IoT. Primarily, the TTOS-RAAM technique utilizes a min-max scaler to scale the input data into a uniform format. Besides, a hybrid of the coati–grey wolf optimization (CGWO) approach is utilized for optimum feature selection. Moreover, the TTOS-RAAM technique employs the conditional variational autoencoder (CVAE) technique to detect adversarial attacks. Finally, the parameter adjustment of the CVAE model is performed by utilizing an improved chaos African vulture optimization (ICAVO) model. A wide range of experimentation analyses is performed and the outcomes are observed under numerous aspects using the RT-IoT2022 dataset. The performance validation of the TTOS-RAAM technique portrayed a superior accuracy value of 99.91% over existing approaches.

Strategies for Real-time Adjustment of Stimulation Patterns in Closed-loop Deep Brain Stimulation for Parkinsons Disease: A Comprehensive Review

Parkinsons disease (PD) is a prevalent neurodegenerative disorder that poses significant healthcare challenges worldwide. Deep brain stimulation (DBS) has proven effective for managing PD, particularly for patients with inadequate medication response. However, traditional high-frequency DBS systems lack the capability to adjust stimulation patterns in real time based on patient responses. In contrast, closed-loop DBS systems, which adjust stimulation based on recorded biomarkers, present a promising alternative that allows more flexible, on-demand electrical pulses delivery, offering possibility of achieving better therapeutic outcomes with less invasive stimulus. This review article explored various strategies for real-time adjustment of stimulation parameters in closed-loop DBS primarily based on electrophysiological biomarkers, particularly local field potentials (LFPs). We categorize these strategies into four categories: simple on-demand methods, control methods in systems theory, optimization or machine learning algorithms, and desynchronization approaches. By synthesizing existing literature, we provide a comprehensive overview of these strategies, highlighting contributions from multiple disciplines and the prospect of optimizing treatment for Parkinsons disease using closed-loop approach.

A Robust Linear Active Disturbance Rejection Control for Renewable Energy Grid-Connected System Based on APF

With the energy transition and changes in electrical load structures, harmonic distortion in power systems is increasingly threatening grid stability, especially during the integration of renewable energy sources. Active power filters (APFs) are commonly used to improve power quality, but current fluctuations and voltage instability caused by DC-side capacitor charging and discharging hinder effective harmonic compensation. To address this, this paper presents a method combining a variable-step-size adaptive linear predictor with linear active disturbance rejection control (VALP-LADRC). By integrating an adaptive linear predictor (ALP) with LADRC, the proposed method effectively reduces the impact of disturbances on control, improving voltage regulation and harmonic compensation, and enhancing renewable energy grid integration. To address delays during the adjustment of initial parameters in the adaptive predictor, we combine it with a variable-step-size algorithm, significantly improving disturbance rejection and tracking accuracy, while solving voltage drop issues. The simulation results in a photovoltaic grid-connected system show that VALP-LADRC effectively mitigates disturbances, ensures voltage stability, and enhances power quality. This approach offers a promising solution for supporting sustainable development through better renewable energy integration and improved power quality.

A practical kinetic model for polyvinyl chloride polymerization in batch reactors

Vinyl chloride monomer plays a significant contribution to the chemical industry, serving as a fundamental component for the synthesis of polyvinyl chloride (PVC). This material is distinguished as one of the most extensively manufactured polymers worldwide, finding wide application due to its physical properties, providing good durability, affordable cost, and ease of handling. Although certain mathematical models can be found in literature, implementing the multiphase model with kinetic and diffusive effects demands a considerable set of adjustable parameters, making it challenging to reproduce in industrial sites. In contrast, this study describes the dynamic process of vinyl chloride (VC) polymerization by suspension in a batch reactor to produce PVC. The proposed approach considers a simplified yet robust model that requires fewer adjustable parameters, enabling easier implementation and corroborating with experimental data from the literature. By solving systems of algebraic and differential equations, the proposed model accurately reproduces experimental data for the conversion of VC to PVC, specifically for the initiators PW-40 and AIBN. Additionally, the model delivers superior results compared to those obtained using Aspen® Plus PVC module, a commercial platform widely employed in the industry for polymerization process modeling. These improvements are achieved without introducing excessive complexity, making the model a practical and efficient tool for industrial applications, such as the modeling and optimization of PVC reactors.

Impact of mercury vacancy states on Shockley–Read–Hall recombination in narrow gap HgCdTe

Abstract Technologically relevant narrow gap Hg1-xCdxTe is known to contain a considerable amount of mercury vacancies that supposedly degrade carrier lifetimes. Using semiclassical approach with no adjustable parameters, we calculate capture coefficients for acceptor states with different binding energy. Resulting Shockley–Read–Hall recombination times agree well with the experimental data and suggest that operating temperature of Hg1-xCdxTe detectors with ~ 20 µm cutoff can be elevated above 77 K.

Super Typhoons Simulation: A Comparison of WRF and Empirical Parameterized Models for High Wind Speeds

As extreme forms of tropical cyclones (TCs), typhoons pose significant threats to both human society and the natural environment. To better understand and predict their behavior, scientists have relied on numerical simulations. Current typhoon modeling primarily falls into two categories: (1) complex simulations based on fluid dynamics and thermodynamics, and (2) empirical parameterized models. Most comparative studies on these models have focused on wind speed below 50 m/s, with fewer studies addressing high wind speed (above 50 m/s). In this study, we design and compare four different simulation approaches to model two super typhoons: Typhoon Surigae (2102) and Typhoon Nepartak (1601). These approaches include: (1) The Weather Research and Forecasting (WRF) model simulation driven by NCEP Final Operational Global Analysis data (FNL), (2) WRF simulation driven by the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), (3) the empirical parameterized Holland model, and (4) the empirical parameterized Jelesnianski model. The simulated wind fields were compared with the measured wind data from The Soil Moisture Active Passive (SMAP) platform, and the resulting wind fields were then used as inputs for the Simulating WAves Nearshore (SWAN) model to simulate typhoon-induced waves. Our findings are as follows: (1) for high wind speeds, the performance of the empirical models surpasses that of the WRF simulations; (2) using more accurate driving wind data improves the WRF model’s performance in simulating typhoon wind speeds, and WRF simulations excel in representing wind fields in the outer regions of the typhoon; (3) careful adjustment of the maximum wind speed radius parameter is essential for improving the accuracy of the empirical models.

Extracting speech spectrogram of speech signal based on generalized S-transform.

In speech signal processing, time-frequency analysis is commonly employed to extract the spectrogram of speech signals. While many algorithms exist to achieve this with high-quality results, they often lack the flexibility to adjust the resolution of the extracted spectrograms. However, applications such as speech recognition and speech separation frequently require spectrograms of varying resolutions. The flexibility of an algorithm in providing different resolutions is crucial for these applications. This paper introduces the generalized S-transform, and explains its fundamental theory and algorithmic implementation. By adjusting parameters, the proposed method flexibly produces spectrograms with different resolutions, offering a novel and effective approach to obtain speech signal spectrograms. The algorithm enhances the traditional Stockwell transform (S-transform) by incorporating a low-pass filtering function and introducing two adjustable parameters. These parameters modify the Gaussian window function of the basic S-transform, resulting with the generalized S-transform with customizable time-frequency resolution. Finally, this paper presents simulation experiments using both synthesized signals and real speech datas, comparing with the generalized S-transform with several commonly used spectrogram extraction algorithms. The experiments demonstrate that the generalized S-transform is feasible and effective, particularly when it is combined with the generalized fundamental frequency profile. The results indicate that this method is a viable and effective in obtaining spectrograms of speech signals, and has potential application in speech feature extraction and speech recognition. The pure speech dataset used in the experiments is sourced from a downloadable database and partially from a recorded speech set.

Impact of mercury vacancy states on Shockley–Read–Hall recombination in narrow gap HgCdTe

Abstract Technologically relevant narrow gap Hg1-xCdxTe is known to contain a considerable amount of mercury vacancies that supposedly degrade carrier lifetimes. Using semiclassical approach with no adjustable parameters, we calculate capture coefficients for acceptor states with different binding energy. Resulting Shockley–Read–Hall recombination times agree well with the experimental data and suggest that operating temperature of Hg1-xCdxTe detectors with ~ 20 µm cutoff can be elevated above 77 K.

Influence of Photoemission Geometry on Timing and Efficiency in 4D Ultrafast Electron Microscopy.

Broader adoption of 4D ultrafast electron microscopy (UEM) for the study of chemical, materials, and quantum systems is being driven by development of new instruments as well as continuous improvement and characterization of existing technologies. Perhaps owing to the still-high barrier to entry, the full range of capabilities of laser-driven 4D UEM instruments has yet to be established, particularly when operated at extremely low beam currents (~fA). Accordingly, with an eye on beam stability, we have conducted particle tracing simulations of unconventional off-axis photoemission geometries in a UEM equipped with a thermionic-emission gun. Specifically, we have explored the impact of experimentally adjustable parameters on the time-of-flight (TOF), the collection efficiency (CE), and the temporal width of ultrashort photoelectron packets. The adjustable parameters include the Wehnelt aperture diameter (DW), the cathode set-back position (Ztip), and the position of the femtosecond laser on the Wehnelt aperture surface relative to the optic axis (Rphoto). Notable findings include significant sensitivity of TOF to DW and Ztip, as well as non-intuitive responses of CE and temporal width to varying Rphoto. As a means to improve accessibility, practical implications and recommendations are emphasized wherever possible.

A laser tracking attitude dynamic measurement method based on real-time calibration and AEKF with a multi-factor influence analysis model

Abstract To address the limitations of six-degree-of-freedom (6-DOF) visual tracking systems in terms of low dynamic performance and the inability to perform measurements under light occlusion, this paper proposes a novel laser tracking attitude dynamic measurement method integrating vision and inertial measurement unit (IMU). A tailored real-time calibration model is developed to enable precise alignment of the vision and IMU coordinate systems during dynamic operations. To overcome the limitations of extended Kalman filter (EKF) that rely on static noise assumptions in complex dynamic measurement scenarios, this study proposes an adaptive mechanism based on multi-factor influence analysis. Through dynamic monitoring of visual observation noise, an adaptive EKF algorithm is designed to enhance performance under varying conditions. When integrated into a vision/IMU fusion system, the algorithm significantly enhances the system’s sensitivity to variations in visual sensor observation noise caused by changes in lighting and motion states. This capability allows rapid adjustment of filtering parameters, leading to substantial improvements in filtering accuracy and stability. Simulation results demonstrate that the proposed algorithm outperforms traditional EKF in fusion measurement accuracy when tested in simulated noise environments featuring sinusoidal and random jump signals. Furthermore, the fusion measurement accuracy is mainly influenced by the data density of the visual sensor and the measurement distance. Finally, the proposed method was validated on a precision turntable. Experimental results reveal that at a 3 m measurement distance and within a 0°–25° angular range, when the turntable rotates at a constant speed of 5°/s along a single axis, the maximum absolute deviation in repeatability of the fused attitude measurements was below 0.11°, and the system is capable of achieving a high measurement frequency of 100 Hz.

Compact Tri-Band Bandpass Filter with Wide Upper Stopband Based on Spoof Surface Plasmon Polaritons and Open-/Short-Circuited Stubs

In this paper, a new U-shaped ring spoof surface plasmon polariton (SSPP) structure is proposed as part of a bandpass filter (BPF) combined with short-circuited stubs (SCSs). The U-shaped ring unit offers miniaturization and multiple adjustable parameters. Furthermore, the lower and upper cutoff frequencies of the passband for the BPF can be adjusted by modifying the structural parameters of the SSPP units and SCSs, respectively. To validate the design, a prototype filter was first created with a frequency range of 2 to 3.7 GHz for the passband and an extended stopband that reached up to 15 GHz. On the basis of the designed BPF, a tri-band filter was realized by introducing multiple transmission zeros by loading multiple open-circuited stubs (OCSs) onto the transmission portion of SSPPs. The center frequencies of the three passbands were 1.20 GHz, 2.03 GHz, and 2.96 GHz, respectively. At the same time, the upper stopband rejection reached up to 12 GHz with an attenuation of −30 dB, about 10 times the center frequency of the first passband. The experimental results demonstrate a strong correlation between the measured and simulated outcomes, thereby validating the proposed structure and design methodology. Notably, the filter measures only 0.38λg × 0.13λg, highlighting its compact size as a significant advantage.