Analysis Of Parameters Research Articles

Shear behavior of RC beams with openings under impact loads: unveiling the effects of HSC and RECC

Incorporating transverse openings in reinforced concrete (RC) beams reduces their load-bearing capacity and stiffness, making them prone to premature failure. This highlights the need for a more nuanced understanding of their behavior to ensure structural integrity, particularly under impact loads. This research delves into a relatively unexplored area, investigating the impact performance of RC beams containing openings through numerical analysis. By comparing different concrete types, the study seeks to identify optimal materials for such applications. The concrete damage plasticity model, accounting for strain rate effects, will be employed to simulate the material behavior of normal-strength concrete (NSC), high-strength concrete (HSC), and a novel eco-friendly alternative: rubberized engineered cementitious composite (RECC). RECC incorporates recycled tire rubber as a partial substitute for traditional concrete aggregates, offering a sustainable solution while mitigating environmental hazards associated with waste tire incineration. The finite element models are validated with experimental results, accurately predicting ultimate capacities, failure modes, and post-cracking response in RC beams (with/without openings) under static/impact loads. A comprehensive parametric analysis investigates the effects of concrete strength, impact energy, impactor mass, drop height, and opening location, providing valuable insights into how these factors influence the impact behavior under drop-weight testing. The results reveal that openings in RC beams under impact loads significantly reduce strength and stiffness, with detrimental effects observed for dual shear-zone openings. Surprisingly, a small mid-span opening can enhance impact response. HSC beams exhibit lower initial displacements but higher residual values, while RECC improves overall behavior in beams with openings, reducing maximum displacement and promoting energy dissipation for improved post-impact recovery.

Unravelling wheat genotypic responses: insights into salinity stress tolerance in relation to oxidative stress, antioxidant mechanisms, osmolyte accumulation and grain quality parameters

BackgroundSalt stress is a prominent abiotic stressor that imposes constraints on grain yield and quality across various crops, including wheat (Triticum aestivum). This study focused on assessing the genetic diversity of 20 wheat genotypes categorized as tolerant, moderately tolerant, and sensitive with three genotypes of unknown tolerance. To address salinity stress-related problems, different morpho-physiological, osmoprotectant, biochemical, yield, and grain quality-related parameters were analyzed under control (pH 8.0, EC 3.9) and saline-sodic (pH 9.4, EC 4.02) conditions in field.ResultsFindings revealed noteworthy variations among the genotypes in response to salinity stress. Greater accumulation of Na+ and lower K+ content were observed in response to salt stress in the sensitive varieties HD1941 and K9162. Proline, a stress indicator, exhibited significantly (p ≤ 0.05) greater accumulation in response to salinity stress, particularly in the tolerant cultivars KRL210 and KH65. Salt stress induced the most significant decrease (p ≤ 0.05) in spike length, thousand-grain weight, and hectolitre weight coupled with increased protein content in sensitive varieties, resulting in diminished yield.ConclusionCorrelation analysis of parameters under salinity stress showed that SOD, proline, and K+ contents can be used as the most efficient screening criteria for salinity stress during early developmental stages. Principal component analysis revealed that DBW187, DBW303, and DBW222 varieties were tolerant to salinity stress and exhibited an effective antioxidant system against salinity. This study will facilitate salt-tolerant wheat breeding in terms of the identification of tolerant lines by screening for limited traits in a wide range of germplasms.

Long-distance transmission conductor condition sensing based on distributed fiber optic sensing technology

Abstract The existing long-distance transmission line perception mainly focuses on the measurement and analysis of electrical parameters. When the line is subject to wind vibration, icing or galloping, the changes of electrical parameters are not obvious and difficult to capture, resulting in poor performance of long-distance transmission line fault state perception. In this regard, the long-distance transmission line condition sensing based on distributed optical fiber sensing technology is studied. This method designs corresponding sensing methods for four working conditions: using the back Brillouin scattering sensor and phase sensitive Rayleigh scattering sensor in the optical fiber sensing technology to form a multi parameter distributed optical fiber sensing device, which is used to sense the surface temperature, vibration, strain and other data of the long-distance transmission line; By analyzing the linear relationship between fiber Brillouin frequency shift and Brillouin power and fiber strain and temperature, the sensing results of icing thickness of long-distance transmission lines are obtained; By calculating the amplitude and phase information of the detection signal, the sensing results of long-distance transmission line vibration are obtained; By calculating the time difference of polarization mutation signal at both ends of long-distance transmission line, combined with the propagation speed of optical signal and the length of long-distance transmission line, the perception result of lightning fault location of long-distance transmission line can be obtained. The experimental results show that this method can more accurately collect the real-time operation data of long-distance transmission lines, and can effectively perceive the galloping, wind vibration and lightning fault location of long-distance transmission lines.

Comparative investigation, analysis, and optimisation of weld reinforcement during powder-fed gas metal arc welding of AISI 1023

ABSTRACT The main aim of the present study is to perform a comparative investigation, experimental analysis, and optimisation of weld reinforcement in the specific domain of powder-fed gas metal arc welding in relation to AISI 1023 steel. The study conducted a comprehensive analysis of several parameters, encompassing statistical methodologies (i.e. Genetic Algorithm and artificial neural networks) and response surface methodology (RSM). The study shows that the advance gas metal arc welding provides maximum of 95% more reinforcement and current and powder feed rate has emerged as the most influential parameter for bead reinforcement. Based on the results obtained, it can be concluded that the Levenberg-Marquardt (5-14-1) model demonstrates the highest level of suitability as an ANN model for the purpose of forecasting output data. As per R value positive correlation of 0.9674 between the empirical data and the forecasts produced by the LM (5-14-1) model. Genetic algorithm suggested that the optimal point is situated within the predetermined range of process variables. According to validation experimentation the ANN model exhibited superior performance compared to the RSM model and then come GA. ANN has shown the maximum error of 0.06 when compared to experimental results.

Exploring patterns of surface urban heat island intensity: a comparative analysis of three Greek urban areas

One of the most pressing issues of our time is the destabilization of the global climate as a result of rapid urbanization. In Europe, according to United Nations reports, almost 73% of the population lives in cities and this percentage is expected to rise to 85% by 2050 (United Nations and Department of Economic and Social Affairs, ‘World Urbanization Prospects 2018 Highlights’, 2018). This observed urban development and its trends pose significant challenges to our urban environments, therefore it is critical to improve our understanding and assess its impact by measuring and analyzing its patterns. This study presents a comprehensive five-year analysis of microclimatic parameters that can contribute to this analysis, such as land surface temperature (LST) and surface urban heat island (SUHI) intensity, using remotely sensed data over three cities in Greece. Specifically, the selected cities are the city of Thessaloniki, which is the second largest city situated in the northern part of Greece, the city of Larissa on the mainland, and finally the city of Tripoli in the Peloponnese peninsula in southern Greece. Each city represents urban areas with different physical and urban characteristics. For this purpose, the vegetation index-based technique for LST extraction was applied within the Google Earth Engine (GEE) platform. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) calculations were also performed over a five-year period. The results highlighted the spatio-temporal variations and trends that occur in the studied urban areas, providing valuable insights into the annual patterns of the SUHI phenomenon in different geographical contexts within Greece. This is particularly relevant in countries like Greece, where the density of World Meteorological Organization (WMO) stations providing reliable temperature data is low, usually limited to one per city. Consequently, LST derived from satellite data can provide more accurate intra-urban results of the SUHI effect, demonstrating the value of remotely sensed data as a cost effective and supportive tool for decision-making in environmental and urban planning policies.

Statistical Analysis of Characteristic Parameters and Probability Distribution of Near-Fault Velocity Pulses—A Case Study on the 1999 Mw 7.6 Chi-Chi Earthquake

Statistical Analysis of Characteristic Parameters and Probability Distribution of Near-Fault Velocity Pulses—A Case Study on the 1999 Mw 7.6 Chi-Chi Earthquake

Analysis and optimization of time-varying meshing parameters of spiral bevel gear with consideration of load sensitivity

Analysis and optimization of time-varying meshing parameters of spiral bevel gear with consideration of load sensitivity

Parametric Analysis of Moment-Resisting Timber Frames Combined with Cross Laminated Timber Walls and Prediction Models Using Nonlinear Regression and Artificial Neural Networks

The light weight and moderate stiffness of multistorey timber buildings make them susceptible to increased lateral displacements and accelerations under service-level wind loading. Therefore, the fulfilment of serviceability requirements is a major challenge. In this study, linear elastic finite element analysis was used to perform a parametric study of moment-resisting timber frames combined with cross laminated timber walls. In the parametric study, various mechanical and geometrical parameters were varied within practical ranges. The results of the parametric study were used to derive simplified analytical expressions and to train artificial neural networks which can be used to estimate fundamental frequency, mode shape, top floor displacement, maximum inter-storey drift, and wind-induced acceleration. The analytical expressions and the artificial neural networks can be used for the preliminary assessment of serviceability performance of moment-resisting timber frames with and without cross laminated timber walls, under service-level wind loading.

Parametric Analysis of the Compact and Long-Term-Operable Passive Residual Heat Removal System

Differing from land-based systems, conventional passive residual heat removal systems (PRHRSs) in nuclear-powered ships encounter challenges for use as safety systems due to limited space and difficulties in adopting safety aids. To resolve this problem, the authors have previously suggested a compact and long-term-operable PRHRS that uses air cooling and seawater cooling in tandem, and its feasibility was confirmed through a design study and transient analysis. Nevertheless, the system performance depends on various parameters, and their sensitivities have yet to be analyzed. The purpose of the current study is to conduct a parametric analysis of the system focusing on four parameters: filling ratio, thermal center difference between heat source and sink, heeling angle, and heat source temperature. The Multi-dimensional Analysis of Reactor Safety (MARS-KS) code was used for the parametric analysis. The results show that the parameters are highly interrelated with each other, and consequently, the system exhibits significantly different behavior depending on the parameter conditions. In particular, the filling ratio determines the overall trends of the system. It was also found that the presence of two heat sinks with a single heat source in the natural circulation loop of the system complicate the natural circulation behavior. Based on the results, we conclude that a thorough examination and careful control of the parameters are necessary to implement the proposed system. The highlights are as follows: 1. Parametric analysis of compact and long-term-operable PRHRS is conducted. 2. Analysis reveals that the filling ratio is the most dominant parameter for system performance. 3. The unique behavior of the natural circulation loop with two heat sinks and a single heat source is analyzed.

Optimizing Optoelectronics Performance: Theoretical and Experimental study on ZnO Thin Film for Al/ZnO/p-Si Photodiode

Abstract In this study investigates the optimization of ZnO thin film photoelectric devices, specifically focusing on ZnO/p-Si heterojunction photosensitive devices and photodiodes (PDs) using Al contacts. Through an experimental and theoretical analysis approach aims to evaluate the effects of important parameters, including ZnO layer thickness, defect density, and contact materials, on PD’s efficiency. Numerical analysis simulations comparatively examine the experimentally fabricated device performance at a 5 nm ZnO layer thickness by balancing photon absorption and carrier formation while minimizing carrier transport limitations. Experimentally process, an Atomic Layer Deposition (ALD) system was used to grow ZnO interlayers on one side of the polished Si wafer. Then, Al metallic contacts were created on the ZnO layers using a hole array mask. The PDs were then subjected to electrical characterization using I-V and I-t measurements under various illumination densities. Al/ZnO/p-Si PD's device with active performance has been produced and analyzed with electrical parameters such as barrier height, photocurrent, spectral response, ideality factor and EQE were derived, analyzed and studied. In conclusion, this work provides a comprehensive understanding of the performance of Al/ZnO/p-Si PD at varying illumination intensities and offering a detailed analysis of key parameters influencing device efficiency for future optoelectronics applications.

Dynamic analysis of human papillomavirus transmission model under vaccine intervention: a case study of cervical cancer patients from Hungary

Nearly 80% of women are estimated to have at least one HPV infection from high-risk types. Although the majority can clear the infection through their immune system, some are at risk for cervical cancer. Persistent high-risk HPV infections are the leading cause of cervical cancer worldwide. This paper proposes a mathematical model that examines the effects of HPV transmission on cervical cancer patients under vaccine intervention. The model’s fundamental properties are investigated, including the stability of equilibrium points and the existence of forward bifurcations. Subsequently, based on cervical cancer patient data collected from Hungary between 2000 and 2020, the model’s optimal parameter values are identified using a nonlinear least squares method. Further, we perform a sensitivity analysis of the key cervical cancer progression parameters. Our results indicate that both direct HPV vaccination in susceptible populations and additional vaccination in individuals who have recovered can improve immune responses and reduce the risk of cervical cancer. In addition, the study of the effects of intervention measures on cervical cancer patients in Hungary from 2000 to 2030 reveals that reducing the contact rate is conducive in the short term to curbing the development of cervical cancer; however, in the long term, relying solely on this measure is not sufficient to lead to a significant decrease in the number of cervical cancer cases.

Effect of electrolyte level on performance and mass transfer of non-aqueous lithium-oxygen battery

To accurately reveal the effect of electrolyte content on mass transfer within lithium-oxygen batteries and their discharge performance, this study utilizes computed tomography(CT) scanning technology and digital reconstruction methods to construct a geometric model of the lithium-oxygen battery cathode that is equivalent to the actual one. Based on the fundamental principles of mass transfer, fluid dynamics, and electrochemistry, a two-dimensional model for the discharge process of lithium-oxygen batteries is established. This model facilitates analysis of various parameters and their influencing factors, including the distribution of reaction products, pore structure, oxygen concentration distribution, battery performance, and overpotential, within the lithium-oxygen battery cathode. The research findings indicate that the deposition of reaction products alters the cathode structure and material transport; lower discharge current density and higher electrolyte levels are conducive to the dispersed deposition of reaction products, thereby reducing the deposition's resistance to internal mass transfer in the cathode.

EXPRESS: Disturbance in Multitier Supply Chain under Competition

The COVID-19 outbreak has raised concerns over the global pharmaceutical supply chain where raw ingredients are produced in a few geographically concentrated regions. Disturbance in upstream production along with the surging demand for medical treatments have increased the risk of medicine shortfalls, and this situation has prompted the need for a more diversified supply chain with geographically dispersed competing suppliers. In this research, we investigate how disturbance spans a supply chain network where supply quantities are endogenously determined by competition behaviors. We evaluate the expected shortfall between post-disturbance supply and intrinsic demand, with an adapted algorithm which pushes pseudoflows in the network and partitions nodes into strong and weak branches in a spanning tree, to solve the equivalent maximum flow problem. Parametric analysis based on simulations reveals that the severity of disturbance loss can be significantly offset with supplier competitions, although the marginal benefit of competition decreases fast in the number of suppliers. In addition, configuration inefficiencies can magnify upstream shortfalls and supply chain efficiency can be improved by pushing high-cost firms to stop production. Our findings indicate that a supply chain with a moderate level of competition and balanced configuration can be robust against disturbance and demand volatility.

Simulating the impact of tumor mechanical forces on glymphatic networks in the brain parenchyma

AbstractThe brain glymphatic system is currently being explored in the context of many neurological disorders and diseases, including traumatic brain injury, Alzheimer’s disease, and ischemic stroke. However, little is known about the impact of brain tumors on glymphatic function. Mechanical forces generated during tumor development and growth may be responsible for compromised glymphatic transport pathways, reducing waste clearance and cerebrospinal fluid (CSF) transport in the brain parenchyma. One such force is solid stress, i.e., growth-induced forces from cell hyperproliferation and excess matrix deposition. Because there are no prior studies assessing the impact of tumor-derived solid stress on glymphatic system structure and performance in the brain parenchyma, this study serves to fill an important gap in the field. We adapted a previously developed Electrical Analog Model using MATLAB Simulink for glymphatic transport coupled with Finite Element Analysis for tumor mechanical stresses and strains in COMSOL. This allowed simulation of the impact of tumor mechanical force generation on fluid transport within brain parenchymal glymphatic units—which include perivascular spaces, astrocytic networks, interstitial spaces, and capillary basement membranes. We conducted a parametric analysis to compare the contributions of tumor size, tumor proximity, and ratio of glymphatic subunits to the stress and strain experienced by the glymphatic unit and corresponding reduction in flow rate of CSF. Mechanical stresses intensify with proximity to the tumor and increasing tumor size, highlighting the vulnerability of nearby glymphatic units to tumor-derived forces. Our stress and strain profiles reveal compressive deformation of these surrounding glymphatics and demonstrate that varying the relative contributions of astrocytes vs. interstitial spaces impact the resulting glymphatic structure significantly under tumor mechanical forces. Increased tumor size and proximity caused increased stress and strain across all glymphatic subunits, as does decreased astrocyte composition. Indeed, our model reveals an inverse correlation between extent of astrocyte contribution to the composition of the glymphatic unit and the resulting mechanical stress. This increased mechanical strain across the glymphatic unit decreases the venous efflux rate of CSF, dependent on the degree of strain and the specific glymphatic subunit of interest. For example, a 20% mechanical strain on capillary basement membranes does not significantly decrease venous efflux (2% decrease in flow rates), while the same magnitude of strain on astrocyte networks and interstitial spaces decreases efflux flow rates by 7% and 22%, respectively. Our simulations reveal that solid stress from growing brain tumors directly reduces glymphatic fluid transport, independently from biochemical effects from cancer cells. Understanding these pathophysiological implications is crucial for developing targeted interventions aimed at restoring effective waste clearance mechanisms in the brain. This study opens potential avenues for future experimental research in brain tumor-related glymphatic dysfunction.

Performance of channel shear connectors in steel frame with reinforced concrete infill walls

To examine the mechanical properties of channel shear connectors employed in steel frame with reinforced concrete infill wall (SRCW) structures, the improved push-out tests were performed on three specimens of channel shear connectors subjected to the monotonic and cyclic loading. This study investigated the influences of loading protocol and concrete strength on the mechanical behavior of channel shear connectors. After the refined finite element (EF) models of channel shear connectors were validated based on the push-out tests, a systematical parametric analysis was conducted to evaluate the effects of RC infill wall thickness, concrete strength, and channel dimensions on its shear strength of the channel shear connectors. In addition, this study also compared the accuracy of the CSA S16, AISC 360 and GB50017 design code in predicting the shear capacity of channel shear connectors in SRCW structures. The experimental results and parametric studies indicated that the current design equations inaccurately evaluate the shear capacity of channel shear connectors used in SRCW structures. Consequently, an alternative equation was developed to estimate the shear-resistant capacity of channel shear connectors in SRCW structures, which provided the desirable calculation precision for engineering design.

QUALITY AND SAFETY PARAMETERS BEFORE AND AFTER PASTEURIZATION OF APPLE JUICE

The research was carried out in September (2023) on the premises of the “Kosovo Agricultural Institute” in the city of Peja. The purpose of this research was to show the influence of pasteurization on the physical, chemical, and organoleptic characteristics of apple juice. During the study, several analyses of the physical, chemical, and sensory parameters of apple juice were carried out. The parameters that have been realized include: °Brix value, Total acidity, Vitamin C, pH and SO2, sensory parameters: aroma, color, taste, and clarity-turbidity. The purpose of this research is the importance of knowing the influence of some parameters on apple juice before and after pasteurization. Pasteurization of apple juice is of great importance due to various factors related to food safety, storage, and consumer health. In the analysis of sensory parameters, the juice before pasteurization had a milder and lighter aroma, had a lighter yellow color, tasted less sweet, less fresh, and had low acidity. Clarity was present but contained some turbidity. In the analysis of sensory parameters, the evaluation of physicochemical parameters and sensory parameters should contribute to valuable knowledge on the physicochemical transformations caused by pasteurization in apple juice. Understanding these changes is very important for maintaining product quality and ensuring customer satisfaction. The study provides a basis for optimizing pasteurization processes to minimize unwanted changes in juice characteristics.

COMPARISON OF SOME QUALITY PARAMETERS IN SOUR CHERRY JUICE IN DIFFERENT PACKAGING

Fruits and their products are of great importance in the food industry. Fruit juices are found in almost every market, which helps consumers to get nutritional value from their consumption. The quality and insurance must be at the right level, but even more important to apply permanent control. Sampling was carried out randomly directly in the supermarket, and the analysis of parameters in the study of sensory quality (color, taste, aroma, and homogeneity) and physical-chemical (pH, °Brix, total acidity, and turbidity) in sour cherry juice is of importance because the same juice in different packaging is compared. After the analyses that have been carried out and the results obtained, they provide additional insight into the analyzed parameters. So, the consumer receives additional information, also confidence in the quality of the product will increase based on the results obtained. In conclusion, even though we are talking about the same sour cherry juice, as a result of the % content of the raw material with different amounts and the different packaging, the analyses for the parameters under study prove the differences, but at the limit, so the analyzed juices are within the standard for quality.

Complex nonlinear dynamics of a multidirectional energy harvester with hybrid transduction

Abstract Mechanical energy harvesting has increasing scientific and technological interests due to novel energetic challenges. A critical issue in classical cantilever-based mechanical energy harvesting systems is the lack of multidirectional energy conversion capabilities and, due to that, deviations from the excitation source can drastically reduce their performance. This limitation has led to the development of energy harvesters with attached pendula, serving as a direction coupling mechanism. Nevertheless, the pendulum structure itself can act as an energy absorber, drastically reducing the harvester performance in certain scenarios. In order to overcome this issue, a hybrid transduction multidirectional pendulum-based energy harvester is proposed integrating a piezoelectric element, to capture energy from the principal direction, and an electromagnetic transducer, to harness rotational energy from the pendulum. This paper presents an in-depth analysis of the hybrid multidirectional pendulum-based energy harvester using a nonlinear dynamics perspective to evaluate the energy harvesting performance. A reduced-order model is proposed to represent the essential characteristics of such systems. A parametric analysis using a nonlinear dynamics perspective is carried out to map the system dynamics and performance. The emergence of complex and rich dynamics is observed, including chaos and hyperchaos. Results reveal the most and least effective combinations of structural parameters in terms of energy conversion. Additionally, the dynamical responses and patterns associated with high performance are identified. These responses are often characterized by a blend of irregular complex behaviors, coupled with a mix of oscillatory and rotational patterns of motion, resulting in wider bandwidth systems.

RetVes segmentation: A pseudo-labeling and feature knowledge distillation optimization technique for retinal vessel channel enhancement

Recent advancements in retinal vessel segmentation, which employ transformer-based and domain-adaptive approaches, show promise in addressing the complexity of ocular diseases such as diabetic retinopathy. However, current algorithms face challenges in effectively accommodating domain-specific variations and limitations of training datasets, which fail to represent real-world conditions comprehensively. Manual inspection by specialists remains time-consuming despite technological progress in medical imaging, underscoring the pressing need for automated and robust segmentation techniques. Additionally, these methods have deficiencies in handling unlabeled target sets, requiring extra preprocessing steps and manual intervention, which hinders their scalability and practical application in clinical settings. This research introduces a novel framework that employs semi-supervised domain adaptation and contrastive pre-training to address these limitations. The proposed model effectively learns from target data by implementing a novel pseudo-labeling approach and feature-based knowledge distillation within a temporal convolutional network (TCN) and extracts robust, domain-independent features. This approach enhances cross-domain adaptation, significantly enhancing the model's versatility and performance in clinical settings. The semi-supervised domain adaptation component overcomes the challenges posed by domain shifts, while pseudo-labeling utilizes the data's inherent structure for enhanced learning, which is particularly beneficial when labeled data is scarce. Evaluated on the DRIVE and CHASE_DB1 datasets, which contain clinical fundus images, the proposed model achieves outstanding performance, with accuracy, sensitivity, specificity, and AUC values of 0.9792, 0.8640, 0.9901, and 0.9868 on DRIVE, and 0.9830, 0.9058, 0.9888, and 0.9950 on CHASE_DB1, respectively, outperforming current state-of-the-art vessel segmentation methods. The partitioning of datasets into training and testing sets ensures thorough validation, while extensive ablation studies with thorough sensitivity analysis of the model's parameters and different percentages of labeled data further validate its robustness.

Hypernetworks for Personalizing ASR to Atypical Speech

Abstract Parameter-efficient fine-tuning (PEFT) for personalizing automatic speech recognition (ASR) has recently shown promise for adapting general population models to atypical speech. However, these approaches assume a priori knowledge of the atypical speech disorder being adapted for—the diagnosis of which requires expert knowledge that is not always available. Even given this knowledge, data scarcity and high inter-/intra-speaker variability further limit the effectiveness of traditional fine-tuning. To circumvent these challenges, we first identify the minimal set of model parameters required for ASR adaptation. Our analysis of each individual parameter’s effect on adaptation performance allows us to reduce Word Error Rate (WER) by half while adapting 0.03% of all weights. Alleviating the need for cohort-specific models, we next propose the novel use of a meta-learned hypernetwork to generate highly individualized, utterance-level adaptations on-the-fly for a diverse set of atypical speech characteristics. Evaluating adaptation at the global, cohort, and individual-level, we show that hypernetworks generalize better to out-of-distribution speakers, while maintaining an overall relative WER reduction of 75.2% using 0.1% of the full parameter budget.