Subjective Evaluation Research Articles

Clinical efficacy of intradermal type I collagen injections in treating skin photoaging in patients from high-altitude areas

BACKGROUND Photoaging, a result of chronic sun exposure, leads to skin damage and pigmentation changes. Traditional treatments may have limitations in high-altitude areas like Yunnan Province. Intradermal Col Ι injections stimulate collagen production, potentially improving skin quality. This study aims to assess the efficacy and safety of this treatment for photoaging. AIM To evaluate the efficacy and safety of intradermal type Ι collagen (Col Ι) injection for treating photoaging. METHODS This prospective, self-controlled study investigated the impact of intradermal injections of Col Ι on skin photodamage in 20 patients from the Yunnan Province. Total six treatment sessions were conducted every 4 wk ± 3 d. Before and after each treatment, facial skin characteristics were quantified using a VISIA skin detector. Skin thickness data were assessed using the ultrasound probes of the Dermalab skin detector. The Face-Q scale was used for subjective evaluation of the treatment effect by the patients. RESULTS The skin thickness of the right cheek consistently increased after each treatment session compared with baseline. The skin thickness of the left cheek significantly increased after the third through sixth treatment sessions compared with baseline. The skin thickness of the right zygomatic region increased after the second to sixth treatment sessions, whereas that of the left zygomatic region showed a significant increase after the fourth through sixth treatment sessions. The skin thickness of both temporal regions significantly increased after the fifth and sixth treatment sessions compared with baseline (P < 0.05). These findings were also supported by skin ultrasound images. The feature count for the red areas and wrinkle feature count decreased following the treatment (P < 0.05). VISIA assessments also revealed a decrease in the red areas after treatment. The Face-Q-Satisfaction with Facial Appearance Overall and Face-Q-Satisfaction with Skin scores significantly increased after each treatment session. The overall appearance of the patients improved after treatment. CONCLUSION Intradermal Col Ι injection improves photoaging, with higher patient satisfaction and fewer adverse reactions, and could be an effective treatment method for populations residing in high-altitude areas.

Shared decision-making support program for older patients with advanced cancer using a question prompt list and geriatric assessment: A pilot randomized controlled trial

IntroductionOlder patients with cancer are less likely to express their treatment preferences than younger patients. Question prompt lists (QPLs) facilitate communication between patients and physicians. Geriatric assessment (GA) is recommended when older patients with cancer make treatment decisions. This study estimated the effect size of a shared decision-making (SDM) support program combining QPLs with GA in terms of patients' subjective evaluation of the SDM process for a future definitive randomized controlled trial. We also evaluated the number and quality of aging-related communication during consultations, and feasibility and acceptability of the study for exploratory purposes. Materials and MethodsThis is a pilot study with randomized allocation and blind evaluation. Patients aged 65 years or older at the National Cancer Center Hospital, Tokyo, Japan, scheduled to discuss the changes of their treatment, were randomly assigned in a 1:1 ratio to the SDM support program or usual care. This program consisted of 30–60 min of face-to-face coaching, with QPLs and GA provided before the coaching. As the primary endpoint, the decisional conflict scores given by the patients immediately after the consultation were compared between the two groups. For the secondary endpoints, the number and quality of aging-related communications during the consultations were assessed by evaluators (blinded) using audio-recordings. Adherence, burden, and usefulness were assessed for evaluating feasibility and acceptability of the SDM support program. ResultsForty patients were enrolled. All patients completed the GA questionnaire, for which 70% did not require any individual assistance. Answering the questionnaires took approximately 11 min. The decisional conflict scores were mean [standard deviation (SD)]: 19.3 [10.8] vs. 18.0 [11.1] (effect size: Cohen's d = 0.12) for the SDM support program and usual care groups, respectively. The number of aging-related communications during the consultation for the new treatment was higher in the SDM support program group than the usual care (mean [SD]: 3.3 [1.2] vs. 2.2 [1.5], effect size: cohen's d = 1.32). Patients felt that the SDM support program was useful but not burdensome or difficult. DiscussionThe SDM support program was considered useful and feasible for older patients and able to facilitate communication regarding aging-related concerns. Trial registration numberThe study protocol was registered on September 23, 2020, in the UMIN Clinical Trials Registry (UMIN000041867).

Development of a decision support system for client acceptance in independent audit process

Intelligent Information Technology (IIT) applications are crucial in the audit process, enhancing quality, effectiveness, and efficiency. The client acceptance process (CAP), one of the critical audit steps, involves subjective evaluations where business managers' claims intersect with independent audit firm managers' expectations. This subjective nature introduces the potential for errors or misjudgments, impacting audit time and costs. In this paper, therefore, we propose a decision support system considering both auditors' subjective judgments and financial data variations for accepting or rejecting a client enterprise. The decision support system consisting of the Fuzzy Analytic Hierarchy Process (AHP), the logistic regression model, and the fuzzy inference system comprises four phases. In the first phase, a logistic regression model is developed using financial ratios to determine the client's probability of being in a close monitoring market (CMM) which represents publicly traded firms that are struggling to meet specific financial indicators or that are exposed to certain risks. In the second phase, the evaluation criteria used by the audit firm to measure the market reputation of the client enterprise are defined, and the weights of the evaluation criteria are obtained by using Fuzzy AHP. In the third phase, the Client Acceptance Score (CAS) representing market reputation of the client is calculated by incorporating the results of a reputation survey and applying the weights assigned to the evaluation criteria obtained in the second phase. Finally, client acceptance risk level (CARL) is obtained by using a fuzzy inference system and a rule-based defined by auditors. The CMM probability value and CAS score obtained in previous phases are used as input values of the fuzzy inference system. The CARL score guides the audit firm in deciding whether to engage with the client. To illustrate the applicability of the proposed model, a case study has been given in the paper.

Evaluating soundscape in the Italian language: Validation of the translation of the English standardized perceptual attributes of the ISO/TS 12913-2:2018 and comparison with other Romance languages

The Soundscape Attributes Translation Project (SATP) has successfully translated English standardized perceptual attributes of the ISO/TS 12913–2:2018 into various languages. This paper validates the Italian translation of the widespread use of the soundscape circumplex model attributes for using standardized listening experiments protocol. The Italian translation of the ISO soundscape circumplex model was compared with the original English version. Furthermore, a comparison with other Romance languages (i.e., Portuguese, Spanish, French) that have already concluded the validation was carried out. The study was conducted following three phases: (i) a consensus on the final eight attributes of the Italian language was achieved, (ii) the recruitment of 30 naïve normal-hearing Italian listeners and the preparation of the set-up for the listening tests was fostered, (iii) listening tests based on the 27 binaural recordings provided by the SATP project to examine the differences in subjective evaluations of the same sound environment were administrated in the Audio Space Lab of Politecnico di Torino. The main findings of the listening tests consisted in the validation of the Italian translations against English, in the comparison of the perceived loudness in the Italian and English listening tests that showed no significant differences, and in the satisfactory match of Italian translations in comparison with other Romance languages. Although slight differences probably due to cultural aspects were found, these outcomes are helpful to properly develop the Italian version of the ISO 12913-2 and to pursue accurate and replicable soundscape studies.

Contrast enhancement algorithm for infrared images based on atmospheric scattering model

With the rapid development of infrared technology, infrared cameras are widely used in the military, medical, and industrial fields. However, the thermal radiation information received by infrared cameras often undergoes degradation due to random factors such as scattering or reflection during transmission. Consequently, the final infrared image suffers from poor contrast and blurred vision. To address these problems, this paper proposes an infrared image contrast enhancement algorithm based on the atmospheric scattering model. Firstly, we modify the hypothesis of the dark channel prior theory, and the pseudo dark channel prior theory is deduced to meet the processing requirements of single-channel images. Secondly, we propose anti-target interference and filtering threshold method to accurately estimate the atmospheric illumination value and transmittance of the atmospheric scattering model. These methods effectively mitigate the influence of infrared targets on the atmospheric illumination value and eliminate the blocking artifact effect caused by pixel value jumps in the transmittance images. Furthermore, we employ adaptive histogram equalization with contrast limitation, highlighting the global contrast. This paper conducts experiments on the M3FD dataset and employs objective evaluations using Visible Edge (E), Figure Definition (FD), Peak Signal-to-Noise Ratio (PSNR), Information Entropy (IE), Structure Similarity Index Measure (SSIM), and Visual Information Fidelity (VIF). The results indicate that the proposed algorithm enhances the overall contrast of images while highlighting fine texture details, exhibiting good visual effects. Both subjective and objective evaluations outperform seven other contrast algorithms, demonstrating the effectiveness of the proposed method.

Impairment in acquisition of conditioned fear in people with depressive symptoms

BackgroundDepression is one of the primary global public health issues, and there has been a dramatic increase in depression levels among young people over the past decade. The neuroplasticity theory of depression postulates that a malfunction in neural plasticity, which is responsible for learning, memory, and adaptive behavior, is the primary source of the disorder's clinical manifestations. Nevertheless, the impact of depression symptoms on associative learning remains underexplored.MethodsWe used the differential fear conditioning paradigm to investigate the effects of depressive symptoms on fear acquisition and extinction learning. Skin conductance response (SCR) is an objective evaluation indicator, and ratings of nervousness, likeability, and unconditioned stimuli (US) expectancy are subjective evaluation indicators. In addition, we used associability generated by a computational reinforcement learning model to characterize the skin conductance response.ResultsThe findings indicate that individuals with depressive symptoms exhibited significant impairment in fear acquisition learning compared to those without depressive symptoms based on the results of the skin conductance response. Moreover, in the discrimination fear learning task, the skin conductance response was positively correlated with associability, as estimated by the hybrid model in the group without depressive symptoms. Additionally, the likeability rating scores improved post-extinction learning in the group without depressive symptoms, and no such increase was observed in the group with depressive symptoms.ConclusionThe study highlights that individuals with pronounced depressive symptoms exhibit impaired fear acquisition and extinction learning, suggesting a possible deficit in associative learning. Employing the hybrid model to analyze the learning process offers a deeper insight into the associative learning processes of humans, thus allowing for improved comprehension and treatment of these mental health problems.

SUBJECTIVE ANSWERS EVALUATION USING ML & NLP

SUBJECTIVE ANSWERS EVALUATION USING ML & NLP

Health-related quality of life does not deteriorate while waiting for anterior cruciate reconstruction.

The aim of this study was to determine how preoperative health-related quality of life (HRQoL) is affected by the duration of the wait time (WT) for anterior cruciate ligament reconstruction (ACLR) once a decision is made to proceed with surgery. This was a multi-centre prospective cohort study. One hundred and twenty-two patients 14 years of age and above waiting for ACLR completed the International Knee Documentation Committee (IKDC) demographic, current health assessmentand subjective knee evaluation (SKF) forms on the day of decision to operate and the day of surgery. Changes in scores were analyzed for the entire cohort, adjusted for WTsand a subset was compared for patients with isolated anterior cruciate ligament (ACL) tears and ACL tears with concurrent meniscal involvement. Changes in HRQoL scores from the day of the decision to operate to the 9-month postoperative appointments were also assessed. Energy/Fatigue (p < 0.05), Pain (p < 0.05), General Health (p < 0.05)and the IKDC-SKF Score (p < 0.05) significantly increased between the day of the decision to operate and the day of surgery. Only the change in IKDC-SKF score remained significantly higher after adjusting for WT. Baseline HRQoL scores significantly improved by the 9-month postoperative appointment. The length of time waiting for ACLR did not adversely influence HRQoL in this study. However, low preoperative HRQoL and the significant improvement in HRQoL of patients followed postoperatively suggest that timely surgery is beneficial for this patient population. Level II.

A Retinex-based network for image enhancement in low-light environments.

Most of the existing low-light image enhancement methods suffer from the problems of detail loss, color distortion and excessive noise. To address the above-mentioned issues, this paper proposes a neural network-based low-light image enhancement network. The network is divided into three parts: decomposition network, reflection component denoising network, and illumination component enhancement network. In the decomposition network, the input image is decomposed into a reflection image and an illumination image. In the reflection component denoising network, the Unet3+ network improved by fusion CA attention is adopted to denoise the reflection image. In the illumination component enhancement network, the adaptive mapping curve is adopted to enhance the illumination image iteratively. Finally, the processed illumination and reflection images are fused based on Retinex theory to obtain the final enhanced image. The experimental results show that the proposed network achieves excellent visual effects in subjective evaluation. Additionally, it shows a significant improvement in objective evaluation metrics, including PSNR, SSIM, NIQE, and so on, when compared to the results in several public datasets.

Multi-focused image fusion algorithm based on multi-scale hybrid attention residual network.

In order to improve the detection performance of image fusion in focus areas and realize end-to-end decision diagram optimization, we design a multi-focus image fusion network based on deep learning. The network is trained using unsupervised learning and a multi-scale hybrid attention residual network model is introduced to enable solving for features at different levels of the image. In the training stage, multi-scale features are extracted from two source images with different focal points using hybrid multi-scale residual blocks (MSRB), and the up-down projection module (UDP) is introduced to obtain multi-scale edge information, then the extracted features are operated to obtain deeper image features. These blocks can effectively utilize multi-scale feature information without increasing the number of parameters. The deep features of the image are extracted in its test phase, input to the spatial frequency domain to calculate and measure the activity level and obtain the initial decision map, and use post-processing techniques to eliminate the edge errors. Finally, the decision map is generated and optimized, and the final fused image is obtained by combining the optimized decision map with the source image. The comparative experiments show that our proposed model achieves better fusion performance in subjective evaluation, and the quality of the obtained fused images is more robust with richer details. The objective evaluation metrics work better and the image fusion quality is higher.

Toward Immersive and Interactive Surgical Training Using Extended Reality Simulator for IoMT

Since the advent of virtual reality (VR), it has been implemented in medical education for surgical training and anatomy education so that the Internet of Medical Things (IoMT) could be further boosted. There have been rare studies on the research trends of the evaluation of endoscopic training through different XR modalities. Position-based dynamics (PBD) has been utilized to optimize the surgical thread simulation, This paper aims to quantitatively evaluate the training performance of each XR modality in general and in terms of the medical fields studied and outcomes measured. Sensors and devices are utilized to form the Internet of Medical Things for healthcare, where the data is uploaded to the cloud and then analyzed as follows before being fed back to the doctor so that he or she can understand his or her level of operation. Through subjective and objective evaluation, the potential promoting effects of vision and touch in module training were discussed.

SNIPPET: A Framework for Subjective Evaluation of Visual Explanations Applied to DeepFake Detection

Explainable Artificial Intelligence (XAI) attempts to help humans understand machine learning decisions better and has been identified as a critical component towards increasing the trustworthiness of complex black-box systems, such as deep neural networks (DNNs). In this paper, we propose a generic and comprehensive framework named SNIPPET and create a user interface for the subjective evaluation of visual explanations, focusing on finding human-friendly explanations. SNIPPET considers human-centered evaluation tasks and incorporates the collection of human annotations. These annotations can serve as valuable feedback to validate the qualitative results obtained from the subjective assessment tasks. Moreover, we consider different user background categories during the evaluation process to ensure diverse perspectives and comprehensive evaluation. We demonstrate SNIPPET on a DeepFake face dataset. Distinguishing real from fake faces is a non-trivial task even for humans, that depends on rather subtle features, making it a challenging use case. Using SNIPPET, we evaluate four popular XAI methods which provide visual explanations: Gradient-weighted Class Activation Mapping (GradCAM), Layer-wise Relevance Propagation (LRP), attention rollout (rollout), and Transformer Attribution (TA). Based on our experimental results, we observe preference variations among different user categories. We find that most people are more favorable to the explanations of rollout. Moreover, when it comes to XAI-assisted understanding, those who have no or lack relevant background knowledge often consider that visual explanations are insufficient to help them understand. We open-source our framework for continued data collection and annotation at https://github.com/XAI-SubjEvaluation/SNIPPET.

Как удовлетворённость жизнью зависит от типа населённого пункта?

Life satisfaction is a multidimensional indicator that is influenced by many factors. Studies of national life satisfaction factors provide adequate estimates when countries are homogeneous “macroregions” with common social, cultural, and institutional foundations. However, within large and heterogeneous national states, of which Russia is one, internal regional differences in living conditions can be significant, which means that assessments of happiness levels are dominated by various factors, including the type of human settlement and its inherent characteristics. The conducted research is deepening the understanding of the factors that have a significant impact on subjective life satisfaction in various types of settlements in Russia - cities, towns, and villages. The Russia Longitudinal Monitoring Survey - Higher School of Economics (RLMS-HSE) was used as the data source. Econometric modeling is based on an ordered logit model with random effects using panel data. We discovered the high significance of subjective evaluation of wealth satisfaction, worried needs, and availability of paid vacations for all Russians, regardless of the type of settlement. The main differences in the set and degree of influence of factors lie along the line separating the values of collectivism and individualism, which are dominant in cities and villages, respectively. There is less concern about health and the possibility of job loss among rural residents reliant on the support of neighbors and family, in which men do not associate life satisfaction with the duration of the working day, and women do not associate life satisfaction with the amount of wage and maternity. In contrast, individualistically oriented residents of cities where nuclear families predominate and connections among people are weak tend to derive life satisfaction from self-realization through earnings, leisure, children, and health care.

Investigation in image quality and immediate patient safety using pre-dual-flow injection for low-contrast dose spectral pulmonary artery CT angiography

PurposeThe patient safety of iodine contrast-enhanced pulmonary artery CT angiography (CTPA) is widely concerned. This study aimed to investigate the image quality and immediate patient safety of spectral CTPA using a lower-contrast dose pre-dual-flow injection method. MethodsThis retrospective study included 120 patients with suspected pulmonary embolisms who received spectral CTPA between February and December 2022. Patients were divided into normal contrast injection (Group A, n=60) and pre-dual-flow group (Group B, n=60). CT values of pulmonary arteries (PAs) at different levels, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), arteriovenous separation performance, and beam hardening artifact (BHA) index of two sets of images were measured or calculated. The subjective image quality and immediate patient safety were also scored using the three-point method. ResultsGroup B had a contrast dose reduction by 42.5 % (60 vs. 34.5 mL). Radiation exposure dose was not statistically different between the two groups (P>0.05). CT values of different-level PAs on group B images were higher than those on group A images (P<0.05). Group B images had higher SNR and CNR, better arteriovenous separation between PA trunk and pulmonary vein, and lower BHA index on soft tissue and PA (all P<0.05). For subjective evaluation of image quality, group B had a better score in beam hardening artifact (P<0.05). For immediate patient safety, the score in comfortability was statistically higher in group B, with P<0.05. ConclusionsComparing with the normal injection method, pre-dual-flow spectral CTPA with a lower contrast dose injected results in better image quality and shows potential in patient-safety promotion.

A polyphenol fraction from Rosa multiflora var. platyphylala reduces body fat in overweight humans through appetite suppression - a randomized, double-blind, placebo-controlled trial.

Rosa species are rich sources of polyphenols with physiological functions. In this study a polyphenol-rich Rosa multiflora (var. platyphylala) petal extract (RoseFit™) was investigated for weight loss in humans. In a randomized, placebo-controlled, parallel-group, double-blind clinical trial seventy overweight male and female subjects (20-50 years) with body mass index (BMI) 25-30kg/m2 were randomly allocated to the active treatment group (RoseFit) and placebo group in a 1:1 ratio. The subjects received 300mg capsules twice daily for 12 weeks. The primary efficacy outcome measures included body weight, BMI, and body composition, as determined using Dual-energy X-ray absorptiometry (DEXA). Secondary measures consisted of serum lipid profile and appetite marker (leptin and ghrelin) analyses. Safety analyses included biochemical and hematological assessments. At the end of the study, a marked reduction in body weight (-1.20 ± 2.62kg, p < 0.05) and BMI from baseline was observed in the RoseFit group. In addition, the body fat % (RoseFit = -1.69 ± 2.59%, placebo = 0.96 ± 3.21%; p < 0.001) and fat mass (RoseFit = -1.75 ± 1.80kg, placebo = 1.61 ± 3.82kg; p < 0.001) were significantly abated in RoseFit group. Importantly, the lean mass was maintained during the intervention. RoseFit ingestion significantly increased the serum leptin levels compared to the placebo (4.85%; p < 0.05). Further, RoseFit group showed reduction in the hunger hormone ghrelin level (2.27%; p < 0.001) from baseline to the end of study, compared to the placebo. The subjective evaluation of appetite using visual analog scale (VAS) questionnaires further confirmed the appetite-suppression effects of RoseFit. The lipid profile significantly improved in RoseFit-treated subjects. No serious adverse events were observed during the study, indicating the tolerability of RoseFit. Supplementation with RoseFit significantly impacts body weight management and can thus be a potential nutraceutical ingredient for sustainable weight loss. CTRI/2019/10/021584 dated 09/10/2019.

Apparent diffusion coefficient and tissue stiffness are associated with different tumor microenvironment features of hepatocellular carcinoma.

To investigate associations between tissue diffusion, stiffness, and different tumor microenvironment features in resected hepatocellular carcinoma (HCC). Seventy-two patients were prospectively included for preoperative magnetic resonance (MR) diffusion-weighted imaging and MR elastography examination. The mean apparent diffusion coefficient (ADC) and stiffness value were measured on the central three slices of the tumor and peri-tumor area. Cell density, tumor-stroma ratio (TSR), lymphocyte-rich HCC (LR-HCC), and CD8 + T cell infiltration were estimated in resected tumors. The interobserver agreement of MRI measurements and subjective pathological evaluation was assessed. Variables influencing ADC and stiffness were screened with univariate analyses, and then identified with multivariable linear regression. The potential relationship between explored imaging biomarkers and histopathological features was assessed with linear regression after adjustment for other influencing factors. Seventy-two patients (male/female: 59/13, mean age: 56 ± 10.2 years) were included for analysis. Inter-reader agreement was good or excellent regarding MRI measurements and histopathological evaluation. No correlation between tumor ADC and tumor stiffness was found. Multivariable linear regression confirmed that cell density was the only factor associated with tumor ADC (Estimate = -0.03, p = 0.006), and tumor-stroma ratio was the only factor associated with tumor stiffness (Estimate = -0.18, p = 0.03). After adjustment for fibrosis stage (Estimate = 0.43, p < 0.001) and age (Estimate = 0.04, p < 0.001) in the multivariate linear regression, intra-tumoral CD8 + T cell infiltration remained a significant factor associated with peri-tumor stiffness (Estimate = 0.63, p = 0.02). Tumor ADC surpasses tumor stiffness as a biomarker of cellularity. Tumor stiffness is associated with tumor-stroma ratio and peri-tumor stiffness might be an imaging biomarker of intra-tumoral immune microenvironment. Tissue stiffness could potentially serve as an imaging biomarker of the intra-tumoral immune microenvironment of hepatocellular carcinoma and aid in patient selection for immunotherapy. Apparent diffusion coefficient reflects cellularity of hepatocellular carcinoma. Tumor stiffness reflects tumor-stroma ratio of hepatocellular carcinoma and is associated with tumor-infiltrating lymphocytes. Tumor and peri-tumor stiffness might serve as imaging biomarkers of intra-tumoral immune microenvironment.

Viral Transmission? A Longitudinal Study of Media Use and Its Relation to Mental Strain During the First 2 Years of the COVID-19 Pandemic.

In light of the dynamic COVID-19 pandemic, the exposure to pandemic-related media coverage may change over time and may be particularly relevant due to associations with psychopathological symptoms. The aims of the present study were to examine changes in media consumption over time and to analyze its prospective associations with psychological strain. The study uses a longitudinal observational design with ten periods of online data collection from March 2020 to April 2022 in an adult convenience sample (N = 8337) of the general population in Germany. Our data revealed that the frequency and duration of pandemic-related media exposure as well as their subjective critical evaluation showed the highest levels at the beginning of the pandemic and peaked again in autumn 2020 and spring 2021. The primarily used media formats changed only slightly over time. The amount of media exposure at baseline was associated with more impairing pandemic-related anxiety 1month, 1year, and 2years later. Our results hint to potentially problematical and long-lasting associations of pandemic-related media consumption with mental strain. Our findings could serve as an orientation for recommendations, further research, and adequate interventions for a responsible dealing with media coverage. The authors have pre-registered this research at clinicaltrials.gov without an analysis plan; retrievable at: https://clinicaltrials.gov/ct2/show/NCT04331106 .

Sampling the user controls in neural modeling of audio devices

This work studies neural modeling of nonlinear parametric audio circuits, focusing on how the diversity of settings of the target device user controls seen during training affects network generalization. To study the problem, a large corpus of training datasets is synthetically generated using SPICE simulations of two distinct devices, an analog equalizer and an analog distortion pedal. A proven recurrent neural network architecture is trained using each dataset. The difference in the datasets is in the sampling resolution of the device user controls and in their overall size. Based on objective and subjective evaluation of the trained models, a sampling resolution of five for the device parameters is found to be sufficient to capture the behavior of the target systems for the types of devices considered during the study. This result is desirable, since a dense sampling grid can be impractical to realize in the general case when no automated way of setting the device parameters is available, while collecting large amounts of data using a sparse grid only incurs small additional costs. Thus, the result provides guidance for efficient collection of training data for neural modeling of other similar audio devices.

The Use of Dixon Magnetic Resonance Imaging Methods for the Quantification of Rotator Cuff Fatty Infiltration: A Systematic Review

Fatty infiltration of the rotator cuff muscles is very common following rotator cuff tears and is one of the most important factors in determining treatment. Current clinical practice relies on subjective evaluation of fatty infiltration through categorical scoring based on the Goutallier classification system. The Dixon magnetic resonance imaging (MRI) sequence provides flexibility in selecting echo times for water–fat separation. The Dixon method, therefore, has the potential to provide robust and high-quality fat quantification that allows for more accurate calculation of fat fraction (%Fat) of the rotator cuff muscles than the Goutallier classification system. However, significant variance exists in sequencing and post-processing methodology within the recent application of Dixon sequences to quantify rotator cuff fatty infiltration. In this paper, we conducted a systematic review to synthesize the relevant literature utilizing Dixon sequencing for the quantification of rotator cuff fatty infiltration. The literature search was extracted from 1094 articles, with 12 studies included in the final review. Regardless of the varying sequencing pattern and post-processing techniques among studies, the findings suggest the Dixon method is reliable for quantitatively calculating the fat fraction of the rotator cuff muscles, even at very low levels of fatty infiltration. In addition, a quantitative difference in fat fraction was observed between participants with different degrees of tear vs. those without any shoulder pathologies. Multi-point Dixon imaging has the potential to be utilized clinically to objectively quantify fatty infiltration and may lead to improved clinical decision making for patients with rotator cuff tears.

Similarity judgements: the comparison of normative predictions and subjective evaluations – A study of the ratio model of similarity in social context

IntroductionThis study examines the consistency between subjective similarity evaluations and the theoretical predictions derived from Tversky’s ratio model of similarity, alongside the impact of additional positive and negative features on perceived similarity to ideal and bad politicians.MethodsUsing a sample of 120 participants, we assessed the similarity of eight candidate profiles to an ideal and bad politician, varying in positive and negative features. Participants’ subjective evaluations were compared with theoretical predictions derived from Tversky’s ratio model. The analysis focused on how candidate and referent valence influenced observed versus theoretical similarity.ResultsSubjective similarity judgments deviated systematically from theoretical predictions, especially for positively featured candidates, indicating a negativity effect. Additional positive features decreased the perceived similarity of favorable candidates to an ideal politician, while additional negative features did not significantly affect similarity judgments of unfavorable candidates.DiscussionOur findings underscore a significant disparity between subjective and objective similarity judgments, notably for favorable candidates. While the ratio model performs well for unfavorable candidates, its applicability diminishes for favorable ones, emphasizing the role of feature valence in decision-making. Further research on feature valence is crucial for a comprehensive understanding across contexts.