Reference Metrics Research Articles

Height-function-based 4D reference metrics for hyperboloidal evolution

Hyperboloidal slices are spacelike slices that reach future null infinity. Their asymptotic behaviour is different from Cauchy slices, which are traditionally used in numerical relativity simulations. This work uses free evolution of the formally-singular conformally compactified Einstein equations in spherical symmetry. One way to construct gauge conditions suitable for this approach relies on building the gauge source functions from a time-independent background spacetime metric. This background reference metric is set using the height function approach to provide the correct asymptotics of hyperboloidal slices of Minkowski spacetime. The present objective is to study the effect of different choices of height function on hyperboloidal evolutions via the reference metrics used in the gauge conditions. A total of 10 reference metrics for Minkowski are explored, identifying some of their desired features. They include 3 hyperboloidal layer constructions, evolved with the non-linear Einstein equations for the first time. Focus is put on long-term numerical stability of the evolutions, including small initial gauge perturbations. The results will be relevant for future (puncture-type) hyperboloidal evolutions, 3D simulations and the development of coinciding Cauchy and hyperboloidal data, among other applications.

OptiRet-Net: An Optimized Low-Light Image Enhancement Technique for CV-based Applications in Resource-Constrained Environments

The illumination of images can significantly impact computer-vision applications such as image classification, multiple object detection, and tracking, leading to a significant decline in detection and tracking accuracy. Recent advancements in deep learning techniques have been applied to low-light image enhancement (LLIE) to combat this issue. Retinex theory-based methods following a decomposition-adjustment pipeline for LLIE have performed well in various aspects. Despite their success, current research on Retinex-based deep learning still needs to improve in terms of optimization techniques and complicated convolution connections, which can be computationally intensive for end-device deployment. We propose an Optimized Retinex-based CNN (OptiRet-Net) deep-learning framework to address these challenges for the low-light image enhancement problem. Our results demonstrate that the proposed method outperforms existing state-of-the-art models in terms of full reference metrics with a PSNR of 21.87, SSIM of 0.80, LPIPS of 0.16, and zero reference metrics with a NIQE of 3.4 and PIQE of 56.6. Additionally, we validate our approach using a comprehensive evaluation comprising five datasets and nine prior methods. Furthermore, we assess the efficacy of our proposed model combining low-light multiple object tracking applications using YOLOX and ByteTrack in versatile video coding (VVC/H.266) across various quantization parameters. Our findings reveal that LLIE-enhanced frames surpass their tracking results with a MOTA of 80.6% and a remarkable precision rate of 96%. Our model also achieves minimal file sizes by effectively compressing the enhanced low-light images while maintaining their quality, making it suitable for resource-constrained environments where storage or bandwidth limitations are a concern.

A causal model for acoustic assessment of informal learning environments

Irrelevant and intelligible speeches are the most effective distractive noise sources in open-plan environments. They are inevitable in informal learning environments as group assignments are very common. Informal learning can be defined as any additional learning activity that takes place outside the classroom and is conducted individually and collaboratively on campus. This study will examine the impact of sound-absorbing materials on students' attention in informal learning environments in a causal manner. To examine the effect of materials, 3D models will be designed by changing the sound-absorbing materials, using reverberation time as a reference metric by applying the auralization process. The control and treatment models will be provided. The statistical significance of the impacts will be investigated using pairwise correlations, ANOVA, and multivariate regression methods.

Analisis Rantai Pasok Jamur Tiram di UMKM Sidimpuan Hiratake Mushroom Dalam Memenuhi Permintaan

Oyster mushrooms, Pleurotus ostreatus, are a type of edible wood mushroom. MSME Sidimpuan Hiratake Mushroom have customer, a family restauran. In order to meet consumer demand with quality and quantity requirements, MSME had issues such as inability to fulfill demand due to insufficient production capacity, and occurrence of returns, where both of these continue to increase. Purpose of this study is to explain the process of supply chain, to find out the actual performance, and to provide some suggestions for improvements to the performance of supply chain. The process flows analyzed in this supply chain are product, information and financial. The product produced by partner farmers is fresh oyster mushrooms, the products that will be sent to restaurant are oyster mushrooms that have been weighed, cleaned and packaged. This research method is qualitative and quantitative. The results of analysis with food supply chain network show that the process flow in the supply chain has been going well. The operational performance measurement results were measured using several supply chain operation reference metrics, that the operational performance of the supply chain cannot be said to be perfect overall. To measure marketing performance, the farmer's share value obtained by partner farmers is 58.3% and the marketing margin for MSME is Rp.5.000. The results of 5 why's analysis, inability to fulfill restaurant's demand was caused due to several things including variations in the growth process as well as limited resources of farmers. Returns are caused by differences in quality control standards and qualifications between restaurant and MSME.

What patient positioning in chest X-ray is still acceptable? – An empirical study on thresholding quality metrics

AbstractBackground and AimIssues in patient positioning during chest X-ray (CXR) acquisition impair diagnostic quality and potentially increase radiation dose. Automated quality assessment was proposed to address this. Our objective is to determine thresholds on some quality control metrics following international guidelines, that represent expert knowledge and can be applied in a comprehensible and explainable AI approach for such an automatic quality assessment.Materials and MethodsAn AI-method estimating collimation distance to the ribcage, balancing between both clavicle heads, and number of ribs above the diaphragm as metrics for collimation, rotation, and inhalation quality was applied on 64,315 posteroanterior CXR images from a public dataset (ChestX-ray8). From this set 920 CXR images were sampled and manually annotated to gain additional trusted reference metrics. Seven readers from different institutions then classified the acquisition quality of these images independently into okay, inadequate, or unacceptable following the criteria of international guidelines. Optimal thresholds on the metrics were determined to reproduce these classes using the metrics only.ResultsA fair to moderate agreement between the experts was found. When disregarding all inadequate rates a classification on the metrics was able to separate okay rated cases from unacceptable cases for collimation (AUC 0.97), rotation (AUC = 0.93) and inhalation (AUC = 0.97).ConclusionSuitable thresholds were determined to reproduce expert opinions in the assessment of the most important quality criteria in CXR acquisition. These thresholds were finally applied on the AI-method's estimates to automatically classify image acquisition quality comprehensibly and according to the guidelines.

High-tech industry development: selection of technology parks operation model elements in the field of high technology

The article presents the justification of model elements choice for technology parks work in the sphere of high technologies in Russia. The methodology for creating the model, which was built on the position of borrowing the best practices of existing models of innovation infrastructure facilities operation and functioning in the country, has been given. It has been proposed to form a rating among operating technology parks, within the framework of which a comprehensive assessment of technology park management companies’ performance has been carried out according to four groups of indicators (sub-indexes) such as subjects investment attractiveness, information openness, technology parks internal ecosystem, and management company efficiency. Based on the rating results, the leaders among technology parks have been identified, and their behavioral models adopted as reference metrics for the model to be developed. Based on the leaders’ position, it has been proposed to choose the best technology parks model in the field of high technologies in terms of their interaction with residents. Recommendations have been presented for the technology parks infrastructural model as the most promising for private companies, while investment projects should be developed considering federal requirements and standards to ensure their successful implementation and achievement of the set goals. Technology parks in the sphere of high technologies play a key role in Russian economy development. They contribute to the development of innovations, new technologies, high-tech industries, and knowledge-intensive business. It is important that such technology parks development is optimally organized with due regard to modern requirements and regulatory measures.

The performance of selected high-frequency trading proxies: An application on Turkish index futures market

This paper intends to provide evidence for how well high-frequency trading (HFT) proxies capture low-latency activity in rarely explored futures markets. We first run suggested identification algorithms using tick-by-tick order and trade message data to derive models’ HFT estimates. Contrasting these with Exchange-provided classification tags (considered as real HFT messages), we interpret the soundness and consistency of these proxies with regard to various reference metrics through an empirical mindset. Our results suggest that certain proxies track low-latency behavior better than others affirming their given credits of reliable HFT identifiers in practice.

Generalizing the Enhanced-Deep-Super-Resolution Neural Network to Brain MR Images: A Retrospective Study on the Cam-CAN Dataset.

The Enhanced-Deep-Super-Resolution (EDSR) model is a state-of-the-art convolutional neural network suitable for improving image spatial resolution. It was previously trained with general-purpose pictures and then, in this work, tested on biomedical magnetic resonance (MR) images, comparing the network outcomes with traditional up-sampling techniques. We explored possible changes in the model response when different MR sequences were analyzed. T1w and T2w MR brain images of 70 human healthy subjects (F:M, 40:30) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) repository were down-sampled and then up-sampled using EDSR model and BiCubic (BC) interpolation. Several reference metrics were used to quantitatively assess the performance of up-sampling operations (RMSE, pSNR, SSIM, and HFEN). Two-dimensional and three-dimensional reconstructions were evaluated. Different brain tissues were analyzed individually. The EDSR model was superior to BC interpolation on the selected metrics, both for two- and three- dimensional reconstructions. The reference metrics showed higher quality of EDSR over BC reconstructions for all the analyzed images, with a significant difference of all the criteria in T1w images and of the perception-based SSIM and HFEN in T2w images. The analysis per tissue highlights differences in EDSR performance related to the gray-level values, showing a relative lack of outperformance in reconstructing hyperintense areas. The EDSR model, trained on general-purpose images, better reconstructs MR T1w and T2w images than BC, without any retraining or fine-tuning. These results highlight the excellent generalization ability of the network and lead to possible applications on other MR measurements.

Anatomy's missing faces: An assessment of representation gaps in atlas and textbook imagery.

Previous research suggests that underrepresentation in medical curricula perpetuates inequities in healthcare. This study aimed to quantify the prevalence of human phenotypic diversity (e.g., skin tone, sex, body size, and age) across 11 commonly used anatomy atlases and textbooks in pre-clerkship medical education, published from 2015 to 2020. A systematic visual content analysis was conducted on 5001 images in which at least one phenotypic attribute was quantifiable. Anatomy images most prevalently portrayed light skin tones, males, persons with intermediate body sizes, and young to middle-aged adults. Of the 3883 images in which there was a codable skin tone, 81.2% (n = 3154) depicted light, 14.3% (n = 554) depicted intermediate, and 4.5% (n = 175) depicted dark skin tones. Of the 2384 images that could be categorized into a sex binary, 38.4% (n = 915) depicted females and 61.6% (n = 1469) depicted males. A male bias persisted across all whole-body and regional-body images, including those showing sex organs or those showing characteristics commonly associated with a specific sex (e.g. for males, facial hair and/or muscle hypertrophy). Within sex-specific contexts, darker skin was underrepresented, but male depictions displayed greater overall skin tone variation. Although most images could not be assigned to a body size or age category, when codable, these images overwhelmingly depicted adults (85.0%; 482 of 567) with smaller (34.7%; 93 of 268) or intermediate (64.6%; 173 of 268) body sizes. Ultimately, these outcomes provide reference metrics for monitoring ongoing and future efforts to address representation inequalities portrayed in anatomical imagery.

Recent advances and challenges in protein complex model accuracy estimation

Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table.

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.

Syllable-rate-adjusted-modulation (SRAM) predicts clear and conversational speech intelligibility.

Objectively predicting speech intelligibility is important in both telecommunication and human-machine interaction systems. The classic method relies on signal-to-noise ratios (SNR) to successfully predict speech intelligibility. One exception is clear speech, in which a talker intentionally articulates as if speaking to someone who has hearing loss or is from a different language background. As a result, at the same SNR, clear speech produces higher intelligibility than conversational speech. Despite numerous efforts, no objective metric can successfully predict the clear speech benefit at the sentence level. We proposed a Syllable-Rate-Adjusted-Modulation (SRAM) index to predict the intelligibility of clear and conversational speech. The SRAM used as short as 1 s speech and estimated its modulation power above the syllable rate. We compared SRAM with three reference metrics: envelope-regression-based speech transmission index (ER-STI), hearing-aid speech perception index version 2 (HASPI-v2) and short-time objective intelligibility (STOI), and five automatic speech recognition systems: Amazon Transcribe, Microsoft Azure Speech-To-Text, Google Speech-To-Text, wav2vec2 and Whisper. SRAM outperformed the three reference metrics (ER-STI, HASPI-v2 and STOI) and the five automatic speech recognition systems. Additionally, we demonstrated the important role of syllable rate in predicting speech intelligibility by comparing SRAM with the total modulation power (TMP) that was not adjusted by the syllable rate. SRAM can potentially help understand the characteristics of clear speech, screen speech materials with high intelligibility, and convert conversational speech into clear speech.

The Upper Quarter Y-Balance Test: A Scoping Review of Reference Values, Reliability, Determinants, and Practical Application

The aim of this scoping review was to comprehensively examine the Upper Quarter Y-Balance Test (UQYBT), including reference values, reliability, determinants, and its practical application. By including studies irrespective of publication date, participant demographics, or research purpose, provided they were in English and incorporated UQYBT as a primary or secondary variable, an extensive dataset was collected. The focus was on limb-length-normalized results to establish standardized reference metrics. Reliability studies highlighted the UQYBT’s consistency across sessions and raters. The application of UQYBT to assess injury risk is questionable. Studies assessing its relationship with sports performance have also yielded varying results. UQYBT seems to be sensitive to various exercise based-interventions, as shown by several clinical trials. This review furnishes practitioners and clinicians with valuable insights for the application of UQYBT in sports and healthcare settings.

Raman spectroscopy of monolayer to bulk PtSe2 exfoliated crystals

Raman spectroscopy is widely used to assess the quality of 2D materials thin films. This report focuses on PtSe2 , a noble transition metal dichalcogenide which has the remarkable property to transit from a semi-conductor to a semi-metal with increasing layer number. While polycrystalline PtSe2 can be grown with various crystalline qualities, getting insight into the monocrystalline intrinsic properties remains challenging. We report on the study of exfoliated 1–10 layers PtSe2 by Raman spectroscopy, featuring record linewidth. The clear Raman signatures allow layer-thickness identification and provides a reference metrics to assess crystal quality of grown films.

Formulation of climate-based daylighting design prediction model for high performance tropical school classrooms

Daylighting is beneficial in buildings, including school classrooms, in which daylight can significantly affect the occupants’ performance. Also, daylight varies across different climates, including within the tropical region. Climate-based daylight modelling is recommended to accurately predict annual daylight performance in building spaces; however, it is often too complicated in practice. It is intended to have certain climate variables that can act as predictors for annual daylight performance in tropical school classrooms, where openings are typically installed on opposing façades. This study thus aims to propose a climate-based daylighting design prediction model for tropical school classrooms with symmetrical and asymmetrical opening typologies. The model considers horizontal shading depth and its height, and window-to-wall ratio, in relation with the climate variables. To achieve the objective, two classrooms with asymmetrical and symmetrical bilateral openings are modelled and optimized for 117 site locations in Indonesia. The spatial average UDI250-750lx is proposed as the reference metric that can ensure satisfaction of all daylight criteria. The annual global horizontal radiation (aGHR) is proposed as the predictor in the model, with prediction errors below 5 %. Simple equations have been derived to predict spatial average UDI250-750lx in tropical classrooms as a function of aGHR.

Detection of heart rate using smartphone gyroscope data: a scoping review.

Heart rate (HR) is closely related to heart rhythm patterns, and its irregularity can imply serious health problems. Therefore, HR is used in the diagnosis of many health conditions. Traditionally, HR has been measured through an electrocardiograph (ECG), which is subject to several practical limitations when applied in everyday settings. In recent years, the emergence of smartphones and microelectromechanical systems has allowed innovative solutions for conveniently measuring HR, such as smartphone ECG, smartphone photoplethysmography (PPG), and seismocardiography (SCG). However, these measurements generally rely on external sensor hardware or are highly susceptible to inaccuracies due to the presence of significant levels of motion artifact. Data from gyrocardiography (GCG), however, while largely overlooked for this application, has the potential to overcome the limitations of other forms of measurements. For this scoping review, we performed a literature search on HR measurement using smartphone gyroscope data. In this review, from among the 114 articles that we identified, we include seven relevant articles from the last decade (December 2012 to January 2023) for further analysis of their respective methods for data collection, signal pre-processing, and HR estimation. The seven selected articles' sample sizes varied from 11 to 435 participants. Two articles used a sample size of less than 40, and three articles used a sample size of 300 or more. We provide elaborations about the algorithms used in the studies and discuss the advantages and disadvantages of these methods. Across the articles, we noticed an inconsistency in the algorithms used and a lack of established standardization for performance evaluation for HR estimation using smartphone GCG data. Among the seven articles included, five did not perform any performance evaluation, while the other two used different reference signals (HR and PPG respectively) and metrics for accuracy evaluation. We conclude the review with a discussion of challenges and future directions for the application of GCG technology.

Correlation of preoperative CT imaging shift parameters of the lateral plateau with lateral meniscal injury in Schatzker IV-C tibial plateau fractures

BackgroundSchatzker IV-C is a high-energy tibial plateau fracture often accompanied by lateral meniscus injuries. While imaging examinations are routine preoperative measurements, the correlation between CT imaging shift parameters of the lateral plateau and lateral meniscal injury in Schatzker IV-C fractures remains uncovered.MethodsThis retrospective study enrolled a total of 60 patients with Schatzker IV-C tibial plateau fractures at the First People’s Hospital of Hefei. Prior to surgery, CT imaging was used to measure the numerical values of lateral plateau depression (LPD) and lateral plateau widening (LPW). The degree of lateral meniscus injury was confirmed based on intraoperative direct vision, with patients being classified into meniscus injury and non-meniscus injury groups. Dichotomous logistic regression was employed to evaluate the correlation between LPD, LPW, and lateral meniscus injury, while the optimal cut-off points for predicting lateral meniscal injury with LPD and LPW were determined using receiver operator characteristic (ROC) curves.ResultsThe meniscus injury group exhibited a mean LPD of 15.3 ± 3.5 mm, which was significantly higher than the non-meniscus injury group’s mean LPD of 8.4 ± 3.4 mm (P < 0.05). Similarly, the meniscus injury group had a larger mean LPW of 9.4 ± 1.8 mm compared to the non-meniscus injury group’s mean LPW of 6.9 ± 0.9 mm (P < 0.05). The optimal cut-off points for predicting lateral meniscal injury were determined to be 8.40 mm for LPD (with a sensitivity of 95%, specificity of 85%, and AUC of 0.898) and 7.90 mm for LPW (with a sensitivity of 75%, specificity of 90%, and AUC of 0.897).ConclusionsPatients with Schatzker IV-C tibial plateau fractures are at a significantly higher risk of lateral meniscal injury when the LPD exceeds 8.40 mm and/or the LPW exceeds 7.90 mm. Our results may provide novel reference metrics for the early diagnosis of lateral meniscal injury in Schatzker IV-C tibial plateau fracture patients when the MRI examination is not available.

Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization.

Speckle noise is a pervasive problem in medical imaging, and conventional methods for despeckling often lead to loss of edge information due to smoothing. To address this issue, we propose a novel approach that combines a nature-inspired minibatch water wave swarm optimization (NIMWVSO) framework with an invertible sparse fuzzy wavelet transform (ISFWT) in the frequency domain. The ISFWT learns a non-linear redundant transform with a perfect reconstruction property that effectively removes noise while preserving structural and edge information in medical images. The resulting threshold is then used by the NIMWVSO to further reduce multiplicative speckle noise. Our approach was evaluated using the MSTAR dataset, and objective functions were based on two contrasting reference metrics, namely the peak signal-to-noise ratio (PSNR) and the mean structural similarity index metric (MSSIM). Our results show that the suggested approach outperforms modern filters and has significant generalization ability to unknown noise levels, while also being highly interpretable. By providing a new framework for despeckling medical images, our work has the potential to improve the accuracy and reliability of medical imaging diagnosis and treatment planning.

From responses of macroinvertebrate metrics to the definition of reference metrics and stressor threshold values

From responses of macroinvertebrate metrics to the definition of reference metrics and stressor threshold values

A SINGLE IMAGE DEHAZING DATASET WITH LOW-LIGHT REAL-WORLD INDOOR IMAGES, DEPTH MAPS AND INFRARED IMAGES

Abstract. Benchmarking of haze removal methods and training related models requires appropriate datasets. The most objective metrics of assessment quality of dehazing are shown by reference metrics – i.e. those in which the reconstructed image is compared with the reference (ground-truth) image without haze. The dehazing datasets consist of pairs where haze is artificially synthesized on ground-truth images are not well suited for the assessment of the quality of dehazing methods. Accommodation of the real-world environment for take truthful pairs of hazy and haze-free images are difficult, so there are few image dehazing datasets, which consists with the real both hazy and haze-free images. The currently researcher’s attention is shifting to dehazing on “more complex” images, including those that are obtained in insufficient illumination conditions and with the presence of localized light sources. It is almost no datasets with such pairs of images, which makes it difficult of objective assessment of image dehazing methods. In this paper, we present extended version of our previously proposed dataset of this kind with more haze density levels and depths of scenes. It consists of images of 2 scenes at 4 lighting and 8 haze density levels – 64 frames in total. In addition to images in the visible spectrum, for each frame depth map and thermal image was captured. An experimental evaluation of state-of-the art haze removal methods was carried out on the resulting dataset. The dataset is available for free download at https://data.mendeley.com/datasets/jjpcj7fy6t.