Objective Method Research Articles

Validation of a semi-quantitative scoring system and workflow for analysis of fluorescence quantification in companion animals.

Many commercially available near-infrared (NIR) fluorescence imaging systems lack algorithms for real-time quantifiable fluorescence data. Creation of a workflow for clinical assessment and post hoc analysis may provide clinical researchers with a method for intraoperative fluorescence quantification to improve objective outcome measures. Scoring systems and verified image analysis are employed to determine the amount and intensity of fluorescence within surgical specimens both intra and postoperatively. Lymph nodes from canine cancer patients were obtained during lymph node extirpation following peritumoral injection of indocyanine green (ICG). First, a semi-quantitative assessment of surface fluorescence was evaluated. Images obtained with a NIR exoscope were analysed to determine fluorescence thresholds and measure fluorescence amount and intensity. Post hoc fluorescence quantification (threshold of Hue = 165-180, Intensity = 30-255) displayed strong agreement with semi-quantitative scoring (k = 0.9734, p < 0.0001). Fluorescence intensity with either threshold of 35-255 or 45-255 were significant predictors of fluorescence and had high sensitivity and specificity (p < 0.05). Fluorescence intensity and quantification had a strong association (p < 0.001). The validation of the semi-quantitative scoring system by image analysis provides a method for objective in situ observation of tissue fluorescence. The utilization of thresholding for ICG fluorescence intensity allows post hoc quantification of fluorescence when not built into the imaging system.

Temporal changes of the Oyashio water distribution east of Japan under the changing climate: development of an objective evaluation method and its application

AbstractThe Oyashio, a southern part of the western boundary current in the North Pacific subarctic gyre, carries cold and fresh seawater with abundant nutrients southward from the high-latitude, influencing regional climate in the East Asia and marine environment in the western mid-latitude North Pacific. Previously, a distribution of the Oyashio water has been evaluated by empirical temperature thresholds; for example, in spring (March–May) when the Oyashio intrudes southward into the east of Japan, the Oyashio water is defined at 100-m depth as ≤ 5 °C. However, this method is not necessarily adequate under the changing climate because upper ocean temperature may change over time due to some causes unrelated to cold water transport by the Oyashio (e.g., surface heat fluxes). In this study, we developed an objective method to evaluate the Oyashio water distribution applicable under the changing climate with a focus on a thermohaline front located at the warm- and salty-side boundary of the Oyashio water. We identified isohalines at 100-m depth best corresponding to the thermohaline front in each month and used them as the Oyashio water threshold. Using the developed method, we further investigated the springtime Oyashio water distribution east of Japan (in the North Pacific south of 43°N, 141–148°E). The area of the Oyashio water shows inter-annual variation and significant long-term decrease. It was suggested that these temporal variation and change reflect changes in a distribution of anti-cyclonic meso-scale eddies off Hokkaido, which block the southward Oyashio intrusion into the east of Japan.

Serving Up Success: Unveiling the Power of Machine Learning for Volleyball League Prediction

This study investigates the efficacy of Artificial Neural Networks (ANN) in predicting volleyball league standings, focusing on the Turkish Volleyball Federation's Sultanlar and Efeler leagues over five seasons (2018-19 to 2022-23). Given the complexity and volume of performance data in volleyball, traditional analysis methods often face challenges such as data overload and high operational costs. ANN models, known for their ability to learn from and generalize data, present a promising solution to these challenges. By analyzing 23 input variables related to match performance, including points scored, services, attacks, and blocks, this study aims to identify the most influential factors on final league standings and provide a more objective, rapid, and economical analysis method. The results indicate significant potential for ANN in sports analytics, demonstrating high accuracy rates in predictions, especially for the Sultanlar League. However, the study also acknowledges limitations such as data quality and model complexity, suggesting areas for future research to enhance predictive accuracy and applicability of ANN in volleyball and other sports analytics.

Analysis of Mobile MOBAs Design Based on Maslow's Hierarchy of Needs Analysis Theory

Modern society is accompanied by the development of Internet information technology, game exploitation technology, and the rapid popularization of smart phones. Multi-player online tactical competitive mobile games have increasingly become one of the most important choices for teenagers to reduce stress and emissions due to the convenience of carrying equipment, popularity of use, and entertainment in the process of use. This paper analyzes the main game needs of mobile game players through the questionnaire, interview, self-participation method, and objective observation method, so as to construct the correspondence between the needs of multiplayer online tactical competitive mobile games and Maslow's hierarchy of needs theory. Based on this correspondence, this paper analyzes the game behaviors and attitudes of mobile game players towards the current major multiplayer online tactical competitive mobile games, and concludes that the positive influence of respect demand, aesthetic demand, and self-actualization demand of multiplayer online tactical competitive mobile games on players' attitudes is more obvious.

VR rehabilitation system evaluator: A fNIRS-based and LLM-enabled evaluation paradigm for Mild Cognitive Impairment

Virtual Reality (VR) has significant potential for Mild Cognitive Impairment (MCI) rehabilitation by offering dynamic and engaging tasks designed to stimulate various cognitive domains. However, how efficiently evaluating the VR tasks is always challenging. The current effectiveness evaluation methods for task evaluation and optimisation typically rely on post-training metrics like game performance or subjective questionnaires, which cannot directly reflect the comprehensiveness and intensity of cognitive stimulation. To bridge these gaps, we propose a novel approach integrating functional near-infrared spectroscopy (fNIRS) data and a Large Language Model (LLM) for VR rehabilitation task evaluation and optimisation. Firstly, we leverage fundamental cognitive theories to propose a systematic evaluation paradigm for assessing the scope of the cognitive domains stimulated by VR tasks. Secondly, we propose fNIRS-derived graph parameters to objectively evaluate the cognitive stimulation with high-time resolution. Finally, we develop a prompt-generating strategy to facilitate Large Language Model (LLM)-based analysis, which transforms fNIRS-derived metrics combined with the scope of stimulated cognitive domains into easy-to-understand evaluation reports and actionable recommendations for task optimisation. We demonstrate this approach with two VR rehabilitation tasks. Several experts evaluated the LLM-based task evaluation results and task optimisation recommendation and agreed that the LLM-generated results are meaningful and applicable. This approach offers a rapid, objective method to evaluate VR task effectiveness and improves their design, marking a significant step in developing more effective MCI interventions.

Infrared remote-sensing image super-resolution based on physical characteristic deduction

In this paper, we propose a high-confidence, super-resolution method for short-wave infrared remote-sensing images based on characteristic deduction form high-resolution visible images. First, based on the radiative transmission mechanism of full remote-sensing imaging link, a ground object characterization model based on the dark object method was established to achieve the inversion of the object’s ground reflection characteristics of high-resolution visible images. Second, the reflectance information of the specified infrared band can be deduced combined with the reflectance matching of the typical ground object spectral library. On this basis, combined with the characterization modeling of remote-sensing imaging link—such as atmosphere, optical system, platform, and detector—a high-resolution infrared remote-sensing image reconstruction method is proposed. Based on the imaging degradation model between high- and low-resolution infrared images, the results of high-resolution reconstruction were corrected using measured infrared images. Lastly, the final fusion-super-resolution image can be obtained. The results show that the proposed fusion-super-resolution method yields high-resolution infrared images more realistically than traditional image fusion algorithms and shows better structural similarity index and visual information fidelity results, verifying the effectiveness of the proposed infrared remote-sensing image fusion-super-resolution method.

A COMPARATIVE STUDY ON THE VIEWS OF KARL BARTH AND EMIL BRUNNER ON NATURAL THEOLOGY AND ITS RELEVANCE TODAY

The purpose of this research is, by investigation into the views of Barth and Brunner on natural theology, to restate and emphasize its relevance for us today—in the first quarter of the twenty-first century. This is achieved via a comparative analysis—by which this research adopts the purely objective method as well as the critical theological analysis and biblical assessment. It is by design naturalistic, which means it is non- manipulative. The research-maintained neutrality and attentiveness to dynamics. The analysis is inductive; given each case its unique orientation, holistic perspective, and context sensitivity. Barth denied that God can be known outside of Christ. This is to say, he is against general revelation but only accepts special revelation. Brunner insists that there is a knowledge of God obtainable in nature — though dimmed as it may be serves as a point of contact between God and man. He accepts general revelation, but it is subservient to special revelation. The position reached in this research agrees more with Brunner, that: natural theology is relevant in that, we can make the most positive use of it for doing theology and witnessing Christ—in our time. While most works on natural theology rests merely on its importance in apologetics, this work have taken it farther, at least, for evangelistic purposes.

Research on the perception method of tiny objects in low-light and wide-field video

At present, many trackers exhibit commendable performance in well-illuminated scenarios but overlook target tracking in low-light environments. As night falls, the tracker's accuracy drops dramatically. Challenges such as high image resolution, intricate backgrounds, uneven illumination, and the resemblance between targets and backgrounds in Hawk-Eye surveillance videos make tracking small objects in low-light and wide-field scenarios exceedingly difficult for previous trackers. To address these challenges, this paper introduces an innovative approach by integrating the difference constraint method into the CF (correlation filters) tracker, which generates a change-aware mask using inter-frame difference information. In addition, a dual regression model and inter-frame difference constraint term are introduced to restrict each other for dual filter learning. In this paper, we construct a new benchmark comprising 41 night surveillance sequences captured by Hawk-Eye cameras. Exhaustive experiments are conducted on this benchmark. The results show that the proposed method maintains superior accuracy, surpasses state-of-the-art trackers in this dataset, and achieves a real-time performance of 27 fps on a single CPU, substantially advancing tiny object tracking on Hawk-Eye surveillance videos in low light and in night scenes.

The Forensic Service (the Case Study of the Sverdlovsk Region in the 1920s–1940s)

The paper describes the process of formation and functioning of a specialized forensic service in the Urals in order to successfully combat crime during a diffi-cult unstable transition period for the country. The authors put museum information resources and archival documents as the substantive foundation of the paper. It is emphasized that the formation of the service begins with the relocation of specialists who organize this work to Sverdlovsk from Ufa. It is noted that in the 1920s there was an urgent need for personnel training for this type of activity, in connection with which courses for the training of forensic and technical experts at the Criminal Investigation Department of the NKVD were actively developing. Attention is focused on the importance of the issue of methodological support. In the 1920s the first textbook on forensic expertise appeared in Russia. During the study period, along with the need to work with traditional traces found at the scene, there was a need to detect and remove traces and objects during research that required the use of physico‑chemical methods in laboratories, in connection with which physicists and chemical experts appeared. Thus, the use of special research methods for objects seized from accident sites began in the late 1920s. In their conclusions, the authors note that the formation of an expert forensic service was carried out on the basis of scientific justification and methodological support of methods to combat unsolved crimes.

Taste Sensor Assessment of Bitterness in Medicines: Overview and Recent Topics.

In recent decades, taste sensors have been increasingly utilized to assess the taste of oral medicines, particularly focusing on bitterness, a major obstacle to patient acceptance and adherence. This objective and safe method holds promise for enhancing the development of patient-friendly medicines in pharmaceutical companies. This review article introduces its application in measuring the intensity of bitterness in medicine, confirming the achievement of taste masking, distinguishing taste differences between branded and generic medicines, and identifying substances to suppress bitterness in target medicines. Another application of the sensor is to predict a significant increase in bitterness when medicine is taken with certain foods/beverages or concomitant medication. Additionally, to verify the sensor's predictability, a significant correlation has been demonstrated between the output of a bitter-sensitive sensor designed for drug bitterness (BT0) and the bitterness responses of the human taste receptor hT2R14 from BitterDB (huji.ac.il). As a recent advancement, a novel taste sensor equipped with lipid/polymer membranes modified by 3-Br-2,6-dihydroxybenzoic acid (2,6-DHBA), based on the concept of allostery, is introduced. This sensor successfully predicts the bitterness of non-charged pharmaceuticals with xanthine skeletons, such as caffeine or related compounds. Finally, the future prospects of taste sensors are discussed.

A DL method to detect multi-type hidden objects in tunnel linings using a comprehensive GPR dataset

This study proposes an enhanced detection method for tunnel lining hidden objects based on the comprehensive Ground Penetrating Radar (GPR) dataset and an improved deep learning (DL) algorithm. To mitigate the issue of data scarcity, a tailored dataset consisting of six types of hidden objects was compiled, which contained 7426 on-site collected GPR images and 71,014 well-labeled objects. This bespoke dataset offered substantial support for algorithm training and validation, making it one of the most extensive datasets in the domain for tunnel lining GPR detection. An enhanced Faster R-CNN algorithm was then developed to effectively detect different types of hidden objects. Modifications were made in four key areas: Feature extraction module, Feature Pyramid Network (FPN), customized anchor scheme and modified loss function. This algorithm was subsequently trained and validated using the comprehensive dataset. Evaluation metrics demonstrated the performance of this approach, with a mean average Precision (mAP) of 81.2%. Results also proved particularly effective in detecting rebars, pipelines, and steel arches while demonstrating a shortfall in detecting voids and cavities due to the data limitation. The results underscore the potential of this enhanced algorithm for the automatic detection of diverse hidden objects in tunnel linings, thus stimulating further research in this vital field.

Разработка методик на основе айтрекинга для диагностики когнитивных функций у детей

&lt;p style="text-align: justify;"&gt;The development of eye-movement-based diagnostic techniques is especially relevant for assessing cognitive function in children who are unable to provide a verbal or motor response due to their age or developmental disabilities. For these children, the use of objective methods for assessing cognitive function based on eye-tracking can be a useful diagnostic tool to complement traditional behavioural questionnaires and scales. In addition, such diagnostic techniques can be used to quickly screen for problems in the development of cognitive functions. In this article, we review researches on the development of such techniques. Studies show that eye-tracking-based techniques can assess attention, receptive speech, especially in young children, and provide screening assessments of intelligence in children with developmental delays. The data obtained using eye-tracking techniques generally significantly correlate with those obtained using traditional behavioural techniques, indicating that further development of such techniques is promising. The application of machine learning methods may help to create simpler and more effective screening techniques for assessing cognitive processes based on the analysis of eye movement data.&lt;/p&gt;

Algorithm optimization of architectural form through sequential landscape evaluation

Designing architectural and urban spaces based on continuous human spatial experience throughout a sequence may lead to architectural and urban forms not bound to conventional concepts. These forms would be made possible through the combination of a quantitative sequential landscape evaluation and morphological optimization utilizing algorithmic processes. In this study, we introduce a method for evaluating sequential landscapes, in which the configuration of buildings, trees, etc., in view changes as pedestrians move along a particular path, using a genetic algorithm optimization of architectural forms to bring them closer to the designer’s ideals. The method’s validity was tested using two case studies, assuming the design of a sequential landscape in an actual city and park. The results present a more efficient, objective, and reproducible method for designing sequences in buildings, cities, and landscapes, compared to traditional methods.

Categorisation of urban open spaces for heat adaptation: A cluster based approach

In the context of climate change, adapting a city's open spaces to heat waves and extreme heat is crucial for mitigating the Urban Heat Island effect and ensuring the wellbeing of its inhabitants. However, the heat adaptation potential of open spaces varies within a city. This study develops an objective and replicable method to categorise urban areas based on their open spaces' adaptive capacity and improvement potential to heat waves at the micro-scale. This is achieved using a clustering approach, eliminating manual operations, using openly available data, employing indicators suitable for every urban fabric type, and using the urban block as the analysis unit. The validation of the method in the case study of Bilbao (Spain) demonstrates its capacity to obtain meaningful insights regarding open space heat adaptation potential. An analysis of the resulting categories reveals significant open space improvement potential throughout the city and a varying adaptive capacity that depends on urban density. Categories with the lowest adaptive capacity have median open space ratios between 0.35 and 0.65 and median tree cover ratios between 0.07 and 0.15. Those with the highest adaptive capacity present median open space ratios between 0.8 and 1 and median tree cover ratios between 0.05 and 0.25. The method can aid local policymakers in identifying opportunity spots for hosting adaptation solutions and understanding the challenges the city may face in planning adaptation action. The method's replicability enables it to be applied in other cities, contributing to a broader exploration of climate change adaptation potential.

An Algorithm for Automated Measurement of Kinetic Tremor Magnitude Using Digital Spiral Drawings

Introduction: Essential tremor is a common movement disorder. Numerous validated clinical rating scales exist to quantify essential tremor severity by employing rater-dependent visual observation but have limitations, including the need for trained human raters and the lack of precision and sensitivity compared to technology-based objective measures. Other continuous objective methods to quantify tremor amplitude have been developed, but frequently provide unitless measures (e.g., tremor power), limiting real-world interpretability. We propose a novel algorithm to measure kinetic tremor amplitude using digital spiral drawings, applying the V3 framework (sensor verification, analytical validation, and clinical validation) to establish reliability and clinical utility. Methods: Archimedes spiral drawings were recorded on a digitizing tablet from participants (n = 7) enrolled in a randomized placebo control double-blinded crossover pilot trial evaluating the efficacy of oral cannabinoids in reducing essential tremor. We developed an algorithm to calculate the mean and maximum tremor amplitude derived from the spiral tracings. We compared the digitally measured tremor amplitudes to manual measurement to evaluate sensor reliability, determined the test-retest reliability of the digital output across two short-interval repeated measures, and compared the digital measure to kinetic tremor severity graded using The Essential Tremor Rating Assessment Scale (TETRAS) score for spiral drawings. Results: This algorithm for automated assessment of kinetic tremor amplitude from digital spiral tracings demonstrated a high correlation with manual spot measures of tremor amplitude, excellent test-retest reliability, and a high correlation with human ratings of the TETRAS score for spiral drawing severity when the tremor severity was rated “slight tremor” or worse. Discussion: This digital measure provides a simple and clinically relevant evaluation of kinetic tremor amplitude that shows promise as a potential future endpoint for use in clinical trials of essential tremor.

An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based On Ensemble Weather Forecast Data

AbstractIn ensemble weather forecast, tropical cyclone (TC) tracks sometimes group together into trajectories parting away from each other. The goal of this study is to propose an objective method, based on a robust clustering approach, to detect such separation scenarios in the Japan Meteorological Agency Meso‐scale Ensemble Prediction System (MEPS) for three TCs: “Dolphin” (2020), “Nepartak” (2021), and “Meari” (2022). Taking advantage of the independence of the density‐based spatial clustering of applications with noise algorithm to the prior choice of the number of clusters, we first describe an objective way to calculate the aggregation distance, by searching the most frequent Euclidean distance between all the tracks. The clustering is then applied to the forecasted tracks, for each initialization time of MEPS (every 6 hr). Separation scenarios exist when the number of clusters is greater than one.

Picture this: A novel model eye for ophthalmoscopy practice

What was the educational challenge? Ophthalmoscopy is a challenging skill to learn but essential for identifying ophthalmic pathology. Typically, there are few opportunities for early learners to practice this skill in the non-clinical setting. What was the solution? We sought to develop a model eye for the practice of direct and indirect ophthalmoscopy, with realistic dimensions and fundus images, using accessible and affordable supplies. How was the solution implemented? A simple model eye was created with over-the-counter materials. A free software program was used to modify fundus photographs so that they could be molded to the posterior pole of the model eye. What lessons were learned that are relevant to a wider global audience? This model eye can be implemented globally given the simple and affordable construction process to facilitate practice of the ophthalmoscopy technique. What are the next steps? Formally assessing the efficacy of our model eye teaching tool with learners, using both self-reported and objective methods.

Microfluidics as a Powerful Tool to Investigate Microvascular Dysfunction in Trauma Conditions: A Review of the State-of-the-Art.

Skeletal muscle trauma such as fracture or crush injury can result in a life-threatening condition called acute compartment syndrome (ACS), which involves elevated compartmental pressure within a closed osteo-fascial compartment, leading to collapse of the microvasculature and resulting in necrosis of the tissue due to ischemia. Diagnosis of ACS is complex and controversial due to the lack of standardized objective methods, which results in high rates of misdiagnosis/late diagnosis, leading to permanent neuro-muscular damage. ACS pathophysiology is poorly understood at a cellular level due to the lack of physiologically relevant models. In this context, microfluidics organ-on-chip systems (OOCs) provide an exciting opportunity to investigate the cellular mechanisms of microvascular dysfunction that leads to ACS. In this article, the state-of-the-art OOCs designs and strategies used to investigate microvasculature dysfunction mechanisms is reviewed. The differential effects of hemodynamic shear stress on endothelial cell characteristics such as morphology, permeability, and inflammation, all of which are altered during microvascular dysfunction is highlighted. The article then critically reviews the importance of microfluidics to investigate closely related microvascular pathologies that cause ACS. The article concludes by discussing potential biomarkers of ACS with a special emphasis on glycocalyx and providing a future perspective.

Generic color correction for tone mapping operators in high dynamic range imaging

One of the significant challenges in tone mapping is to preserve the perceptual quality of the tone-mapped images. Traditional tone mapping operators (TMOs) compress the luminance of high dynamic range (HDR) images with little to no consideration of image color information resulting into suboptimal colors. Existing color management algorithms require either manual parameter tuning or introducing lightness and hue shifts. The current study aimed to develop a color correction model in plausible agreement with the psychophysical phenomenon of color appearance perception for optimal color reproduction of tone mapped images. The proposed model leverages CIECAM16 perceptual correlates i.e., lightness, chroma, and hue. It is adaptive and entirely automatic while preserving the lightness of the tone-mapped image and maintaining the hue from the HDR image. Moreover, it does not require any gamut mapping correction algorithm in natural color reproduction. The effectiveness of the model was validated using objective and subjective methods. The experiments revealed that the model achieved optimal color reproduction in terms of the naturalness of the colors.

Deep Learning–Assisted Two-Dimensional Transperineal Ultrasound for Analyzing Bladder Neck Motion in Women With Stress Urinary Incontinence

BACKGROUNDNo universally recognized transperineal ultrasound parameters are available for evaluating stress urinary incontinence. The information captured by commonly used perineal ultrasound parameters is limited and insufficient for a comprehensive assessment of stress urinary incontinence. Although bladder neck motion plays a major role in stress urinary incontinence, objective and visual methods to evaluate its impact on stress urinary incontinence remain lacking. OBJECTIVETo use a deep learning–based system to evaluate bladder neck motion using two-dimensional transperineal ultrasound videos, exploring motion parameters for diagnosing and evaluating stress urinary incontinence. We hypothesized that bladder neck motion parameters are associated with stress urinary incontinence and are useful for stress urinary incontinence diagnosis and evaluation. STUDY DESIGNThis retrospective study including 217 women involved the following parameters: maximum and average speeds of bladder neck descent, β angle, urethral rotation angle, and duration of the Valsalva maneuver. The fitted curves were derived to visualize bladder neck motion trajectories. Comparative analyses were conducted to assess these parameters between stress urinary incontinence and control groups. Logistic regression and receiver operating characteristic curve analyses were employed to evaluate the diagnostic performance of each motion parameter and their combinations for stress urinary incontinence. RESULTSOverall, 173 women were enrolled in this study (82, stress urinary incontinence group; 91, control group). No significant differences were observed in the maximum and average speeds of bladder neck descent and in the speed variance of bladder neck descent. The maximum and average speed of the β and urethral rotation angles were faster in the stress urinary incontinence group than in the control group (151.2 vs 109.0 mm/s, P=0.001; 6.0 vs 3.1 mm/s, P <0.001; 105.5 vs 69.6 mm/s, P <0.001; 10.1 vs 7.9 mm/s, P=0.011, respectively). The speed variance of the β and urethral rotation angles were higher in the stress urinary incontinence group (844.8 vs 336.4, P <0.001; 347.6 vs 131.1, P <0.001, respectively). The combination of the average speed of the β angle, maximum speed of the urethral rotation angle, and duration of the Valsalva maneuver demonstrated a strong diagnostic performance (area under the curve, 0.87). When 0.481*β anglea + 0.013*URAm + 0.483*Dval = 7.405, the diagnostic sensitivity was 70% and specificity was 92%, highlighting the significant role of bladder neck motion in stress urinary incontinence, particularly changes in the speed of the β and urethral rotation angles. CONCLUSIONSA system utilizing deep learning can describe the motion of the bladder neck in women with stress urinary incontinence during the Valsalva maneuver, making it possible to visualize and quantify bladder neck motion on transperineal ultrasound. The speeds of the β and urethral rotation angles and duration of the Valsalva maneuver were relatively reliable diagnostic parameters.