Accurate Assessment Research Articles

Cardiopulmonary exercise testing excludes significant disease in patients recovering from COVID-19

ObjectivePost-COVID-19 syndrome presents a health and economic challenge affecting ~10% of patients recovering from COVID-19. Accurate assessment of patients with post-COVID-19 syndrome is complicated by health anxiety and coincident symptomatic...

Work-related injury or work-related pain? A qualitative investigation of work-related pain and injury management among rehabilitation professionals1.

Patient mobility tasks place rehabilitation professionals (Physical and Occupational Therapists) working in hospitals at high risk for work-related musculoskeletal disorders (WMSDs). However, when investigating engagement with a Safe Patient Handling and Mobility program (SPHM), administrative records at a level one trauma hospital showed that rehabilitation professionals reported zero work-related injuries over an eight-year period. As part of a qualitative study conducted to discover their unique work experiences, we explored some of the reasons that rehabilitation professionals might not report work-related injuries to their employers. Using a collective case study design, six focus groups were conducted with 25 members of the rehabilitation team within a level-one trauma hospital. Focus groups were recorded; transcripts were analyzed for emergent themes using first and second cycle coding procedures. Participants in this study denied experiencing work-related injuries but frequently described working in pain, often attributed to patient mobility tasks. These experiences were not reported to employers. Self-management of their pain through co-worker treatment, over-the-counter medications, or informal alteration in job tasks were reported as common. Administrative injury records may underrepresent injuries among rehabilitation professionals. This may be due to their perception of work-related pain as something different than work-related injuries, or that many of these rehabilitation professionals treat their own work-related pain and symptoms rather than report them. To get a more accurate assessment of injury frequency among rehabilitation professionals, researchers should gather information directly from the participants, and should inquire about work-related pain in addition to injury.

Big data-assisted student’s English learning ability appraisal model using fuzzy logic system

The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice.

Safety Assessment of Brazilian Concrete Bridges Through Reliability Analysis

This paper addresses the need for accurate and objective safety assessments of bridges in Brazil taking into account the increase in traffic load and limited funding for infrastructure. In Brazil, the usual method of ensuring bridge safety involves strengthening based on design codes using deterministic analysis and partial safety factors. This method leads to an increase in the maintenance cost of the bridges. Structural reliability assessment, a higher-tier assessment method, is recommended globally for assessing the safety of existing bridges beyond current deterministic methods. This paper presents a study that evaluates the structural safety of existing road bridges in Brazil using the semi-probabilistic method defined in Brazilian standards and the full probabilistic method. The study selected six case studies of existing reinforced and prestressed concrete bridges to evaluate their structural safety concerning current normative loads. The results suggest that the partial safety factors defined in the Brazilian standards are consistent with international standards. The reliability analysis can guarantee the bridges’ safety, even with increased traffic loads, without additional costs for structural strengthening. Therefore, the paper highlights the importance of promoting reliability analysis in Brazil to ensure the safety of existing bridges and promote cost-effective and objective safety assessments.

Menstrual Phase Identification Questionnaire (MPIQ): Development and validation of a cross-sectional survey to identify follicular and luteal phases.

Evidence continues to accumulate on the influence of the menstrual phase on several biobehavioral outcomes (e.g., substance misuse). Expansion of this knowledge is limited due to the burdensomeness of accurate menstrual phase assessment. Thus, we sought to create and validate a questionnaire that can be used as a stand-alone item within low-resource settings and numerous study designs (e.g., cross-sectional) to accurately identify both the follicular phase (FP) and the luteal phase (LP). Participants completed the self-administered four-item Menstrual Phase Identification Questionnaire (MPIQ) in two recently completed clinical trials. We assessed the accuracy of two MPIQ scoring criteria (less restrictive and more restrictive), as compared to self-report of onset of menses alone, with progesterone confirmation via dried blood spots. Participants (n = 59) were, on average, 33.7 (standard deviation [SD]: ± 4.3) years old and provided a total of 83 responses. Assessing FP and LP using the self-reported onset of menses alone classified 65.1% of the responses with an overall phase identification accuracy of 60.2%. While the more restrictive MPIQ scoring classified 100% of the responses, it yielded a similar accuracy (68.4%). In contrast, the less restrictive MPIQ scoring classified 100% of the responses and also significantly improved phase identification accuracy to 92.1% (p < .001). The MPIQ, as a stand-alone item, allows all cross-sectional responses to be classified with a high level of accuracy. This low-burden questionnaire can be used alone to identify FP and LP in studies that may be otherwise limited by study design, finances, and/or participant burden. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Observation of ozone deposition flux and its contribution to stomatal uptake over a winter wheat field in eastern China

Ground-level ozone (O3) poses a significant threat to food security in China. Therefore, it is imperative to conduct a comprehensive scientific evaluation of the potential hazards posed by O3 to agricultural crops. This study continuously observed variation in meteorological factors, O3 concentration, O3 deposition rate and flux, and quantified the distribution characteristics of O3 deposition flux in stomatal and non-stomatal pathways. Results suggested that the O3 dry deposition rate and flux in winter wheat fields exhibited relatively stable variations at night but pronounced fluctuations during the day. Their mean values were 0.31 cm s−1 and -0.0044 μmol m−2·s−1, respectively, with peak values occurring at 08:30 and 14:00. The cumulative O3 total flux during the main growth season of winter wheat was 26.1 mmol m−2, with stomatal and non-stomatal fluxes accounting for 7.7 mmol m−2 and 18.4 mmol m−2, respectively. The proportions of total O3 flux in stomatal and non-stomatal channels were 29.5% and 70.5%, respectively, with daytime proportions being 39.7% and 60.3%, respectively. These findings provide crucial insights for future accurate assessments of crop yield loss due to O3 at the earth's surface.

An Integrated Framework with ADD-LSTM and DeepLabCut for Dolphin Behavior Classification

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. This study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 key points using an open-source tool called DeepLabCut, which accurately helped mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the long short-term memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior exceeded 90%.

LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction

Data contamination in evaluation is getting increasingly prevalent with the emergence of language models pre-trained on super large, automatically crawled corpora. This problem leads to significant challenges in the accurate assessment of model capabilities and generalisations. In this paper, we propose LatestEval, an automatic method that leverages the most recent texts to create uncontaminated reading comprehension evaluations. LatestEval avoids data contamination by only using texts published within a recent time window, ensuring no overlap with the training corpora of pre-trained language models. We develop the LatestEval automated pipeline to 1) gather the latest texts; 2) identify key information, and 3) construct questions targeting the information while removing the existing answers from the context. This encourages models to infer the answers themselves based on the remaining context, rather than just copy-paste. Our experiments demonstrate that language models exhibit negligible memorisation behaviours on LatestEval as opposed to previous benchmarks, suggesting a significantly reduced risk of data contamination and leading to a more robust evaluation. Data and code are publicly available at: https://github.com/liyucheng09/LatestEval.

Computational Modelling of Intra-Module Connections and Their Influence on the Robustness of a Steel Corner-Supported Volumetric Module

This paper investigates the robustness of a single 3D volumetric corner-supported module made of square hollow-section (SHS) columns. Typically, the moment–rotation (M-θ) behaviour of connections within the module (intra-module) is assumed to be fully rigid rather than semi-rigid, resulting in inaccurate assessment (i.e., overestimated vertical stiffness) during extreme loading events, such as progressive collapse. The intra-module connections are not capable of rigidly transferring the moment from the beams to the SHS columns. In this paper, a computationally intensive shell element model (SEM) of the module frame is created. The M-θ relationship of the intra-module connections in the SEM is firstly validated against test results by others and then replicated in a new simplified phenomenological beam element model (BEM), using nonlinear spring elements to capture the M-θ relationship. Comparing the structural behaviour of the SEM and BEM, under notional support removal, shows that the proposed BEM with semi-rigid connections (SR-BEM) agrees well with the validated SEM and requires substantially lower modelling time (98.7% lower) and computational effort (97.4% less RAM). When compared to a BEM with the typically modelled fully rigid intra-module connections (FR-BEM), the vertical displacement in the SR-BEM is at least 16% higher. The results demonstrate the importance of an accurate assessment of framing rotational stiffness and the benefits of a computationally efficient model.

ЦЕНА ПРИРОДЫ: ЭКОНОМИЧЕСКАЯ ОЦЕНКА ЭКОЛОГИЧЕСКИХ РЕСУРСОВ И ЕЕ РОЛЬ В УСТОЙЧИВОМ РАЗВИТИИ

The article is devoted to the assessment of environmental resources and their role in sustainable development. In the modern world, awareness of the importance of nature conservation and sustainable use of its resources is becoming increasingly relevant. The features of the economic assessment of natural resources and ecosystem services are considered, as well as the principles and methods of determining their value are analyzed. Special attention is paid to the problems related to the need to integrate the economic assessment of nature into decision-making processes at the level of public policy and business. The importance of taking into account the ecological value of natural resources and ecosystem services in economic models and management practices is emphasized. It is concluded that it is necessary to develop mechanisms that contribute to a more accurate and objective assessment of the value of nature and its consideration in sustainable development.

Objective demonstration of eyelid spasm relief with smartphone and custom software in hemifacial spasm patients.

To objectively demonstrate eyelid spasms relief in hemifacial spasm (HFS) patients using a smartphone and a custom-made software. Nineteen patients with HFS had standardized videos recorded with a smartphone (iPhone 6S, Apple) camera before and 15days after receiving onabotulinumtoxinA injections. Nineteen age-matched control subjects were also assessed. The Eye Aspect Ratio (EAR) is an algorithm previously described to determine whether the eye is opened or closed. When the eye is closed, EAR tends to be closer to zero. Analogously, if the eye is wide open, values are greater. A custom-made software using the EAR concept was developed and pre- and post-treatment EARs were analyzed to assess HFS patients. Botulinum toxin (BoNT) injections led to a significant increase inthe average EAR of the affected side: + 10.4% (p = 0.0175) of HFS patients, compared to baseline. MeanEAR before BoNT applications were significantly lower (16.2%) on the affected side (0.25 ± 0.05) of HFS patients when compared to controls (0.30 ± 0.05, p = 0.004). After BoNT injections, no statistically significant difference was observed forthe average EAR between the affected side of HFS patients (0.27 ± 0.04) and controls (p = 0.20). Use of a smartphone and custom-made software objectively demonstrated eyelid spasm relief in patients with HFS. Additional refinement of this system could permit more accurate assessments of treatment response rates for each patient, making it possible to be used in clinical practice.

Photon-counting CT of degenerative changes and rupture of silicone breast implants: a pilot study

BackgroundAccurate assessment of breast implants is important for appropriate clinical management. We evaluated silicone properties and diagnostic accuracy for characterizing silicone implants and detecting degenerative changes including rupture in photon-counting computed tomography (PCCT).MethodsOver 16 months, we prospectively included patients with silicone implants and available breast magnetic resonance imaging (MRI) who received thoracic PCCT performed in prone position. Consensus reading of all available imaging studies including MRI served as reference standard. Two readers evaluated all implants in PCCT reconstructions for degenerative changes. In a subgroup of implants, mean density of silicone, adjacent muscle, and fat were measured on PCCT reconstructions. Contrast-to-noise ratios (CNRs) were calculated for implant-to-muscle and implant-to-fat.ResultsAmong 21 subjects, aged 60 ± 13.1 years (mean ± standard deviation) with 29 implants PCCT showed the following: high accuracy for linguine sign, intraimplant fluid (all > 0.99), peri-implant silicone (0.95), keyhole sign (0.90), and folds of the membrane (0.81); high specificity for linguine sign, intraimplant fluid, keyhole sign, folds of the membrane (all > 0.99), and peri-implant silicone (0.98); and high sensitivity for linguine sign and intraimplant fluid (all > 0.99). In a subgroup of 12 implants, the highest CNR for implant-to-muscle was observed on virtual unenhanced reconstructions (20.9) and iodine maps (22.9), for implant-to-fat on iodine maps (27.7) and monoenergetic reconstructions (31.8).ConclusionsOur findings demonstrate that silicone breast implants exhibit distinct contrast properties at PCCT, which may provide incremental information for detection of degenerative changes and rupture of implants.Relevance statementThoracic photon-counting computed tomography is a promising modality for the diagnostic assessment of silicone breast implants.Key points• Thoracic photon-counting computed tomography demonstrates unique contrast properties of silicone breast implants.• Iodine map reconstructions reveal strong contrast-to-noise ratios for implant-to-muscle and implant-to-fat.• Thoracic photon-counting computed tomography shows high diagnostic accuracy in detecting implant degeneration and rupture.Trial registrationGerman Clinical Trials Register number DRKS00028997, date of registration 2022–08-08, retrospectively registered.Graphical

Optimized machine learning models for prediction of effective stiffness of rectangular reinforced concrete column sections

To evaluate the response of a reinforce concrete (RC) building subjected to nonlinear dynamic loading, it is recommended to use effective stiffness of the members. Currently, design codes and practices provide an empirical fraction of un-cracked stiffness to consider the effect of reduced stiffness in cracked sections. However, it is imperative to conduct an accurate assessment of effective stiffness as it directly affects the distribution of forces and various demands in nonlinear dynamic analyses. Manual calculation of effective stiffness based on strength is a time-consuming process. Therefore, the present study attempts to estimate the effective stiffness ratio of rectangular column sections using machine learning algorithms with ensemble learners namely, Adaptive Boosting (AdaBoost), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Decision Tree (DT) and Categorical Gradient Boosting (CatBoost) using a database of 226 samples of nonlinear dynamic analysis results, obtained from previous work of the author. Additionally, Bayesian Optimization has been used for optimal hyperparameter selection. The results of the present study exhibit that the CatBoost model outperforms other considered machine learning models with R2 value of 0.9921 and 0.9966. A sensitivity analysis based on SHAP has also been implemented to establish the correlation between input parameters and output parameters.

Establishing age and gender-specific serum creatinine reference ranges for Thai pediatric population.

Accurate assessment of kidney function in children requires age and gender-specific reference ranges for serum creatinine. Traditional reference values, often derived from adult populations and different ethnic backgrounds, may not be suitable for children. This study aims to establish specific reference ranges for serum creatinine in the Thai pediatric population, addressing the gap in localized and age-appropriate diagnostic criteria. This retrospective study analyzed serum creatinine levels from Thai children aged newborn to 18 years, collected from the Laboratory Information System of the Queen Sirikit National Institute of Child Health from January 2017 to December 2021. The Bhattacharya method was employed to establish reference ranges, considering different age groups and genders. The study compared these newly established reference values with international studies, including those of Schlebusch H., Pottel H., and Chuang GT., to validate their relevance and accuracy. A total of 27,642 data entries (15,396 males and 12,246 females) were analyzed. The study established distinct reference ranges for serum creatinine, which varied significantly across different age groups and between genders. These ranges were found to gradually increase with age from 2 months to 18 years. The study also highlighted notable differences in reference values when compared with other ethnic populations. The study successfully establishes tailored reference ranges for serum creatinine in Thai children, providing a valuable tool for more accurate diagnosis and monitoring of kidney health in this demographic. This initiative marks a significant advancement in pediatric nephrology in Thailand and suggests a need for continuous refinement of these ranges and further research in this area.

LEVERAGING AI AND DATA ANALYTICS FOR ENHANCING FINANCIAL INCLUSION IN DEVELOPING ECONOMIES

Financial inclusion, defined as the access and usage of financial services by all individuals and businesses, is critical for fostering economic development and reducing poverty, particularly in developing economies. However, significant portions of the population in these regions remain underserved or excluded from formal financial systems. Leveraging artificial intelligence (AI) and data analytics presents a promising avenue for addressing the challenges of financial exclusion and advancing financial inclusion in developing economies. This Review explores the role of AI and data analytics in enhancing financial inclusion. Firstly, AI technologies such as machine learning and natural language processing enable the analysis of vast datasets to identify patterns, behaviors, and creditworthiness of individuals and small businesses, thereby facilitating more accurate risk assessment and decision-making by financial institutions. Additionally, AI-powered chatbots and virtual assistants offer personalized financial guidance and support, improving accessibility to financial services for marginalized populations who may have limited literacy or access to traditional banking channels. Moreover, data analytics plays a crucial role in expanding financial inclusion by providing insights into customer preferences, spending habits, and transaction histories. By harnessing these insights, financial service providers can tailor their products and services to better meet the diverse needs of underserved communities, thereby increasing uptake and usage of formal financial services. Furthermore, data analytics enables the development of alternative credit scoring models that leverage non-traditional data sources such as mobile phone usage and utility payments, allowing individuals with limited credit histories to access credit on favorable terms. However, several challenges must be addressed to fully realize the potential of AI and data analytics in enhancing financial inclusion. These include concerns related to data privacy and security, ensuring the fairness and transparency of AI algorithms, and bridging the digital divide to ensure equitable access to technology-enabled financial services. In conclusion, leveraging AI and data analytics holds significant promise for enhancing financial inclusion in developing economies. By harnessing the power of these technologies, policymakers, financial institutions, and other stakeholders can work towards building more inclusive and resilient financial systems that empower individuals and businesses to participate more fully in the formal economy.&#x0D; Keywords: AI, Data Analytics, Financial Inclusion, Developing Economies, Leveraging.

Unseen overlap between fishing vessels and top predators in the northeast Pacific.

Accurate assessments of human-wildlife risk associated with industrial fishing are critical for the conservation of marine top predators. Automatic Identification System (AIS) data provide a means of mapping fishing and estimating human-wildlife risk; however, risk can be obscured by gaps in the AIS record due to technical issues and intentional disabling. We assessed the extent to which unseen fishing vessel activity due to AIS gaps obscured estimates of overlap between fishing vessel activity and 14 marine predators including sharks, tunas, mammals, seabirds, and critically endangered leatherback turtles. Among vessels equipped with AIS in the northeast Pacific, up to 24% of total predator overlap with fishing vessel activity was unseen, and up to 36% was unseen for some individual species. Waters near 10°N had high unseen overlap with sharks yet low reported shark catch, revealing potential discrepancies in self-reported datasets. Accounting for unseen fishing vessel activity illuminates hidden human-wildlife risk, demonstrating challenges and solutions for transparent and sustainable marine fisheries.

A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment

A novel self-adjusting weight approximation procedure to minimize non-identical seasonal effects in multimodel ensemble for accurate twenty-first century drought assessment

The feasibility of Raman spectroscopy for accurate assessment of essential criteria in pharmaceutical industry by investigation of Metformin hydrochloride tableting process

AbstractAnalytical and real‐time technology in pharmaceutical manufacturing process is an important need to ensure that manufactured drugs are safe and effective. Raman spectroscopy is an emerging technique that is able to perform quantitative analysis nondestructively due to the molecular structure of many drugs. Monitoring the content uniformity and quantification of active pharmaceutical ingredient (API) in the tablet preparation process, without the assistance of solvent, is one of the key concerns in the formulation design in order to provide stable, pure, and homogenous finished products. In this study, we investigated the possibility of using Raman data as an analytical method to quantify API, the intra‐ and inter‐sample uniformity of content in the tableting process of Metformin hydrochloride tablets (C4H11N5.HCl). Analysis of all standard samples for prediction of API uniformity represents an acceptable accuracy and precision with a relative standard deviation of 2.55%. Further investigation of tablets regarding to relative Raman intensity of some characteristic peaks demonstrates the amount of API content with an accuracy of ≥96%. These values have a good adaption with pharmacopeia monograph. Findings reveal that the Raman method can be routinely utilized for quantifying API, controlling the content uniformity and the stability of drugs in different stages of manufacturing.

Comparison of statistical models for individual’s ability index and ranking

ABSTRACT Economic efficiency demands accurate assessment of individual ability for selection purposes. This study investigates Classical Test Theory (CTT) and Item Response Theory (IRT) for estimating true ability and ranking individuals. Two Monte Carlo simulations and real data analyses were conducted. Results suggest a slight advantage for IRT, but ability estimates from both methods were highly correlated (r=0.95), indicating similar outcomes. The Logistic two-parameter IRT model emerged as the most reliable and rigorous approach.

An analytical method for free vibrations of the fuel rod with non-uniform mass of small modular reactor

The intricate internal structure of fuel rods results in a non-uniform mass distribution, making it imperative to employ analytical methods for accurate assessment. The study utilizes Euler beam theory to derive the transverse vibration equation for beams with varying mass distribution. The approach involves transforming the non-uniform mass beam into a multi-segment beam with concentrated mass points. Modal function relationships between adjacent uniform segments are established based on continuous conditions at connection points. This transformation leads to the conversion of the variable coefficient differential equation into a nonlinear matrix equation. The Newton-Raphson method is then applied to calculate the characteristic equation and mode shapes, essential for determining natural frequencies. To validate precision, the results obtained are compared with those derived from the finite element method. Furthermore, the developed method is employed to assess the impact of gas plenum location and length on the natural frequency of fuel rods. The proposed methodology serves as a rapid design tool, particularly beneficial during the design phase of fuel rods with non-uniform mass distribution, aiding in configuring structural aspects effectively.