Graphical Evaluation Research Articles

The Methodology of Adaptive Levels of Interval for Laser Speckle Imaging

A methodology is proposed for use in the laser speckle imaging field. This methodology modified the graphical and numerical speckle pattern imaging methods to improve their extraction and discrimination capabilities when processing the embedded temporal activity in the images of laser speckle patterns. This is through enabling these methods to adapt the levels of speckle images’ interval during processing to speed up the process and overcome the lack of discrimination when they deal with a complex scattering medium having regions of various scales of activity. The impact of using the new methodology on the imaging methods’ performance was evaluated using graphical and numerical evaluation tests, in addition, an exceptional laser speckle imaging system was designed and implemented to undertake a series of experimental validation tests on this methodology. The evaluation and experimental validation tests show the effectiveness of this methodology on the extraction and discrimination capabilities for the standard imaging speckle pattern methods and prove its ability to provide high performance with the real images of speckle patterns. The results also show an improvement in the processing speed for both graphical and numerical methods when the adaptive levels methodology is applied to them, which reaches 78% for the graphical and 87% for the numerical speckle processing methods.

A resilience-driven emergency maintenance operation scheme optimization method based on risk

System failure caused by unexpected disruptions is unavoidable and can lead to catastrophic consequences. To complete the emergency maintenance operation safely and efficiently, a well-formulated emergency maintenance scheme is crucial. A resilience-driven optimization methodology for the emergency maintenance operation scheme with considering maintenance operational risk is proposed. The maintenance operational risk is assessed by combining Job Safety Analysis, Bayesian networks and the matter element theory. A graphical evaluation and review technique (GERT) model is established based on the informed maintenance operational risk to evaluate the emergency maintenance scheme. According to derived results of the GERT model, the expected time and the expected risk are preliminarily optimized. A re-optimization model focusing on the system resilience is established to optimize the suitable maintenance operation time for each equipment and the reasonable equipment maintenance sequence for the system. The emergency maintenance scheme optimization for the subsea production system is utilized to demonstrate the proposed method.

Enhancing Drought Risk Assessment in the Punjab, Pakistan: A Copula-Based Modeling Approach for Future Projections

Abstract Drought is one of the most complicated and challenging natural hazards, which occurs nearly in every part of the world and poses recurring challenges to agriculture, food security, livestock, human health, and water management. Pakistan has a long history of drought; however, this study focuses on drought analysis and projection in the province of Punjab, Pakistan, as it provides around 60% of the country’s food product, significantly contributing to the national food supply and economy. This study utilized the previous 56 years (1962–2017) of climate data to calculate the reconnaissance drought index (RDI) and then extracted the drought variables of durations and severity for each meteorological station. The best-fit marginal probability distribution and copula models were chosen for the stations based on numerical as well as graphical evaluation. Lognormal and exponential probability distributions, as well as Gumbel, are selected as the best-fit probability distributions for both drought characteristics and bivariate copula model, respectively, for projections. From the projections, we can infer that the smaller return periods indicate high vulnerability while longer return periods with low vulnerability. The results suggest that Faisalabad, Bahawalpur, Bahawalnagar, and Multan stations have the lowest return periods, indicating high vulnerability, and may experience drought more frequently in the future. Mianwali, Khanpur, Lahore, and Sialkot stations may have an intermediate vulnerability to drought events. The stations of Jhelum, Murree, and Sargodha have larger return periods, implying lower susceptibility to drought events in the future. The projected results provide insights for policymakers and stakeholders to optimize the risk of droughts on agriculture production, livestock, water management, human health, and food security in Punjab, Pakistan.

An investigation of mixed-model assembly line balancing problem with uncertain assembly time in remanufacturing

In recent decades, remanufacturing has emerged as an effective way to address resource crises and environmental pollution issues. Unlike traditional manufacturing, remanufacturing production is filled with various variable factors, especially in the assembly phase. Due to changes in part types, quality conditions, and assembly methods, the assembly time becomes highly uncertain. Assembly line balancing is a key challenge to achieve the stable operation of remanufacturing system. This study proposes an evaluation method for remanufacturing assembly time and establishes a multi-objective mathematical model for balancing remanufacturing mixed-model assembly (RMMA) line. The evaluation method utilizes the Fuzzy Graphical Evaluation Review Technique (FGERT) network to predict expected assembly time for each operation. The balancing model aims to optimize remanufacturing takt time and comprehensive balance rate (CBR). To effectively solve this model, an adaptive double-layer genetic algorithm (ADGA) is designed, where layer I ensures production efficiency and layer II optimizes assembly line balance. Finally, an assemble example of high-pressure common rail fuel pumps (HCRFP) is used to validate the effectiveness of the proposed method. The results demonstrate notable improvements compared to traditional single-product assembly (TSPA) line in scenarios with workstation numbers 4, 5, 6, and 7. Specifically, the production takt time is reduced by 4.19% to 9.56%, and CBR is enhanced by approximately 50%. Further comparison with three other classic algorithms confirms the superiority of ADGA. Additionally, it is observed that remanufacturability (proportion of remanufactured parts) has a significant impact on assembly performance. As remanufacturability increases, both takt time and CBR increase, reaching their maximum values when remanufacturability is around 0.5.

Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique

The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox in the four highly infected countries with the monkeypox virus. This approach involved processing the first cumulative confirmed case time series to address variance stabilization, normalization, stationarity, and a nonlinear secular trend component. After that, five single time series models and three proposed ensemble models are used to estimate the filtered confirmed case time series. The accuracy of the models is evaluated using typical accuracy mean errors, graphical evaluation, and an equal forecasting accuracy statistical test. Based on the results, it is found that the proposed time series ensemble forecasting approach is an efficient and accurate way to forecast the cumulative confirmed cases for the top four countries in the world and the entire world. Using the best ensemble model, a forecast is made for the next 28 days (four weeks), which will help understand the spread of the disease and the associated risks. This information can prevent further spread and enable timely and effective treatment. Furthermore, the developed novel time series ensemble approach can be used to forecast other diseases in the future.

A resource scheduling model for complex equipment development projects considering random overlapping and random feedback of activities

ABSTRACT Aiming to address the time and resource conflict problem caused by multiple tasks and frequent overlapping at each level in the process of complex equipment development, this article constructs a design structure matrix–graphical evaluation and review technique (DSM-GERT) hybrid model, takes the shortest duration of the project as the optimization objective and solves it using the genetic algorithm. Under the conditions of meeting the constraints of time, cost and quality, the project’s parameters are improved by adjusting the resource inputs. The study found that when project resources are insufficient, intertask messaging should be avoided as much as possible. If messaging is initiated, early messaging can be effective in reducing resource consumption, and decision makers can make trade-offs based on their preferences for resources.

Influence of oscillatory hydromagnetic field with mixed convection driven Sutterby nanofluid flow over a stretching cylinder

A nonlinear mixed convective sutterby nanofluid flow along a stretching cylinder is being studied in this article. The set of partial differential equations (PDEs) that control fluid flow is nonlinear and subject to surface constraints and similarity transformation reduces a system of PDEs to non-dimensional ordinary differential equations (ODEs), which are then solved in MATLAB Bvp5c solver which is based on the Runge–Kutta method and Shooting technique. We provide a graphic evaluation of the significant contributions made by the various physical terms to engineering measurements in the curiosity and transport domains. The study discovers that temperature distribution increases heat border viscosity but reduces thermal diffusion, whereas nonlinear convection heat improves surface heat transfer. The periodic magnetic field raises the fluid temperature while decreasing the rate of energy transfer. Furthermore, experiments have demonstrated that oscillating magnetic fields induce fluctuations with a sinusoidal pattern. Furthermore, the current study has applications in the design and manufacture of cylinder-shaped objects in various fields such as aerospace engineering, geophysics, and nuclear engineering. These include core catchers in nuclear power plant steam turbines, waxy crude oils or foodstuffs in centrifugal pumps, and deformation rates that frequently occur in practical turbomachinery and other industrial processes.

Empowering healthcare with NLP-driven deep learning unveiling biomedical materials through text mining

This study presents a comprehensive approach to automated biomedical materials discovery in healthcare applications by integrating text mining and deep learning techniques. The research methodology encompasses two main components: exploration of key research questions through graphical representation and evaluation of model performance using precision, recall, and F1-score metrics. The identification of pertinent research questions is visualized using bar charts, offering insights into the distribution of studies across domains such as data harmonization, heterogeneity, industrial textual data, and sequential data performance. The precision comparison chart highlights the strengths and weaknesses of different models, with model 1 demonstrating notable precision. The recall comparison chart emphasizes model 2’s outstanding performance in capturing relevant information, while the f1-score comparison chart showcases the balanced metrics of model 2 and 4. These visual analyses contribute to a nuanced understanding of the research landscape and guide the development of the proposed NLP-ML pipeline. The study’s findings underscore the significance of addressing data harmonization challenges and extracting insights from industrial textual data in advancing biomedical materials discovery. Overall, this research amalgamates exploratory data analysis and quantitative model evaluation to contribute to the evolving field of text mining and deep learning applications in biomedical material discovery for healthcare applications.

Neuroforensomics: metabolites as valuable biomarkers in cerebrospinal fluid of lethal traumatic brain injuries

Traumatic brain injury (TBI) is a ubiquitous, common sequela of accidents with an annual prevalence of several million cases worldwide. In forensic pathology, structural proteins of the cellular compartments of the CNS in serum and cerebrospinal fluid (CSF) have been predominantly used so far as markers of an acute trauma reaction for the biochemical assessment of neuropathological changes after TBI. The analysis of endogenous metabolites offers an innovative approach that has not yet been considered widely in the assessment of causes and circumstances of death, for example after TBI. The present study, therefore, addresses the question whether the detection of metabolites by liquid-chromatography-mass spectrometry (LC/MS) analysis in post mortem CSF is suitable to identify TBI and to distinguish it from acute cardiovascular control fatalities (CVF). Metabolite analysis of 60 CSF samples collected during autopsies was performed using high resolution (HR)-LC/MS. Subsequent statistical and graphical evaluation as well as the calculation of a TBI/CVF quotient yielded promising results: numerous metabolites were identified that showed significant concentration differences in the post mortem CSF for lethal acute TBI (survival times up to 90 min) compared to CVF. For the first time, this forensic study provides an evaluation of a new generation of biomarkers for diagnosing TBI in the differentiation to other causes of death, here CVF, as surrogate markers for the post mortem assessment of complex neuropathological processes in the CNS (“neuroforensomics”).

A comparative study of YOLOv5 and YOLOv8 for corrosion segmentation tasks in metal surfaces

This study delves into the comparative efficacy of YOLOv5 and YOLOv8 in corrosion segmentation tasks. We employed three unique datasets, comprising 4942, 5501, and 6136 images, aiming to thoroughly evaluate the models’ adaptability and robustness in diverse scenarios. The assessment metrics included precision, recall, F1-score, and mean average precision. Furthermore, graphical tests offered a visual perspective on the segmentation capabilities of each architecture. Our results highlight YOLOv8’s superior speed and segmentation accuracy across datasets, further corroborated by graphical evaluations. These visual assessments were instrumental in emphasizing YOLOv8’s proficiency in handling complex corroded surfaces. However, in the largest dataset, both models encountered challenges, particularly with overlapping bounding boxes. YOLOv5 notably lagged, struggling to achieve the performance standards set by YOLOv8, especially with irregular corroded surfaces. In conclusion, our findings underscore YOLOv8’s enhanced capabilities, establishing it as a preferable choice for real-world corrosion detection tasks. This research thus offers invaluable insights, poised to redefine corrosion management strategies and guide future explorations in corrosion identification.

Graphic Design Effect Evaluation Based on CAD and Collaborative Design

Graphic Design Effect Evaluation Based on CAD and Collaborative Design

Comparative Measurement of Horizontal Penetrometry with a Focus on the Degree of Soil Compaction in Real Work Conditions

Potential soil production is closely related to the physical and mechanical properties. The aim of this paper was to evaluate the effect of different levels of soil compaction created by tractor chassis. The total area of the experimental plot was 13.22 ha. Up until 2019, a conventional tillage system had been used. The measurements were carried out with an innovative measuring device that allows for the continuous measurement of the horizontal penetrometry for comparative measurements while driving, which was designed at the Slovak University of Agriculture in Nitra. The measuring device measured the soil resistance in the tire track (On-track) and out of track (Off-track) as well as in three (50 s) sequences within one tractor pass. Three lines were chosen, where in each a pair of combinations was made. The results were subjected, in addition to graphical evaluation, to single factor ANOVA analysis. When comparing individual passes (PH1 to PH6), the statistical analysis showed that the results of the horizontal resistance measurements proved to be statistically significant (p < 0.05) with respect to the weight, number of passes, and tire underinflation. The highest compaction was caused by the first pass, while the higher weight of the tractor during the next pass had a smaller effect. Underinflating the tires ensured a reduction in compaction. Reducing the tractor tire pressure to 0.15 MPa resulted in a reduction in soil compaction of up to 16%.

ALI: The adaptive levels of interval method for processing laser speckle images with superior activity extraction and discrimination capabilities

This paper describes the development of a new laser speckle imaging method for extracting speckle patterns' temporal activity information with superior extraction and discrimination capabilities. It is based on a novel mechanism which uses the speckle images, in an unconventional manner, through adapting the levels of their interval, to deal with the scattering medium that contains regions with different activity scales. This mechanism overcomes the discrimination inability of the other processing methods when they deal with such a medium and speeds up the process of activity map calculation. The developed speckle processing method was evaluated using numerical and graphical evaluation tests, the results showing the efficacy of using the adapting mechanism for the levels of interval. Experimental tests were also undertaken on the proposed method to validate its ability with real speckle images, obtained through a series of experiments performed using a laser speckle imaging system. The experimental results show the high discrimination and activity extraction capabilities of this method against the speckle processing methods and validate its ability to deal with multi-spectral real speckle images. The developed method achieved a faster processing performance in comparison with traditional methods when processing temporal information for the speckle images and calculating the map of their activity.

Depth of invasion as an independent prognostic factor in early-stage oral cavity squamous cell carcinoma

PurposeTo determine the significance of depth of invasion as a predictor of recurrence and mortality in tongue and non-tongue early-stage oral cavity squamous cell carcinoma patients treated with surgery and no postoperative radiotherapy. Materials and methods344 patients with oral cavity squamous cell carcinoma from 2005 to 2022 at a tertiary academic medical center were reviewed. Patients were included if they had newly diagnosed, previously untreated T1-T2N0 disease treated with surgery alone that was observed within a follow-up of 5 years. For each patient, anatomic site of oral cavity squamous cell carcinoma was categorized as either tongue or non-tongue. Cox proportional hazards regression analyses were performed to determine the association of depth of invasion with recurrence and mortality, with anatomic site, smoking status, and age at biopsy as covariates. Model assumptions were tested by statistical and graphical evaluation using Schoenfeld residuals. ResultsOf 108 patients with T1-T2N0 disease, 78 (72.2 %) had tongue disease, and 30 (27.8 %) had non-tongue disease. Median follow-up was 18.2 months (range, 0.01–58.2 months). In the Cox proportional hazards models, with adjustment for anatomic site and other covariates, depth of invasion positively predicted recurrence (HR 1.16, 95 % CI: 1.01–1.32, p = 0.034) and death (HR 1.42, 95 % CI: 1.11–1.83, p = 0.006). ConclusionsDepth of invasion is an independent predictor of recurrence and death across early-stage tongue and non-tongue squamous cell carcinoma. Therefore, depth of invasion may indicate a need for more aggressive treatment than surgery alone, such as postoperative radiotherapy, even in the absence of other adverse features on pathology within the early-stage population.

The Evaluation of Animation and Graphics

The Evaluation of Animation and Graphics

Defining the calibration process for building thermal performance simulation during the warm season: a case study of a single-family log house

ABSTRACT The study aimed to develop a holistic, evidence-based calibration strategy and define a hybrid step-by-step methodology for building thermal simulations during the warm season. The methodology was demonstrated using the thermal performance of a naturally ventilated single-family log house in a temperate climate. The calibration process was supported by on-site measurements of indoor thermal conditions and occupant behavioural reports between April and July. Hybrid calibration methodology combines statistical indicators, such as NMBE, CV(RMSE), and R 2, and a graphical evaluation of the deviation between the simulated and measured values. After 28 proposed calibration steps, a final calibrated model predicted the actual thermal response of the log house with an accuracy of ±1°C at 71.6% or ±2°C at 98.4% of the considered time. The results showed that occupant-related information, material properties and infiltration rates are crucial to improving the reliability of building thermal models. Moreover, in the calibration process of log houses, appropriate determination of building envelope materials’ thermal conductivity, wood's solar absorptivity, and airtightness showed high importance. The study highlights the importance of calibration for reliable building thermal models, specifically in overheating prevention simulations.

Multi-objective assessment of hydrological model performances using Nash–Sutcliffe and Kling–Gupta efficiencies on a worldwide large sample of watersheds

We introduce a new diagnosis tool that is well suited to analyzing simulation results over large samples of watersheds. It consists of a modification of the classical Taylor diagram to simultaneously visualize several error components (based on bias, standard deviation or squared errors) that are commonly used in efficiency criteria (such as the Nash–Sutcliffe efficiency (NSE) or the Kling–Gupta efficiency (KGE)) to evaluate hydrological model performance. We propose a methodological framework that explicitly links the graphical and numerical evaluation approaches, and show how they can be usefully combined to visually interpret numerical experiments conducted on large datasets. The approach is illustrated using results obtained by testing two rainfall-runoff models on a sample of 2050 watersheds from 8 countries and calibrated with two alternative objective functions (NSE and KGE). The assessment tool clearly highlights well-documented problems related to the use of the NSE for the calibration of rainfall-runoff models, which arise due to interactions between the ratio of simulated to observed standard deviations and the correlation coefficient. We also illustrate the negative impacts of classical mathematical transformations (square root) applied to streamflow when employing NSE and KGE as metrics for model calibration.

Global Stability with Lyapunov Function and Dynamics of SEIR-Modified Lassa Fever Model in Sight Power Law Kernel

Lassa fever is an acute viral hemorrhagic disease that affects humans and is endemic in various West African nations. In this study, a fractional-order model is constructed using the Caputo operator for SEIR-type Lassa fever transmission, including the control strategy. The proposed model examines the dynamics of Lassa fever transmission from rodents to humans and from person to person and in territories with infection in society. The model is analyzed both qualitatively and quantitatively. We examine the positively invariant area and demonstrate positive, bounded solutions to the model. We also show the equilibrium states for the occurrence and extinction of infection. The proposed nonlinear system is verified to be present, and a unique solution is shown to exist using fixed point theorems. Using the Volterra-type Lyapunov function, we investigate the global stability of the suggested system with a fractional Caputo derivative. To study the impact of the fractional operator through computational simulations, results are generated employing a two-step Lagrange polynomial in the generalized version of the power law kernel. A graphical evaluation is provided to show the simplicity and dependability of the model, and all rodents that could be source viruses are important in ecological research. The findings with a value equal to 1 are stronger, according to the comparison of outcomes with different fractional orders. The adverse effect of Lassa fever increases when all modes of transmission are taken into account, according to the study, with fractional-order findings indicating less detrimental effects on specific transmission routes.

Impact of Residential Social Deprivation on Prediction of Heart Failure in Patients With Type 2 Diabetes: External Validation and Recalibration of the WATCH-DM Score Using Real World Data.

Patients with type 2 diabetes are at risk of heart failure hospitalization. As social determinants of health are rarely included in risk models, we validated and recalibrated the WATCH-DM score in a diverse patient-group using their social deprivation index (SDI). We identified US Veterans with type 2 diabetes without heart failure that received outpatient care during 2010 at Veterans Affairs medical centers nationwide, linked them to their SDI using residential ZIP codes and grouped them as SDI <20%, 21% to 40%, 41% to 60%, 61% to 80%, and >80% (higher values represent increased deprivation). Accounting for all-cause mortality, we obtained the incidence for heart failure hospitalization at 5 years follow-up; overall and in each SDI group. We evaluated the WATCH-DM score using the C statistic, the Greenwood Nam D'Agostino test χ2 test and calibration plots and further recalibrated the WATCH-DM score for each SDI group using a statistical correction factor. In 1 065 691 studied patients (mean age 67 years, 25% Black and 6% Hispanic patients), the 5-year incidence of heart failure hospitalization was 5.39%. In SDI group 1 (least deprived) and 5 (most deprived), the 5-year heart failure hospitalization was 3.18% and 11%, respectively. The score C statistic was 0.62; WATCH-DM systematically overestimated heart failure risk in SDI groups 1 to 2 (expected/observed ratios, 1.38 and 1.36, respectively) and underestimated the heart failure risk in groups 4 to 5 (expected/observed ratios, 0.95 and 0.80, respectively). Graphical evaluation demonstrated that the recalibration of WATCH-DM using an SDI group-based correction factor improved predictive capabilities as supported by reduction in the χ2 test results (801-27 in SDI groups I; 623-23 in SDI group V). Including social determinants of health to recalibrate the WATCH-DM score improved risk prediction highlighting the importance of including social determinants in future clinical risk prediction models.

>Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China.

Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics.Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error.