Evaluation Techniques Research Articles

Research on bitter taste and efficacy of Ginkgo biloba extract based on UPLC-Q-TOF-MS~E integrated with molecular docking

Based on high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS~E) and molecular docking technique, bitter compounds of Ginkgo biloba extract(GBE) were characterized, and their relationship with bitter efficacy was investigated. Firstly, UPLC-Q-TOF-MS~E was used for qualitative analysis of GBE components, and 60 chemical components were identified. These chemical components were molecular-docked with bitter receptors, and 26 bitter substances were selected, mainly flavonoids. Secondly, sensory and electronic tongue bitterness evaluation techniques were used to verify that total flavones of GBE were the main bitter substances, which was consistent with the molecular docking results. Finally, network pharmacology was used to predict and analyze bitter substances. The relationship between the target of bitter substance and bitter effect was explored. The key targets of bitter substances are CYP2B6, ALOX15, and PTGS2, etc., and bitter substances may exert a bitter efficacy by ac-ting on related disease targets, indicating that bitter substances of GBE are the material basis of the bitter effect. In summary, the study indicated that the molecular docking technique had a guiding effect on the screening of bitter substances in traditianal Chinese medicine(TCM), and bitter substances of GBE had a bitter efficacy. It provides ideas and references for the study of the "taste-efficacy relationship" of TCM in the future.

Short Step Length Estimation for Parkinson's Disease Patients by Using Fusion Data From Camera-IMU in Smart Glasses.

Shortened step length is a prominent motor abnormality in Parkinson's disease (PD) patients. Current methods for estimating short step length have the limitation of relying on laboratory scenarios, wearing multiple sensors, and inaccurate estimation results from a single sensor. In this paper, we proposed a novel method for estimating short step length for PD patients by fusing data from camera and inertial measurement units in smart glasses. A simultaneous localization and mapping technique and acceleration thresholding-based step detection technique were combined to realize the step length estimation. Two sets of experiments were conducted to demonstrate the performance of our method. In the first set of experiments with 12 healthy subjects, the proposed method demonstrated an average error of 8.44% across all experiments including six fixed step lengths below 30 cm. The second set of straightly walking experiments were implemented with 12 PD patients, the proposed method exhibited an average error of 4.27% compared to a standard gait evaluation technique in total walking distance. Notably, among the results of step lengths below 40 cm, our method agreed with the standard technique (R 2=0.8659). This study offers a promising approach for estimating short step length for PD patients during smart glasses-based gait training.

Development of CNN-based sub-sonic speed vehicle body frame micro damage assessment technology using guided wave spectrogram images

Due to advancements in diagnostic technology, sub-sonic speed trains are subjected to significant fatigue stress on their bodies. This phenomenon can be considered a menace, leading to significant accidents on railways due to the tiny size of defects on the train bodies. In this research, a deep learning approach is employed to identify the initial damage status. The goal is to develop an evaluation technique for verifying an initial wall-thinning crack using frequency spectrogram images. The wall-thinning effects of 6.3 mm thick aluminum AL-2024 T6 plates, primarily used in frames and bodies, are confirmed through the frequency responses of guided waves. To verify the effects of wall thinning, both higher-order harmonic frequencies of guided waves and nonlinear ultrasonic techniques are simultaneously used. To identify the tendency of frequency response, frequency spectrogram images, illustrated by short-time Fourier transforming analysis, are chosen. For training the detection system using the Region-based Convolutional Neural Network (R-CNN), spectrogram images are generated for wall-thinning steps with an increment of 0.2 mm (3% of the total thickness). The ResNet-50 is employed in the convolutional layer of the R-CNN engine to address the issue of gradient vanishing. The results of this research indicate that the depth of wall thinning in the plates can be identified. Based on the R-CNN training set, the initial wall-thinning failure can be verified with reliable accuracy.

Development of an integrated and differentiated tahfidz teaching module to improve Quran memorization for students at MI Plus Al-Fatimah Sukorejo Bojonegoro

Teaching modules are independent curriculum frameworks that replace traditional lesson plans. The independent curriculum teaching module, which serves as a replacement for the RPP, has a diverse format that encompasses learning material/content, teaching methods, interpretation, and evaluation techniques. These modules are designed systematically and engagingly to meet the desired success indicators. The primary goal of developing teaching modules is to provide teachers with instructional tools that can guide them effectively in the teaching process. Teachers are given the autonomy to choose or modify the provided modules by the government to better match their students' characteristics or to create their own modules tailored to their students' needs. The developed teaching modules include integrated modules for tahfidz subjects at Madrasah Ibtidaiyah Plus Al-Fatimah, differentiated modules for tahfidz subjects at Madrasah Ibtidaiyah, guidelines for developing tahfidz teaching modules, instruments for developing these modules, and evaluation tools for assessing the module development process. This research and development (R&D) approach aims to create integrated and differentiated tahfidz teaching modules specifically for MI Plus Al-Fatimah. These modules cover the tahfidz teaching program, implementation guides, and evaluation instruments to assess the program's success. The development process involves the principal to ensure that students can achieve the target of memorizing four juz within six years at the madrasah ibtidaiyah level. The development stages include determining learning strategies and media, producing the modules, and creating assessment tools. This phased approach ensures that the integrated and differentiated tahfidz teaching modules are effective and meet the educational goals of the institution.

Data Mining Techniques Against Cyber Threats: A Review

Abstract Data mining is a technique used to extract useful data from existing databases. Those datasets are now being shared globally. Secure communication and confidentiality are required because data from multiple sources must be collected and stored in one central location. Data mining technology involves methods that rapidly and efficiently convert vast quantities of data into relevant insights adapted to the user's requirements. Unfortunately, the utilization of data mining expertise to acquire sensitive personal information poses a threat to individuals' privacy rights. This paper provides a review of the current techniques for preventing cyber risks and safeguarding privacy through the application of data mining. Data mining is used to examine, analyze, and figure out the structure and behavior of data mining organizations. Implementing data mining with optimal outcomes is a challenging task. In the past decade, academics have extensively studied many elements of data extraction. Therefore, it is crucial to provide published research evidence pertaining to this field. For this study, a thorough evaluation was carried out of more than thirty research papers sourced from reputable literature databases. The objective was to extract significant information regarding the field of data mining. The collected data was then used to address various study inquiries about cutting-edge extraction methodologies, data mining mechanisms in cyber dangers, data extraction procedures, algorithms, and evaluation techniques. This paper discusses various research areas and issues in data mining, serving as a valuable reference for researchers in this domain.

Full-Field Strain and Failure Analysis of Titanium Alloy Diamond Lattice

The advancement in additive manufacturing has significantly expanded the use of lattice structures in many engineering fields. Titanium diamond lattice structures, produced by a direct metal laser sintering process, were experimentally investigated. Two cell sizes were selected at five different relative densities. Morphological analysis was conducted by digital microscopy. The compressive tests and digital image correlation technique allowed the evaluation of elastic moduli to be used in the Gibson–Ashby model. Failure mechanisms of the structures have been analysed by digital image correlation, which represents a promising technique for strain evaluation of such structures. A non-linear finite element model of the lattice structures was developed and validated using the experimental data. The analysis of the results highlights the good mechanical properties of the Ti6Al4V alloy lattice structures.

Enhancing productivity through comprehensive evaluation and contextual analysis: A model for continuous improvement

The main objective of the case study is to measure the productivity of a laboratory dedicated to the development of dermatological products from coconut oil and other natural ingredients, as well as to analyze the influence of the external context on it in order to propose an improvement model that will allow it to improve its competitiveness in the market, strengthen its organizational structure and increase its profitability. This will be done through the application of the PESTEL analysis and the Integral Productivity Evaluation Technique in order to identify areas for improvement. It should be noted that the study is descriptive, with a mixed approach (qualitative and quantitative).

Measurement and Simulation of the Propagation of Impulsive Acoustic Emission Sources in Pipes

Abstract: Acoustic Emission (AE) testing is a non-destructive evaluation technique that has gained significant attention in pipeline monitoring. Pencil-lead breaks (PLBs) are commonly used in reproducing and characterising sensors used in AE applications and have emerged as a valuable tool for calibration processes. This technique involves breaking a pencil lead by pressing it on the surface of the test structure and applying a bending moment at a given angle on a surface. The applied force produces a local deformation on the test surface, which is released when the lead breaks. The fracture in these PLBs is assumed to be a step unload; however, this is not the case. In this work, a series of PLB source experiments complemented with parallel numerical simulations were carried out to investigate the actual unload rate by correlating the relationship between AE speed, frequency, and power from PLBs. This was achieved by varying the simulation unload rates recorded over a duration of 2 s on a steel pipe and comparing to the experiment. Analysis of the investigated results from the experimental and numerical models suggests that although the AE line structure of a PLB can be reproduced by simulation for short times only (1 µs), the actual unload rate for PLBs is in the region of 10–8 s. It is concluded that FEA has the potential to help in the recovery of the temporal structure from real AE structures. The establishment of this model will provide a theoretical basis for future studies on the monitoring of non-impulsive AE sources such as impact on pipelines using finite element analysis.

Low-Cost Imaging to Quantify Germination Rate and Seedling Vigor across Lettuce Cultivars.

The survival and growth of young plants hinge on various factors, such as seed quality and environmental conditions. Assessing seedling potential/vigor for a robust crop yield is crucial but often resource-intensive. This study explores cost-effective imaging techniques for rapid evaluation of seedling vigor, offering a practical solution to a common problem in agricultural research. In the first phase, nine lettuce (Lactuca sativa) cultivars were sown in trays and monitored using chlorophyll fluorescence imaging thrice weekly for two weeks. The second phase involved integrating embedded computers equipped with cameras for phenotyping. These systems captured and analyzed images four times daily, covering the entire growth cycle from seeding to harvest for four specific cultivars. All resulting data were promptly uploaded to the cloud, allowing for remote access and providing real-time information on plant performance. Results consistently showed the 'Muir' cultivar to have a larger canopy size and better germination, though 'Sparx' and 'Crispino' surpassed it in final dry weight. A non-linear model accurately predicted lettuce plant weight using seedling canopy size in the first study. The second study improved prediction accuracy with a sigmoidal growth curve from multiple harvests (R2 = 0.88, RMSE = 0.27, p < 0.001). Utilizing embedded computers in controlled environments offers efficient plant monitoring, provided there is a uniform canopy structure and minimal plant overlap.

Utilizing Electrochemical Techniques for Assessing the Probability of Concrete Resistivity and Corrosion Potential in Reinforced Concrete Structures

Corrosion of reinforcing steel is a major durability issue for reinforced concrete structures exposed to aggressive environments. Early assessment of corrosion probability is important for effective maintenance planning. This study investigates the application of non-destructive electrochemical methods like concrete resistivity and half-cell potential measurement, along with integrated assessment and advanced techniques, for evaluating corrosion risk in reinforced concrete. Concrete specimens with and without Albizia amara resin coating were subjected to accelerated corrosion testing. Concrete resistivity and half-cell potential values showed an inverse correlation, with lower resistivity indicating higher corrosion risk. Resistivity and potential results for the coated samples exhibited intermediate values compared to the control and corroded samples, demonstrating the resin's partial passivating effects. Rebar diameter measurements before and after exposure confirmed corrosion led to reductions in diameter and cross-sectional area, which coating mitigated. Statistical analysis validated the significant impacts of original bar diameter and corrosion level on subsequent geometry changes. Mechanical testing of reinforcing steel bars found the control samples had the highest strength and ductility, while the coated bars exhibited properties closer to the controls than the corroded bars. The protective capabilities of the bio-based Albizia amara resin coating were validated through multipoint monitoring of material durability parameters. Overall, results demonstrated the effectiveness of integrated electrochemical testing with non-destructive techniques for comprehensive condition assessment and corrosion risk evaluation in reinforced concrete structures

Energy Consumption Management of Residential Appliances Based on Load Signatures Decomposition

Nowadays, energy consumption management techniques in the residential side have gained significant importance due to their considerable influence on the control of power flow in distribution networks and especially the possibility of managing a huge part of domestic electrical demand during peak-load hours. Since the customer’s data are recorded in an aggregated form, therefore, in order to apply control approaches, it is necessary to use load pattern evaluation techniques (load signature). These methods capable to decomposition effective features that help control approaches to implement with more accuracy. In this article, features of residential loads have been extracted by using the signature of the aggregated consumer’s demand. Then these features have been evaluated by methods such as logistic regression, k-nearest neighborhood, and decision tree. By assessing the results, it was determined that among the extracted features, the first two features (consumed power and injected harmonics) covered more than 89% of the variance of the entire set, and with the help of using the principal component analysis method, it was determined that by reducing the number of features to 2, a considerable amount of computation is reduced and only about 4% of the accuracy is reduced. Also, the convolutional neural network approach was used to estimate the type of load, and by identifying controllable loads and applying remote home energy management methods, it was found that by increasing participation up to 80%, more than 41% of peak-load consumption could be shifted to off-peak hours.

Analytical model of magnetic Barkhausen noise testing on surface-modified ferromagnetic plates with stress distributions

Surface treatment is a critical technology in engineering that enhances the mechanical properties of materials, in which the stress state within the material and the thickness of the surface modification layer are essential for optimizing performance. Magnetic Barkhausen Noise (MBN) serves as an effective nondestructive evaluation technique for ferromagnetic structures. In this work, we develop an analytical model that incorporates the attenuation mechanism of the MBN signal strength, and the strength of the stress-induced magnetic Barkhausen excitation (MBE) as determined by the magneto-mechanical constitutive law of the material. The proposed theoretical model can accurately characterize the MBN signal influenced by internal stresses and surface modification layers, as confirmed by comparison with existing experimental results. The theoretical analysis shows the influence of internal stress fields, layer dimensions, and electromagnetic properties on the MBN signal, revealing a positive relationship between signal intensity and the thickness of surface modification layer and the magnitude of internal stresses. This research provides a valuable reference for interpreting MBN signals and evaluating internal stress distributions for surface-modified ferromagnetic plates.

Supervised learning to covering cost risk through post-construction evaluation of transportation projects by project delivery methods

PurposeThe main objectives of this study are to (1) develop and test a cost contingency learning model that can generalize initially estimated contingency amounts by analyzing back the multiple project changes experienced and (2) uncover the hidden link of the learning networks using a curve-fitting technique for the post-construction evaluation of cost contingency amounts to cover cost risk for future projects.Design/methodology/approachBased on a total of 1,434 datapoints collected from DBB and DB transportation projects, a post-construction cost contingency learning model was developed using feedforward neural networks (FNNs). The developed model generalizes cost contingencies under two different project delivery methods (i.e. DBB and DB). The learning outputs of generalized contingency amounts were curve-fitted with the post-construction schedule and cost information, specifically aiming at uncovering the hidden link of the FNNs. Two different bridge projects completed under DBB and DB were employed as illustrative examples to demonstrate how the proposed modeling framework could be implemented.FindingsWith zero or negative values of change growth experienced, it was concluded that cost contingencies were overallocated at the contract stage. On the other hand, with positive values of change growth experienced, it was evaluated that set cost contingencies were insufficient from the post-construction standpoint. Taken together, this study proposed a tangible post-construction evaluation technique that can produce not only the plausible ranges of cost contingencies but also the exact amounts of contingency under DBB and DB contracts.Originality/valueAs the first of its kind, the proposed modeling framework provides agency engineers and decision-makers with tangible assessments of cost contingency coupled with experienced risks at the post-construction stage. Use of the proposed model will help them evaluate the allocation of appropriate contingency amounts. If an agency allocates a cost contingency benchmarked from similar projects on aspects of the base estimate and experienced risks, a set contingency can be defended more reliably. The main findings of this study contribute to post-construction cost contingency verification, enabling agency engineers and decision-makers to systematically evaluate set cost contingencies during the post-construction assessment stage and achieving further any enhanced level of confidence for future cost contingency plans.

A Generic Framework for Managing Schedule and Cost Risks of Construction Activities Using PERT and the EV Technique

Construction projects are complexity, multidisciplinary, have thousands of activities and details, and involve many participants. This intricate and fragmented nature of construction projects coupled with tight budgets and limited resources makes them good candidates for failure and promotes the emergence of risks. These risks must be addressed in the decision-making process and properly managed to mitigate their effect. However, risk management is one of the most difficult tasks and the assessment and analysis of the cost and schedule risks of construction activities are considered the most challenging tasks in the whole risk management process and require careful considerations throughout the life cycle of a project. Despite the high cost associated with managing risk in construction projects, the outcome of this task normally provides fruitful benefits. To address this crucial issue, this study employs a mixed methodology approach utilizing both qualitative and quantitative methods to gather feedback from construction experts and identify schedule and cost risk events associated with construction activities, prioritize their likelihood of occurrence, and suggest responses to mitigate them. The Program Evaluation and Review Technique (PERT) and earned value (EV) analysis are then used to estimate the expected cost at completion and the risk associated with it. A spreadsheet framework is then developed to help construction practitioners identify the most severe risks and measure their effect on the project’s duration and cost. The framework also suggests risk responses for each of the risk events. The study then provides recommendations to mitigate risks with high impact and severity.

Update on central retinal artery occlusion.

The medical condition referred to as "central retinal artery occlusion" (CRAO) was first documented by Albrecht von Graefe in 1859. Subsequently, CRAO has consistently been identified as a serious medical condition that leads to substantial visual impairment. Furthermore, it is correlated with vascular complications that have the potential to affect crucial organs such as the brain and heart. A considerable amount of research has been extensively published on the various aspects of this topic, which is marked by notable debates and misconceptions, especially regarding its management and outcomes. The primary aim of this review article is to analyze the latest developments in the understanding of CRAO, which includes its causes, techniques for retinal imaging, systemic evaluation, and therapeutic strategies, such as vitrectomy. This review article offers readers a comprehensive learning experience to gain knowledge on the fundamental principles and recent advancements in CRAO.

LogTODIM-PROMETHEE technique for development evaluation of school-enterprise cooperation from the perspective of collaborative education based on the probabilistic linguistic group decision-making

The integration of industry and education can be literally explained as the role of education in providing intellectual support and production services for the development of industries in a country or region, evolving from an auxiliary role in industrial development to a supporting role in industrial development. The government, enterprises, universities, and intermediaries are all the main bodies of industry education integration, and school enterprise cooperation is the core. The two basic functions of universities, namely “applied scientific research” and “social services”, must be highlighted. How to strengthen the cultivation of applied talents and improve the quality of talent cultivation in universities is a problem that universities must face in their transformation. Strengthening school enterprise cooperation and promoting the integration of industry and education is an effective way to solve this problem. The development evaluation of school-enterprise cooperation from the perspective of collaborative education is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Logarithmic TODIM (LogTODIM) and PROMETHEE technique was employed to put forward the MAGDM issues. The probabilistic linguistic term sets (PLTSs) are employed as a technique for characterizing uncertain information during the development evaluation of school-enterprise cooperation from the perspective of collaborative education. In this paper, the probabilistic linguistic LogTODIM-PROMETHEE (PL-LogTODIM-PROMETHEE) technique is constructed to put forward the MAGDM under PLTSs. The MEREC technique is employed to obtain the weight values under PLTSs. Finally, a numerical example for development evaluation of school-enterprise cooperation from the perspective of collaborative education is put forward to validate the LogTODIM-PROMETHEE technique.

Virtual entertainment robot based on artificial intelligence image capture system for sports and fitness posture recognition

Currently, artificial intelligence and virtual technology are widely used in motion monitoring. Virtual entertainment robots can provide adaptive and personalized fitness services for different user groups, allowing users to experience the exercise process in games. This article studies the virtual entertainment robot based on artificial intelligence image capture system for sports and fitness posture recognition. Firstly, this article explores deep learning methods for motion recognition. By constructing a generative adversarial network to obtain discrimination results, and calculating its anti-loss function. This approach can improve the efficiency of human body movement style conversion through adversarial training. Finally, through the evaluation of movement posture and action recognition, quantitative evaluation results and targeted guidance strategies can be provided to help correct incorrect movements and master correct techniques. Using motion capture systems to identify user movements during exercise not only accurately extracts user fitness movement information, but also makes it easier to use and reduces user costs. The system uses motion evaluation techniques for evaluation to help users correct incorrect fitness habits, prevent exercise losses, and exercise more safely and effectively.

Characteristics of peripheral refractive errors in eyes of patients with non-amblyopic myopic anisometropia

BackgroundThis study aims to investigate relative peripheral refractive (RPR) characteristics in children with non-amblyopic myopic anisometropia and explore potential associations between relative peripheral refractive errors (RPRE) and myopia.MethodsRelative peripheral refractive errors were assessed in 64 children diagnosed with non-amblyopic myopic anisometropia utilizing multispectral refraction topography (MRT). Two eyes of each patient were divided into into the more myopia eyes group (ME) and the fellow eyes group (FE). Evaluated parameters encompassed total defocus values (TRDV), defocus values at eccentricities spanning 0 to 15 degrees (RDV-15), 0 to 30 degrees (RDV-30), 0 to 45 degrees (RDV-45), as well as superior (RDV-S), inferior (RDV-I), temporal (RDV-T), and nasal (RDV-N) positions.ResultsThe study revealed a noteworthy contrast in TRDV values between Group ME (0.52 ± 0.36) and Group FE (0.17 ± 0.41), with a substantial significance (P < 0.0001). While no significant RDV-15 difference emerged between Group ME (0.01 ± 0.05) and Group FE (-0.01 ± 0.07) (P > 0.05), a meaningful RDV-30 difference existed between Group ME (0.11 ± 0.14) and Group FE (0.03 ± 0.19) (P = 0.0017). A significant discrepancy in RDV-45 was also observed between Group ME (0.39 ± 0.29) and Group FE (0.13 ± 0.34) (P < 0.001). Notably, RDV-I and RDV-T positions demonstrated marked differences between Group ME and Group FE (P < 0.0001), whereas no significant disparity was noted in RDV-S and RDV-N positions (P > 0.05).ConclusionEyes exhibiting greater myopia manifested more hyperopic peripheral defocus in the context of anisometropia. MRT as a novel ophthalmic evaluation technique, holds promising potential for broader clinical applications in the future.

Cross-Validation Visualized: A Narrative Guide to Advanced Methods

This study delves into the multifaceted nature of cross-validation (CV) techniques in machine learning model evaluation and selection, underscoring the challenge of choosing the most appropriate method due to the plethora of available variants. It aims to clarify and standardize terminology such as sets, groups, folds, and samples pivotal in the CV domain, and introduces an exhaustive compilation of advanced CV methods like leave-one-out, leave-p-out, Monte Carlo, grouped, stratified, and time-split CV within a hold-out CV framework. Through graphical representations, the paper enhances the comprehension of these methodologies, facilitating more informed decision making for practitioners. It further explores the synergy between different CV strategies and advocates for a unified approach to reporting model performance by consolidating essential metrics. The paper culminates in a comprehensive overview of the CV techniques discussed, illustrated with practical examples, offering valuable insights for both novice and experienced researchers in the field.

A physical-based semi-distributed continuous hydrological modelling for Brahmani River Basin, Eastern India

ABSTRACT This paper describes a case study of continuous hydrologic modeling in the Brahmani River basin in India using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC–HMS). The soil moisture algorithm (SMA) was employed as loss method for continuous simulations in the thirteen sub-basins. The sensitivity analysis showed that soil storage, tension storage and soil percolation are most sensitive parameters. The model performance was assessed for calibration period of 15 years (1990–2004) and validation period of 12 years (2005–2016), using statistical and visual evaluation techniques. During the calibration period, the observed and simulated values showed a good fit, with R2 ranging from 0.67 to 0.74, NSE from 0.66 to 0.72, and PBIAS from −8.5 to −9.5%. Similar performances are obtained during the validation period, with R2 ranging from 0.62 to 0.70, NSE from 0.60 to 0.68, and PBIAS from −2.5 to 21.4%. The results of the SMA procedure employed in the HEC-HMS model give valuable information for flood control and water supply management in the Brahmani Basin and similar other regions in Eastern India.