Average Performance Research Articles

G20 ülkeleri içinde 18 ülkenin okyanus ve deniz sağlığı performanslarının analizi: CEBM tabanlı TOPSIS yöntemi ile bir uygulama

This study investigates the critical role of G20 nations in maintaining ocean health, given the significant influence their economic activities have on global maritime ecosystems. Employing the most recent Ocean Health Index (OHI) data (2023) and the CEBM-TOPSIS Multi-Criteria Decision Making (MCDM) method, the research assesses the ocean health performance of 18 countries G20 countries. The CEBM analysis identifies biodiversity, carbon sequestration capacity, fisheries sustainability, water quality, and coastal protection as the most important OHI criteria, respectively. According to the CEBM-TOPSIS method, Russia, Brazil, and France are the top three countries with the highest ocean health performance, while China, India, and South Africa are ranked lowest among the first three countries. Notably, the average performance score indicates that Russia, Brazil, France, the United Kingdom, Australia, Mexico, South Korea, the United States, Germany, Saudi Arabia, and Canada all exceed the average. This suggests a need for improvement among G20 countries with below-average performance to ensure a more substantial contribution to the global economy and interconnected dimensions. Finally, sensitivity, comparison, and simulation analysis validate the CEBM-TOPSIS MCDM method as a reliable tool for evaluating national ocean health performance.

Enhancing Active Disturbance Rejection Control for a Vehicle Active Stabiliser Bar with an Improved Chicken Flock Optimisation Algorithm

An active stabiliser bar significantly enhances the anti-roll capabilities of vehicles. The control strategy is a crucial factor in enabling the active stabiliser bar to function effectively. This paper investigates an active disturbance rejection control (ADRC) strategy. Given the numerous parameters of the ADRC and their significant mutual influence, optimising these parameters is challenging. To address this, an improved chicken flock optimisation algorithm is proposed to optimise the ADRC parameters and enhance its performance. First, a three-degree-of-freedom dynamic model of the vehicle is established, and an active disturbance rejection control-based optimisation model utilising a chicken flock optimisation algorithm is constructed. To tackle the issues of getting stuck in local optima and low precision when dealing with complex problems in the traditional chicken flock optimisation (CFO) algorithm, several strategies, including improved Lévy flight, have been adopted. Subsequently, the twelve parameters of the ADRC are optimised using the improved chicken flock optimisation algorithm. Comprehensive testing on multiple benchmark functions demonstrates that the improved chicken flock optimisation (ICFO) algorithm is distinctly superior to other advanced algorithms in terms of solution quality and robustness. Simulation results show that the ICFO-ADRC controller is significantly superior. In four different complex road condition tests, the ICFO-ADRC controller shows an average performance improvement of 8% compared to the fuzzy PI-PD controller, an average improvement of 82% compared to the non-optimised ADRC controller, and an average improvement of 18% compared to the CFO-ADRC controller. Our findings confirm that this paper was able to provide new solutions for vehicle stability control whilst opening up new possibilities for the application of metaheuristic algorithms.

B - 118 Exploration of Supplemental Recognition Measures for the RBANS

Abstract Objective RBANS is a widely used cognitive screener that lacks memory recognition measures for 2 recall tasks. Performance on verbal and visual recognition measures, relative to recall, may help inform clinical decision-making and treatment planning. Our pilot project aims to understand how individuals with valid cognitive data and normal memory functioning perform on newly developed RBANS story and figure memory recognition tests. Method Of those referred for outpatient neuropsychological assessment at a veterans hospital between 2017–2024, 89 received the newly established recognition measures as part of their clinical evaluation. Our analysis included veterans that displayed valid profiles (TOMM trial 1 ≥ 45, n = 53). Assessment occurred 30% face-to-face and 70% telehealth. In addition to key demographic factors listed in table 1, race/ethnicity breakdown included: 92% White, 4% Black, 2% Asian, 2% Hispanic. These veterans were subdivided into 3 intact memory groups: list recall (≥ 25%ile, n = 31), story recall (≥ 25%ile, n = 37), figure recall (≥ 25%ile, n = 42) to determine average recognition performances across domains. Results The individuals with valid cognitive data and normal memory recall performed as listed in table 1. Although our novel story and figure recognition measures differ from those published in Duff et al. (2021), our pilot project data largely overlapped with their reported findings despite demographic differences. Conclusion(s) These novel recognition scores can help establish normal story and figure recognition performance to assist with clinical diagnosis and treatment planning. With greater and more diverse samples, we may be able to establish relative cut points for expected recognition memory performances across intact, amnestic/cortical, and vascular/subcortical.

Boosting certified robustness via an expectation-based similarity regularization

A certifiably robust classifier implies the one that is theoretically guaranteed to provide robust predictions against any adversarial attacks under certain conditions. Recent defense methods aim to regularize predictions by ensuring consistency across diverse perturbed samplings around the same sample, thus enhancing the certified robustness of the classifier. However, starting from the visualization of latent representations from classifiers trained with existing defense methods, we observe that noisy samplings of other classes are still easily found near a single sample, undermining the confidence in the neighborhood of inputs required by the certified robustness. Motivated by this observation, a novel training method, namely Expectation-based Similarity Regularization for Randomized Smoothing (ESR-RS), is proposed to optimize the distance between samples utilizing metric learning. To meet the requirement of certified robustness, ESR-RS focuses on the average performance of base classifier, and adopts the expected feature approximated by the average value of multiple Gaussian-corrupted samplings around every sample, to compute similarity scores between samples in the latent space. The metric learning loss is then applied to maximize the representation similarity within the same class and minimize it between different classes. Besides, an adaptive weight correlated with the classification performance is used to control the strength of the proposed similarity regularization. Extensive experiments have verified that our method contributes to stronger certified robustness over multiple defense methods without heavy computational costs.

Enhancing Flight Delay Predictions Using Network Centrality Measures

Accurately predicting flight delays remains a significant challenge in the aviation industry due to the complexity and interconnectivity of its operations. The traditional prediction methods often rely on meteorological conditions, such as temperature, humidity, and dew point, as well as flight-specific data like departure and arrival times. However, these predictors frequently fail to capture the nuanced dynamics that lead to delays. This paper introduces network centrality measures as novel predictors to enhance the binary classification of flight arrival delays. Additionally, it emphasizes the use of tree-based ensemble models, specifically random forest, gradient boosting, and CatBoost, which are recognized for their superior ability to model complex relationships compared to single classifiers. Empirical testing shows that incorporating centrality measures improves the models’ average performance, with random forest being the most effective, achieving an accuracy rate of 86.2%, surpassing the baseline by 1.7%.

DuCFF: A Dual-Channel Feature-Fusion Network for Workload Prediction in a Cloud Infrastructure

Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services to meet the performance requirements of users. The workload prediction of the cloud plays a crucial role in proactive auto-scaling and the dynamic management of resources to move toward fine-grained load balancing and job scheduling due to its ability to estimate upcoming workloads. However, due to users’ diverse usage demands, the changing characteristics of workloads have become more and more complex, including not only short-term irregular fluctuation characteristics but also long-term dynamic variations. This prevents existing workload-prediction methods from fully capturing the above characteristics, leading to degradation of prediction accuracy. To deal with the above problems, this paper proposes a framework based on a dual-channel temporal convolutional network and transformer (referred to as DuCFF) to perform workload prediction. Firstly, DuCFF introduces data preprocessing technology to decouple different components implied by workload data and combine the original workload to form new model inputs. Then, in a parallel manner, DuCFF adopts the temporal convolution network (TCN) channel to capture local irregular fluctuations in workload time series and the transformer channel to capture long-term dynamic variations. Finally, the features extracted from the above two channels are further fused, and workload prediction is achieved. The performance of the proposed DuCFF’s was verified on various workload benchmark datasets (i.e., ClarkNet and Google) and compared to its nine competitors. Experimental results show that the proposed DuCFF can achieve average performance improvements of 65.2%, 70%, 64.37%, and 15%, respectively, in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared (R2) compared to the baseline model CNN-LSTM.

Penerapan Model PBL Melalui Pendekatan Deferensiasi untuk Meningkatkan Hasil Belajar Pendidikan Pancasila Materi Gotong Royong pada Kelas IV di SDN Dukuh Kupang II Surabaya

This study is motivated by the low academic performance of fourth-grade students in Pancasila Education, specifically in the topic of mutual cooperation. The aim of this research is to assess the improvement in learning outcomes for fourth-grade students in Pancasila Education through the implementation of the Problem Based Learning (PBL) model with a Differentiation approach. This research falls under the category of classroom action research and was conducted in two cycles. The subjects of the study were 28 fourth-grade students from SDN Dukuh Kupang II Surabaya. Data were collected through observations and tests, and then analyzed using descriptive quantitative methods. The results indicate that the implementation of the PBL model with a Differentiation approach successfully improved student learning outcomes in the topic of mutual cooperation. The average student performance was 57.14 during the pre-cycle. After implementing the PBL model in cycle I, the average score increased to 71.42, and in cycle II, it rose further to 84.72. Based on these results, it can be concluded that the PBL model with a Differentiation approach is effective in enhancing the learning outcomes of fourth-grade students on the topic of mutual cooperation at SDN Dukuh Kupang II Surabaya.

Effects of Technique Asymmetry on 500 m Speed Skating Performance.

This study aimed to determine the effects of technique asymmetry on 500 m straight-track speed skating performance. We analyzed 20 elite skaters, measuring their joint angles, center of mass shift, and times and speeds during the gliding and push-off phases. The technique asymmetry index (ASI) was calculated for each parameter, and paired t-tests were used to compare bilateral asymmetry. Spearman correlation coefficients assessed the relationship between the ASI and both the average straight track speed and overall performance. Significant bilateral asymmetries in the knee, push-off, trunk, and hip angles were found in both male and female participants (p < 0.05). The male participants demonstrated a higher right push-off speed (p = 0.029) and a longer left gliding time (p = 0.048). Significant asymmetry was also observed in the lateral shift of the center of mass during each phase of the straight-track skating gait cycle (p < 0.001). No significant correlation was found between the ASIs and the overall performance (p ≥ 0.067). These findings indicate that while elite speed skaters demonstrated significant bilateral technique asymmetry in straight track skating, these asymmetries did not significantly impact their overall performance.

Utilizing Support Vector Machines to Detect Hate Speech on Social Media

This paper presents a novel application of Support Vector Machines (SVM) in developing automated systems to detect hate speech on social media platforms, addressing a critical need for scalable solutions to enhance online safety and societal cohesion. By leveraging SVM's proven efficacy in managing high-dimensional data and optimizing the balance between precision and recall, the study offers a comprehensive methodology that includes data collection, preprocessing, model training, deployment, and evaluation. The results demonstrate robust average performance across key metrics, affirming the model's reliability in accurately identifying hate speech while minimizing false positives. This research advances the field by showcasing the practical and theoretical contributions of SVM in automated hate speech detection, highlighting its potential to significantly improve content moderation practices. The findings underscore the necessity for ongoing refinement of detection systems and collaborative efforts among researchers, technology firms, and policymakers to create more inclusive online environments that promote respectful discourse and community well-being.

Pengelolaan Cache pada Aplikasi Pencatatan Penjualan Menggunakan Fuzzy Page Replacement Algorithm

The challenges faced in website development are becoming increasingly diverse, particularly in delivering a responsive website that aligns with user preferences. Progressive Web App (PWA) is an advanced web technology that combines the advantages of mobile applications with the ease of access provided by browsers. With features such as offline access and responsiveness across various devices through the utilization of web caching, PWA can offer a better and more interactive user experience. This study aims to develop a sales recording application based on PWA, optimized using an algorithm with a fuzzy approach, namely the Fuzzy Page Replacement Algorithm (FPRA) in cache management. FPRA is applied to enhance cache efficiency through the clustering of pages based on recency, access frequency, and page reference levels using the Fuzzy C-Means algorithm. The research was conducted with a case study on the sales recording application for a fried chicken business, employing the Rapid Application Development (RAD) method for the design and construction of the application. The results indicate that FPRA successfully reduced page load time significantly, achieving up to 37% faster performance based on the average results of five tested scenarios. The implementation of FPRA in the service worker also increased the average performance score of the application by 15 points, from 56 to 71, as evaluated using the Lighthouse testing tool. Thus, the utilization of caching and the application of FPRA in cache management have been proven to enhance application performance.

PENGARUH DISIPLIN KERJA TERHADAP KINERJA KARYAWAN DI PT ALAM JAYA GRAHA

Work discipline is a crucial factor in achieving a company's goals. This study aims to determine the impact of work discipline on employee performance in the Logistics Division of PT Alam Jaya Graha. The company operates in the transportation sector and is based in Bekasi, West Java. The research was conducted using arithmetic mean and simple linear regression methods, with a sample size of 71 respondents and census is adapted. The findings indicate that the average work discipline score is 3.55, which falls into the "Good" category, and the average employee performance score is 3.56, also categorized as "Good." Furthermore, the simple linear regression analysis shows that work discipline has a positive and significant effect on employee performance (0.00 0.05). The coefficient of determination obtained is 97%, indicating that work discipline has a substantial contribution to influencing employee performance. Future research is recommended to use different methods and a combination of more independent variables

The effect of cumulative eye movements’ guidance of experts on transition from novice to expert

AbstractBased on the assumptions of cognitive load theory, this study aims to utilize the eye movement data collected from multiple experts to scaffold novice graphic designers. The study has two main stages. In the first stage, eye tracking was used to record the eye movements of 7 experts, who covered eight topics explaining how to use Photoshop. The areas of interest and fixation durations were analyzed for each topic to extract the common patterns. Instructional videos were produced with the guidance of both eye movement patterns and multimedia design principles. In the second stage of this study, those videos were delivered to 30 university students. The experiment group watched the guided videos, whereas the control group watched straight videos, i.e., content without guidance. The guidance elements were reduced (faded) as students got familiar with the interface. All participants were assigned to perform tasks as soon as they completed the videos. The findings showed that the knowledge scores did not significantly differ between groups. Still, the average performance scores of the experiment group were considerably higher than that of the control group. Considering the transition process, the experiment group outperformed the others. The topics in which experts’ eye movements highly matched each other resulted in high-performance gaps among groups.

Cardiological and Psychophysiological Reaction of Schoolchildren with Different Types of Central Gemodynamics to Prolonged Physical Activity

The present paper considers the issues of optimizing the construction of a lesson of recreational physical education by differentiating the load on the basis of data on the physical fitness of students. It also touches upon the question of the response of stress-realizing systems of schoolchildren to the physical work performed. The aim of the study is to investigate the cardiologic and psychophysiologic response of schoolchildren with different types of central hemodynamics to prolonged physical workload. Methodology. Cardiologic and psychophysiologic parameters of 74 male students aged 12–13 years were studied. The sample included only practically healthy individuals without severe chronic and acute diseases. All students were divided into three groups by physical fitness (based on the results of the PWC170 test) and three subgroups by the type of central hemodynamics (according to N.N. Savitsky: hypokinetic, eukinetic, hyperkinetic). Heart rate and attention response to running exercise of different durations with excellent rest intervals and response were evaluated. Conclusion. The results obtained indicate different reactions of schoolchildren with different types of central hemodynamics to prolonged physical load: the hypokinetic type had stable heart rate values during the load, better recovery after the load and the least decrease in attention according to the B. Bourdon test. The hyperkinetic type, on the contrary, had unstable dynamics of heart rate during exercise, the lowest rate of heart rate recovery and a pronounced decrease in attention. The eukinetic type had average performance on all tests. The results of the study can be used as a tool to optimize the methodology of the educational process through the most effective dosing of the load.

What Super Productive People Do Differently

This article explores seven habits shown through research to distinguish highly productive individuals from average performers. Each habit is grounded in academic literature and illustrated with real-world industry examples. The habits include planning the next day's schedule in advance to be proactive rather than reactive. Minimizing distractions like notifications to focus deeply on priorities. Batching similar tasks together to maintain cognitive momentum. Automating repetitive tasks to streamline workflow. Enforcing strict boundaries between work and personal time to prevent burnout. Dedicating time to continuous self-improvement through skills development. And scheduling regular "deep work" sessions for uninterrupted focus on complex cognitive work. The article argues that organizational leaders who foster a culture supporting these evidence-based productivity strategies can cultivate high-performing teams consistently delivering quality outcomes despite demanding workloads. adopting some principles could significantly increase organizational output.

Chatbot with ChatGPT technology for mental wellbeing and emotional management

&lt;p&gt;There is a growing concern among the world's population about mental health in work, academics, and other contexts where stress, anxiety, and depression are common problems that negatively impact mental health. This study evaluates a chatbot powered by ChatGPT, offering a novel perspective on emotional intervention and mental well-being. It highlights the urgency of this approach in a context where mental health is critical, providing value by combining advanced technologies with emotional management. A multi-faceted approach was implemented to evaluate both usability and technical performance. The usability of the chatbot was evaluated by users using the System Usability Scale (SUS), while the technical performance was evaluated by experts. The active participation of 15 users provided a detailed perspective, resulting in an average usability of 83, reflecting a positive experience in interacting with the system. At the same time, five experts, through technical metrics, assigned an average technical performance of 4.28, indicating solid operational effectiveness. In conclusion, although more research is needed to customize and optimize chatbots over the long term, this approach holds promise for addressing mental health issues in a variety of settings and represents the integration of artificial intelligence to the benefit of those seeking help managing emotional disorders.&lt;/p&gt;

Assessing Team Performance in a Longitudinal Neonatal Resuscitation Simulation Training Program: Comparing Validity Evidence to Select the Best Tool.

Introduction Neonatal resuscitation is a high-acuity, low-occurrence event that requires ongoing practice by interprofessional teams to maintain proficiency.Simulation provides an ideal platform for team training and evaluation of team performance. Our simulation center supports a longitudinal in situ simulation training program for delivery room teams. In addition to adherence to the Neonatal Resuscitation Program standards, team performance assessment is an essential component of program evaluation and participant feedback. Multiple published teamwork assessment tools exist. Our objective was to select the tool with the best validity evidence for our program's needs. Methods We used Messick's framework to assess the validity of evidence for potential teamwork assessment tools. Four possible tools were identified from the literature: the Mayo High Performance Teamwork Scale (Mayo), Team Performance Observation Tool (TPOT), Clinical Teamwork Scale (CTS), and Team Emergency Assessment Measure (TEAM). Relevant context included team versus individual focus, external evaluator versus self-evaluation, and ease of use (which included efficiency, clarity of interpretation, and overall assessment). Three simulation experts identified consensus anchors for each tool and independently reviewed and scored 10 pre-recorded neonatal resuscitation simulations. Raters assigned each tool a rating according to efficiency, ease of interpretation, and completeness of teamwork assessment. Interrater reliability (IRR) was calculated using intraclass correlation for each tool across the three raters. Average team performance scores for each tool were correlated with neonatal resuscitation adherence scores for each video using Spearman's rank coefficient. Results There was a range of IRR between the tools, with Mayo having the best (single 0.55 and multi 0.78). Each of the three raters ranked Mayo optimally in terms of efficiency (mean 4.66 + 0.577) and ease of use (4+1). However, TPOT and CTS scored highest (mean 4.66 ± 0.577) for overall completeness of teamwork assessment. There was no significant correlation to NRP adherence scores for any teamwork tool. Conclusion Of the four tools assessed, Mayo demonstrated moderate IRR and scored highest for its ease of use and efficiency, though not completeness of assessment. The remaining three tools had poor IRR, which is not an uncommon problem with teamwork assessment tools. Our process emphasizes the fact that assessment tool validity is contextual. Factors such as a relatively narrow (and high) performance distribution and clinical context may have contributed to reliability challenges for tools that offered a more complete teamwork assessment.

Current Status and Future Perspectives for Mobility Options Toward Sustainable Transportation With a Focus on Fuel Cells

ABSTRACTThis paper presents a comprehensive analysis of five different vehicle technologies with a particular emphasis on fuel cell vehicles (FCVs) in comparison to conventional gasoline and diesel ICEs, hybrid electric vehicle (HEV), and electric vehicle (EV) technologies. Key parameters in comparison included greenhouse gas emissions (GHG), social cost of carbon (SCC), energy efficiency, fuel consumption, fuel price, driving range, and total cost of ownership (TCO). A normalization approach is employed to rank the technologies across parameters, providing valuable insights for sustainable transportation decision‐making. FCV technology showed the highest average performance (5.343/10) followed by EV technology (4.760/10). The paper also presents current and anticipated hydrogen demand and supply centers, accompanied by a brief overview of commercially available fuel cell technologies. Finally, the sustainability of FCV technology as well as economic, legal, and infrastructural limitations associated with the widespread adoption are also discussed.

Artificial intelligence in orthopaedic education: A comparative analysis of ChatGPT and Bing AI's Orthopaedic In‐Training Examination performance

AbstractBackgroundThis study evaluated the performance of generative artificial intelligence (AI) models on the Orthopaedic In‐Training Examination (OITE), an annual exam administered to U.S. orthopaedic residency programs.MethodsChatGPT 3.5 and Bing AI GPT 4.0 were evaluated on standardised sets of multiple‐choice questions drawn from the American Academy of Orthopaedic Surgeons OITE online question bank spanning 5 years (2018–2022). A total of 1165 questions were posed to each AI system. The performance of both systems was standardised using the latest versions of ChatGPT 3.5 and Bing AI GPT 4.0. Historical data of resident scores taken from the annual OITE technical reports was used as a comparison.ResultsAcross the five datasets, ChatGPT 3.5 scored an average of 55.0% on the OITE questions. Bing AI GPT 4.0 scored higher with an average of 80.0%. In comparison, the average performance of orthopaedic residents in national accredited programs was 62.1%. Bing AI GPT 4.0 outperformed ChatGPT 3.5 and Accreditation Council for Graduate Medical Education examinees, and analysis of variance analysis demonstrated p &lt; 0.001 among groups. The best performance was by Bing AI GPT 4.0 on OITE 2020.ConclusionGenerative AI can provide a logical context across answer responses through its in‐depth information searches and citation of resources. This combination presents a convincing argument for the possible uses of AI in medical education as an interactive learning aid.

ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications. A novel parallel graph convolutional network model for CPI prediction (ParaCPI) is proposed in this study. This model constructs feature representation of compounds using a unique approach to predict unknown CPIs from known CPI data more effectively. Experiments are conducted on five public datasets, and the results are compared with current state-of-the-art (SOTA) models under three different experimental settings to evaluate the model's performance. In the three cold-start settings, ParaCPI achieves an average performance gain of 26.75%, 23.84%, and 14.68% in terms of area under the curve compared with the other SOTA models. In addition, the results of the experiments in the case study show ParaCPI's superior ability to predict unknown CPIs based on known data, with higher accuracy and stronger generalization compared with the SOTA models. Researchers can leverage ParaCPI to accelerate the drug discovery process.

Construction and Performance Evaluation of a Vegetable Shredding Machine

The need for vegetables because of its economic significance and nutritive value cannot be overstated with the expanding global population. As a way of adding value to vegetables before using them domestically, a portable shredding machine has been developed in order to evade the challenges of time consumption, contamination and wounds associated with the manual method of shredding vegetables by means of sharp knife. Vegetable shredding is a process of cutting vegetables into thin slices or pieces using a sharp knife or grater. The vegetable shredding machine which works on shear principle was fabricated using locally sourced materials and tested. The performance evaluation test shows that the machine driven by a 0.5-hp electric motor has an average performance shredding time of 29.2 seconds with a shredding efficiency of 75.28% and a throughput capacity of 17.25 g/secs. The vegetable shredder consists of simple components that can be effortlessly assembled. Hence, the operation and maintenance of the machine are quite simple and easy