Assessment Methods Research Articles

Assessment methods and learning styles for learning satisfaction: the role of self-efficacy

Assessment methods and learning styles for learning satisfaction: the role of self-efficacy

A framework for categorizing sources of uncertainty in in silico toxicology methods: considerations for chemical toxicity predictions.

Improving regulatory confidence and acceptance of in silico toxicology methods for chemical risk assessment requires assessment of associated uncertainties. Therefore, there is a need to identify and systematically categorize sources of uncertainty relevant to the methods and their predictions. In the present study, we analyzed studies that have characterized sources of uncertainty across commonly applied in silico toxicology methods. Our study reveals variations in the kind and number of uncertainty sources these studies cover. Additionally, the studies use different terminologies to describe similar sources of uncertainty; consequently, a majority of the sources considerably overlap. Building on an existing framework, we developed a new uncertainty categorization framework that systematically consolidates and categorizes the different uncertainty sources described in the analyzed studies. We then illustrate the importance of the developed framework through a case study involving QSAR prediction of the toxicity of five compounds, as well as compare it with the QSAR Assessment Framework (QAF). The framework can provide a structured (and potentially more transparent) understanding of where the uncertainties reside within in silico toxicology models and model predictions, thus promoting critical reflection on appropriate strategies to address the uncertainties.

A comprehensive framework for assessing spatial conflicts risk: a case study of production-living-ecological spaces based on social-ecological system framework

Against the backdrop of rapid global urbanization, the expansion of urban-rural spaces has led to conflicts among production-living-ecological spaces, resulting in a progressive decline in global ecological well-being. Coordinating spatial conflicts to minimize the reduction in ecological well-being is a critical scientific issue urgently needing resolution in current national spatial planning. In fact, the existing studies concentrates on the mechanisms, conceptual implications, evolutionary processes, and optimization simulations of historical spatial conflicts, yielding significant achievements. However, spatial conflicts are irreversible. Compared to studying historical spatial conflicts, assessing spatial conflicts risk which refer to potential occurrence of future spatial conflicts can provide more direct and robust support for optimizing territorial spatial planning. Unfortunately, there is a lack of research on the theory and methods of spatial conflicts risk assessment to date. To fill this gap, take the Production-Living-Ecological (PLE) spaces as a case study, social-ecological system framework (advance in deconstructing complex systems) and the land use function theory (advance in spatial quantification) have been combined to systematically construct a comprehensive framework and method for assessing PLE spatial conflicts risk. This newly framework analyzes the formation mechanism of conflicts risk from the dimensions of spatial pattern and governance. Furthermore, a quantitative and refined assessment method for PLE space conflicts risk at the raster scale is developed based on this framework, coupling coordination degree model, and multivariate data. The feasibility of this framework and method is validated through a case study of Zhangbei County, Hebei Province in China, a typical county in an ecologically fragile mountainous area. Moreover, we establish adaptive selection strategy to PLE space optimization under different governance scenarios based on the assessment result of PLE space conflicts risk. Findings of this research contribute new theoretical and methodological insights into addressing spatial conflicts issues while we are facing a more complex regional and urban system and fragmented governance. The case study will serve as a reference for counties in the northern mountainous area of Hebei Province and similar ecologically fragile mountain counties in planning and controlling coordinated and sustainable development of PLE space.

Investigation of Electrochemical Fingerprints for Buzhong Yiqi Tang Extract Granules Using Diamond-Graphene Composite Electrode

This study presents a novel electrochemical fingerprinting method for Buzhong Yiqi Tang extract granules using a diamond-graphene composite electrode. The electrode was fabricated through chemical vapor deposition of graphene on high-pressure high-temperature synthetic diamond, resulting in a few-layer graphene coating as confirmed by Raman spectroscopy (I₂D/IG ratio ≈ 1.2) and XPS analysis (78% sp² C-C signal). Cyclic voltammetry revealed four anodic and three cathodic peaks, while differential pulse voltammetry provided enhanced resolution with eight distinct oxidation peaks. The method demonstrated high sensitivity (LODs 0.08-0.15mg/mL) and good linearity (R² > 0.995) over a concentration range of 0.5-10mg/mL. Comparison of fingerprints from individual herb extracts (Astragalus membranaceus, Panax ginseng, and Glycyrrhiza uralensis) elucidated the origins of major peaks in the Buzhong Yiqi Tang profile. Principal component analysis of fingerprints successfully differentiated between samples from three manufacturers, accounting for 87.3% of total variance. Strong correlations (r > 0.92) were observed between key DPV peak intensities and HPLC-determined concentrations of traditional chemical markers. The rapid analysis time, minimal sample preparation, and holistic representation of multiple electroactive components make this approach a promising complement to existing chromatographic methods for quality assessment of traditional Chinese medicine formulations.

New impulse-based test method for early-age elastic modulus measurement in cementitious materials

Accurate monitoring of the elastic modulus evolution during the early-age fluid-solid transition in cementitious materials is crucial for understanding their mechanical behavior under early stresses. While existing methods, such as EMM-ARM, have proven effective, they are often associated with high costs or complex setups. This study presents the development of a low-cost, easy-to-implement system that employs impulse excitation applied to a composite beam, introducing the EMM-IRM method for the effective assessment of the elastic modulus of cement paste from the earliest stages of hydration. The use of impulse excitation is particularly advantageous in low-cost systems with accelerometers of lower sensitivity, as it enhances signal clarity and reduces interference from background noise, thereby improving measurement reliability. The system was validated through experimental comparisons with conventional EMM-ARM, cyclic compression, and traditional impulse excitation techniques, using Portland cement paste as the test material. The results show that EMM-IRM consistently estimates the elastic modulus, with a maximum deviation of 6.33% from other methods. Moreover, despite its cost-effectiveness, EMM-IRM demonstrates a higher signal-to-noise ratio compared to conventional EMM-ARM. This approach offers a practical solution for early-age characterization of cementitious materials, balancing cost, simplicity, and measurement accuracy.

A new holistic perspective to assess the ecological risk of microplastics: A case study in Baiyangdian Basin, China

By integrating probabilistic ecological risk assessment with the overall risk index method, which considers the multidimensional characteristics of the microplastome, the ecological risks of microplastic pollution were assessed more comprehensively. This study took the Baiyangdian Basin as an example to address the limitations of current risk assessment methods that rely on concentration data or the individual risk of microplastics. Using an exponential regression model, the acute and chronic ecological risk thresholds for the overall risk index method were determined to be 0.43 and 0.30, respectively. The acute and chronic ecological risks of the microplastome occupied 61% and 79% of the Baiyangdian Wetland and 0% and 14% of the Fu River, while the Xiaoyi River did not exhibit risk during the rainy season. Results indicated that intense human activities, poor hydrodynamics, low settling velocity and high levels of environmental chemical pollutants jointly contributed to the high risk of the microplastome in water bodies. Compared with the probabilistic ecological risk assessment method (risk characterization ratio), there was a significant difference in the area of acute and chronic ecological risks caused by the microplastome in the Baiyangdian Basin when using the overall risk index method. This proved that considering only concentration cannot truly reflect the toxicity of microplastics to aquatic organisms.

Mitochondrial quality control measures, systemic inflammation, and lower-limb muscle power in older adults: a PROMPT secondary analysis

ObjectivesThe study was conducted to explore associations between markers of mitochondrial quality control (MQC) from vastus lateralis muscle biopsies, serum inflammatory markers, and measures of muscle power assessed by two different tools in a sample of older adults. DesignSecondary analysis of data collected in the PeppeR develOpMental ProjecT (PROMPT) at the University of Florida (Gainesville, FL, USA). MethodsForty-three older adults (n = 20 women) were included in the study. Muscle volume of the calf and thigh was quantified by three-dimensional magnetic resonance imaging. Lower-limb muscle power was estimated using 5-time sit-to-stand (5STS) muscle power equations and isokinetic test. Protein markers of MQC were measured in muscle samples by Western immoblotting (n = 12–23), while type I and II fiber cross-sectional area (CSA) and their proportion were quantified using immunohistochemistry (n = 12). Cytochrome C oxidase enzyme activity was measured spectrophotometrically. Finally, inflammatory markers were quantified in the serum using a multiplex immunoassay (n = 39). ResultsMean age of participants was 78.1 ± 5.5 years, and the average body mass index was 26.2 ± 4.5 kg/m2. Markers of mitochondrial biogenesis (i.e., PGC-1α), mitochondrial import proteins (i.e., cHsp70 and mtHsp70), and type I fiber CSA were significantly associated with muscle power estimated via both 5STS muscle power equations and isokinetic test (p < 0.05). Specific associations were also found according to the muscle power assessment method. 5STS muscle power measures were negatively correlated with ClvCasp3, P-AMPK, T-AMPK, P-p38, GM-CSF, INF-γ, IL1b, IL6, IL8, and TNF-α, whereas positive associations were found with BAX (p < 0.05). In contrast, isokinetic measures were significantly and positively correlated with RIP140, Hsp60, and type II muscle fiber CSA (p < 0.05). ConclusionsMarkers of mitochondrial biogenesis (PGC-1α), mitochondrial import proteins (cHsp70 and mtHsp70), and type I muscle fiber CSA were significantly linked to lower-limb muscle power in older adults. These results suggest that muscle power is influenced by mitochondrial signaling. We also found that the relationship between mitochondrial mediators, inflammatory markers, and muscle power varied according to the assessment tool used.

FedFAIM: A Model Performance-Based Fair Incentive Mechanism for Federated Learning

Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning paradigm. To motivate data owners to contribute towards FL, research on FL incentive mechanisms is gaining great interest. Existing monetary incentive mechanisms generally share the same FL model with all participants regardless of their contributions. Such an assumption can be unfair towards participants who contributed more and promote undesirable free-riding, especially when the final model is of great utility value to participants. In this paper, we propose a Fairness-Aware Incentive Mechanism for federated learning (FedFAIM) to address such problem. It satisfies two types of fairness notion: 1) aggregation fairness, which determines aggregation results according to data quality; 2) reward fairness, which assigns each participant a unique model with performance reflecting his contribution. Aggregation fairness is achieved through efficient gradient aggregation which examines local gradient quality and aggregates them based on data quality. Reward fairness is achieved through an efficient Shapley value-based contribution assessment method and a novel reward allocation method based on reputation and distribution of local and global gradients. We further prove reward fairness is theoretically guaranteed. Extensive experiments show that FedFAIM provides stronger incentives than similar non-monetary FL incentive mechanisms while achieving a high level of fairness.

Stochastic assessment of voltage sags and techno-economic optimization

Power quality has become an essential concern for industries and domestic energy consumption with the development of sophisticated equipment like micro-electronic devices. When a power quality event happens, such as voltage sag, financial losses for industries increase tremendously. Therefore, voltage sag evaluation becomes crucial for predicting the number of events and severity of voltage sag events of the electrical network. Industries can determine the mitigation investment needed for optimal operation using the knowledge obtained through voltage sag analysis method. Thus, the first part of the study addressed the development of a stochastic assessment method for voltage sag at an interested bus in the power system network, which was an initial objective of the study. The second part of the research investigates dynamic voltage restorer (DVR) control techniques, as well as the necessary capacitance and energy storage dimensions. The studies also recommend a technique for economic evaluation that emphasizes the expense of voltage sag events as well as the cost of investment in DVR with and without voltage sag events. The size of DVR and economic assessment model established in these studies enables industries to rate the cost of voltage sag and the total investment in mitigation devices.

G2LCPS: End-to-end semi-supervised landmark prediction with global-to-local cross pseudo supervision for airway difficulty assessment

Difficult tracheal intubation is a major cause of anesthesia-related injuries, including brain damage and death. While deep neural networks have improved difficult airways (DA) predictions over traditional assessment methods, existing models are often black boxes, making them difficult to trust in critical medical settings. Traditional DA assessment relies on facial and neck features, but detecting neck landmarks is particularly challenging. This paper introduces a novel semi-supervised method for landmark prediction, namely G2LCPS, which leverages hierarchical filters and cross-supervised signals. The novelty lies in ensuring that the networks select good unlabeled samples at the image level and generate high-quality pseudo heatmaps at the pixel level for cross-pseudo supervision. The extended versions of the public AFLW, CFP, CPLFW and CASIA-3D FaceV1 face datasets and show that G2LCPS achieves superior performance compared to other state-of-the-art semi-supervised methods, achieving the lowest normalized mean error (NME) of 3.588 when only 1/8 of data is labelled. Notably, the inclusion of the local filter improved the prediction by at least 0.199 NME, whereas the global filter contributed an additional improvement of at least 0.216 NME. These findings underscore the effectiveness of our approach, particularly in scenarios with limited labeled data, and suggest that G2LCPS can significantly enhance the reliability and accuracy of DA predictions in clinical practice. The results highlight the potential of our method to improve patient safety by providing more trustworthy and precise predictions for difficult airway management.

High-resolution 3D hydrodynamic modeling and comprehensive assessment of tidal current energy resources

As a clean and renewable resource, tidal current holds significant development potential. However, accurately assessing tidal current energy resources has been a persistent challenge. This study proposes a comprehensive assessment method using a small-scale, high-precision, multi-criteria three-dimensional hydrodynamic model to assess resource reserves applied to the Zhairuoshan Waterway. The hydrodynamic model was employed to simulate critical parameters such as tidal level, flow velocity, and flow direction. Multiple statistical indicators were used to evaluate the model's accuracy, showing consistency. The study analysed the flow speed distribution over a complete cycle, revealing an average flow speed of 1.51 m/s, with a maximum speed reaching 3.83 m/s. The generally high flow speeds within the waterway, coupled with the stability of the tidal current, indicate the significant resource potential of the study area. A quantitative analysis of the spatial distribution of tidal current energy was conducted using energy flux density, and the results showed that high energy density areas are widely distributed. Furthermore, the theoretical tidal energy resources of the waterway were calculated, showing that the maximum resource reserves could reach 9.98 MW at the selected cross-sections. Coupled with the analysis of unit-width energy flux, the high-potential areas within the waterway were further identified.

An evaluation method for synergistic effect of air pollutants and CO2 emission reduction in the Chinese petroleum refining technology

The screening and evaluation of co-control technologies for pollution reduction and carbon mitigation represent a crucial prerequisite for researching pathways toward pollution and carbon reduction within petroleum refining industry. A systematic assessment method for technology synergies in pollution reduction and carbon mitigation remains deficient in China. In this study, we propose a method for evaluating the synergistic effects of technologies in pollution reduction and carbon mitigation in the petroleum refining industry. This model evaluates the synergistic control efficacy of technologies from multiple perspectives, including pollution-carbon synergy, synergistic reduction of pollutant-carbon emissions, cost-benefit analysis, and environmental benefits. The developed methodology is validated through its application to typical petroleum refining technology. The results confirm the synergistic benefits of pollution and carbon reduction through five petroleum refining technologies. With the increase in carbon trading prices, bio-jet fuels (T1) and microalgae carbon sequestration (T5) technologies have greater application potential. This study provides a scientific basis and theoretical support for the realization of collaborative control of carbon pollution in the petroleum refining industry.

Credit risk assessment method driven by asymmetric loss function

Credit risk assessment is significantly hindered by the problem of class imbalance, and cost-sensitive methods represent an effective strategy to address this issue. However, most algorithms tend to approach the imbalance from a class perspective, overlooking the finer details at the sample level. Moreover, such methods are susceptible to interference from noise and outliers. In response to these challenges, this paper proposes an asymmetric cost-sensitive support vector machine (QTSVM) that combines the quadratic type squared error loss function (QTSELF) with support vector machine (SVM). It not only leverages the asymmetry of the loss function by applying varying penalties based on classification errors but also employs different processing measures for samples from different classes. Additionally, it enhances model robustness by imposing a tiny penalty on noise or outliers. The adaptive moment estimation (Adam) algorithm is employed to optimize QTSVM. Extensive experiments and statistical tests profoundly demonstrate the superior performance of QTSVM.

A Data-Driven Seismic Fragility Model for Post-Earthquake Repairable Performance of Highway Curved Bridge Portfolios

The rapid assessment of seismic fragility for highway curved bridges is crucial for enhancing the seismic resilience of transportation infrastructure. However, commonly used performance limit states fail to fully capture post-earthquake rehabilitation requirements. To address this, this paper proposes a data-driven, system-level seismic fragility assessment method for post-earthquake repairable performance of highway curved bridge portfolios. A sample database of curved bridge portfolios is generated using uniform design (UD) and a parametric finite element program, which then drives machine learning (ML) algorithms to develop a seismic fragility prediction model for post-earthquake repairable performance. The model is used to analyze the influence of various structural attributes on the median values of fragility curves (mR) for curved bridges. Additionally, a simplified fragility estimation method is developed based on the analysis results, and its reliability is validated through typical case studies. The results show that the central discrepancy of the UD sampling method is 0.0649, providing reliable and comprehensive data support for the ML prediction model. The fragility prediction model based on an artificial neural network (ANN) exhibits favorable fidelity and robustness. Curved bridges with central angles (α) in the range of 00.5rad show significant variations in mR, with a coupling effect observed between α and column height (H). The median absolute percentage error (MAPE) of the simplified fragility parameter calculation formula is below 11%, offering a preliminary reference for assessing the post-earthquake repairable performance of curved bridges.

Evaluating agriculture 4.0 decision support systems based on hyperbolic fuzzy-weighted zero-inconsistency combined with combinative distance-based assessment

Agriculture 4.0 plays a crucial role in shaping sustainable cities and societies by revolutionizing urban food systems. By incorporating advanced technologies like precision farming, vertical gardening, and data analytics, Agriculture 4.0 improves local food production, reduces food transportation, and optimizes resource utilization. This paper introduces an innovative approach using Multi-Criteria Decision-Making (MCDM) to assess Agriculture 4.0 Decision Support Systems (ADSS), contributing significantly to the selection of optimal systems that can drive sustainability in smart agriculture. The novelty of this research lies in developing a comprehensive evaluation framework that extends the hyperbolic fuzzy-weighted zero-inconsistency method for criteria weighting, combined with the combinative distance-based assessment method for benchmarking ADSS. The assessment matrix evaluates 13 ADSS across eight key criteria, including “accessibility,” “re-planning,” “expert knowledge,” “interoperability,” “scalability,” “uncertainty and dynamic factors,” “prediction and forecast,” and “historical data analysis”. Results from the hyperbolic fuzzy-weighted zero-inconsistency approach highlight “re-planning” (0.143) and “prediction and forecast” (0.140) as the most significant criteria, while “expert knowledge” ranked lowest (0.113). In the combinative distance-based assessment, the system labelled “OCCASION” achieved the highest score (3.843), positioning it as the most favourable ADSS, whereas the “MOLP-based beef supply chain” system scored lowest (−3.519). Sensitivity analysis, conducted using varying sets of weights, confirms the robustness and reliability of the proposed approach. This research provides a powerful decision-making tool that can guide stakeholders in selecting the best ADSS, ultimately promoting sustainability and resource optimization in Agriculture 4.0. The findings have important implications for farmers, agribusiness, and smart agriculture, demonstrating the potential of the methodology to enhance decision-making processes in a critical sector.

Individual and System Level Factors Contributing to Guideline Non-Adherent Surgical Antibiotic Prophylaxis at a Tertiary Health Care System: A Qualitative Analysis

Background Antibiotics play a crucial role in preventing surgical site infections, yet adherence to Infectious Disease Society of America (IDSA) guidelines varies widely. This qualitative study aimed to explore factors influencing perioperative antibiotic administration and assess the potential impact of a clinical decision support (CDS) tool on guideline adherence. Methods In this qualitative study, perioperative personnel with diverse roles (surgeons, anesthesiologists, certified nurse anesthetists, trainees, and pharmacists) were interviewed using a semi-structured interview format from September 2023 through April 2024. Interviews were then analyzed for codes which were assigned to concepts using the constant comparison method for assessment of factors that were described as barriers or facilitators of guideline adherence. Results After piloting with three interviews, we conducted nine sessions with a total of 17 participants: 7 attending anesthesiologists, 3 resident trainees, 2 perioperative pharmacists, 3 Certified Registered Nurse Anesthetists (CRNAs), and 2 attending surgeons. Key themes emerged: (1) Limited familiarity with Infectious Disease Society of America (IDSA) antibiotic guidelines, (2) Lack of standardization and optimization of antibiotic decision-making process, (3) Challenges with managing beta-lactam allergies, (4) Difficulty optimizing vancomycin timing, and (5) Perceived benefit of a Clinical Decision Support (CDS) tool in enhancing workflow and guideline adherence. Conclusions Non-adherence to antibiotic guidelines in the perioperative setting often results from a lack of structured workflow. Our interviews provide a foundation for developing a clinical decision support tool tailored to provider needs, aiming to improve user satisfaction and promote better adherence to perioperative antibiotic guidelines.

Ecological conditions of vineyards and the polymorphism of Saccharomyces cerevisiae strains as a prerequisite of terroir identification

Aim. The aim of the study was to assess whether the ecological conditions for vineyards and the phylogenetic analysis of Saccharomyces cerevisiae wine yeast strains isolated from several locations in the Odesa and Mykolayiv regions could be used for terroir identification. Methods. The study employed microbiological, analytical, expedition, cartographic, and comparative assessment methods. Phylogenetic analysis was conducted using the MEGA software program, and VIN-CAD-UKR software was used for the vineyard cadastre and the ampelecological characteristics data management. Results. The study highlighted differences in the ecological conditions of vineyards in the Odesa and Mykolayiv regions and within the Tairovo and Izmail territorial communities in the Odesa region, focusing on soil and microclimatic characteristics. Saccharomyces cerevisiae strains isolated in 2012 and 2013, years with varying weather conditions in temperature and precipitation, were primarily clustered by the year of isolation. Two notable exceptions included a strain isolated from the Isabela variety belonging to a hybrid group (direct producer, contrary to other varieties, which mainly represent Vitis vinifera or its hybrids with Vitis riparia, Vitis amurensis, Vitis berlandieri) and a strain isolated from the Odesa Black variety inoculated at the beginning of fermentation with a commercial strain of wine yeast. The 2013 isolates, collected under conditions of higher precipitation and lower temperature in the first half of the growing season, showed a more differentiated clustering pattern, with two strains isolated from the Sukholymansky White variety forming a distinct cluster, while the rest grouped together, irrespective of the region, formed of isolation. Conclusions. Phylogenetic analysis of 64 strains of Saccharomyces cerevisiae isolated from 36 technical grape varieties based on the ITS1_5.8S_ITS2 nucleotide sequence demonstrated that the year of isolation (reflecting agroclimatic factors) was the primary influence on sample clustering. These factors, as components of the ecological conditions of terroir, form the foundation for terroir identification.

The Implementation of the Food Quality Protection Act (FQPA) in Increasing the Safety Standards for New Pesticides Used on Foods

This paper examines the Food Quality Protection Act (FQPA) and its impact on enhancing safety standards for pesticides used on food in the United States. Initially, the study explores the Delaney Clause—a strict standard prohibiting carcinogenic food additives—which FQPA replaced with a more flexible "reasonable certainty of no harm" criterion. This shift allows the Environmental Protection Agency (EPA) to assess pesticide risk with consideration of potential benefits, even if a substance is carcinogenic at high doses. The paper also critiques the EPA's implementation challenges, including reliance on quantitative risk assessment (QRA) and cumulative risk assessment, which quantify risks of multiple chemical exposures but face informational and regulatory limitations. Recommendations focus on refining judicial review and integrating cumulative risk assessment to strengthen the FQPA’s capacity to safeguard public health, especially for vulnerable populations such as children. This study underscores the importance of regulatory clarity and rigorous assessment methods in pesticide management.

Using Card Sorting Activity as a Strategy for Evaluating Students’ Learning of Computational Thinking Concepts

This study investigated the effectiveness of the ‘Match it’ card sorting activity for evaluating the prospective teachers’ knowledge and understanding of computational thinking (CT) concepts. 146 primary prospective teachers were asked to sort 26 scenarios and words alongside nine images under five main computational concepts: algorithmic thinking, abstraction, decomposition, patterns &amp; generalisation, and evaluation. The study found that the card sorting activity, as a method for assessment was useful, however, the issues around the design and the content of the current card sorting activity were reported by students which suggests that further revisions should be made to improve the effectiveness of the tool.

A hybrid metaheuristic algorithm for antimicrobial peptide toxicity prediction.

The development of new algorithms can aid researchers and professionals in resolving problems that were once unsolvable or discovering superior solutions to problems that were already settled. By recognizing the importance of continuous research on creating novel algorithms, this paper introduced a hybrid metaheuristic algorithm-h-PSOGNDO, which is a combination of Particle Swarm Optimization (PSO) and Generalized Normal Distribution Optimization (GNDO). The proposed algorithm utilizes the Particle Swarm Optimization's strategy for exploitation and the Generalized Normal Distribution Optimization's global search strategy for exploration. Through this combination, h-PSOGNDO is believed to be an effective algorithm that can promote the advantages of its parents' algorithms. Different assessment methods are used to assess the proposed novel algorithm. First, the h-PSOGNDO is set to conduct experiments on two sets of mathematical functions, including twenty-eight IEEE CEC2017 and ten IEEE CEC2019 benchmark test functions, respectively. Then, the h-PSOGNDO algorithm is applied to a case study on the prediction of antimicrobial peptides' toxicity to evaluate its performance on real-life problems. The statistical findings collected from both the test function sets and the case study show that the h-PSOGNDO algorithm works effectively, proving its astonishing ability to yield highly competitive outcomes for complex problems.