Results Of Evaluation Research Articles

Exploring the Reform of Piano Course Teaching based on AGIL Model

University music performance majors are strongly encouraged to take a piano fundamentals course as one of their necessary foundational topics. For college music achievement majors, it is very crucial to develop and raise students' piano playing proficiency and expertise. For the analysis and creation of complex systems, the AGIL model serves as a theoretical framework. This model may be used to create a complete methodology for piano instruction that emphasizes the growth of the student's flexibility, goal achievement, skill integration, and pause in action. Learning to play the piano may test these connections in the brain that control movement in students and people with poor motor abilities, and it can even improve coordination. Thus, we suggested the AGIL model for the reform of piano course teaching to further encourage the creative growth of piano instruction for music majors in colleges and universities in a diverse environment. The dataset used was made up of Chinese piano students. The performance measures used to assess suggested efficacy include ideal teaching, student performance, note-reading skill, teamwork ability, and student support rate. The evaluation's results indicate that the AGIL approach has provided 98.5% which seems to be useful for improving students' piano skills.   

Model-Scale Evaluation of Autonomous Ship Landing Guidance and Control Modes

This paper presents the results of an extensive model-scale experimental evaluation of autonomous ship landing guidance and control modes, with flight tests performed in the Maneuvering and Seakeeping (MASK) Basin at the U.S. Naval Surface Warfare Center Carderock Division. The experiments were performed using a commodities-based multirotor unmanned aerial vehicle (UAV) operating from a 20-ft-long model scale ship subject to scaled wave conditions. During testing, two separate guidance algorithms were evaluated: a quadratic programming (QP) based landing algorithm that plans the trajectory to a forecasted deck state and a simpler “baseline??? method that tracks deck motions while closing the distance between the aircraft and deck at a constant rate. Both algorithms commanded a Froude-scaled explicit model following control law, and the control law parameters were modified to progressively degrade aircraft tracking bandwidths. The results showed that the predictive capabilities of the QP algorithm allowed more direct landing paths to be planned when compared to the baseline algorithm and also allowed the QP algorithm to land with lower tracking bandwidths. But while the QP algorithm performed well in the majority of cases, there were several landings where a combination of poor deck state predictions and how the QP algorithm utilizes predictions to choose a land time resulted in significant terminal velocity and attitude errors. The baseline guidance algorithm, on the other hand, proved to be both simple and reliable when the UAV was in high bandwidth configurations but required a high reference tracking bandwidth.

Reliability evaluation of non-isolated high gain interleaved DC-DC converter

A high gain DC-DC converter is the crucial part in renewable energy systems (RES) and in electric vehicular systems. The reliability of those high-gain converters needs to be assessed for the long-term operation of renewable energy systems. This article presents the reliability analysis of non-isolated high gain interleaved DC-DC converter. The analysis primarily relies on calculating the mean time between failures (MTBF). Based on military handbook (MIL-HDBK-217) criteria, the reliability calculation is performed. Stress factors and predicted failure rate for each component of presented converter is evaluated and tabulated. Reliability evaluation is performed for 1.5 kW hardware prototype. Based on reliability evaluation results, a reliable converter with better operating life time has been introduced.

On the search for efficient face recognition algorithm subject to multiple environmental constraints

From literature, majority of face recognition modules suffer performance challenges when presented with test images acquired under multiple constrained environments (occlusion and varying expressions). The performance of these models further deteriorates as the degree of degradation of the test images increases (relatively higher occlusion level). Deep learning-based face recognition models have attracted much attention in the research community as they are purported to outperform the classical PCA-based methods. Unfortunately their application to real-life problems is limited because of their intensive computational complexity and relatively longer run-times. This study proposes an enhancement of some PCA-based methods (with relatively lower computational complexity and run-time) to overcome the challenges posed to the recognition module in the presence of multiple constraints. The study compared the performance of enhanced classical PCA-based method (HE-GC-DWT-PCA/SVD) to FaceNet algorithm (deep learning method) using expression variant face images artificially occluded at 30% and 40%. The study leveraged on two statistical imputation methods of MissForest and Multiple Imputation by Chained Equations (MICE) for occlusion recovery. From the numerical evaluation results, although the two models achieved the same recognition rate (85.19%) at 30% level of occlusion, the enhanced PCA-based algorithm (HE-GC-DWT-PCA/SVD) outperformed the FaceNet model at 40% occlusion rate, with a recognition rate of 83.33%. Although both Missforest and MICE performed creditably well as de-occlusion mechanisms at higher levels of occlusion, MissForest outperforms the MICE imputation mechanism. MissForest imputation mechanism and the proposed HE-GC-DWT-PCA/SVD algorithm are recommended for occlusion recovery and recognition of multiple constrained test images respectively.

Pengembangan Kapasitas Agribisnis Sapi Potong melalui Bimbingan Teknis Kelembagaan dalam Upaya Inisiasi Terciptanya Sekolah Peternakan Rakyat di Kabupaten Blora

The development of beef cattle is one of the pillars of the people's economy in the Blora Regency. This activity aims to transfer knowledge and technology to develop the capacity of beef cattle farming agribusinesses to manage livestock reproduction and health, feed, breeding, fattening, management, and social, economic, and network development. This activity was carried out through observation, technical guidance (Bimtek), and mentoring. The Bimtek was divided into six themes, and the assistance was carried out for two months. Sixty breeders carried out the activity from two villages, namely Pengkolrejo Village and Palon Village. From the evaluation results, the Bimtek participants absorbed more than 50 percent of the material. It indicated that all participants experienced increased knowledge and skills of livestock management regarding partnerships and collective business, management, social, economic, and network development, animal reproduction and health, forage and concentrate feed banks, breeding and fattening techniques, and waste management.

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs. Modeling reliability without considering the fusion of multisource uncertainties may result in model distortion and may not provide realistic assessment results. This paper proposes a multisource uncertainty fusion method powered by the margin-based variable transformation and the Bayesian network propagation. The interval and fuzzy variables are transformed into random variables to obtain the corresponding generalized probability density function of the CRDM’s feature parameters. After that, the CRDM’s reliability can be evaluated via probability quantization of the Bayesian network inference result through sampling algorithms. A magnetic-jack type CRDM case is presented to verify the proposed method, and the results show that this method can fuse three types of uncertainty variable in a unified way and effectively obtain reliability evaluation results.

Integrated energy system evaluation and optimization based on integrated evaluation model and time-series optimization

Against the backdrop of global attention to climate change and environmental sustainability, the development timing and comprehensive cost of regional renewable energy power generation projects have become a focus of attention. By constructing effective models to evaluate them, it can help promote the healthy development of renewable energy projects. The research aims to quantitatively evaluate the development status of local renewable energy projects by constructing a comprehensive evaluation model, minimize information loss, and improve the accuracy of evaluation results. This study adopted a comprehensive evaluation model that combines Analytic Hierarchy Process (AHP) based on accelerated genetic algorithm, entropy weight method, and ideal point method. Firstly, the subjective weights of the development evaluation indicators for regional renewable energy power generation projects are calculated. Secondly, the entropy weight method is used to analyze the trend of each indicator and obtain objective weights. Finally, combined with the objective weights and the evaluation results calculated using the TOPSIS method, a comprehensive evaluation of renewable energy power generation projects in various regions is conducted. Through analysis, the core indicators of the development level of renewable energy power generation projects in various regions show specific performance, such as Hebei’s evaluation value of 0.4945 in the proportion of comprehensive energy development, and Inner Mongolia’s evaluation value of 0.4045 in the proportion of comprehensive energy installed capacity. Meanwhile, genetic optimization methods exhibit significant advantages in the calculation of optimization schemes compared to dynamic programming methods, possessing strong global search capabilities and high-precision solutions. This study provides a new research method and approach for the evaluation of regional renewable energy power generation projects, demonstrating the practical value and certain advantages of the research method.

A Multi-Hazard Risk Assessment Model for a Road Network Based on Neural Networks and Fuzzy Comprehensive Evaluation

The frequency of extreme weather events has increased worldwide, leading to more intense natural disasters, which pose significant threats to human life and property safety. The main form of disaster occurrence is multi-hazard coupling and multi-hazard chaining. This paper constructs a road natural disaster risk assessment model using a fuzzy comprehensive evaluation method and neural network to quantitatively analyze road disasters with multiple hazards, and provides valuable insights for the predication of road natural disaster risk. Here, ten factors, including temperature, relative humidity, precipitation, elevation, slope, slope orientation, vegetation cover, geologic lithology, historical impact factors, and road density, were selected as input variables, and risk grade was selected as the output value (the evaluation results). The remaining hidden layers use the fully connected neural network. This model was first trained using historical data (from 2011 to 2021) obtained from road networks and natural disasters in Guangxi, China. Then, taking Lingchuan County as an example, the model was used to predict the risk of natural disasters on its roads, and, finally, the prediction accuracy of the model was determined by comparing the results with actual disaster situations. This study can provide theoretical support and technical operations for the development of subsequent early warning systems.

Quality evaluation of physical education in colleges and universities based on fuzzy logic

This study aims to evaluate the quality of physical education in colleges and universities by using the evaluation method based on fuzzy logic. First, summarize the basic concept of fuzzy logic and its application in the evaluation process. Then, a physical education quality evaluation index system covering teaching quality and students’ physical quality, teachers’ team construction, sports facilities and management is constructed. Then, the evaluation object and evaluation level are determined, and the specific evaluation results and ranking are calculated by fuzzy comprehensive evaluation model. In the empirical analysis, use the simulation data for model application and result analysis, and further find the problems existing in college physical education. Finally, in view of these problems, a series of improvement and optimization suggestions are put forward. This study shows that the evaluation method of physical education quality based on fuzzy logic can effectively reflect the diversity and complexity of education quality, and provide a practical evaluation tool for colleges and universities. At the same time, this study also provides a reference for other universities to help them better understand their own problems, so that they can take effective measures to improve. In addition, this method can be applied to other fields of education quality evaluation in the future, so as to provide more useful information for education reform and development.

Forging a Path to College Persistence: An Experimental Evaluation of the Detroit Promise Path Program

Detroit students who obtain a college degree overcome many obstacles to do so. This article reports the results of a randomized evaluation of a program meant to provide support to low-income community college students. The Detroit Promise Path program was designed to complement an existing College Promise scholarship, providing students with coaching, summer engagement, and financial incentives. The evaluation found that students offered the program enrolled in more semesters and earned more credits compared with those offered the scholarship alone. However, at the 3-year mark, there were no discernable impacts on degrees earned. This article examines systemic barriers to degree completion and offers lessons for the design of interventions to increase equity in postsecondary attainment.

Keeping Early Social-Emotional Learning Developmental: The Development, Implementation, and Preliminary Evaluation of a Preventive Intervention Program for Early Childhood Education and Care

Early childhood forms the fundamental basis for developing various social-emotional skills and represents a unique opportunity to lay the foundation for healthy development. Insensitive learning environments in early childhood may lead to social-emotional difficulties, increasing the risk of developing severe behavioral problems. However, professional development (PD) opportunities that support early childhood education professionals’ (ECEPs) knowledge and skills to promote young children’s social and emotional development are scarce. In Finland, notably, the increasing need to enhance competence through further training has been widely recognized. Therefore, this study introduced the POMPedaSens program in early childhood education and care (ECEC) in Finland. The program aims to promote children’s social-emotional learning (SEL) by supporting ECEPs’ engagement and emotional availability. As a universal intervention program, POMPedaSens includes all children in ECEC regardless of risk factors. The present study included 200 children (MAGE = 72.27 months, SD = 5.48; 53% girls) and 62 ECEPs (MAGE = 43.82 years, SD = 11.71; 95% female). ECEPs received the intervention training to implement the curriculum in their everyday daycare routine. The present study describes the program design, implementation, and results from a preliminary evaluation of the social validity of the POMPedaSens program, measured by the ECEPs’ satisfaction with the program. The results of the social validity evaluation suggested high teachers’ satisfaction and improved children’s SEL. These findings prove that the program may effectively build ECEPs’ capacity to promote children’s SEL.

Fe3O4/biochar modified with molecularly imprinted polymer as efficient persulfate activator for salicylic acid removal from wastewater: Performance and specific recognition mechanism

In this study, a novel Fe3O4-based biochar coupled surface-imprinted polymer was constructed via simple hydrothermal route for salicylic acid recognition and degradation in advanced oxidation processes. The material exhibited excellent adsorption capability, up to 118.23 mg g−1, and efficient degradation performance, 87.44% removal rate within 240 min, based on integrating the advantages of both huge specific surface area as well as abundant functional groups from biochars and specific recognition sites from imprinted cavities. Moreover, high selectivity coefficient (11.67) showed stable recognition in single and binary systems. SO4•− and •OH were confirmed as reactive oxygen species in catalytic reaction according to quenching experiments and EPR analysis. The degradation mechanism and pathway were unraveled by DFT calculations and LC-MS. Furthermore, the results of toxicity evaluation, stability and reusability demonstrated application potential in the field of water environment restoration. This study confirmed that molecular imprinting provided a promising solution to targeted removal of emerging environmental pollutants by degrading after the enrichment of pollutants to the composites surface.

Production of fresh cheese analogues; the impact of soy protein isolate and oat germ powder

AbstractPlant-based proteins are being rapidly explored in the food industry to produce healthy and nutritious foods. Herein, the effects of soy protein isolate (SPI) ratio to oat germ powder (OGP) (0:0, 3.7: 6.3, 5:5, 0:10, 10:0, and 6.3:3.7%) were studied on the physicochemical, colour, rheological, microbial, antioxidant, and sensory properties of wheyless cheese. The results showed that by increasing SPI and OGP levels, ash, white index value, elastic modulus, and complex viscosity decreased, while pH, fat in dry matter, Chroma, and total colour (ΔE) increased. Also, syneresis was only observed in the control sample and was not observed in other samples. Control sample together with sample containing 10% of OGP showed the highest microbial count (i.e., mesophilic bacteria and yeasts-moulds), while samples containing 10% of SPI showed the lowest microbial count. Furthermore, the growth of both coliform and psychrophilic bacteria in all samples was negligible. Based on the results of sensory evaluation, with increasing SPI and OGP, the sensory evaluation score of samples decreased but they were within the acceptable range. According to the results obtained in this study, the combinations of 5% SPI and 5% OGP or 3.7% SPI and 6.3% OGP are recommended to be used in wheyless cheese production.

Evaluation of genetic parameters of growth traits in G2 selected generation of Penaeus monodon

Fifteen whole sib families of the second-generation Penaeus monodon were constructed using artificial mating design in 2020. After labeling the families with fluorescent markers, the growth traits and genetic parameters were evaluated in a cement pond for 56 days. The results showed that the coefficient of variation of growth traits was 11.52-47.53%, indicating a high genetic variation. The heritability range of growth traits of the G2 population of P. monodon was 0.25±0.03-0.41±0.13, which belonged to medium and high heritability, and the statistical test results were significant (P<0.01). The heritability of body length and body weight were 0.38±0.11 and 0.41±0.13, respectively. The evaluation results of genetic correlation among growth traits were highly positive, and the statistical test results were significant (P<0.01). The genetic correlation between body weight and body length was the highest (0.99), and the genetic correlation between the width of the first carapace and the height of the first abdominal segment was the lowest (0.71). The phenotypic correlation between body weight and body length was the highest (0.93), and the lowest was head breastplate width and first abdominal segment height (0.53). These results showed that the G2 generation population of P. monodon has high genetic improvement potential, and the combination of family selection and individual selection achieved better genetic gain. The growth traits were highly positively correlated. Body length and body weight could be selected as the key traits for fast growth line selection, while the other growth traits could be indirectly selected through positive genetic correlation.

University of Rizal System’s Quality Management System Toward Sustainability Framework

A quality management system (QMS) is considered key to the sustainable development of any institution. Its proper implementation is crucial in planning, implementation, assessment, and development and control of changes in the processes and procedures of the university. This study sought to assess the QMS implementation of the University of Rizal System based on ISO 9001:2015 standard and ultimately propose a sustainable framework based on the data obtained from the selected stakeholders of the university and evaluation results of the certifying body. A mixed method of research employing a sequential explanatory approach was utilized to adequately gather and analyze quantitative data using survey questionnaires and qualitative data through document analysis. Findings revealed that the University of Rizal System has manifested a functional, efficient, and effective quality management system necessary for the delivery of products and services to ensure stakeholders’ satisfaction with their needs and interests. The quality management system of the university has established and maintained an effective system to ensure compliance with its policy and objectives, that is, the organization’s management system complies with, adequately maintains, and implements the requirements of the ISO 9001:2015 standard. However, while the quality management system of the University remains effective, efficient, and compliant with the standard, improvement in its implementation is inevitable particularly in the control of the operation of processes, monitoring and attending to nonconformities and corrective actions, and on needed support to implement QMS. Hence, a proposed sustainability framework is highly recommended. Keywords: Quality Management System, Sustainability Framework, Sequential Explanatory Approach, University of Rizal System – Philippines

Power sector carbon reduction review for South Korea in 2030

This study investigates the cost-effectiveness and decarbonization of four essential carbon reduction strategies to achieve Korea’s recent 2030 NDC (Nationally Determined Contribution) goal in the power sector. Examining overseas experiences and identifying potentials and challenges in implementing these strategies in Korea, this study explores the performance of future Korean power systems under fifty-four configurations combining these strategies. The evaluation results indicate that: 1) Thirty-one of the fifty-four tested configurations can achieve the emission target (149.9 MtCO2e) with carbon abatement costs (COA) ranging from -$89 to $105/tCO2e. The optimal configuration with the lowest COA is a combination of nuclear restoration and renewable expansion strategies. 2) Renewable expansion significantly reduces the COA and levelized cost of electricity (LCOE). A pure renewable expansion strategy with photovoltaics of 68 GW and wind turbines of 35 GW attains the emission goal at a COA of -$54/tCO2e, in which the generation share of renewables reaches 42%. 3) A carbon tax of $104/tCO2e achieves the target if complemented by nuclear restoration. Otherwise, $120/tCO2e is needed. The COA and LCOE are increases by switching from cheap coal to expensive natural gas in both cases. 4) Twenty percent coal–ammonia co-firing combined with a carbon tax of $32/tCO2e and nuclear restoration can achieve the goal with relatively high costs compared with the optimal strategies. However, a co-firing strategy is limited because the generation shrinks at a minimum level when 40%∼ mixing ratios are applied, or when the fuel price gap between ammonia and natural gas becomes large.

Ordered weighted evaluation method of lifting operation safety risks considering coupling effect

In order to accurately evaluate the safety risk degree of lifting operations and improve the safety control level of lifting operations, firstly, Work Breakdown Structure (WBS) is used to decompose the construction process of lifting operations, Risk Breakdown Structure (RBS) is used to decompose the safety risk, 16 safety risk factors of lifting operations are identified, and the risk evaluation system is constructed. Then, the safety risk assessment model of lifting operations is constructed by integrating risk coupling theory, joint ordered weighting operators, and an interaction matrix to identify key risk factors and safety risk levels. Finally, taking the lifting operation construction project of Yangfanggou Hydropower Station as an example, the evaluation index system and method are applied. The results show that the high-risk safety risks of the lifting operation construction of the project are: 'Low safety awareness causes workers to operate illegally X23', 'Operation error caused by illegal command X33', 'Workers' risky work caused by low safety awareness X34', 'Edge, high and climbing operation protection is not in place X36', and 'Improper binding of components caused by workers' illegal operation X25'. The construction of the lifting operation poses a significant risk level, and the evaluation results are consistent with the actual situation. The rationality of the evaluation system and model constructed in this paper can provide a reference for the safety management and control of the construction site of the lifting operation and the safety of the lifting operation.

First Fit Algorithm: A Graph Coloring Approach to Conflict-Free University Course Timetabling

Aims: Tackling scheduling issues with the most optimal graph coloring algorithm has consistently posed significant difficulties. The university scheduling problem can be expressed as a graph coloring problem, where courses are depicted as vertices and the connections between courses that have common students or teachers are represented as edges. Subsequently, the task at hand is to assign the vertices with the minimum number of colors. In order to accomplish this task, this paper present a graph coloring technique to conflict free university course timetabling using first fit algorithms.
 Methodology: The conflict graph is partitioned into a set of independent color classes to be assigned time slots and transformed into a conflict-free timetable. The Ladoke Akintola University of Technology (LAUTECH) University Course Timetabling Data was adopted. The allocation of venue based on the allotted time slots is done using first fit packing algorithm. The proposed model is implemented using Python programming language. The developed model had courses being represented as vertices and edges. The course conflict graph was created based on the acquired dataset using vertices-edges relationship diagram. The implemented model is evaluated in terms of Halstead complexity metrics: Program Volume (PV), Program Length (PL), Program Effort (PE), Program Difficulty (PD) and Execution Time (ET). The PV, PL, PE, PD and ET values obtained for the implemented model are 18.45kbits, 0.51, 1037684, 1.97 and 20.45 secs, respectively.
 Conclusion: The proposed model shows a significant improvement over the existing models by producing conflict-free course timetabling problem with better evaluation results. This work will be highly useful in solving various scheduling, optimization and NP-hard related computational problems.

Denoising parameter dependence of coronary artery depictability in compressed sensing magnetic resonance angiography.

Using numerical indices and visual evaluation, we evaluated the dependence of coronary-artery depictability on the denoising parameter in compressed sensing magnetic resonance angiography (CS-MRA). This study was conducted to clarify the acceleration factor (AF) and denoising factor (DF) dependence of CS-MRA image quality. Vascular phantoms and clinical images were acquired using three-dimensional CS-MRA on a clinical 1.5T system. For the phantom measurements, we compared the full width at half maximum (FWHM), sharpness, and contrast ratio of the vascular profile curves for various AFs and DFs. In the clinical cases, the FWHM, sharpness, contrast ratio, signal-to-noise ratio, noise level values, and visual evaluation results were compared for various DFs. Phantom image analyses demonstrated that the respective measurements of the FWHM, sharpness, and contrast ratios did not significantly change with an increase in AF. The FWHM and sharpness measurements slightly changed with the DF level. However, the contrast ratio tended to increase with an increase in the DF level. In the clinical cases, the FWHM and sharpness showed no significant differences, even when the DF level was changed. However, the contrast ratio tended to decrease as the DF level increased. When the DF levels of the clinical cases increased, the background signals of the myocardium, fat, and noise levels decreased. We investigated the dependence of the coronary-artery depictability on AF and DF using CS-MRA. Analysis of the coronary-artery profile curves indicated that a better image quality was achieved with a stronger DF on coronary CS-MRA.

Student Competence in Carrying Out Learning Evaluations in the Era of Independent Learning

This era, introduced by the Minister of Education and Culture, Nadiem Makariem, emphasizes the freedom of teachers and students to innovate, learn independently, and be creative. Student competency in learning evaluation is considered crucial to ensure the successful implementation of this new educational program. The research method used is a literature study focusing on scientific work relevant to the concept of Independent Learning, learning evaluation, and the role of students in the evaluation process. The analysis is carried out to detail key concepts, compare the views of various researchers, and synthesize information to provide a holistic picture. The research results show that student competencies include understanding various evaluation methods, planning skills, active participation, data analysis, and effective communication about evaluation results. Students need to be able to face a holistic evaluation approach that covers various aspects, including cognitive, affective, and psychomotor. The research conclusion underlines the importance of developing student competencies in learning evaluation in the Merdeka Belajar era. Research suggestions include the need for support from schools, teachers, and parents and the need to hold training and workshops to improve students' skills. Collaboration between education stakeholders is essential to ensure that the evaluation approach implemented is appropriate to student needs and supports the achievement of national education goals.