Output Quality Research Articles

Wind Turbine Generation System Implemented with a Car Alternator for Use in Isolated Locations

This work presents a wind driven AC electrical generator system for use in isolated locations. The mechanical energy is provided by a vertical axis wind turbine, connected to a standard car alternator. This arrangement requires no mechanical speed control and, as far as possible, can be produced and serviced in many workshops in third word countries, without special tooling and using locally available materials and parts. Controlling the alternator field current a regulated 12 Vdc supply is generated. The 12 Vdc (nominal) output is fed to the power electronic block formed by a chopper up-converter and an inverter stage. Four different output units are available, providing four different quality outputs: a simple non-filtered single phase square wave inverter; a filtered PWM-controlled single phase inverter providing a line-line AC supply (115 V rms, 60 Hz, for Venezuela); a standard low power UPS; and a filtered PWM-controlled three-phase AC supply (208 V rms, 60 Hz, for Venezuela). This work presents the initial test results, using the alternator and the power electronic blocks, and emulating the wind turbine with an AC motor.

A Hybrid Nondominant-Based Genetic Algorithm (NSGA-II) for Multiobjective Optimization to Minimize Vibration Amplitude in the End Milling Process

Aluminium is a noncorrosive, lightweight material used to fabricate parts for the aerospace, automobile, and construction industries. Due to the low-temperature resistance, more heat is generated. At the same time, in machining, tremendous efforts are taken to keep friction and chatter to a minimum and to attain better quality and perfect output, and also more attention is required while selecting the machining process parameters. Spindle speed, rate of feed, radial and axial depth of cut, and radial rake angle of the tool are the parameters utilized to machine aluminium 6063 using the HSS tool on CNC milling to estimate spindle and worktable vibration using a prediction model. In this study, the design of the experiment of the response surface methodology approach is used to create a second-order statistical equation for experimentation with the Design-Expert v12 software. The performance characteristics are analyzed using the ANOVA method. The spindle speed achieved the lowest vibration between 2000 and 3000 rpm. Next, axial and radial depths were the most vibration-affecting parameter compared to the rate of feed and radial rake angle of the tool. To find the best feasible response, the nondominant sorting genetic algorithm II (NSGA II) approach was trained and tested using MATLAB software. Using a Pareto-optimal technique, the optimum worktable vibration ranged from 0.00284 to 0.00165 mm/s2, whereas the spindle vibration ranged from 0.02404 to 0.01336 mm/s2. The predicted values were found to be in an excellent argument when Pareto-optimal solutions are used.

The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching

ABSTRACT This study, rooted in the Technology Acceptance Model (TAM), investigates the multifaceted factors that influence teacher education students in Information-Based Teaching to embrace artificial intelligence technologies. To enrich the TAM framework, we have incorporated elements such as Artificial Intelligence Literacy (AIL), Subjective Norms (SN), and Output Quality (OQ), with the aim of examining their respective effects on the willingness of teacher education students to adopt AI technologies. To substantiate this theoretical framework, we conducted empirical research involving teacher education students from various Chinese universities. Our findings affirm the robustness of the TAM in explaining the inclination of teacher education students, engaged in the actual teaching process within a digitized educational environment, to adopt AI technologies. Through this model, our study underscores the pivotal role of Artificial Intelligence Literacy (AIL) in influencing educators’ acceptance of AI technologies, establishing a foundational cornerstone for subsequent explorations within the theoretical landscape of the TAM. In this study, we identify Perceived Usefulness (PU) and Artificial Intelligence Literacy (AIL) as the primary factors affecting Behavioral Intention (BI) to use AI technologies. Consequently, to foster broader adoption of AI technologies by educators, it is essential to emphasize their tangible benefits and superiority in teaching, with the goal of promoting the extended utilization of AI in digitalized instruction.

Design and Implementation of an RFID System for Order Tracking in Smart Manufacturing: A Case Study of a Mechanical Factory

Six Sigma is a well-liked technique for raising productivity and quality of output in industrial processes and process improvement initiatives. In the Six Sigma methodology, abnormalities in the operational process are reviewed and examined using the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle. Corrective action is then taken to reduce bottlenecks to enhance productivity, ensure stable product quality, and create a tightly connected production cycle. This study proposes the use of integrating statistical hypotheses into the DMAIC cycle of the Six Sigma method and proposes the use of Enterprise Resource Planning (ERP) modules to monitor production processes at a major mechanical processing company. Lean tools are also suggested by the author to be used in the analysis phase. The research results show a reduction in the rate of late product delivery to users from 6.28% to 0.02% per month. The lean tool application model, statistical hypotheses, and industry 4.0 system are integrated into the EPR system at the company's improvement department and are considered a model for manufacturing companies to refer to in implementing improvements for their products. Production processes in different companies.

KL-DNAS: Knowledge Distillation-Based Latency Aware-Differentiable Architecture Search for Video Motion Magnification.

Video motion magnification is the task of making subtle minute motions visible. Many times subtle motion occurs while being invisible to the naked eye, e.g., slight deformations in muscles of an athlete, small vibrations in the objects, microexpression, and chest movement while breathing. Magnification of such small motions has resulted in various applications like posture deformities detection, microexpression recognition, and studying the structural properties. State-of-the-art (SOTA) methods have fixed computational complexity, which makes them less suitable for applications requiring different time constraints, e.g., real-time respiratory rate measurement and microexpression classification. To solve this problem, we propose a knowledge distillation-based latency aware-differentiable architecture search (KL-DNAS) method for video motion magnification. To reduce memory requirements and to improve denoising characteristics, we use a teacher network to search the network by parts using knowledge distillation (KD). Furthermore, search among different receptive fields and multifeature connections are applied for individual layers. Also, a novel latency loss is proposed to jointly optimize the target latency constraint and output quality. We are able to find 2.8 × smaller model than the SOTA method and better motion magnification with lesser distortions. https://github.com/jasdeep-singh-007/KL-DNAS.

Patient-Friendly Discharge Summaries in Korea Based on ChatGPT: Software Development and Validation.

Although discharge summaries in patient-friendly language can enhance patient comprehension and satisfaction, they can also increase medical staff workload. Using a large language model, we developed and validated software that generates a patient-friendly discharge summary. We developed and tested the software using 100 discharge summary documents, 50 for patients with myocardial infarction and 50 for patients treated in the Department of General Surgery. For each document, three new summaries were generated using three different prompting methods (Zero-shot, One-shot, and Few-shot) and graded using a 5-point Likert Scale regarding factuality, comprehensiveness, usability, ease, and fluency. We compared the effects of different prompting methods and assessed the relationship between input length and output quality. The mean overall scores differed across prompting methods (4.19 ± 0.36 in Few-shot, 4.11 ± 0.36 in One-shot, and 3.73 ± 0.44 in Zero-shot; P < 0.001). Post-hoc analysis indicated that the scores were higher with Few-shot and One-shot prompts than in zero-shot prompts, whereas there was no significant difference between Few-shot and One-shot prompts. The overall proportion of outputs that scored ≥ 4 was 77.0% (95% confidence interval: 68.8-85.3%), 70.0% (95% confidence interval [CI], 61.0-79.0%), and 32.0% (95% CI, 22.9-41.1%) with Few-shot, One-shot, and Zero-shot prompts, respectively. The mean factuality score was 4.19 ± 0.60 with Few-shot, 4.20 ± 0.55 with One-shot, and 3.82 ± 0.57 with Zero-shot prompts. Input length and the overall score showed negative correlations in the Zero-shot (r = -0.437, P < 0.001) and One-shot (r = -0.327, P < 0.001) tests but not in the Few-shot (r = -0.050, P = 0.625) tests. Large-language models utilizing Few-shot prompts generally produce acceptable discharge summaries without significant misinformation. Our research highlights the potential of such models in creating patient-friendly discharge summaries for Korean patients to support patient-centered care.

Diversity Design of Traditional Cultural Elements in Interior Design Based on Combinatorial Optimization Algorithm

Abstract There are great limitations in the imagination and play space of traditional interior design methods, and it is very costly to make changes after the design is completed. For this reason, this paper combines and optimizes SIFT and image splicing methods to recognize traditional cultural elements in images. Gray scale processing is performed on the image, unstable edge response points are removed and low contrast feature points are filtered, and the Gaussian pyramid is established by convolving the image with the Gaussian kernel function under different scale factors to establish the dimensions of the feature points. On this basis, an image splicing optimization algorithm that combines the two-dimensional information entropy of the image is proposed to divide the image into detail regions and flat regions, assign corresponding weights to the feature points in the optimization stage of the uni-responsive matrix, and minimize the residual sum to obtain the optimal uni-responsive matrix using the Levenberg-Marquardt algorithm, to improve the splicing quality. In the actual design test, the Chinese chair has the lowest success rate of 94.1%, and the output quality is 0.9215 and 0.9527. The output quality decreases as the scene becomes more complex. The SSIM and DoEM values for the Chinese chair are 0.9582 and 0.9682 for a simple scenario and decrease to a minimum of 0.8543 and 0.8472 as the number of interior pieces and other items in the scenario increases. By proposing the approach in this paper, labor costs and expenses can be saved and a new direction for interior design development can be taken.

Environmental management for car accident precaution and remote notification

Cars have been utilized as reliable means of transportation beyond the capacity of motorcycles. However, the misuse and abuse of these vehicles through unsafe practices like speeding and reckless driving poses a great risk that results in accidents – and sometimes – with casualties. In such unfortunate circumstances, the importance of prompt accident notification is extremely important for the driver. This notification should encompass vital accident details and precise location information. Thus, the implementation of a comprehensive system for accident warnings and the remote transmission of accident data emerges as an imperative safety measure. In this system, accidents are assessed based on the vehicle's slope relative to the road and speed. The vehicle's slope is measured using ultrasonic sensor, which calculates the ground clearance distance at two different locations of the vehicles. The two sensors will provide warning when there is a difference of more than 3 cm. Speed is measured using GPS, with the limit set at 90 km/h. If crashed occurs, Airbag Sensor gives logic ‘1’, then GPS will take current coordinates and date of the accident, then sends it via SIM800L in SMS format to registered number. Thus, the dangerous condition signed by 4,7 cm difference marked by Red LED and formed 7o angles which caused by excessive maneuver while GPS has 95% accuracy with speedometer. Thus, this voice output sure be great while quiet environment which output quality is 45dB at conversation environment.

An Analysis of the Structural Properties of 3D Printed Square Blocks Prepared using Sustainable Thermoplastic Polyurethane (TPU) Material

For a wide range of utilization, 3D printing is a swiftly developing technology that demands meticulous evaluation of materials, production speed, and resolution. Significant outcomes have been obtained from the application of 3D technology in Cultural Heritage (CH) protection, the valorisation process, communication, and asset incorporation; this is especially true of interdisciplinary initiatives including manufacturing engineering, computer-generated records, and CH accessibility. The primary factors influencing the layout and choosing materials for additive manufacturing are applicability and fabrication technique. A wide range of materials, comprising ceramics and metals, hydro-gels, thermoplastic substances and combinations of these substances, can be used. This study investigates the design and fatigue analysis of a 3D-printed square block made of thermoplastic polyurethane (TPU) under various test conditions, including static structural analysis for compression, fatigue analysis and total deformation within the block layer. The uniform stress distribution was also discussed in detail, as well as the design life and safety factors of the block under fatigue conditions, with its natural frequencies observed in experimental results adjusted the printing parameters for and maintained the process to ensure the best output quality. Post-publication steps included detailed analysis and mechanical testing to verify mechanical properties and dimensional accuracy.

Antecedents of cloud-based financial information systems usage: An integrated model

The recent progress of Financial Information System (FIS) has significantly affected businesses’ sustainable production process. Businesses generally employ FIS to automate their operational procedures and increase their corporate efficiencies through improvement in output quality and sustainability. The performance of FIS has been attributed to its key success criteria. Accordingly, this study examined antecedents of FIS intention to use among Small and Medium-Size Enterprises (SMEs) in Jordan at individual level, with specific focus on the acceptance and use of FIS among accounting department employees. Based on 436 respondents from Jordanian SMEs, results showed an impact of COVID-19 risk, trust, performance expectancy, and perceived severity on the intention to use FIS, whereas effort expectancy and perceived vulnerability showed no impact on the intention to use FIS among Jordanian SMEs.

Employee Experiences with Flexible Work Arrangements and Their Impact on Productivity

The evolving nature of work-life dynamics has prompted increased adoption of flexible work arrangements (FWAs). This study investigates the impacts of FWAs on employee experiences and productivity, aiming to identify the benefits and challenges of such arrangements and to understand how they influence work-life balance and job satisfaction. A qualitative research design was employed, utilizing semi-structured interviews to collect data from 30 participants across various industries known for implementing FWAs. Participants were chosen through purposive sampling to ensure diversity in age, gender, job role, and experience. Theoretical saturation was achieved to ensure comprehensive data coverage. Data were analyzed using thematic analysis to identify themes, subthemes, and underlying concepts. Three main themes were identified: Work-Life Balance, Productivity Dynamics, and Challenges and Barriers. Work-Life Balance covered aspects such as time management, family and personal life integration, stress levels, boundary setting, and overall satisfaction. Productivity Dynamics highlighted efficiency, collaboration, adaptability, focus, innovation, and output quality. Challenges and Barriers included technological issues, communication gaps, organizational support, professional isolation, and work overload. FWAs offer significant potential benefits by enhancing work-life balance and productivity. However, their success and sustainability depend on addressing the associated challenges, such as communication difficulties and potential isolation. Effective implementation of FWAs requires tailored strategies that consider individual and organizational needs, emphasizing the need for supportive technologies and robust management practices.

Evaluation of Quality of Prevention of Mother to Child Transmission of HIV Service Provision and Its Determinants: The Case of Health Facility Readiness and Mothers' Perspectives.

Prevention of Mother-To-Child Transmission of HIV (PMTCT) is a comprehensive intervention aimed at reducing vertical transmission. More than 90% of pediatric AIDS cases are caused by MTCT. However, the quality of care has lagged in terms of service integration, client satisfaction, and facility readiness, although improvements have been observed. Evidence on the quality of PMTCT services is required by clinicians, and policymakers. Therefore, this study aimed to evaluate the effectiveness of PMTCT services provided by public health facilities in Northwest Ethiopia. This cross-sectional study was conducted at nine public health facilities in Gondar city between June 1 and July 17, 2022. A total of 334 mothers were participated. The Structure-Process-Outcome components were used to evaluate service quality. About 135 counseling sessions were observed. The SPSS 26 software was used for statistical analyses. To identify mothers' satisfaction with the service, P-values < 0.25, and < 0.05 were considered in the binary and multiple logistic regression analyses to identify significant variables, respectively. The overall performance of the health facilities was 74.09% (72.19-76.01 CI 95%). In 88.89% of cases, the output quality was higher and rated as good. However, the quality of the input and process services was 60.40% and 72.97%, respectively. Women's satisfaction was 67.09%. Women aged 15-24 years (AOR= 0.31; 95% CI:0.06, 0.79), formally educated (AOR=0.59; 95% CI:0.35, 0.67), > one hour of arrival (AOR=0.71; 95% CI:0.55, 0.93), > 30 minutes waiting time (AOR=0.23; 95% CI:0.11, 0.37), sufficient time for counseling (AOR=2.83; 95% CI:1.72, 3.97), and opening hours (AOR=4.27; 95% CI:2.09, 6.67) were factors associated with satisfaction in women. The quality of PMTCT service provision and mothers' satisfaction were low. Reducing waiting time, punctuality of service providers, adequate counselling, and availability of basic resources can boost mothers' satisfaction.

On the efficacy of conditioned and progressive Latin hypercube sampling in supervised machine learning

In this paper, Latin Hypercube Sampling (LHS) method is compared as per its effectiveness in supervised machine learning procedures. Employing LHS saves computer's processing time and in conjunction with Latin hypercube design properties and space filling ability, is considered as one of the most advanced mechanisms in terms of sampling. Although more data usually deliver better results, when using LHS techniques, same quality outputs can be produced with less data and, as a result, storage cost and training time are reduced. Conditioned Latin Hypercube Sampling (cLHS) is one of those techniques, successfully performing in supervised machine learning tasks. Unfortunately, the minimum sufficient training dataset size cannot be known in advance. In this case, progressive sampling is recommended since it begins with a small sample and progressively increases its size until model accuracy no longer improves. Combining Latin hypercube sampling and the idea of sequentially incrementing sampling, we test Progressive Latin Hypercube Sampling (PLHS) while monitoring the performance of the sampling-based training as the sample size grows. PLHS and cLHS algorithms are applied in datasets with discrete variables securing that each sample is provided with the Latin hypercube design properties and preserves the principal ability of LHS for space filling, as illustrated in respective sample projecting diagrams. The performance of the above LHS methods in supervised machine learning is evaluated by the degree of training of the model, which is certified through the accuracy of the produced confusion matrices in test files. The results from the use of the above Latin Hypercube Sampling techniques compared against benchmark sampling method empirically prove that machine learning training process becomes less costfull, while remaining reliable.

How to evaluate a multi-country implementation-focused network: Reflections from the Quality of Care Network (QCN) evaluation.

Learning about how to evaluate implementation-focused networks is important as they become more commonly used. This research evaluated the emergence, legitimacy and effectiveness of a multi-country Quality of Care Network (QCN) aiming to improve maternal, newborn and child health (MNCH) outcomes. We examined the QCN global level, national and local level interfaces in four case study countries. This paper presents the evaluation team's reflections on this 3.5 year multi-country, multi-disciplinary project. Specifically, we examine our approach, methodological innovations, lessons learned and recommendations for conducting similar research. We used a reflective methodological approach to draw lessons on our practice while evaluating the QCN. A 'reflections' tool was developed to guide the process, which happened within a period of 2-4 weeks across the different countries. All country research teams held focused 'reflection' meetings to discuss questions in the tool before sharing responses with this paper's lead author. Similarly, the different lead authors of all eight QCN papers convened their writing teams to reflect on the process and share key highlights. These data were thematically analysed and are presented across key themes around the implementation experience including what went well, facilitators and critical methodological adaptations, what can be done better and recommendations for undertaking similar work. Success drivers included the team's global nature, spread across seven countries with members affiliated to nine institutions. It was multi-level in expertise and seniority and highly multidisciplinary including experts in medicine, policy and health systems, implementation research, behavioural sciences and MNCH. Country Advisory Boards provided technical oversight and support. Despite complexities, the team effectively implemented the QCN evaluation. Strong leadership, partnership, communication and coordination were key; as were balancing standardization with in-country adaptation, co-production, flattening hierarchies among study team members and the iterative nature of data collection. Methodological adaptations included leveraging technology which became essential during COVID-19, clear division of roles and responsibilities, and embedding capacity building as both an evaluation process and outcome, and optimizing technology use for team cohesion and quality outputs.

CANBLWO: A Novel Hybrid Approach for Semantic Text Generation

Semantic text generation is critical in Natural Language Processing (NLP) as it faces challenges such as maintenance of coherence among texts, contextual relevance, and quality output. Traditional language models often produce grammatically inconsistent text. To address these issues, we introduce Convolutional Attention Bi-LSTM with Whale Optimization (CANBLWO), a novel hybrid model that integrates a Convolutional Attention Network (CAN), Bidirectional Long Short-Term Memory (Bi-LSTM), and Whale Optimization Algorithm (WOA). CANBLWO aims to generate semantically rich and coherent text and outperforms the traditional models like Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Bi-LSTM, and Bi-LSTM with attention, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2). Our model achieved 0.79, 0.78, 0.76, and 0.82 scores in Metric for Evaluation of Translation with Explicit Ordering (METEOR), Bi-Lingual Evaluation Understudy (BLEU), Consensus-based Image Description Evaluation (Ciders), and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, respectively. The proposed model also demonstrates 97% and 96% accuracy on Wiki-Bio and Code/Natural Language Challenge (CoNaLa) datasets, highlighting its effectiveness against Large Language Models (LLMs). This study underscores the potential capability of hybrid approaches in enhancing semantic text generation

Premature Closure of Analysis in Qualitative Research: Identifying Features and Mitigation Strategies

Premature closure of analysis refers to finishing data analysis too early, leading to underdeveloped qualitative findings. It is a critical issue in qualitative research affecting the rigor and trustworthiness of qualitative findings. While much has been written about how to conduct rigorous data analysis across a range of qualitative approaches, there has been no discussion of the features of premature closure of analysis and strategies for addressing it. The purpose of this paper is to outline how to spot premature closure of analysis and to describe strategies to mitigate this issue. Three identifying features of premature analysis are: providing thin descriptions with loaded participant quotes, presenting conventional concepts as themes, and using topic summaries as themes. Using a First Approach to qualitative analysis, working in segments to generate a wholistic thematic output, and critical reflection and examination before finalizing the thematic output can be useful strategies to mitigate premature closure of analysis. Themes and patterns that are too vague and meaningless to provide a comprehensive account of the studied phenomenon are a threat to the validity of the study and a waste of researchers’ effort and time. Premature closure of analysis is one of the most common problems affecting the quality of thematic outputs in quality studies. Therefore, researchers should be mindful and critical in their analytical decision-making to prevent this problem.

Cadmium and lead flow analysis as a decisions support data for waste management

Striving for EU membership, the Republic of Serbia must adjust its waste management practices to comply with EU directives, including targets to reduce biodegradable waste disposal in landfills, as outlined in its Waste Management Program 2022?2031. Cadmium and lead, two highly toxic heavy metals, that are present in municipal solid waste, can pose high environmental and human health threats if not properly managed. The research evaluates how different technologies for biodegradable waste treatment influence the transformation of cadmium and lead flows through waste management systems. Hence, two waste management scenarios were modelled and developed for the Republic of Serbia, where the flows of cadmium and lead are monitored. The results indicate the differences between quantities and concentrations of cadmium and lead emitted in environmental media, thus confirming the various impacts of different waste technologies on achieving the vital goal of waste management ? protection of the human health and the environment. The research concludes the crucial role of the versatile approach, where the quality of waste management outputs is highlighted.

Improving Press Bending Production Quality through Finite Element Simulation: Integration CAD and CAE Approach

Efficient operations and output of outstanding quality distinguish superior manufacturing sectors. The manufacturing process production of bending sheet metal is a form of fabrication in the industry of manufacture in which the plate is bent using punches and dies to the angle of the work design. Product quality is influenced by plate material selection, which includes thickness, type, dimensions, and material. Because no prior research has concentrated on this methodology, this research aims to determine V-bending capacity limits utilizing the press bending method. The inquiry employed finite element analysis (FEA), along with Solidworks was the tool of choice to develop drawings of design and simulations. The ASTM E290 standard guides this study. The software in this package may combine CAD (Computer-Aided Design) and CAE (Computer-Aided Engineering) without requiring extra design applications. This study tested SPCC and SPHC plate materials with five thickness variations. The findings embrace the number of failure risks associated with press bending exhibited on the von Mises stress diagram, which is directly proportional to showing the thickness limit of each material type throughout the bending process. The study's findings lay the groundwork for improving manufacturing quality by lowering the number of faulty goods produced by trial and error. Because the maximum allowable die width is 12 mm, the thickness limit of the press bending process is 2 mm. However, due to the greater intensity of the SPCC material, it has a reduced defect rate compared to SPHC material.

Exploring the Characteristics and Path of Internationalization Development of Vocational Education Based on the Information Diffusion Model

Abstract Internationalization of higher education has become an inevitable trend under the impetus of world economic integration and informationization. This paper adopts the information diffusion technique in the fuzzy information optimization method to construct an assessment model for internationalization development of vocational education. Explicit sample data are turned into fuzzy sets, information diffusion points are built using a two-dimensional average information diffusion model, and the estimated loss and probability distribution of information diffusion are calculated to assess the internationalization development level of vocational education. The constructed indicators set people’s awareness of the internationalization development of vocational education and the dissemination effect, analyzed the internationalization development trend of vocational education in the past five years by combining the Google search data, and explored the impact of internationalization development of vocational education based on the indicators of output level and quality level. The results show that the communication effects of internationalization of vocational education are all rated between (3.3, 3.6), and the attention to the internationalization development of vocational education has increased from 10% to 70% in six years. This study provides suggestions and strategies for advancing the internationalization of higher vocational education in China, which is conducive to steadily increasing the internationalization of higher vocational education in China and improving the level of schooling.

1 MHz, 273 W average power Ytterbium-doped rod-type fiber chirped pulse amplification system

Ytterbium-doped ultrafast fiber lasers are widely used in scientific research, industrial processing, medical diagnosis, and other fields due to their excellent beam quality and high power output. The larger mode area allows the fiber to transmit higher peak-pulse power. The commercial rod-type Ytterbium-doped fiber with a core diameter of 85 μm, produced by NKT in Denmark, can produce ultra-short pulses on the order of 100 watts and 100 microjoules. Based on this rod-type fiber, we construct a chirped-pulse amplification (CPA) system in which the high-efficiency transmission gratings and temperature-tunable chirped fiber Bragg grating (CFBG) are used to compensate for dispersion. We investigate the effect of power input on the amplified power and pulse compression quality, and find that higher power input slows down the gain saturation and improves amplification efficiency. At power inputs of 20 W and 30 W, we obtain power outputs of 305 W and 323 W respectively, with an amplification efficiency of about 80%. To reduce the accumulation of nonlinear phase shift, we use circular polarization amplification. At low power outputs (less than 160 W), the effect of nonlinear phase accumulation on the compressed pulse is negligible, and the increase in power input increases the amplification efficiency. When the power output exceeds 200 W, the cumulative increase of nonlinear phase shift reduces the pulse compression quality, which implies that the input power is appropriately reduced to the power range between 5 W and 20 W. With a power input of 20 W and pump power of 429 W, the power output can reach 305 W. After pulse is compressed by using a diffraction-grating pair, this rod-type fiber CPA system can deliver 1 MHz, 264 fs pulses with 273 W in average power. These results provide an important experimental basis for optimizing the performance of high-power and high-energy ultrafast fiber lasers.