Feasible Plan Research Articles

Towards Developing Empathetic Engineers for The Rural Road Project Management

Education of empathetic engineering for the rural road project management and professional excellence for young engineers is very important as the end product can highly impact the rural community. The current research focused on the analysis of the concept from the explicit meaning of empathy of recent literature from five publishers through an advanced computer search. The goal of this paper is to provide a theoretical framework from a holistic list of empathy perceptions affecting the engineer in managing projects. Two research questions were addressed: first, what does empathy mean when the element of engineering is the centre of the study? and; secondly, from what perspective of project management construction stages does empathy influences engineer? Factors contributing to project failure can be categorized into three major factors namely physical and financial constraints, other minor issues, and empathy issues. Empathy issues are the combination of empathy keywords and empathetic perspectives that could be developed and incorporated in the management of rural roads during the feasibility study, planning, design, and construction stage of the project. Towards developing empathetic engineers and further reducing project failures, intervention through empathy training is a viable option for the enhancement of emotional intelligence and project management skill.

Implementing large language model-based artificial intelligence (AI) technology in proposing effective treatment plans in patients with cancer.

e13660 Background: Language Learning Models (LLM), are programs that can respond to queries without previous exposure to the input. This is because the programs are actually generative mathematical models trained on hundreds of terabytes of textual data. The output is thus a statistical assessment of the corpus of human generated text likely indicating the correct answer. ChatGPT is an example of an LLM that can understand questions and synthesize responses based on probability. However Chat GPT’s potential to accurately assist physicians with a diagnosis, remains yet to be determined. The goal of this study is to elucidate AI’s potential in proposing both feasible and accurate treatment plans specifically for patients with cancer. Methods: Fifty patients at a medical oncology practice were recruited for this study. For each patient, the physician’s shorthand notes were recorded and organized. ChatGPT was then asked to come up with a potential treatment plan using these notes according to a predetermined set workflow for each individual. The responses were then reviewed by a medical oncologist for accuracy and feasibility via a qualtrics survey. Results: ChatGPT correctly proposed a treatment plan in agreement with the physician for a total of 18 cancer patients allowing for the agreement rate to be 36%. For the rest of the 32 patients, the physician disagreed with the proposed ChatGPT treatment plan on the basis of lack of personalization, inhibitive cost, incorrect recommendation, and greater alternative options. Thirteen patients fell into the ‘Lack of Personalization in Treatment' category, twelve in the ‘Incorrect Recommendation’ category, four in ‘Inhibitive Cost' category, and three in the ‘Greater Alternative Options’ category. Most patients however fell in the category of personalization, suggesting ChatGPT’s unfamiliarity with the subject of generating personal treatment plans. Conclusions: In the current study, ChatGPT was expected to create a treatment plan with only the clinician’s abridged shorthand notes and without any familiarity with the physician’s personal preferences for treatment. This knowledge gap can account for an agreement rate less than 50%. Despite its flaws, ChatGPT was, however, able to generate an adjusted and corrected version of the treatment plan when prompted further outside the set workflow. This highlights the invaluable role of clinical experience in synthesizing an accurate treatment plan. With its encroachment into the medical field, ChatGPT has shown potential in proposing feasible and accurate treatment plans, especially in the oncology clinical setting. Despite the low agreement rate obtained from this study, ChatGPT has shown promise in developing an agreeable treatment plan with the oversight of the attending physician.

Can Sri Lanka be a net-zero nation by 2050?—Current renewable energy profile, opportunities, challenges, and recommendations

Sri Lanka as a country has tremendous potential for harnessing energy from renewable sources such as solar, wind, and hydro. However, as of 2018, only 39 % of Sri Lanka's energy generation capacity was harnessed through renewable energy sources. The continuous increase in electrical energy demand and the drastic increase in vehicle population over the past few years have resulted in much of its annual income being spent on purchasing fossil fuels from foreign countries. This has placed the country's future at risk due to the predicted shortage of fossil fuel reserves and in release of an unexpected level of harmful emissions to the environment. In the meantime, Sri Lanka also has an ambitious plan of achieving Net Zero by 2050. The study conducted a systematic review followed by a time series analysis to first identify the present state of the renewable energy progress of the country and through the time series analysis recognize any discrepancies in these efforts. The initial findings revealed the lack of coordination amongst relevant institutions and contrasting government policies such as the increase in investment for non-renewable energy resources as well as backing away from providing initial investment needed to boost the usage of renewable sources for businesses and smaller entities. The study further identified sectors such as transportation and non-renewable power generation activities as the two main barriers deterring the country from having a feasible plan for its efforts for net zero by 2050. From a non-governmental perspective, the study also recognized the knowledge gap and lack of awareness in the wider population of the long-term benefits of switching to renewable sources.

Modulated Arc Therapy for hippocampal-avoidance whole brain radiation therapy: planning comparison with intensity modulated Radiation Therapy.

This study aimed to evaluate the modulated arc therapy (mARC) technique as a planning and treatment option for hippocampal sparing whole brain radiotherapy (HS-WBRT) following the Radiation Therapy Oncology Group (RTOG) 0933 dosimetric criteria. Computed tomography (CT) and magnetic resonance imaging (MRI) were selected retrospectively for 15 patients. Two types of plans were created for each patient, namely an intensity-modulated radiation therapy (IMRT) and a mARC plan. IMRT and mARC plans were compared in terms of plan quality indices, absorbed dose to organs at risk (OARs), number of monitor units (MUs), and treatment time. All plans in both techniques were considered clinically acceptable for treatment. However, IMRT plans presented a higher conformity (p = 0.01) as well as a higher homogeneity as compared to mARC plans, but this difference was not statistically significant (p > 0.05). In terms of the preservation of the hippocampus, it was observed that the IMRT plans achieved significantly lower doses for both 100% of its volume and for its maximum dose (p < 0.001). The evaluation of the remaining OARs showed that the IMRT technique resulted in lower doses, and significant differences were observed for the following organs: left cochlea (p < 0.001), left eye (p < 0.001), right eye (p = 0.03), both lenses of the eye (p < 0.001), and right optic nerve (p = 0.02). Despite these differences, the absolute differences in all dosimetric parameters were low enough to bear any clinical relevance. A drastic (close to 65%) and significant (p < 0.001) decrease was observed in the number of MUs for the mARC plans. This resulted in a substantial decrease in treatment time (60.45%, p < 0.001). It is concluded that the mARC technique is a feasible planning and treatment solution for HS-WBRT that meets the RTOG 0933 criteria. The main advantage of using mARC over IMRT for HS-WBRT is the considerable reduction in MUs and treatment time.

Multi-objective hierarchical strategy for university dorm renovation in severe cold areas

In China, there are many old buildings whose renovation could enhance building environment quality, reduce energy consumption, lower carbon emissions, and create economic benefits. However, there is currently a lack of efficient architectural renovation optimization methods to provide decision-makers and designers with highly feasible renovation plans. This paper proposes an innovative optimization method for renovating old buildings to address this issue. The research employs a hierarchical optimization framework integrating Artificial Neural Networks (ANN), the NSGA-II algorithm, and the entropy-weighted TOPSIS method. The first layer's three-objective optimization using the trained neural network model significantly improves the algorithm's efficiency in searching for optimal solutions. The second layer's entropy-weighted TOPSIS optimization includes eight objective optimizations, such as energy, economy, and lighting, thus broadening the assessment dimensions. Taking an old multi-story university dormitory in the Shenyang area as an example, the study optimizes nine passive design variables across eight objectives. The hierarchical optimization results show that the high-scoring renovation plans can save 1.52 to 3.17 million yuan in energy costs over the building's remaining lifecycle and reduce carbon dioxide emissions by 644–962 kg per square meter. SRRC results indicate that external windows significantly impact economic efficiency and energy consumption, emphasizing the importance of selecting the right type of windows in actual engineering projects. The new method proposed in this study can offers a comprehensive and highly feasible renovation plan for old buildings.

Exploring energy selection methods for robust biologically optimized carbon ion arc for head&neck cancer patients

BackgroundDue to the advantageous depth dose profile of carbon ion beams, carbon ion therapy is commonly applied in the form of intensity modulated particle therapy (IMPT) with few treatment fields. Carbon ion arc therapy (C-Arc) has recently been proposed to improve dose conformity and increase the dose-averaged linear energy transfer (LETd) inside the target to levels relevant for overcoming tumor radioresistance. In this work, we investigate different energy selection approaches for C-Arc, including a novel greedy energy layer refinement strategy.MethodsRobust biologically optimized C-Arc plans were generated for six head &neck cancer patient previously treated at the Shanghai Proton and Heavy Ion Center (SPHIC). Different heuristic approaches for mono- and dual-energy C-Arc were implemented, and compared to carbon IMPT pans. A novel greedy energy layer refinement was developed, acting directly on the dose influence matrix, rather than on an iterative plan optimization. A key challenge in C-Arc with few energy layers per angle is the sharpness of the carbon ion Bragg peak, which is challenging for plan robustness. To improve robustness and plan quality, we propose the use of a 6 mm ripple filter instead of the typical 3 mm ripple filter used for carbon IMPT.ResultsMost mono-energetic C-Arc plans were able to meet the established clinical goals, but some of the heuristic energy selection schemes were not universally applicable with low plan quality in some patients. The developed energy layer refinement strategy delivered high quality plans even for complex cases. Mono-energetic C-Arc plans presented an average increase between 10% and 33% in the maximum LETd in the target, with similar or up to 19% improved average target LETd50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_{50\\%}$$\\end{document} compared to the IMPT carbon ion plans. C-Arc plans with 2 energies per treatment angle, for the employed energy selection heuristic, improved dosimetric quality compared to mono-energetic C-Arc plans, but did not provide the same natural improvement in LETd compared to IMPT.ConclusionsFor small, centrally located targets C-Arc demonstrates the greatest potential, but feasible plans could also be achieved for more complex cases in this work. Further development is necessary for improving C-Arc delivery efficiency and plan quality toward possible clinical application.

Possible Ways to Help Raise China's Fertility Rate

This paper explores the reasons why the current problem of fertility reduction in China is mainly due to the unclear future family planning of women and young people and the incomplete inclusion of the responsibility of raising children in their future planning. By comparing the three types of countries in the world - backward countries, developing countries and developed countries - as "three small islands" and comparing the welfare they provide to their citizens, as well as their demands for different productive forces and human resources, we gradually determine the orientation of China and its future development direction. Finally, by citing and learning from the fertility policies of Japan and South Korea, the article gives several feasible plans to help China raise its fertility rate. With mild encouragement and some financial support, young families can be helped to settle down in their plans to start a family.

Attitudes of college seniors toward graduate student loan debt: the role of financial education

PurposeThis paper aims to examine prospective graduate students' attitudes toward educational loan borrowing in an experimental setting.Design/methodology/approachParticipants were randomly assigned to two treatment groups and one control group. Subjects in experimental group 1 received financial education: a short online course on the economic viability of getting a master's degree and how to finance it with a graduate student loan, while subjects in experimental group 2 received financial education along with information on the availability bias.FindingsRelying on a control group in the assessment of financial literacy education intervention impacts, this research finds positive causal treatment effects on individuals’ attitudes toward debt-financed graduate education. In comparison to the control group, experimental subjects perceived the possibility of going into debt with a graduate loan to complete a master’s degree as less stressful and worrying.Practical implicationsThis study has important educational policy implications to prevent students from stopping investing in human capital by perceiving educational loan debt as something stressful or worrying. The results can help potential (and current) grad students develop a feasible financial plan for graduate school by encouraging higher education institutions to implement educational loan information and financial education into university seminar courses for better graduate student loan decision-making.Originality/valueStudent attitudes toward debt have been analyzed in the context of higher education, but only a few researchers internationally have used an experimental design to study personal financial decision-making.

A novel hierarchical task network planning approach for multi-objective optimization

In this paper, we focus on how to generate the multi-objective optimal plan in hierarchical task network (HTN) planning. Many practical HTN planning problems not only require feasible plans, but also require these plans to achieve the best performance on multiple criteria, which is the multi-objective optimization problem in HTN planning. Aiming to generate the multi-objective optimal plan in HTN planning, a novel HTN planning approach is proposed. Our initial step is to express multiple criteria by extending the concept of the HTN planning domain. The primary search process of our proposed HTN planning approach has two components. The first one is heuristic search, which assesses the quality of methods/operators and organizes them according to their dominance relations. The second one is anytime search, which reduces the search space on the basis of the dominance relations between the partial plan and the non-dominated plan set, which is updated during the planning process. After the termination of the HTN planning search, a non-dominated plan set is returned for the decision-maker to choose from. We adopt Zeno Travel domain problems and an emergency evacuation planning problem in an experimental study to demonstrate how effective and practicable the proposed approach is.

A low-carbon evaluation framework for regional power systems

With the escalating challenges of climate change and the imperative for carbon neutrality, evaluating the carbon footprint of regional power systems becomes crucial. To address this issue, a low-carbon evaluation framework for regional power systems is developed. First, this study identifies the fundamental requirements that the index system for the low-carbon power system should follow and specifies the general direction for the index system’s creation. Second, an evaluation model for carbon reduction ability is proposed by considering generation side, grid side and load side of the power system. Then, a comparative evaluation model for the carbon reduction capability of multi-regional power systems is proposed to spatiotemporally compare the carbon reduction capability of different power systems. Next, a quantification method for the weights of low-carbon evaluation indicators is established based on the power system simulation of multi-scenario scheduling and index sensitivity analysis method. Finally, a low-carbon comprehensive score for regional power systems is created by combining the evaluation results of the system’s carbon reduction capacity with index weights. The practical research results in a region of Zhejiang province in China demonstrate that the proposed model can provide a reasonable and feasible evaluation plan for the low-carbon construction of regional power systems.

529 Implementation of a Clinical Research Feasibility Program at an Academic Medical Center

OBJECTIVES/GOALS: The objectives are 1) to describe the creation and implementation of a Clinical Research Feasibility Program at the University of Miami Miller School of Medicine (UMMSOM), and 2) to share early findings demonstrating its effectiveness in improving research operations which may be helpful for other academic medical centers. METHODS/STUDY POPULATION: Many clinical trials are closed prematurely because of low accrual or not being able to meet the target enrollment. The Miami CTSI and UMMSOM Executive Dean for Research office collaborated to establish the Research Feasibility Committee (RFC) focusing on clinical trial selection with upfront feasibility and recruitment planning. Program implementation included: 1) selecting faculty with successful clinical trial track records as committee members; 2) developing processes, tools, and governance; 3) feasibility pilot testing; and 4) feasibility program roll out and refinement. The feasibility review process starts with the PI/Designee completing a REDCap study intake form, followed by an administrative review to ensure completeness of the form. The RFC chair assigns reviewers for the studies. RESULTS/ANTICIPATED RESULTS: The RFC went live on September 1, 2022 reviewing industry sponsor clinical research studies. The RFC conducts a systematic feasibility assessment of the study protocol, operational requirements, enrollment barriers, institutional resources, and study budget (if available) for all applicable research studies prior to IRB submission and contract negotiation at the UMMSOM. To date, the RFC has received over 270 submissions. Based on feedback from users, the committee has made changes to improve the comprehension of questions and added questions to ensure capturing of critical information to assess study feasibility. Initial metrics suggest simply implementing the review process has decreased the number of clinical trial submissions: average number of studies per quarter was 41 pre-RFC vs 24 post RFC. DISCUSSION/SIGNIFICANCE: The development and implementation of the RFC involved many stakeholders from the research enterprise. Clear and frequent communication to the research community was a key factor in the program’s success. The next phase is assessing the impact of the RFC, such as preserving vital resources for trials more likely to be successful.

GOALNET: Interleaving Neural Goal Predicate Inference with Classical Planning for Generalization in Robot Instruction Following

Our goal is to enable a robot to learn how to sequence its actions to perform high-level tasks specified as natural language instructions, given successful demonstrations from a human partner. Our novel neuro-symbolic solution GOALNET builds an iterative two-step approach that interleaves (i) inferring next subgoal predicate implied by the language instruction, for a given world state, and (ii) synthesizing a feasible subgoal-reaching plan from that state. The agent executes the plan, and the two steps are repeated. GOALNET combines (i) learning, where dense representations are acquired for language instruction and the world state via a neural network prediction model, enabling generalization to novel settings and (ii) planning, where the cause-effect modeling by a classical planner eschews irrelevant predicates, facilitating multi-stage decision making in large domains. GOALNET obtains 78% improvement in the goal reaching rate in comparison to several state-of-the-art approaches on benchmark data with multi-stage instructions. Further, GOALNET can generalize to novel instructions for scenes with unseen objects. Source code available at https://github. com/reail-iitd/goalnet.

Backward and forward simulation of polycyclic aromatic hydrocarbons by the grid-scale model _validation and applications for environmental management

Polycyclic aromatic hydrocarbons (PAHs) associated with fine particulate matter (PM2.5) can cause serious health problems, but data are scarce. We have previously developed a grid-scale model (GSM) to simulate PM2.5-PAH levels on an hourly basis. This study aims to verify the applicability of the model to different years and to reasonably assess the long-term spatiotemporal effects on PAH variation in Kaohsiung. From the resulting database, the annual mean of PM2.5-PAH was found to have decreased from 3.18 in 2005 to 1.78 ng m−3 in 2018, representing an overall decrease of 44%. Thus, this study found that the progressive decrease in PAH levels was closely related to green transport and the gradual tightening of industrial operation regulations. Namely, in the urban area near the central public transport hubs, the estimated PAHs had significantly decreased by 16% (0.37 ng m−3) in 2009 with the operation of the high-speed railway in 2007 and the mass rapid transit in 2008. Near industrial areas, PAHs were reduced by environmental regulations, including air pollution control fees and cap-and-trade policies. By using GSM simulation, this study demonstrated a useful and feasible urban planning tool, and can serve as a reference for future studies related to epidemiology and environmental management.

Fleet management for earthmoving operation in the phase of detail estimation using mixed integer nonlinear programming

ABSTRACT Earthmoving operations generally account for about a quarter of the construction budget in high-way constructions. Feasible planning is thus required for delivering the successful performance of such projects. Selecting the best fleet for the operation is one of the crucial factors affecting both duration and cost. However, owing to changing operating circumstances and the uncertainty of construction projects, this task is yet conducted based on the experience of site managers. The existing literature has witnessed that discrete event simulation (DES) and references such as the construction standard production rate (CSPR) are used to assist the decision-making. Those methods have shown limitations for practical application due to the need for expert knowledge of simulation modeling and time-consuming efforts for data collection. Accordingly, this study proposes a more efficient method of using mixed integer nonlinear programming (MINLP) in multi-objective problems, enabling optimal fleet information derivation based on given circumstances. The proposed method uses the CSPR-based mathematical equations for the consideration of productivity generated by the types and sizes of equipment. A comparative study of the proposed method and a DES approach was given for further discussions.

The Current Situation of Learning and Practice in Pharmacy and Discussion on Countermeasures under the Background of New Engineering

Under the background of new engineering, technological innovation and industrial upgrading have put forward higher requirements for the theoretical and practical abilities, as well as innovation and entrepreneurship abilities of pharmaceutical students. The cultivation of high-quality pharmaceutical professionals is of great significance for promoting the healthy development of the pharmaceutical industry, ensuring medication safety, and improving people's quality of life and happiness. This article analyzes the subjective and objective factors of learning and practice, the construction of practical systems, and ideological political education. It summarizes the problems that pharmaceutical students currently face in their learning and practice, and proposes targeted and feasible plans to actively promote the cultivation of innovative and entrepreneurial high-level pharmaceutical talents.

Single‐Atomic‐Site Platinum Steers Middle Hydroxyl Selective Oxidation on Amorphous/Crystalline Homojunction for Photoelectrochemical Glycerol Oxidation Coupled with Hydrogen Generation

AbstractPhotoelectrochemical (PEC) organic conversion is a promising approach to convert low‐cost organics to value‐added chemicals, which urgently needs the development of efficient and highly selective photoanodes. Here, a Pt single‐atom dispersed WO3 amorphous/crystalline homojunction for anodic glycerol oxidation reaction (GOR) coupled with cathodic hydrogen generation is reported. In the coupled photoelectrocatalytic system, the novel Pt‐SA/WOx photoanode reaches a photocurrent density of 2.85 mA cm−2 at 1.2 V versus RHE, with up to 297.3 mmol m−2 h−1 of glycerol conversion rate and 60.2% of dihydroxyacetone (DHA) selectivity, realizing green synthesis of high‐value chemicals. The experimental data and theoretical calculations reveal the regulated band structure, build‐in electric field, and surface charge density of Pt‐SA/WOx, which can improve the carrier lifetime, accelerate charge transfer, and reduce the energy barrier for the GOR process, thus boosting PEC activity and DHA selectivity. This work provides a feasible plan for designing atomic‐level engineering and constructing amorphous/crystalline homojunction on photoanodes for efficient PEC high‐value products from biomass.

A Review of Industry 4.0 Assessment Instruments for Digital Transformation

Nowadays, different Industry 4.0 technologies have been implemented into diverse industries. However, these implementations are not standardized across similar industries and countries. Consequently, companies are actively looking for assessment instruments—maturity models, roadmaps, frameworks, readiness assessments—to assist in their digital transformation, to determine their Industry 4.0 level, and to identify technologies and strategies that should be implemented in specific areas, thus developing a feasible implementation plan. A review is conducted following the PRISMA (Preferred Reporting Items for Systematic Literature Reviews and Meta-Analyses) methodology to analyze the different research works on assessment instruments focused on Industry 4.0. A total of 538 articles, book chapters, conference proceedings, editorial material, reviews, and reports written in the English language were retrieved. Of these, 132 research papers were examined using a mixed analysis format to generate bibliometrics, and 36 articles were then deeply studied for a complete meta-analysis. The findings and insights of this meta-analysis led to a compilation and summary of dimensions, outcomes, enablers, and key components typically involved in Industry 4.0, which are comprehensively integrated to present the best practices for assessing Industry 4.0 and deploying a digital transformation that can impact productivity, flexibility, sustainability, quality, costs, and time.

A Simulation of the Spatial Expansion Process of Shrinking Cities Based on the Concept of Smart Shrinkage: A Case Study of the City of Baishan

The coexistence of urban expansion and shrinkage in China has become increasingly apparent; therefore, the current strategic model of growth-oriented urban planning as the top-level design needs to be adjusted. This paper focuses on the city of Baishan, which is a typical shrinking city in China, and explores the feasibility of implementing the concept of smart shrinkage planning in shrinking cities in China by constructing a coupled PLUS-SD model. The results demonstrate the following conclusions: (1) The overall simulation of the coupled PLUS-SD model is superior to that of the PLUS model. In Baishan, the areas with the most changes in construction land will be located at the edges of the landforms by 2030. (2) Using the traditional planning scenario would only exacerbate the rate of construction land expansion in Baishan, deepening the incongruity between the city’s population and construction land. (3) The smart shrinkage scenario will require strict control of the scale of construction land and optimization of the structure of the urban construction land, which would push the city in the direction of healthy and sustainable development. (4) The concept of smart shrinkage planning is a scientific and feasible plan for realizing the efficient and sustainable use of construction land in shrinking cities.

Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time.

Research on Dynamic Model Construction of MTI Teacher Professional Development under Influence of Translation Technology

Abstract The continuous updating of translation technology has had a huge impact on the Master of Translation and Interpreting (MTI) teacher professional development. Exploring the effects of translation technology on teacher professional development, the composition of abilities, and factors that influence it, this paper draws upon a literature review and action research. Through the theoretical deductive reasoning and action research, the paper has constructed a dynamic model of MTI teacher professional development under the influence of translation technology, providing cases and useful references for the follow-up research of teacher professional development under the new technology environment, in order to provide a feasible implementation plan for the professional development of college teachers.