Project Planning Research Articles

Geometric decomposition and analysis of Konya Sahip Ata mosque portal muqarnas

In the late Anatolian Seljuk period (1243–1308), architectural design took on new symbolic significance, focusing on grandeur and ornamentation as a response to the shifting political landscape. One of the critical features of this era is the transformation seen in the geometric layouts of some muqarnas designs, exemplified explicitly in Konya Sahip Ata mosque muqarnas (1258). While the arrangements may appear irregular at first glance, closer analysis reveals that they are structured according to a dodecagonal system. This study focuses on the geometric layouts in the plan projections of Sahip Ata mosque muqarnas, showing that dodecagrams were used to organize these complex patterns, reflecting an advanced and deliberate approach to design during this transitional period.

Wind Characteristics at Agadez and Tahoua Weather Stations

ABSTRACTThis study investigates the characteristics of wind speed in two synoptic weather stations in Niger. Hence, three hourly wind data including wind speed and direction from 2009 to 2019 were used. Prior to the analysis, the dataset was pre‐processed to remove corrupted and irrelevant data. Statistical analysis was conducted on the dataset to understand the variability of the wind speed and wind direction at the two synoptic stations. The results showed that the highest prevailing wind is recorded in the Agadez region with average wind speeds ranging from [3; 6] m/s and [6; 10] m/s at 10 m altitude. The dominant directions are the East from September to May, then the West from June to August. At the Tahoua station, the prevailing winds have average speeds also belonging to [3; 6] m/s and [6; 10] m/s at 10 m altitude, but the dominant directions are East and East‐North‐East from October to April, then West and South‐West from May to September. These findings are useful for the effective planning of a wind energy project in Niger as they provide useful insight regarding the wind characteristics in two windy regions in Niger.

Optimizing Wind Speed Predictions in Bangladesh: Unveiling the Power of Machine Learning Approaches

Wind speed is a naturally occurring phenomenon that arises from the intricate interplay of various atmospheric processes. Wind speed prediction is pivotal in various sectors worldwide, and Bangladesh is no exception. Beyond its impact on agriculture, water management, and disaster preparedness, wind speed also plays a crucial role in urban planning and construction projects. Architects and engineers rely on accurate wind speed forecasts to design buildings and infrastructure that can withstand local wind conditions. Furthermore, the aviation and maritime industries heavily depend on wind speed predictions to ensure the safety of flights and shipping routes. Predicting wind speeds in Bangladesh poses a significant challenge due to the region's susceptibility to frequent seasonal changes influenced by its coastal location and complex, nonlinear climate patterns. To address this important aspect, we leverage Machine Learning (ML) algorithms, including Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), Random Forest (RF), K Nearest Neighbors (K-NN), and Support Vector Machine (SVM) to forecast wind speeds at various weather stations in Bangladesh. We utilized various accuracy metrics, including precision, sensitivity, specificity, F-measure, and overall accuracy, to evaluate the performances of the algorithms. The RF model outperformed the other models with an overall accuracy of 94.73% for predicting wind speed conditions in Bangladesh. On the other hand, the LDA model exhibited the lowest performance, achieving an accuracy of 93.27% in comparison to the other models. It is noticeable that the aforementioned five models showed more than 90% accuracy for windspeed prediction. Additionally, we complement our analysis with visual representations such as box plots, density plots, dot plots, parallel plots, and scatterplot matrix plots. These empirical results also highlighted the RF as the most suitable method for predicting contemporary wind speed patterns in Bangladesh. International Journal of Statistical Sciences, Vol. 24(2), November, 2024, pp 137-154

The Influence of Planning and Architectural Styles on the Sustainability of the City of Kumasi, Ghana

The city of Kumasi that was once dubbed the ‘Garden City of West Africa’ was founded in the late 17th century and served as the seat of government for the medieval Greater Asante Union. It later served as a commercial hub for the British Colony after undergoing massive transformation in the first two decades of the 19th century. Post-independence, the city went through various urban planning projects that mostly targeted social development. The architecture of Kumasi has also been changing through different periods of its history. This article examines the relationship between the evolution of the city of Kumasi through urban planning and architecture and the general sustainability in construction. The results show that there is massive depletion of the green belt in Kumasi. Moreover, contemporary architecture uses fewer passive designs for indoor climate control and less local materials for construction. Use of technology for improvement of local materials compressive strength and integration of renewable energy sources in architecture are recommended as course of action.

Enhancing Software Effort Estimation with Pre-Trained Word Embeddings: A Small-Dataset Solution for Accurate Story Point Prediction

Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate semantics, and suffer from dimensionality, sparsity, and computational inefficiency. This study used pre-trained word embeddings, including FastText and GPT-2, to improve estimation accuracy in such cases. Seven pre-trained models were evaluated for their ability to effectively represent textual data, addressing the fundamental limitations of TF-IDF through contextualized embeddings. The results show that combining FastText embeddings with support vector machines (SVMs) consistently outperforms traditional approaches, reducing the mean absolute error (MAE) by 5–18% while achieving accuracy comparable to deep learning models like GPT-2. This approach demonstrated the adaptability of pre-trained embeddings for small datasets, balancing semantic richness with computational efficiency. The proposed method optimized project planning and resource allocation while enhancing software development through accurate story point prediction while safeguarding privacy and security through data anonymization. Future research will explore task-specific embeddings tailored to software engineering domains and investigate how dataset characteristics, such as cultural variations, influence model performance, ensuring the development of adaptable, robust, and secure machine learning models for diverse contexts.

Preliminary Design and Thermal-Hydraulic Study of a 250-kW LBE Experimental Spallation Target for CiADS

The CiADS (China initiative Accelerator Driven System) project plans to build a 250-kW experimental target before construction of the 2.5-MW spallation target. In this paper, the effect of proton beam distribution on the performance of the 250-kW target is studied. Hence, a hollow beam distribution, formed by a Gaussian beam scanning around the center of the beam window, is proposed. Then, combined with the orthogonal test method and the CRITIC (Criteria Importance through Intercriteria Correlation) method, four key geometric parameters of the target flow channel are analyzed to simultaneously achieve optimization of the target performance from the viewpoints of both the maximum temperature and velocity under steady-state condition. Finally, transient analysis of the initiation of the Gaussian beam scanning is conducted; the calculation time was 15s. The results show that the temperature on the window rose gradually until reaching its maximum value corresponding to the steady-state analysis. Detailed analyses showed that the optimized target design is capable of meeting the thermal-hydraulic and mechanical requirements.

A Study on OTT Support Project and Policy Improvement Plan in Korea to Revitalize K-Drama; Focusing on France and Canada

With the recent development of Internet technology and the rapid evolution of digital networks, various types of video content distribution platforms have emerged.[1] In particular, in the era of COVID-19, video contents such as Korean movies and dramas are growing with worldwide attention through the global OTT platform.[1] However, after COVID-19, the Korean media and content industries are experiencing more difficulties than ever in 2024, as they have not been able to cope with the global environment compared to the Korean media and content industries. Therefore, in preparation for the media and content industry environment, which has many variables and is constantly changing under the global environment, we would like to once again check the drama-related support policy currently implemented by the government and propose a plan to revitalize the K-drama industry.

Assessing Awareness of Building Information Modeling (BIM) among AEC Professionals in Nepal

The advent of Building Information Modeling (BIM) has led to a revolutionary transformation in the construction sector on a global scale. The implementation of BIM has resulted in a multitude of benefits, including enhanced project outcomes, cost savings, and improved collaboration. However, in Nepal, the adoption of BIM in the Architecture, Engineering, and Construction (AEC) industry presents a distinctive set of challenges and opportunities. A survey of architects and engineers in both construction and academia indicates a moderate level of awareness of BIM, with 59% of respondents reporting familiarity with the technology. Of these, 69% employ BIM primarily during the design phase, thereby underscoring its utility in project planning. Nevertheless, practical applications remain constrained, as a considerable proportion of users have less than two years of experience with BIM. Despite its potential, 41% of professionals remain unaware of BIM, indicating a significant knowledge gap. This suggests the need for increased education and training to fully leverage BIM's benefits. While the current focus is on design, broader integration across all project stages is essential to maximize its impact. Expanding awareness and developing skills in BIM will be key to advancing its implementation in Nepal's AEC sector, driving efficiency and innovation in construction practices.

Pola Pendekatan Inovatif dalam Rancang Merchandise Budaya Berbahan Dasar Bungkus Semen dalam Pelestarian Wayang Beber

The preservation of local culture requires a creative and innovative approach. This implementation aims to develop innovative approaches in recycling cement packaging into cultural merchandise as part of the preservation efforts of Wayang Beber. The method used involves Objective Oriented Project Planning (OOPP) through a study on the development of an ideal qualitative pattern that will be implemented to transform cement packaging into high-value economic and aesthetic products. The results indicate that the pattern of developing awareness of the importance of preserving Wayang Beber is not only ideal in implementation but can also serve as a pilot project for real implementation. This finding emphasizes the importance of collaboration between educational institutions and communities in developing creative solutions for detailed cultural preservation through the development of innovative products focused on the circular economy.

A systems perspective on gaps in the person-centered sick leave and rehabilitation process: a Swedish interview study

Background Consensus on priorities to optimize the sick leave and rehabilitation process (SRP) is lacking. Objective To explore perspectives of stakeholders in the SRP on bridging the gap between desired process scope, and actual practice, from a multi-professional, multi-organizational, and interdisciplinary approach. Design and setting Focus group interviews were conducted with various SRP frontline professionals in Region Västra Götaland, Sweden, using purposive sampling to capture a range of experiences. Participants discussed their perceptions of critical changes and priorities needed to meet patients’ SRP needs in a primary care context. All interviews were analyzed using systematic text condensation, as described by Malterud. Subjects General practitioners (n = 6), rehabilitation coordinators and licensed healthcare professionals from primary healthcare (n = 13), administrators from the Social Insurance Agency, the Employment Agency and Social Services (n = 12). Results Through data analysis, the following themes emerged: 1) The need for rules and regulations to enable coherent process governance 2) Challenges and opportunities in person-centered SRP: Professional collaboration, organizational priorities, and the need for enhanced leadership, and 3) Balancing resources and patient needs in the SRP: How to improve care quality and accessibility. In summary, participants mainly discussed how to improve process efficiency and quality of care while balancing available resources and a heavy workload. A main goal was to prevent negative spirals of suboptimal decision-making in individual cases, which could lead to increased work, unfortunate outcomes, and patient suffering. Conclusions This qualitative study indicated that gaps between a desired process scope and actual practice might be bridged by enabling coherent cross-organizational process governance, prioritizing person-centered ways of working, and balancing available resources and workload. The above changes were believed to improve process quality and overall efficiency. Trial registration The study project plan was pre-registered on September 21st, 2020, in the database FOU i VGR (researchweb.org), project number 274941.

Um processo participativo de desenvolvimento de melhorias em áreas comuns de uso infantil

Abstract Research which evaluates Social Housing (SH) in Brazil often points out unfavorable conditions expressed by residents, in terms of the residential unit and common spaces, as well as surroundings. Participatory processes to improve projects are considered essential for political, social and economic reasons, and user values need to be prioritized. Processes to develop improvements for existing SH, that include residents as protagonists, with active participation, may guide more effective actions. However, participatory processes for home and public area renovations require procedures to effectively achieve their technical objectives and meet the needs and desires of their residents with feasibility of implementation. This research developed a case study with the participation of residents of a small SH project in Campinas - SP, with a specific focus on improvements for the children's leisure areas. The study involved the public targeted, children living in the analyzed SH complex, in defining their leisure space through dynamics and playful activities. Qualitative results are presented of this participatory process, involving children. Knowledge gained is methodological, with lessons learned for the planning and development of participatory co-design projects with children.

Our First Steps: A QI Project to Reduce NICU Admissions of Infants With NAS.

In 2020, the rate of newborns diagnosed with neonatal abstinence syndrome (NAS) in the United States was 6.3 for every 1000 newborn hospitalizations. Resources used to care for this population, particularly NICU beds, are being overwhelmed. In 2020, the state of Ohio saw a rate of 9.5 newborns with NAS for every 1000 newborn hospitalizations. To determine if using the Eat, Sleep, Console (ESC) model of care to guide management of neonates with NAS instead of the Finnegan Scale would reduce the number of admissions of neonates diagnosed with NAS to the neonatal intensive care unit (NICU). The PDSA (Plan, Do, Study, Act) method was used in the planning and implementation of this pilot quality improvement project. Education regarding the ESC model of care was provided to the Mother/Infant Unit (MIU), with ongoing education and resources provided and readily available on the unit. Fifteen neonates were managed with ESC. Of the 5 who would have been admitted to the NICU for pharmacological treatment if Finnegan Scores were the determining factor for admission, 2 were discharged home from the MIU having been managed with ESC. The use of the ESC model of care can be a useful tool in the management and assessment of neonates with NAS. Resource allocation for care of this population must be assessed to provide optimal non-pharmacological interventions.

Recovery for Resilience: Exploring the Disconnect Between Collaborative Planning and Project Implementation in Post-Hurricane Sandy New York City

Collaborative and participatory planning processes that are interactive, thoughtful, and efficient have become increasingly important elements of post-disaster recovery. Meanwhile, rapid action remains critically important to quickly restore destroyed urban systems, but creating long-term resilience demands new ideas that are also implementable. These various tensions create a daunting paradox, and while many communities have attempted to use collaborative planning approaches in recovery, useful assessments and guidance on collaborative recovery planning is sparse. To better understand the nuances of a collaborative recovery, we document and evaluate the effectiveness of the collaborative processes behind the planning of the ESCR resilience project, initiated as BIG U in New York City, which emphasizes community participation and collaborative decision-making. The project is unique because of its innovative and interactive multi-stakeholder processes. The authors have studied the case longitudinally over the past decade, using a variety of research methods to gather relevant information. The ESCR collaborative efforts suggest that, first, there is a disconnect in the dominant rationalities behind climate resilience efforts between the planning and implementation phases. Second, given the disconnect between the planning and implementation phases, collaborative efforts must be refined to remain relevant in the implementation phase. Third, even after project implementation, there are different types of rationality employed, and thus varying levels of satisfaction, depending on the benefits that participants receive. While planners must recognize the disconnect between collaborative planning and project implementation and seek ways to mitigate any abrupt shifts, we conclude that collaborative approaches to recovery planning can be effective and manageable, even in unique recovery situations.

Deep learning using histological images for gene mutation prediction in lung cancer: a multicentre retrospective study.

Accurate detection of driver gene mutations is crucial for treatment planning and predicting prognosis for patients with lung cancer. Conventional genomic testing requires high-quality tissue samples and is time-consuming and resource-consuming, and as a result, is not available for most patients, especially those in low-resource settings. We aimed to develop an annotation-free Deep learning-enabled artificial intelligence method to predict GEne Mutations (DeepGEM) from routinely acquired histological slides. In this multicentre retrospective study, we collected data for patients with lung cancer who had a biopsy and multigene next-generation sequencing done at 16 hospitals in China (with no restrictions on age, sex, or histology type), to form a large multicentre dataset comprising paired pathological image and multiple gene mutation information. We also included patients from The Cancer Genome Atlas (TCGA) publicly available dataset. Our developed model is an instance-level and bag-level co-supervised multiple instance learning method with label disambiguation design. We trained and initially tested the DeepGEM model on the internal dataset (patients from the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China), and further evaluated it on the external dataset (patients from the remaining 15 centres) and the public TCGA dataset. Additionally, a dataset of patients from the same medical centre as the internal dataset, but without overlap, was used to evaluate the model's generalisation ability to biopsy samples from lymph node metastases. The primary objective was the performance of the DeepGEM model in predicting gene mutations (area under the curve [AUC] and accuracy) in the four prespecified groups (ie, the hold-out internal test set, multicentre external test set, TCGA set, and lymph node metastases set). Assessable pathological images and multigene testing information were available for 3697 patients who had biopsy and multigene next-generation sequencing done between Jan 1, 2018, and March 31, 2022, at the 16 centres. We excluded 60 patients with low-quality images. We included 3767 images from 3637 consecutive patients (1978 [54·4%] men, 1514 [41·6%] women, 145 [4·0%] unknown; median age 60 years [IQR 52-67]), with 1716 patients in the internal dataset, 1718 patients in the external dataset, and 203 patients in the lymph node metastases dataset. The DeepGEM model showed robust performance in the internal dataset: for excisional biopsy samples, AUC values for gene mutation prediction ranged from 0·90 (95% CI 0·77-1·00) to 0·97 (0·93-1·00) and accuracy values ranged from 0·91 (0·85-0·98) to 0·97 (0·93-1·00); for aspiration biopsy samples, AUC values ranged from 0·85 (0·80-0·91) to 0·95 (0·86-1·00) and accuracy values ranged from 0·79 (0·74-0·85) to 0·99 (0·98-1·00). In the multicentre external dataset, for excisional biopsy samples, AUC values ranged from 0·80 (95% CI 0·75-0·85) to 0·91 (0·88-1·00) and accuracy values ranged from 0·79 (0·76-0·82) to 0·95 (0·93-0·96); for aspiration biopsy samples, AUC values ranged from 0·76 (0·70-0·83) to 0·87 (0·80-0·94) and accuracy values ranged from 0·76 (0·74-0·79) to 0·97 (0·96-0·98). The model also showed strong performance on the TCGA dataset (473 patients; 535 slides; AUC values ranged from 0·82 [95% CI 0·71-0·93] to 0·96 [0·91-1·00], accuracy values ranged from 0·79 [0·70-0·88] to 0·95 [0·90-1·00]). The DeepGEM model, trained on primary region biopsy samples, could be generalised to biopsy samples from lymph node metastases, with AUC values of 0·91 (95% CI 0·88-0·94) for EGFR and 0·88 (0·82-0·93) for KRAS and accuracy values of 0·85 (0·80-0·88) for EGFR and 0·95 (0·92-0·96) for KRAS and showed potential for prognostic prediction of targeted therapy. The model generated spatial gene mutation maps, indicating gene mutation spatial distribution. We developed an AI-based method that can provide an accurate, timely, and economical prediction of gene mutation and mutation spatial distribution. The method showed substantial potential as an assistive tool for guiding the clinical treatment of patients with lung cancer. National Natural Science Foundation of China, the Science and Technology Planning Project of Guangzhou, and the National Key Research and Development Program of China. For the Chinese translation of the abstract see Supplementary Materials section.

Advancing gender equality through strategic project management: A framework for integrating gender-responsive policies in development projects

This review presents a strategic framework for integrating gender-responsive policies into project management to advance gender equality within development projects. Gender equality is increasingly recognized as a crucial component of sustainable development, yet significant gaps remain in effectively embedding gender considerations in project planning and execution. Through a review of successful case studies and current project management practices, this review explores how structured, gender-responsive strategies can enhance the impact of development projects across diverse sectors. The proposed framework emphasizes a systematic approach to incorporating gender perspectives from the project design phase through implementation and evaluation. Key components include conducting initial gender impact assessments, establishing gender-specific goals and indicators, and ensuring adequate budget allocations for gender-focused activities. Additionally, the framework underscores the importance of continuous monitoring, stakeholder engagement, and the collection of gender-disaggregated data to inform adaptive management. Challenges such as institutional constraints, cultural resistance, and limited gender-specific expertise among project teams are addressed with recommendations for building capacity and securing policy support. Best practices highlighted include fostering partnerships with local women’s organizations, engaging community leaders, and providing gender sensitivity training for project staff. This review concludes that integrating gender-responsive policies in project management not only advances gender equality but also contributes to more inclusive and effective development outcomes. The framework offers a replicable model for project managers and policymakers, advocating for a shift towards projects that prioritize equitable participation and benefits for all genders. By implementing these practices, development initiatives can create sustainable socio-economic improvements and foster gender-inclusive environments.

Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm

Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting for the complexity of activity networks and uncertainties within project environments, the model aims to address key challenges in project forecasting. Unlike traditional methods such as Earned Value Management (EVM) and Earned Schedule Method (ESM), which rely on static metrics, the XGBoost-SA model adapts dynamically to project data, achieving 92% prediction accuracy. This advanced model offers a more precise forecasting approach by incorporating and optimizing features from historical data. Results reveal that XGBoost-SA reduces cost prediction error by nearly 50% and time prediction error by approximately 80% compared to EVM and ESM, underscoring its effectiveness in complex scenarios. Furthermore, the model’s ability to manage limited and evolving data offers a practical solution for real-time adjustments in project planning. With these capabilities, XGBoost-SA provides project managers with a powerful tool for informed decision-making, efficient resource allocation, and proactive risk management, making it highly applicable to complex construction projects where precision and adaptability are essential. The main limitation of the developed model in this study is the reliance on data from similar projects, which necessitates additional data for application to other industries.

Knowledge and skills required for librarians in Digital Humanities: a literature review

This article is a literature review conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify, analyze and synthesize the competencies required of librarians in the Digital Humanities (DH); DH content in Information Science courses and the roles of librarians in DH projects. Competences were identified in all DH activities, particularly technological competences, soft skills, project planning and intellectual property rights. Courses and proposals for DH courses in Information Science were identified, as there are still gaps in training. The roles of librarians were identified at different levels, as suppliers of infrastructure, tools and raw materials, service providers, collaborators and project creators. It is recommended that DH be included both in the formal domain, through its inclusion in Information Science courses, and in the informal domain, through training that allows for lifelong updating.

Peer-Project-Based Learning in CNC Simulation Programming Courses

The challenges of working with various dynamics and diverse job opportunities require students to have self-efficacy and creativity. Selfefficacy emphasizes courage in solving work challenges and creativity related to the supporting capacity of student innovation. This study aims to develop a peer-project-based learning (Peer PbL) model through model feasibility testing and model effectiveness testing. The research method uses a research and development approach by testing the model using an experimental method with a one-shot case study design. A total of 15 students were involved in this research, with the main topic discussion being CNC turning simulation. The results showed that the steps in the Peer-PbL model include team creation, providing challenges, preparing project plans, validating project suitability, doing projects, monitoring team performance, project presentations, and peer assessment. Feasibility assessments from machine learning experts, learning technologists, evaluation experts, CNC professionals, and CAD/CAM professionals indicate strong rater results and correlations. The results of testing the effectiveness of the Peer- PbLmodel in implementing classroom conditions turned out to be able to increase self-efficacy and creativity before and after the Peer-PbL model. This research is a strategy for updating PbL as a learning model by CNC simulation and programming courses.

The Application of Artificial Intelligence Planning and Scheduling in Photovoltaic Plant Construction Projects

ABSTRACTPlanning is one of the most critical areas within Project Management, with adequate task scheduling and resource management being of vital importance, especially at the project's outset. This paper introduces an Artificial Intelligence designed for the automatic planning of photovoltaic plant (PV) construction projects, encompassing various tasks such as engineering, procurement, logistics, construction and commissioning, and including the substation and transmission line, scheduling a total of 100 tasks, which constitute a basic Engineering, Procurement and Construction project planning. The model is trained using a total of 50 real‐case project plans for PVs. The results demonstrate that the model successfully and effectively carries out photovoltaic project planning, marking a significant step towards digital transformation.

Willingness for Land Transfer and Coupling Coordination Analysis in Poverty Alleviation Resettlement Areas: A Sustainable Development Perspective

This study focuses on the land transfer intentions of migrants and surrounding villagers in the SZ resettlement area of BS City, Guangxi. It systematically analyzes the coupling coordination relationship between migrants’ land transfer-in intentions and the land transfer-out intentions of surrounding villagers, verifying the practical value of the “Shared Land Resource Model” in the resettlement area and its surroundings. The study yields the following key conclusions: (1) there is a strong coupling between the land demand intentions of migrants and the land supply intentions of surrounding villagers, yet the actual coordination in the transfer process is limited, which constrains resource allocation efficiency and prevents land transfer from fully utilizing shared resources; (2) in the evaluation of migrants’ land transfer-in intentions, external environmental factors have the greatest influence (with a weight coefficient of 0.7877), while individual characteristics (0.0486) and psychological characteristics (0.0593) have relatively low weight coefficients, indicating that migrants primarily rely on government policy support and lack internal motivation; (3) the land transfer-out intentions of surrounding villagers are most affected by farmland resource endowment (weight coefficient of 0.3284), indicating that the quality and quantity of land resources are key factors affecting villagers’ transfer-out willingness, while individual endowment factors have the smallest impact (weight coefficient of 0.1220). Three recommendations are proposed: stimulating migrants’ intrinsic motivation to enhance livelihood autonomy, protecting villagers’ land rights to increase transfer participation, and building a systematic land resource sharing model to promote sustainable resource allocation. This study provides theoretical support for optimizing the land transfer mechanism in resettlement areas, aiming to improve land use efficiency, support the livelihood transition of migrants, and offer practical insights for land management planning in poverty alleviation and resettlement projects in other countries.