This study proposes a novel approach to optimizing individual work schedules for book digitization using mixed-integer programming (MIP). By leveraging the power of MIP solvers, we aimed to minimize the overall digiti...This study proposes a novel approach to optimizing individual work schedules for book digitization using mixed-integer programming (MIP). By leveraging the power of MIP solvers, we aimed to minimize the overall digitization time while considering various constraints and process dependencies. The book digitization process involves three key steps: cutting, scanning, and binding. Each step has specific requirements and limitations such as the number of pages that can be processed simultaneously and potential bottlenecks. To address these complexities, we formulate the problem as a one-machine job shop scheduling problem with additional constraints to capture the unique characteristics of book digitization. We conducted a series of experiments to evaluate the performance of our proposed approach. By comparing the optimized schedules with the baseline approach, we demonstrated significant reductions in the overall processing time. In addition, we analyzed the impact of different weighting schemes on the optimization results, highlighting the importance of identifying and prioritizing critical processes. Our findings suggest that MIP-based optimization can be a valuable tool for improving the efficiency of individual work schedules, even in seemingly simple tasks, such as book digitization. By carefully considering specific constraints and objectives, we can save time and leverage resources by carefully considering specific constraints and objectives.展开更多
The construction projects’ dynamic and interconnected nature requires a comprehensive understanding of complexity during pre-construction. Traditional tools such as Gantt charts, CPM, and PERT often overlook uncertai...The construction projects’ dynamic and interconnected nature requires a comprehensive understanding of complexity during pre-construction. Traditional tools such as Gantt charts, CPM, and PERT often overlook uncertainties. This study identifies 20 complexity factors through expert interviews and literature, categorising them into six groups. The Analytical Hierarchy Process evaluated the significance of different factors, establishing their corresponding weights to enhance adaptive project scheduling. A system dynamics (SD) model is developed and tested to evaluate the dynamic behaviour of identified complexity factors. The model simulates the impact of complexity on total project duration (TPD), revealing significant deviations from initial deterministic estimates. Data collection and analysis for reliability tests, including normality and Cronbach alpha, to validate the model’s components and expert feedback. Sensitivity analysis confirmed a positive relationship between complexity and project duration, with higher complexity levels resulting in increased TPD. This relationship highlights the inadequacy of static planning approaches and underscores the importance of addressing complexity dynamically. The study provides a framework for enhancing planning systems through system dynamics and recommends expanding the model to ensure broader applicability in diverse construction projects.展开更多
Village revitalization is a major development strategy in China,where village planning plays as its critical component.Taking village planning of De’an County in Jiangxi Province as an example,this paper explored the...Village revitalization is a major development strategy in China,where village planning plays as its critical component.Taking village planning of De’an County in Jiangxi Province as an example,this paper explored the significant importance of village planning in promoting rural revitalization along with its corresponding promote mechanisms.Through in-depth research on the detailed situation of village planning and implementation in Jiangxi,this paper summarized that some challenges exist,including backward planning,poor planning consciousness,difficulties in planning implementation.Based on these findings,the paper analyzed the challenges of village land resource scarcity,village images lacking in uniqueness features,and insufficient rural infrastructure construction.Furthermore,it proposed strategies such as taking the lead in planning from the beginning,advancing practical implementation at a high level,adhering to bottom-line thinking,and coordinating high-quality rural land protection and development,applying strategies such as ecological and pleasant living,building villages suitable for living and working,and construction of beautiful countryside,aiming to provide valuable reference for related research fields.展开更多
Objective:This study aimed to explore the readiness for advance care planning(ACP)among older adults in Macao’s day service centers and investigate the influencing factors.Methods:A cross-sectional study was conducte...Objective:This study aimed to explore the readiness for advance care planning(ACP)among older adults in Macao’s day service centers and investigate the influencing factors.Methods:A cross-sectional study was conducted from October to December 2022 using a convenience sampling method.A total of 312 older adults were selected from 13 day service centers for older adults in Macao,China.The Advance Care Planning Acceptance Questionnaire and the Family Adaptation,Partnership,Growth,Affection,Resolve(APGAR)Scale were used to survey the older adults.Results:A total of 306 older adults completed the survey.The score for advance care planning readiness was 65.55±10.69,and 59.5%of participants(n=182)were willing to participate in ACP.The family function score was 7.24±2.51,while 70.3%of participants were from a highly functional family.The higher family function indicating a higher readiness for advance care planning(r=0.396,P<0.001).The multiple linear regression analysis indicated that the variables“age,”“knowledge of ACP,”“experience with ACP,”and“received resuscitation of yourself,relatives or friends”combined with“family function”can influence advance care planning readiness among older adults(R^(2)=0.317,F=27.898,P<0.001).Conclusions:Older adults in Macao’s day service centers were willing to engage in ACP.The importance of family involvement is highlighted in the ACP readiness.Health education and improved family communication are vital for promoting ACP,which ensures individuals receive care when they lack the capacity to make that choice.Additionally,healthcare professionals should enhance communication and education with older adults during the medical care process.展开更多
BACKGROUND Kidney transplantation is one of the most effective treatments for patients with end-stage renal disease.However,many regions face low deceased donor rates and limited ABO-compatible transplant availability...BACKGROUND Kidney transplantation is one of the most effective treatments for patients with end-stage renal disease.However,many regions face low deceased donor rates and limited ABO-compatible transplant availability,which increases reliance on living donors.These regional challenges necessitate the implementation of kidney paired donation(KPD)programs to overcome incompatibilities such as ABO mismatch or positive cross-matching,even when suitable and willing donors are available.AIM To evaluate the effectiveness of a single-center domino KPD model in both operational planning and clinical management processes and to assess its impact on clinical outcomes.METHODS Between April 2020 and January 2024,we retrospectively evaluated patients enrolled in our center’s domino kidney transplantation program.Donor-recipient pairs unable to proceed due to ABO incompatibility or positive cross-matching with their own living donors were included.Donors and recipients were assessed based on blood group compatibility,HLA tissue typing,and negative cross-match results.A specialized computer algorithm grouped patients into three-way,fourway,and five-way chains.All surgical procedures were performed on the same day at a single center.RESULTS A total of 169 kidney transplants were performed,forming 52 domino chains.These domino KPD transplants accounted for a notable proportion of our center’s overall transplant activity,which included both living donor kidney transplants and deceased donor transplants.Among these chains,the primary reasons for participation were ABO incompatibility(74%),positive cross-matching(10%),and the desire to improve HLA mismatch(16%).Improved HLA mismatch profiles and high graft survival(96%at 1 year,92%at 3 years)and patient survival(98%at 1 year,94%at 3 years)rates were observed,as well as low acute rejection episodes.CONCLUSION The single-center domino KPD model enhanced transplant opportunities for incompatible donor-recipient pairs while maintaining excellent clinical outcomes.By providing a framework that addresses regional challenges,improves operational efficiency,and optimizes clinical management,this model offers actionable insights to reduce waiting lists and improve patient outcomes.展开更多
The global demand for effective skin injury treatments has prompted the exploration of tissue engineering solutions.While three-dimensional(3D)bioprinting has shown promise,challenges persist with respect to achieving...The global demand for effective skin injury treatments has prompted the exploration of tissue engineering solutions.While three-dimensional(3D)bioprinting has shown promise,challenges persist with respect to achieving timely and compatible solutions to treat diverse skin injuries.In situ bioprinting has emerged as a key new technology,since it reduces risks during the implantation of printed scaffolds and demonstrates superior therapeutic effects.However,maintaining printing fidelity during in situ bioprinting remains a critical challenge,particularly with respect to model layering and path planning.This study proposes a novel optimization-based conformal path planning strategy for in situ bioprinting-based repair of complex skin injuries.This strategy employs constrained optimization to identify optimal waypoints on a point cloud-approximated curved surface,thereby ensuring a high degree of similarity between predesigned planar and surface-mapped 3D paths.Furthermore,this method is applicable for skin wound treatments,since it generates 3D-equidistant zigzag curves along surface tangents and enables multi-layer conformal path planning to facilitate the treatment of volumetric injuries.Furthermore,the proposed algorithm was found to be a feasible and effective treatment in a murine back injury model as well as in other complex models,thereby showcasing its potential to guide in situ bioprinting,enhance bioprinting fidelity,and facilitate improvement of clinical outcomes.展开更多
An improved version of the sparse A^(*)algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorith...An improved version of the sparse A^(*)algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorithms.This algorithm considers factors such as initial position and orientation of the ship,safety range,and ship draft to determine the optimal obstacle-avoiding route from the current to the destination point for ship planning.A coordinate transformation algorithm is also applied to convert commonly used latitude and longitude coordinates of ship travel paths to easily utilized and analyzed Cartesian coordinates.The algorithm incorporates a hierarchical chart processing algorithm to handle multilayered chart data.Furthermore,the algorithm considers the impact of ship length on grid size and density when implementing chart gridification,adjusting the grid size and density accordingly based on ship length.Simulation results show that compared to traditional path planning algorithms,the sparse A^(*)algorithm reduces the average number of path points by 25%,decreases the average maximum storage node number by 17%,and raises the average path turning angle by approximately 10°,effectively improving the safety of ship planning paths.展开更多
To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The...To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.展开更多
Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution netwo...Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.展开更多
Under the background of global tourism development,the planning and design of rural tourism landscape in Libo Yaoshan Ancient Village in the buffer zone of a world natural heritage site were discussed.Through the anal...Under the background of global tourism development,the planning and design of rural tourism landscape in Libo Yaoshan Ancient Village in the buffer zone of a world natural heritage site were discussed.Through the analysis of the current resources and characteristics of Yaoshan Ancient Village,the planning and design principles of cultural inheritance and innovation,ecological protection and sustainable development,and integration of landscape diversity and experience based on the concept of global tourism were clarified.A specific planning and design scheme for tourism landscape was put forward,such as creating the entrance service area,core sightseeing area,rural sightseeing area,folk experience area and other functional divisions.Besides,traffic routes should be optimized and adjusted,and the supporting construction of tourism service facilities should be strengthened to comprehensively improve the overall quality of rural tourism landscape of Yaoshan Ancient Village,promote the sustainable development of local rural tourism,and provide useful reference for the planning and design of rural tourism landscape in other similar areas.展开更多
The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,wh...The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation safety.In this paper,these problems were addressed through the following improvements.First,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed.The A^(*)algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion strategy.This algorithm can also optimize path search angles while improving path security.Second,the distance from the current node to the target node was introduced into the heuristic function.The efficiency of the A^(*)algorithm was improved,and the path was smoothed using the Floyd algorithm.For the dynamic adjustment of the weight to improve the efficiency of DWA,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(DWA)algorithm.Finally,combined with the local target point selection strategy,the optimized DWA algorithm was performed for local path planning.The experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.展开更多
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish...This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.展开更多
The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a nove...The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.展开更多
Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning frame...Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.展开更多
Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a gro...Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.展开更多
Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed bas...Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.展开更多
Existing mobile robots mostly use graph search algorithms for path planning,which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity.Due to the limitations of th...Existing mobile robots mostly use graph search algorithms for path planning,which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity.Due to the limitations of the neighborhood search strategy,the robots could hardly obtain the most optimal global path.A global path planning algorithm,denoted as EDG*,is proposed by expanding nodes using a well-designed expanding disconnected graph operator(EDG)in this paper.Firstly,all obstacles are marked and their corners are located through the map pre-processing.Then,the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes.Finally,the EDG*heuristic iterative algorithm is proposed.It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset.Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM.The result shows that the proposed EDG*reduced the planning time by more than 90%and total length of paths reduced by more than 4.6%.Compared to A*,Dijkstra and JPS,EDG*does not show an exponential explosion effect in map size.The EDG*showed better performance in terms of path smoothness,and collision avoidance.This shows that the EDG*algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.展开更多
The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajecto...The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.展开更多
Owing to the far-reaching environmental consequences of agriculture and food systems,such as their contribution to climate change,there is an urgent need to reduce their impact.International and national governments s...Owing to the far-reaching environmental consequences of agriculture and food systems,such as their contribution to climate change,there is an urgent need to reduce their impact.International and national governments set sustainability targets and implement corresponding measures.Nevertheless,critics of the globalized system claim that a territorial administrative scale is better suited to address sustainability issues.Yet,at the subnational level,local authorities rarely apply a systemic environmental assessment to enhance their action plans.This paper employs a territorial life cycle assessment methodology to improve local environmental agri-food planning.The objective is to identify significant direct and indirect environmental hotspots,their origins,and formulate effective mitigation strategies.The methodology is applied to the administrative department of Finistere,a strategic agricultural region in North-Western France.Multiple environmental criteria including climate change,fossil resource scarcity,toxicity,and land use are modeled.The findings reveal that the primary environmental hotspots of the studied local food system arise from indirect sources,such as livestock feed or diesel consumption.Livestock reduction and organic farming conversion emerge as the most environmentally efficient strategies,resulting in a 25%decrease in the climate change indicator.However,the overall modeled impact reduction is insufficient following national objectives and remains limited for the land use indicator.These results highlight the innovative application of life cycle assessment led at a local level,offering insights for the further advancement of systematic and prospective local agri-food assessment.Additionally,they provide guidance for local authorities to enhance the sustainability of planning strategies.展开更多
Coastal zones are dynamic,rich environments,now densely populated,and increasingly challenged by human and climatechange pressures.Effective long-term integrated coastal zone planning is needed to ensure sustainable e...Coastal zones are dynamic,rich environments,now densely populated,and increasingly challenged by human and climatechange pressures.Effective long-term integrated coastal zone planning is needed to ensure sustainable environmental protection and economic development.In this study,we reviewed the history of coastal zone planning since its birth in the 1950s based on the literature retrieved from the Web of Science(Core Collection)from 2000–2023,then summarized the tools and spatial allocation methods commonly used in the planning process,and finally proposed potential solutions to the challenges faced.The results show that after decades of development,coastal zone planning has changed from a decentralized activity to a targeted and integrated one,with an increasing emphasis on the ecosystem approach and the use of multiple planning tools.Spatial analysis techniques and environmental modelling software have become increasingly popular.Linear programming and overlay analysis are common approaches when performing spatial optimization,but land-sea interactions and planning in the marine parts still lack in-depth analysis and practical experience.We are also aware that the challenges posed by the integration of administrative hierarchies,scoping and conservation objectives,stakeholder participation,consideration of social dimensions,and climate change are pervasive throughout the planning process.There is an urgent need to develop more flexible and accurate spatial modelling tools,as well as more efficient participatory methods,and to focus on the holistic nature of the land-sea system to create more resilient and sustainable coastal zones.展开更多
文摘This study proposes a novel approach to optimizing individual work schedules for book digitization using mixed-integer programming (MIP). By leveraging the power of MIP solvers, we aimed to minimize the overall digitization time while considering various constraints and process dependencies. The book digitization process involves three key steps: cutting, scanning, and binding. Each step has specific requirements and limitations such as the number of pages that can be processed simultaneously and potential bottlenecks. To address these complexities, we formulate the problem as a one-machine job shop scheduling problem with additional constraints to capture the unique characteristics of book digitization. We conducted a series of experiments to evaluate the performance of our proposed approach. By comparing the optimized schedules with the baseline approach, we demonstrated significant reductions in the overall processing time. In addition, we analyzed the impact of different weighting schemes on the optimization results, highlighting the importance of identifying and prioritizing critical processes. Our findings suggest that MIP-based optimization can be a valuable tool for improving the efficiency of individual work schedules, even in seemingly simple tasks, such as book digitization. By carefully considering specific constraints and objectives, we can save time and leverage resources by carefully considering specific constraints and objectives.
文摘The construction projects’ dynamic and interconnected nature requires a comprehensive understanding of complexity during pre-construction. Traditional tools such as Gantt charts, CPM, and PERT often overlook uncertainties. This study identifies 20 complexity factors through expert interviews and literature, categorising them into six groups. The Analytical Hierarchy Process evaluated the significance of different factors, establishing their corresponding weights to enhance adaptive project scheduling. A system dynamics (SD) model is developed and tested to evaluate the dynamic behaviour of identified complexity factors. The model simulates the impact of complexity on total project duration (TPD), revealing significant deviations from initial deterministic estimates. Data collection and analysis for reliability tests, including normality and Cronbach alpha, to validate the model’s components and expert feedback. Sensitivity analysis confirmed a positive relationship between complexity and project duration, with higher complexity levels resulting in increased TPD. This relationship highlights the inadequacy of static planning approaches and underscores the importance of addressing complexity dynamically. The study provides a framework for enhancing planning systems through system dynamics and recommends expanding the model to ensure broader applicability in diverse construction projects.
文摘Village revitalization is a major development strategy in China,where village planning plays as its critical component.Taking village planning of De’an County in Jiangxi Province as an example,this paper explored the significant importance of village planning in promoting rural revitalization along with its corresponding promote mechanisms.Through in-depth research on the detailed situation of village planning and implementation in Jiangxi,this paper summarized that some challenges exist,including backward planning,poor planning consciousness,difficulties in planning implementation.Based on these findings,the paper analyzed the challenges of village land resource scarcity,village images lacking in uniqueness features,and insufficient rural infrastructure construction.Furthermore,it proposed strategies such as taking the lead in planning from the beginning,advancing practical implementation at a high level,adhering to bottom-line thinking,and coordinating high-quality rural land protection and development,applying strategies such as ecological and pleasant living,building villages suitable for living and working,and construction of beautiful countryside,aiming to provide valuable reference for related research fields.
文摘Objective:This study aimed to explore the readiness for advance care planning(ACP)among older adults in Macao’s day service centers and investigate the influencing factors.Methods:A cross-sectional study was conducted from October to December 2022 using a convenience sampling method.A total of 312 older adults were selected from 13 day service centers for older adults in Macao,China.The Advance Care Planning Acceptance Questionnaire and the Family Adaptation,Partnership,Growth,Affection,Resolve(APGAR)Scale were used to survey the older adults.Results:A total of 306 older adults completed the survey.The score for advance care planning readiness was 65.55±10.69,and 59.5%of participants(n=182)were willing to participate in ACP.The family function score was 7.24±2.51,while 70.3%of participants were from a highly functional family.The higher family function indicating a higher readiness for advance care planning(r=0.396,P<0.001).The multiple linear regression analysis indicated that the variables“age,”“knowledge of ACP,”“experience with ACP,”and“received resuscitation of yourself,relatives or friends”combined with“family function”can influence advance care planning readiness among older adults(R^(2)=0.317,F=27.898,P<0.001).Conclusions:Older adults in Macao’s day service centers were willing to engage in ACP.The importance of family involvement is highlighted in the ACP readiness.Health education and improved family communication are vital for promoting ACP,which ensures individuals receive care when they lack the capacity to make that choice.Additionally,healthcare professionals should enhance communication and education with older adults during the medical care process.
文摘BACKGROUND Kidney transplantation is one of the most effective treatments for patients with end-stage renal disease.However,many regions face low deceased donor rates and limited ABO-compatible transplant availability,which increases reliance on living donors.These regional challenges necessitate the implementation of kidney paired donation(KPD)programs to overcome incompatibilities such as ABO mismatch or positive cross-matching,even when suitable and willing donors are available.AIM To evaluate the effectiveness of a single-center domino KPD model in both operational planning and clinical management processes and to assess its impact on clinical outcomes.METHODS Between April 2020 and January 2024,we retrospectively evaluated patients enrolled in our center’s domino kidney transplantation program.Donor-recipient pairs unable to proceed due to ABO incompatibility or positive cross-matching with their own living donors were included.Donors and recipients were assessed based on blood group compatibility,HLA tissue typing,and negative cross-match results.A specialized computer algorithm grouped patients into three-way,fourway,and five-way chains.All surgical procedures were performed on the same day at a single center.RESULTS A total of 169 kidney transplants were performed,forming 52 domino chains.These domino KPD transplants accounted for a notable proportion of our center’s overall transplant activity,which included both living donor kidney transplants and deceased donor transplants.Among these chains,the primary reasons for participation were ABO incompatibility(74%),positive cross-matching(10%),and the desire to improve HLA mismatch(16%).Improved HLA mismatch profiles and high graft survival(96%at 1 year,92%at 3 years)and patient survival(98%at 1 year,94%at 3 years)rates were observed,as well as low acute rejection episodes.CONCLUSION The single-center domino KPD model enhanced transplant opportunities for incompatible donor-recipient pairs while maintaining excellent clinical outcomes.By providing a framework that addresses regional challenges,improves operational efficiency,and optimizes clinical management,this model offers actionable insights to reduce waiting lists and improve patient outcomes.
基金supported in part by the National Natural Science Foundation of China(Nos.52205532 and 624B2077)the National Key Research and Development Program of China(No.2023YFB4302003).
文摘The global demand for effective skin injury treatments has prompted the exploration of tissue engineering solutions.While three-dimensional(3D)bioprinting has shown promise,challenges persist with respect to achieving timely and compatible solutions to treat diverse skin injuries.In situ bioprinting has emerged as a key new technology,since it reduces risks during the implantation of printed scaffolds and demonstrates superior therapeutic effects.However,maintaining printing fidelity during in situ bioprinting remains a critical challenge,particularly with respect to model layering and path planning.This study proposes a novel optimization-based conformal path planning strategy for in situ bioprinting-based repair of complex skin injuries.This strategy employs constrained optimization to identify optimal waypoints on a point cloud-approximated curved surface,thereby ensuring a high degree of similarity between predesigned planar and surface-mapped 3D paths.Furthermore,this method is applicable for skin wound treatments,since it generates 3D-equidistant zigzag curves along surface tangents and enables multi-layer conformal path planning to facilitate the treatment of volumetric injuries.Furthermore,the proposed algorithm was found to be a feasible and effective treatment in a murine back injury model as well as in other complex models,thereby showcasing its potential to guide in situ bioprinting,enhance bioprinting fidelity,and facilitate improvement of clinical outcomes.
基金Supported by the Tianjin University of Technology Graduate R esearch Innovation Project(YJ2281).
文摘An improved version of the sparse A^(*)algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorithms.This algorithm considers factors such as initial position and orientation of the ship,safety range,and ship draft to determine the optimal obstacle-avoiding route from the current to the destination point for ship planning.A coordinate transformation algorithm is also applied to convert commonly used latitude and longitude coordinates of ship travel paths to easily utilized and analyzed Cartesian coordinates.The algorithm incorporates a hierarchical chart processing algorithm to handle multilayered chart data.Furthermore,the algorithm considers the impact of ship length on grid size and density when implementing chart gridification,adjusting the grid size and density accordingly based on ship length.Simulation results show that compared to traditional path planning algorithms,the sparse A^(*)algorithm reduces the average number of path points by 25%,decreases the average maximum storage node number by 17%,and raises the average path turning angle by approximately 10°,effectively improving the safety of ship planning paths.
文摘To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A^(*)algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A^(*)algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A^(*)algorithm significantly outperforms the traditional A^(*)algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks.
基金This research was funded by“Chunhui Program”Collaborative Scientific Research Project of the Ministry of Education of the People’s Republic of China(Project No.HZKY20220242)the S&T Program of Hebei(Project No.225676163GH).
文摘Rational distribution network planning optimizes power flow distribution,reduces grid stress,enhances voltage quality,promotes renewable energy utilization,and reduces costs.This study establishes a distribution network planning model incorporating distributed wind turbines(DWT),distributed photovoltaics(DPV),and energy storage systems(ESS).K-means++is employed to partition the distribution network based on electrical distance.Considering the spatiotemporal correlation of distributed generation(DG)outputs in the same region,a joint output model of DWT and DPV is developed using the Frank-Copula.Due to the model’s high dimensionality,multiple constraints,and mixed-integer characteristics,bilevel programming theory is utilized to structure the model.The model is solved using a mixed-integer particle swarmoptimization algorithm(MIPSO)to determine the optimal location and capacity of DG and ESS integrated into the distribution network to achieve the best economic benefits and operation quality.The proposed bilevel planning method for distribution networks is validated through simulations on the modified IEEE 33-bus system.The results demonstrate significant improvements,with the proposedmethod reducing the annual comprehensive cost by 41.65%and 13.98%,respectively,compared to scenarios without DG and ESS or with only DG integration.Furthermore,it reduces the daily average voltage deviation by 24.35%and 10.24%and daily network losses by 55.72%and 35.71%.
基金Sponsored by the Guizhou Provincial Key Technology R&D Program:A Study on the Conservation Model with Technology and Sustainable Development Demonstration of the World Natural Heritages in Guizhou(No.2202023QKHZC).
文摘Under the background of global tourism development,the planning and design of rural tourism landscape in Libo Yaoshan Ancient Village in the buffer zone of a world natural heritage site were discussed.Through the analysis of the current resources and characteristics of Yaoshan Ancient Village,the planning and design principles of cultural inheritance and innovation,ecological protection and sustainable development,and integration of landscape diversity and experience based on the concept of global tourism were clarified.A specific planning and design scheme for tourism landscape was put forward,such as creating the entrance service area,core sightseeing area,rural sightseeing area,folk experience area and other functional divisions.Besides,traffic routes should be optimized and adjusted,and the supporting construction of tourism service facilities should be strengthened to comprehensively improve the overall quality of rural tourism landscape of Yaoshan Ancient Village,promote the sustainable development of local rural tourism,and provide useful reference for the planning and design of rural tourism landscape in other similar areas.
基金Supported by the EDD of China(No.80912020104)the Science and Technology Commission of Shanghai Municipality(No.22ZR1427700 and No.23692106900).
文摘The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation safety.In this paper,these problems were addressed through the following improvements.First,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed.The A^(*)algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion strategy.This algorithm can also optimize path search angles while improving path security.Second,the distance from the current node to the target node was introduced into the heuristic function.The efficiency of the A^(*)algorithm was improved,and the path was smoothed using the Floyd algorithm.For the dynamic adjustment of the weight to improve the efficiency of DWA,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(DWA)algorithm.Finally,combined with the local target point selection strategy,the optimized DWA algorithm was performed for local path planning.The experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.
基金Supported by the Natural Science Foundation of Zhejiang Province(No.LQ22F030015).
文摘This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.
基金Fundamental Research Funds for the Central Universities(2024JBZX038)National Natural Science F oundation of China(62076023)。
文摘The rapid evolution of unmanned aerial vehicle(UAV)technology and autonomous capabilities has positioned UAV as promising last-mile delivery means.Vehicle and onboard UAV collaborative delivery is introduced as a novel delivery mode.Spatiotemporal collaboration,along with energy consumption with payload and wind conditions play important roles in delivery route planning.This paper introduces the traveling salesman problem with time window and onboard UAV(TSPTWOUAV)and emphasizes the consideration of real-world scenarios,focusing on time collaboration and energy consumption with wind and payload.To address this,a mixed integer linear programming(MILP)model is formulated to minimize the energy consumption costs of vehicle and UAV.Furthermore,an adaptive large neighborhood search(ALNS)algorithm is applied to identify high-quality solutions efficiently.The effectiveness of the proposed model and algorithm is validated through numerical tests on real geographic instances and sensitivity analysis of key parameters is conducted.
基金the financial support of the National Key Research and Development Program of China(2020AAA0108100)the Shanghai Municipal Science and Technology Major Project(2021SHZDZX0100)the Shanghai Gaofeng and Gaoyuan Project for University Academic Program Development for funding。
文摘Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.
文摘Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222215,52072051)Chongqing Municipal Natural Science Foundation of China(Grant No.CSTB2023NSCQ-JQX0003).
文摘Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFB4700402).
文摘Existing mobile robots mostly use graph search algorithms for path planning,which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity.Due to the limitations of the neighborhood search strategy,the robots could hardly obtain the most optimal global path.A global path planning algorithm,denoted as EDG*,is proposed by expanding nodes using a well-designed expanding disconnected graph operator(EDG)in this paper.Firstly,all obstacles are marked and their corners are located through the map pre-processing.Then,the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes.Finally,the EDG*heuristic iterative algorithm is proposed.It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset.Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM.The result shows that the proposed EDG*reduced the planning time by more than 90%and total length of paths reduced by more than 4.6%.Compared to A*,Dijkstra and JPS,EDG*does not show an exponential explosion effect in map size.The EDG*showed better performance in terms of path smoothness,and collision avoidance.This shows that the EDG*algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.
基金supported by the National Natural Science Foundation of China(51875302)。
文摘The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.
文摘Owing to the far-reaching environmental consequences of agriculture and food systems,such as their contribution to climate change,there is an urgent need to reduce their impact.International and national governments set sustainability targets and implement corresponding measures.Nevertheless,critics of the globalized system claim that a territorial administrative scale is better suited to address sustainability issues.Yet,at the subnational level,local authorities rarely apply a systemic environmental assessment to enhance their action plans.This paper employs a territorial life cycle assessment methodology to improve local environmental agri-food planning.The objective is to identify significant direct and indirect environmental hotspots,their origins,and formulate effective mitigation strategies.The methodology is applied to the administrative department of Finistere,a strategic agricultural region in North-Western France.Multiple environmental criteria including climate change,fossil resource scarcity,toxicity,and land use are modeled.The findings reveal that the primary environmental hotspots of the studied local food system arise from indirect sources,such as livestock feed or diesel consumption.Livestock reduction and organic farming conversion emerge as the most environmentally efficient strategies,resulting in a 25%decrease in the climate change indicator.However,the overall modeled impact reduction is insufficient following national objectives and remains limited for the land use indicator.These results highlight the innovative application of life cycle assessment led at a local level,offering insights for the further advancement of systematic and prospective local agri-food assessment.Additionally,they provide guidance for local authorities to enhance the sustainability of planning strategies.
基金Under the auspices of National Key R&D Plan (No.2022YFB3903604)the Youth Innovation Promotion Association of Chinese Academy of Sciences (No.2023060)Key Project of Innovation LREIS (No.KPI001)。
文摘Coastal zones are dynamic,rich environments,now densely populated,and increasingly challenged by human and climatechange pressures.Effective long-term integrated coastal zone planning is needed to ensure sustainable environmental protection and economic development.In this study,we reviewed the history of coastal zone planning since its birth in the 1950s based on the literature retrieved from the Web of Science(Core Collection)from 2000–2023,then summarized the tools and spatial allocation methods commonly used in the planning process,and finally proposed potential solutions to the challenges faced.The results show that after decades of development,coastal zone planning has changed from a decentralized activity to a targeted and integrated one,with an increasing emphasis on the ecosystem approach and the use of multiple planning tools.Spatial analysis techniques and environmental modelling software have become increasingly popular.Linear programming and overlay analysis are common approaches when performing spatial optimization,but land-sea interactions and planning in the marine parts still lack in-depth analysis and practical experience.We are also aware that the challenges posed by the integration of administrative hierarchies,scoping and conservation objectives,stakeholder participation,consideration of social dimensions,and climate change are pervasive throughout the planning process.There is an urgent need to develop more flexible and accurate spatial modelling tools,as well as more efficient participatory methods,and to focus on the holistic nature of the land-sea system to create more resilient and sustainable coastal zones.
