Traditional Planning Research Articles

Innovative Research on the Integration of Traditional Chinese Medicine and Artificial Intelligence for the Treatment of Herpes Zoster

Herpes zoster is a skin disease caused by the varicella-zoster virus (VZV), primarily manifesting as painful skin rashes in localized areas. Traditional Chinese medicine has a long history of treating herpes zoster, and the integration of modern artificial intelligence (AI) technologies holds promise for further enhancing treatment efficacy. This study aims to construct an AI-based traditional Chinese medicine treatment plan by analyzing large-scale clinical data and traditional medical literature to establish individualized diagnostic models. The experimental results demonstrate that this method effectively shortens the disease course and reduces the incidence of complications. Additionally, the study reveals the mechanisms of action of various components of traditional Chinese medicine in treatment, providing a basis for further optimization of treatment plans. This innovative integration approach not only improves treatment precision but also offers a new paradigm for combined traditional and Western medicine treatment. This paper not only showcases the potential of AI in the field of traditional Chinese medicine but also provides new insights into the treatment of herpes zoster.

Planning for green infrastructure by integrating multi-driver: Ranking priority based on accessibility equity

Green infrastructure provides multifarious benefits, improving urban resilience and sustainability amid increasing climate change and urbanization. Traditional green infrastructure planning studies were based on the spatial-equity principle, which usually neglected residents’ aggregation pattern, leading to the conflict with equitable green exposure. Using the sponge city of Zhengzhou as a case, this study proposes a novel priority ranking strategy, namely accessibility equity. We first spatially quantified the regional socioecological conditions on 0.25 km2 grids as green infrastructure planning drivers, including stormwater management, urban thermal environment, air quality, habitat maintenance and water purification. Subsequently we integrated these planning drivers with population density to conduct spatial autocorrelation analysis, priority ranking, and grid clustering. While most planning drivers are positively correlated with population density, except air quality, some areas show opposite trends in terms of local perspective, suggesting that the spatial equity may lead to mismatch between residents’ demand and GI priority. The priority order based on accessibility equity, rises in the city center and falls in suburbs and industrial zones. The study area is divided into four categories using k-means clustering, and we propose the corresponding adaptive green infrastructure development strategy. The framework can be a practical tool for guiding green infrastructure and sponge city projects.

Controller Synthesis for Timeline-based Games

In the timeline-based approach to planning, the evolution over time of a set of state variables (the timelines) is governed by a set of temporal constraints. Traditional timeline-based planning systems excel at the integration of planning with execution by handling temporal uncertainty. In order to handle general nondeterminism as well, the concept of timeline-based games has been recently introduced. It has been proved that finding whether a winning strategy exists for such games is 2EXPTIME-complete. However, a concrete approach to synthesize controllers implementing such strategies is missing. This paper fills this gap, by providing an effective and computationally optimal approach to controller synthesis for timeline-based games.

Safe path planning of mobile robot based on improved particle swarm optimization

Path planning is a fundamental aspect of mobile robot navigation, playing a crucial role in enabling robots to autonomously navigate while avoiding obstacles. Nevertheless, traditional path planning algorithms face navigation challenges, including obstacle avoidance and the potential for getting stuck in local minima or deadlocks along the path. To tackle these challenges, the study proposes an enhanced path planning method based on control barrier function (CBF). This approach introduces a safety velocity adjustment mechanism based on CBF and combines it with the particle swarm optimization (PSO), adjusting the safe speed in global planning and addressing the issue of local minima. Experimental simulations are conducted to validate the flexibility and global optimization performance of the proposed path planning method across various obstacle scenarios.

How Federal Government Policy Helped Create Retirement Insecurity

Financial security in retirement is crucial. Without it, people fall into poverty, work until they die, and die younger than they should. In the 21st century, Americans fund their retirement from Social Security, traditional pension plans, defined contributions plans and private retirement savings plans. Of these four, only Social Security is exclusively the creature of government. Nevertheless, federal law regulates the other three to such an extent that it is fair to say that all four reflect government retirement policy. Since 1974, the federal government has spent billions of dollars supporting the private retirement system. In turn, the private retire-ment system has often failed its participants, sometimes leaving them impoverished. In contrast, Social Security has consistent success in reducing poverty and providing a financially strong old age. The last 50 years demonstrate that the private sector is an unre-liable provider of retirement benefits. Private employers deny their workers retirement benefits, siphon off pension monies for corporate use and declare bankruptcy as a means of leaving their losses to the fed-eral government to make right. In turn, the federal government must spend even more money regulating the private sector and protecting pri-vate workers and their retirement monies. Whatever the theories, we have decades of proof that if the federal government wants to ensure financial security for the elderly, then it should stop providing tax ben-efits and direct payments to private sector retirement plans and instead focus on expanding and strengthening Social Security.

Improving 4G LTE network quality using the automatic cell planning

The growing demand for network services leads to an increase in traffic load on eNodeB, resulting in decreased network quality and performance, necessitating optimization. This research analyses the results of optimising 4G reference signal received power (RSRP), signal to interference noise ratio (SINR) and throughput parameters using the automatic cell planning (ACP) method. ACP has been shown to significantly improve the performance and quality of 4G LTE networks compared to traditional cell planning methods. Based on the standard parameter RSRP, increased after ACP optimisation which is dominant in the range ≥ -100 s.d ˃ -85 dBm and obtained an average value of -98.59 dBm with good category. The average SINR has increased by 18.23 dB with a good category. The dominant throughput is in the 14,000 Kbps range with an average value of 50,241.08 Kbps with the excellent category. The ACP method can enhance the performance of 4G LTE networks, potentially addressing operator issues of unstable network quality due to poor coverage. The ACP method significantly enhances 4G LTE network performance, coverage, and user experience, potentially addressing unstable network quality due to poor coverage. This research is crucial for both users and the telecoms industry.

Multisystem health comorbidity networks of metabolic dysfunction-associated steatotic liver disease.

The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) is growing, but its subsequent health consequences have not been thoroughly examined. A phenome-wide association study was conducted to map the associations of MASLD with 948 unique clinical outcomes among 361,021 Europeans in the UK Biobank. Disease trajectory and comorbidity analyses were applied to visualize the sequential patterns of multiple comorbidities related to the occurrence of MASLD. The associations jointly verified by observational and polygenic phenome-wide analyses were further replicated by two-sample Mendelian randomization analysis using data from the FinnGen study and international consortia. The observational and polygenic phenome-wide association study revealed the associations of MASLD with 96 intrahepatic and extrahepatic diseases, including circulatory, metabolic, genitourinary, neurological, gastrointestinal, and hematologic diseases. Sequential patterns of MASLD-related extrahepatic comorbidities were primarily found in circulatory, metabolic, and inflammatory diseases. Mendelian randomization analyses supported the causal associations between MASLD and the risk of several intrahepatic disorders, metabolic diseases, cardio-cerebrovascular disease, and ascites but found no associations with neurological diseases. This study elucidated multisystem comorbidities and health consequences of MASLD, contributing to the development of combination interventions targeting distinct pathways for health promotion among patients with MASLD. X.L. was funded by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001) and the National Nature Science Foundation of China (82204019) and Y.D. was funded by the Key Project of Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province (GZY-ZJ-KJ-24077) and the National Natural Science Foundation of China (82001673 and 82272860).

Path planning and collision avoidance approach for a multi-agent system in grid environments

Abstract In multi-robot systems, ensuring efficient, safe, and conflict-free navigation for each robot is crucial, especially in scenarios requiring the coordination of complex tasks and interactions with the environment. Currently, most related research focuses on simplified grid environments, making it difficult to apply these findings to real-world scenarios. This paper introduces actual physical constraints into traditional grid environment path planning and designs a set of motion rules suitable for grid environments and a collision avoidance algorithm. We also propose a novel multi-agent path planning approach that integrates deep reinforcement learning with collision avoidance algorithms. Through a series of experiments, we have demonstrated that this method effectively guides agents in navigating from their starting points to their targets within grid environments that possess physical constraints, and significantly reduces the probability of collisions when used in conjunction with our collision avoidance algorithm. These findings not only showcase the efficacy of our approach but also open new avenues for deploying multi-agent systems in more complex real-world scenarios.

AI-Driven Urban Planning: Enhancing Efficiency and Sustainability in Smart Cities

Urban planning in smart cities is increasingly leveraging artificial intelligence (AI) to enhance efficiency and sustainability. This article explores the integration of AI-driven technologies to optimize various aspects of urban development and management. Smart cities are characterized by their use of advanced technologies to improve quality of life, resource management, and infrastructure efficiency. Traditional urban planning methods often face challenges in adapting to rapid urbanization and dynamic environmental changes. AI presents opportunities to address these challenges by providing data-driven insights and predictive capabilities. This research employs a case study approach, analyzing the implementation of AI in urban planning processes across several smart cities globally. Key methodologies include data analytics, machine learning algorithms, and predictive modeling techniques applied to diverse urban datasets. The study evaluates how AI-driven decision support systems aid in infrastructure planning, traffic management, energy consumption optimization, and environmental sustainability. The findings demonstrate that AI-enabled urban planning significantly enhances efficiency and sustainability in smart cities. AI algorithms optimize traffic flow, reduce energy consumption through predictive maintenance of infrastructure, and facilitate adaptive urban design based on real-time data analytics. Moreover, AI-driven approaches improve decision-making processes by providing stakeholders with actionable insights for informed policy formulation and resource allocation. This article contributes to the evolving field of smart city technologies by showcasing the transformative potential of AI in urban planning. By harnessing AI capabilities, cities can effectively address complex urban challenges and pave the way for more resilient and sustainable urban environments.

Multi-Stage Coordinated Planning for Transmission and Energy Storage Considering Large-Scale Renewable Energy Integration

Due to the large-scale integration of renewable energy and the rapid growth of peak load demand, it is necessary to comprehensively consider the construction of various resources to increase the acceptance capacity of renewable energy and meet power balance conditions. However, traditional grid planning methods can only plan transmission lines, often resulting in low utilization rates of newly constructed lines. Additionally, static planning methods can only address single-target scenarios and cannot cope with dynamic growth in load and renewable energy. To address these issues, this paper proposes a multi-stage collaborative planning method for transmission networks and energy storage. This method considers the non-line substitution effect of energy storage resources and their characterization methods. It establishes the coupling relationship between resources across different planning stages to achieve coordinated multi-stage planning for transmission networks and energy storage. Based on the IEEE-24 node system and a case study in a northern province of China, the results show that the proposed method reduces investment costs by approximately 30% compared to static planning methods and by about 7.79% compared to conventional grid planning methods. Furthermore, this method can accommodate more renewable energy.

Urban Livability and Vertical Inclusion: Exploring Mixed-Use Development Strategies in Khulna, Bangladesh

As urban populations expand and future needs evolve amidst limited land availability, rethinking traditional planning and construction approaches becomes imperative. Architect Frank Schlesinger advocates for urban environments that embrace mixed- use complexes, conceptualizing urban land as dynamic, three-dimensional blocks accommodating diverse functions above, at, and below ground levels. Shopping facilities, pivotal in urban landscapes, reflect regional and temporal variations in consumer behavior. Integrating public and private amenities is essential for cultivating a thriving urban ecosystem. However, rigid block structures in current city master plan to constrain diversity and livability, are exacerbated by inadequate setbacks that challenge urban identity. In Khulna, bustling daytime cores contrast with deserted evenings and holidays, reflecting segregated land uses that hinder societal inclusivity. Despite demand, office and commercial spaces often remain vacant. This study employs a mixed-method approach, combining quantitative surveys with qualitative methods such as interviews and observational studies. It aims to address critical local issues including land scarcity, traffic management, vertical development challenges, and the provision of sophisticated services to enhance urban comfort. By exploring innovative configurations of civic facilities, both horizontally and vertically, the study seeks to demonstrate how integrating functions can create cohesive and vibrant urban environments. Ultimately, the adoption of vertically integrated spaces holds promise for fostering inclusivity and sustainability in Khulna’s urban fabric.

A novel integrated optimization method of micrositing and cable routing for offshore wind farms

In traditional wind farm planning, the design of wind turbine locations and cable layouts is usually undertaken sequentially. However, this approach may potentially result in suboptimal solutions. While the increased spacing between wind turbines enhances power output by reducing wake losses, it also imposes a negative impact by raising cable costs. Addressing this challenge, we propose a novel multi-objective optimization model that simultaneously considers the micrositing of wind turbines and cable routing. A joint framework of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Mixed-Integer Linear Programming (MILP) is established to optimize the layout of wind turbine locations, points of connection, and cable paths. The results indicate that, compared to the traditional sequential optimization, our integrated optimization exhibits significant economic advantages since improved the balance between micro siting and cable routing. By strategically sacrificing a portion of power generation to reduce cable costs, the overall investment profitability can be remarkably improved, with a maximum gain equivalent to 10.02 % of the cable costs.

Research on Low-Carbon Design and Energy Efficiency by Harnessing Indigenous Resources through BIM-Ecotect Analysis in Hot Climates

In the face of contemporary challenges, such as economic instability, environmental degradation, and the urgent global warming crisis, the imperative of sustainability and energy efficiency has reached unparalleled significance. Sustainability encompasses not only the natural environment, but also extends to our immediate surroundings, including the built structures and the communities they serve. Embracing this comprehensive perspective, we embarked on a mission to conceive and construct a model house that harnesses state-of-the-art energy-efficient technologies. Our goal was to seamlessly integrate these features not only to meet our sustainability objectives, but also to mitigate environmental threats.This model embodies a harmonious fusion of indigenous resources, employing locally sourced stone and employing traditional construction techniques. Through this approach, we achieved significant reductions in carbon emissions and established a framework for passive cooling and heating systems. Moreover, the design is intrinsically attuned to its contextual surroundings, preserving the diverse tapestry of regional architectural styles. This study stands as a testament to the potential of innovative design and technology in shaping a sustainable future. The research employs a multi-dimensional approach, encompassing strategies of architectural design with a traditional planning approach, sustainable material selection, energy efficiency, and life cycle assessment across a diverse set of case studies. Building energy analysis is conducted through the application of BIM (Ecotect), providing insights into how BIM can adapt and thrive in various environments. Key findings underscore that thermal performance, minimizing energy loads, and reducing carbon emissions are pivotal aspects in designating a building as both green and energy efficient.

A Performance and Data-Driven Method for Optimization of Traditional Courtyards

As urbanization and rapid industrialization accelerate, rural areas face increasing pressure on resources and the environment, leading to challenges such as energy waste and reduced comfort. Traditional village planning and design methods are based on economic benefits and often lack consideration of climate adaptability. To address these issues, a comprehensive assessment of building and courtyard performance should be introduced early in the planning of traditional villages. This approach can better adapt the buildings to their climatic conditions. Introducing relevant performance indicators, such as outdoor comfort, indoor lighting, and building energy consumption, at the initial design stage is crucial. This article employs performance-based multi-objective optimization algorithms and machine learning techniques to investigate the design workflow of courtyards and their combinations. The goal is to enhance planners’ design efficiency in village planning by integrating data-driven and performance-driven methods. The research results show that during the performance-driven phase, by adjusting the spatial morphology and architectural parameters, the performance of the courtyard significantly improved compared to the baseline model. Energy efficiency increased by 32.3%, the physiological equivalent temperature (PET) comfort time ratio in winter was enhanced by 8.3%, and the ratio in summer increased by 3.8%. During the data-driven phase, the classification prediction accuracy of courtyard performance can reach 83%, and the F1 score is 0.81. In the project validation phase, it has also been proven that the performance of different plans can be quickly verified. Compared to the base’s original status, the design solutions’ performance score can be improved from 59.12 to 85.62. In summary, this workflow improves the efficiency of the interaction between design decisions and performance evaluation in the conceptual stage of village planning, providing a solid foundation for promoting subsequent solutions.

A self-adaptive agent for flexible posture planning in robotic milling system

In recent years, industrial robots are widely used in the milling processes of the fundamental components such as aeronautic parts, aerospace cabins and blades on the marine propellers of the worships, because of their flexible structure, large operating space, and robust capacity of rapid reconfiguration. Along with the robotic machining process, multiple types of geometrical and physical constraints display the time-varying characteristics, which are difficult to be considered simultaneously by the traditional static planning methods on the robotic milling posture. Therefore, a deep reinforcement learning (DRL) enhanced virtual repulsive potential field flexible model is proposed for the robotic milling posture dynamic planning. The preliminary planning of static milling posture is realized based on the fixed setting of the virtual modal parameters in the posture planning model, which is treated as a theory preparation for training the deep reinforcement learning agent. To develop the static planning model, multiple constraints are established by considering the performances of geometric, kinematic and machining precision simultaneously. Furthermore, a reward function is constructed for the dynamic agent, comprehensively combining the long and short reward benefits in nonlinear correlation with virtual repulsive forces. By taking the virtual modal parameters as the action space of the dynamic agent, a DRL enhanced agent VRPF-SAC is created and trained under the dynamic milling environment from virtual repulsive planning field. Four tasks of large size robotic milling simulations and experiments are designed and developed to validate the effectiveness of the flexible planning model. In comparison with the static model, the flexible planning model displays the excellent global effects covering the kinematic, and precision performance, with 65.78 %, 72.04 %, and 70.25 % reduction in velocity, acceleration, and jerk of robotic joints, and ensuring the effective precision performance from −0.365 to 0.283 mm of large size workpiece with large curvature changes. The proposed flexible posture planning architecture can provide a basic for robotic dynamic milling of core parts with large size, narrow space and sharp curvatures of the aeronautic parts, aerospace cabins and worship propellers.

Research on path planning for clear aligner flexible manufacturing

In recent years, clear aligner can enhance individual appearance with dental defects, so it used more and more widely. However, in manufacturing process, there are still some problems, such as low degree of automation and high equipment cost. The problem of coordinate system mismatch between gingival curve point cloud and dental CAD model is faced to. The PCA-ICP registration algorithm is proposed, which includes coarse match algorithm and improve-ICP registration algorithm. The principal component analysis (PCA) based method can roughly find the posture relationship between the two point clouds. Using z-level dynamic hierarchical, the ICP registration can accurately find the posture between these two clouds. The final registration maximum distance error is 0.03 mm, which is smaller than robot machining error. Secondly, the clear aligner machining process is conducted to verify the registration effectiveness. Before machining, the path is generated based on the well registered gingival curve. After full registration, the tool path is calculated by establishing a local coordinate system between the workpiece and the tool to avoid interference. This path is calculated and generated as an executable program for ABB industrial robots. Finally, the robot was used for flexible cutting of clear aligners and was able to extract products, ensuring the effectiveness of the proposed research. This method can effectively solve the limitations of traditional milling path planning under such complex conditions.

Research on key technologies for automatic planning of multiple links on a single node

Abstract Aiming at the problems of slow speed and low efficiency existing in the traditional manual planning method of multiple links on a single node, which cannot fully meet the rapid deployment needs of communication nodes, this paper studies and puts forward two automatic planning technologies of single-node-multi-link based on shortest distance priority and minimum angle priority. This is an automatic planning technology that uses the shortest distance priority to determine the pairing relationship between the multi-scattering antenna of the local communication node and the far-end scattering antenna. It aims to solve the issue of cross-redundancy of distance in wrong planning. Automatic planning technology of single-node-multi-link based on minimum angle priority simulates manual planning mode. Using the concept of minimum angle priority, the pairing relationship between the multi-scattering antenna of the local communication node and the remote scattering antenna is determined. This is achieved by calculating and optimizing the minimum angle of two rays that are formed from the origin of the coordinate system of the local communication node to the local scattering antenna and the remote scattering antenna, respectively. Comparative analysis and simulation experiments prove that the two key technologies proposed in this paper can not only effectively avoid the problem of mutual interference between multiple scattering antennas in this node but also improve the efficiency of multi-link planning on a single node.

A Low-carbon Dispatching Method for Cascade Hydropower via HVDC Flexible Delivery

Abstract As a country rich in hydropower resources, the transmission of Southwest hydropower through HVDC system can achieve a large range of optimal allocation of resources, but the traditional HVDC transmission plan is often operated in high and low binary value, which is difficult to give full play to the flexibility of HVDC transmission, and increase the carbon emission and operation cost of the power system. In order to fully explore the flexibility of HVDC transmission mode to reduce system carbon emissions, this paper builds a mixed integer programming model based on the scenario of hydropower being sent to the receiving system through the HVDC system, taking into account such factors as low operating costs and carbon dioxide emissions of thermal power units at the receiving end, overload capacity of the HVDC system, and reactive power compensation cost of the HVDC system. A low carbon scheduling method for cascade hydropower HVDC flexible delivery system is proposed. Finally, taking the cross-regional transmission system of cascade hydropower station as an example, the model proposed in this paper is compared with the high-low binary transmission of traditional HVDC system. The results show that the dispatch mode proposed in this paper and the flexible operation of the HVDC system can promote the consumption of new energy, reduce the CO2 emissions of the system and achieve the dispatch goal of both low carbon and economic operation.

Feasibility of simulation free abdominal stereotactic adaptive radiotherapy using an expedited pre-plan workflow

Background and PurposeImproved hounsfield-unit accuracy of on-board imaging may lead to direct-to-unit treatment approaches We aimed to demonstrate the feasibility of using only a diagnostic (dx) computed tomography (CT)-defined target pre-plan in an in silico study of simulation-free abdominal stereotactic adaptive radiotherapy (ART). Materials and MethodsEight patients with abdominal treatment sites (five pancreatic cancer, three oligometastases) were treated using an integrated adaptive O-Ring gantry system. Each patient’s target was delineated on a dxCT. The target only pre-plan served primarily to seed the ART process. During the ART session, all structures were delineated. All simulated cases were treated to 50 Gy in 5 fractions to a planning target optimization structure (PTV_OPT) to allow for dose escalation within the planning target volume. Timing of steps during this workflow was recorded. Plan quality was compared between ART treatment plans and a plan created on a CT simulation scan using the traditional planning workflow. ResultsThe workflow was feasible in all attempts, with organ-at-risk (OAR) constraints met in all fractions despite lack of initial OAR contours. Median absolute difference between the adapted plan and simulation CT plan for the PTV_Opt V95% was 2.0 %. Median absolute difference in the D0.5 cm3 between the adapted plan and simulation CT plan was −0.9 Gy for stomach, 1.2 Gy for duodenum, −5.3 Gy for small bowel, and 0.3 Gy for large bowel. Median end-to-end workflow time was 63 min. ConclusionThe workflow was feasible for a dxCT-defined target-only pre-plan approach to stereotactic abdominal ART.

Development of an integrated and differentiated tahfidz teaching module to improve Quran memorization for students at MI Plus Al-Fatimah Sukorejo Bojonegoro

Teaching modules are independent curriculum frameworks that replace traditional lesson plans. The independent curriculum teaching module, which serves as a replacement for the RPP, has a diverse format that encompasses learning material/content, teaching methods, interpretation, and evaluation techniques. These modules are designed systematically and engagingly to meet the desired success indicators. The primary goal of developing teaching modules is to provide teachers with instructional tools that can guide them effectively in the teaching process. Teachers are given the autonomy to choose or modify the provided modules by the government to better match their students' characteristics or to create their own modules tailored to their students' needs. The developed teaching modules include integrated modules for tahfidz subjects at Madrasah Ibtidaiyah Plus Al-Fatimah, differentiated modules for tahfidz subjects at Madrasah Ibtidaiyah, guidelines for developing tahfidz teaching modules, instruments for developing these modules, and evaluation tools for assessing the module development process. This research and development (R&D) approach aims to create integrated and differentiated tahfidz teaching modules specifically for MI Plus Al-Fatimah. These modules cover the tahfidz teaching program, implementation guides, and evaluation instruments to assess the program's success. The development process involves the principal to ensure that students can achieve the target of memorizing four juz within six years at the madrasah ibtidaiyah level. The development stages include determining learning strategies and media, producing the modules, and creating assessment tools. This phased approach ensures that the integrated and differentiated tahfidz teaching modules are effective and meet the educational goals of the institution.