Traditional Planning Research Articles

Enhancing Urban Landscape Design: A GAN-Based Approach for Rapid Color Rendering of Park Sketches

In urban ecological development, the effective planning and design of living spaces are crucial. Traditional color plan rendering methods, mainly using generative adversarial networks (GANs), rely heavily on edge extraction. This often leads to the loss of important details from hand-drawn drafts, significantly affecting the portrayal of the designer’s key concepts. This issue is especially critical in complex park planning. To address this, our study introduces a system based on conditional GANs. This system rapidly converts black-and-white park sketches into comprehensive color designs. We also employ a data augmentation strategy to enhance the quality of the output. The research reveals: (1) Our model efficiently produces designs suitable for industrial applications. (2) The GAN-based data augmentation improves the data volume, leading to enhanced rendering effects. (3) Our unique approach of direct rendering from sketches offers a novel method in urban planning and design. This study aims to enhance the rendering aspect of an intelligent workflow for landscape design. More efficient rendering techniques will reduce the iteration time of early design solutions and promote the iterative speed of designers’ thinking, thus improving the speed and efficiency of the whole design process.

A security‐enhanced equipment predictive maintenance solution for the ETO manufacturing

SummaryWith the rapid advancement of intelligent manufacturing, ensuring equipment safety has become a crucial prerequisite for enterprise production. In the engineer‐to‐order (ETO) production mode, characterized by diverse equipment types and frequent adjustments in production lines, equipment maintenance has become increasingly complex. Traditional maintenance plans are no longer adequate to meet the evolving demands of equipment maintenance. This paper proposes a security‐enhanced predictive maintenance scheme specifically designed for ETO‐type production equipment. The scheme utilizes industrial Internet of Things (IIoT) technology to monitor machines and equipment, constructs prediction models using machine learning methods, and reinforces the security of the prediction system through adoption of a decentralized architecture with blockchain distributed storage. In this experiment, six supervised learning models were compared, and it was found that the model based on the random forest algorithm achieved an outstanding accuracy rate of 98.88%. Furthermore, the average total response time for generating predictions within the system is 2.0 s, demonstrating a performance suitable for practical equipment maintenance applications.

Integrating green infrastructure in Italian urban plans. Lessons from Turin and Bologna

Spatial planning constitutes an institutional activity of government and governance that sets up reference development frameworks to coordinate policies and actions for cities and territories. This activity is functional to have coherence and support the implementation of European and international policies focused on socio-ecological transition. The study provides an overview of how the concept of green infrastructure (GI) is central to spatial planning to enable a sustainable regeneration of contemporary cities and territories and to protect biodiversity within a climate-proof framework of interventions. The research aim is to explore the modalities of GI methodological integration in urban planning to address the green transition. Two Italian case studies have been selected to investigate how differently structured urban plans at the local scale design and interpret GI. The discussion from the comparative case study analysis focuses on what one can learn from conformative planning approaches in response to contemporary challenges and with respect to the regulatory framework in force. Final reflections concern the need to work in Italy on a review of the traditional urban planning model with new national fundamental principles, providing a framework for coherence and coordination of the national post-pandemic recovery programme.

Dynamic Scene Path Planning of UAVs Based on Deep Reinforcement Learning

Traditional unmanned aerial vehicle path planning methods focus on addressing planning issues in static scenes, struggle to balance optimality and real-time performance, and are prone to local optima. In this paper, we propose an improved deep reinforcement learning approach for UAV path planning in dynamic scenarios. Firstly, we establish a task scenario including an obstacle assessment model and model the UAV’s path planning problem using the Markov Decision Process. We translate the MDP model into the framework of reinforcement learning and design the state space, action space, and reward function while incorporating heuristic rules into the action exploration policy. Secondly, we utilize the Q function approximation of an enhanced D3QN with a prioritized experience replay mechanism and design the algorithm’s network structure based on the TensorFlow framework. Through extensive training, we obtain reinforcement learning path planning policies for both static and dynamic scenes and innovatively employ a visualized action field to analyze their planning effectiveness. Simulations demonstrate that the proposed algorithm can accomplish UAV dynamic scene path planning tasks and outperforms classical methods such as A*, RRT, and DQN in terms of planning effectiveness.

A switcher skip lot-sampling plan under resubmitted lots using the Taguchi capability index for building a solid vendor-customer relationship

Variable sampling plans incorporating process capability indices (PCIs) have been introduced to adapt to the changing landscape of modern manufacturing, where the occurrence of defective goods has significantly decreased. Skip-lot sampling plans (SkSP) are particularly effective for batches characterized by consistent and high product quality. Additionally, manufacturers with a strong track record of producing high-quality goods or possessing empirical knowledge about their production processes can benefit from resampling. It has been demonstrated that resubmitted sampling plans outperform traditional single sampling plans. In this study, a novel variant of skip-lot sampling, referred to as SkSP-SR, is proposed. SkSP-SR utilizes two reference plans: one for normal inspection (utilizing SSP) and the other for skip inspection (utilizing RSP). The approach also considers the loss of information associated with deviations from the target by incorporating the Taguchi capability index. Compared to traditional sampling plans, the suggested approach not only reduces sample sizes but also enhances discriminating power. Comprehensive tables presenting plan parameters for commonly used quality and risk criteria are provided, enabling practitioners to readily implement the recommended plan.

A Hybrided Method for Temporal Variable-Order Fractional Partial Differential Equations with Fractional Laplace Operator

In this paper, we present a more general approach based on a Picard integral scheme for nonlinear partial differential equations with a variable time-fractional derivative of order α(x,t)∈(1,2) and space-fractional order s∈(0,1), where v=u′(t) is introduced as the new unknown function and u is recovered using the quadrature. In order to get rid of the constraints of traditional plans considering the half-time situation, integration by parts and the regularity process are introduced on the variable v. The convergence order can reach O(τ2+h2), different from the common L1,2−α schemes with convergence rate O(τ2,3−α(x,t)) under the infinite norm. In each integer time step, the stability, solvability and convergence of this scheme are proved. Several error results and convergence rates are calculated using numerical simulations to evidence the theoretical values of the proposed method.

Institutional arrangements and urban planning: analysis of Bernardo Sayão Avenue in Imperatriz-MA

This article aims to analyze the relationship of institutional arrangements with the master plan to identify good management practices in favor of urban planning city of Imperatriz (MA), focusing on Bernardo Sayão Avenue. The methodological basis of the work had as its focus the identification of the main types of services developed on the avenue, to later correlate with the best actions of urban planning from the point of view of the master plan. In addition, the main institutional arrangements (hierarchical and hybrid) developed in the area of study were identified. As a result, it was verified that the master plans in Imperatriz (MA) work as traditional plans focused on a technicalist normalization and little tied to the standards of collaboration and participation, in addition, it was found that even if there are works and institutional arrangements in Avenida Bernardo Sayão there are still discontinuities in actions, projects and program to meet the whole of the avenue a characteristic that repeats itself in the city as a whole.

The Micro-renewal of Streets and Alleyways in Qingzhou Ancient City Based on The Study of "Acupuncture Theory"

The microspace design of Dongmen Street and alleys in Qingzhou Ancient City is vital for cultural heritage preservation and urban renewal. Traditional urban planning and microspace design have limitations, requiring a more flexible approach. This study aims to apply "acupuncture and moxibustion" theory to Dongmen Street's microspace design, achieving heritage protection and urban renewal goals. It introduces Qingzhou Ancient City and the theory's basis, including concepts, principles, and methods. The study proposes an elastic design scheme for Dongmen Street's microspace based on this theory. By respecting historical context, local culture, and integrating modern design theories, flexible design schemes can enhance microspace elasticity during urban renewal. This research provides innovative ideas for Qingzhou's ancient city microspace design, contributing to heritage preservation and urban development. Future exploration will refine solutions aligning better with ancient city preservation and urban renewal needs, furthering the city's heritage and development.

Mortgages with non-random time-varying interest rates

AbstractHere we introduce an “alternative” version of the standard traditional amortization plan, where sequences of non-random time-varying periodic interest rates replace the usual constant periodic effective rate, while preserving all the other classical rules. In particular, we use two of these sequences coherently generated by two different specific hyperbolic instantaneous intensity functions. We found that the two standard amortization plans obtained through this approach match perfectly with the two main amortization plans recently proposed under the simple capitalization law. This matching provides thus a clear link between the traditional scheme and the new wave of proposals in simple regime.

Bioactive VS4-based sonosensitizer for robust chemodynamic, sonodynamic and osteogenic therapy of infected bone defects

BackgroundMost bone defects caused by bone disease or trauma are accompanied by infection, and there is a high risk of infection spread and defect expansion. Traditional clinical treatment plans often fail due to issues like antibiotic resistance and non-union of bones. Therefore, the treatment of infected bone defects requires a strategy that simultaneously achieves high antibacterial efficiency and promotes bone regeneration.ResultsIn this study, an ultrasound responsive vanadium tetrasulfide-loaded MXene (VSM) Schottky junction is constructed for rapid methicillin-resistant staphylococcus aureus (MRSA) clearance and bone regeneration. Due to the peroxidase (POD)-like activity of VS4 and the abundant Schottky junctions, VSM has high electron–hole separation efficiency and a decreased band gap, exhibiting a strong chemodynamic and sonodynamic antibacterial efficiency of 94.03%. Under the stimulation of medical dose ultrasound, the steady release of vanadium element promotes the osteogenic differentiation of human bone marrow mesenchymal stem cells (hBMSCs). The in vivo application of VSM in infected tibial plateau bone defects of rats also has a great therapeutic effect, eliminating MRSA infection, then inhibiting inflammation and improving bone regeneration.ConclusionThe present work successfully develops an ultrasound responsive VS4-based versatile sonosensitizer for robust effective antibacterial and osteogenic therapy of infected bone defects.

An Innovative Multi-Objective Rescheduling System for Mitigating Pandemic Spread in Aviation Networks

The novel coronavirus outbreak has significantly heightened environmental costs and operational challenges for civil aviation airlines, prompting emergency airport closures in affected regions and a substantial decline in ridership. The consequential need to reassess, delay, or cancel flight itineraries has led to disruptions at airports, amplifying the risk of disease transmission. In response, this paper proposes a spatial approach to efficiently address pandemic spread in the civil aviation network. The methodology prioritizes the use of a static gravity model for calculating route-specific infection pressures, enabling strategic flight rescheduling to control infection levels at airports (nodes) and among airlines (edges). Temporally, this study considers intervals between takeoffs and landings to minimize crowd gatherings, mitigating the novel coronavirus transmission rate. By constructing a discrete space–time network for irregular flights, this research generates a viable set of routes for aircraft operating in special circumstances, minimizing both route-specific infection pressures and operational costs for airlines. Remarkably, the introduced method demonstrates substantial savings, reaching almost 53.4%, compared to traditional plans. This showcases its efficacy in optimizing responses to pandemic-induced disruptions within the civil aviation network, offering a comprehensive solution that balances operational efficiency and public health considerations in the face of unprecedented challenges.

Input/output-style approach to standardized traditional amortization plans

AbstractWe apply an Input/Output approach to the analysis of the so-called traditional standardized amortization plans (STAP). The approach is based on a clear distinction between Input/trigger and Output of a STAP. The Output is a set of four vectors obtained by applying a set of transformation matrices to an appropriate Input/trigger vector. The main result of the paper is that, by this approach, any STAP reveals a dual nature through the introduction of the twin trigger theory.

Evidence on the Relationship between Pension-Driven Financial Incentives and Late-Career Attrition: Implications for Pension Reform

Retirement plans can create strong financial incentives that have important labor market implications, and many states have adopted alternative plan designs that significantly change these incentives. The authors use longitudinal data to investigate the impact of Washington State’s 1996 introduction of a hybrid retirement plan on late-career attrition. The unique setup of Washington’s plans allows them to provide empirical evidence on the influence of financial incentives created by statutory retirement eligibility thresholds. Findings show that despite facing very different financial incentives, teachers enrolled in the hybrid and traditional plans respond similarly to reaching a key retirement eligibility threshold. The authors hypothesize that teachers are anchoring to the eligibility thresholds, muting the influence of the financial incentives. They also provide evidence that, in the presence of bright-line eligibility thresholds that can anchor workers’ separation behavior, commonly used structural models may overpredict workers’ responsiveness to the financial incentives embedded in retirement plans.

Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.

No Justice, No Streets! Black radical placemaking and its political aesthetics in George Floyd Square

ABSTRACT Building from Black geographies and Black media studies, I propose “Black radical placemaking” as a communicative approach to reimagine urban planning practices toward more racially equitable futures. Using George Floyd Square as a case study and through photographic fieldwork, I argue that Black radical placemaking promotes a political aesthetics constituted of racialized inclusion, mutual aid, and reparative justice. This stands opposite to traditional urban planning practices that have historically privileged whiteness, predatory capitalism, and social control of BIPOC communities. Lastly, the study discusses how Black radical placemaking can transform communication, media, and geography scholarship toward a social justice arc.

Integration of limbic self-neuromodulation with psychotherapy for complex post-traumatic stress disorder: treatment rationale and case study

ABSTRACT Treatment Rationale: Exposure to repeated sexual trauma, particularly during childhood, often leads to protracted mental health problems. Childhood adversity is specifically associated with complex posttraumatic stress disorder (PTSD) presentation, which is particularly tenacious and treatment refractory, and features severe emotion dysregulation. Augmentation approaches have been suggested to enhance treatment efficacy in PTSD thus integrating first-line psychotherapy with mechanistically informed self-neuromodulation procedures (i.e. neurofeedback) may pave the way to enhanced clinical outcomes. A central neural mechanism of PTSD and emotion dysregulation involves amygdala hyperactivity that can be volitionally regulated by neurofeedback. We outline a treatment rationale that includes a detailed justification for the potential of combining psychotherapy and NF and delineate mechanisms of change. We illustrate key processes of reciprocal interactions between neurofeedback engagement and therapeutic goals. Case Study: We describe a clinical case of a woman with complex PTSD due to early and repetitive childhood sexual abuse using adjunctive neurofeedback as an augmentation to an ongoing, stable, traditional treatment plan. The woman participated in (a) ten sessions of neurofeedback by the use of an fMRI-inspired EEG model of limbic related activity (Amygdala Electrical-Finger-Print; AmygEFP-NF), (b) traditional weekly individual psychotherapy, (c) skills group. Before and after NF training period patient was blindly assessed for PTSD symptoms, followed by a 1, 3- and 6-months self-report follow-up. We demonstrate mechanisms of change as well as the clinical effectiveness of adjunctive treatment as indicated by reduced PTSD symptoms and improved daily functioning within this single case. Conclusions: We outline an integrative neuropsychological framework for understanding the unique mechanisms of change conferring value to conjoining NF applications with trauma-focused psychotherapy in complex PTSD.

Finding an optimal distribution strategy path in an unpredictable environment

Purpose: This article introduces an innovative method designed to optimize distribution strategies with respect to future uncertainty. It goes beyond the limitations of traditional scenario-based planning that often leads to suboptimal strategies due to the unpredictability of future developments and the challenge of accurately assigning probabilities to these scenarios. Consequently, the method allows selection of the most economically viable future strategy. Methodology: Our methodology diverges from conventional approaches by refraining from making rigid assumptions about the probabilities of future scenarios. Instead, it comprehensively explores the entire allowable probability space to identify an optimal strategy that works well in possible future developments. We employed this method in the case study of a real-world company based in Czechia, where we devised three viable distribution strategies and four model development scenarios. Results: The application of our method demonstrated its effectiveness in selecting the most advantageous strategy, as evidenced by the results of our case study. However, the applicability of the method is contingent upon the accurate definition of potential future scenarios and the evaluation of the performance of different strategies within these scenarios. Conclusion: Our findings suggest that this approach significantly enhances strategic planning under uncertainty. Future research will seek to refine this method further by integrating causal relationships to convey additional information across different model periods, thereby improving the robustness and applicability of the strategy selection process.

The positive effects of digital media design combined with AI intelligence on art brand planning and promotion

Abstract With the development of science and technology, digital media and AI intelligence are currently spreading to the traditional brand planning and promotion business, and more and more brands will adopt an empowered brand promotion business in the future. This paper is based on natural language processing and other AI intelligent technology, which includes image, copy, comment, and audio data processing for advertising promotion intelligent creation systems. Combined with the characteristics of art brand planning and promotion, based on the principle of comprehensiveness and importance, it constructs a system of factors influencing digital media and AI intelligence on brand promotion from the four dimensions of brand product, brand capability, brand market, and brand support. The results show that the average purchase intention index of the brand promotion mode integrating digital media and AI intelligence is 4.9710, which is significantly higher than that of the traditional promotion mode, which is 4.1354. This study can help art enterprises identify the relationship and key elements between the influencing factors of brand promotion and guide brand enterprises to implement more targeted brand planning and promotion strategies.

Online Trajectory Planning Control for a Class of Underactuated Mechanical Systems

Efficient control of underactuated systems has always been a great challenge, since one has to stabilize both actuated and unactuated states with fewer control inputs. During the past decades, great efforts have been devoted to improving the transient performance and robustness of the system, which are very crucial in plenty of application scenarios. To motivate corresponding research, an online trajectory planning method is proposed for a class of underactuated mechanical systems in this paper. Specifically, reference trajectories are first generated for actuated states. Based on that, appropriate online adjustments are made to these reference trajectories, so as to realize efficient stabilization of the unactuated states. Different from traditional off-line planning methods, the proposed one utilizes real-time feedback to counteract various unfavorable conditions, which endows it stronger robustness and better transient performance. Rigorous Lyapunov-based analysis is performed to show that the closed-loop system is asymptotically stable around the desired equilibrium point. Finally, the proposed method is applied to two underactuated mechanical systems, with simulation/experiment results provided to demonstrate its superior performance.

Design of Matrix Production Systems: New Demands on Factory Planning Methods

The production paradigm of "matrix production", newly discussed in the context of the digital transformation, is challenging traditional factory planning methods. In a turbulent market environment, more and more companies are striving to increase their competitiveness through more flexible and efficient production systems. Traditional factory planning approaches are still based on classical production systems. Conflicts and inefficiencies in the transformation project are inevitable. To avoid controversy, the question of how factory planning can be updated with the support of matrix production systems (MPSs) must be answered. Should an existing framework be adapted or a new one developed? A detailed literature review reveals the need for an update. In essence, it becomes clear that the topics of MPSs and factory planning have already been connected. These connections do not take into account the morphological requirements that are placed on the planning and operation of MPSs in a factory. The result is a conceptual modification of a common guideline for factory planning. Following the result, recommendations for the way to a successful validation are given. The article provides valuable insights into the core issues of MPSs and factory planning and stimulates scientific discourse.