Search Results (3,020)

The polder in this case study addresses several environmental issues, risk management concerns related to localities served by existing non-permanent dams, energy requirements that can meet a locality’s needs during the renewable energy transition, and their impacts on both rural and urban built environments. Cultural landscape preservation or solar regeneration on agricultural plots in Romania’s rural wetland areas focuses on traditionally inspired design, emphasising the technical versus humanistic approach as an optimal path through some inspiring “Dyads”. Briefly, the dyads are related to Bennett’s systematic approach to ensure the knowledge necessary for achieving understanding without experiencing it. With a two-way spiral, the defined methodology applies energy as solar photovoltaic technology to water-related natural aspects in the built environment without reducing or harming the relevant water management related to nature or built cultural heritage. The Solar Regeneration Monad “Nature -Energy- Built” is a holistic visual framework, replicable in any built environment for a “Built” regenerative culture, that enables the best solution to be identified for the conservation of cultural heritage values in an “Energy” transition context with “Nature”, biodiversity, or other water-related issues. Full article

Robot navigation in confined spaces has gained popularity in recent years, but offline planning assumes static obstacles, which limits its application to online path-planning. Several methods have been introduced to perform an efficient robot navigation process. However, various existing methods mainly depend on pre-defined maps and struggle in a dynamic environment. Also, diminishing the moving costs and detour percentages is important for real-world scenarios of robot navigation systems. Thus, this study proposes a novel perceptron-Q learning fusion (PQLF) model for Robot Navigation to address the aforementioned difficulties. The proposed model is a combination of perceptron learning and Q-learning for enhancing the robot navigation process. The robot uses the sensors to dynamically determine the distances of nearby, intermediate, and distant obstacles during local path-planning. These details are sent to the robot’s PQLF Model-based navigation controller, which acts as an agent in a Markov Decision Process (MDP) and makes effective decisions making. Thus, it is possible to express the Dynamic Robot Navigation in a Confined Indoor Environment as an MDP. The simulation results show that the proposed work outperforms other existing methods by attaining a reduced moving cost of 1.1 and a detour percentage of 7.8%. This demonstrates the superiority of the proposed model in robot navigation systems. Full article

This paper introduces a method for estimating the Downlink Angle of Departure (DL-AoD) of 5G User Equipment (UE) from measured signal strengths of directionally transmitted synchronization signals. Based on estimated DL-AoD values, from two or more anchor nodes, the position of the UE was estimated. Unlike most prior work, which is simulation-based or relies on custom testbeds, this study uses real measurements from an operational 5G network in an industrial factory environment. A deterministic estimator was derived, but multipath and unknown beam characteristics limit its accuracy. To address this, machine learning was applied to automatically adapt to the environment. Previous simulation studies reported 90th-percentile DL-AoD estimation errors below 2°, while experimental works achieved best-case accuracies of 5–6°. In this study, the experimental DL-AoD estimation error remained below 4° for 90% of the measurements, indicating improved real-world performance. Reported positioning errors in the literature range from 3.8 m to 140 m, whereas the 13.2 m error obtained here lies near the midpoint of this range, confirming the practicality of the proposed method in industrial environments. Compared to existing approaches, this work demonstrates high angular accuracy using only sub-6 GHz beams in a realistic industrial scenario without detailed knowledge of antenna beam patterns and channel state. The findings demonstrate that standard 5G signals can provide accurate indoor localization without additional infrastructure, offering a practical path toward cost-effective positioning in industrial IoT and automation. Full article

Multi-UAV three-dimensional (3D) path planning is formulated as a high-dimensional multi-constraint optimization problem involving costs such as path length, flight altitude, avoidance cost, and smoothness. To address this challenge, we propose an Adaptive Strategy Hybrid Secretary Bird Optimization Algorithm (ASHSBOA), an enhanced variant of the Secretary Bird Optimization Algorithm (SBOA). ASHSBOA integrates a weighted multi-direction dynamic learning strategy, an adaptive strategy-selection mechanism, and a hybrid elite-guided boundary-repair scheme to enhance the ability to identify local optima and balance exploration and exploitation. The algorithm is tested on benchmark suites CEC-2017 and CEC-2022 against nine classic or state-of-the-art optimizers. Non-parametric tests show that ASHSBOA consistently achieves superior performance and ranks first among competitors. Finally, we applied ASHSBOA to a multi-UAV 3D path planning model. In Scenario 1, the path cost planned by ASHSBOA decreased by 124.9 compared to the second-ranked QHSBOA. In the more complex Scenario 2, this figure reached 1137.9. Simulation results demonstrate that ASHSBOA produces lower-cost flight paths and more stable convergence behavior compared to comparative methods. These results validate the robustness and practicality of ASHSBOA in UAV path planning. Full article

Rapid urbanization and global climate change are leading to intensified Urban Heat Island (UHI) in tropical regions. This study examined and analyzed urban ventilation corridors to mitigate UHI, paying particular attention to the building arrangement and wind environment. The comprehensive review emphasizes the importance of macro-scale urban planning, including the orientation of street grids and the design of breezeways and air paths. After analyzing these strategies, CFD simulations were applied to the design of high-rise buildings in Semarang and residential areas in Jakarta. These studies revealed that in high-rise building areas in Semarang, the proposed design configuration resulted in a 62% increase in ground-level wind speeds. A further analysis of residential areas in Jakarta revealed that the most comfortable location within a house was in the second row, facing the wind, where the distance between houses was 8.5 m, and the average velocity was 2.78 m/s. Research conducted in this area may contribute to the development of more sustainable and resilient urban areas in tropical climates, as well as assist local governments in planning for these areas. Full article

The social-ecological resilience of rural settlements refers to their ability to resist and mitigate the risks posed by internal and external disturbances, and to utilize the external environment to achieve a new equilibrium state. Amid rapid urbanization, it is of great significance for mountainous settlements to improve their risk resistance and development ability. Taking Dong’an Town in Chengkou County, located in the eastern part of Qinling–Bashan Mountains in southwestern China, as the research object, this study constructs an evaluation index system for rural residential resilience based on social-ecological resilience theory. It explores the resilience level of rural residences in mountainous areas from the dimensions of internal resilience and external environmental resilience and scientifically proposes an optimization path for the spatial layout of rural residences. This study provides a reference for optimizing the rural living environment, promoting spatial equity, and improving people’s livelihood according to local conditions. The results showed that: (1) The overall level of security resilience of rural settlements in Dong’an Town was relatively high, with 221 patches above the security level, accounting for 19.53% of the total area of the town. (2) The rural residents in Dong’an Town can be categorized into three types: core structure optimization, peripheral system upgrading, and relocation and withdrawal. Different types of rural settlements adapt to internal and external resource conditions and select optimal spatial layout paths according to local conditions. Full article

As a well-known World Cultural Heritage Site, the Historic Centre of Macao’s historical buildings possess a wealth of decorative patterns. These patterns contain cultural esthetics, geographical environment, cultural traditions, and other elements from specific historical periods, deeply reflecting the evolution of religious rituals and political and economic systems throughout history. Through long-term research, this article constructs a dataset of 11,807 images of local decorative patterns of historical buildings in Macau, and proposes a fine-grained image classification method using the ConvNeXt-L model. The ConvNeXt-L model is an efficient convolutional neural network that has demonstrated excellent performance in image classification tasks in fields such as medicine and architecture. Its outstanding advantages lie in limited training samples, diverse image features, and complex scenes. The most typical advantage of this model is its structural integration of key design concepts from a Transformer, which significantly enhances the feature extraction and generalization ability of samples. In response to the objective reality that the decorative patterns of historical buildings in Macau have rich levels of detail and a limited number of functional building categories, ConvNeXt-L maximizes its ability to recognize and classify patterns while ensuring computational efficiency. This provides a more ideal technical path for the classification of small-sample complex images. This article constructs a deep learning system based on the PyTorch 1.11 framework and compares ResNet50, EfficientNet-B7, ViT-B/16, Swin-B, RegNet-Y-16GF, and ConvNeXt series models. The results indicate a positive correlation between model performance and structural complexity, with ConvNeXt-L being the most ideal in terms of accuracy in decorative pattern classification, due to its fusion of convolution and attention mechanisms. This study not only provides a multidimensional exploration for the protection and revitalization of Macao’s historical and cultural heritage and enriches theoretical support and practical foundations but also provides new research paths and methodological support for artificial intelligence technology to assist in the planning and decision-making of historical urban areas. Full article

In order to solve problems of fragmented information, missing context and difficult-to-capture feature information in short texts, this paper proposes a dual-path classification model combining word-level and sentence-level feature information. Our method is developing the BERT pre-trained model for obtaining word vectors, and presenting attention mechanisms and the BiGRU model to extract local key information and global semantic information, respectively. To tackle the difficulties of models focusing more on hard-to-learn samples during training, a novel hybrid loss function is constructed as an optimization objective, and to address common quality issues in training data, a text data optimization method that integrates data filtering and augmentation techniques is proposed. This method aims to further enhance model performance by improving the quality of input data. Experimental results on three different short text datasets show that our proposed model outperforms existing models (such as Att + BiGRU, BERT + At), with an average F1 score exceeding 90%. Moreover, the performance metrics of the model improved on the datasets optimized with the proposed data optimization method compared to the original datasets, demonstrating the effectiveness of this method in enhancing training data quality and improving model performance. Full article

In complex scenarios, such as industrial parks and underground parking lots, efficient and safe autonomous navigation is essential for driverless operation and automatic parking. However, conventional modular navigation methods, especially the A* algorithm, suffer from excessive node traversal and short paths that bring vehicles dangerously close to obstacles. To address these issues, we propose an autonomous navigation approach based on a layered terrain cost map and a nonlinear predictive control model, which ensures real-time performance, safety, and reduced computational cost. The global planner applies a two-stage A* strategy guided by the hierarchical terrain cost map, improving efficiency and obstacle avoidance, while the local planner combines linear interpolation with nonlinear model predictive control to adaptively adjust the vehicle speed under varying terrain conditions. Experiments conducted in simulated and real underground parking scenarios demonstrate that the proposed method significantly improves the computational efficiency and navigation safety, outperforming the traditional A* algorithm and other baseline approaches in overall performance. Full article

Robot-aided outdoor cleaning is a challenging task with plenty of room for improvement. The cleaning profile of a robot should be adjusted based on the availability of litter to ensure proper and efficient cleaning. This article proposes a novel system that integrates a Glasius Bio-Inspired Neural Network (GBNN) for coverage path planning with a vision-based cleaning profile adaptation scheme. The vision-based adaptation occurs based on the presence of leaves on the ground. The proposed system operates in two phases: an initial phase with low-power cleaning to cover areas with no leaves, while localizing leaf spots that require high-power cleaning, and a secondary phase focusing on cleaning these detected leaf spots using a high-power cleaning profile. This cleaning profile adaptation enables more efficient and effective cleaning. Simulation results showed a 47% improvement in energy efficiency for the proposed method compared to a method with no cleaning profile adaptation. Robot hardware tests conducted using the Panthera 2.0 outdoor cleaning robot have demonstrated the real-world applicability of the proposed method. Full article

Hierarchical Reinforcement Learning (HRL) for UAV Coverage Path Planning (CPP) is hindered by the “subgoal space explosion”, causing inefficient exploration. To address this, we propose a two-stage framework, Hierarchical Reinforcement Learning Guided by Landmarks (HRGL), which synergistically combines HRL with a multi-scale observation space. The framework provides a low-resolution global map for the high-level policy’s strategic planning and a high-resolution local map for the low-level policy’s execution. To bridge the information gap between these hierarchical views, the first stage, ACHMP, introduces a learned Adjacency Network. This network acts as an efficient proxy for local feasibility by mapping coordinates to an embedding space where distances reflect true reachability, allowing the high-level policy to select feasible subgoals without processing complex local data. The second stage, HRGL, further introduces a landmark-guided global guidance mechanism to overcome local myopia. Extensive experiments on a variety of simulated grid-world maps demonstrate that HRGL significantly outperforms baseline methods in terms of both convergence speed and final coverage rate. Full article

This work presents a task-oriented framework for optimizing robotic surface finishing to improve efficiency and ensure feasibility under realistic kinematic and geometric constraints. The approach combines surface subdivision, optimal placement of the workpiece, and region-based toolpath planning to adapt machining strategies to local surface characteristics. A novel time evaluation criterion is introduced that improves our previous kinematic approach by incorporating dynamic aspects. This advancement enables a more realistic estimation of machining time, providing a more reliable basis for optimization and path planning. The framework determines both the optimal position of the workpiece and the subdivision of its surface into regions systematically, enabling machining directions and speeds to be adapted to the geometry of each region. The methodology was validated on several semi-complex surfaces through simulation and experimental trials with collaborative robotic manipulators. The results demonstrate that improved region-based optimization leads to machining time reductions of 9–26% compared to conventional single-direction machining strategies. The most significant improvements were achieved for larger, more complex geometries and denser machining paths, confirming the method’s industrial relevance. These findings establish the framework as a practical solution for reducing cycle time in specific robotic surface finishing tasks. Full article

Controlled environment agriculture (CEA) pivots food production from an outdoor field setting to the indoors where growing conditions can be calibrated to fit crop needs. This research investigates vertical farms as a type of CEA. In particular, using the St. Louis area as a case study, it provides data-driven support for optimizing a vertical farm’s business model including its supply chain. The methodology presented here informs agri-preneurs about what crops to grow in a vertical farm, how much to grow given local market demand, and what vertical farm configuration (e.g., Dutch bucket, nutrient film technique, deep water culture) a facility should use. Based on the case study’s base scenario, the simulated vertical farm business would record an economic loss. However, the study did find several paths to improving profitability. First, reducing fixed and variable costs benefits profitability. Proper facility-level production and resource planning helps with managing the fixed costs. Second, increasing market prices may benefit profitability, but it has diminishing returns. As a result, firms can justify making investments that enhance their reputation and market competitiveness, though the advantage these marketing activities provide will decline as prices increase. Third, growing demand or increasing market share does not necessarily improve profitability. Full article

In response to growing pressures for sustainability in tourism, this paper examines the techno-economic evaluation of green innovations in small and medium-sized tourism enterprises (SMEs). Focusing on a single case study of a hotel in Greece, the research investigates how and why specific sustainability interventions were implemented and assesses their operational and economic impacts. The study adopts an interpretivist approach, combining process tracing with thematic analysis. The analysis is guided by innovation diffusion theory, supported by organizational learning perspectives, to explain the stepwise adoption of sustainability practices and the internal adaptation processes that enabled them. The techno-economic evaluation draws on quantitative indicators and qualitative assessments of perceived benefits and implementation challenges, offering a broader view of value beyond purely financial metrics. Data were collected through semi-structured interviews, on-site observations, and internal documentation. The findings reveal a gradual, non-linear path to innovation, shaped by adoption dynamics and organizational learning, reinforced by leadership commitment, contextual adaptation, supply chain decisions, and external incentives. Key interventions, including solar energy adoption, composting, and the formation of zero-waste partnerships, resulted in measurable reductions in energy use and landfill waste, along with improvements in guest satisfaction, operational efficiency, and local collaboration. Although it is subject to limitations typical of single-case designs, the study demonstrates how even modest sustainability efforts, when integrated into daily operations, can generate multiple types of outcomes (economic, environmental, and operational). The paper offers practical implications for tourism SMEs and policymakers and formulates propositions for future testing on sustainable innovation in the tourism sector. Full article

Greenhouse automation has become increasingly important in facility agriculture, yet multi-span glass greenhouses pose both scientific and practical challenges for autonomous mobile robots. Scientifically, solid-state LiDAR is vulnerable to glass-induced reflections, sparse geometric features, and narrow vertical fields of view, all of which undermine Simultaneous Localization and Mapping (SLAM)-based localization and mapping. Practically, large-scale crop production demands accurate inter-row navigation and efficient rail switching to reduce labor intensity and ensure stable operations. To address these challenges, this study presents an integrated localization-navigation framework for mobile robots in multi-span glass greenhouses. In the intralogistics area, the LiDAR Inertial Odometry-Simultaneous Localization and Mapping (LIO-SAM) pipeline was enhanced with reflection filtering, adaptive feature-extraction thresholds, and improved loop-closure detection, generating high-fidelity three-dimensional maps that were converted into two-dimensional occupancy grids for A-Star global path planning and Dynamic Window Approach (DWA) local control. In the cultivation area, where rails intersect with internal corridors, YOLOv8n-based rail-center detection combined with a pure-pursuit controller established a vision-servo framework for lateral rail switching and inter-row navigation. Field experiments demonstrated that the optimized mapping reduced the mean relative error by 15%. At a navigation speed of 0.2 m/s, the robot achieved a mean lateral deviation of 4.12 cm and a heading offset of 1.79°, while the vision-servo rail-switching system improved efficiency by 25.2%. These findings confirm the proposed framework’s accuracy, robustness, and practical applicability, providing strong support for intelligent facility-agriculture operations. Full article