Search Results (175)

Over the past decade, the rapid development of geospatial tools has significantly expanded the scope of archaeobotanical research, enabling unprecedented insights into ancient plant domestication, agricultural practices, and human-environment interactions. Within the Chinese context, where rich archaeobotanical records intersect with complex socio-ecological histories, GIS-driven approaches have revealed nuanced patterns of crop dispersal, settlement dynamics, and landscape modification. However, despite these advances, current applications remain largely exploratory, constrained by fragmented datasets and underutilized spatial-statistical methods. This paper argues that a more robust integration of large-scale archaeobotanical datasets with advanced ArcGIS functionalities—such as kernel density estimation, least-cost path analysis, and predictive modelling—is essential to address persistent gaps in the field. By synthesizing case studies from key Chinese Neolithic and Bronze Age sites, we demonstrate how spatial analytics can elucidate (1) spatiotemporal trends in plant use, (2) anthropogenic impacts on vegetation, and (3) the feedback loops between subsistence strategies and landscape evolution. Furthermore, we highlight the challenges of data standardization, scale dependency, and interdisciplinary collaboration in archaeobotanical ArcGIS. Ultimately, this study underscores the imperative for methodological harmonization and computational innovation to unravel the intricate relationships between ancient societies, agroecological systems, and long-term environmental change. Full article

In addition to the performance parameters of a mechanical manipulator—such as precision, repeatability, payload and maximum speed—path optimization can bring significant improvements in terms of cycle time and energy consumption. In this paper, a method is proposed for post-processing trajectories initially generated by spline interpolation in joint space (cubic or quintic interpolation), so that the distances traveled are shorter. The principle of least action is used as a theoretical foundation trying to find the best cost function in terms of trajectory lengths using. In the pursuit of minimizing this cost function, an iterative method is applied. Initial trajectories are split into multiple internal nodes that are displaced little by little from their initial positions, recomposing trajectories that pass through these displaced nodes at every iteration. The purpose of this paper is to demonstrate that by post-processing trajectories initially generated by the usual spline interpolation in joint space, alternative, shorter variants can be obtained. Full article

The shortest path problem is algorithmic and involves finding the least costly path (in terms of distance, time, cost, or other criteria) between two nodes in a graph. This problem is fundamental in graph theory and has applications in logistics, networks, mapping, and more. Several methods have been used to solve this problem. In this paper, we applied the ant colony algorithm to optimize the travel path of product quality technicians in a textile company specializing in washing and dyeing denim items. The company aims to minimize distances and costs between its subcontractors. The method has demonstrated a significant impact on distance and cost reduction while contributing to the reduction of the environmental effects. Full article

To address the challenges of radar multi-target detection in marine environments, this paper proposes an information geometry (IG)-based, two-stage track-before-detect (TBD) framework. Specifically, multi-target measurements are first modeled on the manifold, leveraging its geometric properties for enhanced detection. The designed scoring function incorporates both the feature dissimilarity between targets and clutter, as well as the precise inter-target path associations. Consequently, a novel merit function combining feature dissimilarity and transition cost is derived to mitigate the mutual interference between adjacent targets. Subsequently, to overcome the integrated merit function expansion phenomenon, a two-stage integration strategy combining dynamic programming (DP) and greedy integration (GI) algorithms was adopted. To tackle the challenges of unknown target numbers and computationally infeasible multi-hypothesis testing, a target cancellation detection scheme is proposed. Furthermore, by exploiting the independence of multi-target motions, an efficient implementation method for the detector is developed. Experimental results demonstrate that the proposed algorithm inherits the superior clutter discrimination capability of IG detectors in sea clutter environments while effectively resolving track mismatches between neighboring targets. Finally, the effectiveness of the proposed method was validated using real-recorded sea clutter data, showing significant improvements over conventional approaches, and the signal-to-clutter ratio was improved by at least 2 dB. Full article

Integrated sensing and communication (ISAC) is considered a key enabler for the future Internet of Things (IoT), as it enables wireless networks to simultaneously support high-capacity data transmission and precise environmental sensing. Indoor localization, as a representative sensing service in ISAC, has attracted considerable research attention. Nevertheless, its performance is largely constrained by the quality and granularity of the collected data. In this work, we propose an attention-based framework for cost-efficient indoor fingerprint localization that exploits extended trajectory map construction through a novel trajectory-based data augmentation (TDA) method. In particular, fingerprints at unmeasured locations are synthesized using a conditional Wasserstein generative adversarial network (CWGAN). A path generation algorithm is employed to produce diverse trajectories and construct the extended trajectory map. Based on this map, a multi-head attention model with direction-constrained auxiliary loss is then applied for accurate mobile device localization. Experiments in a real 5G indoor environment demonstrate the system’s effectiveness, achieving an average localization error of 1.09 m and at least 34% higher accuracy than existing approaches. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in environmental monitoring, logistics, and precision agriculture. Efficient and reliable path planning is particularly critical for UAV systems operating in 3D continuous environments with multiple obstacles. However, single-UAV systems are often inadequate for such environments due to limited payload capacity, restricted mission coverage, and the inability to execute multiple tasks simultaneously. To overcome these limitations, multi-UAV collaborative systems have emerged as a promising solution, yet coordinating multiple UAVs in high-dimensional 3D continuous spaces with complex obstacles remains a significant challenge for path planning. To address these challenges, this paper proposes a reinforcement-learning-driven multi-strategy continuous ant colony optimization algorithm, QMSR-ACO_R, which incorporates a Q-learning-based mechanism to dynamically select from eight strategy combinations, generated by pairing four constructor selection strategies with two walk strategies. Additionally, an elite waypoint repair mechanism is introduced to improve path feasibility and search efficiency. Experimental results demonstrate that QMSR-ACO_R outperforms seven baseline algorithms, reducing average path cost by 10–60% and maintaining a success rate of at least 33% even in the most complex environments, whereas most baseline algorithms fail completely with a success rate of 0%. These results highlight the algorithm’s robustness, adaptability, and efficiency, making it a promising solution for complex multi-UAV path planning tasks in obstacle-rich 3D environments. Full article

The Rapidly-exploring Random Tree Star (RRT*) algorithm is widely applied in robotic manipulator path planning, yet it does not directly consider motion control, where abrupt changes may cause shocks and vibrations, reducing accuracy and stability. To overcome this limitation, this paper proposes the Kinematically Smoothed, dynamically Biased Bidirectional Potential-guided RRT* (KSBB-P-RRT*) algorithm, which unifies path planning and motion control and introduces three main innovations. First, a fast path search strategy on the basis of Bi-RRT* integrates adaptive sampling and steering to accelerate exploration and improve efficiency. Second, a triangle-inequality-based optimization reduces redundant waypoints and lowers path cost. Third, a kinematically constrained smoothing strategy adapts a Jerk-Continuous S-Curve scheme to generate smooth and executable trajectories, thereby integrating path planning with motion control. Simulations in four environments show that KSBB-P-RRT* achieves at least 30% reduction in planning time and at least 3% reduction in path cost, while also requiring fewer iterations compared with Bi-RRT*, confirming its effectiveness and suitability for complex and precision-demanding applications such as agricultural robotics. Full article

With the dual pressures of accelerating urbanization and global climate warming, understanding the evolution and connectivity of cold island networks has become crucial for managing urban thermal risks. This study explores the spatiotemporal dynamics, driving mechanisms, and scenario-based projections of cold island networks in a rapidly urbanizing region of Southwest China. Using multi-temporal Landsat imagery (2000–2024), ecological resistance surface modeling, and least-cost path analysis, the study evaluated historical changes and simulated future scenarios for 2035 and 2050 under both Natural Development (ND) and Park City (PC) planning interventions. The findings reveal that: (1) Between 2000 and 2024, rapid urbanization significantly expanded high-temperature areas, fragmented cooling sources, and reshaped cold island connectivity into a hierarchical corridor network centered on a dominant ventilation axis; (2) Since 2019, ecological restoration measures have notably enhanced the structural cohesion and connectivity of cooling corridors, partially mitigating previous fragmentation; (3) Scenario simulations indicate that proactive ecological planning could reduce the extent of high-temperature zones by approximately 20% by 2050, demonstrating strong potential for mitigating future thermal risks. Overall, the results emphasize the necessity of incorporating continuous cold island corridors and connectivity principles into urban spatial planning to enhance climate resilience and support sustainable development. Full article

This paper estimates potential changes in the total system cost (TSC) of decarbonization of two regional transmission organizations (RTOs) in the United States (U.S.)—Midcontinent Independent System Operator-North (MISO-N) and Southwest Power Pool (SPP) RTO West. In particular, the study serves to highlight potential differences in technology costs between two decarbonization pathways at carbon reduction rates close to 100% (relative to 2019 levels) while maintaining system reliability. In Pathway A, decarbonization is achieved by replacing fossil energy (FE)-fired thermal power plants with variable renewable energy (VRE) technologies coupled with energy storage (ES). Pathway B considers retrofitting fossil fuel-fired units with carbon capture and storage (CCS) and the addition of VRE and ES. The results show that including CCS technologies in the path to decarbonization has a significant benefit from a system cost perspective. When summing up all system costs and avoided emissions over 30 years of operation of the decarbonized systems, the pathway that includes CCS is significantly more cost-effective. TSCs for MISO-N are at least USD 1279 billion (B) and at most USD 910 B under Pathways A and B, respectively. For SPP RTO West, Pathway A TSCs are at least USD 230 B, and Pathway B TSCs are at most USD 153 B. TSCs of Pathway A are 1.4–8 times larger than the total system costs of Pathway B. When CCS is not included, the cost per ton of carbon dioxide (CO₂) avoided is estimated to be USD 124–489/ton for MISO-N and USD 248–552/ton for SPP RTO West. When CCS is included, the cost of avoided CO₂ is projected to decrease by 29–87% (mid-point estimate of 73%) with values varying between USD 64 and 114/ton and USD 74 and 164/ton for MISO-N and SPP RTO West, respectively. These differences highlight the need for consideration of all low-carbon-intensive technology options in cost-optimal approaches to deep decarbonization and the value of CCS technologies in the energy transition. Full article

The growing importance of social networks has led to increased research into trust estimation and interpretation among network entities. It is important to predict the trust score between users in order to minimize the risks in user interactions. This article enables the identification of the most reliable and least reliable entities in a network by expressing trust scores numerically. In this paper, the social network is modeled as a graph, and trust scores are calculated by taking the powers of the ratio matrix between entities and summing them. Taking the power of the proportion matrix based on the number of entities in the network requires a lot of arithmetic load. After taking the powers of the eigenvalues of the ratio matrix, these are multiplied by the eigenvector matrix to obtain the power of the ratio matrix. In this way, the arithmetic cost required for calculating trust between entities is reduced. This paper calculates the trust score between entities using linear algebra techniques to reduce the arithmetic load. Trust detection algorithms use shortest paths and similar methods to eliminate paths that are deemed unimportant, which makes the result questionable because of the loss of data. The novelty of this method is that it calculates the trust score without the need for explicit path numbering and without any data loss. Full article

This study aims to assess the role of headwater systems (HS) in enhancing ecological connectivity and supporting Green Infrastructure in the Centre Region of Portugal. Specifically, it identifies restoration opportunity areas within HS by analysing land-use changes over the past 70 years, modelling land-use scenarios to promote ecological resilience, and evaluating connectivity between HS and Natura 2000 sites. The methodology integrates spatial analysis of historical land-use data with connectivity modelling using least-cost path approaches. Results show substantial transformation in HS areas, notably the expansion of eucalyptus plantations and a decline in agricultural land. Approximately 58% of the HS are identified as requiring restoration, including areas within the Natura 2000 network. The connectivity assessment reveals that HS can function as effective ecological corridors, contributing to improved water regulation, soil conservation, gene flow, and wildfire mitigation. A total of 61 potential ecological linkages between Natura 2000 sites were identified. These findings highlight the strategic importance of integrating HS into regional and national Green Infrastructure planning and supporting the implementation of the EU Biodiversity Strategy for 2030. The study recommends prioritising headwater restoration through multi-scale planning approaches and active involvement of local stakeholders to ensure sustainable land-use management. Full article

This study aimed to re-evaluate a historical war supply route within the context of cultural tourism, to revitalize its natural, historical, and cultural values, and to integrate it with existing hiking and trekking routes. Remote sensing (RS) and geographic information system (GIS) technologies were utilized, and land surveys were conducted to support the analysis and validate the existing data. Data for slope, one of the most critical factors for hiking route selection, were generated, and the optimal route between the starting and destination points was identified using least cost path analysis (LCPA). Historical, touristic, and recreational rest stops along the route were mapped with precise coordinates, and both the existing and the newly generated routes were assessed in terms of their accessibility to these points. Field validation was carried out based on the experiences of expert hikers. According to the results, the length of the existing hiking route was determined to be 15.72 km, while the newly developed trekking route measured 17.36 km. These two routes overlap for 7.75 km, with 9.78 km following separate paths in a round-trip scenario. It was concluded that the existing route is more suitable for hiking, whereas the newly developed route is better suited for trekking. Full article

Family structure has long been regarded as an important determinant of household saving, yet the empirical evidence for developing economies remains limited. Using the 2018–2022 panels of the China Family Panel Studies (CFPS), a nationwide survey that follows 16,519 households across three waves, the present study investigates how family size, the elderly share, and the child share jointly shape saving behavior. A household fixed effects framework is employed to control for time-invariant heterogeneity, followed by a sequential endogeneity strategy: external-shock instruments are tested and rejected, lagged two-stage least squares implement internal instruments, and a dynamic System-GMM model is estimated to capture saving persistence. Robustness checks include province-by-year fixed effects, inverse probability weighting for attrition, balanced-panel replication, alternative variable definitions, lag structures, and sample filters. Family size raises the saving rate by 4.6 percentage points in the preferred dynamic specification (p < 0.01). The elderly ratio remains insignificant throughout, whereas the child ratio exerts a negative but model-sensitive association. A three-path mediation analysis indicates that approximately 26 percent of the total family size effect operates through scale economy savings on quasi-fixed expenses, 19 percent is offset by resource dilution pressure, and less than 1 percent flows through a precautionary saving channel linked to income volatility. These findings extend the resource dilution literature by quantifying the relative strength of competing mechanisms in a middle-income context and showing that cost-sharing economies dominate child-related dilution for most households. Policy discussion highlights the importance of public childcare subsidies and targeted credit access for rural parents, whose saving capacity is the most constrained by additional children. The study also demonstrates that fixed effects estimates of family structure can be upward-biased unless dynamic saving behavior and internal instruments are considered. Full article

Urban ventilation corridors play a critical role in improving wind environments, mitigating the urban heat island (UHI) effect, and enhancing urban climate resilience. Traditional Computational Fluid Dynamics (CFD) methods offer high accuracy in simulating wind fields but are computationally intensive and inefficient for large-scale, multi-scenario urban planning tasks. To address this limitation, this study proposes a morphology-driven, machine learning-based framework for ventilation corridor identification. The method integrates Lattice Boltzmann Method (LBM) simulations, neighborhood-based feature normalization, and a random forest regression model to establish a predictive relationship between morphological indices and wind speed distributions under prevailing wind conditions. Input features include raw and log-transformed LBM values, neighborhood-normalized indicators within multiple radii (100–2000 m), and porosity statistics. The model is trained and validated using CFD-simulated wind speeds, with the dataset randomly divided into training (80%), validation (10%), and testing (10%) subsets. The results show that the proposed method can accurately predict spatial wind speed patterns and identify both primary and secondary ventilation corridors. Primary corridors are closely aligned with large rivers and lakes, while secondary corridors are shaped by arterial roads and localized open spaces. Compared with conventional approaches such as FAI classification, Least Cost Path (LCP), and circuit theory models, the proposed framework offers higher spatial resolution and better alignment with the CFD results while significantly reducing computational cost. This study demonstrates the feasibility of using morphological and data-driven approaches to support efficient and scalable urban ventilation analysis, providing valuable guidance for climate-responsive urban design. Full article

In recent years, a key problem in wireless sensor networks has been how to effectively deploy the minimum number of wireless charging pads while establishing at least one feasible charging path from the base station. This ensures that the unmanned aerial vehicle can reach and recharge all sensor nodes from the BS. Previous works have often employed greedy algorithms to solve the optimal deployment problem, treating coverage and connectivity as interdependent properties. This has led to excessive constraints on the placement of wireless charging pads, as each newly added charging pad has to satisfy both properties at the same time. Additionally, previous works have overlooked the critical issue of avoiding the occurrence of isolated sensor nodes in uncovered fragmented regions, in deployment. Failing to address this issue requires additional deployment costs to compensate for uncovered nodes. To overcome these limitations, in this work, we propose a sink-outward strategy wireless charging pad deployment algorithm, which deploys charging pads layer by layer from the innermost region outward, prioritizing coverage before connectivity. The proposed sink-outward max covering (SMC) consists of two key steps: initial pad deployment and optimization. The simulation results show that the proposed method SMC combined with the optimization step, called reducing pads by reallocating pads partially (RPRAP), achieves a reduction in pad count of 10.6–19.8% compared with the methods used in previous works, and the execution time demonstrated in previous works is several to tens of times longer than that of SMC combined with RPRAP. Moreover, the proposed redundant pad removal step, RPRAP, not only removes more redundant pads than the methods used in previous works but also drastically reduces processing time in large-scale wireless sensor networks with many redundant pads. Full article