Search Results (7,736)

In recent years, Location-Based Augmented Reality (LAR) systems have been increasingly implemented in various applications for tourism, navigation, education, and entertainment. Unfortunately, the LAR content creation using conventional desktop-based authoring tools has become a bottleneck, as it requires time-consuming and skilled work. Previously, we proposed an in-situ mobile authoring tool as an efficient solution to this problem by offering direct authoring interactions in real-world environments using a smartphone. Currently, the evaluation through the comparison between the proposal and conventional ones is not sufficient to show superiority, particularly in terms of interaction, authoring performance, and cognitive workload, where our tool uses 6DoF device movement for spatial input, while desktop ones rely on mouse-pointing. In this paper, we present a comparative study of authoring performances between the tools across three authoring phases: (1) Point of Interest (POI) location acquisition, (2) AR object creation, and (3) AR object registration. For the conventional tool, we adopt Unity and ARCore SDK. As a real-world application, we target the LAR content creation for pedestrian landmark annotation across campus environments at Okayama University, Japan, and Brawijaya University, Indonesia, and identify task-level bottlenecks in both tools. In our experiments, we asked 20 participants aged 22 to 35 with different LAR development experiences to complete equivalent authoring tasks in an outdoor campus environment, creating various LAR contents. We measured task completion time, phase-wise contribution, and cognitive workload using NASA-TLX. The results show that our tool made faster creations with 60% lower cognitive loads, where the desktop tool required higher mental efforts with manual data input and object verifications. Full article

When the spaceborne satellite target rotates non-uniformly relative to the ladar, the high-order space-variant phase will be introduced into the echo phase along both the range and azimuth direction, which will cause the degree of defocusing of the scatterers on the target to rely on their locations. Traditional imaging algorithms usually assume that the target is in uniform motion and only compensate for second-order phase errors, ignoring spatial phase variations caused by higher-order non-uniform rotation. Consequently, these algorithms are ineffective in accurately focusing on edge scatterers, leading to image blurring at the target boundaries. To solve this problem, an ISAL imaging algorithm for spaceborne non-uniformly rotating targets based on matched Fourier transform (MFT) and a genetic algorithm is proposed in this paper. First, the echo signal model of the non-uniform rotation target is established. Second, the corresponding higher-order space-variant phase compensation method based on the estimated parameters is proposed, with time-domain higher-order phase compensation along the range direction and MFT algorithm along the azimuth direction. Then, the genetic algorithm is employed for parameter estimation. Finally, the results obtained from both simulation experiments and real data experiments verify that the proposed algorithm has good compensation accuracy and robustness. Full article

Given that a robust water resource management strategy requires the knowledge of natural and climatic factors and social and economic factors, we applied a variability and vulnerability assessment as a quantitative tool to characterize water resources in Ceará, Brazil. A methodological approach that identifies and quantifies variability and vulnerability would allow better solutions to management decision problems. This approach functions as an indicator-based framework separating areas with similar water availability and water resources infrastructure, indicating the influence of natural and anthropogenic factors in the area’s water resources. The assessment proceeded with the regions’ delimitation, classifying them according to rainfall amount and spatial variability. The Adaptive Capacity for Water Management Index (ACWM) was evaluated using georeferenced water infrastructure information based on that classification. Most of the state’s area is subjected to low rainfall (below average). Nonetheless, of the areas with low rainfall, 48% have high variability. Within those areas critical water infrastructures are located that supply water to the state’s main industrial and populated city. Thus, the acknowledgment of this characteristic can complement current water management. Lastly, the authors provided recommendations based on the coupling of variability and vulnerability assessments with adaptive management to address improvements in the current water allocation system. Full article

Indexable face milling tools are often used to machine workpieces with large axial and radial depth of cuts, and thus, the inserts quickly wear out in machining. A kernel technique of smart machining is tool wear compensation, which is to regularly and automatically measure the insert radius/length with a laser tool setter on the machine table during machining, and compensate them in the subsequent machining. Another technique is tool condition monitoring, which is to calculate the insert flank wear width for tool condition and compare with its threshold. When it is less than but close to its threshold of invalid inserts, the cutting tool is automatically changed right before it becomes invalid. On-machine laser tool setters have been equipped in CNC machine tools for several years; however, they cannot conduct cutting tool condition monitoring. The main reason is that the insert flank wear width cannot be measured on the on-machine laser tool setter, and the status quo is that the cutting tool is replaced either too early or too late. To address this problem, a novel tool condition monitoring technique of determining the insert flank wear width of indexable face milling tool using on-machine laser tool setters is proposed. According to the insert geometry, the worn cutting edge and a new workpiece milling mechanism proposed in this work, the insert flank wear width can be calculated. In machining, the insert radius wear is measured on the on-machine laser tool setter, and the insert flank wear width is calculated to evaluate whether it is invalid soon. The results indicate that the optimal height for radius measurement is located near the intersection of the corner and side edges point MR₃, and close to the cutting depth point MR₅. A wear land width threshold of 0.10 mm is established to define tool failure. The proposed calculation method achieves high accuracy, maintaining calculation errors within 14.00%. The inserts can be used in good condition with the maximum lifespan. This method has been verified in machining applications and can be directly applied in industry. Full article

Creativity is widely recognized as one of the most important, key competencies supporting the achievement of sustainable development goals. Our paper presents the results of research on the declared level of creativity competence of students at universities located in Kraków (Southern Poland) preparing for the teaching profession. The survey, based on an original questionnaire (49 questions, 7 sections), covered 406 people. The scale was based on an analysis of self-perception of creativity competence among pre-service teachers. Analysis of the results showed that 12.8% of respondents had a high level of creativity, 56.4% had an average level, and 30.8% had a low level. No significant correlations were found between the level of creative competence and gender or age, while place of origin showed a slight tendency to differentiate. Students most often declared reflectiveness, openness to learning, and independence in problem solving, while less often confidence in predicting the effects of their own actions and resistance to routine. The results indicate the significant, albeit partially untapped, creative potential of future teachers. They also emphasize the need to introduce activities in teacher education that strengthen self-confidence, flexibility, and perseverance—qualities necessary to support innovation in education that promotes sustainable development. Full article

Multi-drone operations face significant efficiency challenges when launch pad locations are predetermined without optimization, leading to suboptimal route configurations and increased travel distances. This research addresses launch pad positioning as a continuous planar location-routing problem (PLRP), developing a genetic algorithm framework integrated with multiple Traveling Salesman Problem (mTSP) solvers to optimize launch pad coordinates within operational areas. The methodology was evaluated through extensive experimentation involving over 17 million test executions across varying problem complexities and compared against brute-force optimization, Particle Swarm Optimization (PSO), and simulated annealing (SA) approaches. The results demonstrate that the genetic algorithm achieves 97–100% solution accuracy relative to exhaustive search methods while reducing computational requirements by four orders of magnitude, requiring an average of 527 iterations compared to 30,000 for PSO and 1000 for SA. Smart initialization strategies and adaptive termination criteria provide additional performance enhancements, reducing computational effort by 94% while maintaining 98.8% solution quality. Statistical validation confirms systematic improvements across all tested scenarios. This research establishes a validated methodological framework for continuous launch pad optimization in UAV operations, providing practical insights for real-world applications where both solution quality and computational efficiency are critical operational factors while acknowledging the simplified energy model limitations that warrant future research into more complex operational dynamics. Full article

Background: Anemia remains an important public health problem worldwide. Investigating the potential influencing factors of anemia can provide a reference for improving anemia status. This study aimed to identify factors influencing anemia in school-age children and assess associations/causal relationships between micronutrients (vitamin A, vitamin D, iron, and zinc) and anemia risk. Methods: This study included 1725 school-age children. Factors associated with anemia were identified using multivariable-adjusted logistic regression. Associations of serum micronutrients with anemia were analyzed, and non-linear relationships were examined. Causality was assessed using two-sample Mendelian randomization (MR) analysis. Results: Daytime outdoor activity, milk consumption, school location, picky eating, and serum ferritin deficiency were associated with anemia (p < 0.05). Higher serum vitamin A (Q4 vs. Q1: OR = 0.548; P_trend = 0.027) and higher serum ferritin (Q4 vs. Q1: OR = 0.470; Q3 vs. Q1: OR = 0.609; P_trend = 0.011) were inversely associated with anemia. RCS indicated a J-shaped non-linear relationship between serum ferritin and anemia risk. MR analysis showed that serum 25-hydroxyvitamin D (OR = 0.864, 95%CI: 0.757–0.986, p = 0.030), serum ferritin (OR = 0.656, 95%CI: 0.588–0.731, p < 0.001), and serum iron (OR = 0.793, 95%CI: 0.681–0.925, p = 0.003) significantly reduced anemia risk with the IVW method. Sensitivity analyses showed no heterogeneity, pleiotropy, or reverse causality. Conclusions: This study found that daytime outdoor activity time, weekly milk consumption frequency, school location, picky eating, and serum ferritin deficiency are closely associated with anemia in school-aged children. Additionally, serum vitamin A, vitamin D, serum iron, and serum ferritin levels are also linked to anemia. These findings collectively highlight the importance of lifestyle factors and specific micronutrients in influencing anemia among school-aged children, providing valuable insights for targeted prevention and intervention strategies. Future intervention trials focusing on these key factors could further validate their practical application value. Full article

PM_2.5 pollution events evolve continuously through spatiotemporal diffusion. However, their three-dimensional spatiotemporal variation characteristics are often overlooked, and the interactions among key characteristics (e.g., duration, maximum concentration) have not yet been systematically analyzed. This study established a three-dimensional (longitude, latitude, and time) spatiotemporal framework for identifying contiguous PM_2.5 pollution events based on the high-resolution ChinaHighAirPollutants (CHAP) dataset (1 km spatial and 1-day temporal resolution). The framework applied the meteorological event tracking algorithm (i.e., the Forward-in-Time method) to track PM_2.5 pollution events. Based on this framework, we systematically tracked and characterized the spatiotemporal evolution of PM_2.5 events across China from 2013 to 2021, quantified the relationships among key event characteristics, and tracked their transport pathways. The results show that: (1) The combination of the FiT algorithm and CHAP dataset enables effective tracking and identification of the three-dimensional spatiotemporal evolution of PM_2.5 pollution events across China. (2) Event PM_2.5 totals, average totals per event and pollution events exhibit a distinct right-inclined “T”-shaped pattern, with hotspots located in Xinjiang, the Beijing-Tianjin-Hebei (BTH) region, Shandong, and Henan, where annual event frequency exceeds 15. (3) Event PM_2.5 totals show strong correlations with average duration per event and average maximum concentration per event, particularly in heavily polluted areas where the Pearson correlation coefficient is close to 1. (4) PM_2.5 pollution events are mainly characterized by short durations of 1 day or 2–3 days, accounting for over 80% of occurrences. Long-duration events are mostly concentrated in areas with severe pollution problems, and their persistence is closely linked to spatial coverage, terrain barrier effects, and meteorological conditions. (5) PM_2.5 pollution events consistently exhibit a west-to-east transport pattern. Short-duration events propagate slower across the inland northwest, whereas long-duration events show a pronounced increase in meridional transport speeds along the eastern coastal areas. This study elucidates the continuous spatiotemporal evolution and intrinsic drivers of PM_2.5 pollution events, offering scientific insights to support air quality improvement and the development of targeted management strategies. Full article

The Lake Velence watershed faces increasing challenges driven by local and global factors, including the impacts of climate change, energy resource limitations, and greenhouse gas emissions. These issues, particularly acute in water management, are exacerbated by prolonged droughts, growing population pressures, and shifting land use patterns. Such dynamics strain the region’s scarce water resources, negatively affecting the environment, tourism, recreation, agriculture, and economic prospects. Nadap, a hilly settlement within the watershed, experiences frequent flooding and poor water retention, yet it also boasts the highest solar panel capacity per property in Hungary. This research addresses these interconnected challenges by designing a solar-hydrodynamic network comprising four multi-purpose water reservoirs. By leveraging the settlement’s solar capacity and geographical features, the reservoirs provide numerous benefits to local stakeholders and extend their impact far beyond their borders. These include stormwater management with flash flood mitigation, seasonal green energy storage, water security for agriculture and irrigation, wildlife conservation, recreational opportunities, carbon-smart winery developments, and the creation of sustainable blue-green settlements. Reservoir locations and dimensions were determined by analyzing geographical characteristics, stormwater volume, energy demand, solar panel performance, and rainfall data. The hydrodynamic system, modeled in Matlab, was optimized to ensure efficient water usage for irrigation, animal hydration, and other needs while minimizing evaporation losses and carbon emissions. This research presents a design framework for low-carbon and cost-effective solutions that address water management and energy storage, promoting environmental, social, and economic sustainability. The multi-purpose use of retained rainwater solves various existing problems/challenges, strengthens a community’s self-sustainability, and fosters regional growth. This integrated approach can serve as a model for other municipalities and for developing cost-effective inter-settlement and cross-catchment solutions, with a short payback period, facing similar challenges. Full article

In the Republic of Korea, the problems of low birth rate and population aging are accelerating population decline at the regional level, leading to the phenomena of local extinction and urban shrinkage. These phenomena, coupled with the projected nationwide population decline, pose a fundamental threat to the sustainability of essential infrastructure such as urban parks. The conventional growth-oriented paradigm of urban planning has shown clear limitations in quantitatively forecasting future demand, constraining proactive management strategies for the era of population decline. To address this gap, this study develops a policy-decision-support framework that integrates long-term population projections, grid-based population data, the DEGURBA urban classification system—a global standard for delineating urban and rural areas— and network-based accessibility analysis. For the entire Republic of Korea, we (1) constructed a 1 km resolution time-series population dataset for 2022–2072; (2) applied DEGURBA to quantify transitions among urban, semi-urban, and rural types; and (3) assessed changes in potential user populations within the defined service catchments. The results indicate that while population concentration in the Seoul Capital Area persists, under the low-variant scenario, a projected average decline of 40% in potential user populations by 2072 will lead to significant functional changes, with 53.6% of municipalities nationwide transitioning to “semi-urban” or “rural” areas. This spatial shift is projected to decrease the proportion of urban parks located in “urban” areas from 83.3% to 75.0%, while the total potential user population is expected to plummet from approximately 44.4 million to 25.8 million, a 42.0% reduction. This study underscores the need for urban park policy to move beyond quantitative expansion and toward quality-oriented management based on selection and concentration. By uniquely integrating long-term demographic scenarios, the Degree of Urbanization (DEGURBA), and spatial accessibility analysis, this study provides a foundational scientific basis for forecasting future demand and supports the formulation of sustainable, data-driven strategies for urban park restructuring under conditions of demographic change. Full article

Suboptimal placement of charging infrastructure is a major barrier to the transition to sustainable transportation, even with the growing popularity of electric vehicles (EVs). The research addresses this challenge by proposing a novel hybrid genetic algorithm (GA) to solve the NP-hard Multiple-Choice Multidimensional Knapsack Problem (MMKP) for computationally derived optimal charging station placement and configurations in Sapporo, Japan. The methodology leverages high-granularity human flow data to identify charging demand and a Traveling Salesperson Problem (TSP)-based encoding to prioritize potential station locations. A greedy heuristic then decodes this prioritization, selecting charger configurations that maximize service capacity within a defined budget. The results reveal that as the budget increases, the network evolves through distinct phases of concentrated deployment, expansion, and saturation, with a nonlinear increase in covered demand, indicating diminishing returns on investment. The findings demonstrate the efficacy of the proposed model in providing a strategic roadmap for urban planners and policymakers to make cost-effective decisions that maximize charging demand coverage and accelerate EV adoption. Full article

This paper presents an optimization-based scheduling strategy for battery energy storage systems (BESS) in alternating current microgrids, considering both grid-connected and islanded operation. The study addresses two independent objectives: minimizing energy losses in the distribution network and reducing carbon dioxide emissions from dispatchable power sources. The problem is formulated using a full AC power flow model that simultaneously manages active and reactive power flows in BESS located in the microgrid, while enforcing detailed operational constraints for network components, generation units, and storage systems. To solve it, a parallel implementation of the Particle Swarm Optimization (PPSO) algorithm is applied. The PPSO is integrated into the objective functions and evaluated through a 24-h scheduling horizon, incorporating a strict penalty scheme to guarantee compliance with technical and operational limits. The proposed method generates coordinated charging and discharging plans for multiple BESS units, ensuring voltage stability, current limits, and optimal reactive power support in both operating modes. Tests are conducted on a 33-node benchmark microgrid that represents the power demand and generation from Medellín, Colombia. This is compared with two methodologies reported in the literature: Parallel Crow Search and Parallel JAYA optimizer. The results demonstrate that the strategy produces robust schedules across objectives, identifies the most critical network elements for monitoring, and maintains safe operation without compromising performance. This framework offers a practical and adaptable tool for microgrid energy management, capable of aligning technical reliability with environmental goals in diverse operational scenarios. Full article

Vacuum-assisted biopsy needle is an important tool for minimally invasive tissue sampling. Its procedural efficiency is largely compromised by the limited pneumatic efficiency and the recurrent tissue winding problem. In this study, a fluid dynamics model of vacuum-assisted biopsy is established, and its pneumatic performance is investigated. The analysis focuses on the interplay between pneumatic efficiency and structural design, particularly examining how geometric parameters influence the internal flow dynamics. The results demonstrate that the vacuum pressure applied linearly increases the flow rate. The main energy loss is located at the inlet area. Key findings reveal trade-offs between flow enhancement and winding risks, where anti-winding structures improve tissue winding but impair the pneumatic efficiency. The study can provide guidance for the structural optimization design of vacuum-assisted biopsy needles. Full article

On-demand urban delivery increasingly relies on electric delivery bicycles (EDBs), yet their limited battery capacity creates coupled challenges of routing efficiency and energy replenishment. We study a novel battery swapping cabinet location-routing problem (BSC-LRP) with multiple depots, which jointly optimizes routing and modular energy infrastructure deployment under time-window and battery constraints. To address the problem’s complexity, we design an improved branch-and-price algorithm enhanced with adaptive heuristic-exact labeling (IBP-HL) and a robust arc-based branching scheme. This hybrid framework accelerates column generation while preserving exactness, representing a methodological advancement over standard B&P approaches. Computational experiments on modified Solomon instances show that IBP-HL consistently outperforms Gurobi in both runtime and solution quality on small cases, and achieves substantial speedups and improved bounds over baseline B&P on medium and large cases. These results demonstrate not only the scalability of IBP-HL but also its practical relevance: the framework provides decision support for operators and planners in designing cost-efficient, reliable, and sustainable last-mile delivery systems with battery-swapping infrastructure. Full article

Pinning control is a key strategy for stabilizing complex networks through a limited set of nodes. However, determining the optimal number and location of pinned nodes under dynamic and structural constraints remains a computational challenge. This work proposes an improved version of the Phymastichus–Hypothenemus Algorithm—Minimized and Determinated (PHA-MD) to solve multi-constraint, hybrid optimization problems in pinning control without requiring a predefined number of control nodes. Inspired by the parasitic behavior of Phymastichus coffea on Hypothenemus hampei, the algorithm models each agent as a parasitoid capable of propagating influence across a network, inheriting node importance and dynamically expanding search dimensions through its “offspring.” Unlike its original formulation, PHA-MD integrates variable-length encoding and V-stability assessment to autonomously identify a minimal yet effective pinning set. The method was evaluated on benchmark network topologies and compared against state-of-the-art heuristic algorithms. The results show that PHA-MD consistently achieves asymptotic stability using fewer pinned nodes while maintaining energy efficiency and convergence robustness. These findings highlight the potential of biologically inspired, dimension-adaptive algorithms in solving high-dimensional, combinatorial control problems in complex dynamical systems. Full article