Search Results (87)

Natural and human-made disasters can severely impair terrestrial communication infrastructures and disrupt emergency response coordination in modern smart cities. To address these challenges, this paper introduces the Weighted Average Yo-Yo-based Clustering and Routing (WAY-CR) scheme, an adaptive, secure, and energy-efficient drone-assisted solution for post-disaster network recovery and emergency response. WAY-CR integrates three main components: First, a novel WAY-based metaheuristic optimizer incorporates the concept of Yo-Yo Motion into the conventional Weighted Average Algorithm (WAA), improving the balance between exploration and exploitation during CH selection and clustering. Second, a secure communication model combines the Paillier Homomorphic Cryptosystem (PHC) with a trust evaluation model to provide end-to-end security and authenticity, ensuring that only authenticated and trustworthy drones participate in communication and routing. Third, a Trust-Aware Boltzmann Path Selection method introduces probabilistic decision-making into routing, allowing adaptive selection of secure and energy-efficient routing paths. WAY-CR formulates a multi-objective optimization model that minimizes communication cost and energy consumption while maximizing trust, link stability, and coverage. Stage 1 addresses secure intra-Ground Control Station (GCS) clustering, authentication, and trust management, whereas Stage 2 restores inter-GCS connectivity through a Secure Relay Discovery and Verification procedure based on Boltzmann Path Selection. An adaptive maintenance mechanism further supports dynamic reconfiguration in response to CH failures, mobility, or trust degradation, thereby preserving stable network performance under disaster-induced disruptions. Extensive simulation results show that WAY-CR outperforms state-of-the-art Flying Ad Hoc Network (FANET) baselines in energy efficiency, cluster stability, trust accuracy, and end-to-end packet delivery, highlighting its potential as a resilient, scalable, and secure solution for post-disaster smart-city environments. Full article

This study develops a ternary Fe-based geopolymer system composed of metakaolin (MK), red mud (RM), and fly ash (FA) for the preparation of sustainable water-retaining subgrade materials for sponge-city roadbed applications. Unlike conventional formulations primarily designed for structural strength or rapid permeability, the proposed MK–FA–RM system was designed to improve water-storage capacity while maintaining adequate mechanical support and environmental compatibility. In this ternary system, MK provides highly reactive aluminosilicate species for geopolymer network formation, RM introduces Fe-bearing phases and enhances industrial solid-waste utilization, and FA contributes to particle packing, workability, and resource efficiency. A constrained ternary mixture design implemented using Design-Expert software was adopted to optimize precursor proportions. Within the investigated compositional range, the fitted first-order mixture model showed acceptable statistical adequacy for preliminary composition screening (R² = 0.86). The optimal blend (60% MK, 30% RM, and 10% FA) achieved a 7-day compressive strength of 8.37 MPa and a water retention rate of 35.3% under ambient curing conditions, satisfying the strength requirement considered for the target subgrade/base-layer application. Microstructural and phase analyses suggest that the synergistic interaction of the three precursors promoted Fe-modified aluminosilicate gel formation together with conventional geopolymer gel products, while improving matrix continuity and preserving interconnected pore space for water storage. This multiscale structural effect helps explain how the material achieved a balance between water retention capacity and mechanical support. Under the tested conditions, the material maintained acceptable residual strength after short-term exposure to water, acid, and sulfate-containing solutions. Life-cycle assessment indicated a 70% reduction in CO₂ emissions compared with ordinary Portland cement, while pilot-scale cost analysis showed a 39% lower production cost than MetaMax-based geopolymer materials. Pilot-scale application further demonstrated the constructability and water-regulation potential of the material in practical environments. Overall, the proposed ternary Fe-based geopolymer demonstrates that Fe-rich industrial wastes can be engineered into low-carbon and economically viable water-retaining subgrade materials that balance hydraulic regulation, structural adequacy, and sustainability. Nevertheless, long-term durability, cyclic loading performance, and direct nanoscale characterization of Fe-bearing gel evolution still require further investigation. Full article

Accurate identification of fly species composition and their responses to attractants is critical for risk assessment and targeted vector control. To evaluate the efficacy of different attractants in surveillance and their species-specific trapping biases, a standardized field study was conducted from June to September 2021 across seven representative cities in China’s major zoogeographical regions: Xining, Ürümqi, Yanji, Beijing, Chongqing, Kunming, and Sanya. Cage traps baited with either fish offal or sugar–vinegar solution were deployed, supplemented by hand-net collection. A total of 134 traps were set, yielding 2132 flies belonging to 21 species. Fish offal captured 1961 flies (91.9%), significantly more than the 101 flies (4.7%) caught with sugar–vinegar solution (χ² = 1582.3, p < 0.001). Lucilia sericata was the dominant species (885 individuals, 41.51%), followed by L. cuprina (178, 8.35%), Sarcophaga portschinskyi (127, 5.96%), and Sarcophaga africa (100, 4.70%). High-risk taxa (Calliphoridae and Sarcophagidae) were almost exclusively attracted to fish offal. Our findings demonstrate that protein-based baits, such as fish offal, are substantially more effective than traditional sugar–vinegar solutions for capturing epidemiologically relevant fly species across diverse ecological zones in China. We recommend prioritizing proteinaceous attractants in national fly surveillance programs and advocate for routine species-level identification to enable risk-informed vector monitoring. Full article

Cross-view geo-localization tries to find the matching place in large satellite or aerial pictures from photos taken at ground level, which is useful for applications like self-driving cars, flying drones, and adding virtual objects to real city scenes. However, the traditional deep learning hybrid CNN-Transformer architecture and complex geometric submodules result in a large computational overhead, making it difficult to apply in real-time on resource-constrained devices. To make it light, fast, and accurate, this paper suggests an effective way to make a state-space model for cross-view geo-localization tasks. The model replaces the traditional self-attention structure with a state-space vision backbone, lowering the sequence modeling complexity from quadratic to linear and greatly accelerating the inference process; it devises a channel-group aggregation strategy without any learnable parameters, producing a comprehensive yet lightweight representation, and introduces a dynamic difficulty-aware loss function that assigns varying weights to all negative samples within a batch according to their similarities, greatly improving the efficiency of hard-negative sample mining and the quality of convergence. The results on the authoritative public datasets CVUSA and CVACT indicate that our method has high accuracy and low computational complexity, providing a feasible approach for the lightweight design of more powerful cross-view geolocation models in the future. Full article

Chrysomya megacephala is a synanthropic fly with a high potential to act as a mechanical vector of pathogenic bacteria, surpassing Musca domestica in both bacterial load and diversity. Native to Asia and Africa, it has become a cosmopolitan species, successfully adapting to a wide range of environments, including natural ecosystems. In Colombia, studies on its role as a vector are limited and have largely relied on traditional culturing methods. This study aimed to characterize the pathogenic bacterial microbiota associated with C. megacephala using 16S rRNA gene sequencing in urban, rural, and forest settings of a coastal tourist city. Flies were collected using Van Someren Rydon traps with attractants and sterile materials. Bacterial identification was performed through Oxford Nanopore MinION sequencing (Manufactured by Oxford Nanopore Technologies, Oxford, UK). A total of 49 bacterial species were identified, with urban environments showing the highest taxonomic richness. The forest environment was characterized by a highly dominant community structure, led by Vagococcus carniphilus. Notably, 20 bacterial species of public health relevance were detected, including Clostridium botulinum, Clostridium perfringens, Ignatzschineria ureiclastica, Escherichia coli, and Streptococcus agalactiae. These findings indicate that bacterial community composition varies by environment and underscore the potential role of C. megacephala as a mechanical vector, highlighting the importance of surveillance for its public health implications. Full article

In recent years, flying ad hoc networks (FANETs) have attracted attention as aerial communication platforms for large-scale disasters. In wide, city-scale disaster zones, survivors’ devices often form multiple isolated clusters, while battery-powered unmanned aerial vehicles (UAVs) must periodically return to a ground station (GS). Under such conditions, conventional delay/disruption-tolerant networking (DTN) routing (e.g., PRoPHET) often traps bundles in clusters or UAVs, degrading the bundle delivery ratio (BDR) to the GS. This study proposes a DTN-based FANET architecture that integrates (i) a mobility model assigning UAVs to information–exploration UAVs that randomly patrol the disaster area and GS–relay UAVs that follow spoke-like routes to periodically visit the GS, and (ii) an extended PRoPHET-based routing protocol that exploits exogenous information on GS visits to bias delivery predictabilities toward GS–relay UAVs and UAVs returning for recharging. Simulations with The ONE in a 10 km × 10 km scenario with multiple clusters show that the proposed method suppresses BDR degradation by up to 41% relative to PRoPHET, raising the BDR from 0.27 to 0.39 in the five-cluster case and increasing the proportion of bundles delivered with lower delay. These results indicate that the proposed method is well-suited for relaying critical disaster-related information. Full article

Persistent patrolling with multiple Unmanned Aerial Vehicles (UAVs) remains challenging due to dynamic surveillance priorities, heterogeneous node importance, and evolving operational constraints. We present the novel Chimpanzee Troop Algorithm for Patrolling (CTAP), a decentralized policy inspired by chimpanzees fission–fusion dynamics and territorial behavior. CTAP provides three capabilities: (i) on-the-fly patrol-group instantiation, (ii) importance-aware territorial partitioning of the patrol graph, and (iii) adaptive boundary expansion via a lightweight shared-memory overlay that coordinates neighboring groups without centralization. Unlike the Ant Colony Optimization (ACO), Heuristic Pathfinder Conscientious Cognitive (HPCC), Recurrent LSTM Path-Maker (RLPM), State-Exchange Bayesian Strategy (SEBS), and Dynamic Task Assignment via Auctions (DTAP) baselines, CTAP couples local-idleness reduction with controlled edge-exploration, yielding stable coverage under shifting demand. We evaluate these approaches across multiple maps and fleet sizes using the average weighted idleness, global worst-weighted idleness, and Time-Normalized Idleness metrics. CTAP reduces the average weighted idleness by 7% to 22% and the global worst-weighted idleness by 30–65% relative to the strongest competitor and attains the lowest Time-Normalized Idleness in every configuration. These results show that a simple, communication-limited, partition-based policy enables robust, scalable patrolling suitable for resource-constrained UAV teams in smart-city environments. Full article

Urban air mobility operations, such as flying Uncrewed Aerial Vehicles (UAVs) and small passenger aircraft in and around cities, will be inherently susceptible to the turbulent wind conditions in urban environments. Therefore, understanding UAM aircraft performance under microscale wind disturbances is critical. Gaining such insight is non-trivial due to the lack of sufficient UAM aircraft operational data and the complexities involved in flight testing UAM aircraft. A viable solution to overcome this hindrance is through simulation-based flight testing, data collection, and performance assessment. To support this effort, the present paper establishes a custom Stochastic microscale Wind Model (SWM) capable of efficiently generating high-resolution, spatio-temporally varying urban wind fields. The SWM is validated against wind tunnel test data, and subsequently, the findings are employed to guide targeted refinements of urban wake simulation. Furthermore, to incorporate realistic atmospheric conditions and demonstrate the ability to generate location-specific wind fields, the SWM is coupled with the mesoscale Weather Research and Forecasting (WRF) model. This integrated approach is demonstrated through a case study focused on a potential vertiport site in Milan, Italy, illustrating its utility for assessing operational area-specific UAM aircraft performance and vertiport emplacement. Full article

Despite severe particulate matter (PM) pollution in Central Asia, limited air composition and health impact data are hindering sustainable air quality management and resilient urban planning. This study provides the first comprehensive assessment of PM_2.5 and PM_2.5–10 in the urban environment of Astana, Kazakhstan, a rapidly expanding city with intense winter heating demands. We characterized PM and atmospheric precipitation and assessed health risks using bioaccessible fractions of PM-bound potentially toxic elements (PTEs). Among 388 samples, PM_2.5 and PM_2.5–10 concentrations peaked at 534 and 1564 μg·m⁻³, respectively. Scanning electron microscopy (SEM) identified soot and coal fly ash, indicating fossil fuel combustion as a major source. Precipitation characterization also showed elevated SO₄²⁻ (17.8 μg⋅L⁻¹), V (108 μg⋅L⁻¹), Ni (84.0 μg⋅L⁻¹), and Mn (63.2 μg⋅L⁻¹). Bioaccessibility tests showed high solubility for Fe (16,229 mg·kg⁻¹) followed by V: key indicators of combustion emissions. Non-carcinogenic risk for Ni and V exceeded acceptable limits for adults and children (e.g., HQ: 6.07 for V for adults). Carcinogenic risk exceeded the threshold 10⁻⁶ for Cd (adults), Co, Cr, and Ni. These findings may help advance urban air quality management via integrating bioaccessibility-based health risk assessment and source apportionment, supporting evidence-driven policies for environmentally responsible development in rapidly urbanizing cold-climate regions. Full article

The Costa Rican mid-elevation forests have been found to include some of the richest sites in the world for biodiversity per unit area. We used DNA barcodes to study 28,773 phorid fly specimens that were Malaise-trapped in the Nectandra Cloud Forest Reserve, north of the city of San Ramón, Alajuela, Costa Rica. This survey yielded 1964 BINs (Barcode Index Numbers), with a projected total of 2809, the largest known world phorid fauna. The diversity patterns of phorid flies collected at Nectandra were compared to 133,705 phorid flies collected at four other sites in Área de Conservacíon de Guanacaste in northwestern Costa Rica. All sites were highly diverse but differed significantly in their similarity to Nectandra, with low overlap among sites. Together, the number of BINs in northwestern Costa Rica is projected to exceed the entire described world fauna of Phoridae. Full article

The CORUS-XUAM project defined three two-way U-space corridors linking Frankfurt Airport’s Terminal 2 on the city outskirts with the city-center Trade Fair. These corridors avoid the approach cones of the northern and central runways and bypass hospital no-fly zones and large buildings. In our previous studies, we first used fast-time simulations to evaluate the U-space routing and its operating concept, based on historical air traffic data. Included were arriving and departing airplanes as well as police, and medical helicopters throughout the city. The focus was on the limitations of the airspace, avoiding conflicts with other airspace users and between the airtaxis using a different corridor or delaying the departure, as well as determining the throughput potential of such a corridor system. Building on our previous studies, this study incorporates higher-fidelity traffic simulation data and an updated demand analysis for the airtaxi shuttle service. Our new sizing analysis reveals that ground operations typically, not airspace capacity, constitute the primary bottleneck. Full article

Capturing real-world aerial images for vision-based navigation (VBN) is challenging due to limited availability and conditions that make it nearly impossible to access all desired images from any location. The complexity increases when multiple locations are involved. State-of-the-art solutions, such as deploying UAVs (unmanned aerial vehicles) for aerial imaging or relying on existing research databases, come with significant limitations. TerrAInav Sim offers a compelling alternative by simulating a UAV to capture bird’s-eye view map-based images at zero yaw with real-world visible-band specifications. This open-source tool allows users to specify the bounding box (top-left and bottom-right) coordinates of any region on a map. Without the need to physically fly a drone, the virtual Python UAV performs a raster search to capture images. Users can define parameters such as the flight altitude, aspect ratio, diagonal field of view of the camera, and the overlap between consecutive images. TerrAInav Sim’s capabilities range from capturing a few low-altitude images for basic applications to generating extensive datasets of entire cities for complex tasks like deep learning. This versatility makes TerrAInav a valuable tool for not only VBN but also other applications, including environmental monitoring, construction, and city management. The open-source nature of the tool also allows for the extension of the raster search to other missions. A dataset of Memphis, TN, has been provided along with this simulator. A supplementary dataset is also provided, which includes data from a 3D world generation package for comparison. Full article

Learn to Fly was developed between February 2022 and March 2023 with the goal of fostering greater social participation and intergenerational dialogue around the recognition and solution of pertinent social issues through the development of psychological flexibility and socioemotional competences in children at the start of their academic careers. Based on a participatory methodology and the concepts of the third generation of Cognitive Behavioral Therapies (CBTs) and ACT (Acceptance Commitment Therapy), the target audience included children of ages 5 and 6 (pre-school and first grade), their teachers, and their families. The Learn to Fly pilot initiative was implemented in eight partner institutions on the Portuguese mainland with the participation of 289 children, their families, and 22 educators. Learn to Fly was evaluated after 12 weeks of implementation using a combination of methodologies, including interviews, focus groups, and pre- and post-tests. Teachers emphasized that the initiative brought families closer to the school, thereby strengthening connections between the school and the community, when analyzing the impact of the project on the school community. Positive changes were observed in the children’s behaviors, particularly with respect to hyperactivity, relationship problems with colleagues, prosocial behavior, socioemotional skills, their perceptions of their participation in various scenarios (their city and country), and intergenerational dialogue with their parents. In addition to the teachers’ preconceived notions about child participation, they became more aware of the possibility of children having a say in decision-making and discovered that the program promoted this aspect. Presently equipped with resources, it is envisaged that teachers trained to implement Learn to Fly will play a significant role in promoting positive child development and social engagement. Full article

Emissions from domestic coal burning are generally recognized as the cause of the lung cancer epidemic in Xuanwei City, Yunnan Province, China. To examine the physicochemical characteristics of airborne particles emitted from burning this locally sourced coal, PM_2.5 samples were collected from Hutou village which has high levels of lung cancer, and Xize village located approximately 30 km from Hutou without lung cancer cases. Transmission Electron Microscopy-Energy Dispersive X-ray (TEM-EDX) analysis was employed to study the physiochemical features and chemistry of individual particles. Sulfur and silica are the most abundant elements found in the airborne particles in both of the two villages. Fewer elements in aerosol particles were found in Xize village compared with Hutou village. Based on the morphologies and chemical compositions, the particles in Xuanwei can be classified into five types including composite particles (38.6%); organic, soot, tar balls, and biologicals (28.3%); sulfate (14.1%); fly ash (9.8%); and minerals (9.2%). The particles in Hutou village are abundant in the size range of 0.4–0.8 μm while that in Xize is 0.7–0.8 μm. Composite particles are the most common types in all the size ranges. The percentage of composite particles shows two peaks in the small size range (0.1–0.2 μm) and the large size ranges (2–2.3 μm) in Hutou village while that shows an even distribution in all size ranges in Xize village. Core-shell particles are typical types of composite particles, with the solid ‘core’ consisting of materials such as fly ash or mineral grains, and the shell or surface layer being an adhering soluble compound such as sulfates or organics. The heterogeneous reactions of particles with acidic liquid layers produce the core-shell structures. Typically, the equivalent diameter of the core-shell particles is in the range of 0.5–2.5 μm, averaging 1.6 μm, and the core-shell ratio is usually between 0.4 and 0.8, with an average of 0.6. Regardless of the sizes of the particles, the relatively high core-shell ratios imply a less aging state, which suggests that the core-shell particles were relatively recently formed. Once the coal-burning particles are inhaled into the human deep lung, they can cause damage to lung cells and harm to human health. Full article

Multirotor drones play an increasingly significant role in smart cities and are among the most widely discussed emerging technologies. They are expected to support various applications such as package delivery, data collection, traffic policing, surveillance, and medicine. As part of their services, future drones should be able to solve the last-mile challenge and land safely in urban areas. This paper addresses the path planning task for an autonomous drone searching for a landing place in an urban environment. Our algorithm uses a novel multi-resolution probabilistic approach in which visual information is collected by the drone at decreasing altitudes. As part of the exploration task, we present the Global Path Planning (GPP) problem, which uses probabilistic information and the camera’s field of view to plan safe trajectories that will maximize the search success by covering areas with high potential for proper landing while avoiding no-fly zones and complying with time constraints. The GPP problem is formulated as a minimization problem and then is shown to be NP-hard. As a baseline, we develop an approximation algorithm based on an exhaustive search, and then we devise a more complex yet efficient heuristic algorithm to solve the problem. Finally, we evaluate the algorithms’ performance using simulation experiments. Simulation results obtained from various scenarios show that the proposed heuristic algorithm significantly reduces computation time while keeping coverage performance close to the baseline. To the best of our knowledge, this is the first work referring to a multi-resolution approach to such search missions; further, in particular, the GPP problem has not been addressed previously. Full article