Search Results (509)

An administrator-assisted CLP-SEIRS-T⁺ framework is developed to model malware propagation and urban cyber-resilience in clustered temporal communication networks. The model extends CLP-SEIRS-T by integrating community structure, predicted links, asynchronous node activation, and an endogenous defense layer in which administrator nodes remain infectable, recover faster than ordinary nodes, and trigger local patch diffusion when community-level prevalence exceeds a risk threshold. Unlike formulations that treat defense as an external or perfectly reliable safeguard, the proposed framework embeds administrator intervention directly within the epidemic state space and couples propagation dynamics with resilience-oriented performance measures, including safe functionality, absorptive capacity, spillover attenuation, recovery time, and service continuity. To keep experimental evidence scale-explicit, the validation is organized as a tiered protocol: a 48-node isolated virtual-machine cyber-range verifies safe mechanism realization; emulation-calibrated logical traces and pilot repeated comparisons examine trajectory behavior, pathway composition, and defense-component effects; and expanded numerical sweeps assess scalability, threshold sensitivity, alternative link-prediction scores, and adaptive-stress assumptions. The results show that direct links dominate local amplification, whereas predicted links contribute disproportionately to cross-community spillover. In the pilot comparison, the full CLP-SEIRS-T⁺ configuration achieves the best observed balance, reducing mean peak burden by 56.9%, shortening mean recovery time by 86.7%, increasing absorptive capacity by 37.1%, and improving service continuity by 12.0% relative to the no-intervention baseline. Larger-network sweeps over $N=48,100,150,200,$ and 500 logical hosts preserve the same qualitative mechanism ordering while keeping functionality error below 0.02. Threshold analysis indicates that intermediate trigger values provide a better burden–cost balance than either overly aggressive or delayed patching. Link-score comparisons show that local-neighborhood predictors yield consistent spillover interpretations, whereas degree-driven prediction can increase bridge exposure. Parameterized adaptive-stress tests further indicate that the mechanism remains beneficial under moderate stress but degrades under severe patch suppression, false telemetry, or intensified bridge seeking. These findings suggest that urban cyber-resilience depends jointly on network modularity, temporal availability, structurally likely bridge formation, state-dependent local defense, and the integrity of administrative response. Full article

Infrared small-UAV detection remains difficult because the target often appears as a weak thermal point rather than a clear object. This problem is clear in the SIDD dataset, where most test targets are smaller than 32 × 32 pixels. To address this case, this paper proposes S-Drone-YOLO, a compact YOLO-based detector that maintains a high-resolution P2 prediction path and leverages it carefully during classification. The model starts from a lightweight YOLOv5-style detector. It adds a stride-4 P2 path and replaces the C3 neck blocks with C2fAttn to improve feature reuse before prediction. Two components are then added to the Architecture II design. The Coordinate-Aware Residual C2f Block, CAR-C2f, strengthens the P2 branch using coordinate attention and residual scaling. The P2-Guided Quality-Aware Detection Head (P2-QADH) combines local P2 details with nearby P3 context. It produces a quality map that adjusts the classification logits. The regression branch, output tensor format, and training loss interface remain unchanged. On the SIDD infrared drone dataset, S-Drone-YOLO reaches 0.988 precision, 0.939 recall, 0.699 mAP50-95, and 0.962 F1-score. It uses 6.45 M parameters and 31.3 GFLOPs. Compared with the Architecture I model, recall increases by 0.8 percentage points and mAP50-95 increases by 0.4 percentage points. At the same time, the parameter count decreases by 20.3%, and GFLOPs decrease by 43.7%. Fine-tuning on five RGB UAV datasets and a second thermal dataset (ThermalUAV2UAV) yields F1 scores ranging from 0.941 to 0.999, with an mAP50-95 of 0.843 on the thermal dataset. The background analysis also shows stable F1-scores across sky, sea, city, and mountain scenes. These results suggest that controlled P2 guidance can improve infrared small-UAV detection while keeping the model size practical. Full article

Biofouling presents numerous challenges across various sectors, including aquaculture, agriculture, infrastructure, and medicine. The development of anti-biofouling techniques remains a significant challenge. In the water industry, biofouling on monitoring sensors substantially compromises the accuracy of measurements by interfering with the sensors’ measuring ability. Biofouling also significantly increases the running costs by increasing the frequency of maintenance needed to keep sensors clean and accurate. Consequently, anti-biofouling techniques are widely employed to clean in situ optical sensors, ensuring accurate measurements while minimizing overall system costs. The conventional approach for preventing biofouling from in situ sensors typically involves the application of coatings, mechanical brushes, ultraviolet radiation, and ultrasonic waves, which possess distinct advantages and disadvantages contingent upon their application. The challenges associated with protecting the small windows of water quality sensors from biofouling over extended periods using current methods are either expensive or adversely affect the integrity of monitoring data. This study introduces a low-cost centimeter-scale high-frequency surface acoustic wave (SAW) device to protect the small windows of in situ water quality sensors continuously from biofouling, functioning as an auxiliary anti-biofouling mechanism. This study found that this 16 MHz SAW device can mitigate the formation of biofilms by adhesive diatom strains CS-1664, CS-1665, and by planktonic algae CS-327 by approximately 98% in comparison to control conditions, functioning effectively as an anti-biofouling tool for itself and surrounding surfaces without adversely affecting aquatic organisms. The dimension and resonance frequency (RF) of the SAW device are also capable of being fabricated according to the area requiring cleaning. A miniaturized 16 MHz SAW device can sustain operation for prolonged periods up to a couple of months without maintenance, at a low cost and power consumption, providing a new anti-biofouling technology. This methodology aims to assist the Australian inland and coastal water quality monitoring system by reducing maintenance costs while simultaneously enhancing the longevity of sensors submerged in water for extended periods. Full article

Nonlinear optimization is a central component of visual odometry and simultaneous localization and mapping (SLAM), but its repeated small- and medium-scale linear algebra operations are difficult to deploy efficiently on embedded hardware. This paper presents a synthesizable C++ library for AMD/Xilinx Vitis high-level synthesis (HLS) that provides field-programmable gate array (FPGA)-oriented dense linear algebra kernels and Lie-group primitives on SO(3) and SE(3). The library supports configurable scalar types, including IEEE floating point, posit arithmetic, and reduced-precision floating-point formats, enabling design-space exploration between numerical accuracy and hardware cost. The proposed kernels are integrated into the back-end of a monocular direct mesh-based visual SLAM system and evaluated on an AMD/Xilinx Kria KV260 platform. Compared with the software reference running on the embedded processor, the integrated FPGA implementation reduces the end-to-end optimization iteration time from 32.0 ms to 8.9 ms, corresponding to a speed-up of 3.6×, and reduces the dominant kernel latency from 25.0 ms to 4.9 ms. The most resource-efficient reduced-precision configuration reduces lookup table (LUT) usage by 29.6%, flip-flop (FF) usage by 25.7%, block random-access memory (BRAM) usage by 25.9%, and digital signal processor (DSP) usage by 38.6% relative to the floating-point hardware baseline, while keeping the relative trajectory error within 1.42%. The results show that Lie-group-aware optimization back-ends can be mapped to embedded FPGAs efficiently when fixed-size algebraic kernels, synthesis-aware memory structures, and configurable arithmetic are considered together. Full article

Poly(l-lactic acid) or poly(l-lactide) (PLLA) is an optically active polymer derived from renewable sources and fully biodegradable. It is known that PLLA assumes a left-handed helix in the solid state and also in solution it still keeps a certain degree of helical structure. Here, we examine the Optical Rotatory Dispersion (ORD) behavior of two grades of PLLA (medium molecular weight and hexadecyl-terminated or high molecular weight for 3D printing) in 13 different solvents and analyze the experimental ORD data through the Moffitt–Yang equation. Furthermore, the ORD data of PLLA in additional 6 solvents were taken from the literature and analyzed with the Moffitt-Yang approach. The results suggest that, also in solution, PLLA maintains the left-handed helix, and the most structurizing and helicogenic solvents for PLLA are ethyl acetate, acetonitrile, and certain chlorinated solvents. The equilibrium association constant (K) and other thermodynamic parameters (ΔG°, ΔH° and ΔS°) between PLLA and polyphenylacetylene (PPA, another helical polymer in the solid state and in solution) were determined in trichloromethane, dichloromethane, and tetrahydrofuran. The K values found suggest a strong helix-helix interaction between the two polymers. The ORD analysis of the PLLA-PPA solutions shows evidence of the extrinsic Cotton effect and confirms the chiral helicity induction between the two polymers with 1:1 complex formation. Full article

Unmanned Aerial Vehicles (UAVs) are complex nonlinear systems characterized by high dimensionality. They are prone to aerodynamic effects, structural dynamics, actuation constraints, and networked interactions, requiring advanced mathematical models and precise control. Their governing equations involve nonlinear rigid-body dynamics coupled with fluid and elasticity models, while modern architectures introduce redundancy that creates constrained mappings between generalized forces and actuator inputs. Coordinated UAV teams add another layer of mathematical structure through graph-based interaction models that determine consensus, formation keeping, and distributed stability. These characteristics give rise to several interconnected challenges. High-fidelity aerodynamic and aeroelastic solvers provide accurate results; however, these are computationally intensive, motivating the development of reduced-order models and data-driven approximations that preserve dominant physical behavior. Methods for quantifying uncertainty support robustness assessments by characterizing the effects of parametric variation and model form error. At the actuation level, control allocation problems rely on constrained linear algebra, convex optimization, and dynamic formulations to ensure feasible and stable realization of command forces and moments. In multi-agent systems, the spectral properties of adjacency and Laplacian matrices govern convergence and cooperative behavior. This article reviews the state of the art in these areas, highlights the mathematical foundations that relate them, and provides a coherent perspective on the methods that enable reliable modeling and control of modern UAV systems. Full article

Background/Objectives: Bud sports (somatic mutations) offer a quick way to develop new bougainvillea varieties by altering specific traits while keeping the desirable genetic background of the original cultivar. However, we still lack a comprehensive understanding of their genomic architecture and the molecular mechanisms behind their formation. This study aimed to characterize the population genomic characteristics of bud sports derived from the commercial variety Bougainvillea × buttiana ‘Miss Manila’. Methods: We employed genotyping by sequencing (GBS) on 39 accessions, including 27 bud sports and 12 conventional varieties. Population genomic analyses, such as principal component analysis (PCA), phylogenetic reconstruction, ADMIXTURE, and diversity statistics (π, He, Tajima’s D), were performed on 64,810 high-quality SNPs. Genome-wide scans for differentiation (FST) and selective sweeps (XP-CLR) were also conducted. Results: Bud sports showed significantly lower genetic diversity (π and He) than conventional varieties, which matches their clonal origin. PCA, phylogenetic, and ADMIXTURE analyses (optimal K = 4) revealed clear genetic differentiation and distinct population structures between the two groups. The bud sport population possessed fewer private alleles and a less negative Tajima’s D value. Genomic scans identified regions under selection in bud sports, with functional annotation pointed to genes involved in ubiquitin-mediated proteolysis and RNA transport. Notably, Bou_119143 (UDP-rhamnose rhamnosyltransferase 1) showed a high mutation frequency specifically in bud sports. Conclusions: We provide the first population-genomic evidence that bud sports of ‘Miss Manila’ are genetically distinct clonal lineages, shaped by somatic mutation and selection. These findings support bud sports as efficient sources for germplasm innovation. The identified genomic regions and candidate genes lay a foundation for future marker-assisted selection and molecular breeding in bougainvillea. Full article

Managed pressure drilling (MPD) is the core technology for developing formations with high pressure and narrow density windows. It precisely maintains the bottomhole pressure (BHP) within the safe operating window defined by formation pore pressure and fracture pressure by actively regulating the wellbore pressure profile. If pressure control becomes unstable, it can easily trigger gas kicks or lost circulation, posing a severe threat to operational safety. However, existing model predictive control (MPC) schemes have significant limitations: pure data-driven models exhibit poor generalization under complex conditions, while control algorithms based on traditional mechanistic models struggle to meet the stringent real-time requirements of field control cycles due to high-complexity numerical iteration processes. To balance control precision and real-time performance, this paper proposes a physics-informed model predictive control framework (PINC-MPC). During the training phase, physical prior knowledge such as the law of mass conservation is embedded into the neural network as constraints to construct a physically consistent deep surrogate model, enabling it to characterize complex wellbore characteristics. In the control phase, this surrogate model replaces the time-consuming numerical solving process of the mechanistic model within the MPC loop, achieving near-real-time state prediction and rolling optimization while ensuring physical fidelity. Experimental results indicate that PINC-MPC demonstrates superior control performance. Its median single-step solving time is only 16.81 ms, achieving an 11.1-fold acceleration compared to the mechanistic model-based scheme (187.3 ms). In a 5000 s full-cycle closed-loop control experiment, the total time required for the former is only 1.68 s, while the latter reaches 18.73 s, representing an efficiency improvement of approximately 91%. In terms of control accuracy, the integrated absolute error (IAE), reflecting the total deviation of the control process, significantly decreased from 63.40 MPa·s for the industrial successive linearization MPC (SLMPC) to 12.90 MPa·s, an improvement of 79.7%. Especially in extreme dynamic conditions such as simulated pump shutdowns for pipe connections and sudden gas kicks, the framework demonstrates excellent predictive ability and response efficiency. It can proactively trigger compensation actions to keep BHP fluctuations within 0.30 MPa, significantly outperforming the traditional SLMPC method. The research results prove that PINC-MPC provides an efficient, precise, and robust nonlinear control strategy for MPD systems, offering important engineering reference value for enhancing the automation level of intelligent drilling systems. Full article

The growing density of space debris in Low Earth Orbit poses significant risks to Distributed Space Systems (DSSs), where multiple satellites operate in close proximity. Conventional single-satellite collision avoidance maneuvers do not account for internal formation safety and may induce secondary conjunction risks. This work presents a formation-level trajectory optimization framework for short-term conjunction mitigation that jointly addresses external debris avoidance and inter-satellite collision prevention. The proposed Space-Debris Evasion with Internal-Collision-Avoidance (SDEICA) method formulates the problem as a sequential convex programming scheme. A probabilistic debris keep-out region is modeled as an elliptical collision tube derived from the relative position covariance at the Time of Closest Approach (TCA) and convexified via tangent-plane approximation. Internal safety constraints are incorporated through successive linearization of inter-satellite separation conditions. The framework is evaluated on 1197 conjunction scenarios derived from ESA Collision-Avoidance Challenge data for a three-satellite formation. Results demonstrate a systematic reduction in the probability of collision below the operational threshold of ${10}^{-5}$ in all cases, within numerical tolerance, eliminating intersatellite distance violations, maintaining bounded formation deviation, and requiring only moderate control effort. The median computational time is 17.12 s per scenario. These findings indicate that sequential convex optimization provides a practical approach for coordinated, fuel-efficient collision avoidance in satellite formations operating in increasingly congested orbital environments. Full article

This study describes participants’ views and insights into crafting effective communication aimed at smokeless tobacco and nicotine pouch prevention among fire academy trainees and new recruits. Firefighters have elevated rates of smokeless tobacco use compared with the general population. Nicotine pouches have also gained popularity among this occupational group. We launched a pilot project centered in rural Northern California counties to uncover factors that can be used to communicate smokeless tobacco and nicotine pouch prevention messages within the firefighter workplace. As a first step, we conducted semi-structured interviews with firefighter subject matter experts, including fire chiefs, fire academy instructors, wildlands firefighters, and recent fire academy graduates. This purposive sample (n = 13) was obtained through referrals from the project’s Community Advisory Board, composed of fire service professionals. Interviews were audio recorded and transcribed. Next, the qualitative interviews were thematically analyzed. The results focus on two aspects of effective workplace communication in the service to delivery of smokeless tobacco and nicotine pouch prevention messages: content (core information conveyed in a message), and format (how the message is transmitted or displayed). Examples of the former are the importance of keeping oneself healthy so that one can do one’s job; do not risk a future compensation claim due to smokeless tobacco or nicotine pouch use. Examples of the latter are the use of brevity; humor. Because firefighters often initiate use of these products after they join the fire service, communicating prevention messages in the workplace during the firefighter training and recruitment stage may help disrupt the uptake of nicotine products. Full article

In the framework of Harmony, the 10th ESA Earth Explorer mission, this paper presents a general methodology to optimize the formation parameters relevant to the single-pass, cross-track interferometry (XTI) configuration. The proposed method considers the requested height sensitivity and the maximum allowable temporal lag and derives the formation parameters for an optimal coverage over different ranges of latitudes by leveraging the relative eccentricity and inclination vector formalism. Our approach addresses the problem of interferometric coherence through the wavenumber support alignment method which is able to take into account the specific geometry of XTI in Harmony, which is a long-baseline multistatic configuration with large squint angles. The analysis is completed by an estimate of the propellant budget, required to maintain the optimized formation, which can be used as a further trade-off parameter within the mission design process. The results indicate that the passively stable helix configuration (with relative eccentricity and inclination phase angles set to 90°) provides a robust solution at equatorial and mid-latitude regions with perpendicular baselines up to the order of 1 km and temporal lag below 10 ms. Conversely, for high-latitude and polar regions, two alternative strategies are identified, revealing a trade-off between enhanced interferometric performance and increased formation maintenance requirements. For polar regions, a first strategy adopts relative eccentric and phase angles of 10°, achieving satisfactory performance across most latitudes, whereas an alternative approach retains the value of 90° and optimizes the formation specifically for high latitudes. These two options result in distinct station-keeping demands since the former strategy requires a $\Delta \mathrm{V}$ budget about two orders of magnitude higher, while the latter remains within a $\Delta \mathrm{V}$ range that is typical for missions of the considered class. Full article

Emotions in the Muslim Brotherhood have been largely overlooked in the literature. This article examines how the movement strategically regulates specific emotions—and the processes that generate them—to keep members in as well as to prevent and deter them from leaving. It focuses on conjugal love as it is produced through endogamous arranged marriage practices. Drawing on frame analysis of Brotherhood literature and fieldwork conducted in Egypt, Qatar, Turkey, and the UK, the study shows that the group tightly structures marital formation, including matchmaking, wedding rituals, and the organization of the couple’s household. Conjugal love produced in this marriage is entangled with two additional forms of attachment: love for the group and love for God. This entangled emotional structure transforms marriage and the three loves attached to it into a mechanism of organizational engagement that can prevent and deter members from leaving. For example, the group makes the cost of exit emotionally high through threats of divorce, social ostracism and God’s condemnation. Full article

This study traces the metamorphoses of concealment from Hesiod’s observation that “the gods keep the means of life concealed from human beings” to contemporary attention economies. In Hesiod’s pronouncement the event of concealment generates the dialectic of need and surplus, from which anthropological difference emerges, distinguishing the human from the animal and the divine. Divine concealment simultaneously creates humanity as seeker, sustenance as sought, and technics as necessity. Bataille’s “general economy” expands this framework from its theological to secular dimensions and from human labor to terrestrial life through solar energy, showing how technique generates discrete perception. Platonov’s revolutionary writings attempt to overcome nature’s dialectics through a quasi-theology of labor, yet the resulting socialist tragedy reveals a marked disproportion between technical development and subjective formation. Contemporary digital technologies transform concealment fundamentally: attention becomes liberated from searching for the hidden, only to be captured and commodified. Semiotic surplus manifests everywhere while material access remains restricted. The ancient matrix of concealment persists through digital transformations, assuming new forms while preserving its essential structure across radically different economic and technological conditions. Full article

No-reference image quality assessment (NR-IQA) models achieve high correlation with human mean opinion scores (MOS) on clean benchmarks, yet recent work shows they can be highly vulnerable to small adversarial perturbations that severely degrade ranking consistency, including in black-box settings. We introduce the Spectral Robustness Mixer (SRM), a lightweight neck inserted between an NR-IQA backbone and regression head, designed to reduce adversarial sensitivity without changing the dataset, label format, or target metric. SRM couples (i) deep-to-shallow cross-scale fusion via a Nyström low-rank attention surrogate, (ii) ridge-conditioned landmark kernels with ridge regularization, solved via numerically stable small-matrix factorization (SVD/LU) to improve conditioning, and (iii) variance-aware entropy-regularized fusion gates with a bounded gain cap to limit gradient amplification. We evaluate SRM on TID2013 and KonIQ-10k under a white-box ${\mathcal{l}}_{\infty }/{\mathcal{l}}_2$ attack ensemble that includes per-image regression objectives and a correlation-aware pairwise inversion objective (a ranking-inspired surrogate for correlation inversion), with expectation-over-transformation (EOT) and anti-gradient masking checks. At $ϵ=4/255$ (${\mathcal{l}}_{\infty }$), SRM improves worst-case robust Spearman’s rank-order correlation coefficient (SROCC; defined as the minimum over our fixed attack ensemble) by an absolute $0.06$–$0.08$ SROCC points (i.e., correlation-coefficient units, not percentage gain) across datasets/backbones, while keeping clean SROCC within $0.00$–$0.01$ of the baseline. We observe similar trends for Pearson linear correlation coefficient (PLCC). Full article

Tight-formation control is a key technology for unmanned surface vehicle (USV) swarms in harbor navigation, cooperative berthing, and operations in hazardous environments, yet achieving reliable obstacle avoidance while maintaining formation stability remains highly challenging. Although multi-agent reinforcement learning has shown strong potential in cooperative systems, parallel policy structures in many existing methods still struggle to achieve synchronized coordination in tight formations, leading to behavioral inconsistencies and unstable formation keeping. To address these challenges, an action-aware multi-agent soft actor–critic (AAMASAC) algorithm is proposed that introduces a hierarchical, action-aware decision mechanism. Within each time step, upper-layer actions are propagated as prior signals to lower-layer policies, establishing an ordered, intent-aligned decision flow that mitigates temporal inconsistency and enhances coordination efficiency. The architecture explicitly encodes inter-layer dependencies via a decision priority hierarchy and real-time behavioral information channels, enabling more accurate credit assignment and more stable value estimation and policy optimization. Across three representative validation scenarios, the AAMASAC algorithm significantly outperforms baseline methods in average reward, path-tracking accuracy, formation stability, and obstacle-avoidance performance. These results indicate that introducing a hierarchical model and action awareness effectively improves control accuracy and coordination in a USV swarm. Full article