Search Results (8,882)

The herbaceous layer in subtropical moist forests often faces multiple concurrent growth and development constraints, yet the relative importance of these constraints and their responses to nutrient addition remain poorly quantified. We conducted a three-year factorial experiment in a montane moist secondary evergreen forest in central Yunnan of southwestern China, manipulating fertilization (control vs. fertilization), plant root competition (roots present vs. excluded), and forest litter (litter present vs. removed) in a fully crossed design. We investigated plant species richness, number of individuals, height, biomass, water content, and root-to-shoot (R/S) ratio of the herbaceous layer. The study results revealed a consistent hierarchy of inhibitory effects: root competition suppressed herb layer growth more strongly than litter layer inhibition, and their combined effect was severe enough to eliminate all herbaceous plants in the unfertilized control (i.e., R+L+ treatment). Fertilization significantly increased community-level plant species richness, number of individuals, height, biomass, and water content but substantially reduced R/S ratios in three of the four treatment combinations where comparisons were valid (R+L−, R−L+, and R−L−). This pattern indicates a shift in community biomass allocation toward aboveground organs under nutrient enrichment, although species turnover and intraspecific plasticity could both contribute to this pattern. In the R+L+ treatment, no plants were present in the control, so fertilization enabled the establishment of a plant community with an R/S ratio of 1.18 ± 0.08. The R/S ratios responded hierarchically to both competition and fertilization: it was highest under combined root + litter competition, intermediate under single-factor competition, and lowest when both competition sources were removed. Fertilization consistently lowered R/S ratios in the three treatment combinations where valid comparisons were possible (R+L−, R−L+, and R−L−). Our findings suggest that fertilization can alleviate multi-layer competition and promote herb layer development in this subtropical forest. Full article

Object visibility in a camera field of view does not guarantee that local depth is usable for robotic manipulation. This paper proposes a quality-aware active re-observation supervisory control framework for constrained eye-in-hand stereo manipulators. The framework elevates ROI-level stereo reliability from a post-processing variable to an admission condition in the observation decision loop. Instead of using a binary target-visible judgment, each observation is represented by evaluability, mean ROI risk, reliable ROI coverage, and an integrated quality score. The controller then accepts the current observation, triggers active re-observation, retains the initial result, or records a failure boundary. To avoid the cost of continuous viewpoint search and the impossibility of knowing post-execution quality in advance, an online proxy strategy based on J_proxy is introduced. Four candidate viewpoints are generated from the current observation and filtered by workspace, pose, joint-limit, and visibility constraints; the top-ranked viewpoint is then executed. Experiments are conducted on a four-degree-of-freedom eye-in-hand stereo manipulator platform. The results show that the method identifies observations in which the target is detected but depth is unreliable, and improves target-ROI observation quality and evaluability through a deployable online re-observation strategy. Specifically, compared with the initial observation, the online proxy increases the evaluable rate from 70.0% to 76.7% and the mean integrated quality score from 0.828 to 0.853; the independent manual offline reference reaches an evaluable rate of 93.3% and a mean integrated quality score of 0.916. Relative to the post-execution quality upper bound, the online proxy strategy achieves an effective quality-cost trade-off under limited pre-execution information. These results indicate that active vision for constrained manipulators should incorporate local depth reliability into online observation decisions rather than considering only whether the target is visible or centered. The proposed quality-gated active re-observation framework is applicable to eye-in-hand robotic inspection, pre-grasp verification, and local measurement, especially when the target remains visible but stereo depth within the task ROI is insufficiently reliable for downstream control. Full article

Salt stress is a major abiotic constraint that limits plant growth by inducing oxidative stress, disrupting photosynthetic processes, and damaging cellular membranes. Although Rosa rugosa is considered relatively tolerant to adverse environments, differences in antioxidant defense and physiological responses among its cultivars under sustained salt stress remain insufficiently understood. Comparative evaluation of these responses is important for identifying salt-tolerant germplasm and understanding stress-adaptation mechanisms. In this study, two R. rugosa cultivars, Siji and Fenghua, were subjected to irrigation treatments with solutions containing 0, 50, 100, or 150 mM NaCl for 28 days under controlled conditions. Morphological traits, salt injury, photosynthetic pigments, osmolytes, ROS accumulation, antioxidant enzyme activities, and lipid peroxidation were measured to assess cultivar-specific differences in salt-stress responses. Salt stress caused concentration-dependent growth inhibition, increased salt injury, and reduced chlorophyll content, while oxidative-stress responses differed between the two cultivars, with stronger adverse effects observed in Fenghua. Siji maintained better growth performance, higher fresh and dry mass of sampled leaves, greater chlorophyll retention, better maintenance of several osmolytes, and stronger antioxidant enzyme activities under the higher NaCl treatments. In contrast, Fenghua showed greater ROS accumulation and higher MDA under the higher NaCl treatments, which are consistent with greater oxidative stress and membrane lipid peroxidation. Exploratory correlation and principal component analyses further showed that antioxidant defense, osmotic adjustment, chlorophyll retention, and reduced oxidative damage were closely associated with salt tolerance in Siji. These results indicate that greater antioxidant capacity, osmotic adjustment, and membrane protection were associated with the superior salt-stress performance of ‘Siji’ compared with ‘Fenghua’. The study identifies Siji as a promising R. rugosa candidate for further salt-tolerance evaluation and provides useful physiological and biochemical indicators for salt-tolerance screening in ornamental rose breeding. Full article

Background: Prone positioning and lung-protective ventilation are key components of the management of moderate-to-severe acute respiratory distress syndrome (ARDS). However, protective ventilation may be limited by refractory hypercapnia, acidosis, and hemodynamic instability. Whether extracorporeal multiorgan support can complement prone positioning by alleviating these physiologic constraints remains unknown. Methods: We conducted a retrospective physiologic cohort study of adult patients with severe ARDS treated with prone positioning combined with extracorporeal multiorgan support using the ADVOS system. The cohort represented a highly selected population with bacterial pneumonia-associated ARDS, sepsis, acute kidney injury, and vasopressor dependence. Physiologic variables were recorded from baseline to the end of the first (t16) and second (t40) prone sessions. The primary objective was to assess short-term changes in arterial pH, PaCO₂, PaO₂/FiO₂ ratio, driving pressure, and respiratory system compliance. Results: Fifteen patients were included. Median baseline PaCO₂ was 68.1 mmHg and pH was 7.22. By t16, PaCO₂ decreased to 42.6 mmHg (p = 0.001) and pH increased to 7.41 (p = 0.001). The PaO₂/FiO₂ ratio increased from 82 to 146 (p = 0.001), and driving pressure decreased from 20 to 17 cmH₂O (p = 0.003). Mechanical power decreased from 28.5 to 25.9 J/min (p = 0.033), whereas respiratory rate and respiratory system compliance did not reach statistical significance at t16. Vasopressor requirements also decreased during the observation period. Extracorporeal support was maintained throughout prone positioning in all patients. Conclusions: In this highly selected cohort, combined prone positioning and extracorporeal multiorgan support was associated with rapid correction of hypercapnic acidosis, improved oxygenation, and favorable changes in selected measures of ventilatory load. These findings describe the integrated physiologic response to the combined strategy and warrant evaluation in prospective controlled studies. Full article

During continuous operation on the human back, the actual contact force exerted by a massage robotic arm may deviate from the desired value because of soft-tissue viscoelasticity, body-surface curvature changes, respiratory motion, and random disturbances. To achieve stable constant-force tracking, we propose a coordinated method combining an Extended State Observer (ESO)-enhanced proportional–integral–derivative (PID) controller with a temporally enhanced Soft Actor–Critic (SAC) algorithm to address response lag and high-frequency oscillations under complex noise. The two-layer architecture integrates fast compensation and policy optimization. In the PID-based layer, the ESO estimates the contact-force error, its first derivative, and the total disturbance; these estimates are used to schedule the PID gains and shape the controller output, improving contact establishment and continuous tracking. In the optimization layer, an improved SAC network adds Q-value-guided attention and frequency-gating constraints to long short-term memory (LSTM)-based sequence encoding, improving the utilization of historical states and suppressing high-frequency oscillations. Validation is conducted through PyBullet simulations of dynamic massage environments and experiments across stiffness levels of 1000 N/m, 2000 N/m, and 3000 N/m and target forces of 5 N, 8 N, and 10 N, demonstrating a certain degree of robustness to parameter variations. Under all conditions, the force error remains within ±0.3 N during stable tracking. Full article

In sudden disaster scenarios, the rapid strategy-driven task-chain construction of rescue task chains in UAV-assisted heterogeneous emergency rescue systems is critical for an effective emergency response. Such systems usually involve multiple types of rescue entities, such as reconnaissance UAVs, delivery UAVs, decision centers, and other emergency resources, which provide four-dimensional capabilities including reconnaissance, intelligence analysis, command and control, and rescue disposal. However, existing manual planning and heuristic optimization methods often suffer from low online decision efficiency in large-scale disaster scenarios and rarely examine how different task-chain construction strategies affect task-chain construction. To address this problem, this paper studies a strategy-aware rescue task-chain construction problem for UAV-assisted heterogeneous emergency rescue systems, in which heterogeneous rescue entities are mapped to reconnaissance, intelligence-analysis, command, and disposal nodes according to their four-dimensional capabilities under capability, distance, cost, availability, and strategy constraints. The problem is formulated as a constrained combinatorial optimization problem, and eight task-chain construction strategies are designed from three dimensions: command mode, information-sharing scope, and disposal mode. To solve this problem, a deep reinforcement learning framework integrating a pointer network and entropy-regularized advantage actor–critic (A2C) is proposed. The pointer network generates variable-length entity-selection sequences for task chains, while entropy-regularized advantage actor–critic (A2C) optimizes the selection policy using an entropy-regularized objective that considers success probability, execution cost, and resource utilization. Experiments across 72 scenarios show that the proposed method outperforms the genetic algorithm in terms of timeliness and overall multi-indicator performance, especially in large-scale rescue scenarios. The results further indicate that distributed command strategies achieve a more balanced and robust performance, providing practical guidance for strategy selection and resource scheduling in UAV-assisted heterogeneous emergency rescue. Specifically, compared with the genetic algorithm under the full-scale 72-scenario evaluation, the proposed method improves the cost-control indicator E2 by 51.2% and the topological indicators E4, E5, E6, and E7 by 86.8%, 346.5%, 375.7%, and 421.5%, respectively, all while reducing the online decision time from 36.5 s to 3.0 s (a 91.8% speedup). The genetic algorithm still outperforms the proposed method on the success-rate indicator E1 and entity-utilization indicator E3, which is discussed as a limitation of the proposed method in the discussion of applicability. Full article

Human detection in drone imagery is an important capability for applications that require information on the location and number of people, including search and rescue, urban pedestrian-flow analysis, crowd-density assessment, and public-space safety management. However, in oblique drone imagery, acquiring and annotating sufficient real data remains difficult because of crowd density, privacy constraints, background clutter, and position-dependent scale variation. This study proposes a copy-and-paste-based synthetic data augmentation procedure for imagery acquired using a DJI Phantom 4 RTK under a 30° camera pitch angle and a 30 m flight altitude, following DJI search-and-rescue observation guidelines. The proposed procedure combines human-free drone background images with a public full-body human dataset and incorporates perspective-based geometry-aware scaling, context-aware placement, and density-matched chip selection based on real person-count distributions. Three YOLO-family detectors (YOLOv8x, YOLO11x, and YOLO26x) and the Transformer-based RT-DETR-X were evaluated at three real-data proportions (10%, 25%, and 100%). Across these twelve combinations, four augmentation strategies (random, geometry-, context-, and combined geometry- and context-aware augmentation) were compared under the same 50% synthetic ratio. For the YOLO family, geometry-aware augmentation improved mAP50-95 in eight of the nine combinations and achieved a mean gain of +0.64 percentage points over the real-only baseline, with the largest gain of +1.99 percentage points under the YOLO11x 25% real-data condition. For RT-DETR-X, the corresponding changes were +0.23, −1.83, and −0.15 percentage points at 10%, 25%, and 100% real data, respectively. These results suggest that, under the acquisition conditions evaluated in this study, aligning pasted person size with the image-position-dependent perspective scale can improve the effectiveness of synthetic augmentation when the chip-level person-count distribution is controlled to match that of the real training data. Full article

The rapid expansion of autonomous microsystems, implantable sensors, wireless sensor nodes, Internet-of-Things devices, distributed electronics, and heterogeneous system-on-chip platforms has intensified the demand for compact electrochemical energy-storage systems that can be integrated directly with microfabricated devices. Among the various negative electrode materials, silicon is particularly attractive for miniaturized lithium-ion batteries because of its high theoretical lithium-storage capacity, abundance, compatibility with mature semiconductor processing, and direct availability as both an active material and a structural platform. However, the practical implementation of silicon anodes in on-chip and miniaturized batteries remains difficult because lithiation-induced volume expansion, fracture, unstable solid-electrolyte interphase formation, loss of electrical contact, and process-integration constraints become more severe as the battery footprint is reduced to the microscale. In contrast to conventional slurry-cast silicon electrodes, silicon anodes for microbatteries can exploit microfabrication strategies such as thin-film deposition, photolithography, deep reactive ion etching, metal-assisted chemical etching, nanoimprint lithography, laser patterning, template-assisted growth, atomic layer deposition, and wafer-level encapsulation. These methods enable deterministic control over electrode geometry, areal loading, porosity, current-collector contact, diffusion length, mechanical compliance, interfacial chemistry, and compatibility with complementary metal-oxide-semiconductor and microelectromechanical-system platforms. This review summarizes the recent progress in microfabrication strategies for silicon anodes in on-chip and miniaturized batteries, emphasizing the relationship between the process route, electrode architecture, mechanical stability, electrochemical performance, and manufacturability. Full article

Energy management in ship integrated power systems (IPSs) requires real-time dispatch of diesel generators, battery storage, and shore power under strict operational constraints. Existing deep reinforcement learning (DRL) approaches are economically competitive but cannot guarantee that device constraints are satisfied during training or deployment. This paper proposes MA-SRL, a safe reinforcement learning framework for ship IPSs that couples an execution-layer Safe Projection Layer (SPL) with a training-stage Lyapunov-based policy update. The SPL projects each raw action onto the feasible set of the current mode before execution, whenever that set is non-empty, and quantifies the departure as a constraint-cost signal that drives a Mode-Dependent Constrained Markov Decision Process (MD-CMDP), making feasibility observable to the learner. The Lyapunov update is designed to control the expected discounted constraint cost through a budget condition during training. Under the nominal scenario, this signal drives the raw policy close to the feasible set, lowering constraint violation by 2.2–6.0× and cost by 7.7–16.9% over DRL baselines, with the lowest constraint violation retained under storm conditions. The same architecture and settings are replicated on a second vessel, plant, route, and operating profile, attaining the lowest cost and constraint violation of all compared methods, and sustaining the lowest distance shortfall and unmet load under a single-generator-loss contingency. Full article

Foot-mounted inertial pedestrian navigation is an attractive infrastructure-free solution for pedestrian positioning in environments where Global Navigation Satellite System (GNSS) signals are unavailable, degraded, or intentionally excluded. However, mixed-gait and multi-level motion expose two persistent failure modes: unreliable zero-velocity detection during high-dynamic motion and weak yaw observability after standard zero-velocity-aided filtering. This paper presents a probability-driven adaptive foot-mounted inertial navigation framework that couples a lightweight motion-probability front-end with probability-weighted mode-adaptive zero-velocity detection, local straight-heading constraints, and probability-gated stair-height updates. A 335-dimensional motion-feature LightGBM classifier first outputs six gait probabilities from 128-sample windows of six-axis inertial data. These probabilities continuously determine the zero-velocity detector threshold and the reliability of heading and vertical pseudo-measurements. For planar navigation, a motion-aware local straight-heading constraint (MA-LSHC) establishes local reference headings only over curvature-safe straight segments, and an adaptive heading covariance controls the strength of each heading pseudo-measurement. For stair navigation, height changes are quantized into integer-riser increments only when stair probabilities and vertical inertial evidence agree. All data were collected in outdoor pedestrian environments, and the evaluation includes four parts: motion classification, training and detector diagnostics, closed rectangular planar navigation, and multi-level stair-height reconstruction. The motion-feature classifier achieves 96.24% accuracy and 96.17% macro-F1. In a mixed-gait closed rectangular route, the proposed method obtains a 1.09 m closure error and a 0.68% relative closure error, outperforming fixed SHOE-ESKF, Wang-AWGF, Guo-SIHDC, and Deng-HDR baselines. In stair experiments, the proposed method achieves a grid-node RMSE of 0.13 m and correctly identifies 38 ascending and 38 descending risers. These results demonstrate that motion probabilities can be used not merely as labels, but as continuous reliability cues for adaptive inertial constraints. Full article

Spin ice provides a canonical setting in which geometric frustration, local constraints, and extensive degeneracy generate nontrivial entropy and emergent quasiparticles. In this review, we examine entropic phenomena in spin-ice materials and related pyrochlore magnets, taking entropy as a unifying theme that connects established spin-ice thermodynamics with recent developments in entropy-driven phases, monopole confinement, and interface-engineered monopole matter. We first review the connection between proton disorder in water ice and the ice-rule manifold of pyrochlore spin ice, including the Pauling residual entropy and its calorimetric observation, and then contrast this classical limit with quantum spin ice and lithographically constructed artificial spin ice. We next discuss pyrochlore iridates, where competition between the intrinsic spin-ice interaction and the Ir-induced local exchange field produces two-in–two-out (2I2O), fragmented, and all-in–all-out (AIAO) states with distinct residual entropies, as well as finite-temperature entropy-enhanced phases near competing ground-state boundaries. We next review emergent magnetic monopoles in spin ice, including monopole creation, Dirac strings, magnetic Coulomb interactions, and key experimental signatures of magnetic charge, current, and noise. Building on this framework, we discuss theoretically proposed two-dimensional magnetic monopole gases at spin-ice/pyrochlore iridate interfaces, where boundary conditions stabilize a net magnetic charge and entropy controls the spatial confinement of the monopole gas. Finally, we consider proposed monopole-based devices, including a magnetic field-controlled monopole transistor and position-based monopole traps. By linking classical residual entropy to entropy-driven phase selection, quasiparticle confinement, and device concepts, this review highlights how entropy can evolve from a thermodynamic signature of frustration into a potential design parameter for emergent magnetic matter. Full article

Intensive care units generate dense physiological, laboratory, imaging, microbiological and treatment data, yet much of the clinical reasoning that drives decisions is recorded only in free text. Over the past decade, clinical sentiment analysis has repeatedly shown that the affective tone of nursing and medical notes is associated with mortality. The incremental discrimination over established severity scores has nevertheless been small, of the order of 0.01 in the area under the receiver operating characteristic curve in the largest published intensive care cohort, and general-purpose sentiment tools transfer poorly to critical care text. We argue that this plateau reflects a misspecified prediction target rather than a limitation of natural language processing. Mortality at 28 days is not the question the clinician asks at the bedside. We propose that the clinically useful quantity is the narrative–physiology discordance score: the signed difference, expressed on a common calibrated scale, between the short-horizon deterioration risk implied by the documented assessment and the risk implied by time-aligned multimodal data. We specify this quantity formally, fix index time and forecast horizon, and set out the leakage, copy-forward and provenance controls without which retrospective performance is uninterpretable. We then treat alert burden as a design constraint rather than a post hoc observation, deriving the operating threshold from an explicit and context-dependent alert budget, and we outline a staged evaluation pathway aligned with TRIPOD + AI and DECIDE-AI in which discordant cases are adjudicated by clinicians. Until that adjudication has been performed, discordance is a record-level inconsistency that prompts reassessment, not evidence of missed deterioration. Clinical sentiment analysis becomes useful when it stops predicting death and starts flagging disagreement. Full article

Cryptographic dependency updates can install cleanly and pass visible regression tests while changing project-facing behavior or leaving an application inside a vulnerable version range. This study presents VeriCrypt-Agent, an evidence-grounded workflow for bounded-autonomy cryptographic maintenance. The system combines a frozen snapshot of release/advisory evidence, a public catalog of project-level executable probes, cross-version execution under old and candidate dependency versions, role-specialized LLM reasoning, and a deterministic decision gate. Automatic merging is allowed only when mandatory probes pass, policy constraints are satisfied, and the audit record is complete. On CryptoUpdate-Mini-30, ten unique package-version transitions are evaluated through 30 controlled executions. Visible tests accept all five non-mergeable transitions, while advisory-only screening accepts two of five. The deterministic All-Probes Gate performs best on this benchmark, with perfect transition-level decisions and no review cases, when all relevant probes are already known and inexpensive. The full workflow observes no unsafe auto-merges (0/5; exact 95% CI: 0.000–0.522) and routes 9/30 executions to review (0.30; exact 95% CI: 0.147–0.494). Removing grounding accepts three of five non-mergeable transitions, and a fixed probe budget accepts two of five. On 48 blinded static crypto-API audit files, including 32 unsafe cases, two-pass review detects 20/32 unsafe cases (0.625; exact 95% CI: 0.437–0.789) and reaches macro-F1 $=0.556$. These point estimates characterize evidence-backed release decisions only under the evaluated conditions; they do not establish general superiority over exhaustive deterministic testing or a deployable static vulnerability detector. Full article

Fractional-order control (FOC) represents memory-dependent, hereditary, and nonlocal dynamics by incorporating non-integer integration and differentiation operators into conventional control laws. However, the numerical realization of these operators increases the effective controller order and imposes additional computational and hardware requirements. This systematic literature review synthesizes 136 peer-reviewed studies published between 2010 and 2025 and identified through six scholarly databases and search platforms. Five focal strategy families—Fractional-Order Proportional–Integral–Derivative Control (FOPID), Fractional-Order Sliding Mode Control (FOSMC), Fractional-Order Adaptive Control (FOAC), Fractional-Order Impedance Control (FOIC), and Fractional-Order Optimal Control (FOOC)—are compared across nonlinear mechanical, biological/physiological, and aerospace/vehicle systems. The comparative analysis reveals distinct operational trade-offs. FOPID extends the conventional PID structure from three to five tunable parameters and provides the highest real-time feasibility, but its enlarged tuning space increases design complexity. FOSMC offers strong robustness and disturbance rejection but requires demanding fractional-order stability analysis and does not fully eliminate switching-induced chattering. FOAC accommodates unknown and time-varying dynamics through online adaptation but increases computational load and validation complexity. FOIC effectively captures compliant and viscoelastic force–motion interactions but involves numerous coupled impedance parameters and interaction-stability constraints. FOOC provides a powerful framework for memory-dependent multi-objective optimization but imposes the highest numerical and theoretical burden, often making direct real-time deployment impractical. Across the five strategies, increasing the order of numerical approximations improves the representation of fractional dynamics over the selected frequency range but adds internal filter states, memory requirements, arithmetic operations, and execution latency. Therefore, performance scalability is fundamentally constrained by growth in the effective state dimension and the available embedded-hardware resources. Future research should prioritize reduced-order fractional approximations, standardized benchmarks and software libraries, stability-certified adaptive architectures, and experimentally validated real-time implementations. Full article

This study proposes a human–computer interactive agent collaboration framework for reservoir dam seepage safety assessment. This is to address the large volume of safety assessment reports, inconsistent textual descriptions, low efficiency of manual statistics, and difficulty in maintaining consistent classification criteria. Using a large language model as the core for semantic understanding, the framework decomposes the report review process into subtasks including data preprocessing, knowledge retrieval, semantic classification, result feedback, and manual review. Structured collaboration among multiple agents is achieved through an “entity location-safety hazard” knowledge primary key, semantic rule constraints, and the Model Context Protocol (MCP). While preserving expert control, the system transforms original texts into aggregable and traceable risk statistics, forming a closed-loop mechanism of “machine-enabled efficiency, human oversight, and knowledge accumulation”. Application validation was conducted using more than 2000 reservoir safety assessment reports, from which 2724 valid severe-risk records were extracted. The results show that severe risks were mainly concentrated in key locations, including dam bodies, culverts, spillways, dam foundations, downstream dams, drainage facilities, and abutments. The main risk types were leakage, abnormal seepage behavior, construction quality problems, noncompliant seepage control, structural damage, and noncompliant drainage facilities. The findings indicate that the proposed framework can improve the efficiency of text processing for seepage safety assessments and the consistency of risk statistics, thereby providing intelligent support for reservoir dam hazard screening and scientific decision-making. Full article