Search Results (6,121)

Anacamptis coriophora is a threatened tuberous orchid of the Irano-Turanian calcareous grasslands, subject to unsustainable harvesting for salep, increasing pressure from habitat loss and climate change. Asymbiotic in vitro culture is the principal method for its ex situ propagation; however, nitrogen nutrition, which is a decisive determinant of early morphogenesis, remains poorly optimized for Iranian terrestrial orchids, whose dust-like, endosperm-free seeds require external nutritional support for germination. Using a modified Malmgren basal medium, we investigated how nitrogen form and concentration affected asymbiotic seed germination and early seedling development in A. coriophora. Organic nitrogen (Aminoven at 25%, 50%, and 75% of full strength) was supplied in combination with inorganic nitrogen (KNO₃ and (NH₄)₂SO₄ in four combinations (balanced-low, nitrate-dominant, ammonium-dominant and high-inorganic). Germination declined markedly from 66% under balanced-low treatment to 13% under high inorganic nitrogen treatment, whereas organic nitrogen consistently enhanced germination across all inorganic backgrounds. Increasing Aminoven significantly promoted shoot elongation and biomass accumulation under low and moderate inorganic nitrogen levels but had little effect under high inorganic nitrogen concentrations. Root growth was enhanced by organic nitrogen across all inorganic treatments, whereas plantlet diameter increased under ammonium-dominant conditions, a signature of stress-induced radial swelling rather than enhanced vigor. Under the optimal medium (75% organic nitrogen, with low inorganic nitrogen), germination rate, shoot length, root length and fresh biomass reached 72%, 12.2 mm, 6.4 mm, and 44 mg plantlet⁻¹, respectively. These findings provide a practical and scalable propagation protocol for this pharmaceutically and ecologically valuable Iranian orchid, thereby supporting conservation strategies aimed at mitigating salep overharvesting and environmental degradation. Full article

Surface slag inclusions on aluminum ingots in high-temperature casting lines often appear in dense clusters. Under poor imaging conditions and with ambiguous defect boundaries, annotators frequently disagree on the location, number, and scale of defects, which limits the performance of conventional vision-based detection methods that rely on deterministic labels. To address this issue, this study proposes a Multi-source Annotation Uncertainty Fusion (MAUF) method. First, a spatial clustering and splitting strategy is introduced to decouple controversial labels from multiple sources into independent regions. Then, a controversy degree metric is defined to convert hard labels into soft supervision signals. Finally, a weighted loss function is designed to dynamically balance the model’s attention between high-controversy and low-controversy regions. In addition, to reduce the evaluation bias caused by traditional Intersection over Union (IoU)-based matching in this scenario, an area-based evaluation metric is developed. Experiments on a real aluminum ingot production dataset show that MAUF achieves an F1low of 48% at a confidence threshold of 0.3, improving by 9.84–26.32% over comparative methods in low-controversy regions while maintaining a moderate coverage rate of about 20% in high-controversy regions. The method also shows strong robustness across different confidence thresholds, with F1low remaining around 48%, whereas competing methods fluctuate more noticeably. Overall, MAUF provides a robust solution for defect detection in noisy industrial environments and offers a useful reference for handling multi-source annotation uncertainty. Full article

This study takes the pumped hydro storage (PHS) in arid areas—where the irrigation reservoir in the piedmont plain serves as the lower reservoir—as the research object. It establishes a model framework for the evolution of heat flux in the entire water body of the upper and lower reservoirs, while systematically considering the impacts of power station operation and climate changes in the upper and lower reservoirs on the heat flux and evaporative loss of the irrigation reservoir’s water body. The results show that on a monthly time scale, under the coupled effect of power station operation and climate change, the percentages of each heat flux on the water surface of the upper and lower reservoirs relative to the total heat flux on the entire water surface exhibit significant differences. Both the sensible heat flux and latent heat flux on the water surface of the upper reservoir are higher than those on the water surface of the lower reservoir. The energy balance ratio of each simulated heat flux ranges from 0.98 to 1.03, and the coefficient of residual ranges from 0.033 to 0.060, indicating good performance of the experimental model simulation. The physical framework provides reference significance for the design and operation management of PHS. Full article

Chaos injection–perturbing numerical flow features with a sequence drawn from a chaotic system–has been proposed as a robustness stress test for IoT intrusion detection, but published instances fix a single generator, a single train/test split, and a small classifier pair. We generalize the injection framework to an arbitrary chaotic source and evaluate six generators (logistic, tent, Hénon, Lorenz, Rössler, Chua) against six classifier families across four injection-severity scenarios and eight train/test splits, on a class-balanced working set of 239,981 flows (sub-sampled from 303,082 flows drawn per directory) from all 34 class directories of the public CICIoT2023 corpus. Across 1152 evaluation cells, three findings emerge. First, generator choice produces a statistically decisive but small effect: pooled F1 degradation spans only 5.57–6.11% across the six generators (a factor of 1.10), and the discrete-versus-continuous distinction has no explanatory value. Second, lag-1 autocorrelation does not predict degradation ($r=+0.209$, $p=0.692$), whereas the marginal magnitude of the shaped perturbation does (mean $\left|\psi \right|$: $r=+0.974$, $p=0.001$). Because the operator applies its sequence index-wise to randomly enumerated test rows, we prove that, averaged over that random enumeration, the distribution of the evaluation outcome depends on the sequence only through its empirical marginal (Proposition 1); for the single fixed enumeration of a given split, no such invariance holds in general, and the claim there is empirical. Under the protocol tested, chaos injection therefore behaves as an amplitude stress test rather than a test of chaotic structure. A 4608-cell control grid provides the empirical evidence: permuting the chaotic sequence, or replacing it with i.i.d. noise of matched marginal, changes mean degradation by $-0.002$ and $+0.005$ percentage points, inside a pre-declared equivalence bound of $\pm 0.25$ pp; matching all six generators to a common empirical CDF collapses the between-generator spread by $71.6\%$ but leaves a small, statistically detectable residual generator effect. Third, the three tree ensembles are the least robust family (7.75% versus 3.81%; Wilcoxon $p=0.0078$, paired t-test $p=2.9\times {10}^{-8}$), reversing the prior single-generator ranking, and the ranking is itself conditional on injection severity. This family ordering is not a threshold artifact: it reproduces on threshold-free AUC degradation (6.80% versus 3.44%) and on absolute F1 loss in percentage points (6.56 versus 3.10), and a cluster-respecting reanalysis that treats the eight seeds and six classifiers as clustered rather than independent reduces the generator effect to a single high-amplitude generator separating from the other five. Code, seeds, and raw logs are released openly. Full article

Insulator defect detection in transmission lines suffers from complex background interference and insufficient small-target recognition accuracy. To address these challenges, this paper proposes an SP-YOLO insulator defect detection algorithm based on an improved YOLOv11n model. The algorithm improves upon YOLOv11n as the baseline framework. A CP-Block module, composed of the CBAM attention mechanism and pinwheel convolution, is embedded into the backbone to enhance small-target recognition and background anti-interference capability; a GOLD-YOLO-APF neck network is designed to improve cross-layer feature fusion and transmission efficiency; and a Focaler–ShapeIoU loss function is proposed, which integrates the Focal concept with ShapeIoU to focus on hard samples while optimizing target-contour perception and bounding-box regression accuracy. Experimental results show that each optimization sub-module improves the overall model performance. After integrating all modules, the model achieves an mAP@0.5 of 86.1% and an mAP@0.5:0.95 of 55.1%, a clear improvement over the original YOLOv11n. The proposed approach attains the highest mAP@0.5 and recall among the compared methods while achieving competitive precision, with only 6.25 M parameters and 68.9 FPS, achieving a favorable balance between accuracy and lightweight design. Full article

Distribution network reconfiguration (DNR) and distributed generation (DG) integration are effective strategies for improving the operational efficiency and voltage performance of modern power distribution systems. However, their coordinated optimization constitutes a complex multi-objective problem involving both discrete and continuous decision variables. This paper proposes a Hybrid Genetic Algorithm–Particle Swarm Optimization (HGA–PSO) framework for simultaneous distribution network reconfiguration and optimal DG placement and sizing. The proposed HGA–PSO combines the global exploration capability of the Genetic Algorithm (GA) with the fast convergence characteristics of Particle Swarm Optimization (PSO) to effectively balance exploration and exploitation. The optimization framework simultaneously minimizes active power losses and improves voltage profiles while satisfying power balance, voltage magnitude, branch current, DG operating, and network radiality constraints. The proposed algorithm was implemented in MATLAB R2024a and evaluated using the Institute of Electrical and Electronics Engineers (IEEE) 33-bus distribution network and the IEEE 118-bus benchmark system. The IEEE 33-bus system is used to validate the proposed DNR and DG methodology, while the IEEE 118-bus system is used as a computational benchmark to evaluate the performance of the optimization framework on a larger-scale optimization problem. For the IEEE 33-bus system, active power losses were reduced by 52.22%, while the minimum bus voltage improved from 0.913 p.u. to 0.982 p.u. For the IEEE 118-bus benchmark system, active power losses were reduced by 25.75%, and the minimum bus voltage increased from 0.869 p.u. to 0.966 p.u. The results demonstrate the effectiveness of the proposed HGA–PSO framework under the investigated benchmark systems, modelling assumptions, and simulation conditions. Full article

Ferroptosis is an iron-dependent form of regulated cell death that has emerged as a potential vulnerability in cancer cells under therapeutic stress. However, the mechanisms by which colorectal cancer cells modulate ferroptosis susceptibility remain incompletely understood. In this study, we investigated the role of CWH43, a glycosylphosphatidylinositol-anchor biogenesis-related protein whose role in chemotherapy response has been unclear, in regulating redox balance and ferroptosis-associated vulnerability in colorectal cancer. Integrative analyses of clinical datasets, transcriptomic profiling, and functional experiments were performed using gain- and loss-of-function approaches in CRC cell models. We found that elevated CWH43 expression was associated with adverse clinical outcomes and reduced responsiveness to chemotherapy. Functionally, CWH43 attenuated ferroptosis-associated vulnerability by reducing intracellular reactive oxygen species and lipid peroxidation. Mechanistically, CWH43 promoted redox-adaptive programs involving activation of NRF2-dependent antioxidant signaling, upregulation of the cystine transporter SLC7A11, increased intracellular glutathione availability, and suppression of lipid peroxidation. Genetic or pharmacological disruption of SLC7A11 restored ferroptosis susceptibility and sensitized CWH43-overexpressing colorectal cancer cells to therapeutic stress, whereas ferrostatin-1 partially restored cell viability following erastin-containing treatments. Collectively, these findings identify CWH43 as a regulator of redox adaptation that modulates ferroptosis-associated vulnerability in colorectal cancer, providing insights into stress-adaptive survival mechanisms and chemotherapy resistance in cancer cells. Full article

Reliable personnel detection in underground mines is essential for safe intelligent mining, but uneven illumination, dust occlusion, thermal interference, and cross-modal parallax can degrade RGB-T perception. We propose an Environment-Driven Fusion and Optimization Network (EnvFONet), which uses environmental degradation as a unified prior for feature representation, multimodal fusion, and training optimization. First, Dynamic Receptive Field Relaxation (DRFR) adaptively interpolates original and locally smoothed thermal features to balance fine-detail preservation and contextual robustness. Second, an Env-AdaIN-guided spatially gated fusion module applies environment-conditioned residual affine modulation and high-frequency pixel-level weighting to reduce cross-modal statistical drift and parallax-induced boundary artifacts. Third, Environment-Adaptive Relaxation Loss (EARL) adjusts regression supervision for difficult matched samples according to environmental degradation and localization quality. On the self-collected Anshan underground iron-ore mine dataset, EnvFONet achieves 96.8% mAP50, 63.9% mAP75, and 62.8% mAP50:95. On the public LLVIP benchmark, it achieves 94.4% mAP50 and 59.6% mAP50:95. These results show that environment-conditioned fusion and optimization improve RGB-T personnel detection robustness under the evaluated degraded conditions. Full article

The retinal pigment epithelium (RPE) plays a key role in age-related macular degeneration (AMD). With aging, bisretinoids such as A2E accumulate in RPE lysosomes, and this accumulation is markedly accelerated by ABCA4 loss of function, which impairs the clearance of retinaldehyde from photoreceptor disc membranes. A2E acts as a blue-light photosensitizer, generating reactive oxygen species that cause oxidative stress, lysosomal impairment, and mitochondrial dysfunction. This oxidative burden disrupts the balance between Nrf2-mediated antioxidant defense and NF-κB-driven inflammation. Astaxanthin, a marine-derived xanthophyll carotenoid, may regulate this balance by activating Nrf2/ARE-mediated cytoprotective responses and suppressing NF-κB-dependent inflammation. This review summarizes mechanistic, preclinical, and clinical evidence for astaxanthin’s protective effects on RPE cells, with emphasis on bisretinoid-induced retinal injury. Astaxanthin reduces oxidative stress, inhibits NF-κB activity, and enhances Nrf2-dependent antioxidant defenses in hydrogen peroxide-, light-, and ischemia-induced retinal damage; however, because these models do not directly reproduce A2E-mediated phototoxicity, A2E-specific protection remains an untested hypothesis. Standardized A2E-specific models are needed to determine whether these protective effects extend to A2E and its photoproducts. Clinical evidence is also limited and largely based on multi-carotenoid formulations, which prevents the isolation of the effects of astaxanthin. Full article

This paper proposes event-triggered pinning graph reinforcement learning (EPGRL) for cooperative ramp merging in mixed traffic with connected and automated vehicles (CAVs) and human-driven vehicles. EPGRL represents vehicle interactions as a time-varying directed graph and uses a dual-stream graph encoder with temporal updating to capture evolving traffic couplings. A Lyapunov-based trigger activates cooperation when a stability-oriented surrogate index indicates loss of contractivity, while topology-aware pinning allocates control to influential CAVs. A PPO policy generates longitudinal and lateral actions, which are filtered by a safety shield. SUMO experiments across traffic demands and CAV penetration rates show that EPGRL improves travel time, fuel use, CO${}_2$ emissions, and system-level time-to-collision relative to uncontrolled, infrastructure-based, and learning-based baselines. Event-timing and spatial analyses characterize the distribution of cooperative updates, while the control-update reduction rate ranges from 73.1% to 79.8% across representative settings. Ablation experiments further show that graph modeling, instability-aware triggering, and pinning selection make complementary contributions. These results indicate that selective, state-dependent cooperation can balance mobility, safety, energy use, and communication cost in simulated mixed-traffic merging. Full article

High-voltage direct current (HVDC) transmission systems face protection challenges due to the absence of natural current zero-crossings and the rapid rise of fault currents. This paper proposes a hierarchical fault detection and classification framework for multi-terminal HVDC networks that combines transient feature extraction with gradient boosting decision trees. The proposed method utilizes traveling wave transients distinguish between internal and external faults across a range of fault resistances and noise levels. The three-stage architecture employs asymmetric loss weighting to maintain system security alongside dependability for high-impedance fault detection. When evaluated on a four-terminal VSC-HVDC benchmark network, the protection scheme achieved zero false trips against normal operational fluctuations and remote pole-to-ground disturbances, alongside 92.24% overall dependability across fault resistances up to 500 Ω and noise levels down to 20 dB SNR. For cumulative fault resistances up to 150 Ω, the dependability exceeded 99%. Furthermore, the framework demonstrates generalization across measurement noise and provides interpretability through feature importance analysis. The proposed framework offers a data-driven approach for HVDC protection that balances the requirements of relay security and dependability. Full article

The paper has re-modeled a nonlinear differential equation to investigate the relationship among human population growth, carbon dioxide accumulation, forest biomass depletion, and ozone-depleting gases. The stability around the equilibrium point of the system is analyzed both locally and globally. The study shows the nature of the stability state characterized by the high pollution rates and loss of the forest resources. In response, control measures are designed to limit emissions and conserve forests, which are formulated and examined in the context of optimal control theory. The Pontryagin maximum principle is used to derive the necessary conditions for optimality, and sensitivity analysis is used to investigate the effect of important parameters. Numerical simulations are performed to verify the outcomes. The study proves that proper intervention strategies are necessary to create a balance between the environment and human well-being. Full article

Cloud-model hyper-entropy can be overestimated when a scale-inflated subpopulation broadens the tails. We develop a quantile-based estimator that removes location and common scale before recovering cloud shape. Influence normalization balances the selected contrasts, and a prespecified correction addresses centered scale inflation. The theory proves global injectivity of the shape-contrast map for every positive shape ratio, establishes local stability under data and numerical-score perturbations, derives a mixed-rate limit for the singular cloud center, and gives boundary-projected and fixed-positive finite-step limits for the contamination correction. Independently checked numerical integration supports the reference calculations. Simulations and real-data-calibrated semi-synthetic experiments show a clean-sample efficiency cost and improved recovery under moderate scale inflation. A controlled loss ablation attributes the observed gains to the structural construction and weighting rather than active Huber clipping. Benchmark applications illustrate how the estimator can be integrated into a cloud-clustering workflow. Full article

Coordinatively unsaturated metal sites (CUSs) are central to the adsorption, electron transfer, and oxidant-activation steps that make metal–organic-framework (MOF)-derived catalysts attractive for water purification. Their open coordination environment, however, also creates an intrinsic durability problem: the sites that bind peroxides, periodate, sulfite, ozone, oxygen, or target pollutants can be competitively occupied by natural organic matter, phosphate, carbonate, chloride, transformation products, and water itself. This review develops a site-resolved framework for interpreting fouling, deactivation, regeneration, and self-healing under realistic aqueous conditions. Evidence from Fe-, Co-, Cu-, Ni-, Ti-, Zr-, Ce-, and mixed-metal MOFs, MOF-derived carbons and oxides, single-atom catalysts, heterojunctions, and catalytic membranes is integrated to distinguish four frequently conflated phenomena: loss of pollutant conversion caused by solution-phase scavenging, reversible occupation of CUSs, irreversible rewriting of the first coordination sphere, and transport fouling at the particle or membrane scale. Particular attention is given to diagnostic evidence based on oxidant utilization, dissolved-metal balance, X-ray absorption and photoelectron spectroscopies, pore accessibility, transformation-product analysis, and continuous-flow operation. Regeneration is classified as physical cleaning, chemical reactivation, structural reconstruction, or genuine self-healing. The review concludes with quantitative recovery metrics, a matrix-ladder validation protocol, and design rules that couple local coordination chemistry with pore architecture and reactor operation. The central proposition is that durability should be reported as preservation and recoverability of a chemically identified active state, rather than inferred from a small number of batch reuse cycles. Full article

Gravimetric mass loss in polymer–biogenic filler systems is often reported as polymer biodegradation without establishing which phase of the material is lost. This study proposes and applies an analytical framework that combines FTIR identification of the starting material, a phase balance ceiling, and removable inventory kinetics to assess such claims. Post-consumer cup plastic was blended with pyroconverted cassava (Manihot esculenta) peel dextrin in four matrix/dextrin/turpentine formulations (70/10/20 to 30/50/20% w/w) and exposed for 30 days to Aspergillus niger and A. fumigatus in Rivalier moist chambers with Sabouraud dextrose broth. FTIR revealed that the material sold as polypropylene contained polystyrene: the matrix ranged from essentially polystyrene (formulation B) to polypropylene-rich (C and D), as shown by the aromatic 755/697 ${\mathrm{cm}}^{-1}$ doublet and a PS/PP spectral index spanning more than one order of magnitude. Mass loss profiles followed a first-order removable inventory model with rate constants $\kappa$ of $9.9\times {10}^{-3}$ to $67.8\times {10}^{-3}$${\mathrm{d}}^{-1}$ (nominal residual solvent scenario) and inventory half-lives of 10–70 d. The removal rate increased with dextrin content ($\rho =0.80$ for both species) with a reproducible reversal between 30 and 40% dextrin that survives explicit bounding of the specimen surface-to-volume covariate. Five of the eight conditions remained below the removable fraction ceiling even on a dry basis; the other three required residual solvent fractions of only 1.2, 1.9, and 11.9% w/w, below the 20% nominal loading, so that no observation requires degradation of the PP/PS matrix. A greenness assessment of the framework with AGREE (0.62), MoGAPI (77%), and AGSA (66.7%) places it above the confirmatory methods it is intended to gate (respirometry, high-temperature GPC), with low throughput, manual operation, and the biosafety of the fungal assay as its main remaining limitations. The framework provides a reproducible, low-burden basis for separating selective removal of biodegradable or volatile phases from evidence that can legitimately support polymer biodegradation claims. Full article