Search Results (283)

Pumped-storage power stations (PSPSs) are vital for grid stability, yet pump-turbines (PTs) operating in the S-shaped region often induce severe hydraulic instability. To reveal the mechanism of these self-excited oscillations, this study establishes a nonlinear mathematical model based on rigid water column theory and a cubic polynomial approximation of the PT’s nonlinear characteristics. Both analytical derivations and numerical simulations were conducted. Analytical results indicate that, in the absence of surge tanks, self-excited oscillations occur when the PT’s negative hydraulic impedance modulus exceeds the pipeline impedance. With a single surge tank, the system behaves analogously to the Van der Pol oscillator, exhibiting oscillations that converge to a stable limit cycle governed by system parameters. Numerical simulations for a dual-surge-tank system further reveal that, due to initial negative damping, the PT transitions to alternative stable equilibria. Crucially, the transition direction is governed by the polarity of the initial disturbance: negative perturbations lead to the regular turbine region, while positive ones lead to the reverse pump region. Additionally, pipe friction causes the steady-state discharge to deviate slightly from the theoretical static value, with deviations remaining below 2.96%. This work provides a theoretical basis for stability prediction in PSPSs. Full article

Topaz is one of the most economically important fluorine-rich nesosilicates, which are predominantly colorless in natural crystals. Hence, the trade relies almost entirely on irradiated blue topaz with an unstable color center, which has been shown to fade over time. The cobalt (Co) diffusion treatment is a stable alternative process for converting colorless topaz to blue by a solid-state diffusion mechanism. To investigate the potential role of Co²⁺ substitution in the formation of the blue layer and the coupled behavior of F/OH dehydroxylation in facilitating this process, systematic diffusion treatments have been successfully conducted and compared. In this study, gem-quality topazes were annealed in air at 1000 °C for 20–40 h (hr) along with CoO, Fe₂O₃, Cr₂O₃, and CuO powders. The diffused products were characterized using Scanning Electron Microscope (SEM), Ultraviolet-Visible absorption spectroscopy (UV-Vis), Near-Mid Infrared spectroscopy (NMIR), and X-ray photoelectron spectroscopy (XPS). Parallel runs with CuO, Fe₂O₃, or Cr₂O₃ alone confirmed that none of these oxides produces a stable blue layer, underscoring the unique role of Co. The Co-diffused sample displays an intense blue layer characterized by a Co²⁺ octahedral isomorphism triplet at 540, 580, and 630 nm, which are absent from both untreated and heat-only controls. XPS analysis reveals the emergence of Co²⁺ (binding energy: 780.63 eV) and a concomitant depletion in F⁻, along with the disappearance of the OH overtone absorption at 7123 cm⁻¹. These observations confirm that defluorination generates octahedral vacancies accommodated by the coupled substitution: CoF₂ (solid reactant) + (AlO₂)⁻ (fragment of topaz structure) → AlOF (solid product) + (CoOF)⁻ (fragment of topaz structure). Prolonged annealing leads to decreased relative atomic percentages of K⁺ and F⁻ ions, consistent with volatilization losses during the high-temperature process, thereby directly correlating color intensity with cobalt valence state, which transfers from Co²⁺ to Co³⁺. These findings establish a Co-incorporation chronometer for F–rich aluminosilicate systems, with an optimal annealing time of approximately 20 hr at 1000 °C. Furthermore, the above results demonstrate that the color mechanism in nesosilicate gems is simultaneously governed by volatile release and cation availability. Full article

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the modelling of ice cover development in the Beauharnois Canal along the St. Lawrence River with the presence and absence of ice booms. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019–2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms. Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. Computational efficiencies of the RIVICE model were also sought. RIVICE was originally compiled with a Fortran 77 compiler, which restricted modern optimization techniques. Recompiling with NVFortran significantly improved performance through advanced instruction scheduling, cache management, and automatic loop analysis, even without explicit optimization flags. Enabling optimization further accelerated execution, albeit marginally, reducing redundant operations and memory traffic while preserving numerical integrity. Tests across varying ice cross-sectional spacings confirmed that NVFortran reduced runtimes by roughly an order of magnitude compared to the original model. A test GPU (Graphics Processing Unit) version was able to run the data interpolation routines on the GPU, but frequent data transfers between the CPU (Central Processing Unit) and GPU caused by shared memory blocks and fixed-size arrays made it slower than the original CPU version. Achieving efficient GPU execution would require substantial code restructuring to eliminate global states, adopt persistent data regions, and parallelize at higher level loops, or alternatively, rewriting in a GPU-friendly language to fully exploit modern architectures. Full article

Air conditioning loads play a critical role in maintaining the supply–demand balance of building microgrids (BMGs), yet their distributed nature and volatile response may undermine secure and stable operation. This paper proposes a day-ahead and real-time aggregated control strategy for BMG air conditioning loads with user privacy protection. First, an approximate aggregation model is developed based on building heat transfer characteristics, and the aggregated response potential is evaluated by jointly considering user comfort and willingness. Second, without sharing fine-grained user information, a Building Microgrid Operator (BMO)–Load Aggregator (LA) day-ahead distributed-scheduling model is formulated and solved using the alternating direction method of multipliers (ADMM). Finally, to address load fluctuations caused by heterogeneous initial indoor temperature distributions, a real-time control strategy based on State-Queueing (SQ) temperature-state pre-transfer is proposed. Case studies show that, compared with the baseline scheme, the proposed method reduces the system operating cost from CNY 50,694.58 to CNY 47,131.64, a 7% decrease, and decreases load shedding from 1466.35 kWh to 257.31 kWh, an 82% decrease. Meanwhile, the real-time control effectively suppresses power fluctuations in the early control stage, thereby improving both economic performance and response smoothness. Full article

Wildfire regimes are undergoing rapid transformation under anthropogenic climate change, with major implications for biodiversity, carbon cycling, and ecosystem resilience. This systematic review synthesizes findings from 42 studies across global, continental, and regional scales to assess emerging patterns in fire frequency, intensity, and seasonality, and to identify climatic, ecological, and anthropogenic drivers shaping these changes. Across biomes, evidence shows increasingly fire-conducive conditions driven by rising temperatures, vapor-pressure deficit, and intensifying drought, with climate model projections indicating amplification of extreme fire weather this century. Boreal ecosystems show heightened fire danger and carbon-cycle vulnerability; Mediterranean and Iberian regions face extended fire seasons and faster spread rates; tropical forests, particularly the Amazon, are shifting toward more flammable states due to drought–fragmentation interactions; and savannas display divergent moisture- and fuel-limited dynamics influenced by climate and land use. These results highlight the emergence of biome-specific fire–climate–fuel feedback that may push certain ecosystems toward alternative stable states. The review underscores the need for improved attribution frameworks, integration of fire–vegetation–carbon feedback into Earth system models, and development of adaptive, regionally tailored fire-management strategies. Full article

Biological decision-making under uncertainty relies on somatic markers, which are affective signals that bias choices without exhaustive computation. This study biomimetically translates the Somatic Marker Hypothesis (SMH) into synthetic somatic markers (SSMs), a minimal and interpretable evaluative mechanism that assigns a scalar valence to compressed environmental states in the high-dimensional discrete grid-world Pixelverse, without modelling subjective feelings. SSMs are implemented as a lightweight Python routine in which agents accumulate valence from experience and use a simple threshold rule (θ = −0.5) to decide whether to keep the current trajectory or reset the environment. In repeated simulations, agents perform few resets on average and spend a higher proportion of time in stable “good” configurations, indicating that non-trivial adaptive behaviour can emerge from a single evaluative dimension rather than explicit planning in this small stochastic grid-world. The main conclusion is that, in this minimalist 3 × 3 Pixelverse testbed, SMH-inspired SSMs provide an economical and transparent heuristic that can bias decision-making despite combinatorial state growth. Within this toy setting, they offer a conceptually grounded alternative and potential complement to more complex affective and optimisation model. However, their applicability to richer environments remains an open question for future research. The ethical implications of deploying such bio-inspired evaluative systems, including transparency, bias mitigation, and human oversight, are briefly outlined. Full article

With the intensification of global environmental challenges and the growing demand for sustainable agricultural transformation, understanding how environmental regulation shapes enterprise innovation has become increasingly important. This study examines the impact of command-and-control environmental regulation on green innovation in agricultural enterprises using panel data from agriculture-related enterprises listed on the Shanghai and Shenzhen A-share exchanges. The analysis focuses on the period 2012–2021, which is characterised by relatively stable environmental regulation and reliable data, providing a consistent empirical context for assessing the effects of command-and-control environmental regulation. By analyzing the characteristics of command-and-control environmental regulation and green innovation in agricultural enterprises, this research constructs and estimates a two-way fixed effects model, a moderating effects model, a mediating effects model, and a spatial Durbin model to explore both direct and spillover effects. The empirical results show that the following findings: (1) Command-and-control environmental regulation significantly promotes green innovation in agricultural enterprises, and this effect remains robust across alternative measurements and model specifications. (2) Heterogeneity analysis indicates that the direct effect of command-and-control environmental regulation is most pronounced in eastern regions, non-state-owned enterprises, and enterprises with weaker environmental, social, and governance performance. (3) Moderation analysis shows that agricultural industrial coordination and executive green cognition significantly strengthen the positive relationship between command-and-control environmental regulation and green innovation in agricultural enterprises. (4) Mediation analysis demonstrates that green management costs serve as a partial mediator in this relationship. (5) Spatial analysis reveals that spatial correlation patterns are evolving over time, with significant positive spillover effects observed among geographically and economically adjacent regions. The findings provide theoretical and empirical evidence to inform the design of coordinated environmental regulation frameworks that effectively stimulate green innovation and foster sustainable agricultural development. Full article

Microalgae are photosynthetic microorganisms capable of fixing CO₂ to produce O₂ and a wide variety of metabolites of interest. Attempts have been made to describe their growth dynamics using mathematical models; however, these models fail to fully represent the dynamics of this bioprocess. Therefore, achieving maximum biomass production in the shortest possible time represents a control challenge due to the nonlinear and time-varying dynamics. Some classic control strategies implemented for this bioprocess are totally or partially dependent on a mathematical model, resulting in controllers with low performance, implementation complexity, and limited robustness. This is where the Virtual Reference Feedback Tuning (VRFT) approach becomes relevant, as it is a model-free control strategy. VRFT is based on the iterative generation of a virtual reference with the aim of minimizing steady-state error, without requiring an explicit model of the bioprocess. Its implementation involves the collection of experimental data in open loop, the minimization of a cost function in closed loop, and the linearization of the system around a stable equilibrium point. This work presents the design and implementation of a VRFT-based control strategy applied to the closed cultivation of the microalga Scenedesmus obliquus UTEX 393 in three flat photobioreactors at laboratory scale. The variables controlled using this strategy were temperature, photosynthetically active light intensity, and level. The experimental results showed that the pre-established references were met. A steady-state temperature of 25 ± 0.625 °C, a PAR (Photosynthetically Active Radiation) light intensity of 100 ± 5 µmol·m⁻²·s⁻¹, and level control that ensured a constant volume of the culture medium were achieved. This suggests that VRFT is a viable control alternative for this type of bioprocess under nominal conditions. Full article

Alternative splicing (AS) is a fundamental mechanism governing transcriptomic diversity and cellular identity. Although 293T (human embryonic kidney) and A549 (human lung adenocarcinoma) cell lines are widely used, cell-type-specific splicing dynamics—including responses to receptor overexpression—remain incompletely characterized. To address this, we integrated Oxford Nanopore long-read sequencing with BGI short-read data to profile transcriptomes under both basal and GPCR-overexpressing conditions (ADORA3 in 293T; P2RY12 in A549). Full-length isoform analysis using FLAIR and SQANTI3 revealed extensive transcriptomic complexity, including 18.02% novel isoforms in 293T and 19.52% in A549 cells. We found that 293T cells exhibited a stable transcriptome architecture enriched in splicing-related pathways, whereas A549 cells underwent broader transcriptional remodeling linked to tumorigenic processes. These findings suggest that 293T cells may be a suitable model for investigating splicing regulation, while A549 cells could serve as a relevant system for exploring tumor-related transcriptome dynamics. Our work elucidates context-dependent AS regulation and underscores the value of integrating long-read sequencing with FLAIR/SQANTI3 for dissecting cell-state-specific transcriptome dynamics. Full article

Hydrofoil vessels have recently re-gained interest, presenting a more efficient and comfortable alternative to regular vessels. However, research in hydrofoils is limited by the high cost and complexity of developing a complete vessel to use for experimental testing. This paper presents a novel low-cost, small autonomous hydrofoil that can be used to research different hydrofoil hardware and configurations, control routines for autonomous balancing while foiling, and autonomous driving algorithms for hydrofoil crafts. A modular design with low-cost, off-the-shelf electronics is proposed and developed. Three independent PID control loops were implemented and validated, enabling the boat to remain stable in a foilborne state. An onboard GPS was used to implement autonomous driving, allowing the boat to navigate between GPS waypoints in a river. Experimental testing of the vessel indicated suitability as a low-cost, easy-to-modify, and easy-to-use hydrofoil test bed. Future research should focus on aspects of the mechanical design, investigating new control methodologies to improve performance, and investigating the efficiency gains and feasibility of performing long-range autonomous missions. Full article

Ship maneuverability models are typically defined by three degrees of freedom: surge, sway, and yaw. However, patrol vessels operating in riverine environments often exhibit significant roll motion during course changes, necessitating the inclusion of this dynamic. This study develops interpretable machine learning models capable of predicting vessel behavior in four degrees of freedom (4-DoF): surge, sway, yaw, and roll. A dataset of 125 h of simulated maneuvers was employed, including 29 h of out-of-distribution (OOD) conditions to test model generalization. Four models were implemented and compared over a 15-step prediction horizon: linear regression, third-order polynomial regression, a state-space model obtained via the N4SID algorithm, and an AutoRegressive model with eXogenous inputs (ARX). Results demonstrate that all models captured the essential vessel dynamics, with the state-space model achieving the best overall performance (e.g., NMSE = 0.0246 for surge velocity on test data and 0.0499 under OOD conditions). Variable-wise, surge and sway showed the lowest errors, roll rate remained stable, and yaw rate was the most sensitive to distribution shifts. Model-wise, the ARX model achieved the lowest NMSE for surge prediction (0.0149), while regression-based models provided interpretable yet less accurate alternatives. Multi-horizon evaluation (1-, 5-, 15-, and 30-step) under OOD conditions confirmed a consistent monotonic degradation across models. These findings validate the feasibility of using interpretable machine learning models for predictive control, autonomous navigation, and combat scenario simulation in riverine operations. Full article

Soil degradation and climate-driven stress increasingly compromise crop performance by disrupting microbial communities and weakening soil biological functions. Microbial consortia such as Effective Microorganisms (EM) are widely adopted as nature-based solutions to enhance soil health and plant productivity, yet it remains unclear whether their biostimulant effects arise primarily from microbial activity or from the metabolites they release. This study aimed to disentangle these contributions by comparing the effects of EM and its cell-free extract (EM Extract) on zucchini (Cucurbita pepo L.), grown under controlled conditions. Growth parameters and pigment composition were quantified through morphological and spectrophotometric analyses, while soil microbial communities and metabolic profiles were characterized using metabarcoding and high-resolution FTIR-based soil metabolomics. Both EM and EM-derived cell-free extracts significantly enhanced zucchini growth, increasing plant height, biomass, chlorophyll content and root development. Cultural-based microbial analyses showed complementary shifts in rhizosphere communities, yet no major taxonomic differences were detected. Consistently, both treatments induced similar metabolomic changes in bulk and rhizosphere soils, resulting in a shared functional state shaped by plant inputs. These results suggest EM extract as a stable and effective alternative to live microbial inoculants for sustainable crop bio stimulation. Full article

This study addresses the challenge of electric vehicle power battery recycling by proposing a dynamic deposit-refund system (DRS) under the Extended Producer Responsibility (EPR) framework, as an alternative to the conventional static DRS. An evolutionary game model is developed to capture the strategic interactions between local governments and responsible enterprises, incorporating a feedback mechanism where the deposit level is dynamically adjusted based on corporate EPR fulfillment rates. Using system dynamics simulation, the evolutionary paths under both static and dynamic DRS regimes are compared. The results demonstrate that the dynamic DRS effectively eliminates persistent oscillations and guides the system toward a stable equilibrium. Furthermore, by defining an ideal scenario, key factors are identified and prioritized to assist the government in steering the system toward this desired state. These findings offer actionable insights for designing adaptive regulatory mechanisms and fostering a self-sustaining battery recycling ecosystem. Full article

Deep learning methods based on convolutional neural networks (CNNs) and Mamba have advanced medical image segmentation, yet two challenges remain: (1) trade-off in feature extraction, where CNNs capture local details but miss global context, and Mamba captures global dependencies but overlooks fine structures, and (2) limited feature aggregation, as existing methods insufficiently integrate inter-layer common information and delta details, hindering robustness to subtle structures. To address these issues, we propose a hybrid cascade and dual-path adaptive aggregation network (HCDAA-Net). For feature extraction, we design a hybrid cascade structure (HCS) that alternately applies ResNet and Mamba modules, achieving a spatial balance between local detail preservation and global semantic modeling. We further employ a general channel-crossing attention mechanism to enhance feature expression, complementing this spatial modeling and accelerating convergence. For feature aggregation, we first propose correlation-aware aggregation (CAA) to model correlations among features of the same lesions or anatomical structures. Second, we develop a dual-path adaptive feature aggregation (DAFA) module: the common path captures stable cross-layer semantics and suppresses redundancy, while the delta path emphasizes subtle differences to strengthen the model’s sensitivity to fine details. Finally, we introduce a residual-gated visual state space module (RG-VSS), which dynamically modulates information flow via a convolution-enhanced residual gating mechanism to refine fused representations. Experiments on diverse datasets demonstrate that our HCDAA-Net outperforms some state-of-the-art (SOTA) approaches. Full article

This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability. Full article