Search Results (316)

Between December 1773 and May 1775, several port cities and towns across the American seaboard participated in a “tea party” as an act of political defiance toward the recent onslaught of taxation laws implemented by the British government on American colonists. Indeed, on 19 October 1774, in Annapolis, Maryland, taxpayer Anthony Stewart was coerced by the Sons of Liberty to burn his ship to the water line to prove his patriotism to the American cause, despite his Loyalist leanings. The circumstances that led to the Patriots targeting tea as their symbol for destruction, the Bostonian group to attire themselves as Mohawks and throw boxes overboard, the multiple threats made to Customs officials and Loyalists alike, speak to the American Revolution borne of a relationship between the mechanisms of hydrocolonialism (concentrated at the Custom House and at major trade docks) and countersurveillance systems implemented by the Sons and Liberty (represented by a number of different groups) and enforced by emerging poetic forms rising with the times of revolution. This is most demonstrated in the “poet of the American Revolution,” Philip Morin Freneau, and his poetic responses to the events leading up to and during the American Revolution. Taking the example of the Annapolis Tea Party and Freneau’s poetry under the consideration of hydrocolonialism among other critical interventions, this essay will consider the push and pull of imperial surveillance and patriotic countersurveillance at the breaking point of the American Revolution, when riots between colonists over goods and taxes spoke to larger socioeconomic systems of control that remain ever present in American cultural values. Full article

This study addresses the trade-off between accuracy and efficiency in predicting the aerodynamics and wakes of large wind turbines. We developed a unified immersed boundary–actuator line framework with large-eddy simulation. The actuator line efficiently represents blade loading, while the immersed boundary method (IBM) with a wall model resolves near-blade turbulence. The solver uses a staggered Cartesian discretization and is accelerated by a hybrid CPU/GPU implementation. An implicit signed-distance geometry treatment and a ghost cell wall function based on Spalding’s law reduce near-wall grid requirements and eliminate body-fitted meshing. Flow past a three-dimensional cylinder at Re = 3900 validates the accuracy and good grid convergence of the IBM. For the wind turbine, three meshes show converged thrust and torque, with differences below 1% between the two finer grids. At the rated condition (U_∞ = 11.4 m/s), thrust and torque agree with STAR-CCM+ and FAST, with deviations of 6.3% and 1.2%, respectively. Parametric cases at 4–10 m/s show thrust and torque increasing nonlinearly with inflow, approximately quadratically, in close agreement with reference models. As wind speed rises, the helical pitch tightens, the wake broadens, and breakdown occurs earlier, consistent with stronger shed vorticity. The framework delivers high fidelity and scalability without body-fitted meshes, offering a practical tool for turbine design studies and extensible wind plant simulations. Full article

The durability of Proton Exchange Membrane Water Electrolysers (PEMWEs) under intermittent renewable power is a critical challenge for scaling green hydrogen. This study investigates the influence of solar intermittency on PEMWE performance and degradation in direct-coupled photovoltaic (PV) systems by comparing two contrasting climates: Muscat, Oman (hot-arid, high irradiance) and Brighton, UK (temperate, variable irradiance). A validated physics-based model, incorporating reversible, activation, ohmic, and concentration overpotentials along with empirical degradation laws for catalyst decay, membrane thinning, and interfacial resistance growth, was applied to hourly PV-generation data. The results indicate that Muscat’s high irradiance (985 MWh year⁻¹) produced nearly double Brighton’s hydrogen yield (14,018 kg vs. 7566 kg) and longer operational hours (3269 h vs. 2244 h), but at the cost of accelerated degradation (359.8 μV h⁻¹ vs. 231.4 μV h⁻¹). In contrast, Brighton’s cooler and more humid climate preserved efficiency (65.8% vs. 59.8%) and reduced degradation, although higher daily cycling and seasonal variability constrained total output. The findings reveal a climate-dependent trade-off: hot, stable regions maximise hydrogen productivity at the expense of lifespan, whereas variable, cooler climates extend durability but limit yield. By explicitly linking intermittency to performance and ageing, this work provides a location-specific assessment of PEMWE feasibility, supporting design and operation strategies for renewable hydrogen deployment. Full article

This study exploits the promulgation of China’s E-commerce Law in 2009 as a quasi-natural experiment to construct a difference-in-differences (DID) model, examining the impact and mechanisms of digital economy governance on corporate cost stickiness. Using Chinese-listed manufacturing companies from 2013 to 2020 as research samples, we find that the implementation of the E-commerce Law significantly reduces corporate cost stickiness. Mechanism analysis reveals that the implementation of the E-Commerce Law promotes digital transformation in traditional manufacturing firms and strengthens their supply chain collaboration. These advancements lead to more efficient cost management decisions and reduce corporate cost stickiness. Heterogeneity analysis indicates that this effect is more significant for mature and declining enterprises and state-owned enterprises, as well as in regions with relatively developed economies and low reliance on foreign trade. Further research shows that the implementation of the E-Commerce Law curbs managerial opportunism and enhances managerial ability. Full article

Large-scale transitions to eco-friendly vehicle fleets present complex capital budgeting challenges, requiring the integration of extensive operational data with financial modeling while balancing economic profitability and environmental sustainability. Traditional approaches often struggle to manage this complexity and quantify the inherent trade-offs. This study develops and applies an innovative integrated accounting-and-finance framework to evaluate the economic and environmental implications of green fleet transition projects, explicitly quantifying the trade-off between profitability and sustainability. Focusing on waste vehicle replacement of Iren Spa, a leading European multi-utility company, we employ the recently developed Split-Screen Approach, a unified accounting-and-finance framework grounded in the laws of motion and conservation. It automatically reconciles pro forma financial statements and generates internally consistent valuation metrics, eliminating the manual adjustments and inconsistencies of traditional models. Its built-in diagnostic checks and scalability for highly complex datasets overcome the manual adjustments and inconsistencies inherent in traditional financial models. We process 2303 inputs across multiple “green” scenarios. This methodology integrates an Engineering Model, describing fleet evolution, operating costs, and CO₂ reduction, with a HookUp Model, which serves to transform scenarios into well-defined projects. The latter model is then integrated with a Financial Model that generates pro forma financial statements, incorporates financing and payout policies, and assesses economic profitability through Net Present Value (NPV) and consistent accounting rates of return. Together, these elements form a robust framework for managing complex data integration and analysis. Our research reveals a fundamental trade-off: enhanced environmental sustainability (measured by Net Green Value, NGV), which quantifies CO₂ reduction, is achieved at the expense of economic profitability, measured by NPV. This financial sacrifice is captured by the Net Value Curve, a Pareto frontier, while the NPV-to-NGV ratio provides “shadow prices” for CO₂ reduction, revealing the financial cost per unit of sustainability gained. Based on 21 project scenarios and additional sensitivity analyses on financial inputs and energy prices, the results confirm a decreasing relationship between NGV and NPV. This study makes three main contributions: (1) it demonstrates the practical application of the Split- Screen Approach for capital budgeting under complexity, (2) it introduces the Net Value Curve framework as a useful tool for visualizing and quantifying the trade-off between profitability and sustainability, (3) it provides managers and policymakers actionable insights, supporting more informed decisions in green fleet transition planning where economic and environmental objectives may conflict. The findings provide managers and policymakers with a rigorous and transparent accounting-and-finance framework that enhances the reliability of capital budgeting decisions compared with traditional financial modeling, while offering a Paretian frontier for evaluating environmental trade-offs. Full article

Hybrid PV-TEG systems can harvest both solar electrical and thermoelectric power, but their operating point drifts with irradiance, temperature gradients, partial shading, and load changes—often yielding multi-peak P-V characteristics. Conventional MPPT (e.g., P&O) and fixed-structure integer-order PID struggle to remain fast, stable, and globally optimal in these conditions. To address fast, robust tracking in these conditions, we propose an adaptive fractional-order PID (FOPID) MPPT whose parameters (K_p, K_i, K_d, λ, μ) are auto-tuned by the red-billed blue magpie optimizer (RBBMO). RBBMO is used offline to set the controller’s search ranges and weighting; the adaptive law then refines the gains online from the measured ΔV, ΔI slope error to maximize the hybrid PV-TEG output. The method is validated in MATLAB R2024b/Simulink 2024b, on a boost-converter–interfaced PV-TEG using five testbeds: (i) start-up/search, (ii) stepwise irradiance, (iii) partial shading with multiple local peaks, (iv) load steps, and (v) field-measured irradiance/temperature from Shanxi Province for spring/summer/autumn/winter. Compared with AOS, PSO, MFO, SSA, GHO, RSA, AOA, and P&O, the proposed tracker is consistently the fastest and most energy-efficient: 0.06 s to reach 95% MPP and 0.12 s settling at start-up with 1950 W·s harvested (vs. 1910 W·s AOS, 1880 W·s PSO, 200 W·s P&O). Under stepwise irradiance, it delivers 0.95–0.98 kJ at t = 1 s and under partial shading, 1.95–2.00 kJ, both with ±1% steady ripple. Daily field energies reach 0.88 × 10⁻³, 2.95 × 10⁻³, 2.90 × 10⁻³, 1.55 × 10⁻³ kWh in spring–winter, outperforming the best baselines by 3–10% and P&O by 20–30%. Robustness tests show only 2.74% power derating across 0–40 °C and low variability (Δv^max typically ≤ 1–1.5%), confirming rapid, low-ripple tracking with superior energy yield. Finally, the RBBMO-tuned adaptive FOPID offers a superior efficiency–stability trade-off and robust GMPP tracking across all five cases, with modest computational overhead. Full article

Drug markets display varying levels of violence across urban contexts, and understanding the drivers behind these differences is essential for designing effective interventions. (1) Background: This study investigates why some cocaine markets are more violent than others, focusing on four cities: Ciudad Juárez, Pereira, Frankfurt, and Madrid. (2) Methods: Using fuzzy-set Qualitative Comparative Analysis (fsQCA), we examined complex configurations of institutional, social, and market-related factors. Data were collected through 56 semi-structured interviews and secondary sources from 2015 to 2020. (3) Results: The findings reveal that violence arises from specific combinations of factors rather than isolated variables. In Latin American cities, violence is associated with weak institutional control, dense criminal networks, high social vulnerability, and fragmented market structures. In contrast, European cities show lower levels of violence due to stronger institutions, effective law enforcement, and well-regulated markets. (4) Conclusions: Addressing violence in cocaine markets requires context-specific strategies that take into account institutional capacity, market dynamics, and broader social conditions. These findings challenge simplistic views of drug market violence and emphasize the need for tailored interventions to mitigate violence effectively. Full article

This paper proposes a novel image tone-mapping framework that incorporates meta-learning, a psychophysical model, Bayesian optimization, and light-field virtual diffraction. First, we formalize the virtual diffraction process as a mathematical operator defined in the frequency domain to reconstruct high-dynamic-range (HDR) images through phase modulation, enabling the precise control of image details and contrast. In parallel, we apply the Stevens power law to simulate the nonlinear luminance perception of the human visual system, thereby adjusting the overall brightness distribution of the HDR image and improving the visual experience. Unlike existing methods that primarily emphasize structural fidelity, the proposed method strikes a balance between perceptual fidelity and visual naturalness. Secondly, an adaptive parameter tuning system based on Bayesian optimization is developed to conduct optimization of the Tone Mapping Quality Index (TMQI), quantifying uncertainty using probabilistic models to approximate the global optimum with fewer evaluations. Furthermore, we propose a task-distribution-oriented meta-learning framework: a meta-feature space based on image statistics is constructed, and task clustering is combined with a gated meta-learner to rapidly predict initial parameters. This approach significantly enhances the robustness of the algorithm in generalizing to diverse HDR content and effectively mitigates the cold-start problem in the early stage of Bayesian optimization, thereby accelerating the convergence of the overall optimization process. Experimental results demonstrate that the proposed method substantially outperforms state-of-the-art tone-mapping algorithms across multiple benchmark datasets, with an average improvement of up to 27% in naturalness. Furthermore, the meta-learning-guided Bayesian optimization achieves two- to five-fold faster convergence. In the trade-off between computational time and performance, the proposed method consistently dominates the Pareto frontier, achieving high-quality results and efficient convergence with a low computational cost. Full article

Markov chain Monte Carlo (MCMC) methods are fundamental to Bayesian inference but are often computationally prohibitive for large datasets, as the full likelihood must be evaluated at each iteration. Subsampling-based approximate Metropolis–Hastings (MH) algorithms offer a popular alternative, trading a manageable bias for a significant reduction in per-iteration cost. While this bias–variance trade-off is empirically understood, a formal theoretical framework for its optimization has been lacking. Our work establishes such a framework by bounding the mean squared error (MSE) as a function of the subsample size (m), the data size (n), and the number of epochs (E). This analysis reveals two optimal asymptotic scaling laws: the optimal subsample size is $m^{★}=O\left(E^{1/2}\right)$, leading to a minimal MSE that scales as ${\mathrm{MSE}}^{★}=O\left(E^{-1/2}\right)$. Furthermore, leveraging the large-sample asymptotic properties of the posterior, we show that when augmented with a control variate, the approximate MH algorithm can be asymptotically more efficient than the standard MH method under ideal conditions. Experimentally, we first validate the two optimal asymptotic scaling laws. We then use Bayesian logistic regression and Softmax classification models to highlight a key difference in convergence behavior: the exact algorithm starts with a high MSE that gradually decreases as the number of epochs increases. In contrast, the approximate algorithm with a practical control variate maintains a consistently low MSE that is largely insensitive to the number of epochs. Full article

This paper proposes a scheme for enhancing covert communication in cognitive radio networks (CRNs) using a reconfigurable intelligent surface (RIS), which ensures that transmissions by secondary users (SUs) remains statistically undetectable by adversaries (e.g., wardens like Willie). However, there exist stringent challenges in CRNs due to the dual constraints of avoiding detection and preventing harmful interference to primary users (PUs). Leveraging the RIS’s ability to dynamically reconfigure the wireless propagation environment, our scheme jointly optimizes the SU’s transmit power, communication block length, and RIS’s passive beamforming (phase shifts) to maximize the effective covert throughput (ECT) under rigorous covertness constraints quantified by detection error probability or relative entropy while strictly adhering to PU interference limits. Crucially, the RIS configuration is explicitly designed to simultaneously enhance signal quality at the legitimate SU receiver and degrade signal quality at the warden, thereby relaxing the inherent trade-off between covertness and throughput imposed by the fundamental square root law. Furthermore, we analyze the impact of unequal transmit prior probabilities (UTPPs), demonstrating their superiority over equal priors (ETPPs) in flexibly balancing throughput and covertness, and extend the framework to practical scenarios with Poisson packet arrivals typical of IoT networks. Extensive results confirm that RIS assistance significantly boosts ECT compared to non-RIS baselines and establishes the RIS as a key enabler for secure and spectrally efficient next-generation cognitive networks. Full article

Rising carbon emissions from international road freight transport in the EU—increasing from 29.4% in 2023 to 31.4% in 2025 under the With Existing Measures (WEM) Road Transport scenario—necessitate the implementation of additional measures within the framework of the EU Carbon Border Adjustment Mechanism (CBAM). For Ukraine, operating under martial law and pursuing a post-war green recovery of its transport and trade sectors, the adoption of EU experience in distributed generation (DG) from renewable energy sources (RESs) is particularly critical. This study evaluates the synergy between energy-efficient and low-carbon management in logistics chains for road freight transportation in Ukraine, drawing on EU evidence of DG based on RESs. To this end, a decoupling analysis was conducted to identify the factors influencing low-carbon and energy-efficient management of logistics chains in Ukraine’s freight transport sector. Under wartime conditions, the EU practice of utilising electric vehicles (EVs) as an auxiliary source of renewable energy for distributed electricity generation within microgrids—through Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies—was modelled. The results confirm the relevance of RES-based DG and the integration of EVs as a means of enhancing energy resilience in resource-constrained and conflict-affected regions. The scientific novelty of this research lies in identifying the conditions for achieving energy-efficient and low-carbon effects in the design of logistics chains through RES-based distributed generation, grounded in circular and inclusive economic development. The practical significance of the findings lies in formulating a replicable model for diversifying low-carbon fuel sources via the development of distributed generation of electricity based on renewable resources, providing a scalable paradigm for energy-limited and conflict-affected areas. Future research should focus on developing innovative logistics chain models that integrate DG and renewable energy use into Ukraine’s transport system. Full article

Indirect lighting systems employing light-emitting diodes (LEDs) and diffuse reflective surfaces are prevalent in applications demanding stringent illumination uniformity. However, conventional diffuse reflection approaches exhibit inherent limitations (inevitable light loss from multiple diffuse reflections and trade-off between uniformity and efficiency). To overcome these constraints, we introduce a novel composite freeform surface illumination system that synergistically integrates total internal reflection (TIR) with diffuse reflection. This design leverages the inherent Lambertian radiation characteristics of LEDs and the properties of ideal diffuse reflectors. A rigorous mathematical model is derived based on the luminous intensity distribution of the LED chip, the prescribed illumination requirements on the target plane, the principle of energy conservation, and Snell’s law. The resulting system of nonlinear equations is solved to generate a series of two-dimensional profile curves, which are subsequently synthesized into an off-axis freeform surface. Simulated results demonstrate that the proposed system achieves higher optical efficiency and superior illumination uniformity compared to traditional diffuse reflector configurations. This universal and feasible methodology broadens the application potential of high-performance diffuse indirect lighting. Full article

To address the challenges of insufficient response speed and robustness in optical attitude control systems under highly dynamic disturbances and internal uncertainties, a composite control strategy is proposed in this study. By integrating the proposed piecewise sliding control (P-SMC) with the improved active disturbance rejection control (ADRC), this strategy achieves complementary performance, which can not only suppress the disturbance but also converge to a bounded region fast. Under highly dynamic disturbances, the improved extended state observer (ESO) based on the EKF achieves rapid response with amplified state observations, and the Nonlinear State Error Feedback (NLSEF) generates a compensation signal to actively reject disturbances. Simultaneously, the robust sliding mode control (SMC) suppresses the effects of system nonlinearity and uncertainty. To address chattering and overshoot of the conventional SMC, this study proposes a novel P-SMC law which applies distinct reaching functions across different error bands. Furthermore, the key parameters of the composite scheme are globally optimized using the particle swarm optimization (PSO) algorithm to achieve Pareto-optimal trade-offs between tracking accuracy and disturbance rejection robustness. Finally, MATLAB simulation experiments validate the effectiveness of the proposed strategy under diverse representative disturbances. The results demonstrate improved performance in terms of response speed, overshoot, settling time and control input signals smoothness compared to conventional control algorithms (ADRC, C-ADRC, T-SMC-ADRC). The proposed strategy enhances the stability and robustness of optical attitude control system against internal uncertainties of system and sensor measurement noise. It achieves bounded-error steady-state tracking against random multi-source disturbances while preserving high real-time responsiveness and efficiency. Full article

Growing global supply chain uncertainties significantly threaten China’s forage import security. The evolving characteristics of the global forage trade network directly impact the stability of China’s supply. This study constructs a directed, weighted trade network based on global forage products trade data (2000–2024). Using complex network analysis methods, it systematically analyzes the network’s topological structure and evolutionary patterns, with a focus on their impact on China’s import security. The study addresses the following questions: What evolutionary patterns does the global forage trade network exhibit in terms of its topological structure? How does the evolution of this network impact the import security of forage products in China, specifically regarding supply chain stability and risk resilience? The research findings indicate the following: (1) From 2000 to 2024, the total volume of global forage products trade increased by 48.17%, primarily driven by forage products excluding alfalfa meal and pellets, which accounted for an average of 82.04% of volume annually. Additionally, the number of participating countries grew by 21.95%. (2) The global forage products trade network follows a power–law distribution, characterized by increasing network density, a clustering coefficient that initially declines and then rises, and a shortening of the average path length. (3) The core structure of the global forage products trade network shows an evolutionary trend of diffusion from core nodes in North America, Oceania, and Asia to multiple core nodes, including those in North America, Oceania, Europe, Africa, and Asia. (4) China’s forage products trade network displays distinct phase characteristics; however, imports face significant risks from high supply chain dependency and exposure to international price fluctuations. Based on these conclusions, it is recommended that China actively expands trade relations with potential product-exporting countries in Africa, encouraging enterprises to “go global.” Additionally, China should establish a three-dimensional supply chain security system, comprising maritime, land, and storage components, to enhance risk resistance and import safety. Full article

As more machine learning models are used in sensitive fields like healthcare, finance, and smart infrastructure, protecting structured tabular data from privacy attacks is a key research challenge. Although several privacy-preserving methods have been proposed for tabular data, a comprehensive comparison of their performance and trade-offs has yet to be conducted. We introduce and empirically assess a combined defense system that integrates differential privacy, federated learning, adaptive noise injection, hybrid cryptographic encryption, and ensemble-based obfuscation. The given strategies are analyzed on the benchmark tabular datasets (ADULT, GSS, FTE), showing that the suggested methods can mitigate up to 50 percent of model inversion attacks in relation to baseline models without decreasing the model utility (F1 scores are higher than 0.85). Moreover, on these datasets, our results match or exceed the latest state-of-the-art (SOTA) in terms of privacy. We also transform each defense into essential data privacy laws worldwide (GDPR and HIPAA), suggesting the best applicable guidelines for the ethical and regulation-sensitive deployment of privacy-preserving machine learning models in sensitive spaces. Full article