Search Results (1,375)

Background: Patients are the most frequent perpetrators of physical violence against nurses. In the United States, most states have established laws designating assault against nurses a felony, or serious crime. It is unknown what reasons nurses have for pressing charges or not pressing charges against patients. Purpose: The purpose of this study was to examine nurses’ decisions regarding pressing charges when patients exhibit violent behavior. Methods: This study used a mixed-method, cross-sectional, descriptive design. Three unfolding case studies were presented in an electronic survey. Twelve versions of the survey were randomly assigned to participants. Each described an adolescent, adult, and geriatric patient. The narrative descriptions were identical, but the visual representations of the patients differed. Results: A total of 499 nurses from seven hospitals in the western United States responded. Most nurses indicated that they would not press charges against any of the hypothetical patients. An injury occurring and an assumption of intentionality contributed to nurses’ decisions to press charges. Participants were more likely to press charges against the adolescent and adult patients than the geriatric patient. The hypothetical adolescent and geriatric patients were more likely to have charges pressed against them if presented as female than if presented as male. The hypothetical adult patient was more likely to have charges pressed against them if presented as white than if presented as black. Conclusions: There is no consensus regarding when a nurse ought to pursue legal action against a patient who exhibits violent behavior. In addition to the presence of injury and the assumption of intentionality, it is possible that implicit bias may also play a role in these decisions. More investigation into this is needed. Full article

This paper addresses the optimization challenges in urban logistics with the aim of enhancing the sustainability of last-mile delivery. By focusing on the collaborative delivery between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), we propose a novel approach to reducing energy consumption and operational inefficiencies. A bilevel mixed-integer linear programming (Bilevel-MILP) model is developed, integrating road network topology with dynamic energy constraints. Departing from traditional single-delivery modes, the paper establishes a multi-task continuous delivery framework. By incorporating a dynamic charging point selection strategy and path–energy coupling constraints, the model effectively mitigates energy limitations and the issue of repeated returns for UAV charging in complex urban road networks, thereby promoting more efficient resource utilization. At the algorithmic level, a Collaborative Delivery Path Optimization (CDPO) framework is proposed, which embeds an Improved Sparrow Search Algorithm (ISSA) with directional initialization and a Hybrid Genetic Algorithm (HGA) with specialized crossover strategies. This enables the synergistic optimization of UAV delivery sequences and UGV charging decisions. The simulation results demonstrate that, in scenarios with a task density of 20 per 100 km², the proposed CDPO algorithm reduces the total delivery time by 33.9% and shortens the UAV flight distance by 24.3%, compared to conventional fixed charging strategies (FCSs). These improvements directly contribute to lowering energy consumption and potential emissions. The road network discretization approach and dynamic candidate charging point generation confirm the method’s adaptability in high-density urban environments, offering a spatiotemporal collaborative optimization paradigm that supports the development of sustainable and intelligent urban logistics systems. The obtained results provide practical insights for the design and deployment of efficient UAV–UGV collaborative logistics systems in urban environments, particularly under high-task-density and energy-constrained conditions. Full article

The article explores the current challenges and prospects for the electrification of the bus fleet in urban passenger transport, with a particular focus on the municipal operator Municipal Transport Ruse EAD. The study is motivated by the growing importance of sustainable mobility and the European Union’s policy framework aimed at decarbonization of urban transport systems. A mixed-integer linear programming (MILP) model is developed to optimize the investment and operational strategies for the gradual replacement of diesel buses with electric ones, taking into account capital expenditures, operational costs, charging infrastructure, and environmental benefits. Scenario analysis is employed to compare six different pathways of fleet electrification, ranging from partial to full transition within a defined planning horizon. The results highlight significant trade-offs between financial feasibility and ecological impact, illustrating that an accelerated electrification strategy yields the largest emission reductions but requires substantial upfront investment. Conversely, gradual transition scenarios demonstrate better budget alignment but achieve lower environmental benefits. The discussion emphasizes the practical applicability of the model for municipal decision-makers, offering a tool for strategic planning under economic and ecological constraints. The paper concludes that sustainable electrification of municipal bus fleets requires a balanced approach that aligns environmental objectives with financial and operational capacities. Full article

The global shipping industry is surging ahead, and with it, a quiet revolution is taking place on the water: marine lithium-ion batteries have emerged as a crucial clean energy carrier, powering everything from ferries to container ships. When these vessels dock, they increasingly rely on shore power charging systems to refuel—essentially, plugging in instead of idling on diesel. But predicting how much power they will need is not straightforward. Think about it: different ships, varying battery sizes, mixed charging technologies, and unpredictable port stays all come into play, creating a load profile that is random, uneven, and often concentrated—a real headache for grid planners. So how do you forecast something so inherently variable? This study turned to the Monte Carlo method, a probabilistic technique that thrives on uncertainty. Instead of seeking a single fixed answer, the model embraces randomness, feeding in real-world data on supply modes, vessel types, battery capacity, and operational hours. Through repeated random sampling and load simulation, it builds up a realistic picture of potential charging demand. We ran the numbers for a simulated fleet of 400 vessels, and the results speak for themselves: load factors landed at 0.35 for conventional AC shore power, 0.39 for high-voltage DC, 0.33 for renewable-based systems, 0.64 for smart microgrids, and 0.76 when energy storage joined the mix. Notice how storage and microgrids really smooth things out? What does this mean in practice? Well, it turns out that Monte Carlo is not just academically elegant, it is practically useful. By quantifying uncertainty and delivering load factors within confidence intervals, the method offers port operators something precious: a data-backed foundation for decision-making. Whether it is sizing infrastructure, designing tariff incentives, or weighing the grid impact of different shore power setups, this approach adds clarity. In the bigger picture, that kind of insight matters. As ports worldwide strive to support cleaner shipping and align with climate goals—China’s “dual carbon” ambition being a case in point—achieving a reliable handle on charging demand is not just technical; it is strategic. Here, probabilistic modeling shifts from a simulation exercise to a tangible tool for greener, more resilient port energy management. Full article

In this study, dried biomass of the alga Chlorella vulgaris was ground into a powder as an eco-friendly, low-cost inhibitor to mitigate the corrosion of carbon steel in acidic solutions. Electrochemical and weight loss measurements, surface morphology observations, adsorption isotherms, activation energy, and potential of zero charge calculations were applied to evaluate the inhibition performance. Electrochemical results indicate that C. vulgaris powder can simultaneously inhibit both the anodic and cathodic corrosion processes of carbon steel, demonstrating good inhibition performance and classifying it as a mixed-type inhibitor with both anodic and cathodic characteristics. Weight loss data further confirm that at a concentration of 300 mg/L, the corrosion inhibition efficiency reaches 88%. The fitted adsorption isotherm reveals that the adsorption of Chlorella vulgaris powder on the carbon steel surface follows the Langmuir model. Density functional theory (DFT) and molecular dynamics simulations indicate that the excellent inhibition performance is attributed to the combined effects of physisorption and chemisorption of constituents such as amino acids and cellulose present in C. vulgaris. Full article

The widespread adoption of Electric Vehicles (EVs) is critically dependent on the deployment of efficient charging infrastructure. However, existing facility location models typically treat charging duration as an exogenous parameter, thereby neglecting the traveler’s autonomy to make trade-offs between service time and energy needs based on their Value of Time (VoT). This study addresses this theoretical gap by developing a heterogeneous network design model that endogenizes both charging mode selection and continuous charging duration decisions. A bi-objective optimization framework is formulated to minimize the weighted sum of infrastructure capital expenditure and users’ generalized travel costs. To ensure computational tractability for large-scale networks, an exact linearization technique is applied to reformulate the resulting Mixed-Integer Non-Linear Program (MINLP) into a Mixed-Integer Linear Program (MILP). Application of the model to the Hubei Province highway network reveals a convex Pareto frontier between investment and service quality, providing quantifiable guidance for budget allocation. Empirical results demonstrate that the marginal return on infrastructure investment diminishes rapidly. Specifically, a marginal budget increase from the minimum baseline yields disproportionately large reductions in system-wide dwell time, whereas capital allocation beyond a saturation point yields diminishing returns, offering negligible service gains. Furthermore, sensitivity analysis indicates an asymmetry in technological impact: while extended EV battery ranges significantly reduce user dwell times, they do not proportionally lower the capital required for the foundational infrastructure backbone. These findings suggest that robust infrastructure planning must be decoupled from anticipations of future battery breakthroughs and instead focus on optimizing facility heterogeneity to match evolving traffic flow densities. Full article

Waste electrical and electronic equipment (WEEE) is one of the fastest-growing waste streams globally. Disposable e-cigarettes are among the products that have gained popularity in recent years. Their complex construction and embedded lithium-ion batteries (LIBs) present environmental, safety, and resource recovery challenges. Despite growing research interest, integrated analyses linking material composition with user disposal behavior remain limited. This study is the first to incorporate device-level mass balance, material contamination assessment, battery residual charge measurements, and user behavior to evaluate the waste management challenges of disposable e-cigarettes. A mass balance of twelve types of devices on the Polish market was performed. Plastics dominated in five devices, while non-ferrous metals prevailed in the others, depending on casing design. Materials contaminated with e-liquid residues accounted for 4.4–10.7% of device mass. Battery voltage measurements revealed that 25.6% of recovered LIBs retained a residual charge (greater than 2.5 V), posing a direct fire hazard during waste handling and treatment. Moreover, it was estimated that 7 to 12 tons of lithium are introduced annually into the Polish market via disposable e-cigarettes, highlighting substantial resource potential. Survey results showed that 46% of users disposed of devices in mixed municipal waste, revealing a knowledge–practice gap largely independent of gender or education. Integrating technical and social findings demonstrates that improper handling is a systemic issue. The findings support the relevance of eco-design requirements, such as modular casings for battery removal, alongside the enforcement of Extended Producer Responsibility (EPR) schemes. Current product fees (0.01–0.03 EUR/unit) remain insufficient to establish an effective collection infrastructure, highlighting a key systemic barrier. Full article

This paper presents a comprehensive techno-economic assessment of a community battery energy storage system (BESS) participating concurrently in energy arbitrage and frequency control ancillary services (FCAS) markets, while also providing customer savings through coordinated demand management. The proposed framework employs a mixed-integer linear programming (MILP) model to co-optimize the charging, discharging, and reserve scheduling of the battery under dynamic market conditions. The model explicitly incorporates key operational and economic factors such as round-trip efficiency, degradation cost, market-participation constraints, and revenue from multiple value streams. By formulating the optimization problem within this MILP structure, both the operational feasibility and the economic profitability of the system are evaluated over annual market cycles. Simulation results demonstrate that integrating FCAS participation with conventional energy arbitrage substantially enhances total revenue potential and improves asset utilization, compared with single-service operation. Furthermore, the coordinated management of community demand contributes to additional cost savings and supports local grid reliability. The findings highlight the critical role of co-optimized control and multi-market participation strategies in improving the financial viability and grid-support capabilities of community-scale BESS deployments. Full article

To improve the accuracy and robustness of lithium-ion battery state of charge (SOC) estimation, this paper proposes a generalized mixture maximum correlation-entropy criterion-based adaptive extended Kalman filter (GMMCC-AEKF) algorithm, addressing the performance degradation of the traditional extended Kalman filter (EKF) under non-Gaussian noise and inaccurate initial conditions. Based on the GMMCC theory, the proposed algorithm introduces an adaptive mechanism and employs two generalized Gaussian kernels to construct a mixed kernel function, thereby formulating the generalized mixture correlation-entropy criterion. This enhances the algorithm’s adaptability to complex non-Gaussian noise. Simultaneously, by incorporating adaptive filtering concepts, the state and measurement covariance matrices are dynamically adjusted to improve stability under varying noise intensities and environmental conditions. Furthermore, the use of statistical linearization and fixed-point iteration techniques effectively improves both the convergence behavior and the accuracy of nonlinear system estimation. To investigate the effectiveness of the suggested method, experiments for SOC estimation were implemented using two lithium-ion cells featuring distinct rated capacities. These tests employed both dynamic stress test (DST) and federal test procedure (FTP) profiles under three representative temperature settings: 40 °C, 25 °C, and 10 °C. The experimental findings prove that when exposed to non-Gaussian noise, the GMMCC-AEKF algorithm consistently outperforms both the traditional EKF and the generalized mixture maximum correlation-entropy-based extended Kalman filter (GMMCC-EKF) under various test conditions. Specifically, under the 25 °C DST profile, GMMCC-AEKF improves estimation accuracy by 86.54% and 10.47% over EKF and GMMCC-EKF, respectively, for the No. 1 battery. Under the FTP profile for the No. 2 battery, it achieves improvements of 55.89% and 28.61%, respectively. Even under extreme temperatures (10 °C, 40 °C), GMMCC-AEKF maintains high accuracy and stable convergence, and the algorithm demonstrates rapid convergence to the true SOC value. In summary, the GMMCC-AEKF confirms excellent estimation accuracy under various temperatures and non-Gaussian noise conditions, contributing a practical approach for accurate SOC estimation in power battery systems. Full article

Energetic powder processing includes comminution, sieving, drying, conveying, mixing, and packaging, all of which determine product performance and safety. With growing requirements for efficiency and reliability, numerical simulation has become essential for analyzing mechanisms, optimizing parameters, and supporting equipment design. This review summarizes recent progress in simulation techniques such as the discrete element method (DEM), computational fluid dynamics (CFD), and multi-scale coupling while also evaluating their predictive capabilities and limitations across various unit operations and safety concerns such as electrostatic hazards. It, thus, establishes the core “property–parameter–performance” relationships and clarifies mechanisms in multiphase flow, energy transfer, and charge accumulation, and highlights the role of symmetry in improving simulation efficiency. By highlighting persistent challenges, this work lays a foundation for future research, guiding the development of theoretical frameworks and practical solutions for advanced powder processing. Full article

This paper proposes a dual path mixed coupling wireless power transfer (DPMPT) coupler as a four-port structure for near-field wireless power transfer in drone and unmanned aerial vehicles. The DPMPT coupler integrates orthogonal double-D coils and 8-plates to realize mixed inductive–capacitive coupling at 6.78 MHz without additional lumped matching networks. A four-port equivalent model is developed by classifying the mutual networks into three coupling types and representing them with a transmission-matrix formulation fitted to three-dimensional full-wave simulations. The model is used to identify the main coupling paths and to evaluate the effect of rotation and lateral/diagonal misalignment on power-transfer characteristics. Simulation results at a transfer distance of 70 mm show a maximum transmission coefficient of about 0.82 at 6.78 MHz and high robustness against rotation. When switch-based port selection is applied on the transmit side, blind spots associated with pose variations that cause an abrupt drop in transmission characteristics are significantly reduced, demonstrating that the DPMPT coupler with switch control provides an effective structural basis for enhancing alignment tolerance in mixed coupling wireless power transfer systems. Full article

In the pursuit of cleaner and more efficient internal combustion engines, dual-fuel strategies combining diesel and hydrogen are gaining increasing attention. This study employs detailed computational fluid dynamics (CFD) simulations to investigate the behaviour of pilot diesel injections in dual-fuel diesel–hydrogen engines. The study aims to characterize spray formation, ignition delay and early combustion phenomena under various energy input levels. Two combustion models were evaluated to determine their performance under these specific conditions: Tabulated Well Mixed (TWM) and Representative Interactive Flamelet (RIF). After an initial numerical validation using dual-fuel constant-volume vessel experiments, the models are further validated using in-cylinder pressure measurements and high-speed natural combustion luminosity imaging acquired from a large-bore optical engine. Particular attention was given to ignition location due to its influence on subsequent hydrogen ignition. Results show that both combustion models reproduce the experimental behavior reasonably well at high energy input levels (EILs). At low EILs, the RIF model better captures the ignition delay; however, due to its single-flamelet formulation, it predicts an abrupt ignition of all available premixed charge in the computational domain once ignition conditions are reached in the mixture fraction space. Full article

Rapid deployment of rooftop photovoltaics (PV), electric heating, and electric vehicles (EVs) is stressing low-voltage feeders in cold climates, where winter peaks push aging transformers to their limits. This paper quantifies how much stationary and mobile storage is required to keep feeder power nearly flat over a full year in such conditions. A mixed-integer linear programming (MILP) model co-optimizes stationary battery energy storage systems (BESSs) and EV flexibility, including lithium-ion degradation, under a flatness constraint on transformer loading, i.e., the magnitude of feeder power exchange (import or export) around a seasonal target. The framework is applied to a 48-dwelling neighborhood in Ardahan, northeastern Turkey (mean January ≈ −8 °C) with rooftop PV and an emerging EV fleet. Three configurations are compared: unmanaged EV charging, optimized smart charging, and bidirectional vehicle-to-grid (V2G). Relative to the unmanaged case, smart charging reduces optimal stationary BESS capacity from 4.10 to 2.95 MWh, while V2G further cuts it to 1.23 MWh (≈70% reduction) and increases flat-compliant hours within ±0.5 kW of the target transformer loading level from 92.4% to 96.1%. The levelized cost of demand equalization falls from 0.52 to 0.22 EUR/kWh, indicating that combining modest stationary BESSs with V2G can make feeder-level demand flattening technically and economically viable in cold-climate residential districts. Full article

Large density fluctuations near the QCD critical point can be probed via intermittency analysis, which involves measuring scaled factorial moments (SFMs) of multiplicity distributions in relativistic heavy-ion collisions. Intermittency reflects the emergence of scale invariance and self-similar structures, which are closely related to symmetry principles and their breaking near a second-order phase transition. We present a systematic model study of intermittency for charged hadrons in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 7.7, 11.5, 19.6, 27, 39, 62.4, and 200 $\mathrm{GeV}$. Using the cascade UrQMD model, we demonstrate that non-critical background effects can produce sizable SFMs and a large scaling exponent if they are not properly removed using the mixed-event subtraction method. To estimate the possible critical intermittency signal in experimental data, we employ a hybrid UrQMD+CMC model, in which fractal critical fluctuations are embedded into the UrQMD background. A direct comparison of the second-order SFM between the model and STAR experimental data suggests that a critical intermittency signal on the order of approximately $1.8\%$ could be present in the most central Au+Au collisions at RHIC energies. This study provides practical guidance for evaluating background contributions in intermittency measurements and offers a quantitative estimate for the critical signal fraction present in the STAR data. Full article

In this study, first-principles density functional theory (DFT) calculations were employed to investigate the effects of single and binary gas adsorption of NO, CO, CO₂, NO₂, H₂S, and O₃ on the ZnGa₂O₄(111) surface. For single-gas adsorption, O₃ adsorbed on surface Ga sites induces a pronounced work-function increase of 0.97 eV, whereas H₂S adsorption at surface O sites yields the strongest adsorption energy (−1.21 eV), highlighting their distinct electronic interactions with the surface. For binary co-adsorption, the NO₂-O₃ pair adsorbed at Ga-coordinated sites produces the largest work-function shift (1.88 eV), while adsorption at Zn sites results in the most stable configuration, with an adsorption energy reaching −3.98 eV. These results indicate that co-adsorption of highly electronegative gases can significantly enhance charge transfer and sensing response. In contrast, mixed oxidizing–reducing gas pairs, such as NO₂-H₂S, lead to a markedly suppressed work-function variation (−0.02 eV), suggesting reduced sensor sensitivity due to compensating charge-transfer effects. Overall, this work demonstrates that gas-sensing behavior on ZnGa₂O₄(111) is governed not only by individual gas–surface interactions but also by cooperative and competitive effects arising from binary co-adsorption, providing insights into realistic multi-gas sensing environments. Full article