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PurposeMiniplate shapes determine the fixation stability to promote best healing and osseointegration process of mandibular fracture. In clinical treatment, the common method used two straight-type miniplate or I-shape miniplate; sometimes this method is not stable enough or limited by the fracture geometry and caused high risk of failure due to screw loosening. This paper aims to investigate a new type of miniplate called V-shape miniplate design as an alternative to the standard straight plate based on total displacement, von Mises stress, stress transfer parameter (STP) and strain energy density transfer parameters (SEDPTs) for two types of bite force condition, which is cutting and chewing condition.Design/methodology/approachThe 3D fixation models were constructed and the finite element (FE) simulation is based on the two-bite force load that ranges from 50 to 700 N based on cutting and chewing bite force condition using ANSYS Workbench 19.2.FindingsIn result comparison, the maximum loading of the V-shape miniplate can reduce deformation by 5.9%, reduce stress by 0.58% reduce strain by 8.1% in cutting condition while reducing deformation by 6.43%, reduce stress by 15.25%, reduce strain by 10.1% in chewing condition. To assess the stress transfer behavior of miniplates fixations to the mandibular bone, the STP and SEDPT were evaluated at the normal cortex screw and the locking head screw. In the simulation, the locking head screw is vertical to the bone structure while the cortex screw is 95 degrees to the bone structure, as a result, the STP value for locking head screw is 1.0073 while in cortex screw is 0.7408.Research limitations/implicationsMeanwhile, the SEDPT value for locking head screw is 2.7574 and 1.8412 for cortex screw.Practical implicationsClinically, V-shape miniplate has shown factual data that can be used for prototyping. STP and SEDTP values provide evidence of how fixation stability is better than I-shape miniplate.Originality/valueIn conclusion, the newly designed V-shape miniplate has overall better stability than the standard I-shape miniplate, and the locking head screw has the STP value closer to 1 than the standard cortex screw; it means the locking screw is better in reducing the stress shielding.

Polychlorinated dibenzo- para -dioxins (PCDDs) and dibenzofurans (PCDFs) (collectively and colloquially referred to as ‘dioxins’) as well as polychlorinated biphenyls (PCBs) are persistent and ubiquitous environmental contaminants that may unintentionally enter and accumulate along the food chain. Owing to their chronic toxic effects in humans and bioaccumulative properties, their presence in feed and food requires particular attention. One important exposure pathway for consumers is consumption of milk and dairy products. Their transfer from feed to milk has been studied for the past 50 years to quantify the uptake and elimination kinetics. We extracted transfer parameters (transfer rate, transfer factor, biotransfer factor and elimination half-lives) in a machine-readable format from seventy-six primary and twenty-nine secondary literature items. Kinetic data for some toxicologically relevant dioxin congeners and the elimination half-lives of dioxin-like PCBs are still not available. A well-defined selection of transfer parameters from literature was statistically analysed and shown to display high variability. To understand this variability, we discuss the data with an emphasis on influencing factors, such as experimental conditions, cow performance parameters and metabolic state. While no universal interpretation could be derived, a tendency for increased transfer into milk is apparently connected to an increase in milk yield and milk fat yield as well as during times of body fat mobilisation, for example during the negative energy balance after calving. Over the past decades, milk yield has increased to over 40 kg/d during high lactation, so more research is needed on how this impacts feed to food transfer for PCDD/Fs and PCBs.

When describing steady-state mass-transfer processes, time is often omitted in the main mass-transfer equation. Experimental data on the operation of mass-transfer equipment, however, also indicate the influence of phase contact time on the mass-transfer efficiency of internal contact devices. In this paper, the basic mass-transfer equation is presented in differential form and integrated together with the expressions of the material balance of the mass-transfer process in two phases for cross-flow tray contact devices, the hydrodynamic model of flow movement on which is close to ideal displacement for one of the phases and ideal mixing for the other. As a result, by means of transformations of the obtained equations, expressions for the Murphy efficiency for each of the phases on the specified type of contact devices, which establish a relationship between the mass-transfer efficiency and the parameters of the mass-transfer process and the consumptions of the phases, are obtained. The prognostic nature of the obtained dependences and their advantages over empirical expressions are indicated. The necessity of taking into account the time of the mass-exchange process, including steady-state, in order to determine the efficiency of the contact device is substantiated.

The Ocotea puberula bark powder (OPBP) was evaluated as an effective adsorbent for the removal of crystal violet (CV) from colored effluents. OPBP was characterized and presented a surface with large cavities, organized as a honeycomb. The main functional groups of OPBP were O-H, N-H, C=O, and C-O-C. The adsorption of CV on OPBP was favorable at pH 9 with a dosage of 0.75 g L −1 . The Avrami model was the most suitable to represent the adsorption kinetic profile, being the estimated equilibrium concentration value of 3.37 mg L −1 for an initial concentration of 50 mg L −1 (CV removal of 93.3%). The equilibrium was reached within 90 min. The data were better described by the Langmuir isotherm, reaching a maximum adsorption capacity of 444.34 mg g −1 at 328 K. The Gibbs free energy ranged from − 26.3554 to − 27.8055 kJ mol −1 , and the enthalpy variation was − 11.1519 kJ mol −1 . The external mass transfer was the rate-limiting step, with Biot numbers ranging from 0.0011 to 0.25. Lastly, OPBP application for the treatment of two different simulated effluents was effective, achieving a removal percentage of 90%.

Deep Reinforcement Learning (DRL) algorithms help agents take actions automatically in sophisticated control tasks. However, it is challenged by sparse reward and long training time for exploration in the application of Deep Neural Network (DNN). Evolutionary Algorithms (EAs), a set of black box optimization techniques, are well applied to single agent real-world problems, not troubled by temporal credit assignment. However, both suffer from large sets of sampled data. To facilitate the research on DRL for a pursuit-evasion game, this paper contributes an innovative policy optimization algorithm, which is named as Evolutionary Algorithm Transfer - Deep Deterministic Policy Gradient (EAT-DDPG). The proposed EAT-DDPG takes parameters transfer into consideration, initializing the DNN of DDPG with the parameters driven by EA. Meanwhile, a diverse set of experiences produced by EA are stored into the replay buffer of DDPG before the EA process is ceased. EAT-DDPG is an improved version of DDPG, aiming at maximizing the reward value of the agent trained by DDPG as much as possible within finite episodes. The experimental environment includes a pursuit-evasion scenario where the evader moves with the fixed policy, and the results show that the agent can explore policy more efficiently with the proposed EAT-DDPG during the learning process.

This study addresses the challenges facing tunnel lighting system maintenance, where conventional single-objective optimization strategies and traditional maintenance approaches struggle to balance multidimensional requirements. Focusing on lifecycle maintenance management of tunnel lighting infrastructure, the research transforms multi-objective optimization into a set of Pareto-optimal subproblems through decomposition strategies. The proposed framework establishes a dynamic topological network within the solution space by integrating the Double Deep Q-Network(DDQN) algorithm from deep reinforcement learning with neighborhood gradient transfer strategies. This study proposes an innovative integration of Wiener degradation processes and the DDQN algorithm to establish a dynamic reliability-cost coupling model for equipment performance analysis. A multi-objective deep reinforcement learning (MODRL)-driven intelligent maintenance framework is developed, systematically coordinating degradation dynamics and economic constraints through computational learning mechanisms. The results show that incorporating maintenance costs and reliability as reward components in the multi-objective optimization problem (MOP) simultaneously enhances operational reliability and reduces comprehensive maintenance expenditures by 29.7%. The neighborhood-based parameter transfer strategy reduced single-episode training time by 41.9% and parameter synchronization time by 68.3%, while improving GPU utilization by 34.9%. It achieved faster convergence with 22.8 fewer threshold steps and reduced multi-objective conflict rates by 17.0%. The developed multi-objective optimization framework for tunnel lighting systems overcomes fixed maintenance threshold limitations. The framework demonstrated a 68.5% reliability decline near lighting failure conditions while effectively addressing overconfidence issues. The weight-combination-based adaptive mechanism enables scenario-specific customization of optimization objectives, offering scalable solutions for cost-prioritized, reliability-focused, or balanced operational strategies.

It is increasingly acknowledged that the acceleration of the global water cycle, largely driven by anthropogenic climate change, has a disproportionate impact on sub‐daily and small‐scale hydrological extreme events such as flash floods. These events occur thereby at local scales within minutes to hours, typically in response to high‐intensity rainfall events associated with convective storms. In the present work, we show that by employing physically based representative hillslope models that resolve the main gradients controlling overland flow hydrology and hydraulics, we can get reliable simulations of flash flood response in small data‐scarce catchments. To this end, we use climate reanalysis products and transfer soil parameters previously obtained for hydrological predictions in an experimental catchment in the same landscape. The inverted mass balance of flood reservoirs downstream is employed for model evaluation in these nearly ungauged basins. We show that our approach using representative hillslopes and climate data sets can provide reasonable uncalibrated estimates of the overland runoff response (flood magnitude, storm volume, and event runoff coefficients) in three of the four catchments considered. Given that flash floods typically occur at scales of a few km2 and in ungauged places, our results have implications for operational flash flood forecasting and open new avenues for using gradient resolving physically based models for the design of small and medium flood retention basins around the world. Plain Language Summary Flash floods have become increasingly common worldwide, with catastrophic damages to both human life and the economy. While the extent of global warming and climate change impacting these events is still under much debate, it is almost certain now that we need to be better equipped to understand and model these extremes to prevent and mitigate the possible risk to human life and infrastructure in a warming climate. To test, if we can use first principles derived from thermodynamic conservation laws and process based hydrological models for the same, we modeled flash flood response in four headwater catchments over Southern Germany using the concept of “representative hillslope.” Since the regions considered in our work are poorly gauged, we made use of global climate reanalysis products and parameter transfer from past experiments. The encouraging results obtained in predicting the flood magnitude and volume speak to the overall applicability of our approach. We are able to get decent uncalibrated predictions in three out of the four catchments considered with minimum computational effort. Understanding and managing the adverse impacts of such extreme hydroclimatic events remains one of the crucial hurdles facing humanity toward the sustainable development goals (SDG17) in this decade. Key Points Physically based representative hillslope models can be used for flash flood predictions in small data‐scarce and rural catchments Climate reanalysis data enable the initialization of a process‐based model, helping to reduce the uncertainties in estimating antecedent soil conditions Transfer of model parameters within the same hydrological landscape is feasible

This work describes the successful application of the pore volume and surface diffusion (PVSD) model characterizing the batch adsorption of Cu(II) on a chemically modified Cucurbita moschata biosorbent. The PVSD model captures the convective transport of Cu(II) from the bulk solution to the biosorbent surface, followed by its surface and pore diffusion inside the biosorbent. The adsorption of Cu(II) is mimicked using the Langmuir isotherm. The algebraic, ordinary, and partial differential equations, involved in the PVSD model, are solved using the general process modeling system (gPROMS). The model simulation results, depicted by the Cu(II) concentration decay curve, show an excellent match with experimental data. The external mass transfer coefficient (≈10−3 m/s) indicated no restriction on approaching Cu(II) toward the biosorbent surface. Within the biosorbent, surface diffusion was dominant over pore volume diffusion. The statistical analysis of the PVSD model results has been done by calculating R2, Chi-square value, normalized standard deviation, p-value, and root-mean-square error. The PVSD model approach presented in this work could be beneficial to other heavy metal–biosorbent systems.

Anisotropic reflectance correction (ARC) of satellite imagery is required to remove multi-scale topographic effects in imagery. Commonly utilized ARC approaches have not effectively accounted for atmosphere-topographic coupling. Furthermore, it is not clear which topographic effects need to be formally accounted for. Consequently, we simulate the direct and diffuse-skylight irradiance components and formally account for multi-scale topographic effects. A sensitivity analysis was used to determine if characterization schemes can account for a collective treatment of effects, using our parameterization scheme as a basis for comparison. We found that commonly used assumptions could not account for topographic modulation in our simulations. We also found that the use of isotropic diffuse irradiance and a topographic shielding parameter also failed to characterize topographic modulation. Our results reveal that topographic effects govern irradiance variations in a synergistic way, and that issues of ARC need to be formally addressed given atmosphere-topography coupling. Collectively, our results suggest that empirical ARC methods cannot be used to effectively address topographic effects, given inadequate parameterization schemes. Characterizing and removing spectral variation from multispectral imagery will most likely require numerical modeling efforts. More research is warranted to develop/evaluate parameterization schemes that better characterize the anisotropic nature of atmosphere-topography coupling.

Wind turbine power curve modeling is essential for wind power forecasting, turbine performance monitoring, and predictive maintenance. However, SCADA data often contain anomalies (e.g., curtailment, sensor faults), degrading the accuracy of power curve predictions. This paper presents a parameter-transfer learning strategy within a preprocessing and modeling framework that jointly optimizes anomaly detection (iForest, LOF, DBSCAN) and WTPC regressors (MLP, RF, GP) via a multi-metric objective adaptable to specific modeling requirements. In the source domain, hyperparameters are explored with randomized search, and in the target domain, transferred settings are refined with Bayesian optimization. Applied to real SCADA from different locations and turbine models, the approach achieves a 90% reduction in optimization iterations and consistently improves target domain performance according to the objective, with no observed loss when comparable source and target turbines differ in site or rated power. Gains are larger for more similar source–target pairs. Overall, the approach yields a practical model-agnostic pipeline that accelerates preprocessing and modeling while preserving or improving fit, particularly for newly installed turbines with limited data.