Basis For Optimization Research Articles

Optimizing E-commerce Recommender Systems: A Comprehensive Review of Techniques and Future Directions

Abstract. This paper examines recommender systems in e-commerce by reviewing technologies and real-world applications and identifying the importance of big data analytics in recommender systems. In the study, three kinds of recommendation algorithms are discussed: collaborative filtering, content-based recommendation, and hybrid models. Collaborative filtering methods did well in the case of large-scale user data, but had cold-start and sparsity problems. Based on the content, recommended methods have strong personalized suggestion functions, but the information cocoon phenomenon is a risk, which decreases the contents diversity. Hybrid models are a combination approach between the two techniques, providing a flexible and robustness solution, but only a more complex computationally. This article also looks at the technology trends that have emerged lately, for example, the use of deep learning models, as well as the privacy-preserving techniques utilized in recommender systems. By analyzing and summarizing the existing research, this paper provides a reference basis for future optimization and application of recommender systems and points out potential research directions.

Prolonged cultivation enhances the stimulatory activity of hiPSC mesenchymal progenitor-derived conditioned medium

BackgroundHuman induced pluripotent stem cells represent a scalable source of youthful tissue progenitors and secretomes for regenerative therapies. The aim of our study was to investigate the potential of conditioned medium (CM) from hiPSC-mesenchymal progenitors (hiPSC-MPs) to stimulate osteogenic differentiation of human bone marrow-derived mesenchymal stromal cells (MSCs). We also investigated whether prolonged cultivation or osteogenic pre-differentiation of hiPSC-MPs could enhance the stimulatory activity of CM.MethodsMSCs were isolated from 13 donors (age 20–90 years). CM derived from hiPSC-MPs was added to the MSC cultures and the effects on proliferation and osteogenic differentiation were examined after 14 days and 6 weeks. The stimulatory activity of hiPSC-MP-CM was compared with the activity of MSC-derived CM and with the activity of CM prepared from hiPSC-MPs pre-cultured in growth or osteogenic medium for 14 days. Comparative proteomic analysis of CM was performed to gain insight into the molecular components responsible for the stimulatory activity.ResultsPrimary bone marrow-derived MSC exhibited variability, with a tendency towards lower proliferation and tri-lineage differentiation in older donors. hiPSC-MP-CM increased the proliferation and alkaline phosphatase activity of MSC from several adult/aged donors after 14 days of continuous supplementation under osteogenic conditions. However, CM supplementation failed to improve the mineralization of MSC pellets after 6 weeks under osteogenic conditions. hiPSC-MP-CM showed greater enhancement of proliferation and ALP activity than CM derived from bone marrow-derived MSCs. Moreover, 14-day cultivation but not osteogenic pre-differentiation of hiPSC-MPs strongly enhanced CM stimulatory activity. Quantitative proteomic analysis of d14-CM revealed a distinct profile of components that formed a highly interconnected associations network with two clusters, one functionally associated with binding and organization of actin/cytoskeletal components and the other with structural constituents of the extracellular matrix, collagen, and growth factor binding. Several hub proteins were identified that were reported to have functions in cell-extracellular matrix interaction, osteogenic differentiation and development.ConclusionsOur data show that hiPSC-MP-CM enhances early osteogenic differentiation of human bone marrow-derived MSCs and that prolonged cultivation of hiPSC-MPs enhances CM-stimulatory activity. Proteomic analysis of the upregulated protein components provides the basis for further optimization of hiPSC-MP-CM for bone regenerative therapies.Graphical

Construction of inherent geometric pattern for equal-area hexagonal discrete global grid systems

ABSTRACT Discrete Global Grid Systems (DGGSs) are hierarchical frameworks that provide seamless global coverage and enable the efficient processing and analysis of heterogeneous geospatial data. As cell resolution becomes finer, similarities in cell attributes, such as cell size and shape, increase. Understanding these shared characteristics enhances our knowledge of DGGSs structures, improving their reliability and accuracy across a range of applications. However, existing methods for quantifying these characteristics are limited, often emphasizing overall analysis and visualization rather than detailed assessment. This paper introduces a geometric pattern quantification method for equal-area hexagonal DGGSs by calculating inter-level attribute similarities to identify a reference level, segmenting the spatial distribution image at this reference level, and fitting the boundaries of these sub-images to create iso-feature lines. The results show that this method improves the accuracy and efficiency of cell attribute calculations, with enhancements of at least 5.6 times for study regions and 13.8 times for sample points at lower levels. This pattern provides a robust basis for grid selection and optimization in regional applications, promoting the broader adoption of DGGSs across various fields.

Dynamic Resource Allocation: The Geometry and Robustness of Constant Regret

We study a family of dynamic resource allocation problems, wherein requests of different types arrive over time and are accepted or rejected. Each request type is characterized by its reward, arrival probability, and resource consumption. An upper bound for the collected reward is given by a linear optimization problem with a random right-hand side. This type of problem, known as packing linear program (LP), is ubiquitous in resource allocation. We provide a detailed characterization of the parametric structure of this packing LP. Relying on this geometric understanding, we revisit and expand on BudgetRatio algorithms that achieve constant regret by resolving this same packing LP in each period and accepting requests scored as sufficiently valuable. We illustrate the benefits of the geometric view in proving that (i) BudgetRatio achieves constant regret relative to the offline (full information) upper bound in the presence of inventory that is (slowly) restocked, and (ii) within explicitly identifiable bounds, the algorithm’s regret is robust to misspecification of the model parameters. This gives bounds for the bandits version of the problem in which the parameters have to be learned. (iii) The algorithm has an equivalent formulation as a generalized bid-price algorithm in which the bid prices can be adaptively and efficiently computed. Our analysis focuses on the evolution of the remaining inventory—in turn of the LP that drives BudgetRatio—as a stochastic process. We prove that it is attracted to sticky regions of the state space in which the online algorithm takes actions consistent with the optimal basis of the offline upper bound, a basis that is revealed only in hindsight at the horizon’s end. Funding: This work was supported by the U.S. Department of Defense [Grant W911NF-20-C-0008].

Fracture and fractal characteristics of high-temperature granite under confined space water jet impact

In the application of jet assisted deep drilling technology, the fracture mode and rock fragment characteristics of high-temperature rock in the confined space of jet drilling are not yet clear. We analyzed the fragmentation and debris distribution of high-temperature granite under confined space jet action using 3D reconstruction technology and statistical fractal theory to examine their fragmentation patterns and fractal characteristics. The results show that high-temperature rocks exhibit obvious thermal fracturing characteristics under the impact of confined water jet, and as the rock temperature increases, vertical compression shear cracks connect with radial tensile cracks. The maximum damage area is located near the bottom of the confined hole, and the distribution of cracks is more complex. As the degree of jet confinement decreases, the rock cracking form changes from radial tensile cracking to vertical compression shear cracking. As the rock temperature increases and the confined space size decreases, the total mass and large debris mass percentages of rock debris increase. The particle size distribution of rock debris follows an Rosin-Rammler (R-R) distribution and has obvious fractal characteristics. The fracture and fragmentation of high-temperature rocks under confined water jet impact are mainly caused by the combination of jet impact stress waves and thermal stress. The temperature difference of rocks affects the heat transfer between jet and rock, and the confined space size directly affects the heat transfer efficiency between jet and rock, which is manifested in different numbers of fracture cracks, internal damage area, and rock debris size distribution. These findings can provide a theoretical and parameter optimization basis for jet-assisted deep reservoir drilling applications.

Quantification of streamflow response to climate change and human activities within upstream mountainous areas of the Daqing River Basin, Northern China

The Daqinghe River Basin is located in the North China Plain. In recent years, however, climate warming, drying, and intense human activities have led to declining ecosystem functions and shrinking wetlands in the region. Understanding streamflow changes in the upstream mountainous areas of the Daqinghe River Basin in this changing environment and identifying the driving factors can provide a scientific basis for water resources management and optimization in these areas. This study focuses on the Beihedian River watershed, the Xidayang Reservoir watershed, and the Wangkuai Reservoir watershed in the upstream mountainous areas of the Daqinghe River. It is based on hydro-meteorological data collected between 1963 and 2019. The methods used in the study include the linear tendency estimation method, the non-parametric Mann-Kendall trend test, the elasticity coefficient method, and hydrological simulation methods. The results of this study suggest that the streamflow, precipitation, and potential evapotranspiration (PET) in the three watersheds showed an overall decreasing trend. The minimum precipitation decrease rate ranged from −1.09 to −0.55 mm/a, and the minimum streamflow decreasing rate at the Beihedian Hydrological Station was −1.32 mm/a, with a minimum range of 0–176.03 mm. Change-point analysis revealed that the streamflow in the Beihedian River and Xidayang Reservoir watersheds experienced a significant change point around 1999, with a significant level of α=0.05. As for the Wangkuai Reservoir watershed, a significant change point was observed around 1980, which is likely attributable to land system reforms and protective forest projects. The attribution analysis which combined both climate change and human activities using the elasticity coefficient method and hydrological simulation methods indicated that climate change contributed an average of 32.93%, 34.50%, and 35.12% to the reduction in streamflow in the three watersheds, respectively. Human activities accounted for an average contribution of 67.07%, 65.50%, and 64.88%, respectively. Water conservancy projects, afforestation, and other human activities were identified as the primary factors contributing to streamflow decreases.

DETERMINATION OF DOSE REFERENCE LEVELS AT BENDAN PEKALONGAN REGIONAL HOSPITAL FOR INTERNAL DOSE AUDIT AS AN EFFORT TO OPTIMIZE RADIATION PROTECTION FOR PATIENTS

Unnecessary exposure is less of a concern because it is considered not to cause injury to patients. Radiation protection optimization is carried out for patients to reduce unnecessary exposure. Following BAPETEN Regulation No. 4 of 2020 Article 46, permit holders are required to implement radiation protection optimization [1]. This study aimed to obtain the local dose reference level (TPD) value through an internal dose audit to optimize patient radiation protection. This optimization is carried out in several steps, including conducting an internal dose audit, determining the local TPD, and evaluating the examination. This study is expected to motivate documentation of radiation parameters to obtain sufficient data for analysis. The method used in this study is a literature study through related references and X-ray radiation output data to calculate the dose in the form of incident air kerma (INAK) and entrance surface air kerma (ESAK). The results showed that the local INAK TPD value was 52% below the national TPD, and the local ESAK TPD value was 57% below the national TPD. The expected local TPD value is a TPD value that is lower than the national TPD value. This indicates that the imaging protocol at RSUD Bendan has been well-optimized. However, there are still several types of examinations where the local TPD value exceeds the national TPD, indicating the need to review further the imaging techniques and protocols used. The influence of body weight and radiological image diagnosis was analyzed as a basis for further optimization. Thus, the determination of this local TPD can be an essential reference in efforts to improve patient safety and optimize radiation protection in health facilities. Keywords: radiation protection optimization of patient, Dose Reference Level (DRL), patient dose.

Influence of elevated temperature on the interfacial adhesion properties of cement composites reinforced with recycled coconut fiber

Magnesium phosphate cement (MPC) is the preferred material for rapid repair due to its rapid setting and early strength properties. However, its inherent brittleness renders it less suitable for use in assembled building nodes. To address the brittleness of MPC, coconut fiber (CF), a natural fiber known for its toughness, is often employed as a reinforcement. However, CF-MPC encounter difficulties in high-temperature settings, such as fire emergencies, which may compromise the interfacial bonding properties of this materials. This paper presents an investigation into the interfacial bonding properties of CF-MPC at elevated temperatures. The study employed microscopic analysis to observe the material changes of CF at elevated temperatures, evaluated the mechanical property changes by means of Fiber Tensile Test, and assessed the interfacial bonding performance by means of Fiber Drawing Test between CF and MPC. The findings indicated that as the temperature increased, the carbonized residue of the CF gradually decomposed, resulting in a notable decline in mechanical strength. Upon exceeding 400 °C, the carbonization of CF was complete, resulting in the loss of its mechanical properties. Besides the results also demonstrated that the interfacial bond strength of CF-MPC at a burial depth of 7.5 mm reaches its maximum value of 2.14 MPa at 20 °C. As the temperature rises to 200 °C, the interfacial bond strength at a burial depth of 12.5 mm reaches its maximum value of 1.08 MPa at this temperature. The findings of this study are conducive to promoting the application of CF-MPC at high temperatures and provide a theoretical basis for the subsequent optimization of this material.

New metrics for measuring 2D uniformity in stirring system based on reconstruction of the particle trajectory

In industrial mixing systems, the evaluation method is a crucial indicator for measuring mixing uniformity. This study proposes an assessment method that combines a method for positioning using dual cameras and a point pattern density fluctuation (PD) method based on disordered hyperuniformity. This study employs a method for positioning using dual cameras to investigate the motion trajectories of tracer balls and achieve a 3D reconstruction process. The reconstructed 3D systems are cross-sectioned through on the axis of the stirring paddle to form a 2D planar diagram of the tracer ball trajectories. The PD method is used to evaluate the mixing effectiveness of the 2D trajectory points, establishing evaluation metrics for the 2D mixing system. To ensure the accuracy of the PD method, a feasibility analysis was conducted using the conductometry. A relationship model between λ and mixing time was established, with a Pearson correlation coefficient (r) of -0.9867, indicating a strong negative linear correlation. The relationship between the planes and whole 3D space was analyzed in terms of the whole and the discrete perspective, as well as the existing methods were compared with other approaches. This study not only deepens the theoretical understanding of evaluating mixing uniformity but also provides a crucial foundation and theoretical basis for process optimization of stirring systems and design of stirred reactors. It offers valuable references and insights for engineering design and technological improvements in related fields.

Study on the enhancement effect of EDTA and oxalic acid on phytoremediation of Cr(VI) from soil using Datura stramonium L.

This study investigated the enhancing effects of soil treatment with ethylene diamine tetraacetic acid (EDTA) and oxalic acid (OA) on the remediation of Cr(VI) contaminated soil by Datura stramonium L. A greenhouse pot experiment was conducted, where Cr(VI) contaminated soil was treated with 100 mg/kg Cr(VI) and varying concentrations of EDTA (5 and 10 mmol/kg) and OA (5 and 10 mmol/kg). The effects of these soil treatments on biomass, chlorophyll content, antioxidant enzyme activities, and Cr(VI) enrichment and translocation efficiency of D. stramonium were evaluated. The results showed that added OA to soil significantly increased the biomass and chlorophyll content of D. stramonium. The addition of 10 mmol/kg of OA to soil increased the plant biomass by 67.16 % and chlorophyll b content by 40.01 %. In addition, OA soil treatment significantly enhanced the activities of superoxide dismutase (SOD) by 6.36 %, peroxidase (POD) by 163.13 %, catalase (CAT) by 36.92 %, and ascorbate peroxidase (APX) by 32.12 %, which effectively alleviated the oxidative stress induced by Cr(VI). In contrast, soil treatment with a high concentration of EDTA (10 mmol/kg) significantly reduced plant biomass and chlorophyll content, although it increased the biological concentration factor (BCF) of the stem and leaf, as well as the translocation factor (TF). In conclusion, appropriate amounts of EDTA and OA added to soil can enhance the phytoremediation efficiency of D. stramonium grown in Cr(VI) contaminated soil, with OA added to soil being more effective than addition of EDTA. This study revealed the potential mechanisms of chelating agents EDTA and OA in enhancing the phytoremediation of Cr(VI) contaminated soil by D. stramonium, providing a scientific basis for further optimization of phytoremediation techniques.

Space heterogeneity of nitrogen removal functional genes, nitrogen transformation pathways and mechanisms in MEBR treating mariculture wastewater

Membrane Electro-Bioreactor (MEBR), exhibited excellent nitrogen removal in mariculture wastewater treatment. However, the differences of microbial community and nitrogen transformation pathways on spatial scale caused by the mass transfer of reactive chlorine species (RCS) generated by electrooxidation were unclear. This study provided new insights into the space heterogeneity of ammonia transformation pathways and mechanisms. The results demonstrated an increase in the reduction of nitrate to nitrite through partial denitrification on the membrane. Coupling with dissimilatory nitrate reduction to ammonium and RCS, higher nitrate (0.3 mgN/L) and TN removal (2.6 mgN/L) was obtained in MEBR. Higher relative abundance of narGHI and nirB were obtained. Ammonia oxidation by biofilters was enhanced in MEBR, that ammonia removal contributed by biofilters was 32.0 %, 5.2 times higher than the membrane. Relative abundance of amoABC and Nitrosomonas in biofilters were the highest. The results will provide theoretical basis for reactor configuration and operation optimization.

Parametric structure optimization of a main rotor blade as an application for FSI analysis in main rotor optimization process

Purpose Modern warfare and modern battlefield are very demanding. The recent conflicts showed that the usage of the helicopters is very limited and only the best constructions are able to provide support for the operations. The purpose of this research is to show the possibilities of new design tool for main rotor aerostructural optimization. It is a next chapter of the research that is aimed at finding new solutions for rotorcraft constructions. Design/methodology/approach This work presents a method of preliminary structure optimization of the main rotor blade using parametric modeling. It is the next step in the main rotor optimization studies. It is the next step after preparing the parametric model for the external shape CFD analysis. As a basis for parametric blade structure calculations, the analytical model is provided in this paper. The equations of rigid blade loads and, as a consequence of the strength elements, stresses are shown. The parametric blade modeling is conducted using the Graphic Integrated Programming language. The parametric design method is shown to be used for various blade planform models and different section airfoils. The structure of a blade is generated automatically after the user enters the parameters. The code-inbuilt analysis systems provide a quick inertia examination of the generated geometry, which is the basis for further optimization. The program calculates the blade loads and verifies them with the given material conditions and proposed safety factors. In the analysis, composite materials for the strength elements were proposed. Findings The results of this research showed the application of parametrization into the main rotor blade design loop. It was presented that the main rotor blade structure can be enhanced using fluid-structure interaction (FSI) methods. The time saving with the implementation the process into design loop is shown. Practical implications This work can be practically used in the main rotor blade design process. It provides the possibilities to check various blade aerodynamic configuration in a structure strength aspect. Originality/value To the best of the authors’ knowledge, there were no published research that combines the main rotor FSI analysis. The method, which is presented in the work, provides a new approach to a rotorcraft design. The application of the parametrization and combining it with the FSI method gives a novel solution for helicopters construction enhancement.

Coordinated Planning for Multiarea Wind-Solar-Energy Storage Systems That Considers Multiple Uncertainties

As the scale of renewable energy sources (RESs) expands, it is essential to optimize the configuration of wind, solar, and storage resources across different areas. Nevertheless, the unavoidable uncertainties associated with both energy supply and demand present significant challenges for planners. This study aims to address the challenge of coordinated planning for multiarea wind-solar-energy storage systems considering multiple uncertainties. First, uncertainties related to future peak demand, thermal generation output boundaries, demand variability, and stochastic unit production are analyzed and modeled on the basis of robust optimization and stochastic programming techniques. Then, a hierarchical coordinated planning model that incorporates both system-wide (SW) and local area (LA) planning models is proposed. The SW planning model is designed to manage the optimal capacity configuration of RESs and energy storage systems (ESSs) within each LA, as well as the operational boundary of LAs. The LA planning models aim to further optimize the capacities of RESs and ESSs and minimize the economic cost within each LA on the basis of local resource characteristics. To achieve the optimal solution, the analytical target cascading (ATC) algorithm is integrated with the column-and-constraint generation (C&CG) algorithm. The simulation results validate the effectiveness and reasonableness of the proposed coordinated planning model, which not only outperforms independent planning approaches but also effectively manages the uncertainties.

Evolution characteristics of landscape ecological risk patterns in Shangluo City in the Qinling Mountains, China

Landscape ecological risk assessment (LERA) is the basis of regional landscape pattern optimization, and a tool that can help achieve a win-win situation between regional development and ecological protection. The landscape ecological risk (LER) of the southern end of the Qinling Mountains, China exhibited an increasing trend after the year 2000, but the degree of increase and the spatial and temporal dynamics were not clear, limiting the formulation and implementation of landscape optimization measures in the area. Here, we constructed a landscape pattern risk index ERI by combining data on landscape disturbance and landscape vulnerability from land use information for Shangluo City for years 2000, 2005, 2010, 2015, and 2020; then, we calculated a LER level and its spatial and temporal dynamics for Shangluo City for years 2000 to 2020. Moran’s I and LISA indices were used to characterize the spatial correlation of ERI in Shangluo City. We found that Shangluo had a large proportion of medium-risk areas, and its LER shifted from medium-high, high in year 2000 to medium risk, medium-low and low risk in year 2020, and LER of Shangluo was clustered in space but the degree of clustering decreased in the past 20 years. We conclude that the development strategy of Shangluo should depend on providing a sustainably-developed environment.

Novel Dual Acetyl- and Butyrylcholinesterase Inhibitors Based on the Pyridyl–Pyridazine Moiety for the Potential Treatment of Alzheimer’s Disease

Background: Alzheimer’s disease (AD) is characterized by cholinergic dysfunction, making the inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) critical for improving cholinergic neurotransmission. However, the development of effective dual inhibitors remains challenging. Objective: This study aims to synthesize and evaluate novel pyridazine-containing compounds as potential dual inhibitors of AChE and BuChE for AD treatment. Methods: Ten novel pyridazine-containing compounds were synthesized and characterized using IR, 1H NMR, and 13C NMR. The inhibitory activities against AChE and BuChE were assessed in vitro, and pharmacokinetic properties were explored through in silico ADME studies. Molecular dynamics simulations were performed for the most active compound. Results: Compound 5 was the most potent inhibitor, with IC50 values of 0.26 µM for AChE and 0.19 µM for BuChE, outperforming rivastigmine and tacrine, and showing competitive results with donepezil. Docking studies revealed a binding affinity of −10.21 kcal/mol to AChE and -13.84 kcal/mol to BuChE, with stable interactions confirmed by molecular dynamics simulations. In silico ADME studies identified favorable pharmacokinetic properties for compounds 5, 8, and 9, with Compound 5 showing the best activity. Conclusions: Compound 5 demonstrates strong potential as a dual cholinesterase inhibitor for Alzheimer’s disease, supported by both in vitro and in silico analyses. These findings provide a basis for further optimization and development of these novel inhibitors.

Deformation and optical aberration prediction in ultra-precision Single Point Diamond Turning of optical components

Aluminum is the material of choice for the majority of aerospace components, and, in the past few years, its application has been extended also to the mirrors of space telescopes because of the improved thermal behavior and the possibility to build the entire telescope with the same material. However, the low elastic modulus of such material, combined with the extremely tight tolerances of optical applications, make the production of these components very challenging and, usually, based on a trial-and-error approach. This paper presents a structured methodology for the prediction of the results of manufacturing in Single Point Diamond Turning of optical components, both in terms of absolute deformation as well as optical aberrations (via Zernike polynomials). All the most significant parameters acting on the workpiece have been simulated and combined. The proposed approach has been experimental validated on an actual aluminum mirror, proving its good accuracy (<5 % rms error). While some improvement can be performed to better match the experimental data in terms of Zernike coefficients, especially for non-symmetric aberrations, this paper forms the basis for an off-machine optimization of the SPDT process, drastically reducing the trial-and-error efforts.

Global Stochastic Comprehensive Sensitivity Analysis Based on Robustness Grouping and Improved Pelican Algorithm-Optimized Radial Basis Function Neural Network

Abstract Modeling analysis is one of the important means to analyze practical engineering, and as technology continues to evolve, various models are getting closer and closer to reality, while at the same time, there are more and more parameters in the models. It is important to analyse the impact of these parameters on the project to assist engineers in making plans or decisions. Sensitivity analysis (SA) can describe the effect of changes in these parameters on the model. However, complex models often have dozens or even hundreds of parameters, and most current SA methods struggle to deal reliably and effectively with these high-dimensional problems. In addition, it is difficult to obtain the sensitivity of continuous points in the parameter space with traditional SA methods. Therefore, this paper proposes a method that combines adaptive grouping and an Improved Pelican Optimization Algorithm for an optimal radial basis function (IPOA-RBF) agent model to solve these problems. Firstly, a clustering grouping method considering grouping robustness is established to obtain objective and stable parameter grouping results in high-dimensional sensitivity analysis. Secondly, a proxy model based on radial basis function neural network and an improved Pelican optimization algorithm are proposed to capture the logic of the proxy model to obtain the parameter sensitivity of continuous points in the parameter space. Finally, the superiority and applicability of this method is verified using an arch dam simulation model.

Tuning Na Occupation and Annealing Temperature on Na Diffusion Pathways in P2-Type NaxNi0.25Mn0.75O2 for Sodium-Ion Batteries by Ab Initio Calculations.

The NaxNi0.25Mn0.75O2 cathode material draws ongoing interest owing to its considerable specific capacity along with its elevated average operating voltage. However, its application is limited by weak rate performance and quick capacity fading. In this study, a series of NaxNi0.25Mn0.75O2 (x = 0.65, 0.70, 0.75, 0.80) cathode materials are prepared by the solid-phase method based on an orthogonal experiment. The optimum preparation process is investigated by optimizing factors such as Na content, calcination temperature, calcination time, and heating rate. The Nae/Naf ratio is adjusted, and the Na+ at different sites are rearranged to reduce the Na+/vacancy ordering by changing the Na content on the basis of process optimization. Owing to the fast migration kinetics of Nae sites, experimental results indicate that the P2-Na0.75Ni0.25Mn0.75O2 cathode material, which has Na+ occupying more thermodynamically stable Nae sites, demonstrates superior battery performance. High initial discharge specific capacity (157.2 mAh g-1) along with favorable cycle performance of the Na0.75Ni0.25Mn0.75O2 cathode material can be achieved by modifying the Nae site occupancy and optimizing the experimental conditions. Together with the microscopic control of Na+ occupancy, this novel orthogonal experiment design offers a fresh perspective and approach to a thorough comprehension of cathode materials for sodium-ion batteries.

A Gaussian statistics-based unit-cell modeling method for the mechanical behavior prediction of 2.5D shallow straight-link-shaped woven composites

The shallow straight-link-shaped structure of 2.5D woven composites (2.5D-SSLS-WC) is a novel material with a wide range of promising applications, but the randomness of its internal structure has brought challenges to performance prediction and engineering design. The gap of existing unit-cell models in capturing the internal randomness of the 2.5D-SSLS-WC material limits the accurate prediction of properties and the optimization of engineering design. In this work, the modeling method for random unit-cell model based on Gaussian distribution is proposed for 2.5D-SSLS-WC structure considering random distribution property of weft yarns and extrusion effects between structural surfaces and internal yarns during the molding process, which can more effectively reflect the practical mesoscopic structure of the material, reveal the micro mechanical behavior inside materials and provide theoretical basis for material design and performance optimization. The mechanical property and damage evolution of the proposed model is predicted and analyzed using finite element (FE) analysis and progressive damage method. The maximum errors of equivalent stiffness and strength between prediction and experimental values are 6.7% and 2.3%, respectively. The results show that the proposed model has high prediction accuracy and can reasonably reflect the damage extension and ultimate failure mode of the material during the loading process, which verifies the rationality of the proposed unit-cell modeling strategy. The modeling method proposed in this paper provides a new perspective for the in-depth understanding and description of the microstructure and mechanical behavior of 2.5D-SSLS-WC materials, which is of significant theoretical and engineering value and can be extended to the modeling of other woven composites.

High‐Latitude Eddy Covariance Temporal Network Design and Optimization

AbstractEcosystems at high latitudes are changing rapidly in response to climate change. To understand changes in carbon fluxes across seasonal to multi‐decadal timescales, long‐term in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high‐latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning‐based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter‐term measurements functions better in terms of larger‐scale representativeness than a network with fewer long‐term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. For the Canadian Arctic we show several optimization scenarios and find that especially the Canadian high Arctic and north east tundra benefit greatly from addition sites. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.