Random Stacking Research Articles

DISC FORAGE CROPPER

In the theoretical substantiation of the grinding process of crushed corn stalks in the working area of the disc grinder, the random stacking of particles of irregular shape in the average cross-section was considered. The principle of operation of the shredder consists in breaking pieces of the product in the working zone formed by the surfaces of the moving and stationary discs. At the same time, the intensity of grinding increases as the material moves from top to bottom in the decreasing working gap. The design of the disc feed shredder is substantiated, which will ensure a reduction in the metal content of the structure, and will allow the production of feed mixtures of a given granulometric composition.

Stochastic stability of random stacking of blocks

The paper explores the stability of a tower obtained by stacking identical rectangular blocks on top of each other with an inherent randomness due to slight positional offsets between successive blocks. With probabilistic modeling techniques, the diffusive behavior of the stacking process is studied and the collapse is seen as a first passage time problem. In the considered model, alignment errors are idealized as independent Gaussian random variables with zero mean and given standard deviation. We derive expressions for the joint probability density functions of block positions, and analyze their correlation. The study extends to the stochastic stability of a stack of given height, by exploring the statistical characteristics of the center of gravity of the part of tower above each block. Eventually, the probabilistic analysis of collapse is developed to quantify the statistics of the number of blocks that can be heaped up before the tower topples. Although this problem may initially appear playful, it offers an illustrated introduction to first passage problems on a non homogenous process. From a practical standpoint, this analysis offers a simple understanding of the influence of alignment errors on the overall stability of a stack, which may find several fields of application.

Ensemble surrogate modeling of advective-dispersive transport with intraparticle diffusion model for column-leaching test

Column-leaching tests are a common approach for assessing the leaching behavior and resulting environmental risks of contaminated soils and waste materials, which are frequently reused for various construction purposes. The observed breakthrough curves of the contaminants are influenced by the complex dynamics of solute transport and kinetic inter-phase mass transfer. Disentangling these interactions necessitates numerical models. However, inverse modeling and sensitivity analysis can be time-consuming, especially when sorption kinetics are explicitly described by intraparticle diffusion, which requires discretizing the domain both in the flow direction along the column axis and inside the grains. To circumvent the need for such computationally intensive models, we have developed two different ensemble surrogate models. These models employ two separate ensemble methods: random forest stacking and inverse-distance weighted interpolation. Each method is applied to base surrogate models that cover different parts of the parameter space. The base surrogate models use the method of Extremely randomized Trees (ExtraTrees). The defined parameter range is based on the German standard for column-leaching tests. To optimize the base surrogate models, we utilized adaptive-sampling methods based on three distinct infill criteria: maximizing the expected improvement, staying within a certain Mahalanobis distance to the best estimate (both for exploitation), and maximizing the standard deviation (for exploration). The ensemble surrogate model demonstrates excellent performance in emulating the behavior of the original numerical model, with a relative root mean squared error of 0.09. We applied our proposed ensemble surrogate model to estimate the complete posterior parameter distribution using Simulation-Based Inference, specifically Neural Posterior Estimation, to determine the full parameter distribution conditioned on copper-leaching data from two different soils. Samples drawn from the posterior distribution align perfectly with the observed data for both the surrogate and original models.

IoT-Driven Healthcare an Ensemble Learning Approach for Early Detection and Prevention of Chronic Diseases

This investigate presents an IoT-driven healthcare system leveraging outfit learning methods for early discovery and avoidance of unremitting illnesses. By joining information from wearable sensors, shrewd domestic gadgets, and electronic wellbeing records, the system offers a comprehensive wellbeing checking framework. Gathering learning calculations, counting Random Forest, Slope Boosting, Stacking, and Bolster Vector Machines, are utilized to combine numerous models' qualities, improving forecast exactness and robustness. Experimental comes about on an assorted dataset illustrate the viability of the proposed approach, accomplishing a precision of 85%, accuracy of 87%, review of 84%, F1-score of 85%, and AUC-ROC of 0.92. Comparative examinations with standard calculations and related work within the field emphasize the prevalence of the outfit learning approach in leveraging IoT information for healthcare applications. This investigation contributes to progressing IoT-driven healthcare by giving bits of knowledge into the potential of gathering learning procedures for illness expectation and avoidance. By making strides early location and personalized mediations, the system points to upgraded understanding results, diminishing healthcare costs, and progress in populace well-being management.

CNT modified chopped ultra-thin CF/PET tapes reinforced PET thermoplastic composite laminates with elevated interfacial properties and ultra-low porosity

Carbon fiber (CF) reinforced thermoplastic polyethylene terephthalate (PET) laminate composites with light weight, high strength, excellent damage tolerance, and recyclability have garnered increasing attention. However, the weak interface between inert CF and PET resin, as well as the difficulty of impregnating the considerably high melt viscosity of PET matrix into the CF bundles, have impeded further development of the CF/PET composites. To settle the challenge, the chopped ultra-thin CF tapes (thickness of 40 μm and length of 30 mm) reinforced PET composite laminates were prepared by mechanical spreading technology, following with random stacking and molding technology via the plasma treatment and PET/carbon nanotubes (CNT) sizing. As the results, the optimal interlaminar shear strength (ILSS), tensile strength, and Young's modulus of the chopped ultra-thin CF tapes reinforced PET composites (with 1.0 wt% CNT) achieved 42.4 MPa, 780.9 MPa, and 47.8 GPa, which increased by 41.9 %, 40.7 %, and 68.9 %, respectively, compared to unmodified CF/PET composites. Especially, the porosity of the composites reached 0.68 %, decreased by 84.3 %. Therefore, combining the easy infiltrability of chopped ultra-thin CF tapes technology and the multiscale interfacial reinforced strategy (plasma treatment and PET/CNT sizing) is an effective way to reduce the porosity and enhance the mechanical properties of the thermoplastic composites.

Epitaxial Growth of CsPbBr3 Pyramids/CdS Nanobelt Heterostructures for High-Performance Photodetectors.

Perovskites have great potential for optoelectronic applications due to their high photoluminescence quantum yield, large absorption coefficient, great defect tolerance, and adjustable band gap. Perovskite heterostructures may further enhance the performance of optoelectronic devices. So far, however, most of perovskite heterostructures are fabricated by mechanical stacking or spin coating, which could introduce a large number of defects or impurities at the heterointerface owing to the random stacking process. Herein, we report the epitaxial growth of CsPbBr3 pyramids/CdS nanobelt heterostructures via a 2-step vapor deposition route. The CsPbBr3 triangular pyramids are well aligned on the surface of CdS nanobelts with the epitaxial relationships of (0-22)CsPbBr3||(1-20)CdS and (-211)CsPbBr3||(002)CdS. Time-resolved photoluminescence results reveal that effective charge transfer occurred at the heterointerface, which can be attributed to the type-II band arrangement. Theoretical simulations reveal that the unique CsPbBr3 pyramids/CdS nanobelt structure facilitates diminishing the reflection losses and enhancing the light absorption. The photodetector based on these CsPbBr3 pyramids/CdS nanobelt heterostructures exhibited an ultrahigh photoswitching ratio of 2.14 × 105, a high responsivity up to 4.07 × 104 A/W, a high detectivity reaching 1.36 × 1013 Jones, fast photoresponses (τrise = 472 μs and τdecay = 894 μs), low dark current, and suppressed hysteresis.

Research on the Prediction of Flight Passenger Flow in Holidays based on Machine Learning

This article focuses on predicting holiday flight passenger flow to optimize airline resource allocation. Analyzing daily passenger data during key holidays, including New Year’s Day, Qingming Festival, Labour Day, Dragon Boat Festival, Mid-Autumn Festival, and National Day, the study employs four machine learning models: Random Forest, Multilayer Perceptron, LightGBM, and Stacking. Findings reveal that all models effectively capture holiday flow patterns, with LightGBM demonstrating superior prediction accuracy. Moreover, creating unified models for all holidays outperforms individual holiday-specific models. The study delves into the factors influencing the varying performance of the best model across different features, providing insightful analysis and discussion.

Innovative AI-driven design of patient-specific short femoral stems in primary hip arthroplasty

This innovative study proposes a revolutionary strategy to enhance the efficacy and customization of hip arthroplasty, focusing on a short prosthesis design meticulously tailored to individual patient needs, with special emphasis on the femoral stem. The implemented methodology involves data collection through a meshless software, which quantifies the stress/strain shielding resulting from femoral insertion. Automatic pre-processing is performed using clustering algorithms, allowing the data to be divided into anatomical areas of the body. Subsequently, training of machine learning algorithms, such as random forests and stacking, is carried out to accurately predict the shielding caused by the stem. These predictions are based on design parameters, specifically linked to the patient's femoral cavity dimensions and their relationship to stem size, which are quantified through dimensionless parameters and serve as model inputs. The reliability of the models is confirmed by evaluation of data from patient with different femoral morphology than those used during the training and validation phases. This approach not only provides fast and cost-effective analyses for medical experts and engineers, but the described methodology can also be successfully extended to the development of alternative arthroplasty prostheses, such as shoulder and knee implants.

Precursor screening of fruit shell derived hard carbons for low-potential sodium storage: A low lignin content supports the formation of closed pores

Biomass hard carbons are being explored for anode materials of sodium-ion batteries due to the wide availability. However, the precursor screening for optimal microstructure and sodium storage performance is tedious and time-consuming. Here, we selected three kinds of fruit shells to prepare hard carbons for sodium-ion batteries and investigated their structure characteristics and sodium storage performance. It is demonstrated that a low lignin content in fruit shells is favor to the formation of closed pores in the derived hard carbons, thus generating a high low-potential plateau capacity. Among the main component of biomass, lignin with rich aromatic ring structure possesses higher thermal stability than cellulose and hemicellulose. During the pyrolysis and carbonization, lignin may decompose to large-size crystalline domains, which hinders the random stacking of carbon layers. Therefore, as the lower lignin content than pine nut shell and macadamia nut shell (28.4% vs. 39.2% and 37.4%), the walnut shell derived hard carbon shows a largest interlayer distance of 0.397 nm and a highest closed pore area of 391 m2 g−1, contributing to the supreme specific capacity of 362.6 mAh g−1, with a plateau capacity of 195.8 mAh g−1 as well as a remarkable cycle stability. The assembled WNSC//NVP full-cell remains a discharge capacity of 53.1 mAh g−1 after 100 cycles at 0.1 A g−1. This work gives a deep insight of the relationship between biomass composition and microstructure of the derived hard carbons to explore its high sodium storage ability in low-potential region.

Long indium-rich InGaAs nanowires by SAG-HVPE

We demonstrate the selective area growth of InGaAs nanowires (NWs) on GaAs (111)B substrates using hydride vapor phase epitaxy (HVPE). A high growth rate of more than 50 μm h−1 and high aspect ratio NWs were obtained. Composition along the NWs was investigated by energy dispersive x-ray spectroscopy giving an average indium composition of 84%. This is consistent with the composition of 78% estimated from the photoluminescence spectrum of the NWs. Crystal structure analysis of the NWs by transmission electron microscopy indicated random stacking faults related to zinc-blende/wurtzite polytypism. This work demonstrates the ability of HVPE for growing high aspect ratio InGaAs NW arrays.

Predicting Landslide Using Machine Learning Techniques

In mountainous areas prone to landslides, it’s crucial to map out where these hazardous events are likely to occur to mitigate risks effectively. This study focuses employing an integrated approach to assess landslide susceptibility using Random Forest (RF), Stacking, Vote, AdaBoostM1, and Bagging. 13 factors influencing landslide occurrence are identified for modeling purposes. To evaluate and compare the models’ performance, multiple statistical methods are employed. The analysis highlights the effectiveness of employing machine learning models, Random Forest (RF), Stacking, Bagging, and Vote methods. The results demonstrate the efficiency of the models in accurately predicting landslide susceptibility. The study suggests that similar hybrid models can be effectively utilized in other sensitive regions with comparable geo-environmental conditions for landslide susceptibility studies. By integrating various techniques and leveraging ensemble algorithms, these models offer improved accuracy and reliability in assessing landslide hazards. This comprehensive approach provides valuable insights for disaster management and risk reduction efforts in landslideprone areas worldwide.

Optimization of the different prediction techniques for mastitis disease detection in cows

The pervasive and economically impactful disease in dairy animals, mastitis, presents a significant challenge to the global dairy industry. Accurate and prompt diagnosis is paramount for effective treatment and to curb the spread within the herd. Leveraging advancements in machine learning for disease identification, this article reviews the application of ensemble models in detecting mastitis in cows. The document outlines mastitis, covering its origins, symptoms, and implications for milk supply and animal well-being, emphasizing the limitations of traditional diagnostic methods. It underscores the need for automated, reliable detection strategies and offers a thorough assessment of ensemble machine learning models consists of random forest, gradient boosting, bagging, and stacking. The evaluation scrutinizes model performance, considering datasets, feature selection methods, model designs, and assessment metrics. This review serves as a valuable resource for researchers, veterinarians, and dairy industry professionals seeking to implement machine learning algorithms for mastitis identification. Providing insights into the current landscape pertaining to cow mastitis worldwide , identifying knowledge gaps, and proposing solutions for enhanced accuracy, the review contributes to advancing mastitis detection, ultimately improving cow health and productivity.

Surface atom migration-involved two-dimensional nucleation and growth of nanotwinned copper in DC electrodeposition

Direct-current (DC) Electrodeposited nanotwinned copper (nt-Cu) is a promising candidate for interconnection materials in integrated circuit for its excellent mechanical, electrical and thermal properties. However, the formation mechanism of nanotwins during DC electrodeposition of nt-Cu remains unclear. In this study, a two-dimensional nucleation and growth model of nt-Cu is established. The critical nucleation size and average twin spacing were evaluated theoretically. The results demonstrate a relatively high probability of twin nucleation through random stacking with the average twin spacing derived to be only ∼1 nm. The gap between theoretical and observed twinning probability can be ascribed to the surface migration-induced dissipation of nuclei. Molecular dynamics simulation further confirms the formation and subsequent dissipation of nuclei during deposition of Cu. By providing a clear illustration of nanotwin formation process, this study contributes to the understanding of nt-Cu electrodeposition.

The contribution of image processing in the evaluation of guided bone regeneration

One of the most ambitious goals of modern bone surgery is to predict the shape of the bone defect, monitor the progress of bone regeneration, and assess the quantity and quality of newly formed bone. Calcium phosphate biomaterials such as hydroxyapatite and tricalcium phosphate are commonly used as bone substitutes in maxillofacial and dental surgery. The objective of this work is to use cone-beam computed tomography (CBCT) and image processing to assess the spatial (architectural) layout, rate of bone generation and osseointegration of implanted commercial granules (PAH 40%, β-TCP 60%, size 0.5 to 1 mm) in the bone defect generated after tooth extraction. CBCT measurements were performed at 48 hours and 12 months. The analysis of 3D images and the application of appropriate morphological mathematical operations allowed us to evaluate the volume of the cavity to be filled, the volume occupied by the granules and the volume of porosity generated by the random stacking of the granules. The result shows that the bone generation rate reaches a value of 89% after one year of implantation. This study shows that by using 3D image processing techniques CBCT, the same results as classical anatomical and histological studies can be obtained.

Evaluating the quality of surface carbonized woods modified with a contact charring or a gas flame charring technique

Surface carbonization, or charring, of wooden exterior cladding boards is a modification method that creates a fully organic barrier layer in resemblance to a coating. The process effectively degrades the wood and transforms it into a carbonaceous residue that protects the underlying unmodified wood from environmental stresses. The surface quality of wood modified in this manner is a combination of several factors and depends on the manufacturing method and wood species. To assess the quality of spruce and birch modified with contact and flame charring techniques, several experiments were set up from the nanoscale to macroscopic evaluation of surface resistance to different stresses. The changes in elemental composition are scaled with the modification severity with little differences between wood species. The carbon structures analyzed by high-resolution transmission electron microscopy (HR-TEM) were found to be amorphous, but the electron energy-loss spectroscopy (EELS) revealed higher ordering with what is assumed to be random graphitic stacking of carbon sheets. These carbon–carbon bonds are stable, so a higher ordering is hypothesized to induce improved resistance to exterior stresses. The scanning electron microscopy (SEM) revealed a clear difference between contact-charred and flame-charred woods. The selected contact charring temperature was not high enough to induce the transformation of cell walls from anisotropic into an isotropic material but provided other benefits such as a relatively crack-free, smooth and scratch resistant surface. Surface roughness was able to adequately predict the surface quality of the contact-charred samples, and scratch tests were found to be suitable for evaluating the mechanical stress resistance of the surface instead of abrasion. In terms of overall quality, birch instead of spruce was concluded to better respond to both charring methods, although contact charring eliminates some species-specific characteristics, resulting in more homogeneous surfaces.

Large-scale assembly of isotropic nanofiber aerogels based on columnar-equiaxed crystal transition

Ice-templating technology holds great potential to construct industrial porous materials from nanometers to the macroscopic scale for tailoring thermal, electronic, or acoustic transport. Herein, we describe a general ice-templating technology through freezing the material on a rotating cryogenic drum surface, crushing it, and then re-casting the nanofiber slurry. Through decoupling the ice nucleation and growth processes, we achieved the columnar-equiaxed crystal transition in the freezing procedure. The highly random stacking and integrating of equiaxed ice crystals can organize nanofibers into thousands of repeating microscale units with a tortuous channel topology. Owing to the spatially well-defined isotropic structure, the obtained Al2O3·SiO2 nanofiber aerogels exhibit ultralow thermal conductivity, superelasticity, good damage tolerance, and fatigue resistance. These features, together with their natural stability up to 1200 °C, make them highly robust for thermal insulation under extreme thermomechanical environments. Cascading thermal runaway propagation in a high-capacity lithium-ion battery module consisting of LiNi0.8Co0.1Mn0.1O2 cathode, with ultrahigh thermal shock power of 215 kW, can be completely prevented by a thin nanofiber aerogel layer. These findings not only establish a general production route for nanomaterial assemblies that is conventionally challenging, but also demonstrate a high-energy-density battery module configuration with a high safety standard that is critical for practical applications.

Correlations in randomly stacked solids.

Packing of spheres is a problem with a long history dating back to Kepler's conjecture in 1611. The highest density is realized in face-centered-cubic (FCC) and hexagonal-close-packed (HCP) arrangements. These are only limiting examples of an infinite family of maximal-density structures called Barlow stackings. They are constructed by stacking triangular layers, with each layer shifted with respect to the one below. At the other extreme, Torquato-Stillinger stackings are believed to yield the lowest possible density while preserving mechanical stability. They form an infinite family of structures composed of stacked honeycomb layers. In this article, we characterize layer-correlations in both families when the stacking is random. To do so, we take advantage of the Hägg code-a mapping between a Barlow stacking and a one-dimensional Ising magnet. The layer correlation is related to a moment-generating function of the Ising model. We first determine the layer correlation for random Barlow stacking, finding exponential decay. We next introduce a bias favoring one of two stacking chiralities-equivalent to a magnetic field in the Ising model. Although this bias favors FCC ordering, there is no long-ranged order as correlations still decay exponentially. Finally, we consider Torquato-Stillinger stackings, which map to a combination of an Ising magnet and a three-state Potts model. With random stacking, the correlations decay exponentially with a form that is similar to the Barlow problem. We discuss relevance to ordering in clusters of stacked solids and for layer-deposition-based synthesis methods.

The Structure of Boron Monoxide.

Boron monoxide (BO), prepared by the thermal condensation of tetrahydroxydiboron, was first reported in 1955; however, its structure could not be determined. With the recent attention on boron-based two-dimensional materials, such as borophene and hexagonal boron nitride, there is renewed interest in BO. A large number of stable BO structures have been computationally identified, but none are supported by experiments. The consensus is that the material likely forms a boroxine-based two-dimensional material. Herein, we apply advanced 11B NMR experiments to determine the relative orientations of B(B)O2 centers in BO. We find that the material is composed of D2h-symmetric O2B-BO2 units that organize to form larger B4O2 rings. Further, powder diffraction experiments additionally reveal that these units organize to form two-dimensional layers with a random stacking pattern. This observation is in agreement with earlier density functional theory (DFT) studies that showed B4O2-based structures to be the most stable.

Thermal conductivity of quasi-bilayer graphene without and with perfluorophenylazide molecules

The obstacles in further improving the heat-transport performance of the graphene-based films root in the random stacking configuration of the graphene flakes and their agglomeration in solvents. In order to overcome these obstacles, we design one kind of quasi-bilayer graphene device composed of one upper narrow nanoribbon and one lower wide nanoribbon with absorbed perfluorophenylazide (PFPA) molecules. By investigating the thermal conductivity (TC) of the quasi-bilayer graphene without absorbed PFPA molecules, we find the TC will increase as the length and width increase, while the TC will decrease with the increase of the layer number. This indicates that a quasi-bilayer graphene flake of a large area and a small layer number will be beneficial to increasing its TC. Furthermore, in the presence of the PFPA molecules, the TC of the quasi-bilayer graphene, albeit suppressed by the PFPA molecules, is found to decrease slowly with the increase of the PFPA molecule number. This demonstrates a feasible way to fabricate the high-quality graphene-based films based on the technology of absorbing PFPA molecules. This research should be an important reference for designing the graphene-based thermal functional devices.

Present Situation and Future Development Trend of Domestic Waste Treatment Technology

With the development of economy in China, many kinds of exquisite packaging are produced to improve the appearance of products and protect the integrity and neatness of products. Moreover, with the improvement of people's living standards and the rapid development of urban construction, people's demand for and choice of various products are also increasing. At the same time, more and more domestic waste is being generated, the random stacking of a large amount of household garbage not only takes up a large amount of land resources, but also pollutes the environment and breeds a large number of mosquitoes, flies and bacteria. Over time, it will also cause harm to people's physical and mental health, it causes enormous pressure and pollution to people's living environment. The problems brought by domestic waste have become one of the top ten environmental problems in the world today. Recycling and reducing the amount of domestic waste is an important measure to promote environmental management and energy conservation and emission reduction, and it is of great significance to improve the ecological environment and sustainable development. In order to solve this problem, garbage classification and recycling is imperative. In order to realize the recycling economy, the state puts forward the concept of waste recycling and takes some measures to improve the management and efficiency of waste recycling. Garbage classification and recycling management is an important part of circular economy. On the one hand, the disposal of garbage can reduce the discharge of garbage from the source, on the other hand, it can also be solved by the reuse of resources. At present, China's waste treatment technology is gradually mature, but the classification of waste management is still relatively inadequate. In this paper, the current situation of domestic waste and the problems in the treatment process are analyzed, and the recycling of waste and other solutions are discussed. At present, China's domestic waste treatment is relatively simple, mainly landfill-based, and the next few years will not be much change. In order to realize the goal of harmless, reduction and resource utilization of garbage, it is an inevitable trend to improve the treatment technology and combine various treatment technologies.