Local Maximum Research Articles

A convexity-constrained parameterization of the random effects generalized partial credit model.

An alternative closed-form expression for the marginal joint probability distribution of item scores under the random effects generalized partial credit model is presented. The closed-form expression involves a cumulant generating function and is therefore subjected to convexity constraints. As a consequence, complicated moment inequalities are taken into account in maximum likelihood estimation of the parameters of the model, so that the estimation solution is always proper. Another important favorable consequence is that the likelihood function has a single local extreme point, the global maximum. Furthermore, attention is paid to expected a posteriori person parameter estimation, generalizations of the model, and testing the goodness-of-fit of the model. Procedures proposed are demonstrated in an illustrative example.

Individual Tree Crown Delineation Using Airborne LiDAR Data and Aerial Imagery in the Taiga–Tundra Ecotone

The circumpolar Taiga–Tundra Ecotone significantly influences the feedback mechanism of global climate change. Achieving large-scale individual tree crown (ITC) extraction in the transition zone is crucial for estimating vegetation biomass in the transition zone and studying plants’ response to climate change. This study employed aerial images and airborne LiDAR data covering several typical transitional zone regions in northern Finland to explore the ITC delineation method based on deep learning. First, this study developed an improved multi-scale ITC delineation method to enable the semi-automatic assembly of the ITC sample collection. This approach led to the creation of an individual tree dataset containing over 20,000 trees in the transitional zone. Then, this study explored the ITC delineation method using the Mask R-CNN model. The accuracies of the Mask R-CNN model were compared with two traditional ITC delineation methods: the improved multi-scale ITC delineation method and the local maxima clustering method based on point cloud distribution. For trees with a height greater than 1.3 m, the Mask R-CNN model achieved an overall recall rate (Ar) of 96.60%. Compared to the two conventional ITC delineation methods, the Ar of Mask R-CNN showed an increase of 1.99 and 5.52 points in percentage, respectively, indicating that the Mask R-CNN model can significantly improve the accuracy of ITC delineation. These results highlight the potential of Mask R-CNN in extracting low trees with relatively small crowns in transitional zones using high-resolution aerial imagery and low-density airborne point cloud data for the first time.

Beam Local Stress Prediction Model for Incremental Launching Construction Based on SSA-SVR Algorithm

In the multi-point incremental launching construction process, without setting temporary piers at the mid-span, it is difficult to ensure the synchronization of the launching equipment at the same pier, which can easily cause the beam to deviate from the design axis, leading to changes in the stress of the beam. Calculating the local maximum stress of the beam when lateral deviation occurs is beneficial for establishing a reasonable and reliable threshold for controlling beam deviations. To address this issue, this paper utilizes a finite element model of the bridge under typical adverse conditions, simulating the lateral deviation and asynchronous jacking of the beam to explore stress variation patterns. The results show that the beam stress increases as the lateral deviation and jacking differences increase, severely affecting the structural safety. To overcome the inefficiency of the finite element modeling method when dealing with multiple working conditions and to establish a reasonable deviation control threshold, this study proposes a beam local stress prediction model under the combined influence of lateral deviation and jacking height differences based on the SSA-SVR algorithm. The model calculates the local maximum stress of the beam for 2500 sets of parameters. An engineering case analysis shows that the prediction model has high calculation accuracy, reliable results, fast computational speed, and high efficiency. Based on the stress prediction results, it is recommended to set the jacking height difference control threshold at 20 mm. When the jacking height difference is less than 20 mm, the local maximum stress value under the combined influence of both parameters should be less than the standard limit. The corresponding lateral deviation control threshold can be dynamically adjusted according to the actual travel difference recorded by the jacking equipment, with the control precision improved to 1 mm.

In situ digital image correlation study on the mechanical properties of GH4169 at different temperatures

AbstractGH4169 is a precipitation‐strengthened nickel‐based high‐temperature alloy, and its mechanical behavior at different temperatures is of significant importance for practical applications. Here, an in situ study on the mechanical properties and microstructural evolution of the GH4169 alloy at different temperatures was conducted using scanning electron microscopy in combination with digital image correlation. The results demonstrate the existence of a linear relationship between the local average strain and the macroscopic strain. At temperatures of 20 °C, 100 °C, 180 °C, and 260 °C, the local average strain is 1.96, 2.13, 2.26, and 2.30 times the macroscopic strain, respectively, which shows a clear trend. This indicates that the temperature has a significant influence on the plasticity of the alloy. Additionally, the strain is concentrated below the notch, whereby at a macroscopic strain of 1.7 % at 20 °C and 260 °C, the local maximum strain at the notch is 52 % and 83 %, respectively. Furthermore, when the macroscopic strain reaches 2.12 %, the local maximum strain at the notch is 72 % and 103 %, respectively. At this point, cracks start to initiate and gradually propagate. In situ observations show that at the beginning of the tensile plasticity stage, the plastic strain rate in the grains is twice the rate at the grain boundaries; subsequently, both rates tend to stabilize, and a high‐strain zone appears inside the grains and is coordinately transferred to the surrounding grains through the grain boundaries. The final stage of damage of the alloy consists of perforation fracture, and the presence of a large number of cracked carbides at the fracture results in specimen cracking.

Stochastic collapse: how gradient noise attracts SGD dynamics towards simpler subnetworks*

Abstract In this work, we reveal a strong implicit bias of stochastic gradient descent (SGD) that drives overly expressive networks to much simpler subnetworks, thereby dramatically reducing the number of independent parameters, and improving generalization. To reveal this bias, we identify invariant sets, or subsets of parameter space that remain unmodified by SGD. We focus on two classes of invariant sets that correspond to simpler (sparse or low-rank) subnetworks and commonly appear in modern architectures. Our analysis uncovers that SGD exhibits a property of stochastic attractivity towards these simpler invariant sets. We establish a sufficient condition for stochastic attractivity based on a competition between the loss landscape’s curvature around the invariant set and the noise introduced by stochastic gradients. Remarkably, we find that an increased level of noise strengthens attractivity, leading to the emergence of attractive invariant sets associated with saddle-points or local maxima of the train loss. We observe empirically the existence of attractive invariant sets in trained deep neural networks, implying that SGD dynamics often collapses to simple subnetworks with either vanishing or redundant neurons. We further demonstrate how this simplifying process of stochastic collapse benefits generalization in a linear teacher-student framework. Finally, through this analysis, we mechanistically explain why early training with large learning rates for extended periods benefits subsequent generalization.

Anomalous heat transfer enhancement in separated flow over a zigzag-shaped dense package of inclined grooves in a channel wall at different temperature boundary conditions

Rapid development of the anomalous enhancement of separated turbulent Re = 6000 air flow and heat transfer in an in-line single-row package of 31 inclined grooves, 0.2 in dimensionless depth, in a singled-out longitudinal region of the wall of a narrow channel is studied. It is due to the interference of vortex wakes behind the grooves and the acceleration in the channel flow core with the formation of a zone of ultrahigh longitudinal velocity. The wave-shaped parameter characteristics are stabilized in the region of approximately 15th groove, whereupon the oscillation amplitudes are moderately reduced. The return flows in the grooves are enhanced with distance from the entry section, the minimum negative friction diminishing from −2 to −4. The total relative heat removal from the structured region increases atq= const by a factor of approximately 2.75 and by the factor of two atT= const with increase in the relative hydraulic losses by the factor of 1.7, as compared with the case of a plane–parallel channel. The relative heat removal from the surface bounded by the contour of the 20th inclined groove amounts to 3.7 (q= const) with increase in the hydraulic losses by the factor of 2.2. An increase in the local maximum of the longitudinal velocity up to a factor of 1.5, as compared with the mean-mass velocity, can be observable.

Pleistocene to early Holocene paleoenvironmental evolution of the Abrolhos depression (Brazil) based on benthic foraminifera.

The paleoenvironmental evolution of the Abrolhos Depression (AD) on the southern Abrolhos Shelf during the global post-Last Glacial Maximum (LGM) transgression is investigated through benthic foraminifera analysis. Downcore sediment samples (core DA03A-5B) collected at a depth of 63m provide insights into the formation and paleoenvironmental variations of AD over the past 18 kyr BP. The core is divided into four biofacies based on foraminifera assemblages. At the base, the presence of carbonate concretions indicates a karstic surface, marking the initiation of the paleolagoon formation at approximately 13 kyr BP with low density of foraminifera, where species such as Elphidium sp. and Hanzawaia boueana (EH Biofacies) were more abundant. During the Younger Dryas (YD) (12.8-12.5 kyr BP), the AD exhibits two distinct phases: an initially confined lagoon environment with reduced circulation characterized by the dominance of the species Ammonia tepida (At Biofacies), followed by increased circulation characterized by higher density, richness, and diversity of benthic foraminifera. The end of the YD is identified by a significant biofacies change, indicative of a shallow marine environment, where the dominant species were A. tepida and Elphidium excavatum (AE Biofacies), supported by sedimentological and geochemical proxies. This paleoenvironmental shift is associated with Meltwater Pulse (MWP) -1B, suggesting a connection to a shallow marine environment. As sea levels continue to rise, the AD transitions into an open marine setting. However, around 8 kyr BP, a change occurs with the absence of A. tepida and the occurrence of planktonic and other benthic foraminifera typical of the outer shelf, indicating depths greater than 50m (HQ Biofacies). The findings highlight the complex interplay between climate fluctuations, sea-level changes, and the formation of coastal environments during the LGM transgression. This study contributes to our understanding of paleoenvironmental dynamics, adding valuable insights to the evolutionary history of AD. The results emphasize the importance of integrating benthic foraminifera analysis, radiocarbon dating, and geochemical proxies to reconstruct paleoenvironments accurately. Overall, this research enhances our knowledge of global continental shelf evolution during the post-LGM transgression and provides valuable information for future paleoenvironmental studies.

Deep Learning Evidence for Global Optimality of Gerver’s Sofa

The moving sofa problem, introduced by Leo Moser in 1966, seeks to determine the maximal area of a 2D shape that can navigate an L-shaped corridor of unit width. Joseph Gerver’s 1992 solution, providing a lower bound of approximately 2.2195, is the best known, though its global optimality remains unproven. This paper leverages neural networks’ approximation power and recent advances in invexity optimization to explore global optimality. We propose two approaches supporting Gerver’s conjecture that his sofa is the unique global maximum. The first approach uses continuous function learning, discarding assumptions about the monotonicity, symmetry, and differentiability of sofa movements. The sofa area is computed as a differentiable function using our “waterfall” algorithm, with the loss function incorporating both differential terms and initial conditions based on physics-informed machine learning. Extensive training with diverse network initialization consistently converges to Gerver’s solution. The second approach applies discrete optimization to the Kallus–Romik upper bound, improving it from 2.37 to 2.3337 for five rotation angles. As the number of angles increases, our model asymptotically converges to Gerver’s area from above, indicating that no larger sofa exists.

Environmentally sound geotechnologies for leaching metals from polymetallic ore processing wastes and wastewater

Global challenges (increased consumption of georesources, climatic changes, limited reserves) increase the relevance of the problems of growing waste accumulation and environmentally-sound modernization of mineral extraction. In this regard, the existing approaches to the design of geotechnologies for metal mining need to be improved based on a concept of so-called circulation waste management and ecologization of technological processes. The paper is devoted to the issue of formation of conceptual bases and directions of ecologization of geotechnologies at leaching metals from polymetallic ore processing wastes and wastewater. The study presents recommendations for improving in-situ leaching of ores in blocks, allowing to determine the optimal conditions for increasing the completeness of subsoil use and reducing environmental damage. It was revealed that at metal extraction with solution circulation through brine chambers the content of Na, Cl, SO4 and Ca ions in dialysate was low, while without circulation through brine, it significantly exceeded corresponding MPCs. This proves the fundamental feasibility of controlling natural leaching processes by enhancing the oxidizing potential of natural solvents through the addition of industrial oxidizing agents. It was found that increasing the duration of agitation leaching (both with and without mechanoactivation) leads to a uniform expansion of the local maximums of Pb yield from the pulp when the minimum NaCl concentration decreases from 11–12 to 7% at H2SO4 concentration of 0.6%. One of key results of the study is justifying the expansion of the use of disintegrators to realize targeted activation of tailings. The practical significance of the obtained results lies in the proved feasibility of optimizing the flow sheet of electrochemical extraction of metals from wastewater on the basis of the obtained regularities of the use of brine circulation through brine chambers. In addition, the totality of the obtained results of using a disintegrator for reextraction of lead from geomaterials will allow developing a methodology for calculating the parameters of mechanoactivation action to increase the degree of metal recovery from the tailings of North Ossetia-Alania’s (Zgidskoe, Sadonskoe, Arkhonskoe deposits) polymetallic ores beneficiation. The most promising way for further research is to substantiate methods of using underground space for complete removal of wastes (wastewater and tailings) after their multistage treatment.

Microstructure-based digital twin thermo-electrochemical modeling of LIBs at the cell-to-module scale

As the application of lithium-ion batteries (LIBs) expands beyond conventional electric vehicles (EVs) to heavy vehicles such as electric trucks or trams, the importance of thermal management in LIB systems is increasing, even at the module or pack level. In particular, because monitoring the thermal behaviors of each cell is not feasible, thermo-electrochemical modeling and simulations in the module or pack level are essential for analyzing and ensuring thermal stability. However, because the conventional lumped thermo-electrochemical models cannot reflect the actual structure of LIB cells, there might be considerable differences may exist between simulation and experimental results. To fill these gaps, we have newly developed a 3D microstructure-based digital twin model of a battery module (8.8 Ah/18.5 V, five LIB pouch cells in series) for an unmanned railway vehicle. Unlike traditional lumped models, our digital twin model accurately well reflects the internal structure of cells and can calculate the heat generation of each component inside a cell. As a result, contrary to a lumped model, the digital twin model can not only simulate the inhomogeneous temperature gradient inside a cell, but also estimates higher local maximum temperatures (TDT, max/TL, max = 137.2 °C/123.9 °C @ 10C discharge) in cells which can trigger thermal runaway. Therefore, microstructure-based digital twin modeling can alleviate concerns regarding the thermal runaway of LIB cells, modules, and packs, and provide safe operating conditions.

Local maximum synchrosqueezing adaptive transformation for cross-instantaneous frequencies analysis

Abstract To overcome the shortcomings of existing time-frequency (TF) analysis (TFA) methods in analyzing signals containing cross-instantaneous frequencies (IFs), this paper proposes an adaptive TFA technique combined with image processing methods based on local maximum synchrosqueezing transform. The core idea of the proposed algorithm is to localize the filtering of signals containing several different IF components using kernel functions containing several different directions, respectively, to achieve energy separation at the crossing frequencies. In turn, the local maximum synchrosqueezing transform is used to rearrange the TF energy to the true IF ridges of the signal to improve the TF energy concentration. Simulation data demonstrates that the proposed algorithm has higher energy aggregation and better noise immunity, especially for signals with cross-IFs. Applying the proposed method to animal acoustic and radar wave signals of pedestrians can accurately describe the differences in the frequency change patterns and the temporal distribution of energy in the signals, thereby providing a judgment basis for effectively identifying and classifying the signals.

Autonomous nervous system responses to environmental-level exposure to 5G's first deployed band (3.5GHz) in healthy human volunteers.

Following the global progressive deployment of 5G networks, considerable attention has focused on assessing their potential impact on human health. This study aims to investigate autonomous nervous system changes by exploring skin temperature and electrodermal activity (EDA) among 44 healthy young individuals of both sexes during and after exposure to 3.5GHz antenna-emitted signals, with an electrical field intensity ranging from 1 to 2V/m. The study employed a randomized, cross-over design with triple-blinding, encompassing both 'real' and 'sham' exposure sessions, separated by a maximum interval of 1week. Each session comprised baseline, exposure and postexposure phases, resulting in the acquisition of seven runs. Each run initiated with a 150s segment of EDA recordings stimulated by 10 repeated beeps. Subsequently, the collected data underwent continuous decomposition analysis, generating specific indicators assessed alongside standard metrics such as trough-to-peak measurements, global skin conductance and maximum positive peak deflection. Additionally, non-invasive, real-time skin temperature measurements were conducted to evaluate specific anatomical points (hand, head and neck). The study suggests that exposure to 3.5GHz signals may potentially affect head and neck temperature, indicating a slight increase in this parameter. Furthermore, there was a minimal modulation of certain electrodermal metrics after the exposure, suggesting a potentially faster physiological response to auditory stimulation. However, while the results are significant, they remain within the normal physiological range and could be a consequence of an uncontrolled variable. Given the preliminary nature of this pilot study, further research is needed to confirm the effects of 5G exposure.

Seasonal dynamics and regional distribution patterns of CO2 and CH4 in the north-eastern Baltic Sea

Abstract. Significant research has been carried out in the last decade to describe the CO2 system dynamics in the Baltic Sea. However, there is a lack of knowledge in this field in the NE Baltic Sea, which is the main focus of the present study. We analysed the physical forcing and hydrographic background in the study year (2018) and tried to elucidate the observed patterns of surface water CO2 partial pressure (pCO2) and methane concentrations (cCH4). Surface water pCO2 and cCH4 were continuously measured during six monitoring cruises onboard R/V Salme, covering the Northern Baltic Proper (NBP), the Gulf of Finland (GoF), and the Gulf of Riga (GoR) and all seasons in 2018. The general seasonal pCO2 pattern showed oversaturation in autumn–winter (average relative CO2 saturation 1.2) and undersaturation in spring–summer (average relative CO2 saturation 0.5), but it locally reached the saturation level during the cruises in April, May, and August in the GoR and in August in the GoF. The cCH4 was oversaturated during the entire study period, and the seasonal course was not well exposed on the background of high variability. Surface water pCO2 and cCH4 distributions showed larger spatial variability in the GoR and GoF than in the NBP for all six cruises. We linked the observed local maxima to river bulges, coastal upwelling events, fronts, and occasions when vertical mixing reached the seabed in shallow areas. Seasonal averaging over the CO2 flux suggests a weak sink for atmospheric CO2 for all basins, but high variability and the long periods between cruises (temporal gaps in observation) preclude a clear statement.

Stationary scattering for the nonlinear Schrödinger equation with point-like obstacles: exact solutions

AbstractWe solve the Nonlinear Schrödinger Equation (NLSE) in 1D in presence of one, two and several Dirac delta potentials. With the help of an equivalent central force problem we obtain the analytical solutions in terms of a biparametric family containing the Jacobi functions. Elliptic Jacobi functions are already reported in the literature but they have not been used in the context of a scattering problem under causal boundary conditions. In the simplest examples of one or two Dirac deltas we analyze how the nonlinear term of the equation affects the modulus and phase profiles of the wave function. We also study the transmission curves under the nonlinear modification of the tunneling behavior for the first time. For a Fabri-Perot configuration made of two deltas, we obtain the effect of nonlinear coupling in the positions of the local maxima (resonances). We lay the foundations for nonlinear Anderson localization of 1D BECs in a speckle field. Upon redefinition of parameters these novel results describe the dynamics of a stationary Higgs field in 1D. Finally, we discuss the conditions for soliton formation under the influence of a Dirac comb potential, giving rise to fully correlated locations and intensities of the defects.

Fault diagnosis of bearings under variable operating conditions based on domain adaptation

Aiming at the problem of low fault recognition rate caused by different distribution of training samples and test samples and imbalance of various fault data in bearing fault diagnosis, a domain adaptive fault diagnosis method based on improved residual network ( ResNet ) is designed. In the first layer of the diagnosis network, a multi-dimensional convolution structure is used for feature extraction to obtain fault feature information of different dimensions; the local maximum mean difference (LMMD) is used in the domain adaptive layer to align the distribution of the source domain and the target domain to obtain more fine-grained information; the class balance loss function ( CBLoss ) is used to solve the training problem of unbalanced data, and the Adam optimization network is used to realize fault diagnosis. Experimental results show that the proposed improved method can achieve higher diagnosis results under the imbalance of fault sample categories. Experimental verification is carried out on two bearing data sets and collected wind turbine data. The results show that the proposed improved method has certain advantages. The diagnostic performance of the proposed improved method in the case of unbalanced data samples is better than other deep transfer learning methods, and it can be used as an effective cross-condition fault analysis method.

Fenton type minimax problems for sum of translates functions

Following an insightful work of P. Fenton, we investigate sum of translates functions F(x,t):=J(t)+∑j=1nνjK(t−xj), where J:[0,1]→R_:=R∪{−∞} is a “sufficiently non-degenerate” and upper-bounded “field function”, and K:[−1,1]→R_ is a fixed “kernel function”, concave both on (−1,0) and (0,1), x:=(x1,…,xn) with 0≤x1≤…≤xn≤1, and ν1,…,νn>0 are fixed. We analyze the behavior of the local maxima vector m:=(m0,m1,…,mn), where mj:=mj(x):=supxj≤t≤xj+1⁡F(x,t), with x0:=0, xn+1:=1; and study the optimization (minimax and maximin) problems infx⁡maxj⁡mj(x) and supx⁡minj⁡mj(x). The main result is the equality of these quantities, and provided J is upper semicontinuous, the existence of extremal configurations and their description as equioscillation points w, i.e., w satisfying m0(w)=m1(w)=⋯=mn(w). In our previous papers we obtained results for the case of singular kernels, i.e., when K(0)=−∞ and the field was assumed to be upper semicontinuous. In this work we get rid of these assumptions and prove common generalizations of Fenton's and our previous results, and arrive at the greatest generality in the setting of concave kernel functions.

Streamwise energy-transfer mechanisms in zero- and adverse-pressure-gradient turbulent boundary layers

The present study investigates streamwise ( $\overline {u^2}$ ) energy-transfer mechanisms in the inner and outer regions of turbulent boundary layers (TBLs). Particular focus is placed on the $\overline {u^2}$ production, its inter-component and wall-normal transport as well as dissipation, all of which become statistically significant in the outer region with increasing friction Reynolds number ( $Re_{\tau }$ ). These properties are analysed using published data sets of zero, weak and moderately strong adverse-pressure-gradient (APG) TBLs across a decade of $Re_{\tau }$ , revealing similarity in energy-transfer pathways for all these TBLs. It is found that both the inner and outer peaks of $\overline {u^2}$ are always associated with local maxima in the $\overline {u^2}$ production and its inter-component transport, and the regions below/above each of these peaks are always dominated by wall-ward/away-from-wall transport of $\overline {u^2}$ , thereby classifying the $\overline {u^2}$ profiles into four distinct regimes. This classification reveals existence of phenomenologically similar energy-transfer mechanisms in the ‘inner’ and ‘outer’ regions of moderately strong APG TBLs, which meet at an intermediate location coinciding with the minimum in $\overline {u^2}$ profiles. Conditional averaging suggests existence of similar phenomena even in low $Re_{\tau }$ canonical and/or weak APG TBLs, albeit with the outer-region mechanisms weaker than those in the inner region. This explains the absence of their $\overline {u^2}$ outer peak and the dominance of $\overline {u^2}$ wall-normal transport away from the wall, which potentially originates from the inner region. Given that the wall-ward/away-from-wall transport of $\overline {u^2}$ is governed by the $Q_4$ (sweeps)/ $Q_2$ (ejections) quadrants of the Reynolds shear stress, it is argued that the emergence of the $\overline {u^2}$ outer peak corresponds with the statistical dominance of $Q_4$ events in the outer region. Besides unravelling the dynamical significance of $Q_2$ and $Q_4$ events in the outer region of TBLs, the present analysis also proposes new phenomenological arguments for testing on canonical wall-turbulence data at very high $Re_{\tau }$ .

Enhancement of superconductivity in W5Si3 with Tc ∼ 6.2 K by P-doping

The effects of phosphorus doping on the prototypical transition metal silicide W5Si3, predicted to host a topologically nontrivial band structure and exhibit superconductivity below its critical temperature (Tc) of 2.7 K, have been investigated. Upon P-doping, the lattice parameter a decreases and c increases for W5Si3–xPx up to x = 0.7. Notably, Tc and the upper critical field of W5Si3–xPx are enhanced by over twofold as x increases, achieving 6.2 K and 5.5 T at x = 0.7, respectively. This enhancement in bulk superconductivity is attributed to a shift of the Fermi level toward a local maximum in the density of states upon P-doping, as indicated by the first-principles calculations. Our results suggest an effective doping strategy which significantly enhances the superconductivity in W5Si3. The emerging W5Si3-type superconductors offer a promising platform for realizing possible topological superconductivity.

The Effect of Sodium Hexametaphosphate on the Dispersion and Polishing Performance of Lanthanum-Cerium-Based Slurry.

A lanthanum-cerium-based abrasive composed of CeO2, LaOF, and LaF3 was commercially obtained. The effect of sodium hexametaphosphate (SHMP) on powder dispersion behavior was systematically investigated using the combined techniques of liquid contact angle, turbidity, zeta potential (ZP), scanning electron microscopy (SEM), powder X-ray diffraction (XRD) combined with Rietveld refinements, X-ray photoelectron spectroscopy (XPS), and polishing tests. The results indicated that the addition of 0.5 wt.% SHMP dispersant to the 5 wt.% lanthanum-cerium-based slurry produced the most stable suspension with a high turbidity of 2715 NTU and a low wetting angle of 45°. The as-obtained slurry displayed good surface polishing quality for K9 glass, with low surface roughness (Ra) of 0.642 and 0.515 nm (in the range of 979 × 979 μm2) at pH = 6 and 11, respectively, which corresponds to the fact that it has local maximum absolute values of ZP at these two pH values. SEM images demonstrated that after appropriate grafting of SHMP, the particle aggregation was reduced and the slurry's dispersion stability was improved. In addition, the dispersion mechanism was explained based on the principle of complexation reaction, which reveals that the dispersant SHMP can increase the interparticle steric hindrance and electrostatic repulsions. In an acidic environment, steric hindrance dominates, while electrostatic repulsion prevails under alkaline conditions. As expected, this polishing slurry may find potential applications in manufacturing optical devices and integrated circuits.

Connection of innovation activity effectiveness and technological novelty of innovation output

The paper studies the relationship between the innovation performance of Russian industrial enterprises and the degree of technological novelty of their innovative products. For this purpose, the innovation function is introduced. That function reflects the dependence of the “output” of the innovation process on its factors measured by the costs of process and product innovations. Hypotheses about the properties of this function are discussed. The relationship between the two types of innovation costs is considered. As an indicator of technological efficiency the ratio of the value of innovative products to the innovative resource costs is used. The dependence of the performance indicator on innovation costs is investigated. The points of local maxima (local technological optimum) of the performance indicator for the size classes of Russian industrial enterprises are found. It is shown that there is a relationship between the effectiveness of innovative activity and the degree of technological novelty of innovative products. As an indicator of the degree of technological novelty the share of the innovative products new to the market in all innovative products is used. It was established that there is a change in the trend of innovative production from the production of radical to incremental innovative products when approaching the technological optimum.