Sign-based Stochastic Gradient Descents (Sign-based SGDs) use the signs of the stochastic gradients for communication costs reduction. Nevertheless, current convergence results of sign-based SGDs ...applied to the finite sum optimization are established on the bounded assumption of the gradient, which fails to hold in various cases. This paper presents a convergence framework about sign-based SGDs with the elimination of the bounded gradient assumption. The ergodic convergence rates are provided only with the smooth assumption of the objective functions. The Sign Stochastic Gradient Descent (signSGD) and its two variants, including majority vote and zeroth-order version, are developed for different application settings. Our framework also removes the bounded gradient assumption used in the previous analysis of these three algorithms.
Steel slag is a solid waste produced in crude steel smelting, and a typical management option is stockpiling in slag disposal yards. Over the years, the massive production of steel slags and the ...continuous use of residue yards have led to vast occupation of land resources and caused severe environmental concerns. Steel slag particles can potentially be used as aggregates in concrete production. However, the volume stability of steel slag is poor, and the direct use of untreated steel slag aggregate (SSA) may cause cracking and spalling of concrete. The present research summarizes, analyzes, and compares the chemical, physical, and mechanical properties of steel slags. The mechanism and treatment methods of volume expansion are introduced, and the advantages, disadvantages, and applicable targets of these methods are discussed. Then, the latest research progress of steel slag aggregate concrete (SSAC) is reviewed. Using SSA leads to an increase in the density of concrete and a decrease in workability, but the mechanical properties and durability of SSAC are superior to natural aggregate concrete (NAC). Finally, future research in this field is proposed to motivate further studies and guide decision-making.
construction of the Schottky-junction is considered to be a valid route to boost the spatial charge separation and transfer of the photocatalytic system. Herein, two-dimensional (2D) O-doped g-C3N4 ...nanosheets were prepared by an annealing route, and then a 2D/2D Ti3C2 MXene/O-doped g-C3N4 Schottky-junction was fabricated using an in-situ electrostatic assembly of negatively charged Ti3C2 MXene and positively charged O-doped g-C3N4 nanosheets. The as-prepared Ti3C2 MXene/O-doped g-C3N4 Schottky-junction exhibited almost two times enhanced hydrogen evolution (25124 μmol/g/h) in comparison to pristine O-doped g-C3N4 (13745 μmol/g/h) and Ti3C2 MXene/pristine C3N4 (15573 μmol/g/h). Based on fully characterizations and theory calculation, the enhanced photocatalytic performance could be attributed to the synergy effect of intimate 2D/2D interfacial contact and the construction of Schottky-junction, which result in the short charge transport distance from HCN to Ti3C2 MXene and efficient separation of the photo-generated charge. This study will provide new insight into developing 2D/2D Schottky-junction photocatalysts for the solution of the energy crisis.
Behaving efficiently and flexibly is crucial for biological and artificial embodied agents. Behavior is generally classified into two types: habitual (fast but inflexible), and goal-directed ...(flexible but slow). While these two types of behaviors are typically considered to be managed by two distinct systems in the brain, recent studies have revealed a more sophisticated interplay between them. We introduce a theoretical framework using variational Bayesian theory, incorporating a Bayesian intention variable. Habitual behavior depends on the prior distribution of intention, computed from sensory context without goal-specification. In contrast, goal-directed behavior relies on the goal-conditioned posterior distribution of intention, inferred through variational free energy minimization. Assuming that an agent behaves using a synergized intention, our simulations in vision-based sensorimotor tasks explain the key properties of their interaction as observed in experiments. Our work suggests a fresh perspective on the neural mechanisms of habits and goals, shedding light on future research in decision making.
Carbon dioxide emissions (CO
2
es) are presently a hot topic of worldwide concern. It is of great significance for lessening CO
2
es to wholly understand the transformation pattern of CO
2
es among ...countries, industries, and the main factors (i.e., emission effect, energy intensity, economic development, population size, carbon per unit of land, land per capita, and environmental impact per capita effects) influencing CO
2
es. Thus, to mitigate the country’s CO
2
es efficiently, it is necessary to determine the driving factors of its emissions and damage variations. For this, we use the logarithmic mean Divisia index method. This research decomposes the major two dimensions, such as carbon sources and carbon damage variations from 1986 to 2020, into eight factors. The results show that Pakistan’s CO
2
es increased continuously during the period, with an average annual growth rate of 4.76%. Growing the country’s CO
2
es over 1986–2020, the key influencing factors are economic development, population, and land, while energy intensity and emission factors are the main forces in mitigating CO
2
es. The carbon source and carbon damage dimensions reached 68.75 Mt and 208.56 Mt, respectively, which led to a rise in CO
2
e. The entire set of factors is averagely moving around the major outcomes that provide significant policy measures. Finally, to efficiently reduce CO
2
e, Pakistan should concentrate on specific industrial paths and implement challenging, comprehensive governance to attain a low-carbon chain throughout the process. Thus, based on empirical results, this research put forward policy suggestions for cleaner production to reduce CO
2
emissions further, and environmental policies must be tailored to local conditions.
Angle-resolved XPS combined with argon ion etching was used to characterize the surface functional groups and the chemical structure of Ti3C2Tx MXene. Survey scanning obtained on the sample surface ...showed that the sample mainly contains C, O, Ti and F elements, and a little Al element. Analyzing the angle-resolved narrow scanning of these elements indicated that a layer of C and O atoms was adsorbed on the top surface of the sample, and there were many O or F related Ti bonds except Ti–C bond. XPS results obtained after argon ion etching indicated staggered distribution between C–Ti–C bond and O–Ti–C, F–Ti bond. It is confirmed that Ti atoms and C atoms were at the center layer of Ti3C2Tx MXene, while O atoms and F atoms were located at both the upper and lower surface of Ti3C2 layer acting as surface functional groups. The surface functional groups on the Ti3C2 layer were determined to include O2−, OH−, F− and O−–F−, among which F atoms could also desorb from Ti3C2Tx MXene easily. The schematic atomic structure of Ti3C2Tx MXene was derived from the analysis of XPS results, being consistent with theoretical chemical structure and other experimental reports. The results showed that angle-resolved XPS combing with argon ion etching is a good way to analysis 2D thin layer materials.
This paper presents a multi-algorithm fusion model (StackingGroup) based on the Stacking ensemble learning framework to address the variable selection problem in high-dimensional group structure ...data. The proposed algorithm takes into account the differences in data observation and training principles of different algorithms. It leverages the strengths of each model and incorporates Stacking ensemble learning with multiple group structure regularization methods. The main approach involves dividing the data set into K parts on average, using more than 10 algorithms as basic learning models, and selecting the base learner based on low correlation, strong prediction ability, and small model error. Finally, we selected the grSubset + grLasso, grLasso, and grSCAD algorithms as the base learners for the Stacking algorithm. The Lasso algorithm was used as the meta-learner to create a comprehensive algorithm called StackingGroup. This algorithm is designed to handle high-dimensional group structure data. Simulation experiments showed that the proposed method outperformed other R2, RMSE, and MAE prediction methods. Lastly, we applied the proposed algorithm to investigate the risk factors of low birth weight in infants and young children. The final results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.508 and a root mean square error (RMSE) of 0.668. The obtained values are smaller compared to those obtained from a single model, indicating that the proposed method surpasses other algorithms in terms of prediction accuracy.
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•Presents a Mxene based virtual sensor array (VSA).•Proposes a convenient strategy for VSA.•We demonstrate correct rates of 90.9%, 90.5%, 90% for the different groups of ...VOCs.•Demonstrates an accuracy of 93.2% for the prediction of ethanol concentrations.
Two-dimensional transition metal carbides/nitrides, known as MXenes, have recently received significant attention for gas sensing applications. However, MXenes have strong adsorption to many types of volatile organic compounds (VOCs), and therefore gas sensors based on MXenes generally have low selectivity and poor performance in mixtures of VOCs due to cross-sensitivity issues. Herein, we developed a Ti3C2Tx-based virtual sensor array (VSA) which allows both highly accurate detection and identification of different VOCs, as well as concentration prediction of the target VOC in variable backgrounds. The VSA’s responses from the broadband impedance spectra create a unique fingerprint of each VOC without a need for changing temperatures. Based on the methodologies of principal component analysis and linear discrimination analysis, we demonstrate highly accurate identifications for different types of VOCs and mixtures using this MXene based VSA. Furthermore, we demonstrate an accuracy of 93.2% for the prediction of ethanol concentrations in the presence of different concentrations of water and methanol. The high level of identification and concentration prediction shows a great potential of MXene based VSA for detection of VOCs of interest in the presence of known and unknown interferences.
Natural frequencies of structures are usually convenient to acquire with a high precision in engineering. However, it is still difficult and faces challenges to apply the frequencies in the area of ...structural health monitoring. One of the main reasons is the natural frequency’s sensitivity to the variation of environmental conditions, such as temperature, which often disturbs the frequency based identification and even results in the false evaluation of the structural health condition. To overcome this problem, a novel frequency-based co-integration technique is proposed in this paper. The main principle is that the non-stationarity of the acquired frequencies caused by the variation of environmental temperature, can be transformed to stationary sequence by linear combining two non-stationary frequencies using the co-integration algorithm. The calculated stationary relationship between two frequencies series is insensitive to the influence of environmental variation, and can be employed to detect structural damage. In the study, ADF (Augmented Dickey-Fuller) test and EG (Engle-Granger) test are first introduced to check the non-stationary order and calculate the co-integration coefficients of the frequency series. Subsequently, the theoretical derivation of frequency co-integration based technique is developed using a simply supported beam as an example, and the procedure of damage detection based on frequency co-integration technique under the influence of environmental temperature is introduced in detail. At last, the validity and robustness of the proposed method are illustrated and verified through a numerical simulation of a steel truss bridge and a real cable-stayed bridge under the influence of changing environmental temperature. Both the numerical simulation and the practical application demonstrate that the proposed frequency co-integration technique can effectively eliminate the influence of the changing environmental temperature and identify the structural damage accurately.
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