Linear Weight Research Articles

Chemical compositions prediction of glass artifacts based on logistic regression and linear weighting

Chemical composition analysis is of great significance for studying the category, origin and production age of the ancient glass. In order to consider the influence of weathering on chemical composition, this paper proposes a prediction method of glass chemical composition based on logistic regression and linear weighting. First, the chi-square test shows that whether the glass surface is differentiated is related to the type of glass. Secondly, the chemical composition of glass relics is divided into two categories by binary logistic regression: significantly affected by weathering and not significantly affected by weathering. Then, for the chemical components not significantly affected by weathering, combined with descriptive statistical results, the mean value was used as the chemical composition content before weathering. For the chemical components significantly affected by weathering, the linear weighting method was used to predict the content of chemical components before weathering. The final results show that this method can better reflect the influence of weathering factors on the chemical composition of glass, which is of great significance for the prediction of the glass composition.

A sixth-order finite difference WENO scheme for non-linear degenerate parabolic equations: an alternative technique

PurposeThis paper aims to construct a sixth-order weighted essentially nonoscillatory scheme for simulating the nonlinear degenerate parabolic equations in a finite difference framework.Design/methodology/approachTo design this scheme, we approximate the second derivative in these equations in a different way, which of course is still in a conservative form. In this way, unlike the common practice of reconstruction, the approximation of the derivatives of odd order is needed to develop the numerical flux.FindingsThe results obtained by the new scheme produce less error compared to the results of other schemes in the literature that are recently developed for the nonlinear degenerate parabolic equations while requiring less computational times.Originality/valueThis research develops a new weighted essentially nonoscillatory scheme for solving the nonlinear degenerate parabolic equations in multidimensional space. Besides, any selection of the constants (sum equals one is the only requirement for them), named the linear weights, will obtain the desired accuracy.

Growth patterns of saffron cod <i>Eleginus gracilis</i> Tilesius (Gadidae) in the first year of life

Patterns of linear and weight growth of saffron cod in the first year of life are considered on the data of measurements and cited materials for different habitats of this species. The growth curve is approximated by logistic function. Monthly increments for the body length of larvae and fry are about 50–60 % in the first four months of life with the highest growth rate. Then the relative growth decreases significantly in fall-winter and does not exceed 1–4 % to the end of the first year of life. The absolute length increments increase during the first half of the year up to the maximum value of 25–30 mm/month in late summer – early autumn. Saffron cod grow faster in the southern areas of their habitat. The weight growth patterns are different from the linear growth. The weight growth is higher in the first five months of life, with gradual decreasing of the monthly weight increments form 156 % to 105 %. The relative weight increments exceed significantly the length increments in this period. When the body weight reaches app. 5 g, the relative weight increments decrease abruptly, though the absolute increments continue to increase until the 6–8th months of life when they reach 5–6 g per month for fish with body length > 86 mm. The body weight increases by 60 % in this short period of the highest weight growth. Time lag between the peaks of absolute linear growth (in July-September) and weight growth (in September-November) is about 2 months.

A probabilistic multi-objective optimization mechanical design

Optimization design of machinery is usually a multi-objective one inevitably. At present, the popular mechanical optimization design is limited by the intrinsic shortcomings of the previous multi-objective optimization methods, which leads to the difficulty of non-comprehensive and nonsystematic optimal solutions in the viewpoint of probability theory. In the linear weighting “additive” method, there is inherent problems of normalization and introduction of subjective factors, and the final results depend on the normalization method to a great extent; the Pareto solution set is a “set” instead of an exact solution. In this paper, the probability—based multi-objective optimization, discretization with uniform design and sequential optimization are combined to establish a new approach of multi-objective optimization mechanical design based on probability theory; the probabilistic multi-objective optimization is used to transform the multi-objective optimization problem into single-objective optimization one from the perspective of probability theory; the discretization by means of uniform design provides an effective sampling to simplify the mathematical processing, which is especially important for dealing with multi-objective optimization problems with continuous objective functions; the sequential optimization algorithm is used to conduct the successive deep optimization. Furthermore, the implementation steps are illustrated with two examples. The results show that the approach can not only give excellent optimization results, but also provide a relatively simple processing.

Ceramics Properties of Indurated-Shale Quarry Wastes from Abakaliki, Southeastern Nigeria: Application as Raw Materials in Roofing-Tile Production

AbstractThe present study describes research carried out to evaluate the mineralogical, geochemical and technological properties of indurated shale-quarry wastes and assess the suitability of these low-cost and locally available quarry wastes generated from crushed indurated shales for possible use as alternatives to fresh raw materials in the manufacture of roofing-tile products. Firstly, the mineralogical and chemical properties of the indurated shales were investigated by X-ray diffraction (XRD) and X-ray fluorescence, while their physical properties were identified by grain-size distribution, Atterberg limits, and clay activity. Samples of indurated shale-quarry wastes (ISQWs) were subjected to heat treatment at elevated firing temperatures to provide the required strength and durability and their ceramics properties (linear shrinkage, weight loss, water absorption, bulk density, and flexural strength) were determined. From the results, the ISQWs were composed predominantly of fine particles with medium plasticity and clay activity with values generally >0.75. The mineralogy revealed a predominance of aluminosilicates (illite-kaolinite-smectite-chlorite) with large quartz contents and variable percentages of carbonate and feldspar. The oxides were dominated by SiO2 and Al2O3, small amounts of ferromagnesian minerals, and considerable amounts of alkalis (K2O and Na2O) which act as fluxes. The CaO concentrations were variable and related to carbonate contents. Characterizations based on compositional ternary (total clay mineral-carbonate-quartz + feldspar) systems, Casagrande clay workability charts, and Winkler and McNally diagrams revealed their suitability for ceramics applications as the majority of ISQW samples fell within the specifications for roofing tiles. The ISQWs fired at a high temperature of 1000°C revealed considerable weight loss, reduction in both linear shrinkage and water absorption with insignificant increase in flexural strength. In order to achieve excellent ceramics properties and further reduce sintering temperature for their suitability as raw materials in the production of roofing tiles, beneficiations of ISQWs are highly recommended.

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼103) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.

Современное состояние биологии, запасов и промысла атерины (Atherina boyeri caspia (Eichwald)) в западной части Среднего и Северного Каспия

The article considers the original results of observations and analysis of the catches of atherina (Atherina boyeri caspia (Eichwald)) by months by stationary nets in the Terek-Caspian fishery subarea (Republic of Dagestan). The size-weight and age indicators of the spawning stocks of atherina have been characterized. For the first time in recent years the conditions for atherina spawning in the western part of the Middle and Northern Caspian are described. All biostatistical indicators of atherina obtained in the recent years (2016–2021) are listed: average linear weight parameters, average fatness coefficient according to Fulton (0.944) indicate a high fattening of fish. Size and weight composition of females and males, age-related fluctuations in length and weight, comparative analysis of atherina concentrations, characteristics of atherina reproduction in the conditions of the Northern and Middle Caspian, fecundity of different age groups, quantitative distribution of roe, larvae and fry of atherina, as well as dynamics of the age composition from catches prove a satisfactory state of stocks of this fish species. Comparative analyses of autumn concentrations of atherina in the Middle Caspian in 1958–1963 and in 2018–2021 are presented, as well as spp/h of trawling, age-related fluctuations in the length and weight in the autumn period (2016–2021). It has been found that atherina in the Caspian Sea is the main object of by-catch when fishing for kilka with stationary seines (in average 56.3 tons per year); it plays an important role as a food component for many fish species (sea herring, sturgeon) and mammals (Caspian seal) living in the waters of the Caspian Sea. Prospective volumes of processing atherina for fishmeal are also considered for the first time. On the scale of the Caspian Sea, with intensive kilka fishing by stationary seines as by-catch, it is possible to catch atherina in the amount of 150–200 tons. Despite the small volumes, they would significantly cover the deficit in the fish feed. Due to the increasing population of atherina in the natural conditions it is required to actively involve atherina in the fishing processes of the organizations in order to maximize the development of the commercial stock.

Optimization Strategy of Rolling Mill Hydraulic Roll Gap Control System Based on Improved Particle Swarm PID Algorithm

Medium and heavy plates are important strategic materials, which are widely used in many fields, such as large ships, weapons and armor, large bridges, and super high-rise buildings. However, the traditional control technology cannot meet the high-precision control requirements of the roll gap of the thick plate mill, resulting in errors in the thickness of the medium and heavy plate, thereby reducing the quality of the product. In response to this problem, this paper takes the 5500 mm thick plate production line as the research background, and establishes the model of the rolling mill plate thickness automatic control system, using the Ziegler-Nichol response curve method (Z-N), particle swarm optimization (PSO) algorithm and linear weight particle swarm optimization (LWPSO) algorithm, respectively, optimizes the parameter setting of the PID controller of the system, and uses OPC UA communication technology to realize the online semi-physical simulation of Siemens S7-1500 series PLC (Siemens, Munich, Germany) and MATLAB R2018b (The MathWorks, Natick, Massachusetts, United States). Comparative studies show that when the same roll gap displacement step signal is given, the overshoot of the system response using the LWPSO algorithm is reduced by 14.26% and 10.18% compared with the Z-N algorithm and the PSO algorithm, and the peak time is advanced by 0.31 s and 0.05 s. The stabilization time is reduced by 3.71 s and 4.31 s, which effectively improves the control accuracy and speed of the system and has stronger anti-interference ability. It has certain engineering reference and application value.

Generating Paraphrase Using Simulated Annealing for Citation Sentences

The paraphrase generator for citation sentences is used to produce several sentence alternatives to avoid plagiarism. Furthermore, the generation results need to pay attention to semantic similarity and lexical divergence standards. This study proposed the StoPGEN model as an algorithm for generating citation paraphrase sentences with stochastic output. The generation process is guided by an objective function using a simulated annealing algorithm to maintain the properties of semantic similarity and lexical divergence. The objective function is created by combining the two factors that maintain these properties. This study combined METEOR and PINC Scores in a linear weighting function that can be adjusted for its value tendency in one of the matrix functions. The dataset of citation sentences that had been labeled with paraphrases was used to test StoPGEN and other models for comparison. The StoPGEN model, with the citation sentences dataset, produced a BLEU score of 55.37, outperforming the bidirectional LSTM method with a value of 28.93. StoPGEN was also tested using Quora data by changing the language source in the architecture section resulting in a BLEU score of 22.37, outperforming UPSA 18.21. In addition, the qualitative evaluation results of the citation sentence generation based on respondents obtained an acceptance value of 50.80.

Multiview synthetic aperture radar target recognition method using joint sparse representation and random weight matrix

Multiview synthetic aperture radar (SAR) images could provide richer information for target recognition as reported in previous works. This study exploits multiview SAR images with application to target recognition via the combination of joint sparse representation (JSR) and random weight matrix. First, the multiview SAR images are clustered into several view sets via a coarse clustering algorithm. In this way, the views in each set tend to share high correlations so they can be properly represented with JSR. For the reconstruction errors from different views, they are fused through a random weight matrix, which includes a rich set of linear weight vectors. Afterwards, the fused errors at different choices of weight vectors are analyzed to form a decision value for target recognition, which could better reflect the differences between different classes through multiview SAR images in comparison with traditional JSR-based methods. Therefore, the overall SAR target recognition performance can be effectively enhanced. Experiments are investigated on the moving and stationary target acquisition and recognition dataset under several operating conditions. The results reveal the high effectiveness of the proposed method under standard operating condition and good robustness under typical extended operating conditions including depression angle variance, configuration variants, noise corruption, partial occlusion, and resolution variance.

Ultra-processed foods in a rural Ecuadorian community: associations with child anthropometry and bone maturation.

Frequent ultra-processed food (UPF) consumption is consistently associated with poor health outcomes. Little is known about UPF intake during early childhood and its effects on growth. We assessed UPF in relation to child anthropometry, bone maturation, and their nutrition profiles in a rural Ecuadorian community. Covariate-adjusted regression models estimated relationships between UPF intake from a 24-hour Food Frequency Questionnaire and three outcomes: linear growth, weight status and bone maturation. Nutrient Profiling Models (NPM) evaluated a convenience sample of UPF (n 28) consumed by children in the community. In this cohort (n 125; mean age = 33·92 (sd 1·75) months), 92·8 % consumed some form of UPF the previous day. On average, children consuming UPF four to twelve times per day (highest tertile) had lower height-for-age z-scores than those with none or a single instance of UPF intake (lowest tertile) (β = -0·43 [se 0·18]; P = 0·02). Adjusted stunting odds were significantly higher in the highest tertile relative to the lowest tertile (OR: 3·07, 95 % CI 1·11, 9·09). Children in the highest tertile had significantly higher bone age z-scores (BAZ) on average compared with the lowest tertile (β = 0·58 [se 0·25]; P = 0·03). Intake of savoury UPF was negatively associated with weight-for-height z-scores (β = -0·30 [se 0·14]; P = 0·04) but positively associated with BAZ (β = 0·77 [se 0·23]; P < 0·001). NPM indicated the availability of unhealthy UPF to children, with excessive amounts of saturated fats, free sugars and sodium. Findings suggest that frequent UPF intake during early childhood may be linked to stunted growth (after controlling for bone age and additional covariates), despite paradoxical associations with bone maturation.

An integrated index developed for measuring precipitation complexity: a case study of Jinsha River basin, China

Accurate quantification of precipitation complexity is vital for assessing the impact of changing environments on precipitation processes and guiding precipitation forecasting. However, previous research mostly quantified precipitation complexity from different perspectives, resulting in differences in complexity results. In this study, the multifractal detrended fluctuation analysis (MF-DFA; derived from fractal), Lyapunov exponent (derived from Chao), and sample entropy (derived from entropy) were used for investigating the complexity of regional precipitation. Then, the integrated complexity index was established by using the intercriteria correlation (CRITIC) method and the simple linear weighting (SWA) method. Finally, the proposed method is applied to China's Jinsha River basin (JRB). The research indicates that (1) the discriminability of the integrated complexity index is higher than that of MF-DFA, Lyapunov exponent, and sample entropy, which can better distinguish the precipitation complexity in the Jinsha River basin; (2) the higher complexity of monthly precipitation was mainly concentrated in the southeast, and the lower complexity was mainly located in the northwest; moreover, the monthly precipitation complexity for the selected study area is the highest at 0.854 at Weixi station and the lowest at 0.152 at Batang station; (3) the superimposed effects of the southwest monsoon, terrain, and reservoir construction have become the main factors that influence the spatial variation of complexity for precipitation. This study provides a new idea for developing an integrated complexity index, and the results are of great significance for regional precipitation disaster prevention and water resources management.

Comparison of Three Time Series Forecasting Methods on Linear Regression, Exponential Smoothing and Weighted Moving Average

The purpose of this study is to compare the 3 forecasting methods Linear Regression, Exponential Smoothing and Weighted Moving Average based on the smallest error value or close to zero. From the results of this study, the Linear Regression method was obtained as the correct method with a predicted value of 502 students, the smallest error value was MAD 27.83, MSE 1152.1 MAPE 8.1%. The Tracking Signal value moves between 1 and -1, the movement is within the control limits of the tracking signal standard deviation distribution 4 and -4, meaning that the method is correct. The Moving Range value moves between 68 and -46, this value is within the MR control limits of 117.83 and -117.83, this result shows that this means that this method has been tested for truth and can be accepted as well. Thus, indicating that the Linear Regression method as a forecasting method is appropriate and acceptable as a basis for future decision making. The level of accuracy of the error and the value in control shows that there is a time series data relationship between the x variable, namely time, and the variable y, namely actual data. In addition, it produces trending data movement patterns, meaning that data movements experience a significant increase over a long period of time or for 7 periods.

Improving Probability-Based Diagnostic Imaging Algorithm by Modeling Weight Distribution Coefficient for Instantaneous Baseline Damage Identification

This study used a combination of the instantaneous baseline method and a probability-based diagnostic imaging (PDI) algorithm for damage identification in a stiffener-reinforced aluminium plate. Selecting an appropriate weight distribution function plays a significant role in the PDI algorithm by expressing the probability of damage existence around a sensing path. Weight distribution functions are highly dependent on the weight distribution coefficient (β). This parameter (β) normally is estimated by trial and error, which is incorrect. In addition, determining this parameter is quite costly and time-demanding. To address these issues thoroughly, a new approach is proposed for modelling the weight distribution coefficient based on the physics of wave propagation and the actuator–sensor distance. An appropriate value for β is estimated for each sensing path between actuators and sensors in a specific structure based on the proposed model, making the results more accurate and reasonable. The proposed model was validated by identifying damages at three different locations of a structure for five scenarios (three single-damage and two multiple-damage scenarios). Moreover, the results obtained from proposed model using linear and Gaussian weight distributions were compared with the results extracted from an existing approach. The acceptable localization results clearly indicate the effectiveness of proposed approach.

Comparison of 50° handheld fundus camera versus ultra-widefield table-top fundus camera for diabetic retinopathy detection and grading.

To compare the performance of a handheld fundus camera with standard 50° visual field to ultra-widefield (UWF) table-top fundus camera in diabetic retinopathy (DR) detection and grading. Patients affected by diabetes mellitus and referred to our diabetic retinopathy clinic were enroled and underwent fundus photography in mydriasis. All photos were taken using the ultra-widefield table-top fundus camera Zeiss Clarus™ 500 (four fields per eye) and the Optomed Aurora® handheld fundus camera (3 fields per eye). The following parameters were analysed: the gradability of the images, the grade of DR, and diabetic maculopathy (DM), the presence of hypertensive retinopathy (HR) and the presence of other ocular diseases. We enroled 759 eyes of 384 diabetic patients and analysed 5313 fundus photos. The handheld fundus camera obtained a sensitivity of 84.2% and specificity of 95.4% for referable cases. Moreover, it obtained, compared to UWF, an almost perfect agreement with linear weighting for DR, DM and HR (k = 0.877, k = 0.854, and k = 0.961, respectively). The lowest sensitivity was achieved for proliferative DR (58.7% sensitivity, 100% specificity). Optomed Aurora® handheld fundus camera imaging showed a strong agreement compared to UWF in grading DR, considering all DR and DM grades, in mydriasis. However, the use of UWF imaging increases the detection of referable eyes.

Vertically Stacked Nanosheet FET: Charge- Trapping Memory and Synapse With Linear Weight Adjustability for Neuromorphic Computing Applications

This work shows the feasibility of a vertically stacked nanosheet field effect transistor (NSFET) for charge-trapping memory and artificial synaptic devices. The artificial synapse’s behaviors, long-term potentiation (LTP), and long-term depression (LTD) are analogous to erase (ERS) and program (PGM) of charge-trapping memory, respectively. This NSFET device with a gate length of 50 nm achieves a wider memory window (MW), long retention time for programming, and infinite retention (> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{{8}}$ </tex-math></inline-formula> s) for ERS operation. The results also show linear synaptic features with nonlinearity values of 2.50 and −0.42 for LTP and LTD, respectively. Furthermore, the device conductance values are utilized as synaptic weights for image recognition of Modified National Institute of Standards and Technology (MNIST) dataset in neural networks and achieve 93.30% accuracy. These results make it a promising candidate for next-generation charge-trapping memory and neuromorphic computing due to its wide memory window, long retention, high accuracy, and high density.

Research on Transformer Fault Diagnosis Model Based on Improved GOA-SVM

A transformer fault diagnosis model based on the improved grasshopper optimization algorithm-optimized support vector machine (SVM) is proposed to improve the precision of transformer fault diagnosis and avoid the issues that the traditional grasshopper optimization algorithm (GOA) is prone to falling into local optimal solution and slow convergence. Firstly, the grasshopper population is initialized using the elite backward learning strategy to improve the initial population quality and search efficiency. Then, the Sigmoid function is introduced to improve the linear weight decreasing of the grasshopper algorithm into nonlinear weight decreasing to strike a balance between the algorithm’s capacity for both local and global exploration. Finally, the kernel function parameters and penalty coefficients of the SVM are optimized with the improved grasshopper optimization algorithm (IGOA) to establish a model based on dissolved gas analysis (DGA) in oil-based IGOA algorithm-optimized SVM for transformer fault diagnosis model and verify the effectiveness and superiority of IGOA-SVM to identify transformer fault states by comparing with PSO-SVM and GOA-SVM.

Inkjet OLED printing planning based on deep reinforcement learning and reward-based TLO

With the increasing use of OLED screens in electronic products, there is a focus on how to print OLED screens more efficiently. Deep reinforcement learning is being used to solve the OLED printing planning problem because it effectively uses historical data and is also real-time. This paper uses a modified TLO method to combine deep reinforcement learning for multi-objective optimization of OLED printing. Through experiments, the method is compared with the linear weighting method commonly used in multi-objective reinforcement learning, which is more effective in optimization.

Three-dimensional third-order gas-kinetic scheme on hybrid unstructured meshes for Euler and Navier–Stokes equations

In this paper, a third-order gas-kinetic scheme is developed on the three-dimensional hybrid unstructured meshes for the compressible inviscid and viscous flows. In the classical weighted essentially non-oscillatory (WENO) scheme, the high-order spatial accuracy is achieved by the non-linear combination of lower order polynomials. However, for the hybrid unstructured meshes, the procedures, including the selection of candidate stencils and calculation of linear weights, become extremely complicated, especially for three-dimensional problems. To overcome the drawbacks, a third-order WENO reconstruction is developed on the hybrid unstructured meshes, including tetrahedron, pyramid, prism and hexahedron. A general strategy is given for the selection of big stencil and sub-stencils for hybrid meshes, and the topologically independent linear weights are used in the spatial reconstruction. A unified interpolation is also used for the volume integral of different types of control volumes, as well as the flux integration over different cell interfaces. Incorporate with the two-stage fourth-order temporal discretization, the explicit high-order gas-kinetic schemes are developed for unsteady problems. With the lower–upper symmetric Gauss–Seidel (LU-SGS) methods and Jacobi iteration, the implicit high-order gas-kinetic schemes are developed for steady problems. To accelerate the computation, both explicit and implicit scheme is implemented with the graphics processing unit (GPU) using compute unified device architecture (CUDA). Especially, for implicit parallel computation, Jacobi iteration is used. The speedup of GPU code suggests the potential of high-order gas-kinetic schemes for the large scale computation. A variety of numerical examples, from the subsonic to supersonic flows, are presented to validate the accuracy and robustness for both inviscid and viscous flows.

Short-term prediction for distributed photovoltaic power based on improved similar time period

A short-term prediction method for distributed PV power based on an improved selection of similar time periods (ISTP) is proposed, to address the problem of low output power prediction accuracy due to a large number of influencing factors and the large difference in the degree of influence of various factors. First, the simple correlation coefficient (SCC) based on path analysis is used to screen the main influencing factors with stronger correlation with PV output power, and these factors are classified into three categories. Second, correlations of the three dimensions are calculated, respectively: (i) TOPSIS (with weights optimized by the SCC) determines meteorological correlation, (ii) linear weighting (based on the fuzzy ranking) obtains time correlation, and (iii) load correlation is quantified with existing current parameters. Third, the combined impact correlation (CIC) is obtained by weighting the three correlations above to establish criteria for the selection of similar periods, and a short-term PV power prediction model is established. Finally, experimental results based on real data of Australian Yulara Solar System PV plant demonstrate that errors of proposed ISTP method are respectively improved by 47.06% and 46.09% compared with the traditional ELMAN and similar day method.