Traditional Calculation Research Articles

Quantum computing for several AGV scheduling models

Due to the high degree of automation, automated guided vehicles (AGVs) have been widely used in many scenarios for transportation, and traditional computing power is stretched in large-scale AGV scheduling. In recent years, quantum computing has shown incomparable performance advantages in solving specific problems, especially Combinatorial optimization problem. In this paper, quantum computing technology is introduced into the study of the AGV scheduling problem. Additionally two types of quadratic unconstrained binary optimisation (QUBO) models suitable for different scheduling objectives are constructed, and the scheduling scheme is coded into the ground state of Hamiltonian operator, and the problem is solved by using optical coherent Ising machine (CIM). The experimental results show that compared with the traditional calculation method, the optical quantum computer can save 92% computation time on average. It has great application potential.

Research and Application of the Mine 3D DC Resistivity Method for Detecting Grouting in the Floor of an Ultrawide Working Face, Taking the Yongmei Xinqiao Coal Mine in Henan Province as an Example

Generally, ground grouting is used to treat confined water areas before mining at the working face, but there is a lack of testing methods for determining the effectiveness of such a grouting treatment on the floor of ultrawide working faces. Therefore, we propose a 3D DC resistivity method for mines and apply it to the detection of the effect of grouting on the mine floor. This study took the Yongmei Xinqiao Coal Mine in Henan Province as the research object and used a combination of theoretical analysis, numerical simulation, and measured data analysis to study the effect of the 3D resistivity method on detecting the effect of grouting on the floor of an ultrawide working face in the mine. The research results indicated that compared with the 2D observation mode of same-side power supply and reception, the 3D observation mode of opposite-side power supply and reception using the tunnels on both sides of the working face was more sensitive to the response of the water-rich area 60 m below the coal seam’s floor. Regarding the model’s set-up in this article, when traditional apparent resistivity calculations were used, the apparent resistivity obtained by the 3D observation mode was opposite to the model’s setting, and accurate electrical information of anomalous bodies must be obtained through 3D inversion. The measured data showed that although the ground grouting treatment effectively reduced the water volume in the floor, the treatment’s result was affected by human factors, and the water in the floor was redistributed.

MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models

Aerosols influence weather and climate by interacting with radiation through absorption and scattering. These effects heavily rely on the optical properties of aerosols, which are mainly governed by attributes such as morphology, size distribution, and chemical composition. These attributes undergo continuous changes due to chemical reactions and aerosol micro-physics, resulting in significant spatio-temporal variations. Most atmospheric models struggle to incorporate this variability because they use pre-calculated tables to handle aerosol optics. This offline approach often leads to substantial errors in estimating the radiative impacts of aerosols along with posing significant computational burdens. To address this challenge, we introduce a computationally efficient and robust machine learning approach called MieAI. It allows for relatively inexpensive calculation of the optical properties of internally mixed aerosols with a log-normal size distribution. Importantly, MieAI fully incorporates the variability in aerosol chemistry and microphysics. Our evaluation of MieAI against traditional Mie calculations, using number concentrations from the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive accuracy for aerosol optical properties. MieAI achieves this with errors well within 10%, and it operates more than 1000 times faster than the benchmark approach of Mie calculations. Due to its generalized nature, the MieAI approach can be implemented in any chemistry transport model which represents aerosol size distribution in the form of log-normally distributed internally mixed modes. This advancement has the potential to replace frequently employed look-up tables and plays a substantial role in the ongoing attempts to reduce uncertainties in estimating aerosol radiative forcing.

Influence of theoretical and semi-empirical peak models on the efficiency calculation in chiral chromatography

Height equivalent to theoretical plate (H) equations, such as the van Deemter or Knox-Saleem equations, and other efficiency vs. linear velocity equations (u), provide kinetic insights into chromatographic separations phenomena and column performance. In enantioselective separations, the peak shape of the two enantiomers can differ significantly and are often asymmetric. The peak efficiency calculations heavily impact these efficiency-flow profiles, leading to erroneous estimations of eddy diffusion, longitudinal diffusion, and mass transfer terms. In this work, new asymmetric peak functions are employed for modeling enantiomer peaks based on the Haarhoff-Van der Linde function, its generalized variant (GHVL), once Generalized Asymmetric Gaussian (AGN), and Twice Generalized Gaussian (TGN). The new models (AGN, TGN, and GHVL) incorporate higher statistical moments besides the zeroth, first, and second moments to account for two-sided asymmetry (fronting or tailing). The fit results are compared with the traditional efficiency calculation methods endorsed by official pharmacopeia and numerical estimation of moments from the raw data. Enantiomeric separations of ibuprofen and dl-homophenylalanine were chosen as probe molecules. The results demonstrate that non-linear least squares fitted functions provide better estimations of peak efficiency data even in the presence of high noise. In particular, the generalized models consistently offered the best quality fits for various peak shapes in chiral separations. Conversely, the half-height Gaussian method greatly overpredicted skewed peak efficiencies. This investigation reveals that the commonly held assumptions of peak shape and numerical integration of raw data are highly insufficient for chiral chromatography. The impact of asymmetry on plate height should not be overlooked when accurate data from efficiency-flow rate curves is derived. We advocate for the broader adoption of these new generalized peak (AGN, TGN, GHVL) models because they provide robustness at various SNRs that account for right or left asymmetry while accurately representing peak geometry.

Benchmarking and contribution analysis of carbon emission reduction for renewable power systems considering multi-factor coupling

The renewable power system serves as a vital path towards achieving carbon peaking and neutrality goals. Accurately quantifying the carbon reduction emissions of renewable power systems and evaluating the contribution of various influencing factors to carbon reduction is quite important, which is related to the formulation of energy policies and regional power planning by the power grid or relevant departments. However, in the current research, there are few studies related to the quantification of carbon emission reduction, and in terms of optimal scheduling, the existing studies often only consider a single factor, ignoring the coupling effect between multiple factors, which makes the contribution of each optimization strategy to carbon emission reduction unclear. Therefore, based on the current power system scheduling mechanism and dual-carbon background, this paper optimizes the carbon emission reduction benchmark factor and its corresponding weight, proposes a new carbon emission reduction benchmark of renewable power system, and verifies the accuracy of this benchmark based on the classical scheduling model. On this basis, the mathematical models of thermal power deep peak shaving (DPS), electricity export (ELE) and green certificate trading (GCT) are established. Each factor is introduced into the classical scheduling model to simulate and verify the promoting effect of each influencing factor on carbon emission reduction. Finally, the above different influencing factors were constructed in different coupling scenarios to calculate the carbon emission reduction under each scenario, and the contribution degree of each influencing factor to carbon emission reduction was analyzed sharply according to the value of cooperation game instead of the traditional calculation method. The results show that the proposed benchmark can reduce the error by 2.6%–15.5 %. In the carbon emission reduction contribution simulation, the three factors have coupling effects, and deep peak shaving and electricity export are more sensitive to carbon emission reduction contribution. This study provides valuable insights for the development of carbon reduction strategies for renewable power systems.

Test Method for Rapid Prediction of Steady-State Temperature of Outer Rings of Bearings under Grease Lubrication Conditions

Temperature has a great influence on the stability of bearing performance. For the study of bearing steady-state temperature, this paper proposes a test method to quickly predict the steady-state temperature of the outer ring of a bearing, which solves the problems in traditional theoretical calculations and simulation analysis methods such as the large number of calculations, complex models, and large errors. Firstly, a mathematical prediction model is established according to the bearing temperature-rise law; then, a bearing steady-state temperature detection device is designed; and finally, the prediction model parameters are solved according to the experimental results, and experimental verification is carried out. It is shown that the prediction model has high accuracy under different load and speed conditions, and the error between the predicted steady-state temperature and the tested steady-state temperature is less than 0.7 °C. This prediction method reduces the single test time of the same speed to 60 min, which greatly improves the efficiency of the temperature detection test. The steady-state temperature model has important theoretical significance in guiding the study of the limiting speed of bearings.

Highly efficient photocatalytic hydrogen generation of PtPdy nanoframe/2D-CdS: Shape-controlled preparation and property modulation

This work focuses on the structure design of Pt-based alloys, and their influences on 2D-CdS semiconductor photocatalyst in hydrogen (H2) generation reaction from water-splitting under visible light, using traditional experimental techniques and DFT calculations as Pd concave nanocube (Pd CNCs) as a template, PtPdx alloy with cuboctahedron core-shell structure (PtPdx NPs) and cuboctahedron nanoframe (PtPdy NFs) were successfully synthesized via wet chemical routes. The structure of Pt-based alloys was certified by XRD, HRTEM, XPS, and ICP. The Pt-based co-catalysts were photo-deposited on 2D-CdS in an aqueous solution of (NH4)2SO3 while the reaction for photo-generating of H2 occurred. The obtained photocatalysts show excellent activity towards hydrogen generation, i.e., in ascending order, Pd CNCs/2D-CdS (46.1 mmol/h/g) < PtPd1.8 NPs/2D-CdS (55.4 mmol/h/g) < PtPd0.2 NFs/2D-CdS (71.7 mmol/h/g, QE=61.46%, λ=420 nm). Photoelectrochemical tests, ESR, and work functions results indicate that PtPd0.2 NFs have excellent photoelectrochemical performance, the longest lifetime of electrons, and the highest work function, respectively. Moreover, DFT calculations unveil that PtPd0.2 has the largest surface energy and the lowest activation energy ΔGH* which results in the fast charge transfer rate at the PtPd0.2/2D-CdS interface. This work offers a novel thought into the structural design of Pt-based co-catalysts applied in photocatalytic hydrogen generation from water-splitting.

Shear performance of tapered box girders with corrugated steel webs in extradosed cable-stayed bridges under cable forces

The integration of box girders with corrugated steel webs (CSWs) and an extradosed cable-stayed design gives rise to an innovative and distinctive bridge design, recognized as the extradosed cable-stayed bridge with CSWs. The synergistic integration of the technical benefits facilitates expanding the maximum span and enhancing the adaptability of this structural configuration. Subjected to cable forces, the main bridge superstructure featuring a box girder with CSWs experiences vertical shear, substantial axial force, and bending moments. Shear buckling on the CSWs emerges as a prominent issue influencing design considerations. In response to heightened internal forces near support, the main girder is typically configured with variable height. The Resal effect in the tapered girder leads to additional shear stresses from axial forces and bending moments, consequently modifying the effective shear stress on the CSWs. Therefore, traditional basic calculation assumptions that solely account for the vertical component of cable forces in shear stress calculations are frequently inaccurate. This study presents a modified theoretical method for calculating shear stress in tapered box girders with CSWs subjected to cable forces by considering the Resal effect. The proposed modified method incorporates not only the vertical shear but also considers bending moments and axial forces. The finite element simulation and experimental results showcase its remarkable accuracy, concurrently validating the presence of the Resal effect in tapered box girders with CSWs under cable forces. Additionally, this study quantitatively analyzes the shear force redistribution phenomenon induced by vertical shear, axial force, and bending moments considering the Resal effect. This reveals the shear performance of tapered box girders with CSWs under cable forces. The study is expected to provide a strategy and theoretical guideline for rapidly and efficiently obtaining the shear stresses of tapered box girders with CSWs in extradosed cable-stayed bridges under cable forces.

Performance degradation model and life prediction method of spool pair caused by erosion wear

The erosion of solid particles in the fluid causes the wear of the metering edge of the spool pair, resulting in a decrease in system performance. This paper aims to develop a general method to evaluate performance degradation and life prediction of spool pairs induced by erosion. Considering the diversity of influencing factors and the complexity of operating conditions, traditional numerical calculation methods will consume huge computational costs. It is difficult to qualitatively and quantitatively analyze the evolution of metering edge by numerical calculation. The erosion prediction model (EPM) constructed by machine learning methods can be used to predict the wear profile discretized in time and space. Combined with the mathematical model of the valve-controlled cylinder, a performance degradation model (PDM) that can simulate real-time working conditions is established. Through the data exchange and iterative update between EPM and PDM, a complete digital platform for the calculation of slide valve durability is constructed. After comparing with the experimental results, it can be concluded that the proposed method can realize the performance degradation evaluation and life prediction of the spool pair caused by erosion wear under continuous working conditions.

Handwritten Calculator Using Contour Detection

This project introduces a novel Handwritten Calculator that merges traditional handwritten calculations with modern computing techniques. By leveraging computer vision, machine learning, and human-computer interaction, the calculator interprets handwritten digits and operators to perform mathematical calculations. Through contour detection and Convolutional Neural Network (CNN) model prediction, the calculator offers users a seamless and intuitive way to compute. The project explores the development, applications, and impact of this innovative technology, with potential implications for education, accessibility, and software engineering. The Handwritten Calculator represents a significant advancement in computational methods, bridging the gap between analog and digital approaches to calculation.

Reflection coefficient of a reflectionless kink

Classically, reflectionless kinks transmit all incident radiation. Recently, we have used an analyticity argument together with a solution of the Lippmann-Schwinger equation to write down the leading quantum correction to the reflection probability. The argument was fast, but rather indirect. In the present paper, we calculate the reflection coefficient and probability by methodically grinding through the Schrödinger picture time evolution. We find the same answer. This answer contains contributions not considered in the traditional calculation of meson-kink scattering in 1991. However, as a result of these contributions, our total result is zero in the case of the Sine-Gordon model, and so it is consistent with integrability. Published by the American Physical Society 2024

An Integrated Gather-and-Distribute Mechanism and Attention-Enhanced Deformable Convolution Model for Pig Behavior Recognition.

The behavior of pigs is intricately tied to their health status, highlighting the critical importance of accurately recognizing pig behavior, particularly abnormal behavior, for effective health monitoring and management. This study addresses the challenge of accommodating frequent non-rigid deformations in pig behavior using deformable convolutional networks (DCN) to extract more comprehensive features by incorporating offsets during training. To overcome the inherent limitations of traditional DCN offset weight calculations, the study introduces the multi-path coordinate attention (MPCA) mechanism to enhance the optimization of the DCN offset weight calculation within the designed DCN-MPCA module, further integrated into the cross-scale cross-feature (C2f) module of the backbone network. This optimized C2f-DM module significantly enhances feature extraction capabilities. Additionally, a gather-and-distribute (GD) mechanism is employed in the neck to improve non-adjacent layer feature fusion in the YOLOv8 network. Consequently, the novel DM-GD-YOLO model proposed in this study is evaluated on a self-built dataset comprising 11,999 images obtained from an online monitoring platform focusing on pigs aged between 70 and 150 days. The results show that DM-GD-YOLO can simultaneously recognize four common behaviors and three abnormal behaviors, achieving a precision of 88.2%, recall of 92.2%, and mean average precision (mAP) of 95.3% with 6.0MB Parameters and 10.0G FLOPs. Overall, the model outperforms popular models such as Faster R-CNN, EfficientDet, YOLOv7, and YOLOv8 in monitoring pens with about 30 pigs, providing technical support for the intelligent management and welfare-focused breeding of pigs while advancing the transformation and modernization of the pig industry.

A novel deep learning method based on 2-D CNNs and GRUs for permeability prediction of tight sandstone

The accurate calculation of tight sandstone reservoir permeability is crucial for optimizing production and maximizing natural gas recovery. Traditional physical model-based permeability calculation methods heavily rely on core experimental parameters. However, petrophysical experiments are costly, prompting the development of fast and reliable reservoir permeability prediction methods. In this paper, a novel deep learning model that combines two-dimensional convolutional neural network (2-D CNN) model and gated recurrent neural network is proposed to predict tight sandstone reservoir permeability. The 1-D conventional well logs data is converted into the 2-D geological feature map to fit the model's input dimension. The TimeDistributed layer wrapping technology is used to couple the 2-D CNN and GRU to extract comprehensive features of geological feature maps. By training the model, a depth nonlinear mapping between the spatial and temporal features in the well logs data and permeability is established. The model is divided into two subnetworks, 2-D CNNs and GRUs, which are used to compare and analyze the functions of different modules in the proposed model. Additionally, two traditional machine learning algorithms, SVM and XGBoost, are also introduced as comparison models. Four regression evaluation metrics are used to quantify the predictive performance of different models. The test results in a blind well demonstrate that the proposed method outperforms the comparison models in predicting permeability, with a coefficient of determination (R2) of 0.9305, a root mean square error (RMSE) of 0.0895, a mean squared error (MSE) of 0.0080, and a mean absolute error (MAE) of 0.0725. This model can provide insight into the petrophysical characteristics of reaction reservoirs based on conventional well logs data and can accurately estimate reservoir permeability under limited core data, which has great application potential in improving the exploration accuracy of unconventional oil and gas resources.

Biodiversity Conservation in Xishuangbanna, China: Diversity Analysis of Traditional Knowledge Related to Biodiversity and Conservation Progress and Achievement Evaluation

Biodiversity plays an important role in maintaining the ecological balance of the earth. The study of traditional knowledge related to biological resources is a hot issue in the field of international biodiversity conservation. Xishuangbanna is a key area of biodiversity and a cultural hotspot of international significance. According to the Technical Regulation for Classification, Investigation, and Inventory of Traditional Knowledge Relating to Biological Diversity issued by the Ministry of Ecology and Environment, we investigated and catalogued the traditional knowledge related to biodiversity of the Jino people who have lived in Xishuangbanna for generations, and collected 490 entries of traditional knowledge related to biodiversity of the Jino people. Drawing on the traditional knowledge diversity index calculation method proposed by Wang Guoping, the overall traditional knowledge α-diversity index of the Jino people is 0.63, indicating that the richness of the traditional knowledge of the Jinuo people is relatively high. The traditional culture related to biodiversity, the traditional knowledge related to agricultural genetic resources, and the traditional technology related to the sustainable utilization of biological resources are relatively rich and diverse. The diversity index is 0.86, 0.82 and 0.79, respectively. In addition, Xishuangbanna has invested a lot of energy in biodiversity protection, including the establishment of nature reserves, botanical gardens, zoos, ecological tea gardens and other species reserves, and the promulgation of laws and policies related to biodiversity protection, and has achieved remarkable results in in situ protection and ex situ protection. On the basis of analyzing the progress and achievements of biodiversity conservation in Xishuangbanna, this study points out that Xishuangbanna faces challenges such as the loss of traditional knowledge, insufficient conservation efforts, and great changes in land use, and puts forward corresponding suggestions.

A new method of fuel pebble temperature calculation based on neutronic and thermal coupling equivalent model

As one of the six reactor types in the fourth-generation nuclear energy systems, the Very High Temperature Gas-cooled Reactor (VHTR) possesses inherent safety features. One VHTR fuel pebble contains tens of thousands of randomly distributed TRISO fuel particles, displaying a double heterogeneity characteristic. Current research models employ the homogenized method for fuel pebble temperature calculation, which neglected the neutronic and thermal (N/T) coupling effect and double heterogeneity characteristic. This study proposes a fuel pebble coupled equivalent method (PCEM) for temperature calculation. The method is based on the effective thermal conductivity obtained by the fine structure fuel pebble N/T coupling method. The new method is evaluated on steady-state and transient VHTR multi-scale neutronic and thermal hydraulic (N/TH) coupling calculation. The results show that the traditional temperature calculation method is conservative. Under steady-state conditions, the maximum degree of conservatism of the fuel pebble average temperature and peak temperature are 0.6 K and 14.2 K respectively. For the sudden drop in coolant flow rate transient condition, the average temperature and peak temperature of the fuel pebble calculated by PCEM are smaller than the values calculated by traditional method at the same time. Due to the influence of the fuel Doppler effect, the transient response of the traditional calculation method is slower.

Design of an English vocabulary e-learning recommendation system based on word bag model and recurrent neural network algorithm

This article takes learning English vocabulary as the foundation, constructs precise classification strategies, and designs a recommendation system for English vocabulary learning. This article constructs a recommendation system for English vocabulary learning based on the word bag calculation model and recursive neural network calculation method, which has great significance, and also conducts in-depth research on this system. Thus, a classification calculation method based on the word bag calculation model was proposed. The calculation method in this paper is different from traditional calculation methods, and the classification accuracy in this paper is higher. Meanwhile, recurrent neural networks themselves have one or more feedback loops that can feed back information from the neural network to other neural networks, resulting in recurrent networks with different structures. After a series of research results, it has been found that the SIFT method takes longer to extract than the SURF method, and the number of extracted features is relatively large. Therefore, the recommendation system for English vocabulary learning proposed in this article can be improved according to the different needs of users, in order to better meet their individual requirements.

A framework for computer-aided high performance titanium alloy design based on machine learning

Titanium alloy exhibits exceptional performance and a wide range of applications, with the high performance serving as the foundation for the development. However, traditional material design methods encounter numerous calculations and experimental trial-and-error processes, leading to increased costs and decreased efficiency in material design. The data-driven model presents an intriguing alternative to traditional material design methods by offering a novel approach to expedite the materials design process. In this study, a framework for computer-aided design high performance titanium alloys based on machine learning is proposed, which constructs an intelligent search space encompassing various combinations of 18 elements to facilitate alloy design. Firstly, a proprietary dataset was constructed for titanium alloy materials using feature design and a combination of unsupervised and supervised feature engineering methods. Secondly, six machine learning algorithms were employed to establish regression models, and the hyperparameters of each algorithm were optimized to improve model performance. Thirdly, the model was screened using five regression algorithm evaluation methods. The results demonstrated that the selected optimized model achieved an R2 value of 0.95 on the verification set and 0.93 on the test set, yielding satisfactory outcomes. Finally, a comprehensive model framework along with an intelligent search methodology for designing high-strength titanium alloys has been established. It is believed that this method is also applicable to other properties of titanium alloys and the optimization of other materials.

A Multilevel Approach for Direct Core Calculation Schemes

A method of dynamic homogenization was recently proposed as an alternative technique for three-dimensional (3D) core calculations using a direct approach. This technique allows for producing homogenization parameters for a subdomain within the core at its actual state, and offers the advantage of avoiding off-line calculations, cross-section interpolations, and expensive 3D transport calculations. The methodology has shown a remarkable reduction in computational cost compared to 3D transport. However, the application of direct calculation schemes in multiphysics and multicycle problems still demands an extensive use of computational resources. Within the framework of traditional two-step calculations, several multilevel schemes have been developed to accelerate lattice calculations for the generation of the multiparameter cross-section libraries. With a similar objective, this work investigates how the multilevel approach can be applied within the framework of direct calculation schemes and discusses how depletion calculations may be performed. The objective is to enhance their performance and reduce memory requirements in favor of a high-fidelity model of the matter behavior in the reactor. We have then identified suitable flux solvers for each level and explored various homogenization options. We tested the methodology in a 3D pressurized water reactor core problem inspired by the TVA Watts Bar Unit 1 Multi-Physics Multi-Cycle OECD/NEA Benchmark, and performed a comparative analysis to assess the accuracy and computational efficiency of these new calculation schemes against direct single-level calculations and a two-step calculation based on pin-by-pin homogenization, as well as Monte Carlo simulations. Our analysis proves that the computational resources required to solve the full-core problem using a multilevel scheme are significantly reduced, and the best computational features are provided by multilevel dynamic homogenization. Furthermore, we observed that the multilevel approach not only allows for speedup, but can also be advantageously applied in order to improve the accuracy of the final solution of a calculation scheme when the core solver relies on an approximate transport operator.

Agricultural Extension Model based on Local Wisdom in Creating Household Welfare in the Dayak Tunjung Tribe Community, West Kutai Regency

Agricultural extension is non-formal education for farmers which includes extension activities, knowledge and skills for farmers and their families which takes place through a teaching and learning process. Agricultural instructors must be able to guide farmers, instructors also provide motivation, provide information and increase farmers' awareness so that they can encourage their interest in learning in facing problems in the field. Local wisdom is a view of life and knowledge as well as various life strategies in the form of activities carried out by local communities in responding to various problems in meeting their needs. The traditional culture of the Dayak Tunjung Tribe in West Kutai is rich in various local wisdom and plays an active role in preserving the environment. Local wisdom is the noble values that apply in community life to, among other things, protect and manage the environment sustainably. Local wisdom not only understands the knowledge of indigenous/local communities between humans and how good relations between humans are, but also concerns knowledge, understanding and customs about humans, nature, and the relationship between all, where all knowledge is lived, practiced, taught and passed on from one generation to generation. The Dayak community as local farmers have local wisdom in farming and opening farms. The Dayak community does not burn forests except for farming, and this is also done through traditional calculations that have been taught from generation to generation. The aim of this research is to analyze the factors that influence changes in farmer behavior towards household welfare in the local wisdom of shifting cultivation. This research approach uses a mixed method approach. In this research, it uses a sequential explanatory design where the first sequence uses quantitative methods, and the second sequence uses qualitative methods. The number of samples (n) in this study was 110 farmers. The data analysis used is GSCA, which is an analysis that combines the characteristics found in PLS-SEM with Covariance Based Structural Equation Modeling (CB-SEM). Based on the research results, it was found that factors influence changes in farmer behavior in improving the welfare of farmer households, namely: (1) internal factors of farmers which have a significant influence on changes in farmer behavior in farming. Factors that do not influence changes in farmer behaviour are: (1) external factors of farmers and (2) the role of extension workers does not have a real influence on the farming activities of the local Dayak Tunjung Tribe community. Changes in farmer behaviour in the shifting agricultural activities of the Dayak Tunjung community, namely knowledge (Y1.1), attitudes (Y1.2), and skills (Y1.3), all of these indicators have a real and large influence on improving farmer welfare. farmer household. This means that in order to improve the welfare of farming households, farmers must change the behaviour of farmers in carrying out shifting cultivation activities in Barong Tongkok District

Stochastic Vibration-Driven Fatigue Analysis for Steel Structure Bridges Considering Track Irregularity

This study proposes a method for assessing the effect of stochastic vibrations resulting from track irregularities which are rarely studied but significant on bridge fatigue. First, the pseudo-excitation method is utilized to determine the power spectrum density of stress. Subsequently, the trigonometric series method is employed to acquire the stress time history set. Then, a traditional fatigue life calculation method is employed for analysis. By utilizing a post-sampling method, the need for costly repetitive solutions of the vehicle-bridge coupling system is eliminated. Numerical simulations are conducted on a steel truss bridge, revealing the non-negligible effect of track irregularity on bridge fatigue performance. The study investigates the underlying mechanism, highlighting the pronounced influence of long-wave track irregularity. Moreover, the research explores the correlation between track smoothness and bridge fatigue performance, demonstrating that a decrease in track smoothness leads to an increase in fatigue loss. These findings emphasize the necessity of considering stochastic vibrations when evaluating bridge fatigue, providing valuable insights for bridge design and maintenance.