Method For Models Research Articles

Implementation of the Gravity Location Models Method in Determining Regional Warehouse Locations to Minimize Goods Transport Costs at PT

Supply Chain is a network of companies that work together to create and deliver a product to the end user. PT X is a company engaged in manufacturing whose products are steel pipes, not only steel pipes are produced. Supply chain management at PT X is considered inefficient because it has to supply four warehouses or depots at once, this can cost a lot of shipping costs in the PT X Supply Chain process. Therefore, supply chain management needs to be carried out using the Gravity Location Model method which aims to minimize the costs of transporting goods at PT X. The aim of this research is to determine the variables needed to determine the location point of PT X regional warehouse using the Gravity Location Model method and to determine the regional warehouse location and optimal shipping costs using the Gravity Location Model method at PT X. From the results of research that has been carried out, the variables used to determine the location points for PT X regional warehouse construction include X and Y, Xn and Yn, dn, Cn, Dn, and TC as well as calculating cost minimization through a comparison of 20 iterations, it was found that the minimum cost was in the 11th iteration, namely IDR 386.955,50. with coordinates X = -4.13 and Y = 115,18.

A class of efficient high-order time-stepping methods for the anisotropic phase-field dendritic crystal growth model

In this paper, we develop and analyze a novel class of arbitrarily high-order and unconditionally energy stable schemes for the anisotropic phase-field dendritic crystal growth model, which is a highly nonlinear system that combines the anisotropic Allen–Cahn equation with the thermal equation. The proposed schemes are based on an extrapolated and linearized Runge–Kutta method for an auxiliary variable reformulation of the crystal growth model. A delicate implementation demonstrates that the proposed method can be realized in a very efficient way, requiring only the solution of a coupled linear elliptic system at each time step. We prove that the constructed schemes satisfy the energy dissipation property and demonstrate the convergence order through a consistency error analysis. To the best of our knowledge, this is the first unconditionally stable scheme of arbitrarily high-order for the anisotropic phase-field dendritic crystal growth model. Numerical experiments for two and three dimensional dendritic crystal growth are simulated to verify our theoretical accuracy as well as the efficiency of the proposed method. Furthermore, to emphasize the superiority of the high-order schemes, we present a numerical comparison that directly contrasts its performance with the lower-order schemes.

Deep Learning–Based Production Forecasting and Data Assimilation in Unconventional Reservoir

Summary Developing unconventional reservoirs such as shale oil is vital for fulfilling the need for energy consumption in the world. Oil production from shale reservoirs is still the most complicated and uncertain phenomenon because of its complex fracture networking, low matrix porosity, and permeability. Production forecasting is crucial for decision-making and tactical exploitation of subsurface resources during production. Traditional methods, such as the Arps decline model and reservoir simulation methods, face significant challenges in forecasting hydrocarbon production due to the highly nonlinear and heterogeneous nature of rocks and fluids. These methods are prone to substantial deviations in forecasting results and show limited applicability to unconventional reservoirs. Therefore, it is essential to improve the production forecasting capability with the help of a data-driven methodology. The data set for modeling is collected from two prominent shale oil-producing regions, the Eagle Ford and the Bakken. The Bakken data set is used to train and test the models, and the Eagle Ford data set is used to validate the model. The random search method was used to optimize the model parameters, and the window sliding technique was used to find a suitable window size to predict future values efficiently. The combination of different deep learning (DL) methods has designed a total of six hybrid models: gated recurrent unit (GRU), long short-term memory (LSTM), and temporal convolutional network (TCN). These models can capture the spatial and temporal patterns in the oil production data. The results concluded that the TCN-GRU model performed best statistically and computationally compared with other individual and hybrid models. The robust model can accelerate decision-making and reduce the overall forecasting cost.

Comparing the in-plane shear moduli of cardboard measured by flexural vibration, torsional vibration, static torsion, off-axis vibration, and off-axis tension tests

Abstract Flexural vibration (FV), torsional vibration (TV), static torsion (ST), longitudinal vibration (OAV), and tension (OAT) of 45° off-axis tests of flat pieces of cardboards were analyzed to determine the in-plane shear modulus (IPSM) using samples of various widths. The analyses were performed with simulations using the finite element method (FEM) for models with and without lamination and physical experiments using cardboard samples. For the FV, TV, and ST tests, the IPSM was calculated using binary search (BS) and linear regression (LR). The FEM results suggest that the IPSM values obtained from the TV and ST test simulations were affected by lamination. In contrast, the effect of lamination was not clear in the FV, OAV, and OAT test simulations. The coefficient of variation obtained from the FV experiments was often larger than that obtained from other tests. Despite this variation, the FV test is more attractive than the other tests because the experiments could be performed easily, and the effect of lamination could be reduced.

Mutual inductance parameter measurement and experimental research of double circuit based on different frequency method

AbstractIn order to improve the measurement accuracy of mutual inductance parameters of transmission lines in the environment of double circuit power lines without power outage, this paper establishes simulation models for measuring mutual inductance parameters of transmission lines using the different frequency method in two modes: equal and unequal zero sequence self‐parameters of two circuit lines, using a parallel zero sequence coupling model of double circuit power lines. In the article, simulation analysis is conducted on the line parameters with a coupling coefficient between 0.4 and 0.6 and a line length of 20–50 km. In order to further verify the correctness of the simulation model and measurement methods, a test bench was established based on the principle of line mutual inductance parameter testing in a laboratory dual circuit line without a power outage environment for experimental testing. By comparing the test values under experimental conditions with the standard values, it has been proven that the model and method can meet the actual measurement requirements of engineering.

Research progress on freeze–thaw constitutive model of concrete based on damage mechanics

Abstract In cold areas, freeze–thaw damage seriously affects the long-term use and safe operation of concrete structures. The constitutive model is an important foundation for predicting deformation and strength characteristics of concrete materials and for the non-linear analysis of concrete structures. This study is based on the elaboration of methods for the constitutive model of damaged materials by using damage mechanics and others. This study focuses on the existing constitutive model results of concrete under the static axial compression load, dynamic load, and coupling environmental load, and analyzing the problems in existing studies. Research has shown that segmented models exhibit higher fitting accuracy of concrete freeze–thaw constitutive model under static axial compression loads. By defining coupled damage variables, it is possible to approach the actual freeze–thaw damage of concrete under environmental coupling, and attention should be paid to the differences or interactions between damage factors. In order to meet the actual engineering needs of high altitude and cold areas needs to expand the temperature range of freeze–thaw tests and consider the dynamic loads impact on concrete damage, the establishment of constitutive model of concrete under the actual freeze–thaw damage is the focus of frost-resistant durability research of hydraulic concrete in cold regions.

Precise Evaluation of Gas Expansion Energy Within Coal Bodies in Coal-and-Gas Outbursts: Innovation in Calculation Model and Experimental Methods

Precise Evaluation of Gas Expansion Energy Within Coal Bodies in Coal-and-Gas Outbursts: Innovation in Calculation Model and Experimental Methods

Program Development for Enhancing Teachers’ Competencies in Teaching Railway Control and Maintenance in Vocational Colleges under the Office of Vocational Education Commission

Teacher competency is crucial for the quality development of learners. Teachers with high competence in learning management will result in high-quality learners. This research aims 1) to study the components and indicators of teachers’ competency in teaching railway control and maintenance. 2) to study current conditions, desirable conditions, methods of development, and needs for the development of teachers’ competency in teaching railway control and maintenance. 3) to design and develop a teachers’ competency-enhancing program in teaching railway control and maintenance, and 4) to study the results of implementing the teacher competency-enhancing program in teaching railway control and maintenance. The methodology was research and development conducted in 4 Phases as follows according to the objectives. The results showed that 1) Components and indicators of teachers’ competency in teaching railway control and maintenance have 5 components, and 17 indicators, confirmed by experts, are appropriate at the highest level. 2) Current condition, teachers’ competency in teaching railway control and maintenance, overall was moderate level. The desirable condition, overall was at the highest level. Competency development methods consist of (a) Training, (b) Self-learning, (c) Workshops, (d) Study visits, and (e) Work practice in the workplace, and the priorities of the needs for competency development, including (a) Self-development, (b) Ethics and professional ethics of teachers, (c) Measurement and evaluation of learning outcomes, (d) Curriculum administration and learning management, and (d) Building relationships and cooperation with the community for learning management, respectively. 3) Teachers’ Competency Enhancing Program in Teaching Railway Control and Maintenance consists of (a) Principles, (b) Objectives, (c) Model and development methods, (d) Contents and developing activities consisting of 5 Modules, and (e) Measurement and Evaluation. The result of the program evaluation by qualified experts was appropriate, utility, and possibility at the highest level. 4) The results of implementing the teachers’ competency-enhancing program in teaching railway control and maintenance were used. It was found that (a) knowledge of teacher competency in teaching railway control and maintenance before development received an average score of 17.70 out of 30, representing 58.99% after development, receiving an average score of 25.90, representing 86.33%. Knowledge after development was higher than before development. (b) Teachers’ competencies and overall performance before development were at a moderate level. After development was at the highest level, and (c) the results of the program satisfaction assessment by participants overall and all aspects are at the highest level.

Augmented Lagrangian method for tensor low-rank and sparsity models in multi-dimensional image recovery

Augmented Lagrangian method for tensor low-rank and sparsity models in multi-dimensional image recovery

Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review.

Background/Objectives: To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research.

WhisPAr: Transferring pre-trained audio models to fine-grained classification via Prompt and Adapter

With the rise of Transformer as the backbone networks, pre-trained models have gradually become a hot spot in deep learning–related fields and play an important role in increasing research and production activities. However, current pre-trained models still have the problems of coarse knowledge granularity and poor zero/few-shot performance and cannot be well applied to fine-grained downstream tasks. In audio processing, considerable structure biases exist between the upstream and downstream tasks, making it difficult to realize cross-task transfer learning. Inspired by the success of Prompt and Adapter techniques in natural language processing (NLP) and computer vision (CV), we propose a cross-task transfer method with good generality to address these problems. We contribute the following: (1) a novel transfer method for audio pre-trained models via deep Prompt and Adapter structures to improve model adaptation to downstream tasks, which is effective and generally applicable and has potential to be used in more powerful audio pre-trained models in the future; (2) a fine-grained classification model implementation based on the abovementioned method WhisPAr, which is experimentally shown to outperform the baseline models in various fine-grained classification tasks (0.73 vs. 0.84, 0.76 vs. 0.81, and 0.47 vs. 0.68); and (3) an in-depth exploration of the mechanisms and laws of Prompt and Adapter. These contributions are expected to drive future research on audio processing and transfer learning, among others.

Pre-clinical Models for Geriatric Pharmacotherapy.

With ageing of the population worldwide and discovery of new medications for prevention and management of age-related conditions, there is increasing use of medications by older adults. There are international efforts to increase the representativeness of participants in clinical trials to match the intended real-world users of the medications across a range of characteristics including age, multimorbidity, polypharmacy and frailty. Currently, much of the data on medication-related harm in older adults are from pharmacovigilance studies. New methods in pre-clinical models have allowed for measurement of exposures (such as chronic exposure, polypharmacy and deprescribing) and outcomes (such as health span functional measures and frailty) that are highly relevant to geriatric pharmacotherapy. Here we describe opportunities for design and implementation of pre-clinical models that can better predict drug effects in geriatric patients. This could improve the translation of new drugs from bench to bedside and improve outcomes of pharmacotherapy in older adults.

Top Executives’ Overseas Background on Corporate Green Innovation Output: The Mediating Role of Risk Preference

Abstract This study employs balanced panel data encompassing Shanghai and Shenzhen A-share listed companies in China spanning the period from 2014 to 2021 as a research dataset, employing a mixed OLS regression model and Bootstrap methods, to investigate the influence mechanism of the overseas background and risk preference tendencies of top executives on the corporate green innovation output. The results show that the top executives’ overseas experience has a substantial influence on corporate green innovation, while top executives’ risk preference level plays a mediating role. In addition, the educational background of top executives is identified as a moderating element in this mechanism. Thus, the conclusions of this article extend prior research by identifying a new connection between top executives’ overseas backgrounds and corporate green innovation and by offering a new perspective to advance and improve the green economy.

Automatic segmentation and quantification of OCT images before and after cataract surgery using deep learning

Obtaining quantitative geometry of the anterior segment of the eye, generally from optical coherence tomography (OCT) images, is important to construct 3D computer eye models, used to understand the optical quality of the normal and pathological eye and to improve treatment (for example, selecting the intraocular lens to be implanted in cataract surgery or guiding refractive surgery). An important step to quantify OCT images is segmentation (i.e., finding and labeling the surfaces of interest in the images), which, for the purpose of feeding optical models, needs to be automatic, accurate, robust, and fast. In this work, we designed a segmentation algorithm based on deep learning, which we applied to OCT images from pre- and post-cataract surgery eyes obtained using anterior segment OCT commercial systems. We proposed a feature pyramid network architecture with a pre-trained encoder and trained, validated, and tested the algorithm using 1640 OCT images. We showed that the proposed method outperformed a classical image-processing-based approach in terms of accuracy (from 91.4% to 93.2% accuracy), robustness (decreasing the standard deviation of accuracy across images by a factor of 1.7), and processing time (from 0.48 to 0.34 s/image). We also described a method for the 3D models’ construction and their quantification from the segmented images and applied the proposed segmentation/quantification algorithms to quantify 136 new eye measurements (780 images) obtained from OCT commercial systems.

Preclinical Direct Endoluminal Assessment of Endothelialization After Flow Diversion With Microangioscopy

Background Direct endoluminal imaging, such as with our previously described microangioscope, is an emerging adjunct in endovascular cerebral aneurysm management that enables clinicians to delineate between thrombi and visualize neoepithelialization after stent placement with high resolution. The present study sought to study flow diversion in vivo under direct endoluminal imaging, and to validate our findings with histopathology. Methods In a rabbit model, we implanted each left common carotid artery with a shielded flow diverter (FD) (Medtronic Pipeline Vantage) and nonshielded FD (Medtronic Pipeline Flex) in the right common carotid artery. We studied 9 animals in 3 groups: (1) no periprocedural antiplatelet therapy, (2) aspirin 81 mg daily, and (3) aspirin 81 mg and clopidogrel 75 mg daily. FD thrombosis, stenosis, malapposition, and neoepithelialization were all evaluated by diagnostic cerebral angiography and microangioscopy after 30 days. Diagnostic cerebral angiography and angioscopic video were analyzed by independent evaluators and compared with histopathologic analysis. Results In the aspirin and dual antiplatelet therapy groups, there were no significant observed differences in stent thrombosis, stenosis, malapposition, or neoepithelialization between the shielded and non‐shielded FD groups. There was significantly more thrombus formation in Group 1. Neointimal thickness as measured by the microangioscope was highly correlated with histology (r = 0.72; P = 0.016). Interrater agreement of microangioscope videos was highest for FD thrombosis and stenosis. Conclusion In‐stent thrombosis, stenosis, malapposition, and neopithelialization demonstrated no significant difference between shielded and non‐shielded FDs. Microangioscopy measurements for neointimal thickness were highly correlated with pathology and may be a helpful adjunct to diagnostic cerebral angiography in FD follow‐up.

World Modeling for Autonomous Wheel Loaders

This paper presents a method for learning world models for wheel loaders performing automatic loading actions on a pile of soil. Data-driven models were learned to output the resulting pile state, loaded mass, time, and work for a single loading cycle given inputs that include a heightmap of the initial pile shape and action parameters for an automatic bucket-filling controller. Long-horizon planning of sequential loading in a dynamically changing environment is thus enabled as repeated model inference. The models, consisting of deep neural networks, were trained on data from a 3D multibody dynamics simulation of over 10,000 random loading actions in gravel piles of different shapes. The accuracy and inference time for predicting the loading performance and the resulting pile state were, on average, 95% in 1.2 ms and 97% in 4.5 ms, respectively. Long-horizon predictions were found feasible over 40 sequential loading actions.

EC-YOLO: Improved YOLOv7 Model for PCB Electronic Component Detection.

Electronic components are the main components of PCBs (printed circuit boards), so the detection and classification of ECs (electronic components) is an important aspect of recycling used PCBs. However, due to the variety and quantity of ECs, traditional target detection methods for EC classification still have problems such as slow detection speed and low performance, and the accuracy of the detection needs to be improved. To overcome these limitations, this study proposes an enhanced YOLO (you only look once) network (EC-YOLOv7) for detecting EC targets. The network uses ACmix (a mixed model that enjoys the benefits of both self-attention and convolution) as a substitute for the 3 × 3 convolutional modules in the E-ELAN (Extended ELAN) architecture and implements branch links and 1 × 1 convolutional arrays between the ACmix modules to improve the speed of feature retrieval and network inference. Furthermore, the ResNet-ACmix module is engineered to prevent the leakage of function data and to minimise calculation time. Subsequently, the SPPCSPS (spatial pyramid pooling connected spatial pyramid convolution) block has been improved by replacing the serial channels with concurrent channels, which improves the fusion speed of the image features. To effectively capture spatial information and improve detection accuracy, the DyHead (the dynamic head) is utilised to enhance the model's size, mission, and sense of space, which effectively captures spatial information and improves the detection accuracy. A new bounding-box loss regression method, the WIoU-Soft-NMS method, is finally suggested to facilitate prediction regression and improve the localisation accuracy. The experimental results demonstrate that the enhanced YOLOv7 net surpasses the initial YOLOv7 model and other common EC detection methods. The proposed EC-YOLOv7 network reaches a mean accuracy (mAP@0.5) of 94.4% on the PCB dataset and exhibits higher FPS compared to the original YOLOv7 model. In conclusion, it can significantly enhance high-density EC target recognition.

A Method of Integrating Air Conditioning Usage Models to Building Simulations for Predicting Residential Cooling Energy Consumption

Great deviations in building energy consumption simulation are attributed to the simplified settings of occupants’ air conditioning (AC) usage schedules. This study was designed to develop a method to quantify the uncertainty and randomness of AC usage behavior and incorporate the model into simulations, in order to improve the prediction performance of AC energy consumption. Based on long-term onsite monitoring of household thermal environments and AC usage patterns, two stochastic models were built using unsupervised cluster and statistical methods. Based on the Monte Carlo method, the AC operation schedule was generated through AC opening duration, setpoints, and other relevant parameters, and was further incorporated into EnergyPlus. The results show that the ideally deterministic AC operation settings from the standard significantly overestimate the cooling energy consumption, where the value based on the fixed mode was 6.35 times higher. The distribution of daily AC energy consumption based on the stochastic modeling was highly consistent with the actual situation, thanks to the accurate prediction of the randomness and dynamics of residents’ AC usage patterns. The total cooling energy consumption based on two stochastic models was found to be much closer to the actual values. The work proposes a method of embedding stochastic AC usage models to EnergyPlus 22.1 benefits for an improvement in building energy consumption simulation and the energy efficiency evaluation regarding occupant behavior in the future.

Improved Bayesian Model Updating Method for Frequency Response Function with Metrics Utilizing NHBFT-PCA

To establish a high-fidelity model of engineering structures, this paper introduces an improved Bayesian model updating method for stochastic dynamic models based on frequency response functions (FRFs). A novel validation metric is proposed first within the Bayesian theory by using the normalized half-power bandwidth frequency transformation (NHBFT) and the principal component analysis (PCA) method to process the analytical and experimental frequency response functions. Subsequently, traditional Bayesian and approximate Bayesian computation (ABC) are improved by integrating NHBFT-PCA metrics for different application scenarios. The efficacy of the improved Bayesian model updating method is demonstrated through a numerical case involving a three-degrees-of-freedom system and the experimental case of a bolted joint lap plate structure. Comparative analysis shows that the improved method outperforms conventional methods. The efforts of this study provide an effective and efficient updating method for dynamic model updating based on the FRFs, addressing some of the existing challenges associated with FRF-based model updating.

Profiles of child labour: prevalence, activities, contexts, and children’s harm extent in Raya-Kobo and Angot Districts, Ethiopia

Using a mixed research design, the overall objective of this study is to investigate the profiles of child labour in Ethiopia’s districts of Raya-Kobo and Angot. The study’s specific objectives are to examine types of activities and prevalence of child labour, to identify the children’s working contexts, and to evaluate the extent of harm children face in workplaces. The study hypothesizes the existence of significant variations in the profiles of child labour (activity types, prevalence rates, settings, and level of harm to children) as a result of several circumstances. While survey methodology was used to collect primary data from 431 randomly selected respondents, desk review and document analysis were also used to gather secondary data. The sampling procedures used include multi-stage purposive and systematic random sampling. Descriptive and inferential (Ordered Probit Regression model) methods of data analysis are utilized to analyze the data. The results of the descriptive analysis demonstrate a high incidence and magnitude of child labour, as well as children’s early entry to work. The regression analysis reveals children’s exposure to high levels of harm at work, which is directly exacerbated by household age, income diversification, land fragmentation, livestock number, shocks, and the predicted value of remittances. Contrarily, access to rural transportation, household heads’ education level, cart ownership, and family size reduce children’s harm extent while working. The study highlights the need to design strategies for age-appropriate work to protect children from hazardous and high-intensity activities, undertake community awareness campaigns about the impact of child labour on children, and strengthen local stakeholders’ capacity. Additionally, connecting rural areas and farmers’ croplands to towns or major routes via repaired or new roads would be beneficial.