Practical Model Research Articles

A practical lateral spreading pressure model for piles in inclined liquefied ground

Liquefaction-induced lateral spreading is a main cause of pile failure during earthquakes because it places considerable soil pressure on piles. The lateral spreading pressure originates not only from liquefied soil but also from nonliquefied soil when it overlays a liquefied soil layer. In this study, we develop a practical lateral spreading pressure model for pseudostatic analysis to analyze pile response in inclined liquefied ground. The model comprises two parts. First, using a laminar flow model, the lateral spreading pressure of liquefied soil is modeled as the equivalent fluid flowing pressure. The coefficient of viscosity of the liquefied soil is constructed on the basis of shaking table tests in the literature. The normalized lateral spreading pressure depends on the liquefied soil thickness-to-pile diameter ratio. Second, for nonliquefied soil, individual p–y curves for uphill and downhill soils are used to simulate their distinctive soil reaction mechanisms. The uphill soil is subjected to lateral spreading soil displacement, whereas the downhill soil is subjected to pile displacement. This approach can elucidate the net soil reactions of the downhill and uphill soils that vary depending on pile stiffness. The proposed model is validated through simulations involving 1-g shaking table tests, centrifuge tests, and case histories reported in the literature.

A practical ball‐grid‐array transition modelling methodology for accurate and fast multi‐interposer package simulation

AbstractAn equivalent circuit modelling methodology for the ball‐grid‐array (BGA) transition is proposed and validated with full‐wave electromagnetic simulation and measurement results. The BGA transitions modelled with the proposed methodology are validated to have a good accuracy up to 40 GHz. Unlike the existing BGA modelling methodologies which cannot model the BGA transition with short transmission lines within the anti‐pad region, the proposed methodology is developed from the practical BGA modelling scenario and models the irregular‐shape solder pads with transmission lines within the anti‐pad region. The BGA transitions modelled with the proposed methodology can be used for accurate and fast signal integrity simulations, analyses, and optimizations on 2.5D and 3D packaging. Additionally, factors impacting the accuracy of the equivalent circuit model are revealed by the proposed methodology.

Mathematical programming model for cost-optimized and environmentally sustainable supply chain design

Sustainability is becoming increasingly important as a topic in general supply chains and cold chains. This study addresses the critical need for an integrated and comprehensive approach to supply chain design that simultaneously maximizes cost efficiency and incorporates environmental sustainability, focusing specifically on cold chains. Prior research has identified a gap in existing studies, highlighting the absence of an overarching framework from the logistics providers’ perspective. Our novel mathematical programming model introduces a unique strategy that carefully balances environmental concerns and cost optimization, addressing the shortcomings found in previous works. By integrating variables related to distribution, transportation, and inventory management, the model aims to minimize overall expenses and environmental impact, specifically regarding energy consumption and carbon emissions. We formulate and solve the optimization problem through rigorous mathematical programming, offering a flexible solution applicable to various logistics scenarios. The results demonstrate a significant reduction in overall expenses and environmental effects, providing logistics providers with a powerful tool for informed decision-making and sustainable supply chain management. This research contributes to the evolving field of supply chain sustainability by presenting a practical and effective model for maximizing cost savings while promoting environmental responsibility. The study concludes with compelling statistics, revealing a noteworthy decrease in overall expenses by €25 128 146.73 (100.25%) and environmental impact by 17 359.39 (100.04%). The outcomes show a notable decrease in general expenses and environmental effects, giving logistics providers a strong instrument for wise choices and sustainable supply chain management.

A Path to Risk: Critical Elements of a Structured Approach.

Value-based care arrangements have been the cornerstone of accountable care for decades. Risk arrangements with government and commercial insurance plans are ubiquitous, with most contracts focusing on upside risk only, meaning payers reward providers for good performance without punishing them for poor performance on quality and cost. However, payers are increasingly moving into downside risk arrangements, bringing to mind global capitation in the 1990s wherein several health systems failed. In this article, the authors focus on their framework for succeeding in value-based arrangements at University Hospitals Accountable Care Organization, including essential structural elements that provider organizations need to successfully assume downside risk in value-based arrangements. These elements include quality performance and reporting, risk adjustment, utilization management, care management and clinical services, network integrity, technology, and contracting and financial reconciliation. Each of these elements has an important place in the strategic roadmap to value, even if downside risk is not taken. This roadmap was developed through an applied approach and intends to fill the gap in published practical models of how provider organizations can maneuver value-based arrangements.

Analysis Of The Effectiveness Of Using Learning Media During The Covid-19 Period With The Multi-Attribute Utility Theory

Learning activities during the Covid-19 pandemic were carried out online with the aim of taking advantage of technological advances using online learning media. All high schools have implemented online learning, one of which is SMAN 1 Bukit Batu. This aims to ensure that the learning process is carried out remotely without having to meet in person. Online learning has advantages and disadvantages that can affect the achievement of learning objectives, especially in theoretical and practical PKOK subjects. So it is necessary to assess learning theory and practice of PJOK to determine the effectiveness of both. Research the use of 6 criteria and compare theoretical and practical PJOK learning models to determine the best model. The method used is Multi-Attribute Utility Theory (MAUT) with a quantitative approach where data collection is carried out through surveys. There were 193 respondents, namely all 12th grade students of SMAN 1 Bukit Batu as the population in this study. The results of calculations using the MAUT method on the effectiveness of using online learning media during the Covid-19 pandemic resulted in the choice of the Theory subject assessment (0) as the lowest assessment, while the Practice subject (0.32) was the highest assessment. This assessment states that online learning can be said to be effective for practical subjects, while theoretical subjects are more effective if done offline

Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review

BackgroundUnscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) models can incorporate a wide range of complex predictors to identify high-risk patients and reduce errors to save time and cost. However, the accuracy and practicality of such models are questionable. This review compares the predictive power of multiple ML models and examines the effects of multiple research factors on these models’ performance in predicting URVs to EDs.MethodsWe conducted the present scoping review by searching eight databases for data from 2010 to 2023. The criteria focused on eligible articles that used ML to predict ED return visits. The primary outcome was the predictive performances of the ML models, and results were analyzed on the basis of intervals of return visits, patient population, and research scale.ResultsA total of 582 articles were identified through the database search, with 14 articles selected for detailed analysis. Logistic regression was the most widely used method; however, eXtreme Gradient Boosting generally exhibited superior performance. Variations in visit interval, target group, and research scale did not significantly affect the predictive power of the models.ConclusionThis is the first study to summarize the use of ML for predicting URVs in ED patients. The development of practical ML prediction models for ED URVs is feasible, but improving the accuracy of predicting ED URVs to beyond 0.75 remains a challenge. Including multiple data sources and dimensions is key for enabling ML models to achieve high accuracy; however, such inclusion could be challenging within a limited timeframe. The application of ML models for predicting ED URVs may improve patient safety and reduce medical costs by decreasing the frequency of URVs. Further research is necessary to explore the real-world efficacy of ML models.

Macrophages enhance regeneration of lateral line neuromast derived from interneuromast cells through TGF-β in zebrafish.

Macrophages play a pivotal role in the response to injury, contributing significantly to the repair and regrowth of damaged tissues. The external lateral line system in aquatic organisms offers a practical model for studying regeneration, featuring interneuromast cells connecting sensory neuromasts. Under normal conditions, these cells remain dormant, but their transformation into neuromasts occurs when overcoming inhibitory signals from Schwann cells and posterior lateral line nerves. The mechanism enabling interneuromast cells to evade inhibition by Schwann cells remains unclear. Previous observations suggest that macrophages physically interact with neuromasts, nerves, and Schwann cells during regeneration. This interaction leads to the regeneration of neuromasts in a subset of zebrafish with ablated neuromasts. To explore whether macrophages achieve this effect through secreted cytokines, we conducted experiments involving tail amputation in zebrafish larvae and tested the impact of cytokine inhibitors on neuromast regeneration. Most injured larvae remarkably regenerated a neuromast within 4 days post-amputation. Intriguingly, removal of macrophages and inhibition of the anti-inflammatory cytokine transforming growth factor-beta (TGF-β) significantly delayed neuromast regeneration. Conversely, inhibition of the pro-inflammatory cytokines interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) had minor effects on the regeneration process. This study provides insights into how macrophages activate interneuromast cells, elucidating the pathways underlying neuromast regeneration.

Gaussian process emulation to improve efficiency of computationally intensive multidisease models: a practical tutorial with adaptable R code

BackgroundThe rapidly growing burden of non-communicable diseases (NCDs) among people living with HIV in sub-Saharan Africa (SSA) has expanded the number of multidisease models predicting future care needs and health system priorities. Usefulness of these models depends on their ability to replicate real-life data and be readily understood and applied by public health decision-makers; yet existing simulation models of HIV comorbidities are computationally expensive and require large numbers of parameters and long run times, which hinders their utility in resource-constrained settings.MethodsWe present a novel, user-friendly emulator that can efficiently approximate complex simulators of long-term HIV and NCD outcomes in Africa. We describe how to implement the emulator via a tutorial based on publicly available data from Kenya. Emulator parameters relating to incidence and prevalence of HIV, hypertension and depression were derived from our own agent-based simulation model and other published literature. Gaussian processes were used to fit the emulator to simulator estimates, assuming presence of noise for design points. Bayesian posterior predictive checks and leave-one-out cross validation confirmed the emulator’s descriptive accuracy.ResultsIn this example, our emulator resulted in a 13-fold (95% Confidence Interval (CI): 8–22) improvement in computing time compared to that of more complex chronic disease simulation models. One emulator run took 3.00 seconds (95% CI: 1.65–5.28) on a 64-bit operating system laptop with 8.00 gigabytes (GB) of Random Access Memory (RAM), compared to > 11 hours for 1000 simulator runs on a high-performance computing cluster with 1500 GBs of RAM. Pareto k estimates were < 0.70 for all emulations, which demonstrates sufficient predictive accuracy of the emulator.ConclusionsThe emulator presented in this tutorial offers a practical and flexible modelling tool that can help inform health policy-making in countries with a generalized HIV epidemic and growing NCD burden. Future emulator applications could be used to forecast the changing burden of HIV, hypertension and depression over an extended (> 10 year) period, estimate longer-term prevalence of other co-occurring conditions (e.g., postpartum depression among women living with HIV), and project the impact of nationally-prioritized interventions such as national health insurance schemes and differentiated care models.

Construction and Optimization of Corrosion Map in a Broad Region of Acidic Soil via Machine Learning

Machine learning has been widely applied to exploring the key affecting factors for metal corrosion in some local regions. However, there is a lack of systemic research and a practicable prediction model for metal corrosion in a broad region. In this paper, the corrosion map of Q235 steel in a broad region of acidic soils of Hunan province of Central China was constructed and optimized via field experiment and machine learning. Both the experimental and optimized corrosion maps confirmed that the corrosion rate of the steel decreased from the western to the eastern part of the province. The concentrations of pH, F−, Cl−, NO3−, HCO3−, K+, and Mg2+ were the key affecting factors in the broad region of acidic soils of the province. Among them, the contribution rate of the HCO3− concentration was higher than that of other factors. The optimization model based on the ordinary least squares could be used for the optimization of the corrosion map of steels in a broad region of acidic soils. The optimized corrosion map was a good alternative to the estimation methods for the corrosion rate of steel in soil.

Security-based distributed fuzzy funnel cooperative control for uncertain nonlinear multi-agent systems against DoS attacks

This article focuses on the distributed funnel cooperative control of nonlinear multi-agent systems suffering denial-of-service (DoS) attacks, which aim to disrupt communication links between agents and can lead to discontinuities in system outputs. First, an actual measurement model of output signals is given, and a fuzzy switching observer is designed by utilizing the practical output model to obtain the unknown states. Second, a funnel-based tracking control strategy is presented by skillfully blinding the funnel function and backstepping technique to defend against DoS attacks. A crucial stability criterion is established by combining an improved average dwell time (ADT) approach and Lyapunov stability theory that integrates the constraints imposed by DoS attacks. In particular, based on an appropriate error transformation, the developed control law has a simpler structure than existing barrier function methods to solve performance constraints. It guarantees that the output of each follower agent yi is able to track the reference signal yd, and synchronization errors ϵi=∑s=1Mai,s(yi−ys)+bi(yi−yd) are consistently confined within a prearranged performance funnel Fϕ, (that is, (t,ϵi(t))∈Fϕ,∀t). Ultimately, a simulation example is presented to prove the validity of the established solution.

Channel-level Matching Knowledge Distillation for object detectors via MSE

Knowledge distillation (KD) has been widely used in different tasks as a practical model compression technique. Due to the poor performance of directly using Mean Square Error (MSE) between the intermediate features of the teacher and student, most feature-based detector distillation methods are primarily concerned with proposing diverse attention mechanisms and employing MSE to guide the student in learning critical information. However, the significance of MSE in detector distillation is often overlooked. To enhance the distillation performance of MSE and provide a novel perspective, we propose channel-level Matching Knowledge Distillation via MSE (MKD). The idea of MKD is simple but effective: based on the match or mismatch of student and teacher channel features in Feature Pyramid Networks (FPN), we divide channels at the same location in the FPN layers of the student and teacher into match channels and mismatch channels and then assign different weights to them to calculate the MSE-based distillation loss, which forces the student to distinguish the teacher’s channels and imitate the features of the corresponding channels to achieve better performance. We conduct extensive experiments on MS COCO and PASCAL VOC benchmarks to prove that MKD is superior to state-of-the-art distillation methods. For example, with a powerful RetinaNet-ResNeXt101 detector as the teacher, ResNet-50 based RetinaNet and RepPoints with our distillation method achieve 40.9% and 42.2% mAP on COCO2017, which are 3.5% and 3.6% higher than the baseline, respectively.

Возможности цифровой трансформации малых и средних сельскохозяйственных предприятий в современных условиях

Abstract. The author presented the results of scientific developments of data transformation when performing certain tasks (inventory, sale of grain to the population) by a visiting employee using a mobile application. The purpose of the study is to develop theoretical provisions and practical recommendations to ensure the solution of economic problems of the functioning of small and medium-sized businesses of the agro-industrial complex using a mobile application. Methods. In the course of the study, general scientific methods of theoretical and empirical knowledge were used: analysis and synthesis, monographic, basic array, design-constructive, deduction and induction, economic-statistical and economic-mathematical methods (comparison, summary and grouping, tabular, graphic). Experimental method, practical modeling, including conducting a statement experiment, analysis and generalization of the obtained data. Results. The development of small and medium-sized agricultural enterprises as a socio-economic phenomenon affects many objects and generates certain connections between them, which allows us to consider small and medium-sized businesses from the point of view of systematic analysis. The conceptual interest and capabilities of agricultural producers in a system approach to human resources management with optimization of the main technological process management system will allow achieving an effective result of activities in the innovative environment of the organization. The results of research work can serve as a theoretical and practical basis for the activities of owners, leaders of small and medium-sized businesses in order to improve the process of digitalization of agricultural production; for the activities of state authorities – support for the development of small and medium-sized businesses with the aim of economic growth of the country; for investors – assessment of investment opportunities in the agro-industrial complex. Scientific novelty. A mechanism has been developed to optimize and improve the efficiency of production processes; attracting domestic and foreign investments in the development of industries of the agro-industrial complex. Feeding the country is a great mission, fulfilling it one of the main components of national security and preserving the integrity of the country. The undisputed priority will be interest in an effective, balanced production process, innovative and technical development and digitalization of agriculture.

A cascading failure model of cyber-coupled power system considering virus propagation

Cyber attacks from the information layer can have a serious impact on the operation of the cyber-coupled power system. In this paper, a practical cascading failure model used for investigating the impact of virus propagation on the cascading failure of cyber-coupled power system is proposed. The model takes into account the actual functionality of cyber-coupled system and the dynamic virus propagation in cyber network, and describes the effects of cyber node failure caused by virus infection and power flow overload on the cascading failure. Moreover, the impact of defense upgrade on the virus propagation is considered. Centralized control based on dispatching center and local regulation based on static power-frequency characteristic are introduced to describe the actual monitoring function of cyber system on the power grid. Then, the effects of defense upgrade strategy and modular community structure on the failure evolution are analyzed. The results indicate that the spread of virus destroys the monitoring function of cyber network on the power network and leads to more serious failure results. The effectiveness of defense upgrade strategies is related to the upgrade probability of information stations. In addition, the stronger community structure of cyber network can improve the robustness of cyber-coupled power system.

Computing Parameter Estimates of a Homogeneous Nested Piecewise Linear Regression

Objective. The aim of the study is to develop an algorithm for identifying the parameters of a homogeneous nested piecewise linear regression model of the first type by the method of least modules. Method. Estimation of its unknown parameters is carried out with the help of reduction to the problem of linear Boolean programming. Its solution should not cause computational difficulties due to a significant number of effective software tools - for example, the well-established and freely available program LPsolve on the Internet. Result. The generated linear programming problem has an acceptable dimension for solving practical modeling problems. Conclusion. The results of solving a numerical example indicate the effectiveness of the method proposed in the work for calculating parameter estimates for a homogeneous nested piecewise linear regression model of the first type by the method of least modules.

Early and consistent safe sleep practices in the neonatal intensive care unit: a sustained regional quality improvement initiative.

To increase compliance with standardized safe sleep recommendations for patients in a cohort of regional level III/IV neonatal intensive care units (NICUs) in accordance with recently revised guidelines issued by the American Academy of Pediatrics (AAP). A regional quality improvement (QI) initiative led by a multidisciplinary task force standardized safe sleep criteria across participating NICU sites. Universal and unit-specific interventions were implemented via Plan-Do-Study-Act (PDSA) cycles with evaluation of compliance through routine crib audits, run chart completion, and Pareto chart analysis. Following QI implementation, compliance with safe sleep guidelines for eligible NICU infants improved from 34% to 90% from October 2019 through September 2022. Compliance with early, consistent modeling of safe sleep practices nearly tripled in this cohort of regional NICUs. A standardized, timely approach to safe sleep transition demonstrated dramatic and sustained improvement in the practice and modeling of safe sleep behaviors in the NICU.

Leaching of Phytochemicals from Beans during Hydration, Kinetics, and Modeling.

In the current era, there is a growing emphasis on the circular economy and the valorization of waste products. Bean processing industries generate substantial nutrient-rich waste laden with valuable phytochemicals. Understanding the leaching patterns and kinetics of major phytochemicals is key to designing better processes leading to increased sustainability. This review investigates phytochemical leaching mechanisms and kinetic modeling methods. Firstly we lay the foundation with a broad theoretical framework, and later deal with kinetic modeling approaches and promising areas for future research. Currently, the composition of industrial-scale bean wastewater remains undocumented in the open literature. Nonetheless, drawing from existing studies and general bean composition knowledge, we proposed a multi-phase leaching process. We hypothesize three distinct phases: initial leaching of phytochemicals from the outer seed coat, followed by a second phase involving polysaccharides, and concluding with a third phase wherein phenolic acids within the cotyledons leach into the hydration water. This review aims to shed light on the complex process of phytochemical leaching from common beans during hydration. By combining theoretical insights and practical modeling strategies, this work seeks to enhance our understanding of this phenomenon and ultimately contribute to the optimization of food processing methods with reduced environmental impact.

Non-linear modeling of a bi-layer magnetostrictive cantilever considering [formula omitted]E effect

Magnetostrictive cantilever beams are reliable and straightforward energy harvesting devices for powering wireless sensors using environmental vibrations. However, despite their widespread use, a comprehensive analytical model encompassing both mechanical and electromagnetic main features is still lacking. The mechanical-to-magnetic coupling of a magnetostrictive beam, with its various working conditions and nonlinear characteristics, can be effectively approximated by the Euler–Bernoulli (E–B) equation with damping (viscous and Kelvin–Voigt) coupled with a non-linear magnetostrictive model. In this study, we propose a practical and convenient model for a general concept device designed to convert vibrations into electrical energy. The model is thoroughly compared with experimental measurements aimed to prove the goodness of the approach rather than energy harvesting highest performance.

Analisis penerapan RT-RW Net menggunakan Topologi Mesh-Wireless untuk meningkatkan pemahaman Administrasi Sistem Jaringan Siswa

Network Administration is a crucial subject within the vocational education curriculum, particularly for Computer and Network Engineering majors. This subject is designed to provide students with knowledge and skills related to network administration and management. This study aims to test the implementation of RT-RW Net using Wireless Mesh topology with a practical model to enhance the understanding of 30 students from SMKN 3 Selong in the Computer and Network Engineering department for the Network Administration subject, covering DHCP Server, IP addressing, subnetting, and mesh topology, utilizing Mikrotik and Winbox v.3. The method used was quantitative with primary data collection through questionnaires and analysis using the Likert scale. The results showed that 84.68% of students responded "Very Understand" to the material provided with an average score of 76.21 out of a maximum score of 90. The application of the RT-RW model using Wireless Mesh topology in network administration subjects with direct practice can enhance student understanding. Therefore, it is crucial for teachers to ensure compatibility between teaching methods and the technology used to optimize student learning

Numerical modelling of bridge deck reinforcement corrosion based on analysis of GPR data

AbstractThis study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck.

Quantumness near a Schwarzschild black hole

The merging of quantum information science with the relativity theory presents novel opportunities for understanding the enigmas surrounding the transmission of information in relation to black holes. For this purpose, we study the quantumness near a Schwarzschild black hole in a practical model under decoherence. The scenario we consider in this paper is that a stationary particle in the flat region interacts with its surroundings while another particle experiences free fall in the vicinity of a Schwarzschild black hole’s event horizon. We explore the impacts of Hawking radiation and decoherence on the system under investigation and find that these effects can limit the survival of quantum characteristics, but cannot destroy them completely. Hence, the results of this study possess the potential to yield valuable insights into the comprehension of the quantum properties of a real system operating within a curved space-time framework.