Concurrent Model Research Articles

Improved Design of Concurrent Synchronization System Controller Based on Petri Net

At present, the advancement of science and technology has contributed to the emergence of various network parallel environments, web service concurrency environments and massively parallel processors. In order to abstract some practical systems into concurrent system models, it is necessary to conduct model analysis, and to perform deadlock detection of the concurrent system. Despite prior studies on the control problem of the synchronous concurrent system, there remains room for improvement in terms of workload, time, and efficiency. Some very practical methods are explored by introducing locking and unlocking to synchronize the concurrent system, and the algorithm that involves the specific regulation is provided. The Petri net model of the controller is developed to provide an effective method applicable to find the deadlock and to prevent and eliminate the deadlock for synchronous concurrent systems.

MMDataLoader: Reusing Preprocessed Data Among Concurrent Model Training Tasks

MMDataLoader: Reusing Preprocessed Data Among Concurrent Model Training Tasks

Acute Effects of Two Different Concurrent Training Models on Physiological Parameters, Explosive Strenght and Balance Performance in Soccer Players

In the study, the acute effects of two different concurrent training models including high intensity interval training (HIIT) and high intensity interval functional training (HIFT) plus strength training (S) on physiological indicators (lactate and heart rate) and performance (balance and explosive power) parameters were examined in soccer players. A total of 13 moderately active young soccer players (19.92; ±1.44 years) participated in the study. In the study, body composition measurements, heart rate, lactate, explosive strength measurement and balance testing were measured. Training models (S+HIIT-S+HIFT) were applied sequentially with a crossover design. The training sessions were conducted with a three-day interval. Parameters were analyzed with SPSS24 (SPSS Inc., Chicago, IL, USA). Repeated Measure Analysis of Variance (ANOVA) was used for repeated measures and Bonferroni Correction was used for pairwise comparisons. Significance level was accepted as p

Enhancing Wave Energy Conversion Efficiency through Supervised Regression Machine Learning Models

The incorporation of machine learning (ML) has yielded substantial benefits in detecting nonlinear patterns across a wide range of applications, including offshore engineering. Existing ML works, specifically supervised regression models, have not undergone exhaustive scrutiny, and there are no potential or concurrent models for improving the performance of wave energy converter (WEC) devices. This study employs supervised regression ML models, including multi-layer perceptron, support vector regression, and XGBoost, to optimize the geometric aspects of an asymmetric WEC inspired by Salter’s duck, based on key parameters. These important parameters, the ballast weight and its position, vary along a guided line within the available geometric resilience of the asymmetric WEC. Each supervised regression ML model was fine-tuned through hyperparameter optimization using Grid cross-validation. When evaluating the performance of each ML model, it became evident that the tuned hyperparameters of XGBoost led to predictions that strongly aligned with the actual values compared to other models. Furthermore, the study extended to assess the performance of the optimized WEC at the designated deployment test site location.

An Axiomatic Basis for Computer Programming on the Relaxed Arm-A Architecture: The AxSL Logic

Very relaxed concurrency memory models, like those of the Arm-A, RISC-V, and IBM Power hardware architectures, underpin much of computing but break a fundamental intuition about programs, namely that syntactic program order and the reads-from relation always both induce order in the execution. Instead, out-of-order execution is allowed except where prevented by certain pairwise dependencies, barriers, or other synchronisation. This means that there is no notion of the 'current' state of the program, making it challenging to design (and prove sound) syntax-directed, modular reasoning methods like Hoare logics, as usable resources cannot implicitly flow from one program point to the next. We present AxSL, a separation logic for the relaxed memory model of Arm-A, that captures the fine-grained reasoning underpinning the low-overhead synchronisation mechanisms used by high-performance systems code. In particular, AxSL allows transferring arbitrary resources using relaxed reads and writes when they induce inter-thread ordering. We mechanise AxSL in the Iris separation logic framework, illustrate it on key examples, and prove it sound with respect to the axiomatic memory model of Arm-A. Our approach is largely generic in the axiomatic model and in the instruction-set semantics, offering a potential way forward for compositional reasoning for other similar models, and for the combination of production concurrency models and full-scale ISAs.

Exploitation of Children's Criminal and Economics To Become A Drug Course In The Urban Of Makassar Indonesia

Many studies have examined the sexual exploitation of children few have explored the economic and criminal exploitation, mostly using qualitative research. In addition, not many studies have been done on children who are economically exploited by their own parents who force them to work as drug couriers. This study used a mixed methods concurrent embedded model to obtain more comprehensive data with 15 child victims of exploitation and 150 respondents as informants. Data collection, data analysis, and data validity were done using qualitative and quantitative methods. The results showed that drug couriers were a form of economic exploitation as well as criminal exploitation of children by families. The contributing factor is the child’s fear of parents who always commit violence, although children worked as drug couriers because of their own desire to help the family economy and personal needs. The social environment contributes to the exploitation of children because it is in the social environment of children that helps parents work, thus undermining parents’ care for their children. Various methods are used if the child does not want to work as a drug courier, such as physical violence, psychological violence and threats, and sometimes giving gifts and freedom of association. The actions of parents are economic exploitation and crime against children.
 Keywords: exploitation, drug couriers, economic, family

On ‘Becoming’ Contemplative Academic Development Practitioners: Reflections through Collaborative Autoethnography

The onslaught of COVID-19 heralded a turn in higher education as society has begun to question the purpose, values and practices of higher education. COVID-19 presented the unique opportunity to re-imagine and re-envision how one conceptualises the roles and responsibilities of academic development practitioners. How are they responding to their own evolving selves, their environmental cues, and shaping the shifts of redirection within higher education? In this article, we draw from two research traditions to examine the processes of re-imagining academic developers’ becoming: contemplative pedagogy and collective autoethnography. In our data collection, we use the concurrent model in autoethnography as we draw on personal memory data, archival materials, self-observation, self-reflection, self-analysis and interviews with each other. This contributed to a thick description of our personal and professional lives and practices. We show how these collective research traditions and approaches provided nuanced understandings of the role of place and space in evolving academic development in higher education. This article argues for the value of a contemplative autoethnography as an approach to transforming academic development practices, particularly in providing insight into professional development, identity and personal experience.

A tensor based varying-coefficient model for multi-modal neuroimaging data analysis.

All neuroimaging modalities have their own strengths and limitations. A current trend is toward interdisciplinary approaches that use multiple imaging methods to overcome limitations of each method in isolation. At the same time neuroimaging data is increasingly being combined with other non-imaging modalities, such as behavioral and genetic data. The data structure of many of these modalities can be expressed as time-varying multidimensional arrays (tensors), collected at different time-points on multiple subjects. Here, we consider a new approach for the study of neural correlates in the presence of tensor-valued brain images and tensor-valued covariates, where both data types are collected over the same set of time points. We propose a time-varying tensor regression model with an inherent structural composition of responses and covariates. Regression coefficients are expressed using the B-spline technique, and the basis function coefficients are estimated using CP-decomposition by minimizing a penalized loss function. We develop a varying-coefficient model for the tensor-valued regression model, where both covariates and responses are modeled as tensors. This development is a non-trivial extension of function-on-function concurrent linear models for complex and large structural data, where the inherent structures are preserved. In addition to the methodological and theoretical development, the efficacy of the proposed method based on both simulated and real data analysis (e.g., the combination of eye-tracking data and functional magnetic resonance imaging (fMRI) data) is also discussed.

Institutional Factors Influencing Participatory Monitoring and Evaluation in NGOs in Uganda: A Case of TASO Mbarara and Rukungiri Service Centers

The participatory approach to Monitoring and Evaluation (M&E) is considered core to project development and implementation because it seeks to involve people who will be affected by the project throughout its entire process. Over the years, Non-Governmental Organizations (NGOs) have played a significant role in implementing community-centered projects in Uganda. However, the level of community participation has been mainly limited to needs assessments, consultative meetings during the design of the project and baseline studies. This has greatly hindered project sustainability and effectiveness in achieving the intended objectives due to less ownership of the interventions by local people, a phenomenon attributed to institutional-based factors. The study examined the influence of institutional factors (leadership styles and staff competencies) on Participatory Monitoring and Evaluation (PM&E) and explored how organizational structure influences PM&E and the stakeholder perspectives towards PM&E at The AIDS Support Organization (TASO) in Mbarara and Rukungiri service centers. The researchers adopted a mixed methods approach with a concurrent triangulation model involving a sample of 76 respondents whose views were obtained using questionnaires and 10 participants in key informant interviews. Quantitative data was analyzed using descriptive and inferential statistics in SPPS version 20.0, while qualitative data was analyzed using thematic analysis. The study found that leadership styles and staff competencies had a statistically significant relationship with PM&E. Qualitative findings further revealed that organizational structure in terms of policies and roles determines the extent of participation, the frequency, and how stakeholders are engaged. Stakeholder perspectives aligned towards technical skills needed for meaningful participation in M&E, policy limitations, and financial constraints influenced participation at all stages. Rigorous stakeholder mapping and analysis by institutions is key in understanding interests and capacities of stakeholders which can be addressed and aligned to enhance participatory monitoring and evaluation.

Institutional Factors Influencing Participatory Monitoring and Evaluation in NGOs in Uganda: A Case of TASO Mbarara and Rukungiri Service Centers

The participatory approach to Monitoring and Evaluation (M&E) is considered core to project development and implementation because it seeks to involve people who will be affected by the project throughout its entire process. Over the years, Non-Governmental Organizations (NGOs) have played a significant role in implementing community-centered projects in Uganda. However, the level of community participation has been mainly limited to needs assessments, consultative meetings during the design of the project and baseline studies. This has greatly hindered project sustainability and effectiveness in achieving the intended objectives due to less ownership of the interventions by local people, a phenomenon attributed to institutional-based factors. The study examined the influence of institutional factors (leadership styles and staff competencies) on Participatory Monitoring and Evaluation (PM&E) and explored how organizational structure influences PM&E and the stakeholder perspectives towards PM&E at The AIDS Support Organization (TASO) in Mbarara and Rukungiri service centers. The researchers adopted a mixed methods approach with a concurrent triangulation model involving a sample of 76 respondents whose views were obtained using questionnaires and 10 participants in key informant interviews. Quantitative data was analyzed using descriptive and inferential statistics in SPPS version 20.0, while qualitative data was analyzed using thematic analysis. The study found that leadership styles and staff competencies had a statistically significant relationship with PM&E. Qualitative findings further revealed that organizational structure in terms of policies and roles determines the extent of participation, the frequency, and how stakeholders are engaged. Stakeholder perspectives aligned towards technical skills needed for meaningful participation in M&E, policy limitations, and financial constraints influenced participation at all stages. Rigorous stakeholder mapping and analysis by institutions is key in understanding interests and capacities of stakeholders which can be addressed and aligned to enhance participatory monitoring and evaluation.

Unveiling Recurrence Patterns: Analyzing Predictive Risk Factors for Breast Cancer Recurrence after Surgery.

Breast cancer (BC) stands as the second-leading cause of female-specific cancer-related fatalities globally, necessitating comprehensive research to address its critical aspects. This study aimed to explore the time intervals between surgery and disease recurrence in BC patients and their survival utilizing various parametric and semi-parametric models. After the examination of data collected from 2010 to 2021 at a BC Center in Tehran, Iran, 171 cases met the criteria for analysis out of 2246 datasets. Model fitting, was assessed through the Akaike Information Criterion (AIC), and indicated the logistic distribution as the most fit one among concurrent and independent variable models. The Cox proportional hazard regression model consistently demonstrated superior fitting, characterized by the lowest AIC values. The average age at diagnosis was 50.39 years, with a standard deviation of 11.13. Typical survival time was estimated 53.44 months, falling within a confidence interval of 51.41-55.48 months at a 95% confidence level. The 1-year survival rate was determined at 0.92 (95% CI: 0.89-0.94). Notably, patient age while cancer diagnosis, progesterone receptor (PR), tumor grade, and tumor stage were found to be statistically significant (P < .05) risk factors for prediction of BC recurrence after surgery in Iran by Cox model. Our findings underscore the importance of further exploration and consideration of the identified risk factors in BC research and treatment strategies.

Accelerating automatic model finding with layer replications case study of MobileNetV2.

In this paper, we propose a method to reduce the model architecture searching time. We consider MobileNetV2 for 3D face recognition tasks as a case study and introducing the layer replication to enhance accuracy. For a given network, various layers can be replicated, and effective replication can yield better accuracy. Our proposed algorithm identifies the optimal layer replication configuration for the model. We considered two acceleration methods: distributed data-parallel training and concurrent model training. Our experiments demonstrate the effectiveness of the automatic model finding process for layer replication, using both distributed data-parallel and concurrent training under different conditions. The accuracy of our model improved by up to 6% compared to the previous work on 3D MobileNetV2, and by 8% compared to the vanilla MobileNetV2. Training models with distributed data-parallel across four GPUs reduced model training time by up to 75% compared to traditional training on a single GPU. Additionally, the automatic model finding process with concurrent training was 1,932 minutes faster than the distributed training approach in finding an optimal solution.

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

This article presents the design, implementation, and evaluation of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FLSys</b> , a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps. FLSys is designed to work on smart phones with mobile sensing data. It balances model performance with resource consumption, tolerates communication failures, and achieves scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. Furthermore, FLSys provides advanced privacy preserving mechanisms and a common API for third-party app developers to access FL models. FLSys adopts a modular design and is implemented in Android and AWS cloud. We co-designed FLSys with a human activity recognition (HAR) model. HAR sensing data was collected in the wild from 100+ college students during a 4-month period. We implemented HAR-Wild, a CNN model tailored to mobile devices, with a data augmentation mechanism to mitigate the problem of non-Independent and Identically Distributed data. A sentiment analysis model is also used to demonstrate that FLSys effectively supports concurrent models. This article reports our experience and lessons learned from conducting extensive experiments using simulations, Android/Linux emulations, and Android phones that demonstrate FLSys achieves good model utility and practical system performance.

Adaptive Multi-Fidelity (AMF) modelling of damage in composites under Low-Velocity impact and compression after impact

In this paper, the recently developed adaptive multi-fidelity (AMF) approach is extended for progressive damage analysis of composite laminate subjected to low-velocity impact (LVI) and compression after impact (CAI). This approach is more computationally efficient by utilizing a combination of low-fidelity shell elements and high-fidelity brick elements in a single concurrent model. A general set of criteria has been proposed to govern the transition of shell and brick elements from one to the other and vice versa, as dictated by the mechanics of damage evolution. When matrix cracks or interfacial delamination initiates, shell elements transit to brick elements and cohesive elements are inserted to capture the damage growth. The brick elements partitioned by non-critical cracks are reverted to smeared shell elements when the numerical crack density becomes saturated in a laminate. Geometrical non-linearity is adopted here to predict large deformation in the composite laminate for both implicit quasi-static and dynamic analyses. The performance of AMF models is benchmarked with high-fidelity models and experimental data reported in literature. The predicted results show reasonable agreement with experimental observation and high-fidelity models, and up to 51% improvement in computational efficiency is achievable.

A machine learning method of accelerating multiscale analysis for spatially varying microstructures

Multiscale computing for heterogeneous materials provides a powerful and fidelity approach to handling situations where a suitable macroscopic constitutive model is unavailable. However, there have been very few instances in the industrial sector where concurrent multiscale modeling techniques have been adopted, primarily because they require significant computational resources. In particular, when dealing with large numbers of representative volume elements (RVEs) with different microstructure morphologies throughout the macrostructure. In this paper, the novelty lies in providing an artificial neural network (ANN) model-based multiscale approach for accelerating multiscale analysis of the elliptical inclusion-reinforced composites with spatially varying microstructures, which give up the assumption of global periodicity. To tackle this challenge, firstly, a series of RVEs with different microstructure morphologies characterized by inclusion orientations and aspect ratios, under different load conditions, are simulated to generate training data. Second, the back propagation neural network (BPNN) is trained offline using data acquired from the RVE simulation for learning the underlying and possibly RVE morphologies. The trained BPNN replaces the complex microscopic mechanical solution problems over each macroscopic integration point at each iteration, to update the macroscopic stress-strain responses and it significantly reduces the computational cost of multiscale simulations. Then, the trained neural network is implemented on ABAQUS via user-defined materials (UMAT) and its effectiveness is verified by comparison with the classical multiscale finite element method (FE2) method. Finally, the outstanding computational capability of the proposed approach is further demonstrated through two numerical examples.

A direct FE[formula omitted] method for concurrent multilevel modeling of piezoelectric materials and structures

Multiscale piezoelectric materials and structures have shown their promising potential of applications in sensor, energy harvester, actuator and so on. However, the large complexity of such materials and structures poses a significant challenge to the accurate and efficient prediction of their piezoelectric behavior which is significant for guiding and optimizing their applications. In this paper, a Direct FE2 method was proposed for concurrent multilevel modeling of the coupled electromechanical behavior of multiscale piezoelectric materials and structures. The energy equilibrium and kinematic constraints between macro- and meso-scales were satisfied by prescribing periodic boundary conditions for both displacement and electric potential, which were derived from the governing equation of FEM. Another boundary condition was prescribed to the center node of each RVE, to prevent rigid motion and shifting of electric potential of meso-scale RVEs. All these boundary conditions can be easily prescribed to the Direct FE2 using multiple points constraints (MPCs), a commonly used feature in most commercial software. The accuracy, computational efficiency and ease of numerical implementation of the proposed Direct FE2 method were validated using a series of multiscale simulations, including the quasi-static response of homogeneous piezoelectric panels, an arc honeycomb structure, a regular honeycomb structure and a piezoelectric composite panel, as well as the dynamic response of the piezoelectric composite panel. It shows that the proposed Direct FE2 method will be valuable for large-scale simulations of complicated multiscale piezoelectric materials and structures.

A chance-constrained stochastic chiller sequencing strategy considering life-expectancy of chiller plant

This article focuses on addressing the chiller sequencing problem of chiller plants by establishing a comprehensive energy management framework. The contribution of this work is threefold. Firstly, a distributive modeling architecture is presented that establishes five concurrent models as input to the framework. A synergy among these models is exploited to formulate a chance-constrained stochastic chiller sequencing problem. Secondly, a quantified life expectancy model of the chiller plant is introduced and a case is made for why it is influential in delivering industrially applicable solutions. The model attempts to strike a balance between economic optimality and improved reliability. Thirdly, a chiller data pre-processing protocol incubating two heuristic algorithms is proposed to address measurement uncertainties of the chiller plant state variables. Furthermore, this work develops a robust ensemble model to accurately forecast the cooling load and embeds a chain of reformulations to improve the global solution's optimality. The developed framework is realized with the plant at the Indian Institute of Technology Gandhinagar. The results confirm that the proposed framework leads to a significant amount of power savings. In comparison to conventional scheduling, the chiller plant power consumption can be reduced by up to 6.2 %, thereby illustrating its efficacy.

Latent Class Analysis of Social Needs in Medicaid Population and Its Impact on Risk Adjustment Models

Background: A growing number of US states are implementing programs to address the social needs (SNs) of their Medicaid populations through managed care contracts. Incorporating SN might also improve risk adjustment methods used to reimburse Medicaid providers. Objectives: Identify classes of SN present within the Medicaid population and evaluate the performance improvement in risk adjustment models of health care utilization and cost after incorporating SN classes. Research Design: A secondary analysis of Medicaid patients during the years 2018 and 2019. Latent class analysis (LCA) was used to identify SN classes. To evaluate the impact of SN classes on measures of hospitalization, emergency (ED) visits, and costs, logistic and linear regression modeling for concurrent and prospective years was used. Model performance was assessed before and after incorporating these SN classes to base models controlling for demographics and comorbidities. Subjects: 262,325 Medicaid managed care program patients associated with a large urban academic medical center. Results: 7.8% of the study population had at least one SN, with the most prevalent being related to safety (3.9%). Four classes of SN were determined to be optimal based on LCA, including stress-related needs, safety-related needs, access to health care–related needs, and socioeconomic status–related needs. The addition of SN classes improved the performance of concurrent base models’ AUC (0.61 vs. 0.58 for predicting ED visits and 0.61 vs. 0.58 for projecting hospitalizations). Conclusions: Incorporating SN clusters significantly improved risk adjustment models of health care utilization and costs in the study population. Further investigation into the predictive value of SN for costs and utilization in different Medicaid populations is merited.

Novel cellular coil design for improved temperature uniformity in inductive heating of carbon fibre composites

Electromagnetic induction has great potential for energy efficient manufacturing of polymer composites. Carbon fibre can be directly heated inductively, achieving rapid, localised, and volumetric heating. The main obstacle to wider adaption of induction in composites processing is the non-uniformity of temperature distribution in-plane and through thickness of composite laminates. This study puts forward a new cellular architecture of the coil and examines the feasibility of addressing the problem of field uniformity. The geometrical parameters of the coil were examined in concurrent modelling and experimental work, aiming at reducing the effect of field cancellation in neighbouring cells and maximising both the coil efficiency and the field uniformity. The paper demonstrates good potential of the new coil designed with significantly improved uniformity of temperature field compared to traditional commercially available coils. This result has been further optimised with parametric finite element analysis to reduce the standard deviation in surface temperature by a further 37%.

Stuart has got the PoWeR! Skeletal muscle adaptations to a novel heavy progressive weighted wheel running exercise model in C57BL/6 mice.

Murine exercise models are developed to study the molecular and cellular mechanisms regulating muscle mass. A progressive weighted wheel running model, named 'PoWeR', was previously developed to serve as a more translatable alternative to involuntary resistance-type exercise models in rodents, such as synergist ablation. However, mice still run great distances despite the added resistance as evidenced by a large glycolytic-to-oxidative shift in muscle fibre type. Thus, PoWeR reflects a blended resistance/endurance model. In an attempt to bias PoWeR further towards resistance-type exercise, we developed a novel heavy PoWeR model (hPoWeR) utilizing higher wheel loads (max of 12.5g vs 6g). Adult male C57BL/6 mice voluntarily performed an 8-week progressive loading protocol (PoWeR or hPoWeR). Running distance peaked at ∼5-6kmday-1 in both treatments and was maintained by PoWeR mice, but declined in the hPoWeR mice as load increased beyond 7.5g. Peak isometric force of the gastrocnemius-soleus-plantaris complex tended to increase in wheel running treatments. Soleus mass increased by 19% and 24% in PoWeR and hPoWeR treatments, respectively, and plantaris fibre cross-sectional area was greater in hPoWeR, compared to PoWeR. There were fewer glycolytic and more oxidative fibres in the soleus and plantaris muscles in the PoWeR treatment, but not hPoWeR. Collectively, these data suggest hPoWeR may modestly alter skeletal muscle supporting the aim of better reflecting typical resistance training adaptations, in line with decreased running volume and exposure to higher resistance. Regardless, PoWeR remains an effective hypertrophic concurrent training model in mice.