Operational Model Research Articles

Effects of Tranexamic Acid on Human Osteoblasts as Proxy for Fracture Healing.

To investigate the effect of tranexamic acid (TXA) through in vitro culture of primary human osteoblasts (HOB) and in vivo using an operative rat femur fracture model. It was hypothesized that there would not be any effect on fracture healing in both studies. Primary HOBs were exposed to varying concentrations of TXA over different time periods. Cells were assessed for viability, metabolism, and mineralization. For the in vivo model, fractures were created in the femora of adult rats, exposed to either TXA or saline, and then assessed for healing at different time points. A modified radiographic union score for tibia was used to evaluate radiographs, callus mineralization was assessed with microcomputed tomography, and biomechanical tests were performed. Overall, HOB viability and metabolism decreased as TXA concentration and exposure time increased. However, at concentrations below 56.44 mg/mL, HOB viability was not affected. Similarly, mineralization also decreased as TXA concentration and exposure time increased. In both groups, in vivo results demonstrated increasing radiographic healing, callus mineralization, and biomechanical strength as a function of time. There was a trend for increased healing in the TXA group at 6 weeks after fracture; however, the difference compared with untreated animals was not statistically significant. Although a degradation of HOB viability and metabolism occurred with increased TXA concentrations and exposure times, clinically relevant concentrations do not adversely affect HOB viability, metabolism, or mineralization. In addition, there were no noticeable adverse effects of TXA administration in the in vivo model.

Pre- and postsynaptic signatures in the prelimbic cortex associated with "alcohol use disorder" in the rat.

The transition to alcohol use disorder (AUD) involves persistent neuroadaptations in executive control functions primarily regulated by the medial prefrontal cortex. However, the neurophysiological correlates to behavioral manifestations of AUD are not fully defined. The association between cortical neuroadaptations and behavioral manifestations of addiction was studied using a multi-symptomatic operant model based on the DSM-5 diagnostic criteria for AUD. This model aimed to characterize an AUD-vulnerable and AUD-resistant subpopulation of outbred male Wistar rats and was combined with electrophysiological measurements in the prelimbic cortex (PL). Mirroring clinical observations, rats exhibited individual variability in their vulnerability to develop AUD-like behavior, including motivation to seek for alcohol (crit 1), increased effort to obtain the substance (crit 2), and continued drinking despite negative consequences (crit 3). Only a small subset of rats met all the aforementioned AUD criteria (3 crit, AUD-vulnerable), while a larger fraction was considered AUD-resilient (0 crit). The development of AUD-like behavior was characterized by disruptions in glutamatergic synaptic activity, involving decreased frequency of spontaneous excitatory postsynaptic currents (sEPSCs) and heightened intrinsic excitability in layers 2/3 PL pyramidal neurons. These alterations were concomitant with a significant impairment in the ability of mGlu2/3 receptors to negatively regulate glutamate release in the PL but not in downstream regions like the basolateral amygdala or nucleus accumbens core. In conclusion alterations in PL synaptic activity were strongly associated with individual addiction scores, indicating their role as potential markers of the behavioral manifestations linked to AUD psychopathology.

Operational Performance Evaluation Model for Food Processing Machinery Industry Chain

This study aims to create a performance evaluation model for the food processing machinery industry. The goal is to help food processing plants improve both process quality and competitiveness. Additionally, component failures may disrupt the continuous operation of the food processing machine, potentially resulting in insufficient production and delays in delivery, which in turn leads to cost losses. For the sold food processing machinery, decreases in the average number of failures within a unit of time, the average repair response time when a failure occurs, and the average repair duration are three crucial factors in minimizing the total expected loss due to machine failures. Based on these three important factors, this study established the following evaluation indices: (1) the processing performance index, (2) the repair reporting performance index, and (3) the maintenance performance index. These indices serve as tools for assessing the performance of the three key operational aspects. This study employed a radar chart to construct the evaluation model, which can directly compare the critical values with the point estimates of three indices. Consequently, this approach can judge whether the operational performance has achieved the required level. This can maintain the simplicity and usability of point estimates while reducing the risk of misjudgment due to sampling errors.

The Rise of Digital Banking in the UK Financial Sector and Its Impact on Commercial Banks

In the contemporary financial domain, the emergence of digital banks has revolutionized operational models in traditional commercial banking. This article delves into how digital banks, through advanced technology and customer-centric services, compel commercial banks to revaluate their operational and regulatory frameworks. Specifically, it focuses on the digital banking market in the UK, analyzing how digitalization and technological progress are driving the evolution of competitive dynamics within the financial sector. As one of the worlds foremost financial hubs, the UKs trends in financial market development have profound implications for the global financial system. The British government and regulatory bodies have proactively adapted to these shifts, framing policies that facilitate innovation while ensuring security and compliance within digital banking. This backdrop sets the stage for this report to explore the transformative impact of digital banks on traditional banking models in the UK, aiming to understand the regulatory adaptations and the overall influence of digital entities on the conventional banking landscape.

When the ‘inclusive turn’ fuels the entrepreneurial city: Critical perspectives from Singapore

The rise of the notion of ‘inclusion’ in urban planning, seen in phrases like inclusive city, calls for a critical analysis of its evolving meaning and its spatial, social and political implications. Paradoxically, the meaning of urban inclusion has narrowed such that it now primarily refers to accessibility for people with disabilities. At the intersection of urban studies and critical disability studies, our article investigates the conceptions, criteria and modes of production underlying the implementation of urban inclusion in Singapore's ‘Enabling Village’, a purportedly inclusive space opened in 2015. We use a mixed‐methods approach, analysing official narratives and conducting site visits to understand the site as both an appropriated and a branded space. We show that, in Singapore, the inclusion agenda interacts with the city‐state's distinctive approach to planning and governance, where social issues are ‘engineered’ and give rise to replicable operational models. Our hypothesis is that implementing the inclusive city through the production and promotion of ‘inclusive’ urban projects such as the Enabling Village fuels the expansion of Singapore as a state‐led ‘entrepreneurial city’ (Harvey, Geografiska Annaler. Series B, Human Geography 1989; 71(1):3–17). In particular, operationalising ‘urban inclusion’ in this way allows for the reinvention of Singapore as a global urban model.

Evaluation of resurgence following differential reinforcement of alternative behavior with and without extinction in a human operant model.

One of the most common treatments for severe challenging behavior involves placing the challenging behavior on extinction and differentially reinforcing an alternative response (DRA). However, extinction is not always feasible and may be unsafe or impractical to implement in some circumstances. Thus, implementing a DRA without extinction intervention may be necessary for some cases. Currently, the extent to which DRA without extinction produces durable treatment outcomes, particularly as it relates to the resurgence of challenging behavior, is unclear. The present study investigated resurgence following DRA with and without extinction using a three-phase resurgence evaluation in a translational human operant model with college students as participants. All participants demonstrated resurgence across both experimental groups. Although there were no statistically significant differences in the prevalence, magnitude, or persistence of resurgence between groups, levels of resurgence magnitude were relatively higher in the DRA-without-extinction group than in the DRA-with-extinction group. Clinical implications of these findings and directions for future human operant investigations of resurgence are discussed.

Advancing Subseasonal Surface Air Temperature and Heat Wave Prediction Skill in China by Incorporating Scale Interaction in a Deep Learning Model

AbstractAccurate subseasonal predictions of high surface air temperature (SAT) and heat wave events 10–30 days in advance are crucial for mitigating the risks of extreme weather; however, they pose a challenge for current operational models. In this study, we trained a convolutional neural network (CNN)‐based deep learning model to exploit the modulations in China's SAT by precursor signals across different timescales to improve predictions of future SAT and heat wave events. This CNN model demonstrated superior capability in capturing the evolution of SAT anomalies and the occurrence of heat wave events with forecast lead times beyond 20 days, compared with that of the operational models of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts. Explainability analysis highlighted that subseasonal SAT predictability in China is driven primarily by large‐scale intraseasonal perturbations from both lower‐ and higher‐latitude regions of Eurasia and interannual variability.

Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals.

The Problems and Solutions of Enterprise Financial Management in the Perspective of Digital Economy

From the perspective of the digital economy, enterprise financial management is facing unprecedented challenges and opportunities. Traditional financial management models are no longer suited to current development needs. Fine-tuning financial management is essential to support the modernization of enterprises, guard against operational risks, and promote coordination across the entire value chain for greater economic efficiency. With the help of digital technology, data-driven, highly interconnected, and intelligent decision-making processes are becoming more prominent, profoundly transforming the operational and financial management models of enterprises. This enables financial management to keep pace with modern developments. In light of this, the paper explores the connotations and mechanisms of the digital economy and enterprise financial management, clarifies relevant conceptual characteristics, and identifies problems in financial management under the digital economy. It also offers strategies for optimization and problem-solving, with the aim of providing valuable insights for educators and practitioners.

Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction.

Stroke continues to be a major adverse event in advanced congestive heart failure (CHF) patients after continuous-flow left ventricular assist device (CF-LVAD) implantation. Abnormalities in mitochondrial oxidative phosphorylation (OxPhos) have been critically implicated in the pathogenesis of neurodegenerative diseases and cerebral ischemia. We hypothesize that prior stroke may be associated with systemic mitochondrial OxPhos abnormalities, and impaired more in post-CF-LVAD patients with risk of developing new stroke. We studied 50 CF-LVAD patients (25 with prior stroke, 25 without); OxPhos complex proteins (complex I [C.I]-complex V [C.V]) were measured in blood leukocytes. Both at baseline (pre-CF-LVAD) and postoperatively (post-CF-LVAD), the prior-stroke group had significantly lower C.I, complex II (C.II), complex IV (C.IV), and C.V proteins when compared to the no-prior-stroke group. Oxidative phosphorylation proteins were significantly decreased in prior-stroke group at post-CF-LVAD compared to pre-CF-LVAD. Machine learning Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest modeling identified six prognostic factors that predicted postoperative stroke with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.93. Oxidative phosphorylation protein reduction appeared to be associated with the new stroke after implantation. Our study found for the first time the existence of mitochondrial dysfunction at the peripheral level in CHF patients with prior ischemic stroke even before CF-LVAD implantation. The changes in OxPhos protein expression could serve as biomarkers in predicting new post-CF-LVAD strokes.

Receiving Paths Improvement of Digital Phased Array Antennas Using Adaptive Dynamic Range

In contemporary radar technology, the observation and detection of objects with low radar cross-sections remains a significant challenge. A multi-functional radar model employing a digital phased array antenna system offers notable advantages over traditional radar in addressing this issue. Nonetheless, to fully capitalize on these benefits, improving the structure of the receiving path in digital transceiver modules is crucial. A method for improving the digital receiving path model by implementing a matched filter approach is introduced. Given that the return signals from objects are often lower than the internal noise, the analog part of the digital transceiver modules must ensure that its dynamic range aligns with the level of this noise and the weak signal. The output signal level of the analog part must correspond to the allowable input range of the analog-to-digital converter. Improvements in the receiving path to achieve a fully matched model can reduce errors in the phase parameters and amplitudes of the useful signal at the output. The simulation results presented in this paper demonstrate a reduction in amplitude error by approximately 1 dB and a phase error exceeding 1.5 degrees for the desired signal at the output of each receiving path. Consequently, these improvements are expected to enhance the overall quality and efficiency of the spatial and temporal accumulation processes in the digital phased array antenna system. Furthermore, to maintain the matched filter model, we also propose incorporating an adaptive “pseudo-expansion” of the linear gain range. This involves adding a feedback stage with an automatic and adaptive bias voltage adjustment for the intermediate-frequency preamplifier in the analog part of the receiving path. Simulations to qualitatively verify the validity of this proposal are conducted using data from practical operational radar system models.

A Reversible Perspective on Petri Nets and Event Structures

Event structures have emerged as a foundational model for concurrent computation, explaining computational processes by outlining the events and the relationships that dictate their execution. They play a pivotal role in the study of key aspects of concurrent computation models, such as causality and independence, and have found applications across a broad range of languages and models, spanning realms like persistence, probabilities, and quantum computing. Recently, event structures have been extended to address reversibility, where computational processes can undo previous computations. In this context, reversible event structures provide abstract representations of processes capable of both forward and backward steps in a computation. Since their introduction, event structures have played a crucial role in bridging operational models, traditionally exemplified by Petri nets and process calculi, with denotational ones, i.e., algebraic domains. In this context, we revisit the standard connection between Petri nets and event structures under the lenses of reversibility. Specifically, we introduce a subset of contextual Petri nets, dubbed reversible causal nets , that precisely correspond to reversible prime event structures. The distinctive feature of reversible causal nets lies in deriving causality from inhibitor arcs, departing from the conventional dependence on the overlap between the postset and preset of transitions. In this way, we are able to operationally explain the full model of reversible prime event structures.

Research on Status Assessment and Operation and Maintenance of Electric Vehicle DC Charging Stations Based on XGboost

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards.

Free surgery for CHD through philanthropy—a sustainable model?

AbstractAmong the multiple challenges faced by children from low- and middle-income countries (LMICs) with congenital heart disease (CHD), the economics of care remains foremost, contributing significantly to morbidity and mortality. This paper evaluates the four existing finance models available for healthcare systems and proposes a new model—the GIVE model (government, institutions and individuals, values, and engagements)—as the fifth model for global sustainable healthcare systems. The paper presents an evaluation of a chain of three paediatric cardiac hospitals in India to assess the sustainability of their philanthropy-based operational model, through which surgeries are offered completely free of cost to children with CHD. The three Sri Sathya Sai Sanjeevani Centres for Child Heart Care in India have been proponents of this philanthropic model for over a decade. From February 2013 to January 2024, 19,684 patients with CHD received surgeries at no cost. The average cost of surgery was reported to be USD (United States Dollar) 1800. A case study of one of the Sanjeevani Centres showed that 23.8% of patients were in STAT Category 3 and above, as defined by the Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery, with an in-hospital mortality rate of 2.08%. The evaluation highlighted the institution’s numerous strategies to enable sustainability in key aspects of operations, economics, and social impact. Despite challenges, the Sai Sanjeevani philanthropic model, which encompasses both economic and social impact, is dependable and can be replicated. The proposed GIVE model is recommended for adoption by LMICs as a global way forward to enable free CHD surgeries through sustained philanthropy, strengthened by a shared vision and collaborations.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

Assessment of Urban Resilience to Floods: A Spatial Planning Framework for Cities

Urbanization-led economic growth drives infrastructure investments and population accumulation in cities, hence exploiting natural resources at an extreme rate. In this context, coastal cities have become vulnerable to climate change-induced extreme weather events and human-made disasters in recent history, where effective measures to improve the resilience of cities are pivotal for developing sustainable living environments. This study proposes a framework for assessing urban resilience to natural disasters (floods) using bottom-up spatial interactions among natural, physical, and social systems within cities and regions. It is noted that seminal studies focus on either the mitigation or adaptation strategies within urban environments to assess disaster resilience, where limited multidisciplinary and operational models hinder evaluations at the city scale. Therefore, urban system interactions and quantifiable parameters proposed in this framework are essential for policymakers and disaster management agencies in the timely allocation of resources to optimize the recovery process. Moreover, spatial planning agencies can adopt resilience mapping to identify the potential risk zones and orient sustainable land use management. Urban resilience can be embodied in spatial strategies with the operational framework proposed here, and future urban growth scenarios can be tested in multiple disaster conditions.

Rainfall Enhancement Downwind of Hills Due to Stationary Waves on the Melting Level and the Extreme Rainfall of December 2015 in the Lake District of Northwest England

This paper investigates how stationary gravity waves generated by flow over orography enhance rainfall, with particular attention to the role of induced waves in the melting level. The findings reveal a new mechanism by which gravity wave flow focuses precipitation, amplifying rainfall intensity downwind of hills. This mechanism, which depends on the differential velocities of rain and snow, offers fresh insights into how orographic effects can intensify rainfall. A two-dimensional diagnostic model based on linear gravity wave theory is used to investigate the record-breaking rainfall of December 2015 in the Lake District of northwest England. The pattern of ascent is shown to have a qualitatively good fit to that of the Met Office’s operational high-resolution UKV model averaged over 24 h, suggesting that orographically excited stationary waves were the principal cause of the rain. Precipitation trajectories imply that a persistent downstream elevated wave caused by the Isle of Man supported a spray of seeding ice particles directed towards the Lake District, and that these grew whilst suspended in strong upslope flow before being focused by the undulating melting-level into intense shafts of rain.

Operational optimization of a rural multi-energy system supported by a joint biomass-solid-waste-energy conversion system and supply chain

Biomass, as one of the most accessible renewable resources, could reduce dependency on fossil fuels and mitigate associated environmental impacts. With economic development and improved living standards, biomass in rural areas is converted into various forms of energy to meet high energy demands. However, existing studies on rural multi-energy system operations assume energy production from biomass is a given input parameter and consider it to be a fixed constant or subject to upper and lower bounds, independent of the biomass supply chain. Indeed, biomass energy production is significantly influenced by the collection, transportation, storage, and other processes within the supply chain. This work proposes a mixed integer linear program (MILP) for the joint optimization of an integrated rural multi-biomass-solid waste energy conversion system with a source-grid-demand-based biomass-solid waste supply chain (IRMBS-BSC system). The IRMBS-BSC system encompasses three sub-systems: the biomass-solid waste source sub-system, the biomass-solid waste grid sub-system, and the biomass-solid waste demand sub-system. Various biomass-solid wastes are classified, and a source model is established to evaluate their availability. Subsequently, a supply chain model is developed within the biomass-solid waste grid sub-system. Based on the available and transported quantity from the biomass-solid waste source sub-system and grid sub-system, a multi-energy system operation model, considering refined biomass conversion processes, is proposed. A case study involving a multi-energy system that integrates multiple rural community biomass-solid waste supplies is conducted. The results demonstrate that the proposed MILP, which considers the biomass-solid waste supply chain, can reduce energy imports during the load peak periods and can also decrease the overall operational costs of the multi-energy system.

A value chain modeling approach for upscaling the production of fine flavor cocoa in Arauca (Colombia)

AbstractThe fine flavor cocoa (FFC) market offers additional income for small farmers. Despite the potential of several Latin American countries to produce FFC, their lack of standardized postharvest processes hinders compliance with high‐quality standards demanded by the FFC market, as these processes have a direct impact on cocoa's organoleptic characteristics. This work supports the design of a collective postharvest transformation model that coordinates the main steps in the cocoa agri‐food chain: transport of the cocoa harvest, classification, fermentation, drying, storage, and commercialization. Our methodology includes a modeling scheme combining optimization and simulation models, considering variability in the processes, and covering operational (transport and resource allocation), tactical (design and sizing of the processing plant), and strategic (sales dynamics) decisions across the cocoa value chain. We illustrate this methodology in a case study in Arauca, Colombia. Our findings emphasize the importance of combining these operational and strategic models since their inputs and outputs are interconnected influencing the decision‐making within the cocoa value chain. Our study also identifies key determinants for the postharvest design, highlighting the postharvest center capacity, FFC prices, and the market selling mode (export or local).

Integrating an urban fire model into an operational wildland fire model to simulate one dimensional wildland–urban interface fires: a parametric study

Background Wildland fires that occur near communities, in the wildland–urban interface (WUI), can inflict significant damage to urban structures. Although computational models are vital in wildfires, they often focus solely on wildland landscapes. Aim We conducted a computational study to investigate WUI fire spread, encompassing both urban and wildland landscapes. Methods We developed a 1D landscape-scale semi-physical model by integrating a semi-physical urban fire spread model into an Eulerian level set model of wildfire. The model includes ignition and spread through radiation, direct flame contact and ember deposition. Key results Through a parametric study, we compare the relative change of spread rate from various structural properties and landscape layouts represented by model parameters, highlighting the significant impact of fire-resistant structure materials over surface treatments. Layout configurations play a pivotal role in fire spread, with isolated islands of combustibles effective in reducing spread rate, aligning with existing mitigation strategies. Conclusion Despite using a 1D domain and limitations on spatial and temporal variability, our model provides insights into underlying phenomena observed in WUI fires and their mitigation. It offers early-stage development of strategies for managing structure materials and landscape layouts. Implications Our model and findings provide insights into WUI fire dynamics, paving the way for advanced mitigation strategies.