Control Policies Research Articles

On the evaluation and selection of network-level traffic control policies: Perimeter control, TUC, and their combination

Perimeter control (PC) of urban traffic networks can be effective in increasing network-wide efficiency. PC operates on the border of a protected region of a traffic network. Most studies thus far considered fixed-time plans for the inner part of these regions. A few studies have shown that combining PC with locally actuated or decentralized traffic control systems may have positive effects on traffic performance, including better-defined Network Macroscopic Fundamental Diagrams (NMFDs), increased network throughput, and reduced delays. The Traffic-responsive Urban Control (TUC) is a real-time network-wide traffic control system with particular design characteristics, such as the balancing of link’s occupancies and an inherent gating feature. These characteristics suggest that TUC may enhance the traffic network performance when combined with PC whilst improving the resulting NMFDs and network throughput and delays. In this work, we investigate the effect of feedback perimeter control (FPC), TUC, and their combination on the NMFD and on the traffic conditions of general traffic and public transport in the microsimulation of a realistic model of the Christchurch Central Business District in New Zealand. We perform a thorough investigation of practical aspects of both control strategies and their combination, including parameter tuning and infrastructure requirements, and how they may affect the control system choice. Results show higher throughput and less hysteresis on the NMFDs, particularly when TUC is involved. PC provides benefits concentrated in the protected region which can greatly benefit public transportation if there is an overlap with the transit network. The combination of TUC and FPC boosts network-wide throughput.

Composite water value: a way forward to balance the development and protection of transboundary lakes

Transboundary lakes are shared by multiple administrative regions. The key to balance the development and protection of transboundary lakes is to properly measure the value of water resources. Most of previous studies on the measurement of lake water resources value have not fully considered the ecosystem service function. This paper proposes a new concept “composite water value” to measure the value of transboundary lakes by integrating the external runoff value and the internal runoff value of water resources. The study constructs a composite water value measurement system for transboundary lakes, further analyzes its influencing factors,and applies the system to the case of Nansi Lake, a representative transboundary lake in eastern China. The results show that: (1) The composite water value of lakes is influenced by various factors, including industrial structure, water withdrawal, and water use methods, which impact the external runoff water value; meanwhile, the composite water quality and fluctuations in lake level are closely associated with the internal runoff water value. From 2008 to 2021, the average annual composite water value of Nansi Lake was 39.628 billion yuan, exhibiting a "rising-falling-fluctuating rising" trend due to pollution control policies, reduced precipitation, and enhanced water-saving technologies successively. (2) From a long-term perspective, it is necessary to focus on the internal runoff water use value of lakes. The internal runoff water value of Nansi Lake has been over 75% of the composite water value, and flood storage and water conservation are important manifestations of its ecosystem service value. (3) The external runoff water value of lake is closely related to the internal runoff water value, and relevant departments need to consider the balance between the water withdrawal of multiple cities along the lake and the retained water volume of the lake to achieve the maximum benefit of composite water value.

Impact of coronavirus disease 2019 on the utilization of hospital services and development of optimal pandemic control strategy in Chinese tertiary hospitals during the Omicron wave

BackgroundThis study aimed to assess the impact of coronavirus disease 2019 (COVID-19) on hospital service utilization and revenue in Chinese tertiary hospitals and develop an optimal pandemic control strategy (OPCS) for the peak period of the Omicron wave.MethodsRetrospective data from three Chinese tertiary hospitals (provincial, city, and county level) were analyzed for three phases: pre-outbreak (Jan-Apr 2019), outbreak (Jan-Apr 2020), and post-outbreak (Jan-Apr 2021). OPCS was developed under the guidance of the China government pandemic control policy during post-break phase of COVID-19. A decision-tree model was constructed to compare OPCS to strict pandemic control strategy during outbreak phase for the hospital service utilization and hospital revenue in a provincial tertiary hospital during the Omicron wave.ResultsOutpatient, emergency room (ER) visits, hospitalizations, and intensive care admissions dropped by 33.8–53.4% during the outbreak, with the provincial hospital being the most affected. Hospital revenue also declined, especially for the provincial hospital (40.1%). Post-outbreak, most services recovered, but ER visits remained lower (11.6% decrease for provincial hospital, 46.5% for county hospital). Total income and expenditure decreased, with the provincial hospital experiencing the most significant revenue reduction (45.7%). OPCS showed greater utilization of medical services (31.6 times more outpatient visits; 1.7 times more inpatient days; 3.4% more surgery volume) and higher revenue (¥220.8 million more) compared to the strict pandemic control strategy.ConclusionsCOVID-19 measures were associated with less hospital service utilization and revenue in Chinese tertiary hospitals. The developed OPCS in Chinese tertiary hospitals, focusing on isolating infected inpatients but not shutting down the hospital facilities exposed to virus, could be effective in optimizing hospital service utilization and hospital revenue during the Omicron wave.

Nursing leadership in tobacco dependence treatment to advance health equity: An American Academy of Nursing policy manuscript

BackgroundTobacco use remains the leading cause of preventable disease, disability, and death in the United States and is a significant cause of health disparities. PurposeThe purpose of this paper is to update the Tobacco Control policy paper published over a decade ago by the American Academy of Nursing’s Health Behavior Expert Panel Tobacco Control subcommittee. MethodsMembers reviewed and synthesized published literature from 2012 to 2024 to identify the current state of the science related to nurse-led tobacco dependence treatment and implications for nursing practice, education, and research. FindingsThe results confirmed that nurse-led tobacco dependence treatment interventions are successful in enhancing cessation outcomes across settings. DiscussionRecommendations for nursing leaders include: promote tobacco dependence treatment as standard care, accelerate research on implementation of evidence-based treatment guidelines, reduce health disparities by extending access to evidence-based treatment, increase nursing competency in providing tobacco treatment, and drive equity-focused tobacco control policies.

Delayed reinforcement learning converges to intermittent control for human quiet stance

The neural control of human quiet stance remains controversial, with classic views suggesting a limited role of the brain and recent findings conversely indicating direct cortical control of muscles during upright posture. Conceptual neural feedback control models have been proposed and tested against experimental evidence. The most renowned model is the continuous impedance control model. However, when time delays are included in this model to simulate neural transmission, the continuous controller becomes unstable. Another model, the intermittent control model, assumes that the central nervous system (CNS) activates muscles intermittently, and not continuously, to counteract gravitational torque. In this study, a delayed reinforcement learning algorithm was developed to seek optimal control policy to balance a one-segment inverted pendulum model representing the human body. According to this approach, there was no a-priori strategy imposed on the controller but rather the optimal strategy emerged from the reward-based learning. The simulation results indicated that the optimal neural controller exhibits intermittent, and not continuous, characteristics, in agreement with the possibility that the CNS intermittently provides neural feedback torque to maintain an upright posture.

Childhood Cancers; Diagnostic Challenges in Sub-Sahara Africa

Childhood cancers pose a significant public health challenge in sub-Saharan Africa due to late diagnoses, limited treatment options, and poor long-term survivorship. This paper aims to describe the key childhood cancers affecting the region, explore barriers to diagnosis and treatment, and propose potential solutions. The most common childhood cancers in Nigeria and other sub-Saharan countries include leukemia, brain tumors, lymphomas such as Burkitt's lymphoma, and solid tumors like neuroblastoma and Wilms' tumor. However, lack of diagnostic capacity, scarce chemotherapy resources, and high costs frequently delay diagnosis and prevent optimal treatment. Poverty is also associated with worse survival outcomes for pediatric cancer patients. Solving this crisis requires coordinated, multi-pronged efforts. Areas of focus include establishing childhood cancer surveillance and control policies, training primary providers to recognize early signs and symptoms, strengthening referral networks, increasing pediatric oncology services in underserved areas, expanding research on childhood cancers unique to the region, and enhancing financial access to treatment. With government support for childhood cancer programs and global partnerships to address shortages in skilled staff and technologies, survival rates have the potential to significantly improve for Africa's children with cancer. Keywords: Childhood cancer, malignancy, Diagnosis, Challenges.

Housefly gut microbiomes as a reservoir and facilitator for the spread of antibiotic resistance.

Arthropods, such as houseflies, play a significant role on the dissemination of antimicrobial resistance (AMR); however, their impact has often been overlooked in comparison to other AMR vectors. Understanding the contribution of arthropods to the spread of AMR is critical for implementing robust policies to mitigate the spread of AMR across "One Health" sectors. Herein, we investigated the in-situ transfer of a gfp-labelled AMR plasmid (IncA/C carrying a mcr-8 gene, pA/C_MCR-8) in the gut microbiota of housefly (Musca domestica) by applying single-cell sorting, 16S rRNA gene amplicon sequencing, and whole genome sequencing. Our findings demonstrate that the pA/C_MCR-8 positive E. coli donor strain is capable of colonizing the gut microbiome of houseflies and persists in the housefly intestine for five days, however, no transfer was detectable above the detection threshold of 10-5 per cell. The conjugative plasmid, pA/C_MCR-8 demonstrated a high transfer frequency ranging from 4.1 × 10-3 to 5.0 × 10-3 per cell in vitro, and exhibited transfer across various bacterial phyla, primarily encompassing Pseudomonadota and Bacillota. Phylogenic analysis has revealed that Providencia stuartii, a human opportunistic pathogen, was a notable recipient of pA/C_MCR-8. The conjugation assays further revealed that newly formed P. stuartii transconjugants readily transfer pA/C_MCR-8 to other clinically relevant pathogens (e.g. Klebsiella pneumoniae). Our findings indicate the potential transfer of AMR plasmids from houseflies to human opportunistic pathogens and further advocates the adoption of a One Health approach in developing infection control policies that address AMR across clinical settings.

Changes in the epidemiological characteristics of influenza in children in Zhengzhou, China, in the post-COVID-19 era

BackgroundInfluenza is a contagious respiratory disease posing a huge burden of disease for children around the world. The purpose of this study was to investigate the epidemiologic changes in childhood influenza in Zhengzhou, China, before, during, and after the COVID-19 outbreak. The aim of this study was to determine the impact of the COVID-19 outbreak and related prevention and control policies on the children’s influenza epidemiological trend.MethodsAll influenza report card data from the Children’s Hospital Affiliated with Zhengzhou University’s Disease Surveillance Reporting Management System were collected and analyzed monthly from January 2018 to December 2023. The period of the study was divided into three phases for comparison: the pre-pandemic period, the pandemic period, and the post-pandemic period.ResultsBetween January 2018 and December 2023, a total of 82,030 children with influenza were diagnosed at our hospital, including 46,453 males and 35,577 females. A total of 11,833 of them had to be hospitalized for influenza, and 321 of them were brought to the ICU. Influenza showed low-level epidemiologic status during the COVID-19 pandemic, and there was a substantial rise in influenza and a surge in the number of cases after the COVID-19 pandemic period. The year 2023 will had the most influenza cases (40,785). The peak incidence of influenza changes in 2022, from July to October, and in 2023, from February to April and from October to December. During the post-pandemic period, the proportion of new-borns and young children among influenza patients decreased, while the proportion of school-age children increased significantly, and the proportion of influenza patients hospitalized and the proportion of ICU admissions decreased.ConclusionInfluenza showed low-level epidemiologic status during the COVID-19 pandemic. In the post-pandemic period, there is a large increase in influenza incidence, with a double peak in influenza incidence. The proportion of school-age children with influenza has also increased. As a result, we recommend that influenza vaccination for key populations, particularly school-age children, be completed by October of each year in Henan Province, and that the government and schools increase education about nonpharmacological influenza prevention approaches.

Integrated Optimization of Quality Control and Maintenance Policies for Light-Emitting Devices

Integrated Optimization of Quality Control and Maintenance Policies for Light-Emitting Devices

Data quality in Brazilian population-based cancer registries for gastrointestinal cancers

BackgroundPopulation-based cancer registries (PBCRs) are the primary source of information for cancer surveillance and monitoring. Currently, there are 30 active PBCRs in Brazil. The objective of this study was to analyze the data quality of five gastrointestinal cancers (esophagus, stomach, colorectal, liver, and pancreas) according to the criteria of comparability, validity, completeness, and timeliness in Brazilian cancer registries.MethodsThis study included data from Brazilian PBCRs with more than ten years of historical data starting in the year 2000, regardless of the type of defined geographical coverage (state, metropolitan region, or capital), totaling 16 registries. Brazilian PBCRs were evaluated based on four international data quality criteria: comparability, validity (accuracy), completeness, and timeliness. All cancer cases were analyzed, except for nonmelanoma skin cancer cases (C44) and five gastrointestinal tumors (esophageal cancer, stomach cancer, colorectal cancer, liver cancer, and pancreatic cancer) per cancer registry and sex, according to the available period.ResultsThe 16 Brazilian PBCRs represent 17% of the population (36 million inhabitants in 2021) according to data from 2000 to 2018. There was a variation in the incidence in the historical series ranging from 12 to 19 years. The proportion of morphologically verified (MV%) cases varied from 74.3% (Manaus) to 94.8% (Aracaju), and the proportion of incidentally reported death certificate only (DCO%) cases varied from 3.0% (São Paulo) to 23.9% (Espírito Santo). High-lethality malignant neoplasms, such as liver and pancreas, had DCO percentages greater than 30% in most cancer registries. The sixteen registries have more than a 48-month delay in data release compared to the 2022 calendar year.ConclusionThe studied Brazilian cancer registries met international comparability criteria; however, half of the registries showed indices below the expected levels for validity and completeness criteria for high-lethality tumors such as liver and pancreas tumors, in addition to a long delay in data availability and disclosure. Significant efforts are necessary to ensure the operational and stability of the PBCR in Brazil, which continues to be a tool for monitoring cancer incidence and assessing national cancer control policies.

Multi-factor Authentication Framework for Secured Data Access in Cloud Computing

Aims and Background: Accessing data services on the cloud raises serious concerns about security and privacy. To verify a client's stated identity, the existing authentication approach uses just one authentication factor. However, traditional techniques of one-factor authentication are inefficient since they demand an excessive amount of user information and expose their private data to eavesdropping. Methods: Cloud-based data storage is susceptible to both external and internal threats. Information security relies heavily on authentication, which is a method for verifying user identities before granting them access to protected data. When it comes to protecting data in a cloud computing environment from the most cutting-edge methods of attack, traditional password authentication simply isn't enough. For the purpose of protecting user identities in the cloud, this study presents interactive multi-layer authentication systems. Various access control policies are used when validating users in the cloud. Registration, permissions, and authentication factors for users are all parts of the cloud app's security process. Results: An intrusion detection system is built into the security mechanism to help identify people who could be malevolent. Various techniques are available for user identity verification, including intrusion detection, access control, and multi-factor authentication. The last step is to encrypt the data so that no one else can read it. Conclusion: To guarantee the effectiveness and accuracy of the suggested frameworks and methodology, they are experimentally evaluated. There were few false positives and a high detection rate.

Physics‐informed reinforcement learning for optimal control of nonlinear systems

AbstractThis article proposes a model‐free framework to solve the optimal control problem with an infinite‐horizon performance function for nonlinear systems with input constraints. Specifically, two Physics‐Informed Neural Networks (PINNs) that incorporate the knowledge of the Lyapunov stability theorem and the convergence conditions of the policy iteration algorithm are utilized to approximate the value function and control policy, respectively. Then, a Reinforcement Learning (RL) algorithm that does not require any first‐principles or data‐driven models of nonlinear systems is developed to iteratively learn a nearly optimal control policy. Furthermore, we provide a rigorous theoretical analysis showing the conditions that ensure the stability of closed‐loop systems with the control policy learned by RL and guarantee the convergence of the iteration algorithm. Finally, the proposed Physics‐Informed Reinforcement Learning (PIRL) method is applied to a chemical process example to demonstrate its effectiveness.

Estimation-based model predictive control with objective prioritization for mutually exclusive objectives: Application to a power plant

This work presents an algorithm for estimation-based model predictive control with objective prioritization such that distinct objectives may be defined for mutually exclusive operational regions. The objective prioritization algorithm is built by using logical conditions that define regions of operation which are incorporated into the objective function, thus allowing smooth transitions between a bank of objectives. The control objective prioritization is cast in the framework of model predictive control that is coupled with an extended Kalman filter for estimation of critical yet unmeasured state variables. The algorithm is applied to the challenging control problem of an industrial superheater (SH)-reheater (RH) system of a natural gas combined cycle plant under load following operation where smooth transitions among various control objectives is desired – operation under nominal conditions, avoidance of spraying to saturation at the inlet of the SH and RH systems, and avoidance of main steam temperature excursions. The results from the estimator framework are compared with the industrial data from an operating power plant. The control algorithm is evaluated by simulating a servo control problem and disturbance rejection scenarios as expected under load-following operation of the power plant. This algorithm is generic and can be applied to accomplish local control policies for safety, economics, quality control, state constraints, and others. Topical HeadingProcess Systems Engineering

Asymptotic Scaling of Optimal Cost and Asymptotic Optimality of Base-Stock Policy in Several Multidimensional Inventory Systems

Asymptotic Optimality of Simple Heuristic Policies for Multidimensional Inventory Systems Stochastic inventory systems with multidimensional state spaces, such as lost-sales system with positive lead time and perishable inventory system, are challenging to manage because of the curse of dimensionality, and their optimal control policies are extremely complex. In “Asymptotic Scaling of Optimal Cost and Asymptotic Optimality of Base-Stock Policy in Several Multidimensional Inventory Systems,” Bu, Gong and Chao consider three classes of such systems in the regime of large unit penalty cost, and they establish the asymptotic optimality of (modified) simple base-stock policies as well as an explicit expression for the optimal cost rate in each of these systems. These results justify the applications of such policies in real-world applications, and they are achieved by constructing tight newsvendor upper and lower bounds on the systems’ costs and analyzing the asymptotic scaling of newsvendor costs with large unit penalty cost. This approach is expected to be useful in studying other multidimensional stochastic inventory systems.

Alcohol control policy and alcohol-attributable disease burden in Finland and the Baltic countries: A longitudinal study 1995-2019.

Alcohol remains a significant contributor to mortality and morbidity in Finland and the Baltic countries, particularly among men. This study aimed to assess alcohol policy restrictiveness in this region from 1995 to 2019 using a modified version of the Bridging the Gap (BtG-M) policy scale and examine its association with alcohol-related disease burden. The study utilised national laws to score policy restrictiveness (higher BtG-M scores mean stricter policies) and age-standardised rates of disability-adjusted life years (DALY), years of life lost, years lived with disability and deaths per 100,000 from the 2019 Global Burden of Disease Study (GBD). Spearman correlation tests and panel data regression models were applied to assess the association between policy score and burden of disease. Finland maintained a high BtG-M score, while the Baltic countries experienced recent increases from initially lower scores. Alcohol-related disease burden showed an inverse association with policy changes in these countries. Strongest association was seen between the BtG-M score and DALY rates attributed to injuries. Premature mortality among men constituted the largest proportion of disease burden. Despite challenges in accessing and comparing policy data over time, we showed a strong association between alcohol policy and alcohol-related harm in Finland and the Baltic countries. This study is one of the first to use the BtG-M scale to monitor changes in alcohol policies over time and their relationship to alcohol-related harm using GBD methodology. The study highlights the effects of national alcohol policies on levels of alcohol-related harm.

Impact of secondhand smoke on air quality in partially enclosed outdoor hospitality venues: a review

BackgroundSmoking is a leading cause of premature mortality and morbidity globally. The pollutants generated from smoke are not only harmful to smokers, but also to those exposed to secondhand smoke. As a result of increasingly restrictive indoor smoke-free policies in many countries, there is a tendency for tobacco smoking to move outdoors into partially enclosed settings in hospitality venues. The aim of this systematic review was to evaluate the impact of secondhand smoke on air quality in outdoor hospitality venues.MethodsTwo electronic databases PubMed and Scopus were searched from 1 January 2010 to 30 June 2022 for studies of air quality impacts from tobacco smoking in outdoor hospitality venues. A total of 625 studies were screened and 13 studies were included in this review.ResultsThe majority (9 studies) of reviewed studies monitored PM2.5 concentration as an indicator of secondhand smoke. PM2.5 was reported from 10.9 µg/m3 to 91.0 µg/m3 in outdoor smoking areas, compared to 4.0 µg/m3 to 20.4 µg/m3 in outdoor control sites unaffected by smoking. Secondhand smoke can also drift into adjacent outdoor areas or infiltrate into indoor environments thus affecting air quality in spaces where smoking is not permitted.ConclusionsThe reviewed studies indicated that air quality within outdoor hospitality venues where smoking is permitted is unlikely to meet current World Health Organization (WHO) ambient air quality guidelines for PM2.5. Customers and staff in outdoor hospitality venues with active smoking, and in adjacent outdoor and indoor non-smoking areas, are potentially exposed to secondhand smoke at levels exceeding WHO guidelines. Stronger smoking control policies are recommended for outdoor hospitality venues to protect the health of customers and staff from harmful secondhand smoke exposure.Prospero registrationCRD42022342417.

A compartmental model for smoking dynamics in Italy: a pipeline for inference, validation, and forecasting under hypothetical scenarios

We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

Enhancing Alcohol-Related Research in Africa: Possibility for a Continental-Wide Alliance.

Alcohol-related harms constitute a major public health concern in Africa, with high rates of alcohol consumption and associated negative health and social consequences. Despite this, there is a lack of comprehensive and coordinated research on alcohol use and related harms across the continent. This paper discusses common factors such as diverse traditions and cultures, prevailing low socioeconomic status, gendered approach, limited policy and weak regulation and compliance to existing alcohol control policies that contribute to alcohol-related problems, and the challenges facing alcohol-related research including limited research, isolated and uncoordinated research, and resource scarcity; it further proposes strategies for advancing a continental-wide alliance to enhance alcohol-related research in Africa. The alliance would bring together researchers, policymakers, and other stakeholders to collaborate on research projects, share data and resources, and develop evidence-based interventions to reduce alcohol-related harms. Furthermore, the paper outlines the potential benefits of such an alliance, including improved data collection and analysis, greater capacity building for researchers, and more effective policy and program development. The paper concludes that a continental-wide alliance on alcohol-related research in Africa has the potential to significantly enhance our understanding of alcohol use and related harms on the continent and to inform effective prevention and intervention strategies.

Genetically-Based Active Flow Control And PIV Measurements Of A Circular Cylinder Wake

In the field of flow manipulation, considerable attention is devoted to controlling the wake of bluff bodies, which are characterized by the phenomenon of vortex shedding. Specifically, the control of the wake behind a single circular cylinder has garnered substantial research interest due to its engineering applications, such as reducing aerodynamic drag in the automotive and aerospace industries. Therefore, in the current work, through the application of the planar PIV technique, the fluid dynamic behavior of a circular cylinder wake controlled via synthetic jets (SJ) and Machine Learning (ML) methods is analyzed and discussed. ML algorithms are used to determine the optimal voltage signal for the loudspeaker, which effectively minimizes the aerodynamic drag of the cylinder. This approach overcomes the limitations associated with the a priori assumption of a parametric waveshape. In particular, the gradient-enriched machine learning control (gMLC) algorithm is chosen as optimization tool to search the best open-loop control policy for aerodynamic drag reduction. The cost function for this problem has been defined considering both the drag reduction contribution and the electrical power spent for the actuation. Initial optimization tests are performed by minimizing a cost function considering only the reduction in aerodynamic drag. Subsequently, a term related to the electrical power consumption for actuation is incorporated into the cost function. Once obtained the optimal control policies for these cases (hereinafter renamed CC1 and CC2, respectively), the physical mechanism responsible for reducing the aerodynamic drag is investigated more closely by analyzing the effect of the control laws on the turbulent flow statistics. Figure 1 reports a comparative assessment of the CC1 and CC2 with the baseline configuration and the configuration controlled by a sinusoidal law (CCS). The baseline configuration is characterized by an extended region of localized turbulent fluctuations in the far wake, where the time-averaged signature of von Kármán vortices is found. In contrast, all the controlled configurations show a global reduction in wake turbulence, with a peak of turbulent energy at the rear stagnation point due to the momentum injection from the SJ actuator. Notably, the CCS and CC2 configurations share similar flow field morphologies, while the CC1 configuration shows a greater reduction in turbulent fluctuations compared to the other controlled cases. This control law also achieves the largest drag reduction among all configurations studied reporting a 9.7% of drag reduction, compared to CCS and CC2, which exhibit similar reductions of 8.4% and 8.5%, respectively. Therefore, the optimization procedure has proven the capability of finding control policies able to outperform the traditional sinusoidal control widely studied in literature.

Simple heuristics for the joint inventory and pricing models with fixed replenishment costs

This study considers the joint inventory and pricing problem of a firm selling a single item over a multi-period planning horizon. A fixed replenishment cost is incurred whenever the replenishment decision is made. Period demands are considered to be non-stationary, stochastic, and price-dependent. The literature shows that the optimal policy in such a system is of an (s, S, p)-type. The implementation of this type of policy might be challenging in practice due to the prevailing computational difficulty in determining the optimal policy parameters. This study, therefore, aims to develop computationally efficient heuristic approaches for the joint inventory and pricing problem. The proposed heuristics provide different levels of flexibility in making inventory and pricing decisions. Furthermore, they are essentially built on the concept of the replenishment cycle and do not require solving a stochastic dynamic program. Our numerical study demonstrates the profit efficiency of the proposed heuristic approaches against the optimal policy. The results also indicate that firms lacking the technical ability to employ a dynamic pricing strategy might still gain significant profit improvement by only using a dynamic inventory control policy.