Maximization Strategy Research Articles

Cognitive warfare and the logic of power: reinterpreting offensive realism in Russia’s strategic information operations

ABSTRACT This article examines how Russia deploys cognitive warfare in the Russo-Ukrainian conflict through the theoretical lens of offensive realism. By conceptualizing cognitive warfare as a non-kinetic strategy of power maximization, the study argues that such tactics are not merely reactive or defensive but serve as proactive instruments to reshape the international strategic environment. Methodologically, the research adopts a qualitative content analysis of political statements, social media narratives, and government documents from both Russian and Ukrainian sources, complemented by in-depth case studies of digital influence operations. The findings demonstrate that Russia’s narrative manipulation – particularly through social media, state media, and strategic deception – aligns with the offensive realist imperative to enhance relative power without direct military engagement. By analyzing cognitive warfare through the lens of offensive realism, this study expands the framework’s analytical reach to contemporary information conflict, offering insights into how digital influence operations shape power dynamics among states.

Smart Contract Fuzzing Towards Profitable Vulnerabilities

Billions of dollars are transacted through smart contracts, making vulnerabilities a major financial risk. One focus in the security arms race is on profitable vulnerabilities that attackers can exploit. Fuzzing is a key method for identifying these vulnerabilities. However, current solutions face two main limitations: 1. a lack of profit-centric techniques for expediting detection and, 2. insufficient automation in maximizing the profitability of discovered vulnerabilities, leaving the analysis to human experts. To address these gaps, we have developed VERITE, a profit-centric smart contract fuzzing framework that not only effectively detects those profitable vulnerabilities but also maximizes the exploited profits. VERITE has three key features: 1. DeFi action-based mutators for boosting the exploration of transactions with different fund flows; 2. potentially profitable candidates identification criteria, which checks whether the input has caused abnormal fund flow properties during testing; 3. a gradient descent-based profit maximization strategy for these identified candidates. VERITE is fully developed from scratch and evaluated on a dataset consisting of 61 exploited real-world DeFi projects with an average of over 1.1 million dollars loss. The results show that VERITE can automatically extract more than 18 million dollars in total and is significantly better than state-of-the-art fuzzer ItyFuzz in both detection (29/10) and exploitation (134 times more profits gained on average). Remarkably, in 12 targets, it gains more profits than real-world attacking exploits (1.01 to 11.45 times more). VERITE is also applied by auditors in contract auditing, where 6 (5 high severity) zero-day vulnerabilities are found with over $2,500 bounty rewards.

Responses of Convective Heavy Rainfall to Atmospheric Moisture Amplification: Implications for Probable Maximum Precipitation Estimation

Abstract Mesoscale convective systems (MCSs) can produce extreme precipitation and flash flooding, emphasizing its importance for Probable Maximum Precipitation (PMP). To estimate PMP using a numerical weather prediction model, the Relative Humidity Maximization (RHM) method has been most commonly used, in which atmospheric grids in the modeling domain are fully saturated to maximize moisture availability for the storm. However, such drastic saturation practice may weaken thermodynamic gradients that are essential for MCSs development, potentially reducing rather than maximizing precipitation. This study investigated the response of MCS-induced heavy rainfall to various atmospheric moisture maximization strategies using the Weather Research and Forecasting (WRF) model, focusing on an extreme rainfall event in August 2022 in Japan. Applying RHM to all grids in the atmosphere resulted in weakened convective organization, reduced precipitation magnitude, and altered spatial rainfall distribution due to atmospheric stabilization and diminished low-level convergence. To address this, we selectively applied RHM to lower atmospheric layers and high-humidity regions to maintain a strong thermodynamic gradient, based on our understanding of the storm mechanism. This selective RHM approach successfully increased atmospheric moisture during the storm while maintaining (even enhancing) convective organization. The RHM practice below 900 hPa with the threshold of relative humidity > 90% preserved strong vertical and horizontal thermodynamic gradients and moisture flux convergence, leading to a 25.8% increase in 48-hour maximum precipitation over the Arakawa Basin. Thus, both vertical and horizontal gradients in thermodynamics matter for maximizing precipitation of MCSs. Further studies should apply similar methodologies to a broader range of MCS events to generalize our findings and to develop an effective PMP estimation methodology for MCSs.

The Financialization of Healthcare in France: Trends and implications.

The healthcare system in France, once celebrated for its universal coverage and accessibility, now grapples with profound transformations driven by corporatization, polarization, and financialization. Initially founded on principles of solidarity and government support, the system provided ample opportunities for doctors to practice either in public hospitals or private settings, with fees regulated to ensure affordability. However, recent decades have seen a shift towards agreements that allow specialists to charge additional fees beyond standard rates, which are covered by private insurance or paid directly by patients. The landscape is further complicated by demographic shifts such as an aging population and rising incidences of chronic diseases, exacerbating healthcare demand while the supply of medical professionals stagnates. Urbanization has concentrated medical services, leading to dominant practices in certain specialties and longer waiting times, especially in rural areas. Financialization has emerged as a pivotal force, with private investors increasingly influencing healthcare delivery. This trend is evident in sectors like medical biology and radiology, where consolidation and profit maximization strategies may prevail, potentially compromising care quality and access. While financial influx may temporarily address funding gaps, it also risks eroding professional autonomy and patient care standards. These developments mark a schism from traditional values of the French healthcare as a public good, raising concerns about equity, regulation, and the ethical implications of intertwining medical practice with financial imperatives in France.

Should Private Equity Firms Own Residency Slots?

Private equity (PE) margin maximization and profit-making strategies focus on acquisition, short-term ownership, and sale of health care entities, including residency program opportunities. PE ownership durations generally have 3 purposes: reduce staff, sell assets, and refinance debt. The purpose of graduate medical education (GME), however, is to provide learning and training opportunities in a variety of clinical, academic, technical, and research domains. This article offers examples of PE involvement in residency training and argues that PE and GME purposes not only conflict but add instability to graduate medical education learning environments. This article also suggests reasons why PE investment in GME, including residency "slot" ownership, undermines academic health centers' ethical and educational obligations to trainees in their GME programs.

Modular Multilevel Converter Control Strategy for AC Fault Current Maximization and Grid Code Compliance

This paper proposes a dynamic current limit for modular multilevel converters (MMCs) that maximizes the injection of current during grid faults in order to mitigate the voltage dip, reduce voltage imbalances in case of an asymmetrical fault, and ensure the proper operation of protective relays. The reduced short-circuit capacity of MMCs, and power converters in general, is one of their main limitations. In the event of a fault, the converter’s current is significantly lower than that of the synchronous generators, which may impact both the performance of power system protective relays and the mitigation of voltage drops during faults. Usually, to protect the MMCs themselves, their output current is limited by their control. However, the current flowing through the power semiconductors is the arm current, not the output current, and this consists of an AC and a DC component. A new current saturation strategy aiming at maximizing fault current injection, in compliance with the most recent grid codes, is proposed. This strategy limits the arm currents by dynamically adjusting the output current limit while injecting reactive currents (both positive- and negative-sequence) and active current according to the grid codes, the fault type, and voltage sag level. A theoretical analysis is carried out to determine the maximum current injection that will not exceed the arm limits, and this is then validated through detailed PSCAD simulations. With the proposed strategy, the supplied current can be increased by approximately 40%.

A Multi-Participant Benefit Maximization Model and Strategy Optimization in Market Transactions Incorporating Game Theory

A Multi-Participant Benefit Maximization Model and Strategy Optimization in Market Transactions Incorporating Game Theory

Sales or Profits? Managerial Decision‐Making in a Duopoly With Symmetric and Asymmetric Costs

ABSTRACTThe paper examines the performance of firms when managers can choose sales or profit maximization strategies and when costs can be symmetric or asymmetric. Three competitive scenarios are examined: both firms choose profit maximization strategies, both firms choose sales maximization strategies, and one firm chooses profit and its rival chooses sales maximization. The paper uses theoretical and numerical analysis to examine a number of symmetric and asymmetric cost scenarios and identifies six different types of industry equilibrium. It confirms the well‐known prisoners' dilemma behavior but demonstrates that this occurs only at relatively low levels of cost.

Health care institutions and their physicians are the greatest promoters of COVID-19 vaccine acceptance among health care workers.

Health care institutions and their physicians are the greatest promoters of COVID-19 vaccine acceptance among health care workers.

Vertical energy seascapes and diving behaviour modulate metabolic scope in a pelagic predator.

Pelagic fishes must obtain resources in prey-sparse habitats and may be considered energy speculators with maximization, gambling high energy costs (e.g. metabolism) for a high rate of return (prey capture). As such, they may have to carefully use their energy seascape to obtain the resources necessary for high growth rates. For diving animals, their energy seascape will also have a vertical component in addition to a horizontal one, which is rarely considered. Dolphinfish, Coryphaena hippurus, embody the maximization strategy as they have high metabolic rates and fast growth rates. We coupled biologging on wild individuals with lab-based respirometry to estimate dolphinfish swimming metabolic rates and vertical energy seascapes. Dolphinfish performed continuous yo-yo dives with deeper dives at night but higher activity during the day. Dive descents were approximately 27% less costly than the ascents. Fish modulated their behaviour so that metabolic costs during the descent/ascent phases of deeper dives were less than those for shallow dives. While temperature is likely the primary limit of dive depth, the vertical energy seascape may be secondary and limit maximum dive depths. Studies of pelagic animal energy seascapes should consider the vertical component that will help determine their ability to access and utilize prey.

Constraining impedance-based controllers: generalised framework and experimental validation

Wave energy converters (WECs) hold significant potential in the global energy market, though their high energy costs compared to traditional sources remain a challenge. Recent research has focused on power maximisation strategies to make WECs more competitive, with optimisation-based control methods offering one solution. However, these methods are often complex and difficult to implement in real-time scenarios. Simpler, non-optimisation-based approaches, such as impedance based control, have gained interest due to their ease of implementation and ability to provide representative control actions. Despite their advantages, impedance based controllers often suffer from unconstrained control forces, potentially leading to suboptimal performance and exceeding the validity range of the system model. This study introduces a novel constrained impedance based control strategy that maintains the simplicity of traditional methods while addressing the limitations of unconstrained controllers. The proposed approach is experimentally validated using the Wavestar prototype at the Aalborg facility in Denmark, under both monochromatic and panchromatic wave conditions. Results show that the constrained controller effectively confines the device motion within predefined limits, improving power output under nonlinear force saturation. These findings demonstrate that constrained impedance-based control can provide more accurate and practical control actions, enhancing the performance of WECs in real-world applications.

Optimization of efficiency maximization strategy of automobile transmission system based on simulated annealing algorithm

Abstract Under the dual pressures of the global energy crisis and environmental protection, improving the efficiency of automotive transmission systems has become one of the core issues in the industry. This paper focuses on automobile transmission systems, proposes an innovative optimization scheme, and applies an SAA algorithm to solve the efficiency maximization problem. SAA is derived from the annealing principle in the physical field, which can effectively avoid local optimal traps and realize global search. It is especially suitable for dealing with complex multivariable optimization tasks. Firstly, a detailed mathematical model of the variable speed transmission system is established, covering key links such as gear meshing and friction loss, ensuring the accuracy of the analysis. Subsequently, many accurate vehicle test data were introduced, covering various driving conditions, such as urban roads and expressways, to verify algorithm effectiveness. Experiment mental data show that under normal working conditions, the average efficiency of the optimized transmission system is improved by about 5%. In contrast, under extreme conditions, such as high load and low speed, the efficiency is improved by more than 8%. The power ride comfort is unaffected, indicating the solution has high performance and practicality. In addition, through sensitivity analysis, it is found that several key parameters, such as drive shaft diameter and lubrication conditions, significantly impact the system efficiency, providing a direction for further refined adjustment.

A large-eddy simulation analysis of collective wind farm axial-induction set points in the presence of blockage

Abstract. Over the past few years, numerous studies have shown the detrimental impact of flow blockage on wind farm power production. In the present work, we investigate the benefits of a simple collective axial-induction set point strategy for power maximization and load reduction in the presence of blockage. To this end, we perform a series of large-eddy simulations (LESs) over a wind farm consisting of 100 IEA 15 MW turbines and build wind farm power and thrust coefficient curves under three different conventionally neutral boundary layers and one truly neutral boundary layer. As a result of the large-scale effects, we show that the wind farm power and thrust coefficient curves deviate significantly from those of an isolated turbine. We carry out a trade-off analysis and determine that, while the optimal thrust set point is still correctly predicted by the Betz limit under wake-only conditions, it shifts towards lower operating regimes under strong blockage conditions. In such cases, we observe a minor power increase with respect to the Betz thrust set point, accompanied by a load reduction of about 5 %. More interestingly, we show that for some conditions the loads can be reduced by up to 19 %, at the expense of a power decrease of only 1 %.

CHINA’S ROLE IN THE UNITED NATIONS IN ADDRESSING CLIMATE CHANGE DURING THE XI JINPING ERA

Climate change has become a critical global challenge in the 21st century, with China as the largest emitter playing a vital role in addressing it. This research aims to analyze China’s transforming role within the UN framework in addressing climate change during Xi Jinping’s era, focusing on diplomatic posture changes and policy implementation. Using qualitative research methods with a descriptive-analytical approach through library research, this study analyzes various official documents, journal articles, and policy reports from 2020-2024. The discussion reveals that under Xi Jinping’s leadership, China has undergone a significant transformation from a defensive position to proactive leadership in global climate diplomacy, reflected through its 2060 carbon neutrality commitment and the establishment of a 3 billion USD South-South Climate Cooperation Fund. Analysis through an offensive realist perspective reveals that China’s active engagement in the global climate regime manifests a power maximization strategy in the international system. In conclusion, China’s transforming role has positively contributed to global climate governance while maintaining its national strategic interests through a combination of ambitious commitments and various policy instruments.

Optimisation of decision making for construction projects of assembly buildings based on improved PSO algorithm

A cutting-edge construction method called fitted construction allows for several parallel lines of work to speed up construction and enhance building quality. However, achieving optimal project decisions for global construction projects demands a high level of objective decision-making. To enhance the decisionmaking process, this research utilizes particle swarm algorithms to optimize construction project decisions in assembled buildings. To tackle the issue of early convergence in particle swarm algorithms, three swarm enhanced particle swarm algorithms are proposed by merging the variational mechanism of the differential evolution algorithm and quantifying the decision making tasks for assembly building construction projects to be solved by the enhanced particle swarm algorithm. Regarding the research results, the upgraded particle swarm algorithm achieved a fundamental convergence in 20 iterations whilst resolving the Sphere, Rosebrock, Rastrigin, and Griewank functions. The improved particle swarm algorithm converges to an optimal solution of –19.208 within 20 iterations on the Holder function, with an optimal domain of [8.055, –9.665]. The results of the optimization study for the decision-making problem of the assembly building project demonstrate that implementing Sigmoid smoothing yields a minimum duration problem of 0.755 and a minimum duration of 45 days. The optimal cost and time required to solve the problem of economic maximisation strategy using the enhanced particle swarm method are 500,000 and 52 days, respectively. The results indicate that the improved particle swarm approach outperforms conventional algorithms in the decision-making process for assembly building projects, maintaining computational accuracy throughout.

Group Service or Individual Service? Differentiated Pricing for a Private or Public Service Provider

Facilitated by information technology and online platforms, group service has emerged in many service systems, such as one-to-many tour guide service, one-to-many tutoring courses and one-to-many medical diagnosis. We consider a service provider offering both group service and individual service and model it as a single-server queue in which customers with heterogeneous waiting costs make both joining/balking and group/individual service decisions. We solve the private provider’s service assortment and pricing strategies for profit maximization (system C), whose solution leads to six equilibrium structures. Furthermore, both equilibrium service volume and service price are nonmonotonic in the potential arrival rate, which contradicts classical theory due to the follow-the-crowd (FTC) characteristics associated with group service. This leads to a counterintuitive and novel pricing implication: sustaining full coverage in a larger market may necessitate an increase in service prices when group service is offered. We then numerically show that the optimal group size is unique. Furthermore, compared with scenarios where only individual service is provided, the simultaneous provision of both services can create a win-win-win situation, particularly when the potential arrival rate is relatively high. We extend the discussion to the scenario with a welfare-maximizing objective (system S) and show that the aforementioned results continue to hold. Furthermore, we find that the socially optimal price is lower than the profit-maximizing price, resulting in a larger total number of customers served in system S. Notably, our findings suggest that system S is more likely to provide group service and achieve full market coverage than system C.

Navigating the Carbon Challenge: Strategic Integration of Hybrid Policies in Green Supply Chains

In the context of climate change, the increasing urgency to mitigate environmental impacts has driven firms to adopt green supply chain strategies. Existing research primarily focuses on either carbon tax or emission trading schemes, leaving a gap in understanding the combined impact of hybrid carbon policies. This study addresses this gap by developing a dual-tier supply chain model with a manufacturer and retailer, exploring the effects of a carbon tax, emission trading, and a hybrid policy on emission reduction strategies and pricing decisions. Using a reverse inductive method within a Stackelberg game framework, we identify optimal strategies for emission reduction and profit maximization under each policy scenario. Results indicate that the hybrid policy achieves the lowest unit carbon emissions when the manufacturer’s initial pollution level is below a critical threshold. This research contributes to the literature by providing actionable insights into the strategic advantages of hybrid carbon policies for firms seeking both profitability and sustainability in green supply chains.

Challenges and Support Systems for Headteachers in the Implementation of Professional Learning Communities Concept in Effutu Municipality, Ghana

Professional Learning Communities (PLCs) concept was introduced in the pre-tertiary education system in Ghana in 2019 as a model of continuous professional development policy, following the education reforms. The aim is to offer teachers opportunity to take charge of their own professional learning so as to become competent professionals in the field of education as well as to promote collaboration and collegiality among them. This study explored the challenges facing the basic school headteachers in Effutu Municipality of Ghana in the implementation of PLCs concept as well as the support systems they needed. It was qualitative research underpinned by the interpretivist paradigm and employed a case study research design. Using maximal variation sampling strategy, 15 headteachers were selected for a semi-structured interview and the data collected was analysed thematically. The study revealed that the challenges confronting the headteachers in the implementation of PLCs in the municipality included time constraints and limited space for reflective activities, perceived lack of value, and inadequate knowledge about the concept. It also emerged that financial and material support from both internal and external sources within the fraternity were needed for effective implementation of the PLCs in the municipality, The study recommended that the Municipal Education Directorate should intensify school sensitization campaigns to raise awareness, and to promote the benefits of PLCs among the headteachers, teachers, and relevant stakeholders to ensure effective implementation of the concept.

Hiring Strategies

Abstract We investigate a hiring process where a sequence of applicants is sequentially interviewed, and a decision on whether to hire an applicant is immediately made based on the applicant’s score. For the maximal and average improvement strategies, the decision depends on the applicant’s score and the scores of all employees, i.e. previous successful applicants. For local improvement strategies, an interviewing committee randomly chosen for each applicant makes the decision depending on the score of the applicant and the scores of the members of the committee. These idealized hiring strategies capture the challenges of decision-making under uncertainty. We probe the average score of the best employee, the probability of hiring all first N applicants, the fraction of superior companies in which, throughout the evolution, every hired applicant has a score above expected, etc.

도쿄 에어비앤비 호스트의 매출 극대화 전략 - 호스트 단위 분석

The sharing economy challenges traditional industries by offering new opportunities and innovations. Digital platforms like Airbnb have emerged as significant players in the tourism and accommodation sectors, providing individuals with additional income opportunities and consumers with more diverse and economical options. This study examines the total revenue of an Airbnb host particularly in Tokyo, Japan, where regulatory frameworks limit annual operation days and require licenses for extended operations. Using quarterly panel data from Q2 2020 to Q4 2023, the study employs econometric modelling, specifically the Hausman-Taylor estimator, to identify key variables affecting Airbnb hosts’ revenue. This model is particularly useful for addressing endogeneity issues by allowing some variables to be treated as endogenous, providing a more robust analysis. This study proposes business strategies for revenue maximization. We find that past performance positively impacts current revenue, while high vacancy rates are linked to revenue declines. Additionally, spatial diversification strategy is shown to positively affect total revenue, whereas there is a negative relationship between hosts’ revenue and housing rental rates. These findings offer Airbnb hosts valuable insights for maximizing revenue, under regulatory constraints.