Competitive Tool Research Articles

Historical Memories of Russians Today (history of the XX century Russia in the optics of families’ stories)

Referring to the data of long-term sociological research by the Federal Scientific Research Center of the Russian Academy of Sciences, the author studies how the historical memory in contemporary Russian society is forming under the influence of the discursive politicization of the national past. The purpose of the article is to study either the images of the recent national past that are rooted in the mass consciousness of Russians being the basis of collective identity, or the changes in attitudes towards the past under the influence of the state rhetoric “historization”. The realization of the scientific purpose involves to solve some research tasks: to study the dynamics of respondents’ interest in the state’s past when political leaders used to apply to historical memory, to analyze what people in Russia think about historical truth, about the ways and the actors of its protection. Under the contemporary political context, when the past and history become tools of political competition, many persons in Russia, for whom the Soviet heritage is the foundation of national identity and family memory, support the idea of the need to protect the socially accepted interpretation of Russian history. In 2020’th the majority of the respondents (37%) claimed the need to react harshly to the distorted interpretation of some events in Russian history when whey answered the question “How should Russia react to the false interpretation of certain historical events in some Western countries?”. The historical truth is considered by the Russia’s citizens as a universal interpretation of historical events, so the majority (48%) claims it is necessary to protect historical memory by the preservation of those interpretations of the historical events, that are admitted by the society. Supporting the idea that historical truth should be protected, Russian citizens rather support harsh political statements and defensive foreign policy discourse than demonstrate real involvement into historical context, their own daily interest in protecting accepted interpretations of the past and personal concern about their correctness. In 2022 less than a third of the respondents (29%) considered the ordinary citizens should protect the historical truth, while the majority claimed the state’s leadership (67%), professional historians (49%) and social scientists (38%) should defend national historical memory.

Influence of Corporate Digital Responsibility on Financial Performance: The Mediating Role of Firm Reputation

ABSTRACTThis study examines the mediating role of firm reputation in the relationship between corporate digital responsibility (CDR) and financial performance in an emerging market, Ethiopia. An online cross‐sectional survey was used to collect data from 126 agricultural, manufacturing, and service firms. The study used partial least squares structural equation modeling (PLS‐SEM) to analyze the hypothesized relationship. Our findings reveal that the impact of CDR on financial performance is indirect only as firm reputation plays a full, complementary mediation role in the CDR—financial performance nexus. This implies that firms could leverage CDR as a competitive “inducing” strategy to enhance their firm reputation, which, in turn, can boost their financial prospects. Our study significantly contributes to the business ethics and digital economy literature by offering a pioneering empirical validation of the CDR phenomenon in an emerging context, thus extending the signaling and stakeholder theories to digitalization and reputation management domains. The findings offer managers fresh insight into the potential impact of a CDR strategy on firm reputation and financial performance, showing that firms can leverage CDR as a loss prevention strategy to gain a competitive advantage. Policymakers are therefore urged to promote soft‐law regimes and policies on CDR to motivate companies to leverage it as a competitive tool.

The role of emotional marketing in enhancing strategic customer behavior through financial knowledge: An analytical study of a sample of shopping malls in Iraq

The research aims to diagnose the logical relationships between emotional marketing and its role in enhancing strategic customer behavior through financial knowledge to reach results in the dynamic environment of shopping malls. The paper was designed in light of adopting the descriptive analytical approach to variables and collecting data and information through the questionnaire tool distributed to the shopping malls of the research sample. Using statistical analysis programs to reach the results, the study sample reached (25) shopping malls in the local environment of the country. The results showed that there is a threat in the local environment in which these malls operate through the entry of foreign investment projects in this field, which led to an increase in supply and competition in terms of quality, prices and delivery services to gain customers. The studied malls had to develop a strategy on how to attract and retain customers by using emotional marketing practices and relying on financial knowledge to influence strategic customer behavior, as the current and future customer has become rapidly changing in tastes, needs, desires and transformation through technological development and the world has become a small village that can be viewed at any time and in short periods of time. The research also concluded that the threat of new investment through the establishment of new malls as new investment projects affects supply and demand in the environment in which these studied malls operate. The research also concluded that there is a correlation and influence relationship between the research variables of the studied malls, which includes a package of results that benefit the research community. The paper aims to bridge the knowledge gap between the variables of the paper in the dynamic environment by using emotional marketing as a competitive tool in influencing strategic customer behavior through financial knowledge to gain and maintain the current customer and gain the potential customer.

Study On the Factors Affecting the Audit Fees of Small and Medium Enterprises in Vietnam

The competitive pressure within the group of domestic audit firms is becoming increasingly intense, and pricing is considered one of the most effective competitive tools. This also stems from the fact that many small businesses tend to prioritize low costs when selecting an audit firm. Through a survey conducted on 70 companies, the author utilized SPSS 26 software to analyze the data and identified five factors. The results from the correlation test between two variables and the multivariate linear regression model both indicated that for small and medium-sized enterprises in Vietnam, two factors significantly impact the audit fees for client companies: the size of the audited company and the reputation of the auditing firm.

Cybersecurity and the Future of International Relations

This research examines the growing influence of cybersecurity on international relations, focusing on how cyber threats, digital espionage, and cyber warfare are transforming state sovereignty, diplomacy, and global security. With the rise of state-sponsored cyberattacks and the increasing involvement of non-state actors, cybersecurity has become a central issue in the global power dynamics between major nation-states and international organizations. The study analyzes high-profile cyber incidents, such as the SolarWinds and Stuxnet attacks, and investigates their geopolitical implications, including how they have reshaped cyber norms and governance strategies. Through a comparative analysis of national cybersecurity strategies in countries like the U.S., China, and the EU, this research assesses the effectiveness of current international treaties, such as the Budapest Convention on Cybercrime, and multilateral efforts to establish cybersecurity norms. Preliminary findings suggest that cybersecurity has evolved into a strategic tool in global competition, with cyberattacks being used for coercion and disruption. Moreover, the rise of cyber espionage has heightened the stakes in digital diplomacy, leading to the establishment of specialized cyber units within foreign ministries. This study advocates for the development of comprehensive international treaties focused on cyber governance, with an emphasis on conflict prevention, cyber deterrence, and collective defense mechanisms. Additionally, it calls for greater involvement of multilateral organizations, such as the United Nations and NATO, in creating transparent and accountable frameworks for state-sponsored cyber activities. The research underscores the urgent need for cohesive global governance models to address the complex challenges posed by cybersecurity in shaping the future of international relations.

An enhanced slime mould algorithm with triple strategy for engineering design optimization

Abstract This paper introduces an enhanced slime mould algorithm (EESMA) designed to address critical challenges in engineering design optimization. The EESMA integrates three novel strategies: the Laplace logistic sine map technique, the adaptive t-distribution elite mutation mechanism, and the ranking-based dynamic learning strategy. These enhancements collectively improve the algorithm’s search efficiency, mitigate convergence to local optima, and bolster robustness in complex optimization tasks. The proposed EESMA demonstrates significant advantages over many conventional optimization algorithms and performs on par with, or even surpasses, several advanced algorithms in benchmark tests. Its superior performance is validated through extensive evaluations on diverse test sets, including IEEE CEC2014, IEEE CEC2020, and IEEE CEC2022, and its successful application in six distinct engineering problems. Notably, EESMA excels in solving economic load dispatch problems, highlighting its capability to tackle challenging optimization scenarios. The results affirm that EESMA is a competitive and effective tool for addressing complex optimization issues, showcasing its potential for widespread application in engineering and beyond.

Choosing between additive and conventional manufacturing of spare parts: On the impact of failure rate uncertainties and the tools to reduce them

Recently, there has been great interest in using additive manufacturing (AM) to produce spare parts: allowing to produce spare parts with short lead times and close to the point of use, AM reduces the need for large inventories otherwise required by conventional manufacturing (CM) techniques to deal with intermittent spare parts demand. However, using AM to produce spare parts is limited by two main drawbacks: high production costs and uncertain failure rates arising from AM still being a relatively new production technique. While the former can be counterbalanced by inventory cost reductions, it is unclear how the latter impacts the sourcing option decision (i.e. AM or CM). We aim to fill this gap by studying a periodic review model in which spare parts demands follow a Poisson process. To make our analysis accurate and reliable, we leverage a material science approach to obtain realistic values for the failure rate uncertainties. From the results, it emerges that AM is heavily penalized by failure rate uncertainties much higher than those of CM. Consequently, we then focus on some recent tools developed to reduce AM failure rate uncertainties: porosity assessment and in-situ monitoring. We find that the failure rate uncertainty should be reduced by five and six percentage points to make it worthwhile to invest in porosity assessment and in-situ monitoring, respectively. Finally, we determine under which circumstances each tool is preferred over the other and found that porosity assessment is typically the most competitive uncertainty reduction tool.

Accurate optimal quantum error correction thresholds from coherent information

Quantum error correcting (QEC) codes protect quantum information from decoherence as long as error rates fall below critical error thresholds. In general, obtaining thresholds implies simulating the QEC procedure using, in general, suboptimal decoding strategies. In a few cases and for sufficiently simple noise models, optimal decoding of QEC codes can be framed as a phase transition in disordered classical spin models. In both situations, accurate estimation of thresholds demands intensive computational resources. Here we use the coherent information of the mixed state of noisy QEC codes to accurately estimate the associated optimal QEC thresholds already from small-distance codes at moderate computational cost. We show the effectiveness and versatility of our method by applying it first to the topological surface and color code under bit-flip and depolarizing noise. We then extend the coherent information based methodology to phenomenological and quantum circuit level noise settings. For all examples considered, we obtain highly accurate estimates of optimal error thresholds from small, low-distance instances of the codes, in close agreement with threshold values reported in the literature. Our findings establish the coherent information as a reliable competitive tool for the calculation of optimal thresholds of state-of-the-art QEC codes under realistic noise models. Published by the American Physical Society 2024

Strategic Management of Luxury Brands: A Case Study of Louis Vuitton’s Market Performance and Consumer Loyalty

This study explores the critical role of brand strategy management in the luxury goods industry, focusing on Louis Vuitton (LV) as a case study. With the continuous development of the global economy and heightened consumer expectations, competition in the luxury market has intensified. Brand strategy management, encompassing positioning, communication, maintenance, and extension, emerges as a vital competitive tool that influences market performance and consumer loyalty. This research aims to uncover the unique aspects of LV's brand strategy, its impact on market performance and consumer loyalty, and the challenges the brand faces. Through an in-depth literature review and case analysis, the study identifies key success factors of LV's brand strategy, providing strategic recommendations for other fashion brands to enhance their market competitiveness. The findings contribute to the existing literature by filling gaps in the understanding of luxury brand management and offering practical insights for industry practitioners. Additionally, the study highlights the importance of innovation and adaptation in maintaining brand prestige and relevance in an ever-evolving market landscape, emphasizing how LV's strategic decisions have solidified its position as a leader in the luxury sector. This comprehensive examination not only sheds light on LV's enduring success but also offers valuable lessons for emerging brands aiming to navigate the complexities of the luxury market.

Models and applications of stochastic programming with decision‐dependent uncertainty in power systems: A review

AbstractStochastic programming is a competitive tool in power system uncertainty management. Traditionally, stochastic programming assumes uncertainties to be exogenous and independent of decisions. However, there are situations where statistical features of uncertain parameters are not constant but dependent on decisions, classifying such uncertainties as decision‐dependent uncertainty (DDU). This is particularly the case with future power systems highly penetrated by multi‐source uncertainties, where planning or operation decisions might exert unneglectable impacts on uncertainty features. This paper reviews the stochastic programming with DDU, especially those applied in the field of power systems. Mathematical properties of diversified types of DDU in stochastic programming are introduced, and a comprehensive review on sources and applications of DDU in power systems is presented. Then, focusing on a specific type of DDU, that is, decision‐dependent probability distributions, a taxonomy of available modelling techniques and solution approaches for stochastic programming with this type of DDU and different structural features are presented and discussed. Eventually, the outlook of two‐stage stochastic programming with DDU for future power system uncertainty management is explored, including both exploring the applications and developing efficient modelling and solution tools.

RUFCO: A Deep Learning Framework to Postprocess Subseasonal Precipitation Accumulation Forecasts

Abstract Postprocessing is a critical step in attaining calibrated and reliable probabilistic forecast output from numerical weather prediction models. A novel deep learning framework is proposed to postprocess 20 years of 7- and 14-day precipitation accumulation reforecasts from the Global Ensemble Forecast System at subseasonal time scales (week 1, week 2, and combined weeks 3–4 forecasts) over the contiguous United States. The network builds upon previous studies and is a combination of three parallel-trained components suitable for subseasonal prediction. The first is a ResUnet architecture which learns nonlinear relationships between binned observed precipitation and input images of weather and geographical variables. The second conditions the network to the month-of-year via a feature-wise linear modulation (FiLM) layer. The third helps the network learn when to revert the forecast to that of climatology. The RUFCO (named for its components ResUnet, FiLM, and Climatological-Offramp) forecasts are compared against raw and climatological forecasts as well as those from a state-of-the-art distributional regression postprocessing model, “censored, shifted gamma distribution (CSGD),” and a simple bias-corrected model. At week 1, every method exhibited a competitive advantage over climatological forecasts. At week 2, RUFCO generated forecasts with statistically significant improvement over climatology at 82%–94% of the domain, beating CSGD’s coverage of 76%–90% of the grid points. At week 3, RUFCO’s skillful coverage was 65%–85%, while CSGDs dropped to only 12%–37%. At the longer lead times, RUFCO achieved the highest domain-averaged skill scores across seasons. However, the network tends to “smooth” forecast skill, making it less competitive with CSGD in limited areas with strongly spatially varying biases. Significance Statement Precipitation accumulation forecasts 1, 2, and 3–4 weeks in advance are increasingly in-demand for a variety of decision-making applications around hydrologic forecasting, flood and drought awareness, and wildfire preparedness. However, raw forecasts from numerical weather prediction systems have errors that hinder skill. Postprocessing methods remove those errors and provide more reliable and skillful forecasts. We show that a new neural network technique is an effective and competitive postprocessing tool compared to more traditional techniques.

Foreground biases in strong gravitational lensing

Strong gravitational lensing is a competitive tool to probe the dark matter and energy content of the Universe. However, significant uncertainties can arise from the choice of lens model, and in particular the parameterisation of the line of sight. In this work, we consider the consequences of ignoring the contribution of foreground perturbers in lens modelling. We derive the explicit form of the degeneracy between the foreground shear and the ellipticity of a power law lens, which renders the former quantity effectively unmeasurable from strong lensing observables, and biases measurements of the latter by a few percent. Nonetheless, we demonstrate that this degeneracy does not affect measurements of the Einstein radius. Foreground tidal effects are also not expected to bias the slope of the potential, and any biases in this slope should not affect the recovery of the Hubble constant. The foreground convergence term adds an additional uncertainty to the measurement of H 0, and we show that this uncertainty will be on the order of 1% for lensing systems located along random lines of sight. There is evidence to indicate that the probability of strong lensing is higher towards overdense lines of sight, and this could result in a small systematic bias towards overestimations of H 0.

A Study on Quality Control as Competitive Tool for Small Scale Enterprises

A Study on Quality Control as Competitive Tool for Small Scale Enterprises

Collaborating with C-Level Executives to Align Security with Business Goals: How Strategic Security Initiatives were Integrated with Broader Business Objectives

A link between cybersecurity and the organization's objectives is crucial in contemporary business. With the advancement of technology, today's digital transformation has enshrined cybersecurity not as a technical problem but as an essential tool for business competition. This paper aims to understand how cybersecurity professionals work alongside C-level executives to ensure security mirrors organizational goals and objectives. By beginning with business objectives in mind, emphasizing communication, and incorporating security into transformation projects, cybersecurity can be positioned to align with business goals to support business advancement. The paper also identifies Risk management according to business risk appetite, business continuity management, and regulatory compliance as essential framework components. Rapid threat intelligence and innovative tools such as cloud security and Zero Trust Network Access (ZTNA) are key examples of how protection can precede business initiatives. This collaboration shields business operations against cyber risk and advances competitive advantage by strengthening consumers' perceptions and optimizing business functions. Therefore, with the ever-advancing threat environment to its systems, organizations must embrace AI-driven threat sensors, automated compliance solutions, and effective incident response. Finally, cybersecurity must be recognized as one of the main enablers that drive innovation and business growth and allow companies to succeed in safe digital environments. The paper offers practical advice to cybersecurity professionals on communicating with the board and other executives, thereby achieving the organizational security culture necessary to succeed in the long run.

Consumer Perception of Private Label Products in Hungary

Private labels have emerged as a significant force in the retail sector, particularly in the fast-moving consumer goods (FMCG) market. Their importance has grown as consumers increasingly prioritise value for money, often choosing these products over manufacturer-branded items. As a result, private labels have become a competitive tool for retailers, driving down costs and fostering customer loyalty through consistent quality at lower prices. This study investigates Hungarian consumers' perceptions of private label products, focusing on quality comparison between private labels and manufacturer brands. A comprehensive online survey was conducted between April and June 2024, with over 10,000 respondents providing insights into their shopping habits and quality assessments of private label products. The primary findings reveal that more than half of the respondents (52.3%) believe that the quality of private label products is comparable to that of manufacturer-branded products, while 14.7% even consider it superior. Interestingly, 33% of the respondents perceive private label products as inferior in quality, although the differences are often seen as slight. These perceptions are influenced by both objective and subjective factors, including brand loyalty, price, and packaging. Demographic analysis using Cramer's V coefficient indicates that factors such as age, gender, income, and education have minimal impact on these quality perceptions. However, slight variations were noted, with men more likely to view private label products as lower in quality compared to women. Additionally, those with higher education tend to rate the quality of private label products more favorably. The study also explores consumer behavior in hypothetical purchasing scenarios, finding that 85.9% of respondents would prefer a private label product if it were identical in content and produced by the same manufacturer as a more expensive branded option. This suggests a strong potential for growth in the private label sector, particularly if retailers continue to emphasize the quality and value of these products. In conclusion, the research highlights the growing trust in private-label products among Hungarian consumers, driven by the perception that these products offer comparable quality at lower prices. Retailers are advised to leverage this trust by expanding their private label offerings, particularly in categories where consumer preference is already strong, and by continuing to market these products effectively to different demographic groups.

Serialized on-grid lift-in sectioning for tomography (SOLIST) enables a biopsy at the nanoscale

Cryo-focused ion beam milling has substantially advanced our understanding of molecular processes by opening windows into cells. However, applying this technique to complex samples, such as tissues, has presented considerable technical challenges. Here we introduce an innovative adaptation of the cryo-lift-out technique, serialized on-grid lift-in sectioning for tomography (SOLIST), addressing these limitations. SOLIST enhances throughput, minimizes ice contamination and improves sample stability for cryo-electron tomography. It thereby facilitates the high-resolution imaging of a wide range of specimens. We illustrate these advantages on reconstituted liquid–liquid phase-separated droplets, brain organoids and native tissues from the mouse brain, liver and heart. With SOLIST, cellular processes can now be investigated at molecular resolution directly in native tissue. Furthermore, our method has a throughput high enough to render cryo-lift-out a competitive tool for structural biology. This opens new avenues for unprecedented insights into cellular function and structure in health and disease, a ‘biopsy at the nanoscale’.

Principles and challenges of whole cell microbial biosensors in the food industry.

Whole cell microbial biosensors (WCMB) are mostly genetically modified microorganisms used to detect target molecules as indicators of biological and chemical contaminants as well as in the identification of compounds of interest in the food industry. The specificity and sensitivity of these biosensors are achieved through the design of genetic circuits that make use of genetic sequences such as promoters, terminators, genes encoding regulatory proteins or reporter proteins, among others. Despite the advances of WCMBs for their application, significant challenges are faced, such as cell stability, regulatory restrictions, and the need to optimize response times so that they can be a competitive detection tool in the market. This review explores the technological progress, potential and limitations of WCMBs in the food industry, starting by reviewing the operating principles of biosensors. The importance of selecting appropriate chassis cells and the integration of recognition elements and transducers to maximize their effectiveness in the detection of contaminants and compounds of interest in the food industry is highlighted.

The Geoeconomics of Regional Currency Contest: How Bilateral Swap Arrangements of India and Japan Counter the Rise of RMB

ABSTRACT India and Japan are the only two non-dollar issuing countries that have signed dollar-denominated bilateral swap arrangements (BSAs) with foreign central banks. These arrangements indicate that India and Japan are committed to protect their BSA partners’ financial security even at the expense of their own dollar reserves. What has motivated the two countries to make such policy choices? This article argues that India and Japan’s dollar-denominated BSAs not only enhance financial security but also limit the recipients’ motivations to look for alternative settlement currencies for trade or investment. Even if the BSAs are not directly aimed to contain China, therefore, this effectively limits the potential expansion of the RMB’s influence and allows India and Japan to maintain their structural power by supporting regional dollar dominance. This article finds, therefore, that India and Japan’s dollar-denominated BSAs serve as a tool in the geoeconomic competition with a rising China in support of the incumbent regional powers.

Public Opinion and Implications of Nepal’s Involvement in Hydropower Cooperation with India

As two neighboring but power-divided countries, Nepal and India not only have deep historical ties but also have experienced many fluctuations in the development of bilateral relations. The water issue has always been one of the sensitive factors affecting the bilateral relationship between Nepal and India. In recent years, with the increase in Nepal’s power generation capacity and India’s focus on energy issues, hydropower has gradually become an important and controversial area of cooperation between the two countries. India’s use of Nepal’s hydropower as a tool for strategic competition with China and its attempts to increase its control over Nepal’s hydropower has also sparked multiple discussions in Nepal.

IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network

IntroductionImmune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments. ObjectivesIdentifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients. MethodsThe proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients’ data from several clinical trials covering melanoma, gastric cancer, and bladder cancer. ResultsOur results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu. ConclusionIRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.