Flexible Strategy Research Articles

A study on the integration and flexible operation strategy of sodium-based CO2 capture system in cogeneration power plants and economic analysis

A study on the integration and flexible operation strategy of sodium-based CO2 capture system in cogeneration power plants and economic analysis

P-702. Epidemiology of Respiratory Pathogens and the Impact of Seasonality, Patient Demographics, and Diagnostic Testing Approach: A Retrospective Analysis from 2022 to 2023

Abstract Background Respiratory tract infections (RTIs) are a significant concern for public health due to their widespread transmission and high rates of illness and death reported globally. The use of multiplex molecular methods has allowed for the detection of a broad range of respiratory viruses and their subtypes, as well as some bacteria. This study investigates the epidemiology of respiratory pathogens detected in different demographic populations, emphasizing the importance of a flexible diagnostic testing approach to adapt to the complex dynamics of respiratory pathogens during the peak season. Distribution Rates of Respiratory Targets in Young, Adult, and Elderly Patients (n=1,520) Analysis of distribution rates of 19 respiratory targets during the respiratory season of 2022-2023, stratified by age. Methods During the 2022-23 respiratory disease season, the Diasorin LIAISON PLEX® Respiratory Flex Assay was used to prospectively test 1,520 patients across six clinical sites in the United States. As part of an observational study, retrospective analyses were conducted to compare the detection rates and frequencies of 19 respiratory targets (14 viral and 5 bacterial targets). The results were stratified by age. Results Many respiratory pathogens displayed age- and season-specific patterns. Enterovirus/rhinovirus was detected more frequently in patients < 21 years old (27%) than in the adult group (14%) and elderly group (9%), p< 0.01. Flu A prevalence was found to be higher in adults (21-65 y) at 25% and 33% in the elderly ( >65 y) population, p≤0.05. Individuals < 21 years had a lower incidence of SARS-CoV-2 (8%) compared to adults (35%) and the elderly (40%), p< 0.001. The most common viruses found in all three populations were flu A, human coronavirus, SARS-CoV-2, and flu A-H1, while the other pathogens were more population-specific. Conclusion The results of the study offer valuable insights into the complex dynamics of respiratory pathogens. They emphasize the importance of flexible multiplex PCR testing strategies that consider the different circulating patterns of these pathogens across different populations. This approach can help customize respiratory testing based on factors such as seasonality, patient demographics, and acceptable costs, and in line with diagnostic stewardship, adapt testing protocols to meet the specific needs of respiratory testing. Disclosures All Authors: No reported disclosures

Strategies for inclusive digital education: problem/project-based learning, cooperative learning, and service learning for students with special educational needs

IntroductionInclusive digital education is a growing field that aims to provide equitable learning opportunities for all students, including those with special educational needs (SEN). This study investigates the effectiveness of three pedagogical methods - problem/project-based learning (PBL), cooperative learning (CL), and service learning (SL) - in promoting inclusive digital education.MethodsWe adopted a quantitative approach, collecting data through a structured questionnaire administered to nine media education and pedagogy experts from Italy and Austria.ResultsResults showed that PBL, CL, and SL are generally effective but require differentiated implementation approaches to maximize inclusivity. Practical implementation in the inclusive context of PBL requires promoting student reflection and self-assessment adapted to students’ individual needs; in CL, group projects supported by digital collaboration tools (e.g., Google Docs) facilitate structured dialogue, peer collaboration, and shared responsibility, thereby increasing engagement; in SL, linking learning to real-world practice and designing experiences that reflect students’ abilities and interests emerge as crucial, especially for students with SEN.DiscussionThe implications of these findings suggest that educators should adopt flexible and adaptive strategies to successfully implement PBL, CL, and SL to ensure that they meet the diverse needs of students in digital environments. Limitations of the study and recommendations for future investigations are discussed to further develop this area of research.

Future threat landscapes: The impact on intelligence and security services

This article examines the evolving nature of antagonistic threats in the context of intelligence and security services, with a focus on small and medium-sized countries. It explores the impact of hybrid threats and non-linear warfare in an increasingly blurred security landscape between war and peace. The study aims to understand the emerging dynamics of the grey zone and the new challenges these evolving threats pose to intelligence and security services. The article adopts a qualitative methodology, drawing on global examples and including the strategic use of hybrid warfare by both state and non-state actors. In addition, the study examines technological advancements, particularly in artificial intelligence (AI) and machine learning (ML), to assess their role in shaping modern threats. The article argues that modern antagonistic threats differ from traditional ones in both intensity and complexity. Hybrid threats operate across multiple domains, blending military and non-military tactics while exploiting societal vulnerabilities. The article highlights the growing importance of AI and ML in both offensive and defensive strategies as well as the challenges posed by rapid technological advancements beyond state control. The article concludes that intelligence and security services must adapt to these multi-dimensional threats by embracing flexible integrated strategies. Enhanced international collaboration, advanced technological integration, and a focus on resilience will be the key to countering hybrid threats. The findings underscore the need for intelligence services to operate beyond traditional boundaries to effectively manage the complexities of future security environments.

Flexible extended warranty considering products’ heterogeneous reliability

ABSTRACT Customers often have the option to extend the warranty of a new durable product by paying extra for an extended warranty (EW). A critical issue in EW design is EW cost estimation, which highly depends on the field reliability of the product. An inaccurate warranty cost estimation may develop inappropriate risk management leading to high or low financial capacity with warranty claims and inefficiency on customer service. In practice, the field reliability of sold items differs due to variations in manufacturing process, usage, and operating conditions. However, this heterogeneity is not considered in traditional uniform warranty strategies. To recognize the reliability of the item, we apply Bayesian inference to the lifetime model with a given prior based on the observed failure data. Moreover, we propose a flexible EW strategy allowing delayed EW purchases within the base warranty period by considering the EW cost estimation based on the field reliability. Specifically, we consider different preventive maintenance (PM) strategies based on the updated reliability model and analyze the EW costs for different scenarios. Numerical experiments and actual examples of diesel engines are presented to validate the proposed method.

Implementation of the I-DECIDED® tool for PIVC assessment and decision making: discussion paper.

PIVCs often cause pain, irritation, or infection. Regular and careful catheter checks can decrease complications and improve patient outcomes. Implementation of the I-DECIDED® tool led to fewer idle catheters and complications. We present ideas for implementing the tool and how to overcome some common barriers. Peripheral intravenous catheter (PIVC) assessment and decision making should be evidence based to minimize risks and enhance patient care. Exploring implementation strategies from successful outcome studies can raise health care professionals' awareness, boost adherence to evidence-based protocols, and elevate PIVC care standards. To describe the implementation of the I-DECIDED® device assessment and decision tool for PIVC management in the Australian and Brazilian contexts. The Promoting Action on Research Implementation in Health Services (PARIHS) framework (evidence, context, facilitation) was used to prospectively plan the implementation of the tool in adult inpatient units in Australia and to retrospectively analyze the implementation of the tool in a pediatric inpatient unit in Brazil. Similarities and differences in the implementation studies were explored. Implementation of the tool in Australia and Brazil demonstrated that prevention and early detection of PIVC complications is achievable with standardized assessment and decision prompts. Both contexts witnessed a reduction in idle PIVCs, decreased insertion site complications, improved dressing quality, and significant improvements in documentation following implementation. Common implementation strategies included champions, education, badge cards, and posters. Enabling flexibility and context-specific education strategies was essential. Standardization of PIVC assessment and decision making using the I-DECIDED® tool reduced the prevalence of idle catheters and complications in Australia and Brazil. Implementation of the tool in different contexts confirms its utility and relevance for PIVC management in diverse health care settings. Promotion and adoption of the tool as a PIVC management bundle could increase patient safety and reduce health care costs.

“I felt like I was missing ‘me’": Long-term experiences of intrapersonal loss, grief, and change in adults with an acquired brain injury

ABSTRACT Experiences of loss and change following acquired brain injury (ABI) are frequent and multi-contextual, yet the long-term experiences of people with ABI are not well understood. This study explored the experiences of intrapersonal loss, grief and change in people with ABI, a decade after their injury. Twelve adults with ABI were interviewed 10–13 years post-injury. Using interpretative phenomenological analysis, we identified four overarching themes: a dawning realization of the impact of injury; loss of personhood; loss and liminality; and learning to live with loss and grief. Our findings indicate that in the decade following ABI, people continue to develop awareness of the impact of their injury and experience oscillating acceptance towards enforced changes. Participants reported a diminished sense of agency and autonomy in how they were perceived by and interacted with following injury. Additionally, identity may be lost, suspended, and renegotiated. Findings suggest dynamic, active, and flexible coping strategies that continue to be present over the long term. Considering the persistent nature of the injury and the evolving needs of the individual, a longer term view of rehabilitation outcomes may be required, contextualised by relational and intrapersonal challenges that may present over the longer term.

Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN

To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. The algorithm uses Rainbow DQN as the core framework, incorporating vehicle position, speed, and acceleration information into the state space. The reward function simultaneously considers two objectives: reducing vehicle waiting times and minimizing carbon emissions, with the vehicle queue length as a weighted factor. Additionally, an ACmix module, which integrates self-attention mechanisms and convolutional layers, is introduced to enhance the model’s feature extraction and information representation capabilities, improving computational efficiency. The model is tested using an actual intersection as the study object, with a signal intersection simulation built in SUMO. The proposed approach is compared with traditional Webster signal timing, actuated signal timing, and control strategies based on DQN and D3QN models. The results show that the proposed strategy, through real-time signal timing adjustments, reduces the average vehicle waiting time by approximately 27.58% and the average CO2 emissions by about 7.34% compared with the actuated signal timing method. A comparison with DQN and D3QN models further demonstrates the superiority of the proposed model, achieving a 15% reduction in average waiting time and a 6.5% reduction in CO2 emissions. The model’s applicability is validated under various scenarios, including different proportions of electric vehicles and traffic volumes. This study aims to provide a flexible signal control strategy to enhance intersection vehicle flow and reduce carbon emissions. It offers a reference for the development of green, intelligent transportation systems and holds practical significance for promoting urban carbon reduction efforts.

Ultrahigh piezoelectric performances of (K,Na)NbO3 based ceramics enabled by structural flexibility and grain orientation

(K,Na)NbO3-based ceramics are deemed among the most promising lead-free piezoelectric materials, though their overall piezoelectric performance still lags behind the mainstream lead-containing counterparts. Here, we achieve an ultrahigh piezoelectric charge coefficient d33 ∼ 807 pC·N−1, along with a high longitudinal electromechanical coupling factor (k33 ∼ 88%) and Curie temperature (Tc ∼ 245 °C) in the (K,Na)(Nb1-xSbx)O3-Bi0.5Na0.5ZrO3-BiFeO3 (KNN-xSb) system through structural flexibility and grain orientation strategies. Phenomenological models, phase field simulations and high-angle annular dark-field scanning transmission electron microscopy reveal that the structural flexibility originates from the high Coulomb force between K+/Na+ ions and Sb ions in the KNN-xSb system, while the grain orientation promotes the displacement of B-site cations leveraging the engineered domain configuration. As a result of its excellent comprehensive piezoelectric properties, the textured KNN-5Sb/epoxy 1-3 piezoelectric composite is found to possess a broader bandwidth BW = 60% and higher amplitude output voltage than commercial PZT-5 and other KNN counterparts. These findings suggest that the textured KNN-5Sb ceramics could potentially replace current lead-based piezoceramics in transducer applications.

THE EFFECT OF COMPETITION IN THE PRODUCT MARKET AND FINANCIAL FLEXIBILITY ON BUSINESS STRATEGY: A PAKISTANI CONTEXT

Purpose: This study examines the relationship between competition in the product market (PMC), financial flexibility (FF), and business strategies in Pakistan, providing both theoretical and practical implications for companies. It contributes to the body of knowledge on strategic management, particularly related to emerging markets, and addresses the institutional voids impacting firms. Design/Methodology: For data collection, 200 companies listed on the Pakistan Stock Exchange (PSX) were chosen via using the systematic elimination method for 2010-2020. Since the study involved binary dependent variables, probit and logit regression models were applied to test the hypothesis. The study adopted Ittner and Larcker (1997), Herfindahl-Hirshchman Index (HHI), and Frank and Goyal (2009) models to assess Business Strategy, PMC, and FF respectively. Findings: PMC has a significant impact on companies to adopt a certain business strategy (i.e., defensive, opportunistic, analytical, and invasive) and the level of financial flexibility. Similarly, financial flexibility provides companies to adapt defensive and opportunistic strategies, and it also make companies decrease the adaptation of analytical and invasive strategies. Originality: This study endeavors to provide a significant insight for companies to focus on the importance of PMC and keep track of their level of financial flexibility. The study also suggests that exploring the opportunities of investment, which are competitive in nature, can be effective and helpful in selecting the appropriate business strategy. Following this roadmap, companies can better be able to maximize their performance and value to sustain their competitiveness.

Rational multienzyme architecture design with iMARS.

Biocatalytic cascades with spatial proximity can orchestrate multistep pathways to form metabolic highways, which enhance the overall catalytic efficiency. However, the effect of spatial organization on catalytic activity is poorly understood, and multienzyme architectural engineering with predictable performance remains unrealized. Here, we developed a standardized framework, called iMARS, to rapidly design the optimal multienzyme architecture by integrating high-throughput activity tests and structural analysis. The approach showed potential for industrial-scale applications, with artificial fusion enzymes designed by iMARS significantly improving the production of resveratrol by 45.1-fold and raspberry ketone by 11.3-fold invivo, as well as enhancing ergothioneine synthesis in fed-batch fermentation. In addition, iMARS greatly enhanced the invitro catalytic efficiency of the multienzyme complexes for PET plastic depolymerization and vanillin biosynthesis. As a generalizable and flexible strategy at molecular level, iMARS could greatly facilitate green chemistry, synthetic biology, and biomanufacturing.

Research on the ordering strategy problem in supply chain with profit allocation under two-level price-fluctuation sales.

This paper investigates optimal ordering strategies in supply chains under two-level price fluctuations and initial profit allocation. By utilizing Copula functions to model the complex relationship between fluctuating prices and uncertain demand, the study develops both continuous and discrete decision models for practical applications. A discrete algorithm is proposed to approximate the optimal solution, with its convergence rigorously proven. Numerical experiments demonstrate that profit allocation ratios significantly impact optimal order quantities and overall supply chain profit. Price fluctuations, particularly at the discount level, present critical challenges, necessitating flexible and adaptive ordering strategies. The study also investigates the influence of different Copula relationships on optimal ordering decisions, revealing how varying market conditions-from moderate price sensitivity to high volatility-affect optimal order quantities. By examining ordering strategies in the context of profit allocation contracts, this research offers a new perspective on how supply chain members can collaboratively navigate uncertain markets. The findings provide actionable insights for managers to mitigate risks, improve coordination, and seize new opportunities. Extending traditional models to incorporate price fluctuations and profit allocation, this study makes theoretical and practical contributions to supply chain management, offering robust strategies to strengthen supply chain resilience.

Application of Particle Swarm Optimization in Optimal Asset Allocation

Particle Swarm Optimization (PSO) is an effective tool for solving nonlinear, non-convex optimization problems, offering a quick and efficient way to identify rational asset allocations. In PSO, each particle represents a specific asset allocation and moves within the search space to optimize the defined criteria. Particles update their positions based on personal experience (individual best) and the collective experience of the swarm (global best), gradually converging toward an optimal solution. Research on applying PSO to asset allocation demonstrates that this algorithm not only optimizes expected returns but also minimizes risk in investment portfolios. With its adaptability and computational speed, PSO can become a valuable tool for investors in formulating flexible and effective asset allocation strategies in a volatile financial environment.

The Impact of Workforce Flexibility on Organizational Operations: Promoting Productive Employment

Objective: Workforce flexibility strategies are increasingly becoming a critical means for enterprises to enhance competitiveness and adapt to the rapidly changing market environment. Theoretical Framework: By integrating workforce flexibility with the theoretical framework of Sustainable Development Goals (SDGs), this study examines how flexible strategies serve as key drivers to improve market adaptability, productivity, and employee satisfaction, and provides an in-depth analysis of their application in real-world cases. Method: Using an empirical case analysis approach, three globally influential companies—GitHub, Microsoft, and Flex—were selected for study to systematically analyze their experiences and outcomes in implementing workforce flexibility strategies. Results and Discussion: The results demonstrate that flexible work arrangements significantly improve market adaptability and productivity, while also enhancing employee satisfaction and organizational loyalty. Furthermore, workforce flexibility fosters work-life balance, increases employee autonomy, and creates more opportunities for career development. Research Implications: These findings provide an empirical foundation for management when formulating human resource strategies and suggest integrating workforce flexibility measures into future HR planning to achieve optimal organizational performance. Originality/Value: The study emphasizes the value of flexible strategies in promoting industry innovation and achieving SDGs, offering a fresh management perspective for tackling globalization challenges.

IMPLEMENTATION OF DIFFERENTIATED LEARNING: STUDENT AND TEACHER PERSPECTIVES IN THE CONTEXT OF MATHEMATICS IN PRIMARY SCHOOLS

Differentiated learning is an approach that aims to accommodate students' needs and learning styles in the learning process. In learning mathematics in primary schools, students often face challenges in understanding abstract concepts, so flexible and responsive teaching strategies are required. This study aims to explore the application of differentiated learning methods in mathematics at State 2 Wameo Primary School, Baubau City, and evaluate teachers' and students' perspectives on the effectiveness of this approach. The research used a qualitative method with a case study approach. Data collection techniques included in-depth interviews with teachers and students, observation of learning activities, and analysis of learning documents. Participants consisted of fourth and fifth grade students who have various learning styles, as well as teachers involved in the implementation of differentiated learning. The results show that differentiated learning helps students understand math materials better through media and tasks that suit their learning styles. However, teachers face challenges in providing equal attention, adjusting learning strategies and distributing tasks fairly due to limited time and resources. This research contributes to the development of more inclusive and adaptive mathematics learning strategies and provides a basis for educational policies that support students' diverse learning needs, but still provides a basis for educational policies that support students' learning needs.

ModeHunter: A Package for the Reductionist Analysis, Animation, and Application of Elastic Biomolecular Motion.

ModeHunter is a modular Python software package for the simulation of 3D biophysical motion across spatial resolution scales using modal analysis of elastic networks. It has been curated from our in-house Python scripts over the last 15 years, with a focus on detecting similarities of elastic motion between atomic structures, coarse-grained graphs, and volumetric data obtained from biophysical or biomedical imaging origins, such as electron microscopy or tomography. With ModeHunter, normal modes of biophysical motion can be analyzed with various static visualization techniques or brought to life by dynamics animation in terms of single or multimode trajectories or decoy ensembles. Atomic structures can also be refined against volumetric densities with flexible fitting strategies. The software consists of multiple stand-alone programs for the preparation, analysis, visualization, animation, and refinement of normal modes and 3D data sets. At its core, two spatially reductionist elastic motion engines are currently supported: elastic network models (typically for a Cα level of detail and rectangular meshes) and bend-twist stretch (for trigonal or tetrahedral meshes or trees resulting from spatial clustering). The programs have recently been modernized to Python 3, requiring only the common numpy and scipy external libraries for numerical support. The main advantage of our modular design is that the tools can be combined by the end users for specific modeling applications, either standalone or with complementary tools from our C/C++-based Situs modeling package. The modular design and consistent look and feel facilitate the maintenance of individual programs and the development of novel application workflows. Here, we provide the first complete overview of the ModeHunter package as it exists today, with an emphasis on functionality and workflows supported by version 1.4.

Contextual cues can be used to predict the likelihood of and reduce interference from salient distractors.

Our attention can sometimes be disrupted by salient but irrelevant objects in the environment. This distractor interference can be reduced when distractors appear frequently, allowing us to anticipate their presence. However, it remains unknown whether distractor frequency can be learned implicitly across distinct contexts. In other words, can we implicitly learn that in certain situations a distractor is more likely to appear, and use that knowledge to minimize the impact that the distractor has on our behavior? In two experiments, we explored this question by asking participants to find a unique shape target in displays that could contain a color singleton distractor. Forest or city backgrounds were presented on each trial, and unbeknownst to the participants, each image category was associated with a different distractor probability. We found that distractor interference was reduced when the image predicted a high rather than low probability of distractor presence on the upcoming trial, even though the location and (in Experiment 2) the color of the distractor was completely unpredictable. These effects appear to be driven by implicit rather explicit learning. We conclude that implicit learning of context-specific distractor probabilities can drive flexible strategies for the reduction of distractor interference.

Modeling household's willingness to pay for sustainable rural drinking water supply using hand-pumped boreholes: a bivariate probit analysis in Chilga District, Northwestern Ethiopia

ABSTRACT Access to safe drinking water remains a critical issue in rural areas of developing countries, including Ethiopia, where many communities rely on unimproved sources. This study assesses households’ willingness to pay (WTP) for improved drinking water services in rural Chilga District, Northwestern Ethiopia, focusing on hand-pumped boreholes as a sustainable solution. Using a contingent valuation method (CVM) with a double-bounded dichotomous choice format and a bivariate probit model, the study identifies key factors influencing WTP. Results reveal that female-headed households are more supportive of water improvement projects, while older respondents and those with higher perceived water quality are less likely to pay. Education significantly increases WTP, and households spending more time collecting water are more inclined to pay for improvements. Marginal effects of income on WTP suggest the need for flexible pricing models. The study underscores the need for targeted interventions, educational campaigns, and flexible bidding strategies to enhance community engagement and ensure equitable access to improved water services.

Flexible switching method of control strategy for MMC-HVDC converter based on AC power grid strength

Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) has been widely used in the large-scale transmission of renewable energy sources (RESs). However, the operating conditions of MMC-HVDC system with RESs are complex and variable, making it challenging to apply a single converter control strategy to different operating conditions, which affects the safe and stable operation of the whole system. In this paper, a flexible switching method of control strategies is proposed for MMC-HVDC converter. Firstly, the state-space model of MMC-HVDC under different control strategies is established, and the small signal stability and stable operation ranges under these control strategies are analyzed. Then, according to the proposed control strategy switching principle, a flexible control switching method for MMC-HVDC converter is proposed. This flexible control switching method ensures a smooth switching between different control strategies and causes a minimal disturbance to the overall system. Finally, a simulation model is established on the PSCAD/EMTDC platform, and the feasibility of the flexible control switching strategy for the MMC-HVDC system is verified. The simulation results show that very small disturbance is observed during the control switching and the flexible control switching strategy can effectively maintain the stable operation of MMC-HVDC system under different operating conditions. The proposed flexible control switching method can be activated according to the change of AC grid strength and it will help improve the stable operation of MMC converter.

The Role of Livelihood Assets in Affecting Community Adaptive Capacity in Facing Shocks in Karangrejo Village, Indonesia

This study addresses a theoretical gap by examining how multiple livelihood assets collectively enhance rural communities’ adaptive capacity and contribute to rural resilience theory. Using structural equation modeling, data were collected from June to August 2024 from 372 randomly selected households in Karangrejo Village, Indonesia, to test whether livelihood assets significantly influence adaptive capacity in response to diverse economic, social, and environmental shocks. The findings reveal that human, natural, physical, and social capital show a strong, positive effect on adaptive capacity, whereas financial capital alone does not significantly enhance resilience. Despite the limited geographic scope, the results underscore that comprehensive asset combinations—rather than reliance on a single form of capital—strengthen a community’s capacity to withstand shocks. This integrated perspective suggests that balanced investments across multiple forms of capital foster sustainable and flexible adaptation strategies, enabling communities to navigate uncertainty and maintain stability. The study highlights the critical importance of diversifying livelihood assets to foster long-term rural resilience and improve quality of life, offering practical insights for policymakers, practitioners, and researchers in developing holistic interventions that support adaptive capacity.