Balancing Strategy Research Articles

State Compensation Model in Adaptive Event-Triggered Predictive Control: A Novel Approach to Mitigating Moving Bottlenecks

Moving bottlenecks, characterized by their high frequency and unpredictability, pose significant challenges to timely response and management, often resulting in road congestion and increased risk of traffic accidents. To address these issues, this paper proposes an adaptive event-triggered variable speed limit (AET-VSL) method based on a state compensation model, which emphasizes the concept of symmetry in the optimization of multi-segment speed limits. This symmetry approach facilitates a balanced and efficient control strategy that adjusts speed limits in a way that harmonizes traffic flow across multiple road segments, reducing congestion and improving overall traffic stability. The state compensation model builds on the classical METANET traffic flow model, incorporating coordination between road segments to reduce congestion while minimizing disruptions to traffic flow stability. By dynamically adjusting speed limits using real-time traffic data, the AET-VSL method addresses fluctuations in traffic conditions and ensures adaptive control to manage bottlenecks efficiently. A simulation framework was employed to evaluate the proposed strategy across varying traffic scenarios. Results demonstrate that AET-VSL outperforms traditional methods, providing consistent improvements in traffic performance. For instance, under low-traffic-flow conditions, AET-VSL reduced waiting time (WT) by 41.36%, potential collisions (PCs) by 51.92%, and fuel consumptionfuel consumption (FC) by 34.07%. This study highlights the novelty and effectiveness of AET-VSL, offering a scalable and reliable solution for dynamic traffic management and showcasing its potential to enhance traffic safety and efficiency.

Nurses' Perceptions of Patients With Co-Occurring Psychiatric and Substance Use Disorders in Psychiatric Settings: A Grounded Theory Study.

To explore psychiatric and mental health nurses' perceptions of patients with co-occurring psychiatric and substance use disorders in psychiatric settings. An exploratory qualitative study design based on grounded theory, employing Straussian analytic procedures. Semi-structured interviews were conducted with 11 psychiatric and mental health nurses. Participants were recruited at a public psychiatric hospital located in the Paris metropolitan area of France. Face-to-face interviews took place between February 2024 and May 2024. Field notes and interview data were continuously analysed during open, axial and selective coding phases. The core category identified was 'waiting for a patient's will to change to emerge' when caring for patients with co-occurring substance use disorders. This active waiting period involved a balancing strategy between two discrete care objectives: stabilising the patient's clinical status and keeping the nurse-patient relationship intact. Identifying a patient's will to change provided an opportunity to reconcile these two aspects of psychiatric nursing through the addition of a collaborative component to the relationship. The substantive theory generated by our findings contributes to understanding the interactional and relational processes that occur between nurses and patients presenting with co-occurring substance use disorders in the context of psychiatric care. Implications of our study include the need for further emphasis on collaborative approaches that can be applied even in early or pre-contemplative stages of recovery. Organisational policies should systematically include training in the field of addiction care, given the high prevalence of co-occurring substance use disorders in psychiatric settings. Our findings can assist nurses in critically examining the challenges they experience when engaging with patients with co-occurring substance use disorders. This research adheres to the COREQ (COnsolidated criteria for REporting Qualitative research) Checklist. No patient or public contribution.

Statistical analysis of work-life balance and job satisfaction: an empirical study of female employees in the cigarette industry

The concept of work-life balance has become increasingly popular in recent years. The demands of dedicating oneself to work, managing household responsibilities, and fulfilling non-work obligations have led many modern workers to adopt work-life balance strategies to harmonize their professional and personal lives. This study aims to analyze the effect of work-life balance on job satisfaction among female employees at Pitoe Cigarette Factory, Trenggalek. The research employed a simple linear regression analysis method, using data collected from questionnaires completed by 95 female employees as samples. The results of the simple linear regression test indicated a positive relationship between work-life balance and job satisfaction, with a p-value of 0.000, which is less than the significance threshold of 0.05. This finding demonstrates that improved work-life balance can contribute to increased job satisfaction among female employees. The coefficient of determination (R²) was found to be 0.413, indicating that 41.3% of the variation in job satisfaction is explained by work-life balance. Furthermore, the study revealed that both work-life balance and job satisfaction levels were categorized as moderate, suggesting the need for greater attention to managing the balance between work and personal life. These findings are expected to provide insights for Pitoe Cigarette Factory management and other companies in designing policies that support work-life balance, ultimately enhancing job satisfaction and productivity among female employees.

Work-Life Balance Strategies and their Influence on Employee Productivity

The aim of this study is to explore the impact of work-life balance strategies on employee productivity and organizational well-being at Sekolah Luar Biasa Erha Pabelan. As work environments become increasingly flexible with the rise of remote and hybrid work models, it is essential to understand how these strategies influence both individual and organizational outcomes. This research adopts a qualitative approach, utilizing semi-structured interviews and focus group discussions with the principal, teachers and educational staff at Sekolah Luar Biasa Erha Pabelan. The data collected were analyzed through thematic analysis, identifying key themes related to flexible working arrangements, managerial support, wellness programs, and barriers to effective implementation. The findings reveal that flexible working hours and remote work options significantly enhance employee productivity and satisfaction, while supportive workplace culture and wellness programs further contribute to well-being and organizational engagement. However, challenges such as limited awareness of policies, resistance from middle management, and resource constraints hinder the full potential of these initiatives. The study concludes that organizations must proactively address these barriers and continuously adapt their strategies to foster a sustainable work-life balance. Future research should investigate the effectiveness of personalized work-life balance solutions, the role of technology in managing boundaries, and cross-cultural differences in the implementation of these strategies.

Speech Emotion Recognition Model Based on Joint Modeling of Discrete and Dimensional Emotion Representation

This paper introduces a novel joint model architecture for Speech Emotion Recognition (SER) that integrates both discrete and dimensional emotional representations, allowing for the simultaneous training of classification and regression tasks to improve the comprehensiveness and interpretability of emotion recognition. By employing a joint loss function that combines categorical and regression losses, the model ensures balanced optimization across tasks, with experiments exploring various weighting schemes using a tunable parameter to adjust task importance. Two adaptive weight balancing schemes, Dynamic Weighting and Joint Weighting, further enhance performance by dynamically adjusting task weights based on optimization progress and ensuring balanced emotion representation during backpropagation. The architecture employs parallel feature extraction through independent encoders, designed to capture unique features from multiple modalities, including Mel-frequency Cepstral Coefficients (MFCC), Short-term Features (STF), Mel-spectrograms, and raw audio signals. Additionally, pre-trained models such as Wav2Vec 2.0 and HuBERT are integrated to leverage their robust latent features. The inclusion of self-attention and co-attention mechanisms allows the model to capture relationships between input modalities and interdependencies among features, further improving its interpretability and integration capabilities. Experiments conducted on the IEMOCAP dataset using a leave-one-subject-out approach demonstrate the model’s effectiveness, with results showing a 1–2% accuracy improvement over classification-only models. The optimal configuration, incorporating the joint architecture, dynamic weighting, and parallel processing of multimodal features, achieves a weighted accuracy of 72.66%, an unweighted accuracy of 73.22%, and a mean Concordance Correlation Coefficient (CCC) of 0.3717. These results validate the effectiveness of the proposed joint model architecture and adaptive balancing weight schemes in improving SER performance.

Soft balancing in the regions: causes, characteristics and consequences

Abstract US decline and retrenchment is increasing the importance of regional dynamics across the international system. Over the past decade, International Relations scholars have described and conceptualized this development variously as ‘de-centred globalism’, a ‘multi-order world’, a ‘multiplex world’ or a multicultural ‘no one's world’, but the links between the regional and global levels and the roles and functions of regional institutions in power politics continue to be poorly understood. This special section uses the analytical lens of soft balancing—that is, attempts at restraining a threatening power through diplomatic and institutional delegitimation—to explore these links. Soft balancing has been used extensively to understand developments at the great power level, but its focus on diplomatic and institutional strategies holds considerable potential for explaining how rising powers, middle powers and smaller states seek to navigate the emerging international order. Contributors to the section discuss developments in the Indo-Pacific, central Asia, Europe and BRICS to answer questions such as: what are the characteristics of soft balancing in the regions? How do regional actors apply different soft balancing strategies? When and under what conditions will soft balancing strategies be effective? What is their impact on regional and global orders? This introduction offers an overview of soft balancing in theory and practice and their relevance to the world's regions.

Energy Efficient and Balanced Task Assignment Strategy for Multi-AAV Patrol Inspection System in Mobile Edge Computing Network

Energy Efficient and Balanced Task Assignment Strategy for Multi-AAV Patrol Inspection System in Mobile Edge Computing Network

Adaptive Probabilistic Neural Network based Edge Data Center Authentication for Secure Load Balancing in Fog Computing

The growth of current technological advances has promising of fog computing, that increases the processing capacity of devices and provides fresh approaches for conventional applications in industry. Fog computing has become increasingly vital given the huge and sophisticated system of sensors to manage the information stream. Node authentication and secure load balancing are important in fog computing. The rationale behind developing this model is that the existing deep learning models are affected by the node authentication performance and also generate issues like high latency developed by the cloud-based architecture while processing huge amount of data, and node employment. Thus, the objective of the developed model is to implement an advanced deep learning method for better node authentication and an appropriate enhanced optimization scheme for performing optimal resource allocation. Additionally, secure load balancing is done at edge node centers in fog computing to allocate the workload for each node securely. Initially, the network initialization attributes in fog computing are provided to the authentication process. Here, the deep learning-based Edge Data Centre (EDC) authentication takes place with the help of the Adaptive Probabilistic Neural Network (A-PNN) model. The major intention of introducing an A-PNN method is to check whether the node is authenticated or not. If the EDC node is authenticated, then the A-PNN model suggests the particular EDC node for the resource allocation process, or else the node will be ignored or removed from the process. Here, the new hybrid approaches as Hybrid Heap with African Buffalo Optimization Algorithm (HH-ABO) are suggested for execute the process of optimization. Here, the parameters of the A-PNN model are tuned for maximizing accuracy, precision, and NPV and also minimizing the False Positive Rate (FPR) rate. Then the authenticated node is subjected to the load-balancing scheme. The allocation of allocation is carried out in the load balancing scheme. Here, the diverse constraints like resource allocation, energy consumption, cost, the execution time of the task, security by authentication, latency, and task completion time are considered for formulating the objective function using the HH-ABO algorithm. At last, it is performed well than the traditional methods using diverse parameters. The outcome demonstrate that the developed model achieved better results on load balancing at edge nodes in fog computing. The proposed model effectively resolves the load balancing issues and also it distributes workload for each node accurately. It effectively resolves the latency and deployment issues. In addition, the modern applications based on the proposed scheme will allow million of workloads from one node to another in a fast and reliable manner.

SOC balancing strategy for parallel-connected power conversion system based on PQ decoupling control

SOC balancing strategy for parallel-connected power conversion system based on PQ decoupling control

Development of ROS-triggered masked liposomes for activated cellular delivery using a charge balance strategy.

We report a charge balance strategy for reactive oxygen species (ROS)-triggered activation of liposome cell delivery by unmasking cationic membranes. Zeta potential experiments, microplate-based vesicle interaction assays, and cellular delivery studies confirmed that modification of anionic lipid 1 by ROS led to the uncaging of cationic liposomes, thereby driving cellular association.

Integration of Multi-Source Landslide Disaster Data Based on Flink Framework and APSO Load Balancing Task Scheduling

As monitoring technologies and data collection methodologies advance, landslide disaster data reflects attributes such as diverse sources, heterogeneity, substantial volumes, and stringent real-time requirements. To bolster the data support capabilities for the monitoring, prevention, and management of landslide disasters, the efficient integration of multi-source heterogeneous data is of paramount importance. The present study proposes an innovative approach to integrate multi-source landslide disaster data by combining the Flink-oriented framework with load balancing task scheduling based on an improved particle swarm optimization (APSO) algorithm. It utilizes Flink’s streaming processing capabilities to efficiently process and store multi-source landslide data. To tackle the issue of uneven cluster load distribution during the integration process, the APSO algorithm is proposed to facilitate cluster load balancing. The findings indicate the following: (1) The multi-source data integration method for landslide disaster based on Flink and APSO proposed in this article, combined with the structural characteristics of landslide disaster data, adopts different integration methods for data in different formats, which can effectively achieve the integration of multi-source landslide data. (2) A multi-source landslide data integration framework based on Flink has been established. Utilizing Kafka as a message queue, a real-time data pipeline was constructed, with Flink facilitating data processing and read/write operations for the database. This implementation achieves efficient integration of multi-source landslide data. (3) Compared to Flink’s default task scheduling strategy, the cluster load balancing strategy based on APSO demonstrated a reduction of approximately 4.7% in average task execution time and an improvement of approximately 5.4% in average system throughput during actual tests using landslide data sets. The research findings illustrate a significant improvement in the efficiency of data integration processing and system performance.

Connected Healthcare System Technology Interventions to Improve Patient Safety by Reducing Medical Errors: A Systematic Review

ABSTRACT Medication or medical mistakes, the third highest cause of death in the United States, occur from prescription writing to administering the therapy, with serious clinical and cost repercussions. Digital health technologies, such as connected healthcare systems, have the potential to reduce pharmaceutical errors and increase patient safety. This systematic review was conducted to find literature evidence to improve patient safety and reduce medication errors with connected healthcare interventions. This systematic review was conducted using the PRISMA 2020 guidelines. PubMed, SCOPUS, EBSCO, and Google Scholar databases were searched from January 1, 2000 to June 30, 2024 using keywords: medication errors, patient safety, and connected healthcare. A qualitative narrative analysis was conducted for the review. The detailed search yielded 9524 papers in total. In the process of duplicate removal, 4856 duplicate articles were found. After the removal of duplicate articles, 4615 were found not suitable or relevant to the topic of this study and were removed. Finally, 53 articles were chosen for the review study after screening and duplication removal. Ten of the 53 articles were review articles (18.9%), and 43 (81.1%) were original. The research indicates that various connected healthcare system technologies are more effective in minimizing errors and enhancing care quality. Integrating computerized physician order entry and clinical decision support systems may further reduce medical errors. However, many areas require additional research, and the outcomes are mixed. A balanced strategy that combines innovation, practical safety, and outcome evaluation is preferable.

INTERPERSONAL COMMUNICATION, EMOTIONAL INTELLIGENCE, CONFLICT RESOLUTION, RELATIONAL SATISFACTION AMONG INTIMATE PARTNERS

Romantic relationships significantly impact individuals' mental health, emotional well-being, and life satisfaction. Effective communication, emotional intelligence, and conflict resolution are critical to maintaining relational harmony. However, contemporary challenges, including technology use and work-life balance, complicate relationship dynamics, necessitating a deeper exploration of these factors. This study investigates the interplay between communication patterns, emotional intelligence, conflict resolution techniques, and relational satisfaction in intimate relationships. The objectives include identifying key communication strategies, exploring the role of emotional intelligence, analyzing the impact of technology-mediated communication, and understanding the balance between work-life and relational satisfaction. A qualitative, exploratory design was employed, involving semi-structured interviews with 25 couples (N=50). The data were collected through guided discussions on communication styles, emotional intimacy, conflict resolution, and relationship satisfaction. Thematic analysis was conducted using NVivo software to identify key themes and subthemes. The study revealed six major themes: effective communication, emotional intimacy, conflict resolution, technology-mediated communication, work-life balance, and relational satisfaction. Active listening, empathy, and open communication emerged as pivotal for fostering intimacy and satisfaction. Technology-mediated communication demonstrated both benefits and challenges, while work-life balance significantly influenced relational harmony. This research underscores the importance of cultivating emotional intelligence, balanced communication strategies, and effective conflict resolution to enhance relationship dynamics. The innovative application of blockchain technology concepts in managing relational conflicts was also noted as a novel approach to selective information sharing. Future studies should examine the long-term impacts of technology-mediated communication, delve into diverse relational contexts, and develop interventions that integrate emotional intelligence and conflict resolution strategies to strengthen intimate relationships.

El temor a perder el empleo como motivación laboral

One of the most complex issues in organizations is work motivation; however, the phenomenon of fear as a motivating force at work has been little examined. Thus, the aim of this paper is to examine fear, understood as a work motivator, to boost performance and productivity. A qualitative methodology with a hermeneutic approach is used to explore the fear of losing one’s job as a motivating force in the work environment. Likewise, it is an exploratory and descriptive-analytical approach based on the review of scientific papers and a thematic analysis to identify patterns and recurring themes. The main results reveal that the fear of losing one’s job impacts both employees and the organization in general, generating psychological, physiological and behavioral reactions that affect productivity and social interactions, also damaging the culture and work environment. Fear can encourage work efforts in the short term, but it damages emotional and psychological well-being in the long term, decreasing creativity and innovation. In conclusion, based on the theories analyzed, it was found that, although fear can boost temporary performance, its effectiveness as a motivational driver depends on intrinsic factors and the balance between rewards and consequences. Fear can negatively affect competitiveness and organizational sustainability; therefore, leaders are recommended to apply balanced strategies that prioritize employee well-being, avoiding generating unnecessary anxiety, in order to improve performance and quality of work.

Path planning and engineering problems of 3D UAV based on adaptive coati optimization algorithm

In response to the challenges faced by the Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive Coati Optimization Algorithm (ACOA). ACOA achieves a balanced exploration–exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness. Additionally, to prevent entrapment in local optima, ACOA introduces an Adaptive Levy Flight strategy to maintain population diversity, thereby improving convergence accuracy. Furthermore, underperforming individuals are eliminated using a cosine disturbance-based differential evolution strategy to enhance the overall quality of the population. The efficacy of ACOA is assessed across four dimensions: population diversity, exploration–exploitation balance, convergence characteristics, and diverse strategy variations. Ablation experiments further validate the effectiveness of individual strategy modules. Experimental results on CEC-2017 and CEC-2022 benchmarks, along with Wilcoxon rank-sum tests, demonstrate superior performance of ACOA compared to COA and other state-of-the-art optimization algorithms. Finally, ACOA’s applicability and superiority are reaffirmed through experimentation on five real-world engineering challenges and a complex urban three-dimensional unmanned aerial vehicle (UAV) path planning problem.

Harnessing Artificial Intelligence to Achieve Sustainable Development Goals: Opportunities, Challenges, and Ethical Considerations

This critical analysis examines the role of artificial intelligence (AI) in attaining the Sustainable Development Goals (SDGs), emphasizing its potential advantages and related problems. AI technologies can markedly improve resource efficiency, optimize public service delivery, and stimulate economic growth and development, thereby advancing multiple Sustainable Development Goals (SDGs), including poverty alleviation, responsible consumption and sustainable urban development. The incorporation of artificial intelligence brings ethical challenges, including the potential for perpetuating biases and aggravating inequality, which may impede progress toward sustainable development. This assessment underscores the necessity of responsible deployment of Artificial Intelligence, promoting strong governance frameworks, collaborative efforts among multiple stakeholders, and focused investments in education and capacity building to minimize poverty significantly. By connecting artificial intelligence research and uses with the Sustainable Development Goals and ensuring equal access to its advantages, stakeholders may leverage AI as a potent instrument for sustainable development. In conclusion, leveraging the revolutionary potential of AI for sustainable development necessitates a multi-stakeholder approach that includes governments, researchers, civil society, and the corporate sector. This collaboration must emphasize the establishment of strong governance frameworks, the involvement of data management specialists, the assurance of algorithmic transparency and accountability, and the facilitation of equal access to AI technologies. This study ultimately advocated for a balanced strategy that optimizes the beneficial effects of AI while mitigating its problems to promote a more sustainable and equitable future for all citizens.

Neuroethical implications of AI-driven productivity tools on intellectual capital: a theoretical and econometric analysis

PurposeThis study explores the dual impact of AI-driven productivity tools on employee performance, well-being and sustainability, focusing on the key metrics of productivity, innovation, stress, job satisfaction and sustainability.Design/methodology/approachThe study uses econometric models and simulated data and applies difference-in-differences and regression analysis to assess the effects of AI tools on 1,000 simulated employees.FindingsAI tools enhance productivity but offer limited support for innovation. They increase stress and reduce job satisfaction while improving governance outcomes and transparency.Research limitations/implicationsUsing simulated data may limit real-world applicability. Future studies should validate findings with empirical data regarding long-term sustainability impacts.Practical implicationsA balanced AI strategy combining training and well-being support is vital to mitigate stress and sustain employee satisfaction during AI integration.Social implicationsMonitoring employee well-being and sustainability metrics is fundamental to ensure responsible AI adoption.Originality/valueThis research provides new insights into balancing AI’s productivity benefits with its psychological and ethical implications, offering guidelines for responsible implementation.

Under the lens of ads: Digital surveillance and pre-roll advertising avoidance dynamics

Programmatic advertising operates within a surveillance framework that monitors consumer behaviour and generates personalised content to deliver more targeted advertisements to audiences. While this framework analyses large-scale data from users and has the potential to enhance the relevance of advertisements, it can also negatively impact the user experience. This study examines the factors influencing avoidance behaviours towards pre-roll video advertisements by analysing the relationships between perceived marketing surveillance, ad clutter, skippable ad irritation, and perceived autonomy. For this purpose, data collected from 547 participants were analysed using structural equation modelling (SEM). The study's findings show that increasing the relevance of advertisements mitigates surveillance-induced negative attitudes and reduces viewers' tendency to avoid skippable ads. The results underscore the importance of granting viewers greater autonomy to improve the user experience and minimise ad avoidance. Furthermore, the study highlights the necessity of developing more balanced and ethical advertising strategies that enhance consumers' sense of control and alleviate the intrusive effects of programmatic advertising's surveillance nature.

Business Strategy Innovation and Investment Risk Prevention and Control under Sustainable Development

This research investigates the relationship between innovation strategies and investment risk, with a particular focus on the moderating effect of Environmental, Social, and Governance (ESG) practices. Utilizing data from Chinese listed companies, we employ multiple regression models to assess the impact of R&D investments on systematic risk (Beta) and explore how ESG scores influence this relationship. Our findings indicate that increased R&D investment is associated with higher investment risk, highlighting the inherent uncertainties and potential volatility of innovation activities. However, strong ESG practices mitigate these risks, suggesting that companies with robust ESG frameworks can reduce the additional risk introduced by high levels of innovation. The study also reveals significant interrelationships among key financial metrics, underscoring the complexity of managing innovation and investment risk. These insights provide valuable implications for companies, investors, and policymakers, emphasizing the need for balanced growth strategies that incorporate effective risk management and sustainable practices. This research contributes to the literature by offering empirical evidence on the interplay between innovation, ESG, and investment risk, ultimately advocating for more resilient and sustainable business practices.

The Effects of Social Media on Mental Health Among Adolescents

This study delves into the intricate relationship between social media and adolescents’ mental health, a pressing concern in the digital era. The development of social media is a product of the times and a reflection of social progress. For individuals, the impacts are diverse. Social media platforms, while promoting social interaction and a sense of belonging, also harbor dangers like addiction and cyberbullying that can negatively impact self-esteem and mental health. The paper offers a succinct literature review, emphasizing social media's dual impact as a source of support and stress for high school students. It highlights the pivotal role of mental well-being in countering social media's negative influences and advocates for additional research to develop strategies that encourage positive social media engagement. The study concludes with a call for a balanced strategy to maximize social media’s benefits for adolescents while protecting them from its potential detriments. This study explores the relationship between social media and the mental health of adolescents, a pressing issue in the digital age. The development of social media is a product of the times and a reflection of social progress. There are both positive and negative consequences for individuals. While social media platforms promote social interaction and a sense of belonging, they also pose dangers such as addiction and cyberbullying, which can harm self-esteem and mental health. The paper provides a concise review of the literature, emphasizing the dual impact of social media as a source of support and stress for students. It highlights the central role of mental well-being in countering the negative influences of social media and advocates for further research to develop strategies to promote positive social media engagement. The study concludes with a call for a balanced strategy to maximize the benefits of social media for young people while protecting them from its potential harm.