Management Level Research Articles

Adaptive hierarchical energy management strategy for fuel cell mobile robot hybrid power system based on working condition recognition

Energy management of hybrid power is critical to maintain the economical and efficient operation of fuel cell mobile robots. To improve the energy distribution between the proton exchange membrane fuel cell (PEMFC) and battery under different working conditions, this paper proposes an adaptive hierarchical energy management strategy (AHEMS) based on the recognition and management levels. Firstly, the recognition level realizes the identification of different working conditions based on the machine learning (ML) methods including the K-means and KNN. Secondly, the fuel cell hydrogen consumption and efficiency are both optimized by adaptive multi-objective particle swarm optimization (AMOPSO) at the management level. Specifically, an adaptive flight parameter strategy based on the particle dispersity (PD) information is proposed to balance the convergence and diversity of Pareto solutions. Besides, to overcome the parameter uncertainty caused by different working states and improve the system performance, an interval optimization scheme is proposed based on the Pareto solutions. Finally, the fuzzy decision combined with the recognition results and state of charge (SOC) of the battery is performed to find the most appropriate power distribution of the PEMFC and battery. The proposed AHEMS algorithm is compared with different algorithms in the numerical simulation and hardware-in-loop (HIL) experiments. These results demonstrate that the hybrid power system with the proposed optimization scheme performs better than the base model and classical optimization algorithms in terms of the hydrogen consumption and efficiency indexes, revealing the success of this AHEMS approach in solving the energy distribution problem in different working conditions.

Job satisfaction and career plans among Irish radiography services managers and clinical specialist radiographers

IntroductionRadiography Service Managers (RSMs) and Clinical Specialist Radiographers (CSRs) play a pivotal role in the overall provision and seamless operation of the diagnostic imaging services provided to patients. The essential role played by these professionals makes it important to consider both job satisfaction and career plans amongst this cohort as both these variables are inextricably linked with the quality of care provided to patients and the occupational performance of staff. This study explored the job satisfaction and career plans of RSMs and CSRs. MethodA well-established, validated, questionnaire-based job satisfaction assessment tool, the Job Satisfaction Survey (JSS), was used to provide data relating to 36 statements across nine subscales related to job satisfaction in healthcare. Additionally, there were four open-ended questions which provided participants the opportunity to provide input related to career plans. An email link with access to the electronic survey was circulated to the National RSMs Group who further distributed the survey to other RSMs and all CSRs at their respective clinical sites. The collected data was analysed in Excel with the data analysis add-in to assess statistical significance. ResultsThe findings of this study demonstrated predominately ‘ambivalent’ opinions among CSRs and RSMs, with regard to job satisfaction. Areas of highest satisfaction amongst both cohorts included ‘Nature of work’, ‘Co-workers’, and ‘Supervision’, while areas of dissatisfaction included ‘Pay’, unclear systems of ‘Contingent Rewards’, and ‘Operating Conditions’. ConclusionThis study highlighted areas where measures could be explored to increase job satisfaction and encourage the retention of both RSMs and CSRs in diagnostic imaging departments. Implications for practiceThe enhancement of job satisfaction at management levels should translate through to other grades of radiographers, impacting the overall quality of patient care and professional well-being in the department.

Study on the modularization of project operations under the “oil company” model

Abstract A rational and efficient business management and operation model is a prerequisite for the smooth operation of an organization. This study aims to build an intensive and efficient business management model with the characteristics of the “oil company” model. Compared with the traditional business management model, the “oil company” model has significant advantages in terms of organizational structure, communication efficiency, work style and work efficiency, etc. Due to the compression of management levels and the shortening of business chains, the communication efficiency and work efficiency are higher, and the composition of personnel and work style are more flexible. Therefore, based on the theory of the “oil company” and on the idea of compressing management levels and shortening management chains, business management departments should be integrated, streamlined and optimized, and business chains should be sorted out.

Time will tell the truth: Performance commitments and earnings management in M&A

This study investigates the impact of performance commitments on the earnings management levels of acquiring firms in the three years following mergers and acquisitions (M&As), as well as the resulting performance reversal phenomenon. The empirical results indicate that listed companies significantly increase their earnings management levels in the three years after making performance commitments. This suggests that firms may engage in upward earnings management during the commitment period to inflate profits and achieve the promised performance. Moreover, our findings reveal that M&A transactions involving stock payments and performance commitments exhibit even higher levels of earnings management. However, such practices are unsustainable; once the commitment period ends, the previous earnings management behavior is gradually rectified, leading to a performance reversal. Further analysis demonstrates that performance commitments result in a significant decline in companies' return on assets (ROA) in the fourth and fifth years post-M&A.

Effectiveness of Risk Management through Risk Maturity Measurement: A Study at PT Pelabuhan Indonesia

This research aims to evaluate the effectiveness of risk management at Pelindo post-merger and assess its risk management maturity level. Additionally, the study intends to offer relevant recommendations for the company in developing its risk management strategy and implementation. The research adopts a qualitative descriptive approach with a case study strategy, where the researcher analyzes an organization as a unit of analysis in a natural setting without manipulation. Data collection techniques include interviews with representatives from the Group Risk Management, Governance, and Compliance as the corporate-level risk managers. Document analysis includes textbooks, academic and professional journals, theses, articles, and company department reports. Data analysis follows the Miles and Huberman model. The research indicates that the maturity level of risk management at Pelindo post-merger has reached the good practice phase with a score of 2.94. This reflects a consistent application of risk management in accordance with industry standards, although its effectiveness varies. The organizational structure, framework, processes, and risk culture have begun to be implemented. Recommendations for improvement include strengthening the risk culture, enhancing the effectiveness of risk management functions, and aligning risk strategies with the ERM transformation plan for better effectiveness and compliance.

Cytokine Storms and Anaphylaxis Following COVID-19 mRNA-LNP Vaccination: Mechanisms and Therapeutic Approaches.

Acute adverse reactions to COVID-19 mRNA vaccines are a major concern, as autopsy reports indicate that deaths most commonly occur on the same day of or one day following vaccination. These acute reactions may be due to cytokine storms triggered by lipid nanoparticles (LNPs) and anaphylaxis induced by polyethene glycol (PEG), both of which are vital constituents of the mRNA-LNP vaccines. Kounis syndrome, in which anaphylaxis triggers acute coronary syndrome (ACS), may also be responsible for these cardiovascular events. Furthermore, COVID-19 mRNA-LNP vaccines encompass adjuvants, such as LNPs, which trigger inflammatory cytokines, including interleukin (IL)-1β and IL-6. These vaccines also produce spike proteins which facilitate the release of inflammatory cytokines. Apart from this, histamine released from mast cells during allergic reactions plays a critical role in IL-6 secretion, which intensifies inflammatory responses. In light of these events, early reduction of IL-1β and IL-6 is imperative for managing post-vaccine cytokine storms, ACS, and myocarditis. Corticosteroids can restrict inflammatory cytokines and mitigate allergic responses, while colchicine, known for its IL-1β-reducing capabilities, could also prove effective. The anti-IL-6 antibody tocilizumab also displays promising treatment of cytokine release syndrome. Aside from its significance for treating anaphylaxis, epinephrine can induce coronary artery spasms and myocardial ischemia in Kounis syndrome, making accurate diagnosis essential. The upcoming self-amplifying COVID-19 mRNA-LNP vaccines also contain LNPs. Given that these vaccines can cause a cytokine storm and allergic reactions post vaccination, it is crucial to consider corticosteroids and measure IL-6 levels for effective management.

Organizational culture and leadership style in Spanish Hospitals: Effects on knowledge management and efficiency

Hospital culture and leadership style have attracted considerable attention in research, with compelling evidence indicating their potential competitive advantages, including their crucial role in ensuring the successful implementation of knowledge management and its impact on hospital efficiency. The aim of this paper is to identify the effects of organizational culture and leadership style on knowledge management and hospital efficiency. Fuzzy cognitive maps (FCMs) are relational models that can be used to represent the opinions and knowledge of expert to infer cause-effect relationships among different concepts. The use of FCMs as a simulation tool enables the evaluation of potential scenarios based on different organizational cultures and leadership styles in hospitals. Developing an FCM for this study involved several steps. Firstly, data were collected through interviews with 21 experts in hospital management. The interviews were conducted between May and September 2023 either face-to-face or via videoconference. Once individual cognitive maps had been created, consensus among them was achieved through a multicriteria decision-making process, wherein the expert opinions were averaged. The separate cognitive maps of each expert were then integrated to produce a single FCM using the augmented FCM approach. Reflecting expert insights from the FCM, hospitals with a hierarchy culture exhibit diminished levels of knowledge creation, management, and overall hospital efficiency, whereas those with an adhocracy culture show improvements in knowledge creation, knowledge exploitation, and overall hospital efficiency in comparison to alternative ones. From the experts' FCM perspective regarding leadership style, transformational leadership achieves the highest level of knowledge management and hospital efficiency in hospitals with an adhocracy culture. Finally, this paper offers a reference for practising knowledge management and improving hospital efficiency through adhocracy culture and transformational leadership.

Lesson from stock price crash: Changes in managerial confidence and incentives

This paper investigates the consequences of stock price crashes. I find that stock price crash risk subsequently reduces managerial confidence levels, as proxied by the CEO's option-based and earnings call transcript's text-based measures. I also find that stock price crash risk reduces CEO compensation and equity incentives, suggesting that a firm seeks to adjust managerial incentives after its stock price crashes to prevent future occurrences. Furthermore, CEOs with high confidence are more likely to curtail overinvestment after their crash experiences relative to others, thereby contributing to shareholder value. Overall, this paper provides novel evidence that CEOs and firms appear to learn from their experiences of stock price crashes, suggesting that stock price crashes may induce experience-driven conservatism that influences CEO and corporate decisions.

Оценка финансовой безопасности домохозяйств арктических регионов Российской Федерации на микроуровне

Analyzing household financial security is closely related to the formation and analysis of financial resources and their allocation. This study aims to analyze the financial security of households in the Russian Arctic regions at the micro level. Key objectives include exploring various approaches to defining and evaluating household financial security, developing a system of coefficients to measure this security, applying it to Arctic regions over time, and identifying common risks and threats to household financial security in these regions. The study synthesizes existing research on household financial security, providing a comprehensive perspective on its definition and assessment. Household financial security is assessed from two angles: first, by evaluating an individual household's financial resources and its ability to cover current expenses, save, and withstand unexpected financial shocks; second, by considering its role in regional financial stability and socio-economic development. For a thorough analysis, the study proposes categorizing financial security into manageable and unmanageable levels. Results from applying the developed coefficients reveal a disparity among Arctic regions: three regions exhibit high financial security, two regions face critical levels of insecurity, and the remaining regions fall into the average category. The study identifies key threats to financial security in each region, with low purchasing power being a common problem for all regions. The study finds that households prioritize securing their financial stability before investing their financial resources into regional development, underscoring the need for ongoing monitoring of financial conditions. The role of government in supporting household financial security is also emphasized. The practical significance of this study lies in its potential to inform the development and adjustment of regional socio-economic programs based on the findings.

Effect of weed management and fertilizer levels on growth and yield of rainfed cotton (Gossypium hirsutum L.) under south Gujarat condition

Effect of weed management and fertilizer levels on growth and yield of rainfed cotton (Gossypium hirsutum L.) under south Gujarat condition

Environmental regulation and renewable energy technology innovation: Firm-level evidence from China

This paper deploys a quasi-natural experiment formed by the implementation of China's New Environmental Protection Law (NEPL) and evaluates the impact of environmental regulation on renewable energy technology innovation (RETI). The findings indicate that the NEPL results in a substantial and robust increase in firm-level RETI. Further research show that this stimulus impact can be achieved by alleviating financing constraints and optimizing managerial levels. The effect appears more pronounced for state-owned enterprises, during the growth stage, and in regions with high marketization and high-income levels.

Research and application of 3D spatial safety electronic fence technology based on beidou high-precision positioning in substation construction projects

Abstract In recent years, the Beidou Navigation Satellite System (BDS) has developed rapidly, achieving global coverage and being widely used in various fields. The high-precision positioning capability of the Beidou system provides strong technical support for a variety of application scenarios, especially in fields requiring high-accuracy location information, such as safety monitoring in substation engineering. Substations are critical components of the power system, and their operational safety is vital for the stable operation of the entire grid. However, due to the complexity of the equipment and harsh working environments in substation engineering, utilizing Beidou’s high-precision positioning capabilities can significantly enhance the safety management level of substation construction projects and reduce the occurrence of accidents. This paper primarily explores and investigates high-precision positioning-based three-dimensional (3D) spatial safety electronic fence technology for substation construction projects. The process begins with using laser radar to capture 3D scene information and extract key parts of the point cloud model. Comprehensive positioning sensors, integrating the Beidou Navigation Satellite System (BDS) and inertial navigation, are then installed on crane booms to achieve real-time monitoring of the boom’s posture. Finally, a software platform is established to calculate the real-time 3D spatial distance between the crane boom and surrounding sensitive facilities, ensuring the safety of large-scale construction machinery. Field test results validate the effectiveness of the proposed method.

Application and practice of industrial IoT cloud platform in oilfield intelligent transformation

Abstract Under the context of global energy transformation and low-carbon development, the traditional petroleum industry is facing a series of challenges such as technological innovation, market competition, environment protection, etc. Therefore, there is an urgent demand to realize green, safe, economical, and efficient development of oil and gas through oilfield intelligent transformation. This study develops a novel cloud platform covering the whole business based on the technical architecture of the industrial Internet of Things (IoT) for assisting the intelligent transformation of oilfields. Since the application of the IoT-based cloud platform in Qingcheng Oilfield, functions such as automatic collection of whole-domain data, remote monitoring, early warning, as well as intelligent analysis and decision-making have been realized. Furthermore, compared with the traditional platform, the new platform has improved eight indicators, such as data processing efficiency and sharing capacity, by more than 20%. Consequently, the developed IoT-based cloud platform can significantly improve the production and management level of the oilfield, and efficiently support oilfield intelligent transformation.

Personalized Health Management Strategies Based on Deep Reinforcement Learning in the Network Environment

In order to study the optimal personalized motion push target, the author proposes a personalized health managementstrategy based on deep reinforcement learning in the network environment. Firstly, the research problem is defined, and a real-time interactive personalized motion target decision-making model is constructed; Subsequently, in response to the uncertain characteristics of user behavior in the problem, a deep reinforcement learning algorithm was adopted, combined with the departure strategy temporal difference learning method and neural network nonlinear fitting method, in order to learn strategies from user historical data; Finally, the effectiveness of the proposed method was validated using a real dataset from Fitbit. The research results indicate that personalized motion goal push services based on deep reinforcement learning can help users cultivate a healthy lifestyle and improve their personal health management level by analyzing user behavior data in real-time, providing scientific guidance and timely incentives.

Dynamic matching of energy and heat conduction in production workshops and financing for manufacturing enterprises based on the Internet of Things

With the advancement of Industry 4.0, the application of Internet of Things technology in manufacturing enterprises is increasingly extensive, and energy heat transfer, as an important factor affecting production efficiency and cost, needs to be optimized by new technical means. This study explores the energy heat conduction characteristics of production workshops based on the Internet of Things and its impact on the dynamic matching of financing for manufacturing enterprises, providing an innovative energy management model to promote the sustainable development of enterprises. The research uses the Internet of Things technology to monitor the energy flow and heat transfer in the production shop in real time through sensors and data acquisition systems. Combined with big data analytics and machine learning algorithms, in-depth analysis of energy consumption data is performed to identify bottlenecks and optimization points for heat transfer. At the same time, the financing dynamic matching model is constructed to analyze the financing needs and costs of enterprises under different energy management levels. The study found that iot technology can significantly improve the thermal efficiency of production plants and reduce energy waste. On the basis of the optimization of energy heat transfer, the financing demand and financing cost of enterprises have also undergone dynamic changes. The optimization of energy heat transfer based on the Internet of Things not only improves the energy efficiency of the production floor, but also provides a new impetus for the financing of manufacturing enterprises. Implementing an effective energy management strategy will help companies stay ahead of the curve in an increasingly competitive market.

Association Between Pericoronary Fat Attenuation Index as Evaluated by Coronary Artery CT Angiography and Clinical Interventions in Lipid Management Among Patients with Coronary Artery Disease.

This study aims to evaluate the relationship between the pericoronary fat attenuation index (FAI), derived from coronary artery computed tomography angiography, and post-lipid management levels of low-density lipoprotein cholesterol in patients with coronary artery disease (CAD). Additionally, the study investigates coronary inflammation across different lipid management strategies. We selected a cohort comprising 521 CAD patients who met the inclusion criteria. Patients were categorized into well-managed (LDL-C<2.6mmol/L) and poorly managed (LDL-C≥2.6mmol/L) groups based on lipid management efficacy. We collected anthropometric measures (height, weight, body mass index, and body surface area) and clinical indicators, including Gensini score, and FAI-related parameters for coronary atherosclerotic lesions. We analyzed the interrelations along these parameters and lipid management using statistical methods and assessed diagnostic value via receiver operating characteristic (ROC) curve analysis of these parameters was assessed through. The poorly managed group exhibited significantly higher levels of total cholesterol, triglycerides, and lower levels of high-density lipoprotein compared to the well-managed group (P < 0.05). Significant differences were observed between the groups in terms of lesion length in the proximal segment of the left anterior descending artery, FAI value in the proximal segment of lesions in the right coronary artery (RCA), volume thickness in the middle segment of RCA lesions, and lesion length in the distal segment of RCA (P < 0.05). ROC curve analysis revealed areas under the curve ranging from 0.484 to 0.660 for the parameters, indicating limited diagnostic efficacy. The FAI in the RCA varies with lipid management strategies, suggesting it as a valuable metric for monitoring both perivascular inflammation and lipid status in CAD patients. However, its current diagnostic efficacy is limited, indicating the need for further research to improve its clinical utility.

Association of hypertension and long-term blood pressure changes with new-onset diabetes in the elderly: A 10-year cohort study.

To explore the correlation between new-onset diabetes (NOD), hypertension and blood pressure management among elderly individuals in China. A cohort analysis involved 1380 participants aged 60 years or older, initially free of diabetes in 2008, from the Chinese Longitudinal Healthy Longevity Survey. Follow-up assessments occurred every 2-3 years. The relationship between hypertension, blood pressure changes and NOD was analysed using multivariable-adjusted Cox regression. By 2018, 102 participants developed diabetes, while 1278 remained without diabetes. The cumulative diabetes prevalence increased from 3.1% at 3 years to 7.4% at 10 years. Hypertension prevalence increased from 20.9% at baseline to 41.0% at 10 years, with higher rates in those diagnosed with diabetes during follow-up. Multivariate analysis identified age, gender, baseline hypertension and systolic blood pressure (SBP) as independent predictors of NOD. Hypertension combined with overweight/obesity significantly increased the risk of NOD (hazard ratio [HR] 2.837; 95% confidence interval [CI], 1.680-4.792). We evaluated participants' blood pressure management levels in 2008 and 2011, then tracked the onset of diabetes from 2011 to 2018. Compared with participants with an average SBP below 120 mmHg in 2008 and 2011, those with SBP of 140 mmHg or higher had an 8-fold higher risk of developing NOD (adjusted HR8.492, 95% CI 2.048-35.217, P = .003), the highest risk group. Participants with SBP of 130-139.9 mmHg also had a significantly increased risk (adjusted HR 5.065, 95% CI 1.186-21.633, P = .029), while those with SBP of 120-129.9 mmHg showed no significant difference (HR 2.730, 95% CI 0.597-12.481, P = .195). Consistently high SBP (≥ 130 mmHg) further increased NOD risk (adjusted HR 3.464, 95% CI 1.464-8.196, P = .005). Significant predictors of NOD included age, gender, baseline hypertension and blood pressure management. Maintaining SBP consistently below 130 mmHg may be an effective strategy to reduce the incidence of NOD in the general elderly population.

INFORMATION SYSTEM FOR ASSESSING THE LEVEL OF HUMAN CAPITAL MANAGEMENT

The article offers conceptual foundations for formalizing the process of assessing a level of human capital (HC) management at the enterprise using mathematical and computer modeling based on neural network technologies. The methodological approach for assessing the level of human capital management has been improved. This allows the use of neural network tools to identify accurately and reasonably the level of HC management with the help of self-learning multilayer perceptron. The weight coefficients of such a network were calculated. An appropriate artificial neural network – a multilayer perceptron – was built using the mathematical software MatLab and it was successfully diagnosed. The improved mathematical model for assessing the level of HC management at the enterprise makes it possible to display transparently a set of input parameters on a set of output solutions, to decompose such a process, and to simplify the procedure of its formalization. The designed neural network allows us to determine quickly and accurately the level of HC management at the enterprise. The conceptual approach proposed by the authors has several significant advantages over existing alternative methods: accuracy of assessment; taking into account a wide range of various evaluation parameters of impact; high speed of making decisions and self-learning ability. The proposed approach was successfully implemented to assess the level of HC management at 24 domestic enterprises. The information system "HC" developed by the authors allows to calculate the estimated parameters of the evaluation process; to determine the level of HC management based on the mathematical apparatus of the multilayer perceptron. Such estimates correlate with the estimates obtained by the experts of these enterprises which indicates the adequacy of the approach proposed by the authors. Therefore, the proposed information system for assessing the level of management of the HC allows accurate implementation of such a process with minimal time and money costs.

중대재해처벌법 시행에 따른 물류사업장 내 안전의식수준 차이에 대한 연구

Purpose: This study examines whether the rise in logistics safety incidents has led to changes in workers’ safety awareness levels in distribution centers. Research design, data, and methodology: A t-test determined if there were changes in the safety awareness levels of managers and workers, analyzed by year and workplace. Results: The level of safety consciousness in the workplace and the level of safety consciousness in managers and workers according to the year, all showed significant differences. In addition, depending on the type of workplace, only the safety consciousness levels of managers showed significant differences between the groups. Conclusions: This study reviewed empirical analysis of the levels of awareness about logistics safety (in workplaces, managers, and workers) and presents theoretical conclusions and practical implications.

EFFECTIVE MANAGEMENT DECISIONS: METHODS AND TECHNIQUES

The article is devoted to the study of methods and techniques for making and managing effective managerial decisions. It is established that managerial decisions are multi-faceted and varied. Managerial decisions are classified depending on whether they require the development and/or modification of the methods and techniques for their implementation, based on the results of research by scholars. The conclusion is made about the necessity of conducting an in-depth analysis of management methods and techniques for companies to make effective decisions, considering diverse influencing factors on their activities under precedent conditions and circumstances, in the context of adapting to these and subsequently developing theoretical-methodological tools. Attention is focused on the fact that modern business faces constant challenges related to russia's full-scale war against Ukraine, deepening imbalances, and the continuation of negative trends as a result. It is concluded that, under such conditions, decision-making requires extensive application of modern methods and approaches at all levels of management, with appropriate modification and optimization of the decision-making process itself. The activities of LLC "Nova Poshta" are analyzed as a prominent representative of a business that successfully uses innovative approaches to management but must continuously improve and adapt methods and techniques to new challenges for successful market operation. It is determined that LLC "Nova Poshta" serves as an example of a successful combination of innovation and traditional approaches, which contributes and can have a significant impact on ensuring not only the company's viability during russia's full-scale war against Ukraine but also dynamic changes in the post-war market. The conclusion is drawn that effective managerial decision-making is the foundation of successful enterprise functioning.