Efficient System Research Articles

Enhancing Spectrum Sharing Efficiency in Large-Scale MIMO Systems over Integration of Cognitive Radio and Reinforcement Learning

Enhancing Spectrum Sharing Efficiency in Large-Scale MIMO Systems over Integration of Cognitive Radio and Reinforcement Learning

Association between personal characteristics and anxiety and burnout in orthopedic residents: a cross-sectional cross-sectional study

ObjectiveBurnout is becoming a major problem in medicine, and some articles have pointed to the link between burnout and anxiety. Several factors can contribute to burnout and anxiety, and recognizing each can improve medical education environments. The purpose of this study is to determine the relationship between personal characteristics, anxiety and burnout of orthopedic residents.MethodIn this cross-sectional study, the anxiety rate, depression, and burnout were evaluated in 94 orthopedic residents above the second year and orthopedic fellowships in three hospitals. Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and Maslach Burnout Inventory—Human Services Survey (MBI-HSS) were used to measure the levels of depression, anxiety, and burnout, respectively. Multivariate logistic regression analysis was used to evaluate predictor variables for burnout.ResultsSevere anxiety and depression were reported in 17% and 12.77% of residents, respectively. The rate of burnout for Emotional Exhaustion (EE), Depersonalization (DP), and low sense of Personal Accomplishment (PA) subscales was 18.09%, 28.72%, and 43.62% of people with high levels of burnout, respectively. Multivariate analysis of logistic regression showed that Works > 60 h per week, Anxiety score > 28, Time spent in the hospital > 9 h per Day, Academic year and Experience being abused by the upstream, Balance between work and life, Feeling the need for support from nurses in the hospital were significantly associated with burnout.Conclusionstress and anxiety can be considered a severe factor in burnout; this issue can provide a clear perspective of the psychological cycle that ultimately leads to a decrease in the efficiency of the medical system and related services.

How the Inclusion of Pigeon Pea in Beef Cattle Diets Affects CH4 Intensity: An In Vitro Fermentation Assessment

Tropical pastures intercropped with legumes have been gaining prominence for increasing the efficiency of livestock production systems when compared to pasture monocultures. Here, our objective was to understand the fermentation processes that tropical grass and legumes underwent when included in ruminant diets, which have previously been found to optimize animal performance while reducing the intensity of enteric CH4 emissions. For this purpose, three areas containing pigeon pea (Cajanus cajan) and Urochloa spp. were sampled. Samples were dried, grounded, chemically analyzed, and included in five proportions (0%, 25%, 50%, 75%, and 100%) of pigeon pea in the diet. The diets were then analyzed using an in vitro fermentation technique. Statistical analysis was performed using SAS statistical software, considering bottles as replicates, and our results suggest that a 25% inclusion of pigeon pea is optimal for balancing CH4 mitigation and fermentation efficiency, highlighting the importance of more studies with this legume due to its benefits, especially as a supplement during drought periods that impact the production and quality of tropical pastures. It is important to consider that pigeon pea’s secondary compounds may have positively modulated the fermentation process and reduced CH4 emissions. However, excessive legume inclusion can negatively affect digestibility and animal health, impairing animal performance and the sustainability of pasture-based production systems.

Enhancing production efficiency through optimizing plant density in maize–soybean strip intercropping

IntroductionDue to limited arable land resources, intercropping has emerged as an efficient and sustainable production method for increasing total grain yield per unit land area. Maize–soybean strip intercropping (MSSI) technology is being widely promoted and applied across China. However, the combination of optimal density for achieving higher production efficiency of both soybean and maize remains unclear. The objective of this study was to evaluate the differences in yield, economic benefits, land, and nitrogen (N) efficiency in MSSI systems under different densities.MethodsFive maize/soybean density combinations (67,500/97,500 plants ha−1, D1; 67,500/120,000 plants ha−1, D2; 67,500/142,500 plants ha−1, D3; 60,000/142,500 plants ha−1, D4; 52,500/142,500 plants ha−1, D5) were set under the same N input in the field experiment.Results and discussionThe results demonstrated that optimizing the density in the intercropping system could enhance production efficiency. Increasing the density of soybean and maize significantly increased the total grain yield (D3 > D2 > D1 > D4 > D5). The D3 treatment, exhibiting the best comprehensive performance, also promoted increases in leaf area index, dry matter accumulation, and N absorption and utilization. Path analysis indicated that density had the most substantial impact on maize yield, while grain number had the greatest influence on soybean yield, with contribution rates of 49.7% and 61.0%, respectively. These results provide valuable insights into optimal field density for summer planting in MSSI, facilitating its wider adoption.

On the Methodology for Calculating the Economic Efficiency of Energy Storage Systems

On the Methodology for Calculating the Economic Efficiency of Energy Storage Systems

An efficient non-binary QC-LDPC coded modulation scheme for long-haul optical systems

Abstract Coded modulation (CM), which combines channel codes with high-dimensional modulation schemes, is an attractive technique to improve the spectral efficiency in high-speed optical transmission systems. Non-binary low-density parity check (NB LDPC) codes can provide larger coding gains with smaller codeword lengths compared to their binary counterpart, in addition to reducing the latency of the system. The majority of works based on NB LDPC CM focus on bit-interleaved CM (BICM) and the widely used additive white Gaussian noise (AWGN) channel model. However, the Gaussian channel model is not a realistic model for long-haul intensity-modulated direct-detection (IM/DD) optical systems using erbium-doped fiber amplifiers. In this paper, it is mathematically proved that noise in the received signals after photodetection can be more accurately represented using chi-squared distribution. The efficiency of CM depends on the mapping between coded symbols and the constellation signal points of the modulation scheme. Hence, a symbol interleaved CM with subcarrier modulation for NB LDPC codes based on the proposed channel model is presented in this paper to improve the bit-error rate (BER) performance. The simulation results show that the proposed CM for NB LDPC codes with M-ary quadrature amplitude modulation schemes provides better performance than BICM. The analysis of the error performance of the proposed system gives a coding gain of 1 dB at a BER of 10−7 compared to that of the AWGN channel model. The performance improvement of the system is also evaluated in terms of spectral efficiency and link distance.

Technical efficiency analysis of advanced medical centers in Burkina Faso

IntroductionBurkina Faso faces many challenges in the health domain, with no real opportunity for an increase in public health expenditures. In Burkina Faso, as in all low-income countries, health spending efficiency is crucial. The objective of this paper is to assess the efficiency of Advanced Medical Centers (AMCs)—which correspond to district hospitals—in Burkina Faso over the 2017–2020 period and identify the factors that promote—or, on the contrary, limit—the efficiency of these health structures.MethodWe first assessed the efficiency level of the 45 AMCs running in the country between 2017 and 2020 using a bootstrap Data Envelopment Analysis (DEA) methodology. Inputs include the number of doctors, nurses, other medical staff, non-medical staff, and beds, while output variables correspond to the number of inpatients, surgeries, outpatients, and inpatient days. In a second step, determinants of AMC’s efficiency levels were explored using a double-bootstrap procedure. The roles of AMCs’ internal and environmental factors were both considered.ResultsWe found a mean efficiency score of 0.51 over the study period, indicating that AMCs could have almost doubled their healthcare production without needing additional resources. The size, education level, and health status of the covered population and the density of the healthcare supply in the district appeared to be the driving factors of AMCs’ efficiency.ConclusionOur results indicate that improving the efficiency of AMCs should be a high-level priority for the Burkinabe health policy. Resources could be reallocated across AMCs to increase the overall efficiency of the health system.

Employing machine learning for advanced gap imputation in solar power generation databases

This research evaluates the application of advanced machine learning algorithms, specifically Random Forest and Gradient Boosting, for the imputation of missing data in solar energy generation databases and their impact on the size of green hydrogen production systems. The study demonstrates that the Random Forest model notably excels in harnessing solar data to optimize hydrogen production, achieving superior prediction accuracy with mean absolute error (MAE) of 0.0364, mean squared error (MSE) of 0.0097, root mean squared error (RMSE) of 0.0985, and a coefficient of determination (R2) of 0.9779. These metrics surpass those obtained from baseline models including linear regression and recurrent neural networks, highlighting the potential of accurate imputation to significantly enhance the efficiency and output of renewable energy systems. The findings advocate for the integration of robust data imputation methods in the design and operation of photovoltaic systems, contributing to the reliability and sustainability of energy resource management. Furthermore, this research makes significant contributions by showcasing the comparative performance of traditional machine learning models in handling data gaps, emphasizing the practical implications of data imputation on optimizing hydrogen production systems. By providing a detailed analysis and validation of the imputation models, this work offers valuable insights for future advancements in renewable energy technology.

Deep learning-driven channel coding: enhancing performance in LDS-CDMA and NOMA systems

This paper presents an innovative approach to channel coding for LDS-CDMA and NOMA systems through the integration of deep learning techniques. By leveraging neural networks within the channel coding process, the proposed model achieves substantial improvements in error correction, leading to a significant reduction in Bit Error Rate (BER) and computational complexity when compared to traditional methods such as Unpunctured Turbo Trellis Coded Modulation (UTTCM). The results demonstrate that the deep learning-based system is particularly effective in challenging communication environments, such as those with low Signal-to-Noise Ratios (SNR) and high levels of multi-user interference, where it outperforms conventional coding techniques. This research underscores the potential of deep learning to enhance the efficiency and adaptability of wireless communication systems, particularly as networks evolve towards 6G and beyond. The flexibility offered by this approach allows for improved handling of dynamic and complex environments, ensuring higher data throughput and reduced latency. Future work may focus on implementing the proposed model in real-time applications, as well as investigating its synergy with emerging technologies like massive MIMO and NOMA, which could lead to even greater performance enhancements in next-generation communication systems.

Social vulnerability to food insecurity in Sub-Saharan Africa: exploring the connection between climate change, funeral rites and food insecurity in rural Ghana

ABSTRACT Climate Change and food security remain one of the topical issues discussed globally. In Ghana, the literature has reported on the effects of climate change on the food consumption patterns of many households due to low adaptive capacity and inefficient food production systems. In addition to climate change as a threat to food security, funeral rites also play a major role in household food insecurity. However, the link between climate change, funeral rites and food security as indices of social vulnerability has been less investigated. Using a qualitative-dominance approach to research with 8 key informant interviews, 4 focus group discussions (FGDs) and a questionnaire, the analysis revealed that climate change and funeral rites are critical determinants of food insecurity in the Kassena Nankana Municipality of the Upper East Region of Ghana. The results show that while climate change affects the food production systems, the poorly harvested farm produce and livestock are used for the funeral rites of the deceased’s household family. While being sensitive to the cultural aspects of the uses of farm produce and livestock for funeral rites, and given the prevailing climatic conditions, the study recommends the need for traditional authorities and households to consider holding less expensive funeral rites which do not expend future food needs and resources. Again, localised climate change adaptation strategies need to be developed by the Ministry of Food and Agriculture to support the activities of agriculture in the municipality which is widely known to be susceptible to climate change.

Robust cryptographic scheme for reliable data communication in VANET (RCSRC) using clustering approach

In contemporary times, as accident rates continue to rise, there is a growing interest in vehicular ad hoc networks (VANETs) as a means to reduce accidents and vehicle damage, as well as enhance public safety. VANETs facilitate the transmission of messages within predefined areas to ensure system safety and efficiency. However, ensuring message authenticity in such a dynamic environment presents a significant challenge, with previous research focusing less on security aspects. Some previous protocols send plain message, which leads to the compromise of the message confidentiality. Although few algorithms work on security, they involve the communication to the trusted authority, which lead to increased latency. Thus, in this paper we propose a robust cryptographic scheme for reliable data communication in VANET which minimizes the involvement of trusted authority and uses communication ID of vehicle for communication in place of real entities. Security analysis of the proposed scheme is showing the sturdiness of the proposed protocol again various types of safety spasms. The evaluation of its performance includes assessing communication cost and time, revealing the proposed algorithm's efficiency compared to others.

Understanding Solid Oxide Fuel Cell Hybridization: A Critical Review

A holistic literature review of 399 articles is presented on hybrid solid oxide fuel cell (SOFC) systems and taxonomized through a unique classification methodology that considers primary energy sources, component configurations, system outputs and relevant analysis. In the reviewed research articles, primarily four different types of technologies are used to complement the energy production of the fuel cell stack. As a result, four respective categories were established in terms of the coupling components, inclusive of (1) wind turbines (WTs), (2) solar systems (SS), (3) thermal power plant turbines (TPPTs) and (4) piston engines (PEs). Additional classification was needed to further subdivide the evaluated system configurations, due to the wide spectrum of system outputs encountered in the reviewed literature. That involved (a) electrical power generation (PG), (b) four types of cogeneration (CG), (c) four types of trigeneration (TG) and (d) two types of multigeneration (MG). Depending on the type of assessment in respective research, literature was further categorized as computational, experimental, or both computational and experimental. The most prevalent hybrid system amongst the 102 designs, as adapted from the reviewed literature, is the SOFC/gas turbine (GT) application, with the overwhelming majority producing exclusively electrical power output. The particular system is physically demonstrated in an existing 220-kW experimental facility established by the National Fuel Cell Research Center (NFCRC). The identified systems were predominantly computationally examined and research focused on operating conditions around specific design load ratings. Energy, exergy, economic, and environmental assessments were typically followed by sensitivity studies in the reviewed literature. The primary decision variables in most cases revolved around SOFC operating conditions, such as temperatures over 1000 °C as well as elevated pressures. Those resulted in the deterioration of the electrochemical cell and failed to be addressed by the majority of encountered studies. Improvements in the conversion efficiency of PG applications were enabled by natural gas (NG)-fed pressurized SOFCs, directly coupled to GTs. The most efficient power configuration in terms of thermodynamic performance was achieved by coupling PEs to SOFCs and a metal hydride reactor (MHR) to unlock a maximum net system efficiency of 79.54%. CG systems integrating solar cells (SCs) and photovoltaic panels (PVs) to SOFC subsystems can result in a combined efficiency of approximately 90% and sustain this over a broad energy demand range. TG systems incorporating gasifiers to SOFC/GT subsystems achieved a combined efficiency of 93%, whereas the optimal combined efficiency in MG configurations was identified at 79.5% by coupling SCs to the SOFC/GT/organic Rankine cycle turbine (ORCT) subsystems. In terms of cost-effectiveness, configurations that relied on increasing levels of renewable energy (RE) technologies resulted in high overall levelized cost of electricity (LCOE). This cost was increased further when power storage units were deployed to address wind and solar intermittency challenges. Configurations classified under TPPTs operating on low carbon-content fuels were preferred in terms of providing lower LCOE, while applications operating on gasified biomass and coal blends should deploy additional CO2 capture and storage units to mitigate respective emissions.

Insights into turbulent airflow structures in blind headings under different ventilation duct distances

The paper explores the 3D stationary vortex structure of turbulent airflow near the dead-end face of a blind heading, ventilated via a forcing ventilation system. Despite its significance in blind heading ventilation, previous studies primarily focused on temporal dynamics of harmful impurities, overlooking flow structure details crucial for mass transfer processes. Our study delves into the ventilation flow structure across diverse parameters of the auxiliary ventilation system. Alongside standard flow visualization tools, we introduce three dimensionless indicators to comprehensively characterize flow structure, facilitating analysis of its variations with parameter changes and quantitative evaluation of system efficiency. Analysis revealed the formation of a single large-scale vortex within the entire range of considered ventilation system parameters in the heading. This vortex induces a significant increase in air circulation, approximately 2.5–3.5 times greater than airflow emerging from the ventilation duct’s end, thus intensifying mass transfer processes within the heading. We found that ventilation efficiency of the dead-end face zone in a blind heading with a 29.2 m² cross-section decreases linearly with increasing distance between the ventilation duct’s end and the dead-end face. However, compensating for this distance by increasing duct velocity is feasible, bearing significant implications for mine ventilation safety, particularly in ventilating blind headings with distant ducts from the dead-end face.

Distributed Secondary Control of DC Microgrid with Power Management Based on Time-of-Use Pricing and Internal Price Rate

This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability, fluctuating electricity prices, battery state of charge (SOC) levels, and frequent plug-ins and plug-outs of electric vehicles (EVs). Uncertainties in DCMG systems often lead to inefficiencies, power imbalances, and inexact voltage regulation issues within DCMGs. In addition, to maintain the power balance and constant voltage regulation under various operational states, the proposed scheme also incorporates secondary control into the DCMG power-management system. Unlike the existing approaches that often fail to adapt dynamically to changing conditions, the proposed method is the first approach to consider the concept of internal price rate in designing the DCMG power management. To address this challenge, this approach proposes a more resilient power-management strategy to enhance the efficiency and adaptability of DCMG systems. Extensive simulations and experimental validations demonstrate the practicality and adaptability of the proposed control strategy under diverse test conditions, including operation transitions between grid-connected mode (GCM) and islanded mode (IM), low battery SOC condition, operation transition from the current control mode (CCM) to distributed secondary control mode (DSCM), and EV plug-in scenarios. The test results confirm that the proposed method enhances the reliability, efficiency, and economic viability of DCMG systems, making it a promising solution for future smart grid and renewable energy integrations.

Effects of electrode/electrolyte materials on heavy-metal removal in industrial wastewater with flow capacitive deionization method

The expansion of industrial facilities poses a significant environmental risk due to the generation of extensive wastewater containing complex components that effluent human health. Flow-electrode Capacitive Deionization (FCDI) technology has emerged as a promising solution for wastewater treatment due to its unlimited adsorption capacity and continuous long-term operations compared to conventional systems. This study investigates the efficiency of the FCDI system in purifying industrial wastewater by extracting complex heavy metal ions, intending to achieve complete recycling and zero discharge. Different electrolyte/electrode materials were investigated to achieve high removal efficiency, including electrolytes such as sodium chloride (NaCl) and sodium sulfate (Na2SO4), and electrodes such as active carbon (AC) and C65 carbon black (C65 CB) used in lithium-ion batteries. We found that the innovative combination of Na2SO4 electrolyte with 0.1 g of C65 CB exhibited an exceptional salt removal efficiency of 90 %, surpassing NaCl electrolyte which showed 68 % efficiency. Additionally, we observed higher adsorption with C65 CB slurry compared to AC when using Na2SO4 electrolyte. To deepen our insights, Molecular Dynamics (MD) simulations were conducted to explore heavy metal ions interactions with electrode/electrolyte and membrane materials under an electric field. The results indicated that Ni2+ ions had the highest passage rate of 80 % across the membrane, followed by Cr2+, Cu2+, and Zn2+. Furthermore, superior adsorption of Ni2+ ions onto the C65 CB electrode was noted, contributing to enhanced charge transfer and overall conductivity. Furthermore, we evaluated the economic and environmental implications associated with implementing the FCDI system in practical. This study presents a novel approach by using C65 carbon black (CB) with Na2SO4 as the electrolyte, indicating high adsorption efficiency at low carbon loading electrode. This combination offers a cost-effective and sustainable solution for improving complex heavy metal ion removal in FCDI systems. Overall, our study’s findings significantly contribute to offering crucial insights into optimizing FCDI technology for efficient heavy metal ions removal in industrial wastewater treatment using lithium-ion battery anode/cathode electrode material.

Mimicomes: Mimicking Multienzyme System by ArtificialDesign.

Enzymes are widely distributed in organelles of cells, which are capable of carrying out specific catalytic reactions. In general, several enzymes collaborate to facilitate complex reactions and engage in vital biochemical processes within cells, which are also called cascade systems. The cascade systems are highly efficient, and their dysfunction is associated with a multitude of endogenous diseases. The advent of nanotechnology makes it possible to mimic these cascade systems in nature and realize partial functions of natural biological processes both in vitro and in vivo. To emphasize the significance of artificial cascade systems, mimicomes is first proposed, a new concept that refers to the artificial cascade catalytic systems. Typically, mimicomes are able to mimic specific natural biochemical catalytic processes or facilitate the overall catalytic efficiency of cascade systems. Subsequently, the evolution and development of different types of mimicomes in recent decades are elucidated exhaustedly, from the natural enzyme-based mimicomes (immobilized enzyme and vesicle mimicomes) to the nanozyme-based mimicomes and enzyme-nanozyme hybrid mimicomes. In conclusion, the remaining challenges in the design of multifunctional mimicomes and their potential applications are summarized, offering insights into their future prospects.

Comparative assessment of the epidemic state of tuberculosis before and during the war in Ukraine

BACKGROUND. The war has a negative impact on patient care and the efficiency of the health care system; access to medical care is limited, coverage of preventive examinations and detection of tuberculosis (TB) are deteriorating. There is a threat of exacerbation of the epidemic situation with TB in Ukraine. OBJECTIVE. To assess the epidemic situation with TB during the war in Ukraine. MATERIALS AND METHODS. Comparison of data from the Ministry of health on TB (epidemiological indicators and indicators on detection, diagnosis and prevention) in the pre-war (2018-2021) and war (2022-2023) periods. RESULTS. The following were revealed: 1) a positive trend towards a decrease in epidemiological indicators of TB in 2018-2020, which slowed down in 2021 due to the COVID-19 pandemic, and a negative trend towards an increase in almost all indicators of TB incidence, separate indicators of mortality from TB and its prevalence in 2022-2023; 2) significant increase in the incidence of TB among contacts and children; 3) increase by ⅓-. of mortality from TB in certain regions; 4) correlation between regional rates of morbidity, mortality and prevalence of TB. In 2018-2023, the following decreased: fluorography examination, tuberculin diagnostics, detection of TB during preventive examinations (by 2-4 times), preventive vaccinations for children of the first year of life, detection of newly diagnosed and recurrent TB by smear, preventive treatment in contacts. CONCLUSIONS. Incidence is a marker of response to negative external factors and the main criterion for assessing the epidemic state of TB during the war. Fluorography examination, tuberculin diagnostics, detection of TB during professional examinations, preventive vaccinations for children of the first year of life, detection of newly diagnosed and recurrent TB by smear, preventive treatment of contacts are the main criteria for evaluating the activity of the anti-TB service during the wartime, since they are the first to significantly respond to the challenges of war.

Finite Element Analysis and Computational Fluid Dynamics for the Flow Control of a Non-Return Multi-Door Reflux Valve

This paper presents a comprehensive analysis of a multi-door check valve using computational fluid dynamics (CFD) and finite element analysis (FEA) to evaluate flow performance under pressure test conditions, with an emphasis on its ability to prevent backflow. Check valves are essential components in various industries, ensuring fluid flow in one direction only while preventing reverse flow. The non-return multi-door reflux valve is increasingly preferred due to its superior backflow prevention, fluid control, and effective flow regulation. Rigorous testing under varying pressure conditions is essential to ensure that these valves perform optimally. This study uses CFD and FEA simulations to evaluate the structural integrity and flow characteristics of the valve, including pressure drop, flow velocity, backflow prevention effectiveness, and flow coefficient. A high-fidelity 3D model was created to simulate the valve’s behavior under various conditions, analyzing the effects of parameters such as the number of doors, their orientation, geometry, and operating conditions. The CFD results demonstrated a significant reduction in backflow and pressure drop across the valve. However, localized turbulence and flow separation near the valve doors, particularly under partially open conditions, have raised concerns about potential wear. The velocity profiles indicated a uniform distribution at full opening with laminar velocity profiles and minimal resistance to flow. The results of the FEA showed that the stresses induced by the fluid forces were below critical levels, with the highest stress concentrations observed around the hinge points of the valve doors. Although the valve structure remained intact under normal operating conditions, some areas may have required reinforcement to ensure long-term durability. Combined CFD and FEA analyses demonstrated that the valve effectively preserves system integrity, prevents backflow, and maintains consistent performance under various pressure and flow conditions. These findings provide valuable insights into design improvements, performance optimization, and enhancing the efficiency and reliability of reflux valve systems in industrial applications.

Research on wind turbines preventive maintenance strategies based on reliability and cost-effectiveness ratio

Purpose The purpose of this study is to optimize the operational efficiency of the entire system by developing a reasonable maintenance strategy for wind turbines that improves component reliability and safety while reducing maintenance costs. Design/methodology/approach A hybrid incomplete preventive maintenance (PM) model based on boundary intensity process is established to give dynamic PM intervals for wind turbines using an iterative method with reliability as a constraint; the selection method of PM and replacement is given based on the cost-effectiveness ratio, which in turn determines the optimal number of PM for wind turbines. Findings The reliability is used to obtain the components’ maintenance cycle, and the cost-effectiveness ratio is used to select the number of maintenance times, thus, getting the optimal maintenance strategy. The validity of this paper’s method is verified by arithmetic cases, which provides a new method for formulating a reasonable PM strategy for wind turbines. Practical implications The wind turbine preventive maintenance strategy for Boundary intensity process proposed in this paper can scientifically formulate the maintenance strategy, optimize the cost-effectiveness per unit of time of the wind power generation system, and solve the problems of difficulty in formulating a reasonable maintenance strategy for the wind turbine components and high operation and maintenance costs. Originality/value In this paper, the authors describe the failure pattern by a Boundary intensity process, establish a hybrid incomplete PM model by introducing a failure intensity increment factor and an age reduction factor and establish a maintenance strategy optimization model with comprehensive consideration of reliability and cost-effectiveness ratio. Finally, the validity of the model in this paper is verified by arithmetic case analysis. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0153/

Оподаткування і фіскальна соціологія: фінансова думка Заходу та уроки для України

Introduction. In the process of fair interaction between authorities and taxpayers, a relationship of trust is created. This trust ensures support for government fiscal initiatives from the business sector. Conversely, if the relationship is not transparent and equitable, it can lead to a passive or even negative attitude towards taxes, thereby reducing the efficiency of the tax system. Problem Statement. There is a gap in Ukrainian fiscal theory, which is still underdeveloped in terms of fiscal sociology. Compared to Western countries, where fiscal sociology has already become an integral part of economic analysis, in Ukraine this issue has been studied only by a narrow circle of scholars. This limitation necessitates a deeper study and integration of sociological approaches into financial science, because without it, Ukrainian researchers and policy makers lose the opportunity to gain a more complete understanding of the interaction between economic and social processes. The purpose is to substantiate the directions of integration of fiscal sociology into Ukrainian financial theory and to develop recommendations for its inclusion into Ukrainian financial practice. Methods. The methods used are systematic, structural-functional, comparative analysis, analogy, grouping, description, comparison and generalization. Results. It is proved that by integrating fiscal sociology, Ukrainian fiscal theory can benefit from a more nuanced understanding of the socio-economic factors influencing fiscal policy. Such integration could lead to more effective policy making and a deeper understanding of the economic challenges facing Ukraine. Conclusions. Ukrainian academics and policymakers should prioritize the interdisciplinary study of fiscal policy, taking into consideration the rich tradition of fiscal sociology in the West. Joint efforts should be made to develop tax policies that strengthen state legitimacy by addressing issues of fairness, transparency, and trust. Strategies for managing public debt and improving tax morale should be a central element of fiscal policy to ensure that Ukraine's financial system can withstand socioeconomic pressures and contribute to long-term stability and development.