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Purpose -- European paper industry has been struggling with margins and profitability for more than decade time period. At typical in markets of west, paper product demand is at long-term decline, mostly driven by continuously increasing internet use. However, in emerging markets demand still exists, and in Europe numerous small markets in east have even some growth available. The paper aims to discuss these issues. Design/methodology/approach -- The authors analyse in this research work with longitudinal data (period of 2002-2009) from one large Finnish paper mill and data envelopment analysis (DEA) approach, how distribution efficiency to selected eight East European markets has evolved. Findings -- In general distribution efficiency has improved, but this has taken place in step-wise manner rather than being linear year-to-year development (year 2006 found to be the threshold). Reason is mostly in better management of transportation costs, and in particular lower monthly deviation of these costs. It is surprising that case paper mill has been able to manage transportation costs in rapidly increasing energy cost environment so efficiently. Maybe European Union enlargement of 2004 and 2007 has had its effects on distribution efficiency. Research limitations/implications -- The research is limited to the deliveries of one paper mill located in Finland. Also East European markets in the early periods of this study were emerging papers markets, and distribution practices were clearly evolving. Practical implications -- Based on the study East European paper market distribution should give more attention on transportation cost control, and trying to find solutions to minimize it with low monthly fluctuation. Originality/value -- Very few studies exist from East European distribution issues, and particularly that of paper industry. Also used quantitative method of DEA is relatively new in this context and gives valuable insights for the distribution efficiency development. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is an effective way to address the issues of economic development, energy saving and emissions reduction. For any important industries it is therefore necessary to measure energy efficiency and set a practical target for it. In this paper, we use CCR, SBM and energy intensity to measure the energy efficiency of the paper industries of 22 EU countries. Results indicate that the SBM and CCR efficiency value is more meaningful for policy makers than that of energy intensity, as measurement results of energy intensity deviate from reality and economic efficiency. The CCR and SBM have roughly the same fluctuation trends and the average SBM energy efficiency value is 0.71, always 10 percent lower than CCR model, as it takes simultaneous account of both the optimal input-output and has more discriminatory power in efficiency measurement. Furthermore, EU policy makers could improve energy efficiency by raising energy prices. As for the 2030 EU target of energy saving and emission reduction, the EU should pay more attention to five major paper producers: Finland, Sweden, Germany, the United Kingdom and Italy. [ABSTRACT FROM AUTHOR]

The accurate evaluation of green innovation efficiency is a critical prerequisite for enterprises to achieve sustainable development goals and improve environmental performance and economic efficiency. This paper evaluates the green innovation efficiency of 72 new-energy enterprises by using a hybrid method of Data Envelopment Analysis (DEA) and a random forest model. The non-parametric DEA model is combined with the parametric SFA model to analyze the real green innovation efficiency on the basis of removing environmental factors and random factors. Then, the random forest model based on a nonlinear relationship is used to evaluate factors impacting green innovation efficiency. This paper proposes a comprehensive evaluation method designed to assess the green innovation efficiency of new-energy enterprises. By applying this method, companies can gain a comprehensive understanding of the current performance in green innovation, facilitating informed decision-making and accelerating sustainable development. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to test the aggregates chosen for an efficient application of granulated chemical fertilisers of different types during the optimal period, as well as to reduce the time and costs required for the agricultural work performed. The experiments were carried out within the IF Raicsics Mihailo Dragan Farm in the Commune of Sânpetru Mare, Timiș County, Romania. Depending on the capacity of the fertiliser hoppers of these machines, we chose the tractor with which to work in the aggregate. Obtaining high-quality crops at the lowest possible costs can only be achieved through the application of specific, perfected technologies, which allow a superior utilisation of both natural resources and inputs. Therefore, comparing the fertiliser machines studied, we notice a difference in terms of direct costs due to the technological characteristics of each one. That is why it is very important to calculate the economic efficiency of the types of aggregates chosen for different agricultural works. [ABSTRACT FROM AUTHOR]

Internal audit has become globally recognized as a vital component of corporate governance, contributing to transparency, accountability, and organizational performance. The aim of the study is to provide an understanding of developments and trends related to internal audit at an interdisciplinary level, thus bibliometric analysis was used to examine 648 scientific articles, published between 1990 and 2022, which had the keyword "internal audit effectiveness". The study suggests that continued research and development in this area is essential, given the constant changes in the business environment and the need to continuously improve the quality, effectiveness and performance of internal audit. [ABSTRACT FROM AUTHOR]

This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective. [ABSTRACT FROM AUTHOR]

With the development of distributed generation and power electronics technology, DC microgrids have made certain progress with their advantages of simple structure, economic efficiency, and easy control. Considering factors such as the geographical location of some residential areas, microgrids with fuel cells that can operate independently are also receiving increasing attention. In this paper, an online control method named virtual hydrogen consumption is proposed based on an independent DC microgrid which solves the problem of electro‐hydrogen conversion successfully. By solving the overall consumption function, the analytical solution is further obtained to realize the online control. Based on the results of RT‐LAB platform, the proposed method can maintain the output of fuel cell system in its high efficiency range with a lower volatility. [ABSTRACT FROM AUTHOR]

Mariculture has not fundamentally reduced its dependence on resources and the environment; ecological and economic efficiency has become an important target of concern for the sustainable and healthy development of China's mariculture industry. Environmental regulation (ER) is a core tool to realize the synergistic development of economic development and environmental resource protection in mariculture. In this paper, the provincial panel data of China's coastal areas from 2007 to 2020 was selected, and the level of ecological and economic efficiency of mariculture in different provinces of China was measured by using the super slacks‐based measurement–data envelopment analysis model considering the undesirable output of mariculture, and the impact of different ERs on ecological and economic efficiency was explored. It was found that the impacts of different ER methods on the eco‐economic efficiency (EEF) of mariculture may be non‐linearized, and there is also regional heterogeneity in the optimal choice and combination of ER methods to improve the EEF of mariculture. [ABSTRACT FROM AUTHOR]

The potato is a key crop in addressing global hunger, and deep learning is at the core of smart agriculture. Applying deep learning (e.g., YOLO series, ResNet, CNN, LSTM, etc.) in potato production can enhance both yield and economic efficiency. Therefore, researching efficient deep learning models for potato production is of great importance. Common application areas for deep learning in the potato production chain, aimed at improving yield, include pest and disease detection and diagnosis, plant health status monitoring, yield prediction and product quality detection, irrigation strategies, fertilization management, and price forecasting. The main objective of this review is to compile the research progress of deep learning in various processes of potato production and to provide direction for future research. Specifically, this paper categorizes the applications of deep learning in potato production into four types, thereby discussing and introducing the advantages and disadvantages of deep learning in the aforementioned fields, and it discusses future research directions. This paper provides an overview of deep learning and describes its current applications in various stages of the potato production chain. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present an analysis of the economic efficiency of sunflower production in the pedoclimatic conditions specific to the outskirts of the city of Timișoara different technological options in the period 2020-2022. The experiment was trifactorial (of the 4 x 3 x 3 type), with 3 factors: D - density; F - fertility; T - technology. Ten plants were chosen from each plot and a series of measurements were made. Based on the values obtained, descriptive statistical indices related to the different technological factors were calculated, namely: arithmetic mean, error of the mean and coefficient of variation. The results showed that the profit had a variation between 849 RON/ha in the case of plants grown at a density of 49,261 g.g./ha on unfertilized agrofund prepared by scarification + ploughing and 1,990 RON/ha for plants grown at a density of 59,524 g.g./ha on the agricultural land fertilized with N50P50K0 and prepared by ploughing. In 55% of cases, the technological variants produced a profit of over 1,500 RON/ha. The research related presented in this paper concerns useful information that will allow superior, high-quality, and sustainable productions under the conditions of the current climate changes. [ABSTRACT FROM AUTHOR]

The development of alternative soil tillage technologies, which ensure the conservation and maintenance of its productive capacity as well as the reduction of energy consumption per unit of processed area, is today a necessity in developing a sustainable agriculture. The study presented in this paper was carried out in the area of Oravița, Caraş-Severin County, Romania, and refers to the minimum tillage technology in wheat culture. The study points to the unfavourable effect of intensive soil works -- classic (conventional) system -- on soil erosion. This has led to the development of some research oriented towards new technologies of soil working in the conservation system. The purpose of this paper is to promote the introduction of alternative technologies in agricultural works, by dealing with the main technical, economic and environmental aspects that compete for the achievement of efficient agriculture. From an economic point of view, this method is less expensive because fuel consumption and machine working time are reduced. The use of modern mechanization technologies has a great economic efficiency due to the following aspects. [ABSTRACT FROM AUTHOR]

The snow-shaped distribution network (SDN) is a cable distribution network composed of eight (or six) 10 kV feeders from four (or three) substations in a regular connection. Compared with the traditional 10 kV distribution network, SDN can support a wide range of load transfer among six or eight feeders. Aiming at the problem of load spatial-temporal unbalanced condition caused by the integration of distributed generators (DGs) and different load types in different feeders, this paper proposes a dynamic reconfiguration strategy for load balancing in SDN considering DGs and energy storage system (ESS). Firstly, the basic structure of SDN is analyzed and the power flow model for its dynamic reconfiguration is developed. Secondly, the dynamic reconfiguration optimization model for load balancing in SDN considering DGs and ESS is proposed to utilize the load transfer capability to mitigate the load unbalanced condition and reduce active power loss. Thirdly, the original nonconvex model is converted into a mixed-integer second-order cone programming (MISOCP) model by applying the second-order cone relaxation and the big-M method, which is solved by CPLEX solver. Finally, the effectiveness of the proposed model and method are verified by an actual case in Tianjin and IEEE 33-node system. The analysis results show that the proposed method can significantly alleviate the load unbalanced spatial-temporal distribution and improve the economic efficiency by regulating the operation of SDN including ESS optimization and dynamic reconfiguration. [ABSTRACT FROM AUTHOR]

This paper proposes a supplementary design method for a three‐power source hybrid power transmission system based on the lever method, aimed at enhancing its power performance and economic efficiency. It makes up for some defects of traditional configuration design methods. Firstly, in order to better show the connection between the two planetary rows in the composite distribution configuration, an equivalent lever model with four nodes was established. The rod model was screened based on the connection relationship between the mechanical point distribution and power source. The rod model was analyzed in combination with the motor speed and torque characteristics, and the value interval of the two mechanical points of the composite distribution rod model was obtained. Then, the four‐node rod model was split. By adding the clutch and brake to control the connection between different nodes of the planetary bank, the working mode of the configuration can be switched. The supplementary design was carried out by increasing the input distribution to compensate for the low efficiency of the composite distribution configuration when driving at low speed, and three optimal configurations were obtained. (Based on the constraints of factors such as rod length, there are finally three solutions that meet the requirements.) Finally, the vehicle dynamics model was established, and the simulation results based on the dynamic programming principle showed that compared with the typical three‐power configuration Prius+, the fuel economy of the optimal configuration was improved in different degrees. Configuration (II) was selected as the object for parameter optimization, and the results showed that the power performance of the vehicle is improved by 1.8%, and the economy is improved by 5.47%, which verifies the validity of the configuration design method proposed in this paper. [ABSTRACT FROM AUTHOR]

The present paper presents a statistical analysis of the Gross Domestic Product/person, at the national level, in relation to the member countries of the European Union, using statistical data published by Eurostat, Naational Institute of Statistics (INSSE), FAOSTAT and the World Bank. Based on the data centralization, calculations were made to determine some absolute and relative statistical indicators, such as the absolute change, the dynamic index or the dynamic rate. The purpose of this paper is to analyze and observe GDP per inhabitant evolution, in the period 2017 - 2022, as well as the hierarchy of European member countries, according to this macroeconomic indicator. Going through the work, it can be observed that the order of each member state in different hierarchies based on the GDP level, nominal or real, differs, depending on the perspective from which the data is analyzed. Not always a country with a high level of GDP is characterized by a higher level of well-being of the population. Also, a country that has not yet reached economic maturity in terms of GDP level, may have advantages over the already mature European economic markets over a longer period of time. [ABSTRACT FROM AUTHOR]

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Maize, one of the most popular cereal, originates from the Central America and is very versatile, being used in various fields. It is used in food, as feed and in industry. The purpose of the paper regards the choice of hybrids which is one of the most important factors influencing the productivity and quality of the maize. The area where the crop is located influences the choice of the most suitable hybrid, as it must be selected according to the average temperature during the growing period and the type of soil. In this study, the experiments were carried out in the climate and soil conditions of Moara Domneasca Research Station. To find the right hybrid, several hybrids were selected to grow and later to compare their harvest and choose the optimal variant. To obtain the highest production of maize, the chosen hybrid should be also as resistant as possible to extreme weather conditions, such as periods of drought grown in the Romanian Plain, on a specific soil and fertilized with different doses of chemical fertilizers. The economic efficiency of the applied fertilization system was also analyzed. As a conclusion, the two hybrids tested reacted positively to chemical fertilization. The SC4140 hybrid behaved better in the non-fertilized version, achieving higher yields than the P9903 hybrid. [ABSTRACT FROM AUTHOR]

The paper focuses on the presentation of key results of the project of applied research oriented on evaluation of the increased safety and reliability of the railway network due to the implementation of relevant railway network projects and their projection into the investment project socio-economic evaluation using Cost-Benefit Analysis. The subject research described in the article is the design and verification of the functionality of the methodological procedure for the evaluation of these benefits for their further use in the assessment of public projects on railways. The methodological part summarizes the basic outputs of the previous research. These outputs were subsequently applied at the formulation of basic rules for the assessment of benefits connected with increased railway reliability and safety, which were later processed into the form of simplified methodological procedure. The simplified approach uses average data on the occurrence impact for the entire railway network, broken down into line and station sections. The functionality of individual approaches was checked on case studies of actually implemented projects. [ABSTRACT FROM AUTHOR]

Regional Integrated Energy Systems (RIESs) and Shared Energy Storage Systems (SESSs) have significant advantages in improving energy utilization efficiency. However, establishing a coordinated optimization strategy between RIESs and SESSs is an urgent problem to be solved. This paper constructs an operational framework for RIESs considering the participation of SESSs. It analyzes the game relationships between various entities based on the dual role of energy storage stations as both energy consumers and suppliers, and it establishes optimization models for each stakeholder. Finally, the improved Differential Evolution Algorithm (JADE) combined with the Gurobi solver is employed on the MATLAB 2021a platform to solve the cases, verifying that the proposed strategy can enhance the investment willingness of energy storage developers, balance the interests among the Integrated Energy Operator (IEO), Energy Storage Operator (ESO) and the user, and improve the overall economic efficiency of RIESs. [ABSTRACT FROM AUTHOR]

As distributed generation (DG) becomes increasingly integrated into the distribution grid, the structure of the distribution network is becoming more complex. To enhance the safety and cost-effectiveness of distribution systems, distribution network reconfiguration is gaining significant importance. Achieving optimal distribution network reconfiguration entails two key considerations: A feasible topology and economic efficiency. This paper addresses these challenges by introducing a novel approach that combines the potential island detection in undirected-graphs and the application of a whale optimization algorithm (WOA) for network reconfiguration optimization. To begin, we identified island categories based on the type of switchable-branches connected to these islands, allowing for the construction of potential island groups. Subsequently, unfeasible topologies were eliminated based on the conditions under which islands form within these potential island groups. Feasible topologies were then used to construct a model for network reconfiguration optimization. The optimal distribution network topology is determined using the WOA. In the final phase, the proposed method's effectiveness was demonstrated through a case study on the IEEE-33 node distribution network under scenarios with and without DG integration. The results showed that the proposed method exhibited better performance than traditional approaches in distribution network reconfiguration. [ABSTRACT FROM AUTHOR]

Under the premise of ensuring the safe and stable operation of a wind–thermal–storage power system, this paper proposes an optimization model aimed at improving its overall economic efficiency and effectively reducing the peak-to-valley load difference. The model transforms the multi-objective optimization problem to solve a feasible interval of weight coefficients. We introduce a novel fusion model, where a Convolutional Neural Network (CNN) is melded with a Long Short-Term Memory Network (LSTM) to form the target network structure. Additionally, for datasets with limited samples, we incorporate a Self-Attention Mechanism (SAM) into the Model-Agnostic Meta-Learning (MAML). Ultimately, we build an MAML-SAM-CNN-LSTM network model to solve the interval of weight coefficients. An arithmetic validation of a modified IEEE 30-node system demonstrates that the MAML-SAM-CNN-LSTM network proposed in this paper can adeptly solve the feasible intervals of weight coefficients in the optimization model of the wind-thermal storage system. This is achieved under the constraints of the specified wind-thermal storage power system operation indexes. The evaluation indexes of the network model, including its accuracy, precision, recall, and F1 score, all exceed 98.72%, 98.57%, 98.30%, and 98.57%, respectively. This denotes a superior performance compared to the other three network models, offering an effective reference for optimizing decision-making and facilitating the enhanced realization of multi-objective, on-demand scheduling in the wind-thermal storage power system. [ABSTRACT FROM AUTHOR]

A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, which is a crucial consideration for the emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication links. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyber-attacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control as well as modern probabilistic and data-driven approaches for modeling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids. [ABSTRACT FROM AUTHOR]

The transformer is a fundamental piece of equipment for power grids. The analysis and optimization of their life-cycle costs are of great importance to reinforce the economic efficiency of electrical networks. This paper constructs a comprehensive transformer life-cycle cost (LCC) model by fusing life-cycle cost theory with relevant transformer expenditure. It proceeds to examine the life-cycle cost aspects of the transformer, delving into its cost dynamics under various influencing factors, establishing interconnections between these factors and analyzing the cost relationship. By employing MATLAB software (Matlab 2021a) along with the whale optimization algorithm (WOA) this paper optimizes the objective function. Through this, it establishes the LCC model for 20 power transformers, obtaining the optimal objective function curve and the maximum value for LCC optimization of the transformer. Unlike previous research, this study adds a detailed analysis of several factors that influence LCC. At the same time, it develops a more complete, scientific and rational LCC optimization model. An illustrative example validates the model and the superiority of the whale optimization algorithm. The algorithm not only shows its scientific basis and superiority, but also serves as a guiding mechanism for LCC management in transformer engineering practices. Ultimately, it emerges as a fundamental tool to improve the efficiency of power grid asset management. [ABSTRACT FROM AUTHOR]

Digital economy has become the primary motivation for high-quality economic development. Upon comprehensively analyzing the connotation of digital economy, this paper constructs an evaluation system from development environment, digital industrialization, industrial digitalization, and digital governance to measure China’s digital economy index from 2016 to 2018 using the entropy-based TOPSIS method. Also, based on the improved Feder dual sector model, this paper discusses the theoretical mechanism of digital economy promoting high-quality economic development directly and through improving economic efficiency and economic structure, and carries out an empirical test. According to findings, on the one hand, China is scaling up digital economy and accelerating digital transformation; on the other hand, the greater the likelihood that high-quality economic development is constrained by low economic efficiency and unreasonable economic structure, the greater the role of digital economy in promoting high-quality economic development. therefore, this paper puts forward recommendations for China’s high-quality economic development, such as advancing new infrastructure construction, developing digital economy, improving economic efficiency and economic structure, and transforming the government governance model. [ABSTRACT FROM AUTHOR]

Sturgeon farming poses an interesting decision: give priority to highly prized caviar or meat. Indeed, sturgeons are mainly reared in aquaculture to produce caviar. However, the economic profitability of a sturgeon farm also depends on the existence of a meat market. Meat is still an important product for sturgeon farmers once the stock of caviar-producing females has been established. This paper takes a case study approach and analyses how an Italian producer addresses the trade-off, adopting a methodology that measures the value of the economic productivity of production inputs, e.g., the measure of the profitability of the inputs, and compares them with the costs of the inputs and the prices of the outputs. This analysis is the first attempt, to our knowledge, to adopt an economic analysis to address this choice problem, based on a very representative case study. This helps monitor whether the farm's production choices are economically efficient and effective and whether and how those can be improved. The results show that the farm maximizes profit but does not reach its optimal production level. This implies that both (1) producing more output and (2) a further switch to caviar production for less meat are economically efficient production strategies. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our bidirectional model outperforms state-of-the-art conventional unidirectional models, providing a more reliable tool for short and medium-term load forecasting. This research highlights the importance of bidirectional context in time-series forecasting and its practical implications for grid stability, economic efficiency, and resource planning. [ABSTRACT FROM AUTHOR]

This paper investigates the structural performance of novel steel-plate concrete containment structures, focusing on third-generation nuclear power plants. To address the challenges of increased complexities and costs associated with double-layer containment designs, this study explores the potential of steel-plate concrete structures to enhance safety, economic efficiency, and construction simplicity. The steel-plate concrete structure, characterized by its core concrete and dual steel plates, shows superior compressive strength, bending resistance, and elastoplasticity. Extensive numerical analyses, including finite element modeling and thermal-stress coupling, were conducted under various load conditions. Under structural integrity test conditions, the maximum radial displacement observed was 24.59 mm. Under design basis conditions, the maximum radial displacement was 47.61 mm; under severe accident conditions, it was 53.83 mm. The ultimate bearing capacity was 0.91 MPa, 2.17 times the design pressure. This study concludes that the steel-plate concrete containment structure maintains a high safety margin under all tested conditions, with stress and strain well within acceptable limits. It can effectively serve as a robust barrier against radioactive leakage and malicious impacts, providing a viable alternative to conventional containment designs. [ABSTRACT FROM AUTHOR]

The finishing mill is a critical link in the hot rolling process, influencing the final product's quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi-LSTM. Depthwise separable convolution and Bi-LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat-headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering. [ABSTRACT FROM AUTHOR]

This paper proposes an automated approach to the technology selection of High-Voltage Alternating Current (HVAC) Offshore Substations (OHVS) for the integration of Oil & Gas (O&G) production and Offshore Wind Farms (OWF) based on Artificial Intelligence (AI) techniques. Due to the complex regulatory landscape and project diversity, this is enacted via a cost decision-model which was developed based on Knowledge-Based Systems (KBS) and incorporated into an optioneering software named Transmission Optioneering Model (TOM). Equipped with an interactive dashboard, it uses detailed transmission and cost models, as well as a technological and commercial benchmarking of offshore projects to provide a standardized selection approach to OHVS design. By automating this process, the deployment of a technically sound and cost-effective connection in an interactive sandbox environment is streamlined. The decision-model takes as primary inputs the power rating requirements and the distance of the offshore target site and tests multiple voltage/rating configurations and associated costs. The output is then the most technically and economically efficient interconnection setup. Since the TOM process relies on equivalent models and on a broad range of different projects, it is manufacturer-agnostic and can be used for virtually any site as a method that ensures both energy transmission and economic efficiency. [ABSTRACT FROM AUTHOR]

The off-grid Hybrid Renewable Energy Systems (HRES) demonstrate great potential to be sustainable and economically feasible options to meet the growing energy needs and counter the depletion of conventional energy sources. Therefore, it is crucial to optimize the size of HRES components to assess system cost and dependability. This paper presents the optimal sizing of HRES to provide a very cost-effective and efficient solution for supplying power to a rural region. This study develops a PV-Wind-Battery-DG system with an objective of 3E analysis which includes Energy, Economic, and Environmental CO2 emissions. Indispensable parameters like technical parameters (Loss of Power Supply Probability, Renewable factor, PV fraction, and Wind fraction) and social factor (Human Developing Index) are evaluated to show the proposed modified Harris Hawks Optimization (mHHO) algorithm's merits over the existing algorithms. To achieve the objectives, the proposed mHHO algorithm uses nine distinct operators to obtain simultaneous optimization. Furthermore, the performance of mHHO is evaluated by using the CEC 2019 test suite and the most optimal mHHO is chosen for sizing and 3E analysis of HRES. The findings demonstrate that the mHHO has achieved optimized values for Cost of Energy (COE), Net Present Cost (NPC), and Annualized System Cost (ASC) with the lowest values being 0.14130 $/kWh, 1,649,900$, and 1,16,090$/year respectively. The reduction in COE value using the proposed mHHO approach is 0.49% in comparison with most of the other MH-algorithms. Additionally, the system primarily relies on renewable sources, with diesel usage accounting for only 0.03% of power generation. Overall, this study effectively addresses the challenge of performing a 3E analysis with mHHO algorithm which exhibits excellent convergence and is capable of producing high-quality outcomes in the design of HRES. The mHHO algorithm attains optimal economic efficiency while simultaneously minimizing the impact on the environment and maintaining a high human development index. [ABSTRACT FROM AUTHOR]

In the construction industry, the professional evaluation of construction schemes represents a crucial link in ensuring the safety, quality and economic efficiency of the construction process. However, due to the large number and diversity of construction schemes, traditional expert review methods are limited in terms of timeliness and comprehensiveness. This leads to an increasingly urgent requirement for intelligent check of construction schemes. This paper proposes an intelligent compliance checking method for construction schemes that integrates knowledge graphs and large language model (LLM). Firstly, a method for constructing a multi-dimensional, multi-granular knowledge graph for construction standards is introduced, which serves as the foundation for domain-specific knowledge support to the LLM. Subsequently, a parsing module based on text classification and entity extraction models is proposed to automatically parse construction schemes and construct pathways for querying the knowledge graph of construction standards. Finally, an LLM is leveraged to achieve an intelligent compliance check. The experimental results demonstrate that the proposed method can effectively integrate domain knowledge to guide the LLM in checking construction schemes, with an accuracy rate of up to 72%. Concurrently, the well-designed prompt template and the comprehensiveness of the knowledge graph facilitate the stimulation of the LLM's reasoning ability. This work contributes to exploring the application of LLMs and knowledge graphs in the vertical domain of text compliance checking. Future work will focus on optimizing the integration of LLMs and domain knowledge to further improve the accuracy and practicality of the intelligent checking system. [ABSTRACT FROM AUTHOR]

Given the complex powertrain of fuel cell electric vehicles (FCEVs) and diversified vehicle platooning synergy constraints, a control strategy that simultaneously considers inter-vehicle synergy control and energy economy is one of the key technologies to improve transportation efficiency and release the energy-saving potential of platooning vehicles. In this paper, an energy-oriented hybrid cooperative adaptive cruise control (eHCACC) strategy is proposed for an FCEV platoon, aiming to enhance energy-saving potential while ensuring stable car-following performance. The eHCACC employs a hybrid cooperative control architecture, consisting of a top-level centralized controller (TCC) and bottom-level distributed controllers (BDCs). The TCC integrates an eco-driving CACC (eCACC) strategy based on the minimum principle and random forest, which generates optimal reference velocity datasets by aligning the comprehensive control objectives of the platoon and addressing the car-following performance and economic efficiency of the platoon. Concurrently, to further unleash energy-saving potential, the BDCs utilize the equivalent consumption minimization strategy (ECMS) to determine optimal powertrain control inputs by combining the reference datasets with detailed optimization information and system states of the powertrain components. A series of simulation evaluations highlight the improved car-following stability and energy efficiency of the FCEV platoon. [ABSTRACT FROM AUTHOR]

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master–slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators' profit, while the lower-level objective is to minimize the user's power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks. [ABSTRACT FROM AUTHOR]

Today at a time of global economic and financial crisis, timber industry in Slovakia, as well as other industry, is not just trying to look inside for company reserves in production processes, but also identifies new trends in production and its competitiveness in world markets. It is obvious that the pulp and paper industry needs to keep up the competitive pressure with new trends in efficient production, which is certainly linked to investments and their expected effects (capacitance, substitution, income effect). One of the key factors of economic efficiency of production of SAQ pulp is a complex issue of ensuring the required inputs.