In response to the problem of increasing climate change and energy security, investment in renewable energy sources has increased significantly both in Europe and globally. Wind and solar power plants are expected to be the largest contributors to global decarbonization, ranking first and second in projected capacity by 2050. Since all power plants have some impact on the environment, this also applies to photovoltaic power plants. Because of the large capacities projected for PV installed capacity, their environmental impacts should be evaluated. The environmental impacts of PV power plants change over time. Improving the manufacturing technology of PV system components, increasing the efficiency of solar cells, and using materials that are less harmful to the environment will reduce these impacts. Manufacturing PV system components is a highly energy-intensive process that involves greenhouse gas emissions. As new renewable energy capacity is built, the amount of "green" electricity on the grid increases, reducing CO2 emissions per kWh consumed. The objective of this paper is to analyze the current status of the environmental impact of PV power plants under these changing conditions in terms of CO2 emissions, land use, pollutant and noise emissions, and water consumption. The capacity installed to date will reach the end of its lifetime by 2050, which means that the amount of waste associated with it will increase over time. This can have a significant impact on the environment, which is why part of the work is dedicated to this problem. In addition to the available information from the literature, in the part related to land use, the authors also made their own estimates based on data on newly installed PV power plants and PV modules available on the market. The results of the analysis show that there is enough land both in Europe and worldwide to install the planned capacities of rooftop and ground-mounted PV power plants. CO2 emissions are at the same level as for concentrated solar power, with a decreasing trend. Pollutant emissions, noise and water consumption are not a major problem compared to other types of power plants. Overall, it can be concluded that the expansion of PV capacity has a very positive impact on the environment.

This paper analyzes and compares models for predicting average magnet losses in interior permanent-magnet motors with fractional-slot concentrated windings due to harmonics in the armature reaction (assuming sinusoidal phase currents). Particularly, loss models adopting different formulations and solutions to the Helmholtz equation to solve for the eddy currents are compared to a simpler model relying on an assumed eddy-current distribution. Boundaries in terms of magnet dimensions and angular frequency are identified (numerically and using an identified approximate analytical expression) to aid the machine designer whether the more simple loss model is applicable or not. The assumption of a uniform flux-density variation (used in the loss models) is also investigated for the case of V-shaped and straight interior permanent magnets. Finally, predicted volumetric loss densities are exemplified for combinations of slot and pole numbers common in automotive applications.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in China over recent years.

Modern building automation systems implement plenty of advanced control and monitoring functions considering various parameters like users’ activity, lighting, temperature changes etc. Moreover, new solutions based on Internet of Things and cloud services are also being developed for smart buildings to ensure comfort of use, user safety, energy efficiency improvements and integration with smart grids and smart city platforms. Such a wide spectrum of technologies and functions requires a new approach in building automation systems design to provide effective way of their implementation and flexibility during operation. At the same time, in the building design and operation industries, tools based on building information modeling and digital twins are being developed. In this paper, the authors discuss development directions and application areas of these solutions, identifying new trends and possibilities of their use in smart homes and buildings. In particular, the focus is on procedures for selecting automation functions, effective integration, and interoperability of building management systems with Internet of Things considering organization of prediction mechanisms and dynamic functional changes in buildings and smart networks. Chosen solutions and functions should consider the requirements from EN ISO 52120 standard and the guidelines defined for the Smart Readiness Indicator.

The electric vehicle (EV) industry is experiencing rapid growth, accompanied by continuous advancements in charging infrastructure to satisfy the rising need for fast and reliable charging. Particularly, the focus has shifted towards EV fast charging (EVFC) and its impact on the power grid. This paper presents a comprehensive analysis and survey of scholarly literature and projects conducted in this field over the past decade. The findings highlight the rising demand for EVFC and the need to address its potential adverse effects on the power grid. However, there is a noticeable lack of clear research guidance regarding charging time. To tackle these challenges and pave the way for future solutions, extensive research on DC fast charging, ultra-fast charging, and the integration of vehicle-to-grid (V2G) systems is required. This review paper thoroughly investigates the development of fast charging technology for electric vehicles (EVs), including its advantages and comparative analyses from various perspectives. Furthermore, it delves into the advancements in DC fast charging infrastructure, emphasizing charging standards and control modes. The study also investigates different categories of battery chargers for both on-board and off-board charging. Additionally, it explores the classification of DC-DC converters in the context of DC fast charging stations, discusses control strategies for EV systems, identifies existing challenges, and outlines future trends in EV fast charging.

The period of the COVID-19 pandemic resulted in a rapid development of innovative education methods, in particular using e-learning and remote work tools. The effects of this development are also post-pandemic changes in formal teaching regulations, in particular in higher education, allowing classes and lectures to be carried out with Blended Learning or Flipped Classroom methods. This paper provides information about a case study of implementing ICT technologies with elements of the mentioned methods in one of the subjects at a technical university. The implemented tools and solutions in the field of e-learning and interactivity are presented, along with information regarding their perception by students during two academic years in the post-COVID-19 period. The analysis of results of these information and student evaluations indicates their generally positive approach to such innovations in the educational path, and at the same time shows significant challenges for teachers to increase the attractiveness and effectiveness of the teaching process and the development of practical, technical skills of students.

The assessment of Photovoltaic (PV) systems, a key component of sustainable energy solutions, is of paramount importance in the pursuit of a greener future. This abstract provides an overview of environmental assessments associated with PV systems, considering their life cycle, energy production, and ecological impact. The study explores various methodologies and tools employed in the evaluation of PV systems’ environmental performance, addressing issues such as material sourcing, manufacturing, installation, and end-of-life disposal. Additionally, it delves into the trade-offs and challenges in achieving sustainability objectives in the solar energy sector. By critically examining the environmental implications of PV systems, this research contributes to informed decision-making and the sustainable integration of solar power into our energy landscape.

Ghana, blessed with abundant solar resources and has strategically invested in solar photovoltaic (PV) technologies to diversify its energy mix and reduce the environmental impacts of traditional energy technologies. The 50 MW solar PV installation by the Bui Power Authority exemplifies the nation's dedication to utilizing clean energy for sustainable growth. This study seeks to close the knowledge gap by providing a detailed analysis of the system's performance under different weather conditions, particularly on days with abundant sunshine and those with cloudy skies. The research consists of one-year monitoring data of the climatic and AC energy output fed into the grid. This data was used to analyze PV performance on each month's sunniest and cloudiest days. The results show that the total amount of AC energy output fed into the grid each month on the sunniest day varies between 229.3 MWh in December and 278.0 MWh in November, while the total amount of AC energy output fed into the grid each month on the cloudiest day varies between16.1 MWh in August and 192.8 MWh in February. Also, the percentage variation in energy produced between the sunniest and cloudiest days within a month ranges from 16.9% (December) to 94.1% (August). The reference and system yield analysis showed that the PV plant has a high conversion efficiency of 91.3%; however, only the sunniest and overcast days had an efficiency of 38% and 92%, respectively. The BPA plant's performance can be enhanced by using this analysis to identify erratic power generation on sunny days and schedule timely maintenance to keep the plant's performance from deteriorating. Optimizing a solar PV system's design, installation, and operation can significantly improve its AC energy output, performance ratio, and capacity factor on sunny and cloudy days.

A Partial discharge (PD) occurrence in high-voltage (HV) and medium-voltage (MV) power assets is a warning sign of insulation system degradation or defective manufacturing/assembly, that may cause catastrophic damage and eventually lead to the breakdown of the power equipment. Ultra-high frequency (UHF) PD measurement technology has extensively been used to monitor the insulation health of the HV or MV equipment due to its high immunity against ambient noise and conducted disturbances. This paper presents experimental validation of a new design of a UHF planar rectangular microstrip antenna that can be used for PD monitoring of HV and/or MV power equipment, such as power transformers. The designed planar rectangular microstrip antenna has a compact size of 80mmx70mmx1.6mm. Laboratory experiments were performed to validate the antenna sensitivity to PD activity caused by insulation defects created inside an oil-filled transformer tank model. PD measurement results confirm that the proposed rectangular microstrip antenna has high sensitivity and high signal-to-noise ratio (SNR), thanks to its capability to suppress external noise disturbances, especially during online PD monitoring. Thus, the proposed UHF antenna seems to be a promising and alternative PD sensor that can be employed in UHF PD monitoring systems of HV or MV power equipment.

In the face of escalating climate concerns and the push for sustainable development, the global shift towards renewable and decentralized energy systems presents new challenges and opportunities. This study investigates integrating decentralized energy production, particularly photovoltaic (PV) systems, into national energy planning, aiming to optimize energy strategies that balance local production and consumption with national objectives. By analyzing the Swiss energy model, the research employs the EnergyScope and REHO models to assess the strategic implications of decentralized versus centralized energy systems. Results show that a decentralized approach can significantly reduce PV installation needs to 35 GW, about 23% of potential capacity, and decrease annual system costs by 10% to CHF 1230 per capita. This strategy emphasizes local consumption, minimizes grid reinforcement demands, and leverages economic advantages while addressing overproduction challenges through effective energy storage and grid management. Conclusions underline the strategic value of combining centralized and decentralized methods for resilient and sustainable energy planning. The study contributes to the discourse on energy policy and infrastructure planning, advocating for a hybrid model that accommodates both local conditions and broader energy objectives, urging further research into climate impacts and technology integration for a comprehensive energy future.

Because of the rapid advancement in the use of photovoltaic (PV) energy systems, it has become critical to look for ways to improve the energy generated by them. The extracted power from the PV modules is proportional to the output voltage. The relationship between output power and array voltage has only one peak under uniform irradiance, whereas it has multiple peaks under partial shade circumstances (PSC). There is only one global peak (GP) and many local peaks (LPs), where the typical maximum power point trackers (MPPT) may become locked in one of the LPs, significantly reducing the PV system's generated power and efficiency. The metaheuristic optimization algorithms (MOAs) solved this problem, albeit at the expense of the convergence time, which is one of these algorithms' key shortcomings. Most MOAs attempt to lower the convergence time at the cost of the failure rate and the accuracy of the findings because these two factors are interdependent. To address these issues, this work introduces the dandelion optimization algorithm (DOA), a novel optimization algorithm. The DOA's convergence time and failure rate are compared to other modern MOAs in critical scenarios of partial shade PV systems to demonstrate the DOA's superiority. The results obtained from this study showed substantial performance improvement compared to other MOAs, where the convergence time is reduced to 0.4 s with zero failure rate compared to 0.9 s, 1.25 s, and 0.43 s for other MOAs under study. The optimal number of search agents in the swarm, optimal initialization of search agents, and optimal design of the dc-dc converter is introduced for optimal MPPT performance.

More and more works are using machine learning techniques while adopting supervisory control and data acquisition (SCADA) system for wind turbine anomaly or failure detection. While parameter selection is important for modelling a wind turbine’s health condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. Moreover, after proving that Copula, a multivariate probability distribution for which the marginal probability distribution of each variable is uniform is capable of simplifying the estimation of mutual information, an empirical copula based mutual information estimation method (ECMI) is introduced for an application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

For offshore wind power generation, accessibility is one of the main factors that has great impact on operation and maintenance due to constraints on weather conditions for marine transportation. This paper presents a framework to explore the accessibility of an offshore site. At first, several maintenance types are defined and taken into account. Next, a data visualization procedure is introduced to provide an insight into the distribution of access periods over time. Then, a rigorous mathematical method based on finite state Markov chain is proposed to assess the accessibility of an offshore site from the maintenance perspective. A five-year weather data of a marine site is used to demonstrate the applicability and the outcomes of the proposed method. The main findings show that the proposed framework is effective in investigating the accessibility for different time scales and is able to catch the patterns of the distribution of the access periods. Moreover, based on the developed Markov chain, the average waiting time for a certain access period can be estimated. With more information on the maintenance of an offshore wind farm, the expected production loss due to time delay can be calculated.

To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

Based on the dynamic strength variation mechanism of concrete under complex loading conditions, two approaches were devised: numerical testing and CT scanning, guided by the principle of minimizing energy dissipation. A methodology for calculating concrete's failure area and measuring its strength was developed. The analysis delves into the mechanisms behind failure patterns and variations in concrete specimen strength, focusing on crack initiation, development, and the resultant failure surface formed by crack propagation, along with the stress distribution in concrete specimens subjected to diverse loading conditions. Through evaluating the strength and computed failure area of concrete specimens, it is evident that the loading method influences both the strength and failure area of concrete specimens. In general, a larger failure area correlates with a higher measured strength.

Coal gasification with carbon dioxide is a process for generating clean gaseous fuels and relieving greenhouse effect. Zhundong coal has high alkali and alkali earth metals (AAEMs) content, medium volatile and low ash in nature. Isothermal CO₂ gasification of char derived from Zhundong coal (R-char) and char from acid washing R-char (AR-char) are performed in thermo-gravimetric analyzer (TGA). The effect of AAEMs is investigated on the gasification behavior in the range of temperatures 1073 K to 1273 K. The carbon conversion increases rapidly with increasing reaction temperature and CO₂ concentration. R-char has high gasification rate and carbon conversion compared with AR-char. The accuracy of the free-model approach for calculating activation energy at different conversions is validated by compared with different kinetic models (volume reaction model, distributed activation energy model). Moreover, R-char gasification with CO₂ shows a compensation effect as the Arrhenius parameters (EA and k0) increase or decrease simultaneously.

In numerical simulations, achieving high accuracy without significantly increasing computational costs is often challenging. To address this, this paper presents an improved finite volume weighted essentially non-oscillatory (WENO) scheme tailored for applicability in computational fluid dynamics (CFD) and implemented in the flow solver NUAA-Turbo for simulating turbomachinery flows using both RANS and RANS/LES coupling. Firstly, the new WENO scheme is validated against classic numerical test cases to assess its stability and reliability in handling discontinuities, the Double problem, and Raleigh-Taylor (RT) instability issues. Compared to the original format, this enhanced finite volume WENO scheme demonstrates superior stability near discontinuities and resolves flow features with the same grid resolution more effectively. Next, for engineering applications related to turbomachinery, such as compressor and turbine characteristics, computations are performed using RANS, and the results obtained using WENO-ZQ3 and WENO-JS3 are compared. Finally, the new fifth-order WENO scheme is applied to RANS/LES coupled simulations of turbine wakes and film cooling. The results show that the enhanced finite volume WENO scheme offers improved stability and accuracy in engineering applications, allowing for high-precision calculations with fewer grid points to capture more detailed flow physics, thereby reducing computational costs in aerodynamic applications.

To conduct high-precision and high-resolution numerical simulation of complex flow structures in turbomachinery, a high-order finite volume weighted essentially non-oscillatory (WENO) scheme for the large eddy simulation (LES) is improved and embedded into the three-dimensional viscous unsteady CFD solver NUAA-Turbo. Firstly, the spectral characteristics and unsteady convergence of the improved WENO scheme are studied. Compared with the classical WENO scheme, the improved WENO scheme has better dissipation and dispersion characteristics and faster convergence speed. Then, the coefficient CW of the Wall-Adapting Local Eddy-viscosity (WALE) model is calibrated by decaying homogeneous isotropic turbulence (DHIT) test case. Finally, the scheme is applied to conduct high-precision LES calculations of turbulent boundary layer flow, supersonic compression corner flow, and low-pressure turbine cascade separation flow. The numerical simulation results show that this WENO scheme has excellent shock wave capture ability and turbulence resolution and can capture more flow field details with less mesh size, greatly reducing the computational cost and laying a foundation for large-scale engineering application of LES.

The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in MATLAB/SIMULINK evaluated these algorithms’ performance under various shading scenarios. The results indicate that while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. The study concludes that integrating AI and ML into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and support the green energy transition.

In modern warfare, the diversified tasks can sometimes be challenging to fulfill with a single projectile, necessitating the incorporation of cooperation constraints in trajectory planning. Limited by the stringent requirements of the launch platform and lower costs, the maneuverability and ability to adjust the flight time range of projectiles are weak. Therefore, the rationality of performance indicators and flight time coordination strategies is especially critical. To fully exploit the control capabilities of projectiles, a battlefield area segmentation method is proposed, differentiated performance indicators and a cooperative trajectory planning model that considers the entire range of projectiles are established. Considering the limitations of existing flight time coordination strategies in applications, targeted improvements have been made in different cooperation scenarios, and a bi-level adaptive cooperation strategy(BACS) with both optimality and flexibility is proposed. To rapidly determine feasible conditions, a simple and universal method for obtaining the capability ranges of cooperation is introduced. The multi-phase optimal control problems are transformed into nonlinear programming problems using the Radau pseudo-spectral method, and simulation results show that the proposed methods are capable of handling trajectory planning tasks in different cooperation scenarios. The improved strategies effectively enhance the planning success rate under relatively harsh conditions and BACS demonstrates good compatibility with the problems studied.

Due to constraints in launch platform and cost, the maneuverability of gliding-guided projectiles is limited, necessitating a rational design of their trajectory schemes. To reduce the sensitivity of trajectory schemes to uncertainties while ensuring compatibility between flight schemes and guidance control systems, and fully exploiting the control capability of the projectile, a closed-loop robust trajectory planning method is proposed. Models of major uncertain factors and states deviation at the control-start point are established. Based on the NIPCE method, the stochastic dynamic model is transformed into a high-dimensional deterministic model with PCE coefficients as state variables, and the uncertainty propagation law is obtained. A PID algorithm is employed to design a tracking guidance law based on position error feedback, and open-loop and closed-loop robust trajectory planning models are established accordingly. The optimal control problem is solved by transforming it into a nonlinear programming problem using the direct shooting method. Simulation results indicate that the NIPCE method can significantly improve the computational efficiency of uncertainty propagation while ensuring accuracy. Open-loop robust planning can effectively mitigate the sensitivity of gliding trajectories to uncertainties but cannot completely eliminate terminal dispersion. Closed-loop robust planning effectively improves control effort consumption on the basis of open-loop planning.

Due to the high solar irradiance or energy price, certain regions in the U.S. may reach 100% PV penetration and experience degradation of frequency response greater than the interconnection as a whole. Therefore, in this section, the 100% PV penetration region in each interconnection is simulated to study the local high PV penetration effects. The study was performed by quantifying RoCoF, frequency nadir, and settling frequency at different regional PV penetration levels. The impact of high regional PV penetration on the compliance of grid code on frequency response is also studied.

Three kinds of inorganic alkali are introduced into tetraethylenepentamine (TPEA) and polyethyleneimine (PEI) modified MCM-41 as the CO2 adsorbents. FT-IR and TGA are used to characterize the surface groups and the thermal stability of adsorbents. Chemical titration method is used to measure the alkali amounts of adsorbents. Thermo-gravimetric analysis with 10%CO2/N2 as the simulated flue gas is used to test the CO2 adsorption performance of adsorbents. The results show that all three kinds of inorganic alkali containing adsorbents exhibit promoted CO2 adsorption capability than traditional TPEA and PEI modified samples. Ca(OH)2 and PEI modified samples exhibit the highest adsorption capacity and stable regeneration property. The introduction of inorganic alkali changes the chemical adsorption mechanism between CO2 and increases the effective alkali amounts due to its hydroxyl groups. The CO2 adsorption capacities have a linear dependence relation with the alkali amounts of adsorbents, indicating that alkali amount is a critical factor for novel adsorbents exploring.

The article discusses issues related to the optimization of the operation of medium voltage networks, focusing on the optimization of network division points and the selection of the optimal network configuration aimed at reducing power and energy losses. The article provides a critical review of selected scientific works from recent years on the issue of network configuration optimization. The developed solution uses non-deterministic network models and statistical and probabilistic approaches. Optimization studies were carried out using heuristic optimization methods and proprietary algorithms for forecasting the load and power generated from renewable energy sources. The solution proposed in the article has many application possibilities. It can be used to optimize the location of network division points (reducing economic costs related to the modernization of network infrastructure) as well as to optimize the reconfiguration process in order to reduce power losses while maintaining voltage criteria.

In the proposed article, the issues of increasing the synchronizing ability of the system of coordinated rotation of asynchronous motors are considered. Based on the T-shaped equivalent circuit, electromechanical relationships were obtained with changes in the value of the total rotor resistance and the supply voltage of individual motors. An algorithm for calculating the torques, stator and rotor currents of the motors of the system has been compiled. Based on the calculations obtained, the mechanical characteristics of the system asynchronous motors are constructed for various values of total resistance, supply voltages and rotor positions. The obtained data and graphs confirm the adequacy of the theory obtained in an analytical way. Functional and structural diagrams in small scales have been compiled, which allow simulating transient operation modes of multi-motor asynchronous electric drives in the Matlab application package. Multi-motor asynchronous electric drives with adjustable supply voltages, as well as with preliminary synchronization and synchronous braking, have been developed. Compilation of a program for the stability of a nonlinear system of a multi-motor asynchronous electric drive (MAEDs) with a thyristor voltage converter (TVC) in Matlab. Also, the modes of soft start, load shedding and surge on the motor shaft are simulated, confirming the stability of the system.

The development and utilization of wind energy is of great significance to the sustainable development of China's economy and the realization of the goal of "dual carbon". Under the typhoon system, the randomness and volatility of wind speed become an important basis for affecting the energy efficiency utilization and design of wind turbines. Therefore, based on wind speed and wind direction data at 10m, 30m, 50m and 70m heights of a wind power tower in Yancheng, Jiangsu Province, this paper analyzes the change characteristics of wind speed and wind direction by using BFAST method and Hurst index. Based on typhoon body data, topographic data and mesoscale system wind direction data, the causes of the occurrence and development of wind speed and wind direction of wind tower are further analyzed, and a conclusion is drawn: (1) In BEAST method, there are 5, 5, 6 and 6 change points at the height of 10m, 30m, 50m and 70m respectively. Among them, the change points at the height of 10m, 30m and 50m all change before and after node 325, and the change time point at the height of 70m is inconsistent with other heights. Hurst index results show that the inconsistency at 70m altitude is stronger than that at other altitudes. (2) The wind direction sequence at 10m, 30m, 50m and 70m altitude is fitted by stages, and the direction of wind direction is SE-E-SE-SW-W-NW, where, At the height of 70m, the deviation of the fitting trend line from other altitudes is larger in the wind speed strengthening stage and weakening stage. (3) The wind speed at the height of 10m and 70m has different response degrees to the typhoon body. The correlation between the wind speed and the distance between the wind measurement tower and the typhoon near the center is stronger at the height of 10m than at the height of 70m. The surface type and the wind direction of the mesoscale system also have certain effects on the wind speed and direction. In this paper, the change characteristics and influencing factors of wind speed and direction of wind tower are studied, which provides methods and theoretical support for the study of short-term wind speed when typhoon passes through, and provides reliable support for the selection of wind power forecast indicators and the selection and design of wind turbines under extreme gale weather systems.

This research presents a maximum power point tracking of traditional single-ended primary converter (SEPIC) with the aid of fuzzy logic controller (FLC) for regulating the voltage and perturbation and observation(P&O) algorithms for regulating the reference voltage and reaching the maximum power point tracking. This is a promising technique for the conventional SEPIC converter to achieve maximum power point tracking with much less error. Moreover, the method is simple, reliable, and understandable for photovoltaic systems.

The paper explores the features of numerical simulation to analyze the dynamic behavior of drives controlled by pulse-width modulators. Modern motor control systems widely use pulse width modulation. Effective numerical modeling of such systems has specific features because the models employed are continuous-event and have hybrid behavior due to the presence of nonlinear links with discontinuities of the first kind. Therefore, it is essential to have special integration methods with variable steps, which should be factored in when developing the model. The paper shows how these problems are solved when modeling an electric drive with a DC motor in the SimInTech software.

Combining modern thermodynamics theory branches, including finite time thermodynamics or entropy generation minimization, constructal theory and entransy theory, with metallurgical process engineering, this paper provides a new exploration on generalized thermodynamic optimization theory for iron and steel production processes. The theoretical core is to thermodynamically optimize performances of elemental packages, working procedure modules, functional subsystems, and whole process of iron and steel production processes with real finite-resource and/or finite-size constraints with various irreversibilities toward saving energy, decreasing consumption, reducing emission and increasing yield, and to achieve the comprehensive coordination among the material flow, energy flow and environment of the hierarchical process systems. A series of application cases of the theory are reviewed. It can provide a new angle of view for the iron and steel production processes from thermodynamics, and can also provide some guidelines for other process industries.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

The design complexity of the new generation of civil aero-engines results in higher demands on engines’ components, higher component temperature, higher heat generation, and, finally, critical thermal management issues. This paper will propose a methodological approach to create physics-based models for heat loads developed by sources as well as a systematic sensitivity analysis to identify the effects of design parameters on the thermal behaviour of civil aero-engines. The ranges and levels of heat loads generated by heat sources (e.g., accessory gearbox, bearing, pumps, etc.) and heat absorption capacity of heat sinks (e.g., engine fuel, oil, and air) are discussed systematically. The practical research challenges for thermal management system design and development for the new and next generation of turbofan engines will then be addressed through a sensitivity analysis of the heat load values as well as the heat sinks flow rates. The potential solutions for thermal performance enhancements of propulsion systems will be proposed and discussed accordingly.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

This paper provides a comparative analysis of AES (Advanced Encryption Standard) and Salsa20 algorithm implementations, focusing on power consumption efficiency in passive RFID (Radio Frequency Identification) tags and ultra-low-power devices. The main objective of this work is to determine which of these algorithms is more suitable to operate in these types of devices. For this purpose, ASIC (Application-Specific Integrated Circuit) implementations of AES and Salsa20 based on low-power approaches were developed and their power consumption was evaluated. Results demonstrate that Salsa20 power consumption is lower than AES (2.82μW and 4.01μW, respectively - both in 0.18μm), indicating that Salsa20 is a better choice than AES for passive RFID tags.

Natural polluted insulator surfaces are always coated with various kinds of soluble constituents, and those constituents affect flashover performance differentially. Currently this fact is not considered either in laboratory experiments or field pollution degree measurements, causing the existing insulation selection method to be deficient. In this paper a systematical research on insulator flashover voltage gradient correction involving types of soluble pollution constituents was presented. Taking typical type glass insulator as the sample, its flashover tests polluted by typical soluble chemicals NaCl, NaNO₃, KNO₃, NH₄NO₃, MgSO₄, Ca(NO₃)₂ and CaSO₄ were carried out. Then the flashover gradient correction was made combining the flashover performance of each soluble constituent, the ESDD contribution of the seven constituents, as well as the saturation performance of CaSO₄. The correction was well verified with the flashover test results of insulator polluted by three types of soluble mixture. Research results indicate that the flashover gradient correction method proposed by this paper performs well in reducing the calculating error. It is recommended to carry out component measurements and flashover gradient correction to better select outdoor insulation configuration.

The present study aims at investigating the relationship between Initial Public Offerings (IPOs) variables mainly underpricing on one hand, and the brand value measures on the other. Our final data set is 104 international brand companies. We implement empirical approach using hierarchical OLS regression and descriptive statistics. We show that underpricing is positively related to brand value which emphasizes the marketing role of going public and underpricing in enhancing brand equity through the product market, which additionally confirms some information asymmetry models. We also find that on average brand companies had not been recognized as brands at the IPO time. Moreover, we show the positive role of private equity in enhancing brand value, additionally, the non-linear association between underpricing and brand value is not evident. Finally, we draw some policy implication and suggestions for future research.

Part II, like Part I, discusses geometry, kinematics and statics in detail without taking into account power losses in meshing. The main goal of Part II, however, is to prove that there are complex and structurally coupled planetary gear systems, i.e. containing a closed circuit in which no circulation of power flow is generated. The power flow analysis is also carried out using the direct method, with a slight modification in the way of writing the equations identifying active and passive gears, i.e. at the same time the directions of power flow on all transmission paths. The analysed planetary gear system (PGS) is characterised by mobility equal to 1, i.e. single degree of freedom (DoF). It consists of three subsystems in the form of the 2KH type planetary units connected in series-parallel, specially selected to demonstrate the thesis of this paper. It consists of three subsystems in the form of single planetary subsystems of the 2KH type connected series-parallel, specially chosen to illustrate the thesis of this paper.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

This article proposes a particular strategy to proceed with a progressive electrification of public transport systems in cities. Starting from a bus operation model, the possible electrification of two routes is analysed, one urban and another extra-urban in the city of Pisa. An estimate is made of the energy uses associated with certain operating modes. The maximum level of consumption is estimated at approximately 280 kWh per day per bus for the urban route and excluding some special days, less than 215 kWh per day for the extra-urban route, for which a hybrid bus is proposed. Starting from an estimate of the daily consumption for the management of the two routes, the sizing of a photovoltaic system distributed on some modular shelters which serves to power the same routes is carried out. The resulting system has a power of the order of 190-200 kW. The modular solution is also outlined, and an installation is proposed. The analyzed case lends itself to being easily replicated.

This paper proposes a novel single-stage buck-boost three-Level neutral-point-clamped (NPC) inverter with two independent dc sources coupled for the grid-tied photovoltaic (PV) application, which can effectively solve the unbalanced operational conditions generally appeared between two coupled independent PV sources induced by the unequal irradiation and temperature distribution. The proposed control scheme can simultaneously guarantee the maximum power point (MPP) operation of both PV sources and maintain the output waveform quality. Compared to the traditional two-stage PV inverter, the proposed NPC inverter could reduce the PV array voltage requirement and dc-link capacitors’ voltage rating, meanwhile show the advantage in operational efficiency. MATLAB simulations and the captured experimental results are presented to show the performance of the proposed three-level inverter.

This study investigates cloud cavitation suppression around a model scale NACA66 hydrofoil using active water injection and explores the effect of multiple injection parameters. Numerical simulations and a mixed-level orthogonal test method are employed to systematically analyze the impact of jet angle αjet, jet location Ljet, and jet velocity Ujet on cavitation suppression efficiency and hydrofoil energy performance. The study reveals that jet location has the greatest influence on cavitation suppression, while jet angle has the greatest influence on hydrofoil energy performance. The optimal parameter combination (Ljet = 0.30C, αjet = +60 degrees, Ujet = 3.25 m/s) effectively balances energy performance and cavitation suppression, reducing cavitation volume by 49.34% and improving lift-drag ratio by 8.55%. The study found that the jet's introduction not only enhances vapor condensation and reduces the intensity of the vapor-liquid exchange process but also disrupts the internal structure of cavitation clouds and elevates pressure on the hydrofoil suction surface, thereby effectively suppressing cavitation. Further analysis shows that positive-going horizontal jet components enhance the lift-drag ratio, while negative-going components have a detrimental effect. Jet arrangements near the trailing edge negatively impact both cavitation suppression and energy performance. These findings provide valuable reference for selecting optimal injection parameters to achieve a balance between cavitation suppression and energy performance in hydrodynamic systems.

With the global objectives of achieving a ‘carbon peak’ and ‘carbon neutrality’, along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from January 1st to December 30th in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications.

Building integrated photovoltaics (BIPV) consists of PV panels that are integrated into the building as part of the construction. This technology has advantages such as the production of electricity without necessary additional land area. This paper provides a literature review about the recent developments in urban building energy modelling, including tools and methods, and how they can be used to predict the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The microclimate, namelly the urban heat island concept, is introduced and related with the existence of PV systems. It is concluded that urban building energy modelling (UBEM) can be effective to study the performance of PV systems in the urban environment. It allows to simultaneously predict the building energy performance and the microclimate effect. However, there is a need to develop new methodologies to overcome the challenges associated to UBEM, especially in what concerns non-geometric data, which leads to a major source of errors. and to find an effective method to predict the effect of PV systems in the urban environment.

The solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the quantity of solar panels and the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to forecast the solar irradiance of typical irrigation areas. The impact of the meteorological parameters on the solar irradiance is studied, and four different parameter combinations are formed and considered as input to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict the solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as the input parameters. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model, with predicted values falling within acceptable ranges. The model developed using mixed data of multiple stations can be applied for forecasting solar irradiance in different regions. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.

In this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

One of the most crucial and economically beneficial tasks for energy customer is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may warry. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data driven forecasting method based on fuzzy logic for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, left and right from load peak, searching for BESS capacity equal. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) with respect to BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak shaving method, offline digital time-domain simulations have been performed on a real-life practical test micro grid system in MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical micro grid system with real-life data obtained from smart meter and also, compared with several previously-reported methods.

The oscillating heat pipe (OHP) is a new member in the family of heat pipes, and it has great potential applications in energy conservation. However, the fluid flow and heat transfer in the OHP as well as the fundamental effects of inner diameter on them have not been fully understood, which are essential to the design and optimization of the OHP in real applications. Therefore, by combining the high-speed visualization method and infrared thermal imaging technique, the fluid flow and thermal performance in the OHPs with inner diameters of 1, 2 and 3 mm are presented and analyzed. The results indicate that three fluid flow motions, including small oscillation, bulk oscillation and circulation, coexist or, respectively, exist alone with the increasing heating load under different inner diameters, with three flow patterns occurring in the OHPs, viz. bubbly flow, slug flow and annular flow. These fluid flow motions are closely correlated with the heat and mass transfer performance in the OHPs, which can be reflected by the characteristics of infrared thermal images of condensers. The decrease in the inner diameter increases the frictional flow resistance and capillary instability while restricting the nucleate boiling in OHPs, which leads to a smaller proportion of bubbly flow, a larger proportion of short slug flow, a poorer thermal performance, and easier dry-out of working fluid. In addition, when compared with the 2 mm OHP, the increasing role of gravity induces the thermosyphon effect and weakens the 'bubble pumping' action, which results in a little smaller and bigger thermal resistances of 3 mm OHP under small and bulk oscillation of working fluid, respectively.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

The decarbonization of the electricity grid as one among the actions to reduce fossil fuel emissions, and thus their impact on global warming in the future, will be achieved mainly via the integration and widespread diffusion of renewable power sources. This is going to be supported also by the shift from the paradigm production-transmission-distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will reduce the reliability of the grid very likely, unless appropriate countermeasures are taken/implemented, hopefully at the same time the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might determine in renewable-based power systems. This paper surveys the current state-of-the-art on anomalies and faults affecting wind-PV-storage hybrid power systems, i.e., the main renewable technology ensemble that will establish the future grid, and highlights possible research directions that may help to fill the literature gaps.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

This paper experimentally reveals some of the resources offered by the evolution of the instantaneous active electric power in describing the state of three-phase AC induction asynchronous electric motors (with squirrel-cage rotor) operating under no-load conditions. A mechanical power is required to rotate the rotor at no-load and this mechanical power is satisfactorily reflected in the constant and variable part of instantaneous active electric power. The variable part of this electrical power should necessarily have a periodic component with the same period as the period of rotation of the rotor. The paper proposes a procedure for extracting this periodic component description (as a pattern, by means of a selective averaging of instantaneous active electrical power) and analysis. The time origin of this pattern is defined by the time of a selected first passage through the origin of an angular marker placed on the rotor, detectable by a proximity sensor (e.g. a laser sensor). The usefulness of the pattern in describing the state of the motor rotor has been demonstrated by several simple experiments which show that a slight change in the no-load running conditions of the motor (e.g. by placing a dynamically unbalanced mass on the rotor) has clear effects in changing the shape of the pattern.

For a long time, a company's Product Development Process (PDP) was seen as supporting the operations department, although PDP decisions and mistakes have a considerable impact on market performance. This is critical even in agriculture where bad habits and practices in the PDP can lead rural producers to great losses. Therefore, this research investigated the effect on the market performance of rural products (banana) in the southern region of Brazil, based on two analyses: (i) how sustainability practices support the PDP phases and (ii) how the phases of the Product Life Cycle Assessment (LCA) mediate sustainability practices and PDP phases. This study presents a quantitative analysis using Confirmatory Factor Analysis (CFA) and hierarchical ordinary least squares (OLS) regression of data obtained from a survey of 110 rural producers who directly participate in the banana production and planning process in southern Brazil. Our results shows that sustainability practices support the PDP, and we confirm that the product development and post-development phase has an effect on market performance. In addition, we identified that in the pre-development phase of the PDP, dealing with rural products (bananas), the maturity stage of the LCA mediates sustainability. In the PDP development phase, we concluded that rural families that develop economic and environmental practices with their products in the market growth phase may have reduced results. As for the post-development phase of the PDP, we conclude that when companies invest in environmental and social practices, there is a complete mediation of the effect, where these practices lose strength if the product is in the introduction and maturity phases in the market. As originality, our study contributed to demonstrate value on the product life cycle for the product development process in agriculture using sustainability practices through a systemic approach, filling the gap in the literature due to the lack of research on these areas seen in an integrated way.

Abiotic stress, especially drought stress poses a significant threat to the terrestrial plant growth, development, and productivity. Although mulberry have great genetic diversity and extensive stress tolerant traits in agroforestry systems, only a few reports offer preliminary insight into biochemical responses of mulberry leaves under drought conditions. In this study, we performed a comparative metabolomic and transcriptomic analysis on the “drooping mulberry” (Morus alba var. Pendula Dippel) under PEG-6000-simulated drought stress. Our research revealed that drought stress significantly enhanced flavonoid accumulation and up-regulated the expression of phenylpropanoid biosynthetic genes. Furthermore, the activities of superoxide dismutase (SOD), catalase (CAT) and malondialdehyde (MDA) content were elevated. In vitro enzyme assays and fermentation tests indicated the involvement of flavonol synthase/flavanone 3-hydroxylase (XM_010098126.2) and anthocyanidin 3-O-glucosyltransferase 5 (XM_010101521.2) in the biosynthesis of flavonol aglycones and glycosides, respectively. The recombinant MaF3GT5 protein was found to recognize kaempferol, quercetin, and UDP-glucose as substrates, but not 3-/7-O-glucosylated flavonols and UDP-rhamnose. MaF3GT5 is capable of forming 3-O- and 7-O-monoglucoside, but not di-O-glucosides, form kaempferol. This implies its role as a flavonol 3, 7-O-glucosyltransferase. The findings from this study provided insights into the biosynthesis of flavonoids and could have substantial implications for the future diversified utilization of mulberry.

Computational models of homologous protein groups are essential in sequence bioinformatics. Due to the diversity and rapid evolution of viruses, the grouping of protein sequences from virus genomes is particularly challenging. The low sequence similarities of homologous genes in viruses require specific approaches for sequence- and structure-based clustering. Furthermore, the annotation of virus genomes in public databases is not as consistent and up-to-date as for many cellular genomes. To tackle these problems, we have developed VOGDB, a database of Virus Orthologous Groups. VOGDB is a multi-layer database that progressively groups viral genes into groups connected by increasingly remote homology. The first layer is based on pair-wise sequence similarities, the second layer is based on the sequence profile alignments and the third layer uses predicted protein structures to find the most remote homology. VOGDB groups allow for more sensitive homology searches of novel genes and increase the chance of predicting annotations or inferring phylogeny. VOGDB uses all virus genomes from RefSeq and partially re-annotates them. VOGDB is updated with every RefSeq release. The unique feature of VOGDB is inclusion of both prokaryotic and eukaryotic viruses in the same clustering process which makes it possible to explore old evolutionary relationships of the two groups. VOGDB is freely available at https://vogdb.org under the CC BY 4.0 license.

Photovoltaic(PV) power generation is highly nonlinear and stochastic. Accurate prediction of PV power generation plays a crucial role in grid connection as well as the operation and scheduling of power plants. To predict the PV power combination model, this paper suggests a method based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Auto-Regressive Neural Networks with Exogenous Input (NARXNN), Long Short Term Memory (LSTM) Neural Network, and Light Gradient Boosting Machine (LightGBM) algorithms. To attempt to reduce the non-smoothness of PV power, the weather variable features with the greatest effect on PV power are first identified by correlation analysis. Following this, the PV power modal decomposition is split and reorganized into a new feature matrix. Finally， a NARX is used to obtain preliminary PV power components and residual vector features， and the PV power is predicted by combining three models of LightGBM， LSTM, and NARX and then the final prediction results are obtained by combining the PV power prediction results using error inverse method weighted optimization. The prediction results demonstrate that the model put forth in this paper outperforms those of other models and validate the model's validity by utilizing real measurement data from Andre Agassi College in the United States.

In this paper, a study of modeling PV cell (solar cell), PV module (solar modules) and PV array solar array) using Matlab/Simulink software is presented. This study is focused on the study to design photovoltaic cell, photovoltaic modules, photovoltaic panels and PV array (solar array) and to analyze the change in PV performance according to changes in different parameters such temperature, solar irradiation, reverse saturation current, series resistance and shunt resistance. This study shows the PV and IV characteristics curve for each model from which MPP (maximum power point) is determined. In this study, different PV and IV characteristic curves are gained for varying temperatures with specified solar irradiation conditions,s and then the MPP is tracked. The highest power output is also compared between the designed PV module and the selected PV module (MX Solar USA MX60-245)

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consist of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to LNG terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics.

In this paper, a new axial-type magnetic gear with auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AFS-AMG, it has the higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterpart in RMGs, AFS-AMGs achieve small size. Then, the geometrical parameters of AFS-AMGs are optimized to obtain the better electromagnetic performances, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

Solar energy is one of the most important renewable energy sources for many settings, such as households, solar-powered vehicles, power grid support, etc. However, the amount of solar energy produced is directly proportional to the surface area of the solar panel exposed to the sun rays, and so the challenges arise for the space required to install solar panels in order to get maximum area coverage of sun radiation energy absorption in constricted spaces such as vehicles powered by solar, small residential apartments, etc. The challenge with solar energy is that once a plate is installed, the space under it or above it cannot be used for another solar panel installation. This is due to the fact that the shadow cast by the above solar panel or structure prevents sun rays from reaching the below solar panel. The introduction of flexible solar panels that can take on any shape has given us the opportunity to explore the possibility of maximising solar plate surface area using surface manifold techniques. In this optimisation study, the constraint is that we have a space with fixed dimensions of length, width, and height, and the objective is to maximise the surface area of the panel within the given space dimensions to maximise solar energy absorption. Thus, this paper then proposes curving the panel surface and/or protruding the surfaces of the panel using the concept of a topology surface manifold to maximise the surface area: Protruding panel surface with shapes such as pyramidal, cone, hemisphere, sinusoidal wavy curve, etc. are analysed to check if the panel surface increases at all. The geometrical design result shows that the panel surface can increase by more than 20%. Although this paper only presents concept work and details of experimental design are yet to be performed, it should also be noted that curving surfaces have drawbacks, such as some protruding parts of solar panels when the plate is tilted, not facing the sun directly, which may cast shadows on parts of the solar panel, causing hindrance.

The battery energy storage systems (BESS) are most promising solution for increasing efficiency and flexibility of distribution networks (DNs) with significant penetration level of photovoltaic (PV) systems. There are various issues related to the optimal operation of DNs with integrated PV sys-tems and BESS that need to be addressed to maximize DN performance. This paper deals with the day-ahead optimal active-reactive power dispatching in unbalanced DNs with integrated sin-gle-phase PV generation and BESSs. The objectives are the minimization of cost for electricity, en-ergy losses in the DN and voltage unbalance at three-phase load buses by optimal managing of active and reactive power flows. To solve this highly constrained nonlinear optimization problem, a hybrid particle swarm optimization with sigmoid-based acceleration coefficients (PSOS) and cha-otic gravitational search algorithm (CGSA), called PSOS-CGSA algorithm, is proposed. A scenario based approach encompassing the Monte Carlo simulation (MCS) method with the simultaneous backward reduction algorithm is used for the probabilistic assessment of the uncertainty of PV generation and power of loads. The effectiveness of the proposed procedure is evaluated through the series test cases in a modified IEEE 13-bus feeder.

Power semiconductor devices are essential from the operation point of view, size, efficiency and cost, these components are used in a myriad of applications, providing features that make them an important part of the system in which they are operating. This document analyzes and compares the basic structure, properties, design aspects, as well as temperature performance, stability and switching losses, present in devices on silicon (Si), silicon carbide (SiC) and new generation devices fabricated in gallium nitride (GaN) applied in renewable energy systems. The main objective is determinate the viability of the new generation components, which present a superior performance in view of an increase in efficiency, conductivity, decreases in switching losses, lower resistances and parasitic capacitances as well as higher operating frequency range. Therefore demonstrating the GaN components are a strong and viable candidate to solve some of the problems present in renewable energy systems.

Abstract: Like other plants, every PV power plant will one day reach the end of its service life. Calculations show that 20,400 tons of PV waste will be generated worldwide by 2030 and 60.2 million tons by 2050, not including the mass of the support structure. Such large amounts of waste pose a potential threat to the environment and people if not properly managed. The paper analysed the options for dealing with PV waste, namely reuse, recycling and landfilling. For recycling as the best option in terms of environmental protection and circular economy, an overview of recycling technologies and the percentage of achievable recycling for the materials contained in each PV system component is given. In addition, the current situation of legislation and recycling of PV modules in Europe was examined with special reference to the Balkan countries. There are a small number of factories for recycling PV modules in Europe, but none in the Balkan countries. The main reason for this is the small amount of PV waste in these countries and consequently the economic unprofitability. For this reason, PV modules (after dismantling the aluminum frame and cables) are mostly disposed of as non-hazardous waste in landfills in these countries. Finally, the main barriers to faster implementation of PV module reuse and recycling are listed, along with guidelines for their removal. The cost of recycling Si modules is about ten times higher than the cost of disposal. To change this ratio in favor of recycling, cheaper recycling methods need to be developed and taxes on landfill disposal need to be increased.

This paper proposed three-dimensional numerical simulation method by coupling of electrostatic and fluid fields to evaluating the performance of electrical sensor in the concentration measurement of gas/solid two-phase flow. Compared with the static numerical simulation, this real-time dynamic 3-D simulation method can research on a designed capacitance sensor combining the dynamic characteristics of the two-phase flows for concentration measurement. Several fluid-electrostatic models of transmission pipes with different sensor structures are built. Under different test positions and different particle concentrations, the flow characteristics and the corresponding electric signals can be obtained, and the correlation coefficient between the concentration values and the capacitance values are used for performance evaluation of the sensors. The effects of flow regimes on concentration measurement are also been investigated in this paper. To validate the results of simulation, an experimental platform with horizontal straight pipe for phase volume concentration measurement of solid/air two-phase flow is built, and the experimental results agree well with simulation conclusions. The simulation and test results show that the coupling models can give constructive reference opinions for the sensor design and collection of installation position in different transmission pipelines, which are very important for the practical process of pneumatic conveying system.

Outliers seriously affect the accuracy in geometric model fitting. Previous works in coping with outliers involve threshold selection and scale estimation. However most scale estimators suppose the inlier distribution as a Gaussian model, which usually poorly meets the cases in geometric model fitting. Outliers, considered as the points with big residuals to all the true models, share common items with big values in quantized residual preferences, thus making the outliers gather away from the inliers in quantized residual preference space. In this paper we make use of this outlier consensus in quantized residual preference space by extending the usage of energy minimization to combine the model error and the spatial smoothness in quantized residual preference space for outlier detection. The energy minimization based outlier detection process follows an alternate sampling and labeling framework. After the outlier detection, an ordinary energy minimization method is employed to optimize the inlier labels, which also follows the framework of alternate sampling and labeling. The experimental results in this study show that the energy minimization based outlier detection method can detect most of the outliers in the data. Furthermore, the proposed energy minimization based inlier segmentation process segments the inliers into different models quite accurately. Overall, the performance of the proposed method is better than most of the state-of-the-art methods.

A novel hybrid CSP-PV power plant is presented. Instead of the integration used in current hybrid power plants, where part of the PV production is charged into the thermal energy storage system through electrical resistors, the proposed system integrates both PV and thermal solar fields using a high-temperature heat pump. Both the heat pump and the heat engine are based on Brayton supercritical CO2 thermodynamic cycles. Such integration allows for charging the molten salts storage as if a central tower receiver field supplied the thermal energy, whereas parabolic trough collectors are employed. Unlike conventional hybrid plants, where the storage of PV production leads to a decrease in power injected into the grid throughout the day, the power injected by the proposed system remains constant. The heat engine efficiency is 44.4%, and the COP is 2.32. The LCOE for a 50 MWe plant with up to 12 hours of storage capacity is 171 $/MWh, which is lower than that of existing CSP power plants with comparable performance. Although the cost is higher compared to a PV plant with batteries, this hybrid system offers two significant advantages: it eliminates the consumption of critical raw materials in batteries, and all the electricity produced comes from a synchronous machine.

A high step-up DC-to-DC converter that integrates an isolated transformer and a switched-clamp capacitor is presented in this study. The voltage stress of the main power switch should be clamped to 1/4 V by using the turn ratio and switched-clamp capacitor of an isolated transformer to achieve a high voltage gain. In addition, a passive clamp circuit is employed reduce voltage stress on the main power switch. The energy of the leakage inductor can be recycled by the clamp capacitor because of the passive clamp circuit, thereby improving the power converter efficiency. The converter consists of one isolated transformer, one main switch, three capacitors, and four diodes. Operating principle and steady-state analyses are also discussed. Finally, a 24-V-input voltage to 200-V-output voltage and a 150 W output power prototype converter are fabricated in the laboratory. The maximum efficiency of the converter is 95.1 at 60 W.

This paper generalizes the actuator disc theory to the application of crosswind kite power systems. For simplicity, it is assumed that the kite sweeps an annulus in the air, perpendicular to the wind direction (i.e. straight downwind configuration with tether parallel to the wind). It is further assumed that the wind flow has a uniform distribution. Expressions for power harvested by the kite is obtained, where the effect of the kite on slowing down the wind (i.e. the induction factor) is taken into account. It is shown that although the induction factor may be small for a crosswind kite (of the order of a few percentage points), neglecting it in calculations may result in noticeable overestimation of the amount of power harvestable by a crosswind kite system.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system's safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system's safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Specifically leveraging the Lyapunov function, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

Accurately predicting the power of solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for the balanced operation and optimized dispatch of the power grid system, and reduces operating costs. Solar PV power generation depends on weather conditions, which are prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility and intermittency. Recently, the demand for further investigation and effective use on the uncertainty of short-term solar PV power generation prediction has been getting increasing attention in many application of renewable energy sources. In order to improve the predictive accuracy of output power of solar PV power generation and develop a precise predictive model, the authors worked predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is one of the important aspects for optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods in the reviewed literature are classified according to the input data source used for accurate predictive models, and the case studies and examples proposed are analyzed in detail. The contributions, advantages and disadvantages of the predictive probabilistic methods are compared. Finally, the future studies of short-term solar PV power forecasting is proposed.

We suppose that a Cooper pair (CP) will experience a damping force exerted by the condensed matter. A Langevin equation of a CP in two dimensional condensed matter is established. Following a method similar to Nelson's stochastic mechanics, generalized Schr\"{o}dinger equation of a CP in condensed matter is derived. If the CPs move with a constant velocity, then the corresponding direct current (DC) electrical conductivity can be calculated. Therefore, a Drude like formula of resistivity of CPs is derived. We suppose that the damping coefficient of CPs in two dimensional cuprate superconductors is a linear function of temperature. Then the resistivity and scattering rate of CPs turn out to be also linear-in-temperature. The origin of linear-in-temperature resistivity and Planckian dissipation in cuprate superconductors may be the linear temperature dependence of the damping coefficient of CPs.

Solar photovoltaic (PV) generation suffers from multiple serious issues, for example shading effects directly affect the performance of installed PV arrays. By studying these issues, it can play a key role in improving the limitations and performance of PV panels. The study of PV arrays under partial shading conditions is mentioned in many papers as a part of other studies and as an independent phenomenon. Therefore, it is essential to improve these studies and update them with new research and study this problem in detail to know how to limit the effects of partial shading irradiance conditions on PV arrays performances, including economical aspects. This paper proposes to study the effects of different partial shading irradiance conditions for a PV array in various configurations. The analysis is proved in detail through the simulation of the proposed PV array in the Matlab/Simulink environment. The obtained results show the performances of interconnected PV arrays under different partial shading irradiance and temperature conditions, including the solar array I-V and P-V characteristics. In addition, different PV array configurations with and without bypass and blocking protection diodes improve and minimize the effect of partial shading conditions and avoid the reductions in output power.

The increasing deployment of intermittent renewable energy sources necessitates effective methods for balancing the electrical grid to ensure stability and safety. Energy storage systems that can store excess electrical power during periods of low demand and release it during high demand are crucial for both short-term and long-term applications. Power-to-Gas is an innovative energy storage solution that converts surplus renewable electricity into gas fuels, such as hydrogen, which can be stored and used later. This hydrogen can serve multiple purposes, including energy storage and as fuel for transportation modes like cars, trams, trains, and buses. While most hydrogen today is derived from fossil fuels, solid-oxide electrolysis (SOE) offers a cleaner alternative by producing hydrogen without harmful emissions. SOE is also the most efficient method of electrolysis. This study aims to determine the operational parameters for an SOE system, including lower heating value (LHV)-based efficiency and total input power, using a mathematical model. Results are presented for three different operating temperatures and four different steam utilization ratios. The paper is structured as follows: the introduction provides the context and motivation for the study; the second section outlines the fundamentals of electrolysis and its various types; the third section delves into solid-oxide electrolysis and electrolyser systems; the fourth section describes the methodology, including the mathematical formulation and simulation software used; the fifth section presents the calculation results and conclusions; and the final section summarizes the findings.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

The aim of this research work is to study the influence of temperature, solar irradiation and wind dust on the production of solar energy. In addition, it aims to study the assessment of water quality, in particular groundwater used for cooling and cleaning photovoltaic panels (quality analysis). it's an important source, stable and sustainable can be used entirely for cooling and cleaning PV modules from the sales dust, in order to improve their performance. To date, all research studies carried out in this context of cooling and cleaning using water have not taken account of the nature of this water. We have taken a sample of water from a well in a rural area in the Darra Tafilalet region of Morocco. This region has an arid and semi-arid climate, with very high temperatures and dust-laden winds, particularly in summer. These meteorological characteristics influence the efficiency of cells, particularly photovoltaic (PV) panels, reducing their performance. Next, the water sample was analysed in the laboratory. The results obtained show that the groundwater in this area is very sensitive to cooling and cleaning photovoltaic panels. Moreover, these results indicate that the temperature, solar irradiation and wind-blown dust have a significant influence on energy production.

The new unique technology for granulation of metals and nonmetallic melts is presented in the paper. It was created on the newly developed controlled film flow disintegration with rapid cooling of the droplets and solidified granules in amorphous state. Three new phenomena discovered by us created the base for 3 different methods for granulation of metals and nonmetallic melts in a wide range of granules’ sizes. Electromagnetic resonance method allows producing granules of metals in the range of 1 mm and over. The soliton-like vibrational regime is available for production of any granulation material in the range 0.1-1 mm. Moreover, the shockwave regime is applicable for producing particles from 10 micrometers by size. All methods produce particles of the stated size with a narrow distribution by size: deviation from the average size no more than 50%. The other known methods do not produce granules with such high cooling rate, which can realize the amorphous state and cannot regulate size of particles in such bright but narrow range from 10 micrometers to 1-2 mm.

The purpose of this paper is to explore the implementation of Retrieval-Augmented Gen- 14 eration (RAG) technology with open-source Large Language Models (LLMs). For this purpose, a 15 web-based application named PaSSER that integrates RAG with three models - Mistral:7b, 16 Llama2:7b: Orca2:7b, is developed. In its development various technologies are used, including 17 Blockchain for managing and storing assessment results of built-in testing modules. PaSSER em- 18 ploys a set of evaluation metrics for LLMs’ performance - METEOR, ROUGE, BLEU, Perplexity, 19 Cosine similarity, Pearson correlation, and F1 score. The specialized knowledge base is in the smart 20 agriculture domain. As an illustration, the paper presents results and analysis of two tests. First one 21 is assessing the performance of LLMs across different hardware configurations, the other is deter- 22 mining the model that delivers most accurate and contextually relevant responses within the RAG. 23 The paper also discusses the integration of blockchain technology with LLMs to manage and store 24 assessment results within a blockchain environment. The tests revealed that Mistral:7b demon- 25 strates the best results across most of the evaluated metrics. The discussion section comments on 26 technical and hardware considerations that affect the performance of LLMs. Future development 27 will focus in leveraging new larger pre-trained open-source LLMs, exploring different finetuning 28 approaches, and further integration with Blockchain and IPFS.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.

This paper studies a multihop amplify-and-forward (AF) simultaneous wireless information and power transfer (SWIPT) relaying system. Each relaying node harvests energy using power splitting (PS) scheme from a part of its received signal to amplify and forward the rest the received signal to the next relay. Based on this system model and signal flow, we derived and solved the convex energy minimization problem with optimal PS ratio. The influence of processing cost was then investigated for the AF-SWIPT system with the decode-and-forward SWIPT as benchmark, where AF-SWIPT was found to be superior.

Building energy consumption is the main urban energy consumption component, which mainly serves people-centered work and living energy demands. Based on the physical requirements of humans in urban buildings and to determine the comfortable body temperature in each season, this paper establishes a sizing optimization model of building-type integrated energy systems (IES), where the cooling and heating loads required to maintain indoor somatosensory body comfortable temperature are calculated. Depending on the external energy price, internal power balance and other constraints, the model develops an optimal sizing and capacity panning method of energy conversion and storage unit in a building-type IES with PV generation. The operating principle is described as follows: the PV generation is fully consumed, a gas engine satisfies the electric and thermal base load requirements, while the power system and a heat pump supply the remaining loads. The gas price, peak-valley electricity price gap and heat-topower ratio of gas engines are considered as important factors for the overall techno-economic analysis. The developed method is applied to optimize the economic performance of building-type IES and verified by practical examples. The results show that using the complementary characteristics of different energy conversion units is important to the overall IES cost.

X-ray Fluorescence Computed Tomography (XFCT) is an emerging non-invasive imaging technique providing molecular-level data, gaining attention for high-resolution imaging. However, increased sensitivity with benchtop X-ray sources raises radiation exposure. Artificial Intelligence (AI), particularly deep learning (DL), has revolutionized medical imaging by delivering high-quality images in the presence of noise. In XFCT, traditional methods rely on complex algorithms for noise reduction, but AI holds promise in addressing high-dose concerns. We present an optimized SCUNet model for noise reduction in low-concentration XRF images. Compared to higher-dose images, our method’s effectiveness is evaluated. While various denoising techniques exist for X-ray and CT, few address to XFCT. The DL model is trained and assessed using the augmented data, focusing on background noise reduction. Image quality is measured using PSNR and SSIM, comparing outcomes with 100% X-ray dose images. Results show the proposed algorithm achieves high-quality images from low-dose and low-contrast agents, with a maximum PSNR of 39.05 and SSIM of 0.86. The model outperforms BM3D, NLM, DnCNN, and SCUNet in both visual inspection and quantitative analysis, particularly in high-noise scenarios. This indicates the potential of AI, specifically the SCUNet model, in significantly improving XFCT imaging by mitigating the trade-off between sensitivity and radiation exposure.

One of the major problem of photovoltaic grid integration is limiting the solar-induced imbalances since these can undermine the security and stability of the electrical system. Improving the forecast accuracy of photovoltaic generation is becoming essential to allow a massive solar penetration. In particular, improving the forecast accuracy of large solar farms generation is important both for the producers/traders to minimize the imbalance costs and for the Transmission System Operators to insure stability. In this article, we provide a benchmark for the day-ahead forecast accuracy of utility scale PV plants in 1325 locations spanning the country of Italy. We then use these benchmarked forecasts and real energy prices to compute the economic value of forecast accuracy and accuracy improvement in the context of the Italian energy market regulatory framework. Through this study, we further point out some several important criticisms of the Italian “single pricing” system that brings to paradoxical and counterproductive effects regarding the need to reduce the imbalance volumes. Finally, we propose a new market-pricing rule and innovative actions to overcome these undesired effects of the current dispatching regulations.

The rapid growth of the sharing economy has propelled shared mobility to the forefront of attention. The continuous advancement of autonomous driving technology also brings new opportunities and challenges to the shared mobility market. This study comprehensively analyzes the market potential concerning autonomous vehicles (AVs) to provide shared mobility services, utilizing SWOT analysis, PESTLE analysis, and Porter’s Five Forces. The findings reveal that AVs can provide improved shared mobility services by increasing transportation safety, reducing emissions, reducing costs, enhancing traffic efficiency, and increasing customer satisfaction and the profitability of shared mobility services. However, challenges such as technological and policy uncertainties, safety concerns, high initial costs, inadequate public communication infrastructure, and the absence of standardized regulations can hinder the widespread adoption of AVs. The benefits are also restricted with a low market penetration rate of AVs. To promote AVs in the shared mobility market, this study also provides implications for AV stakeholders tailored to the evolving shared mobility market dynamics.

This study evaluates the effectiveness of Darknet53 and VGG16 pretrained models to develop a hybrid ensemble model named HybridPlantNet23 deep learning model for effective plant disease classification, using the publicly available dataset ‘A Dataset of Leaf Images’ from Mendeley webpage. The preprocessing of the dataset involved resizing the images to the respective requirements of the pretrained models to develop a combined datastore which served as the input. All experiments were conducted using MATLAB R2023b deep network designer interface. Model performances were evaluated based on the conventional accuracy, precision, recall, and F1-score. VGG16 achieved the highest accuracy (97.25%), precision (96.30%), recall (96.96%), and F1-score (96.55%), outperforming both Darknet53 and the ensemble HybridPlantNet23 model. Despite the advanced architecture of the ensemble model, its performance (accuracy: 95.37%, precision: 93.56%, recall: 94.20%, F1-score: 93.74%) was not significantly superior, thus highlighting scientific reasons behind this development. Recommendations for future works were provided accordingly.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

Grid energy storage system for PV Applications is connected with three different power sources i.e. PV Array, Battery and the Grid. It is advisable to have Isolation between these three different sources to provide safety for the equipment. The configuration proposed in this paper provides the complete isolation between the three sources. A Power Balancing Control (PBC) for this configuration is proposed to operate the system in three different modes of operation. Control of a dual active bridge (DAB) based battery charger which provides a galvanic isolation between batteries and other sources is explained briefly. Various modes of operation of a Grid energy storage system are also presented in this paper. Hardware-In-Loop (HIL) Simulation is carried out to check the performance of the system and the PBC algorithm. Power circuit (comprises of inverter, dual active bridge based battery charger, grid, PV cell, batteries, contactors and switches) is simulated and the controller hardware and user interface panel are connected as HIL with the simulated power circuit through Real Time Digital Simulator (RTDS). HIL simulation results are presented to explain the control operation, steady state performance in different modes of operation and the dynamic response of the system.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

Design process can be divided into stages of analysis and synthesis. This paper presents the application of the object-oriented technique to the synthesis as well as theoretical considerations concerning to design of gear size ranges. The synthesis consists in composition (aggregation) of single details into units – according to the rule: “from details up to general view”. The basics of the object-oriented synthesis were formulated. Furthermore, the consecutive stages of synthesis were formulated. The interactions between these stages were discussed. Some aspects were described in details e.g.: creation of interfaces for performance of the embodiment phase the designed artefact via CAD system drawing with specification (dimensions are still parametrically set). The final step of the procedure is creation of so called substantial (concrete) form of artifacts i.e. final assignment of dimensions which stored in the special data base. The final stage of the design process is issuing of technical documentation (technical drawings) created automatically via CAD system. The considerations are terminated by the list of advantages connected with the object-oriented approach to design (in comparison to the traditional way).

The self-assembly is an intriguing process of great importance in general soft matter and thus re-ceives an ever-growing scientific interest. Its most trivial example is the entropy-driven crystalli-zation of hard spheres. Past works have established the similarities and differences in the phase behavior of monomers and chains made of hard spheres. In the present work, inspired by the dif-ference in the melting points of the pure components, we study, through Monte Carlo simulations, the phase behavior of athermal mixtures composed of fully flexible polymers and individual monomers of uniform size. We analyze how the relative number fraction and the packing density affect crystallization and the established ordered morphologies. A synergetic effect is observed in the crystallization leading to synchronous crystallization of the two species. Structural analysis of the resulting ordered morphologies shows perfect mixing and thus no phase separation, which could split the system into polymer-rich and monomer-rich domains. Due to the constraints imposed by chain connectivity, the local environment of the individual spheres is systematically more spherical and more symmetric compared to the one of spheres belonging to chains. In general, increasing polymer content leads to reduction of the degree of crystallinity. According to the present findings, relative concentration is another determining factor to control the phase behavior of hard colloidal systems based on polymers.

Bats are natural hosts for a wide variety of viruses and could therefore be the reservoir, source and spillover for several mammalian viruses, including adenoviruses. We examined faecal samples from 12 bats (five species of the family Vespertilionidae) from central Russia (Moscow Region) and found that two of them (16%) were positive for mastadenoviruses. One of the identified viruses was isolated from faeces of Nyctalis noctule and named BatAdV/MOW15-Nn19. The genome of the BatAdV/MOW15-Nn19 was 37,915 bp in length. Comparative studies have shown significant divergence of the nucleotide sequence of the BatAdV/MOW15-Nn19 genome from European mastadenoviruses, the highest genome sequence similarity was to isolate WA3301 from an Australian bat. Phylogenetic analysis, as well as a comparative analysis of the protein coding genes to homologous available from public database, showed evidence of novel species within Mastadenovirus genus (preliminary named «K»). Analysis of the short part of the DNA polymerase gene of BatAdV/MOW15-Nn19 reveals few NCBI records from Europe with high identity to the new virus, i.e. mastadenoviruses related to the new species are circulating in European bats. The partial genome of another mastadenovirus from Pipistrellus nathusii was identified, it represented bat adenovirus species B, which is widely distributed in European Vespertilionidae bats. New data on the genetic diversity of bat adenoviruses may help to better understand how they evolved in mammals and spread across Europe.

This article develops a multi-perspective view on motivations and methods for tobamovirus purification through the ages, and presents a novel efficient, easy-to-use approach that can be well-adapted to different species of native and functionalized virions. We survey the various driving forces prompting researchers to enrich tobamoviruses: from the search for the causative agents of mosaic diseases in plants to their increasing recognition as versatile nanocarriers in biomedical and engineering applications. Best practice and also rarely applied options for the serial processing steps required for successful isolation of tobamoviruses are then reviewed. Adaptations for distinct particle species, pitfalls and 'forgotten' or underrepresented technologies are considered as well. The article is topped off with our own development of a method for virion preparation, rooted in historical protocols. It combines selective re-solubilization of polyethylene glycol (PEG) virion raw precipitates with density step gradient centrifugation in biocompatible iodixanol formulations, yielding ready-to-use particle suspensions. This newly established protocol and some considerations for perhaps worthwile further developments could serve as putative stepping stones towards preparation procedures appropriate for routine practical uses of the multivalent soft-matter nanorods.

This paper presents some results from a computational fluid dynamics (CFD) model of a multi-megawatt crosswind kite spinning on a circular path in a straight downwind configuration. The unsteady Reynolds averaged Navier-Stokes equations closed by the k−ω SST turbulence model are solved in the three-dimensional space using ANSYS Fluent. The flow behaviour is examined at the rotation plane, and the overall (or global) induction factor is obtained by getting the weighted average of induction factors on multiple annuli over the swept area. The wake flow behaviour is also discussed in some details using velocity and pressure contour plots. In addition to the CFD model, an analytical model for calculating the average flow velocity and radii of the annular wake downstream of the kite is developed. The model is formulated based on the widely-used Jensen’s model (Technical Report Risø-M; No. 2411, 1983), which was developed for conventional wind turbines, and thus has a simple form. Expressions for the dimensionless wake flow velocity and wake radii are obtained by assuming self-similarity of flow velocity and linear wake expansion. Comparisons are made between numerical results from the analytical model and those from the CFD simulation. The level of agreement was found to be reasonably good. Such computational and analytical models are indispensable for kite farm layout design and optimization, where aerodynamic interactions between kites should be considered.

The growing interest in the use of energy storage systems to improve the performance of tramways has prompted the development of control techniques and optimal storage devices, displacement, and sizing to obtain the maximum profit and reduce the total installation cost. Recently, the rapid diffusion of renewable energy generation from photovoltaic panels has also created a large interest in coupling renewable energy and storage units. This study analyzed the integration of a photovoltaic power plant, supercapacitor energy storage system, and railway power system. Random optimization was used to verify the feasibility of this integration in a real tramway electric system operating in the city of Naples, and the benefits and total cost of this integration were evaluated.

Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tool and the left-handed tool. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. For gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP.

The solar heat pump system based on photovoltaic photothermal (PV/T) modules is a new technology that integrates photovoltaics and heat reutilization. It can improve the efficiency of photovoltaic conversion and recovery the remaining heat generated during the photoelectric conversion process, thereby improving the comprehensive efficiency of the whole system. Simulink software is a visual simulation tool, which has the advantages of strong applicability, clear simulation structure and calculation process. The design of its graphical programming greatly simplifies the user modeling process and allows for direct viewing of results, therefore improving the simulation efficiency. At present, there is little research on simulation of solar heat pump systems with PV/T modules based on Simulink software. In this paper, the mathematical models of each component contained in the PV/T solar heat pump system were established based on the system working principle, energy conservation equations and some empirical formulas, and then the operation of the system was simulated by the Simulink software. Due to the operation characteristic of PV/T modules is the key influencing factor for the whole system performance, the model and simulation process for PV/T modules were introduced in detail. The results showed that the surface temperature of PV/T modules under different operating conditions was significantly lower than that of ordinary photovoltaic panels, and the temperature change amplitude was lower. As results, the power generation and photovoltaic conversion efficiency were significantly improved.

Load Frequency Control (LFC) is essential to ensure the stability and performance of power systems. In this study, a novel optimization algorithm, the Sea Horse Optimizer (SHO) algorithm, was proposed for optimizing controller parameters in LFC problems of power system. The proposed SHO algorithm was tested on a two-area power system with photovoltaic system and reheat thermal units, under three different scenarios: a 10% load change in both area, large load disturbances, and varying solar radiation. The proposed algorithm optimizes the gain parameters of PI/PID controllers using performance metrics such as Integral of Absolute value of the Error (IAE), Integral of Square Error (ISE), Integral of Time multiplied by Square Error (ITSE), and Integral of Time multiplied by Absolute Error (ITAE). The study compared the performance of the SHO-optimized controller with other reported optimization algorithms such as Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA). The study also evaluated the accuracy and effectiveness of the controllers using performance metrics such as Settling Time, Overshoot (M+), and Undershoot (M-). The results show that the SHO-tuned controller significantly reduces overshoot, undershoot, and settling time of the system oscillations compared to other algorithms. The study provides valuable insights into the optimization of LFC controllers and presents a significant contribution to the literature with its novel algorithm. The proposed SHO algorithm can be considered an effective alternative solution method for LFC in power systems.

The article delves into the crucial realm of compressing hydrogen in its gaseous state, a pivotal process for enhancing its viability in both civil and industrial sectors. The study initiates by providing a concise overview and comparison of diverse hydrogen storage methodologies, laying the groundwork with an in-depth analysis of hydrogen's thermophysical properties. It scrutinizes plausible configurations for hydrogen compression, aiming to strike a delicate balance between energy consumption, predominantly derived from the fuel itself, and the requisite number of compression stages. Notably, to render hydrogen storage competitive in terms of volume, pressures of at least 350 bar are deemed essential, albeit at an energy cost amounting to approximately 10% of the fuel's calorific value. Multi-stage compression emerges as a crucial strategy, not solely for energy efficiency, but also to curtail temperature rise, with an upper limit set at 200°C. This nuanced approach is underlined by the exploration of compression levels commonly cited in the literature, particularly 350 bar and 700 bar. Ultimately, the study advocates for a three-stage compression system as a pragmatic compromise, capable of achieving high-pressure solutions while keeping compression work below 10 MJ/kg, a threshold indicative of sustainable energy utilization.