bluuebunny/crossref_metadata_2025 huggingface dataset:
Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework
Authors: Zhang Wei , Salmi Abderrahmane , Yang Chifu , Jiang Feng
Published: August 2024

Low-dose computed tomography (LDCT) imaging is a critical tool in medical diagnostics due to its reduced radiation exposure. However, this reduction often results in increased noise levels, compromising image quality and diagnostic accuracy. Despite advancements in denoising techniques, a robust method that effectively balances noise reduction and detail preservation remains a significant need. Current denoising algorithms frequently fail to maintain the necessary balance between suppressing noise and preserving crucial diagnostic details. Addressing this gap, our study focuses on developing a deep learning-based denoising algorithm that enhances LDCT image quality without losing essential diagnostic information. Here we present a novel supervised learning-based LDCT denoising algorithm that employs innovative noise extraction and denoising techniques. Our method significantly enhances LDCT image quality by incorporating multiple attention mechanisms within a U-Net-like architecture. Our approach includes a noise extraction network designed to capture diverse noise patterns precisely. This network is integrated into a comprehensive denoising system consisting of a generator network, a discriminator network, and a feature extraction AutoEncoder network. The generator network removes noise and produces high-quality CT images, while the discriminator network differentiates real images from denoised ones, improving the realism of the outputs. The AutoEncoder network ensures the preservation of image details and diagnostic integrity. Our method improves the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 7.777 and 0.128 compared to LDCT, by 0.483 and 0.064 compared to residual encoder–decoder convolutional neural network (RED-CNN), by 4.101 and 0.017 compared to Wasserstein generative adversarial network–visual geometry group (WGAN-VGG), and by 3.895 and 0.011 compared to Wasserstein generative adversarial network–autoencoder (WGAN-AE). This demonstrates that our method has a significant advantage in enhancing the signal-to-noise ratio of images. Extensive experiments on multiple standard datasets demonstrate our method’s superior performance in noise suppression and image quality enhancement compared to existing techniques. Our findings significantly impact medical imaging, particularly improving LDCT scan diagnostic accuracy. The enhanced image clarity and detail preservation offered by our method open new avenues for clinical applications and research. This improvement in LDCT image quality promises substantial contributions to clinical diagnostics, disease detection, and treatment planning, ensuring high-quality diagnostic outcomes while minimizing patient radiation exposure.
A New Sum-Channel Radiating Element for a Patch-Monopole Monopulse Feed
Authors: Miklavčič Peter , Batagelj Boštjan
Published: August 2024

This article describes a new design for the sum-channel, circularly-polarized, radiating patch element of a multimode patch-monopole monopulse feed. Such feeds are suitable for the prime-focus illumination of a deep symmetric reflector. A relatively simple and compact feed design allows for a compact single-mirror antenna with monopulse tracking. The feed is about a wavelength in diameter, also making it suitable for illuminating smaller antennas, for example, in LEO-satellite ground (client) stations. The design applies a slotted circular patch as a circularly-polarized sum-channel element. The design variables are optimized mainly for the sum-channel aperture illumination efficiency in an S-band satellite ground station. After a few cut-and-try iterations, the final feed prototype was produced and measured. In addition to an additional degree of freedom in the geometry and potentially easier fabrication, the new sum-channel radiating-element shape allows for a slightly better monopulse channel isolation, and a higher radiation-pattern symmetry compared to the previous design.
Reinforcement Learning-Based Energy-Saving Path Planning for UAVs in Turbulent Wind
Authors: Chen Shaonan , Mo Yuhong , Wu Xiaorui , Xiao Jing , Liu Quan
Published: August 2024

The unmanned aerial vehicle (UAV) is prevalent in power inspection. However, due to a limited battery life, turbulent wind, and its motion, it brings some challenges. To address these problems, a reinforcement learning-based energy-saving path-planning algorithm (ESPP-RL) in a turbulent wind environment is proposed. The algorithm dynamically adjusts flight strategies for UAVs based on reinforcement learning to find the most energy-saving flight paths. Thus, the UAV can navigate and overcome real-world constraints in order to save energy. Firstly, an observation processing module is designed to combine battery energy consumption prediction with multi-target path planning. Then, the multi-target path-planning problem is decomposed into iterative, dynamically optimized single-target subproblems, which aim to derive the optimal discrete path solution for energy consumption prediction. Additionally, an adaptive path-planning reward function based on reinforcement learning is designed. Finally, a simulation scenario for a quadcopter UAV is set up in a 3-D turbulent wind environment. Several simulations show that the proposed algorithm can effectively resist the disturbance of turbulent wind and improve convergence.
Dynamic Radiant Barrier for Modulating Heat Transfer and Reducing Building Energy Usage
Authors: Stevens Tyler R. , Parsi Behzad , Mulford Rydge B. , Crane Nathan B.
Published: August 2024

Buildings consume significant energy, much of which is used for heating and cooling. Insulation reduces undesired heat transfer to save on heating and cooling energy usage. Radiant barriers are a type of insulation technology that reduces radiant heat absorbed by a structure. Applying radiant barriers to buildings reduces costs and improves both energy efficiency and occupant comfort. However, homes often have favorable thermal gradients that could also be used to reduce energy usage if the insulation properties were switched dynamically. This article introduces two dynamic radiant barriers intended for residential attics, which can switch between reflecting and transmitting states as needed. These radiant barriers are manufactured as a single deformable assembly using sheet materials and are compatible with various actuation mechanisms. The efficacy of these radiant barriers is reported based on a hotbox experiment and numerical calculations. The experimental results demonstrate that both proposed dynamic radiant barrier designs increase effective thermal resistance by factors of approximately 2 when comparing insulating to conducting states, and by approximately 4 when comparing the insulating state to the case without a radiant barrier. Additionally, the dynamic radiant barriers achieve heat flux reductions up to 41.9% in the insulating state compared to tests without a dynamic radiant barrier.
Pitch Actuator Fault-Tolerant Control of Wind Turbines via an L1 Adaptive Sliding Mode Control (SMC) Scheme
Authors: Fayazi Ali , Ghayoumi Zadeh Hossein , Ahmadian Hossein , Ghane Mahdi , Seryasat Omid Rahmani
Published: August 2024

Effective fault identification and management are critical for efficient wind turbine operation. This research presents a novel L1 adaptive-SMC system designed to enhance fault tolerance in wind turbines, specifically addressing common issues such as pump wear, hydraulic leakage, and excessive air content in the oil. By combining SMC with L1 adaptive control, the proposed technique effectively controls rotor speed and power, ensuring reliable performance under various conditions. The controller employs an adjustable gain and an integrated sliding surface to maintain robustness. We validate the controller’s performance in the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) simulation environment using a 5-megawatt wind turbine under high wind speeds. Simulation results demonstrate that the proposed L1 adaptive-SMC outperforms traditional adaptive-SMC and adaptive control schemes, particularly in the presence of faults, unknown disturbances, and turbulent wind fields. This research highlights the controller’s potential to significantly improve the reliability and efficiency of wind turbine operations.
A Review of Life Cycle Assessment (LCA) Studies for Hydrogen Production Technologies through Water Electrolysis: Recent Advances
Authors: Shaya Negar , Glöser-Chahoud Simon
Published: August 2024

Climate change is a major concern for the sustainable development of global energy systems. Hydrogen produced through water electrolysis offers a crucial solution by storing and generating renewable energy with minimal environmental impact, thereby reducing carbon emissions in the energy sector. Our research evaluates current hydrogen production technologies, such as alkaline water electrolysis (AWE), proton exchange membrane water electrolysis (PEMWE), solid oxide electrolysis (SOEC), and anion exchange membrane water electrolysis (AEMWE). We systematically review life cycle assessments (LCA) for these technologies, analyzing their environmental impacts and recent technological advancements. This study fills essential gaps by providing detailed LCAs for emerging technologies and evaluating their scalability and environmental footprints. Our analysis outlines the strengths and weaknesses of each technology, guiding future research and assisting stakeholders in making informed decisions about integrating hydrogen production into the global energy mix. Our approach highlights operational efficiencies and potential sustainability enhancements by employing comparative analyses and reviewing advancements in membrane technology and electrocatalysts. A significant finding is that PEMWE when integrated with renewable energy sources, offers rapid response capabilities that are vital for adaptive energy systems and reducing carbon footprints.
Should We Have Selfish Microgrids?
Authors: Feleafel Hanaa , Radulovic Jovana , Leseure Michel
Published: August 2024

Substantial breakthroughs in renewable energy have been made in order to reduce energy-induced climate change. Yet our reliance on these sources is still insufficient. The UK’s objective of attaining net-zero emissions by 2050 is highly dependent on shifting to an electrical system that exclusively relies on zero-carbon generation. This entails integrating renewable energy sources, along with other low-carbon sources such as nuclear power, into the energy mix. However, the primary barrier to incorporating additional renewable energy sources into the grid is their intermittent and volatile nature. Therefore, there is a pressing need to stabilise the generation of renewables and manage this volatility by enhancing the balancing mechanism between microgrids and the national grid. This paper examines previous research on microgrids and smart grids, specifically from a supply chain perspective. It has been observed that the majority of the current literature focuses on documenting selfish microgrids that strive to optimise performance at the microgrid level. However, there is an alternative approach that draws inspiration from the field of supply chain management. Consequently, it is possible to enhance a microgrid’s performance within the broader system that it belongs to by reconsidering the timing and location of storage utilisation.
On the Thermal Stability of Selected Electrode Materials and Electrolytes for Na-Ion Batteries
Authors: Samigullin Ruslan R. , Bobyleva Zoya V. , Zakharkin Maxim V. , Zharikova Emiliya V. , Rozova Marina G. , Drozhzhin Oleg A. , Antipov Evgeny V.
Published: August 2024

Sodium-ion batteries are a technology rapidly approaching widespread adoption, so studying the thermal stability and safety of their components is a pressing issue. In this work, we employed differential scanning calorimetry (DSC) and ex situ powder X-ray diffraction to study the thermal stability of several types of sodium-ion electrolytes (NaClO4 and NaPF6 solutions in PC, EC, DEC, and their mixtures) and various cathode and anode materials (Na3V2(PO4)3, Na3(VO)2(PO4)2F, β-NaVP2O7, and hard carbon) in combination with electrolytes. The obtained results indicate, first, the satisfactory thermal stability of liquid Na-ion electrolytes, which start to decompose only at 270~300 °C. Second, we observed that charged vanadium-based polyanionic cathodes, which appear to be very stable in the “dry” state, demonstrate an increase in decomposition enthalpy and a shift of the DSC peaks to lower temperatures when in contact with 1 M NaPF6 in the EC:DEC solution. However, the greatest thermal effect from the “electrode–electrolyte” interaction is demonstrated by the anode material: the heat of decomposition of the soaked electrode in the charged state is almost 40% higher than the sum of the decomposition enthalpies of the electrolyte and dry electrode separately.
An Innovative Double-Frontier Approach to Measure Sustainability Efficiency Based on an Energy Use and Operations Management Perspective
Authors: Zhang Linyan , Xu Chunhao , Zhang Jian , Lei Bingyin , Xie Anke , Shen Ning , Li Yujie , Gao Kaiye
Published: August 2024

China’s economic development has achieved great success in recent years, but the problems of energy scarcity and environmental pollution have become increasingly serious. To enhance the reliability and efficiency between energy, the environment and the economy, sustainable development is an inevitable choice. In the context of measuring sustainability efficiency, a network data envelopment analysis model is proposed to formulate the two-stage process of energy use and operations management. A double frontier is derived to optimize the available energy for sustainable development. Due to nonlinearity, previous linear methods are not directly applicable to identify the double frontier and calculate stage efficiencies for inefficient decision-making units. To address this problem, this study develops the primal-dual relationship between multiplicative and envelopment network models based on the Lagrange duality principle of parametric linear programming. The newly developed approach is used to evaluate the sustainability efficiency of 30 administrative regions in China. The results show that insufficient sustainability efficiency is a systemic problem. Different regions should take different measures to conserve energy and reduce pollutant emissions for sustainable development. To increase sustainability efficiency, regions should support energy-saving and emission-reducing technologies in production processes and strengthen their capacity for technological innovation. Compared with energy use efficiency, operations management efficiency in China has a wider range of changes. During the operations management stage, there is not much difference between the capacity and quantity of each region. Based on benchmark regions at the efficiency frontier, there is an opportunity to improve operations management in the near future. Blockchain technology can effectively improve energy allocation efficiency.
Deep Learning Models for PV Power Forecasting: Review
Authors: Yu Junfeng , Li Xiaodong , Yang Lei , Li Linze , Huang Zhichao , Shen Keyan , Yang Xu , Yang Xu , Xu Zhikang , Zhang Dongying , Du Shuai
Published: August 2024

Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy management. In recent years, deep learning technology has made significant progress in time-series forecasting, offering new solutions for PV power forecasting. This study provides a systematic review of deep learning models for PV power forecasting, concentrating on comparisons of the features, advantages, and limitations of different model architectures. First, we analyze the commonly used datasets for PV power forecasting. Additionally, we provide an overview of mainstream deep learning model architectures, including multilayer perceptron (MLP), recurrent neural networks (RNN), convolutional neural networks (CNN), and graph neural networks (GNN), and explain their fundamental principles and technical features. Moreover, we systematically organize the research progress of deep learning models based on different architectures for PV power forecasting. This study indicates that different deep learning model architectures have their own advantages in PV power forecasting. MLP models have strong nonlinear fitting capabilities, RNN models can capture long-term dependencies, CNN models can automatically extract local features, and GNN models have unique advantages for modeling spatiotemporal characteristics. This manuscript provides a comprehensive research survey for PV power forecasting using deep learning models, helping researchers and practitioners to gain a deeper understanding of the current applications, challenges, and opportunities of deep learning technology in this area.
Assessment of CO2 Sequestration Capacity in a Low-Permeability Oil Reservoir Using Machine Learning Methods
Authors: Fan Zuochun , Tian Mei , Li Man , Mi Yidi , Jiang Yue , Song Tao , Cao Jinxin , Liu Zheyu
Published: August 2024

The CO2 sequestration capacity evaluation of reservoirs is a critical procedure for carbon capture, utilization, and storage (CCUS) techniques. However, calculating the sequestration amount for CO2 flooding in low-permeability reservoirs is challenging. Herein, a method combining numerical simulation technology with artificial intelligence is proposed. Based on the typical geological and fluid characteristics of low-permeability oil reservoirs in the Liaohe oilfield, the CMG 2020 version software GEM module is used to establish a model for CO2 flooding and sequestration. Meanwhile, a calculation method for the effective sequestration coefficient of CO2 is established. We systematically study the sequestration rules in low-permeability reservoirs under varying conditions of permeability, reservoir temperature, and initial reservoir pressure. The results indicate that, as the permeability and sequestration pressure of the reservoir increase, oil recovery gradually increases. The proportion of structurally bound sequestration volume increases from 55% to 60%. Reservoir temperature has minimal impact on both the recovery rate and the improvement in sequestration efficiency. Sequestration pressure primarily improves sequestration efficiency by increasing the dissolution of CO2 in the remaining oil and water. The calculation chart for the effective sequestration coefficient, developed using artificial intelligence algorithms under multi-factor conditions, enables accurate and rapid evaluation of the sequestration potential and the identification of favorable sequestration areas in low-permeability reservoirs. This approach provides valuable technical support for CO2 flooding and sequestration in pilot applications.
An MPPT Control Strategy Based on Current Constraint Relationships for a Photovoltaic System with a Battery or Supercapacitor
Authors: Lai Guohong , Zhang Guoping , Li Shaowu
Published: August 2024

When the battery or supercapacitor is connected to the output of a PV system, the conventional voltage equation expressing its mathematical model usually must be replaced by the current relationship to study the maximum power point tracking (MPPT) control theory. However, hitherto, there is a lack of an attempt to disclose the current constraint relationships at the maximum power point (MPP), which leads to the potential risk of MPPT failure. To solve this problem, in this paper, the MPPT constraint conditions on the basis of currents are built and then a new MPPT control strategy is proposed. In this strategy, a linearized model parameter of a PV cell is used as the bridge to find the current relationships. On the basis of them, some expressions involving the duty cycle are built to directly calculate the control signal of the MPPT controller. Meanwhile, an implementation method is designed to match this proposed MPPT strategy. Finally, some simulation experiments are conducted. The simulation results verify that the proposed MPPT constraint expressions are accurate and workable and that the proposed MPPT strategy and its implementation process are feasible and available. In addition, the simulation results also show that the proposed MPPT strategy has a better MPPT speed and the same MPPT accuracy when the P&O method and fuzzy algorithm are compared. By this work, the MPPT constraint conditions based on current relationships are first found, representing a breakthrough in disclosing the inherent relationships between different currents when the PV system is operating around the MPP.
Mechanical Behavior of Sediment-Type High-Impurity Salt Cavern Gas Storage during Long-Term Operation
Authors: Wang Jian , Li Peng , Bai Weizheng , Lu Jun , Fu Xinghui , Fu Yaping , Shi Xilin
Published: August 2024

With the development of salt cavern gas storage technology, the construction of large-scale salt cavern gas storage using sediment voids is expected to solve the problems of low effective volume formation rate and poor construction economy of high-impurity salt mines. At present, there are few studies on the long-term operational mechanical behavior of salt cavern gas storage under the influence of sediment accumulation. The present paper studies the influence of sediment height, particle gradation, and operating pressure on the stability of salt caverns by constructing a coupling model of sediment particle discontinuous medium and surrounding rock continuous medium. The continuous–discontinuous coupling algorithm is suitable for analyzing the influence of sediment height and particle gradation on the creep shrinkage of salt caverns. The increase in sediment height slows down the creep shrinkage of the cavern bottom, which strengthens the restraining effect on the surrounding rock of the cavern. As a result, the position of the maximum displacement of the surrounding rock moves to the upper part of the cavern. The sediment particle gradation has little effect on the cavern volume shrinkage rate. The greater the coarse particle content, the smaller the cavern volume shrinkage rate. The greater the operating pressure, the more conducive to maintaining the stability of the cavern. This situation slows down the upward movement of the sediment accumulation and increases the gas storage space in the upper part of the cavern. The obtained results can provide a reference for evaluating the long-term operational stability of sediment-type high-impurity salt cavern gas storage.
Impact of Non-Residential Users on the Energy Performance of Renewable Energy Communities Considering Clusterization of Consumptions
Authors: Veronese Elisa , Lauton Luca , Barchi Grazia , Prada Alessandro , Trovato Vincenzo
Published: August 2024

Renewable energy communities foster the users’ engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects.
Simulation and Energy Analysis of Integrated Solar Combined Cycle Systems (ISCCS) Using Aspen Plus
Authors: Al Mhanna Najah M. , Al Hadidi Islam , Al Maskari Sultan
Published: August 2024

The aim of this research is to simulate and analyze a combined power cycle (Steam turbine and gas turbine cycles) by studying the effect of changing the natural gas flow rate on the developed power. Therefore, reducing the amount of used natural gas in the combustion chamber of the gas turbine cycle from 9.2 to 4 kg/s showed a significant drop in the power produced by the gas turbine, i.e., from 123.7 to 57.7 MW. Additionally, this change in the combusted natural gas amount affected the heat recovered in both heat recovery steam generators (HRSGs), i.e., from 219.79 to 100.35 MW, respectively. Consequently, the amount of generated steam in the high pressure HRSGs and the power developed in the steam turbine changed from 60.88 to 27.79 kg/s and from 56.39 to 27.13 MW, respectively. A heat exchanger (HFHX) utilizing a heating fluid was used as an external source of energy to compensate the reduction in the generated heat and to increase the amount of generated steam up to 157.32 kg/s, which keeps the power plant capacity at 180 MW. Existing combined local plant data were used in this study and were simulated in Aspen Plus software V11. A sensitivity analysis was made to optimize the cycle operating conditions that use less natural gas and produce the same amount of power.
A Review of the Energy System and Transport Sector in Uzbekistan in View of Future Hydrogen Uptake
Authors: Yakhshilikov Jamshid , Cavana Marco , Leone Pierluigi
Published: August 2024

This study explores the potential role of hydrogen in decarbonizing the transport sector in Uzbekistan by examining different aspects of the country’s energy system and transport final use. In road transport, Uzbekistan has already gained experience with the use of alternative fuels through the “Compressed Natural Gas—Mobility” initiatives and has achieved a fleet coverage of 59%. These existing frameworks and knowledge can ease the integration of hydrogen into road transport. The rail sector also has the potential for hydrogen uptake, considering that 47% of rail lines are not electrified. The results of this study indicate that powering all CNG vehicles with a 10% hydrogen blend (HCNG) could reduce road transport emissions by 0.62 MtCO2eq per year, while replacing diesel trucks with hydrogen-based vehicles could contribute to an additional reduction of up to 0.32 MtCO2eq per year. In rail transport, hydrogen-powered trains could reduce emissions in non-electrified lines by up to 0.1 kgCO2eq/km of journey. In assessing the potential infrastructure for hydrogen logistics, this study also identifies opportunities for hydrogen export by repurposing the existing natural gas infrastructure. Focusing on Uzbekistan, this study provides a regional perspective on the potential for the integration of hydrogen into the transport sector in Central Asia.
Multi-Layer Energy Management and Strategy Learning for Microgrids: A Proximal Policy Optimization Approach
Authors: Fang Xiaohan , Hong Peng , He Shuping , Zhang Yuhao , Tan Di
Published: August 2024

An efficient energy management system (EMS) enhances microgrid performance in terms of stability, safety, and economy. Traditional centralized or decentralized energy management systems are unable to meet the increasing demands for autonomous decision-making, privacy protection, global optimization, and rapid collaboration simultaneously. This paper proposes a hierarchical multi-layer EMS for microgrid, comprising supply layer, demand layer, and neutral scheduling layer. Additionally, common mathematical optimization methods struggle with microgrid scheduling decision problem due to challenges in mechanism modeling, supply–demand uncertainty, and high real-time and autonomy requirements. Therefore, an improved proximal policy optimization (PPO) approach is proposed for the multi-layer EMS. Specifically, in the centrally managed supply layer, a centralized PPO algorithm is utilized to determine the optimal power generation strategy. In the decentralized demand layer, an auction market is established, and multi-agent proximal policy optimization (MAPPO) algorithm with an action-guidance-based mechanism is employed for each consumer, to implement individual auction strategy. The neutral scheduling layer interacts with other layers, manages information, and protects participant privacy. Numerical results validate the effectiveness of the proposed multi-layer EMS framework and the PPO-based optimization methods.
Determinants of Non-Hydro Renewable Energy Consumption in China’s Provincial Regions
Authors: Hu Yuanbo , Huang Weilun , Dai Aibi , Zhao Xuemeng
Published: August 2024

This study investigates the determinants of non-hydro renewable energy (NHRE) consumption across 31 provincial regions in China, spanning from 2015 to 2022. Utilizing fixed effects and moderating effects, the research analyzes the impacts of research and development intensity (RDI), urbanization (UR), and human capital (HC) on NHRE consumption (NHREC). Additionally, the moderating roles of industrial structure (IS) and tax (TA) are examined, along with control variables such as informationization and economic development. The findings reveal that increased RDI significantly boosts NHREC by enhancing technological advancements. UR also positively affects NHREC, particularly in rapidly urbanizing regions, while HC provides a skilled workforce that drives renewable energy projects. However, the study identifies that concentrated industrial structures and high taxes can negatively moderate these positive impacts, highlighting the complex interplay of these factors. Policy recommendations include creating “Renewable Energy Innovation Hubs” in underdeveloped regions to attract startups and researchers, developing “Solar Cities” with mandated solar panels on all buildings, and introducing a “Carbon Offset Lottery” to incentivize investments in renewable energy.
Electromagnetic–Structural Finite Element Analysis of Copper and Aluminum Windings in Power Transformers under Short-Circuit Conditions
Authors: Cornelius Richard Gonçalves , Lenhard Betina Luísa , Medeiros Leonardo Hautrive , Fontoura Herber Cuadro , Correa Cristian Hans , Carraro Rodinei , Beltrame Rafael Concatto , dos Santos Tiago , Marchesan Tiago Bandeira , Bender Vitor Cristiano
Published: August 2024

Electromagnetic forces can lead to the structural failure of power transformers due to extreme loading conditions, vibration, and/or fatigue. Therefore, studying the nature and the magnitude of these forces is a key task in the design and failure analysis of such important equipment. Keeping this issue in mind, this work aims at conducting a numerical analysis in order to evaluate the mechanical stresses and displacements of windings in power transformers due to the action of electromagnetic forces. With this purpose, a finite element model is developed considering electromagnetic and mechanical effects assuming short-circuit conditions. The study compares the cases employing copper and aluminum windings with header tap, in different temperatures. The model developed in this work is verified against analytical solutions. The numerical calculations allow for performing a detailed analysis in terms of the distribution of both displacements and stresses along the windings, which is of great relevance for identifying critical structural points and avoiding structural failure. Overall, the obtained results demonstrate that the finite element model is a useful tool for the structural design of power transformers that allow for investigating and optimizing key aspects before manufacturing.
Recycling and Reuse of Mn-Based Spinel Electrode from Spent Lithium-Ion Batteries
Authors: Thapa Arjun K. , Nouduri Abhinav C. , Mohiuddin Mohammed , Reddy Kannapu Hari Prasad , Bai Lihui , Wang Hui , Sunkara Mahendra K.
Published: August 2024

In this paper, we introduce an environmentally friendly approach to recycle used batteries and recover highly valuable manganese-based cathode materials. This study demonstrates the feasibility of fast plasma pyrolysis to recover LiMn2O4 electrode materials (e.g., lithium manganese oxide, LMO) and demonstrate their reuse in newly assembled Li-ion cells. The electrochemical performance of as-recycled cathodes shows an initial discharge capacity of 72 mAh/g and is stable for 100 cycles at 0.1 C. After adding 20 mole % of excess LiOH, the recycled LMO after relithiation at 660 °C can deliver an initial discharge capacity of 96 mAh/g and retain a decent discharge capacity of 88 mAh/g after 50 cycles at a 0.2 C rate. Without relithiation, the as-recycled LMO cathode after heating at 1000 °C delivers the best electrochemical cycling performance, including an initial discharge capacity of 94 mAh/g and 50th cycle capacity of 91 mAh/g at a 0.2 C rate. This study highlights a feasible approach for recycling electrode materials in spent LIBs. Recycling of lithium-ion batteries and especially electrode materials is crucial for the sustained growth of the lithium-ion battery industry and reduced environmental issues.
Sex-Specific Associations between Prenatal Exposure to Bisphenols and Phthalates and Infant Epigenetic Age Acceleration
Authors: England-Mason Gillian , Merrill Sarah M. , Liu Jiaying , Martin Jonathan W. , MacDonald Amy M. , Kinniburgh David W. , Gladish Nicole , MacIsaac Julia L. , Giesbrecht Gerald F. , Letourneau Nicole , Kobor Michael S. , Dewey Deborah
Published: August 2024

We examined whether prenatal exposure to two classes of endocrine-disrupting chemicals (EDCs) was associated with infant epigenetic age acceleration (EAA), a DNA methylation biomarker of aging. Participants included 224 maternal–infant pairs from a Canadian pregnancy cohort study. Two bisphenols and 12 phthalate metabolites were measured in maternal second trimester urines. Buccal epithelial cell cheek swabs were collected from 3 month old infants and DNA methylation was profiled using the Infinium MethylationEPIC BeadChip. The Pediatric-Buccal-Epigenetic tool was used to estimate EAA. Sex-stratified robust regressions examined individual chemical associations with EAA, and Bayesian kernel machine regression (BKMR) examined chemical mixture effects. Adjusted robust models showed that in female infants, prenatal exposure to total bisphenol A (BPA) was positively associated with EAA (B = 0.72, 95% CI: 0.21, 1.24), and multiple phthalate metabolites were inversely associated with EAA (Bs from −0.36 to −0.66, 95% CIs from −1.28 to −0.02). BKMR showed that prenatal BPA was the most important chemical in the mixture and was positively associated with EAA in both sexes. No overall chemical mixture effects or male-specific associations were noted. These findings indicate that prenatal EDC exposures are associated with sex-specific deviations in biological aging, which may have lasting implications for child health and development.
Co-Digestion and Mono-Digestion of Sewage Sludge and Steam-Pretreated Winter Wheat Straw in Continuous Stirred-Tank Reactors—Nutrient Composition and Process Performance
Authors: Kreuger Emma , Tosi Virginia , Lindblad Maja , Davidsson Åsa
Published: August 2024

Wheat straw (WS) constitutes a considerable biomass resource and can be used to produce the energy carrier methane through anaerobic digestion. Due to the low contents of several nutrient elements and water in harvested WS, the use of sewage sludge (SS), consisting of primary sludge and waste-activated sludge, as a nutrient source in co-digestion with steam-pretreated wheat straw (PWS) was investigated theoretically and practically. WS was steam-pretreated, with acetic acid as the catalyst, at 190 °C for 10 min, ending with a rapid reduction in pressure. Process stability and specific methane production were studied for the mono-digestion and co-digestion of PWS and SS in continuous stirred-tank reactors for 208 days. The HRT was 22 days and the OLR 2.1 gVS L−1 d−1. In co-digestion, the OLR was increased to 2.8 gVS L−1 d−1 for one week. Nutrient elements were added to PWS mono-digestion at two different concentration levels. Co-digestion was stable, with a total concentration of short-chain fatty acids (SCFAs) at a safe level below 0.35 g L−1 at both OLRs. The higher OLR during co-digestion would require an increase in reactor volume of 14%, compared to the mono-digestion of SS, but would increase the annual production of methane by 26%. The specific methane production levels for PWS mono-digestion, SS mono-digestion, and co-digestion were 170, 320, and 260 mL g−1VS, respectively. Co-digestion did not result in a synergistic increase in the methane yield. SCFAs accumulated in the mono-digestion of PWS when using lower levels of nutrient supplements, and the concentrations fluctuated at higher nutrient levels. The main conclusion is that PWS and SS can be co-digested with long-term process stability, without the addition of chemicals other than water and acetic acid. The specific methane production for mono-digestion of PWS was relatively low. The effect of using higher concentrations of micronutrients in PWS mono-digestion should be evaluated in future studies.
Morpho-Anatomical Degeneration of Dopaminergic Neurons in Adult Zebrafish Brain after Exposure to Nickel
Authors: Cacialli Pietro , Ricci Serena , Lazzari Maurizio , Franceschini Valeria
Published: August 2024

Chronic exposure to heavy metals has been widely demonstrated to induce pathological features in different tissues and, in particular, in the central nervous system. Specific neurons, including dopaminergic neurons, were observed to be more susceptible to toxic agents. Several previous studies performed on zebrafish (Danio rerio) models observed that exposure to nickel (one of the most abundant heavy metals) induces impairment of memory and anxious-like behaviors. Nevertheless, this phenotypical evidence has not been associated with dopaminergic system damage, and no reports showing the effects of nickel on dopaminergic neurons are available. In this study, we aim to analyze the precise distribution and variation in dopaminergic neurons in adult zebrafish after chronic (96 h) exposure to nickel ions dissolved in water at different sub-lethal doses (0.4 mg L−1; 2 mg L−1 and 4 mg L−1). The effects of treatment on dopaminergic neurons were evaluated by measuring transcript and protein levels of tyrosine hydroxylase (TH), described as a dopaminergic neuron marker. As shown, the expression of the th1 and th2 genes was reduced in the entire brain of zebrafish treated with nickel. Immunostaining analysis allowed us to localize TH-expressing neurons mainly in the posterior tuberculum, where they were observed to be reduced after nickel treatment in a dose-dependent fashion. Consistently, the TUNEL assay revealed a significant increase in apoptosis of TH-expressing cells after treatment with 2 mg L−1 and 4 mg L−1 of nickel. Our findings represent the first evidence of the effect of nickel on the dopaminergic system.
Correction: Duman et al. Description of a Novel Fish Pathogen, Plesiomonas shigelloides subsp. oncorhynchi, Isolated from Rainbow Trout (Oncorhynchus mykiss): First Genome Analysis and Comparative Genomics. Fishes 2023, 8, 179
Authors: Duman Muhammed , García Valdés Elena , Ay Hilal , Altun Soner , Saticioglu Izzet Burcin
Published: August 2024

Text Correction [...]
Pressure Drop Estimation of Two-Phase Adiabatic Flows in Smooth Tubes: Development of Machine Learning-Based Pipelines
Authors: Bolourchifard Farshad , Ardam Keivan , Dadras Javan Farzad , Najafi Behzad , Vega Penichet Domecq Paloma , Rinaldi Fabio , Colombo Luigi Pietro Maria
Published: August 2024

The current study begins with an experimental investigation focused on measuring the pressure drop of a water–air mixture under different flow conditions in a setup consisting of horizontal smooth tubes. Machine learning (ML)-based pipelines are then implemented to provide estimations of the pressure drop values employing obtained dimensionless features. Subsequently, a feature selection methodology is employed to identify the key features, facilitating the interpretation of the underlying physical phenomena and enhancing model accuracy. In the next step, utilizing a genetic algorithm-based optimization approach, the preeminent machine learning algorithm, along with its associated optimal tuning parameters, is determined. Ultimately, the results of the optimal pipeline provide a Mean Absolute Percentage Error (MAPE) of 5.99% on the validation set and 7.03% on the test. As the employed dataset and the obtained optimal models will be opened to public access, the present approach provides superior reproducibility and user-friendliness in contrast to existing physical models reported in the literature, while achieving significantly higher accuracy.
Enhancing Deer Sous Vide Meat Shelf Life and Safety with Eugenia caryophyllus Essential Oil against Salmonella enterica
Authors: Kačániová Miroslava , Garzoli Stefania , Ben Hsouna Anis , Ban Zhaojun , Elizondo-Luevano Joel Horacio , Kluz Maciej Ireneusz , Ben Saad Rania , Haščík Peter , Čmiková Natália , Waskiewicz-Robak Božena , Kollár Ján , Bianchi Alessandro
Published: August 2024

Modern lifestyles have increased the focus on food stability and human health due to evolving industrial goals and scientific advancements. Pathogenic microorganisms significantly challenge food quality, with Salmonella enterica and other planktonic cells capable of forming biofilms that make them more resistant to broad-spectrum antibiotics. This research examined the chemical composition and antibacterial and antibiofilm properties of the essential oil from Eugenia caryophyllus (ECEO) derived from dried fruits. GC-MS analyses identified eugenol as the dominant component at 82.7%. Additionally, the study aimed to extend the shelf life of sous vide deer meat by applying a plant essential oil and inoculating it with S. enterica for seven days at 4 °C. The essential oil demonstrated strong antibacterial activity against S. enterica. The ECEO showed significant antibiofilm activity, as indicated by the MBIC crystal violet test results. Data from MALDI-TOF MS analysis revealed that the ECEO altered the protein profiles of bacteria on glass and stainless-steel surfaces. Furthermore, the ECEO was found to have a beneficial antibacterial effect on S. enterica. In vacuum-packed sous vide red deer meat samples, the anti-Salmonella activity of the ECEO was slightly higher than that of the control samples. These findings underscore the potential of the ECEO’s antibacterial and antibiofilm properties in food preservation and extending the shelf life of meat.
Comparative Evaluation of Chemical Composition and Nutritional Characteristics in Various Quinoa Sprout Varieties: The Superiority of 24-Hour Germination
Authors: Cao Beier , Bao Changjian , Zhu Zhiqiang , Gong Yanning , Wei Junyu , Shen Zhenguo , Su Nana
Published: August 2024

Quinoa (Chenopodium quinoa Willd) sprouts are rich in bioactive compounds that offer numerous health benefits. However, limited research exists on their cultivation, nutritional value, and processing potential. This study compared the nutritional composition and antioxidant activity of quinoa sprouts from different varieties at various time points. Results showed a general increase in most nutrients over time. At the 24 h mark, JQ-W3 exhibited a 17.77% increase in leucine, 1.68 times higher than in eggs, along with a 6.11-fold elevation in GABA content. JQ-B1 exhibited the preeminent antioxidant potency composite (APC) score. Saponins, known for their bitter taste, decreased at 12 h but returned to original levels by 24 h. Based on nutritional components and saponin content, 24 h sprouted black quinoa JQ-B1 and white quinoa JQ-W3 were selected, providing a basis for quinoa sprout development in the food industry. These findings contribute to the understanding and utilization of quinoa sprouts.
Optimizing Chlorella vulgaris Cultivation to Enhance Biomass and Lutein Production
Authors: Wu Kangping , Lai Jiangling , Zhang Qi , Wang Yunpu , Cui Xian , Liu Yuhuan , Wu Xiaodan , Yu Zhigang , Ruan Roger
Published: August 2024

Lutein is widely used in medicine, health care, and food processing due to its antioxidant effects; however, it is difficult for the traditional extraction of lutein using marigolds to meet the increasing market demand for lutein. To achieve high-efficiency lutein production, we investigated the effects of different conditions on the biomass accumulation and lutein yield of Chlorella vulgaris. The optimized cultivation conditions include mixotrophic cultivation using sodium nitrate as a nitrogen source, maintaining a total-organic-carbon-to-total-nitrogen ratio of 12:1, a total-nitrogen-to-total-phosphorus ratio of 10:1, and lighting duration of 24 h. The results of the study indicated that under these specific conditions, Chlorella vulgaris attained a final biomass concentration, biomass productivity, and growth yield of 6.08 g·L−1, 1.00 g·L−1·d−1, and 1.67 g biomass/g TOC, respectively. Additionally, the concentrations of total chlorophyll, carotenoid, lutein, and protein reached 139.20 mg·L−1, 31.87 mg·L−1, 15.02 mg·L−1, and 2.17 g·L−1, respectively, and the content of lutein reached 2.47 mg·g−1. This study supplies a theoretical basis for the industrial application of lutein production using Chlorella vulgaris.
Camembert-Type Cheese with Sweet Buttermilk: The Determination of Quality Properties and Microstructure
Authors: Szkolnicka Katarzyna , Dmytrów Izabela , Mituniewicz-Małek Anna , Meghzili Batoul
Published: August 2024

Camembert is a type of surface-mold-ripened soft cheese traditionally produced from cow’s milk. Buttermilk, a by-product of butter production with beneficial nutritional and technological properties, is increasingly being used in various applications, including cheesemaking. Therefore, this study aimed to use sweet buttermilk (BM) in combination with milk at concentrations of 10% (w/w) (BM10) and 20% (w/w) (BM20) for the production of Camembert-type cheese. A control cheese made entirely from milk was also produced. The cheese samples underwent a 28-day ripening process during which their composition, acidity, water activity, color, and sensory properties were examined at 1-week intervals. The microstructure of the matured Camembert-type cheese samples was analyzed using scanning electron microscopy (SEM), and their texture was evaluated. The production yield of BM20 cheese (18.03 ± 0.29 kg/100 kg) was lower (p < 0.05) than that of the control (19.92 ± 0.23 kg/100 kg), with BM10 showing the distinctly lowest yield (14.74 ± 0.35 kg/100 kg). The total solid and fat content of BM Camembert-type cheese samples was lower than the control. However, the total protein content in cheese BM20 at the end of the ripening period was the same as that of the control. The changes in acidity in all samples were typical for Camembert cheese, and water activity was high (above 0.92). The sensory properties of all samples were characteristic of the cheese type, while the color of BM cheese samples differed from the control. The microstructure of BM10 and BM20 cheese variants was similar, namely homogenous and less porous compared to the control. In terms of texture, the BM samples had significantly lower hardness, adhesiveness, and gumminess. This study indicates that sweet BM, particularly at a concentration of 20%, may be effectively used in the production of Camembert-type cheese.