bluuebunny/crossref_metadata_2025 huggingface dataset:
A State-Feedback Control Strategy Based on Grey Fast Finite-Time Sliding Mode Control for an H-Bridge Inverter with LC Filter Output
Authors: Chang En-Chih , Wu Rong-Ching , Chang Heidi H. , Cheng Chun-An
Published: May 2024

An H-bridge inverter with LC (inductor-capacitor) filter output allows the conversion of DC (direct current) power to AC (alternating current) power that has been used in a variety of applications, such as uninterruptible power supplies, AC motor drives, and renewable energy source systems. The fast finite-time sliding mode control (FFTSMC) features acceleration of the system state towards the equilibrium position as well as conserving insensitivity against internal parameter fluctuations as well as external load disturbances falling within the predetermined bounds. However, the FFTSMC would potentially witness chattering or steady-state errors as indefinite margins come to be exaggerated or underestimated. The chattering in the sliding mode control practice is oscillatory defective behavior. It induces inefficient operation, higher switching power losses in the transistor circuits, as well as saturated actuators, thus impairing the inverter’s output energy efficiency and raising harmonic distortion. Therefore, this paper presents the H-bridge inverter with LC filter output, which is controlled by a grey prediction fast finite-time sliding mode trajectory tracking. A more highly accurate grey prediction model based on the centered approximation methodology is deployed to vanish the chattering as well as steady-state errors. Taking into account the union of grey prediction and FFTSMC, a feedback-controlled H-bridge inverter with LC filter output allows attaining a highly efficient as well as quality sine-wave output voltage. The presented state-feedback control strategy is robust, less complex, attains more rapid convergence, and is highly accurate. The design process, computer simulation, as well as experimental results of the proposed state-feedback control strategy established that the H-bridge inverter with LC filter output has the capability to exhibit fast dynamic response time as well as good steady-state tracking behavior of the output voltage under step-loading changes and nonlinear loading conditions.
Rolling Bearing Residual Useful Life Prediction Model Based on the Particle Swarm Optimization-Optimized Fusion of Convolutional Neural Network and Bidirectional Long–Short-Term Memory–Multihead Self-Attention
Authors: Yang Jianzhong , Zhang Xinggang , Liu Song , Yang Ximing , Li Shangfang
Published: May 2024

In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper proposes a technique that utilizes particle swarm optimization (PSO) in combination with the fusing of a one-dimensional convolutional neural network (CNN) and a multihead self-attention (MHSA) bidirectional long short-term memory (BiLSTM) network called PSO-CNN-BiLSTM-MHSA. Initially, the original signals undergo correlation signal processing to calculate the features, such as standard deviation, variance, and kurtosis, to help identify the beginning location of the rolling bearing degradation stage. A new dataset is constructed with similar degradation trend features. Subsequently, the particle swarm optimization (PSO) algorithm is employed to find the optimal values of important hyperparameters in the model. Then, a convolutional neural network (CNN) is utilized to extract the deterioration features of rolling bearings in order to predict their remaining lifespan. The degradation features are inputted into the BiLSTM-MHSA network to facilitate the learning process and estimate the remaining lifespan of rolling bearings. Finally, the degradation features are converted to the remaining usable life (RUL) via the fully connected layer. The XJTU-SY rolling bearing accelerated life experimental dataset was used to verify the effectiveness of the proposed method by k-fold cross-validation. After comparing our model to the CNN-LSTM network model and other models, we found that our model can achieve reductions in mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 9.27%, 6.76%, and 2.35%, respectively. Therefore, the experimental results demonstrate the model’s accuracy in forecasting remaining lifetime and support its ability to forecast breakdowns.
Deep Pre-Training Transformers for Scientific Paper Representation
Authors: Wang Jihong , Yang Zhiguang , Cheng Zhanglin
Published: May 2024

In the age of scholarly big data, efficiently navigating and analyzing the vast corpus of scientific literature is a significant challenge. This paper introduces a specialized pre-trained BERT-based language model, termed SPBERT, which enhances natural language processing tasks specifically tailored to the domain of scientific paper analysis. Our method employs a novel neural network embedding technique that leverages textual components, such as keywords, titles, abstracts, and full texts, to represent papers in a vector space. By integrating recent advancements in text representation and unsupervised feature aggregation, SPBERT offers a sophisticated approach to encode essential information implicitly, thereby enhancing paper classification and literature retrieval tasks. We applied our method to several real-world academic datasets, demonstrating notable improvements over existing methods. The findings suggest that SPBERT not only provides a more effective representation of scientific papers but also facilitates a deeper understanding of large-scale academic data, paving the way for more informed and accurate scholarly analysis.
Aggregation Equivalence Method for Direct-Drive Wind Farms Based on the Excitation–Response Relationship
Authors: Yan Gangui , Wang Yupeng , Fan Yuxing , Yang Cheng , Yue Lin
Published: May 2024

The grid interconnections for direct-drive wind farms have triggered multiple new sub-synchronous oscillation events, which can prevent the power system from operating safely and stably. However, the excessively high order of the detailed model for large-scale wind farms with multiple direct-drive permanent magnet synchronous generators (PMSGs) connected to power systems poses a challenge when investigating small disturbance stability and instability mechanisms. This study establishes a model of the grid-connected PMSG system based on the voltage/power excitation–response relationship to describe the dynamic characteristics of the port of the PMSG, and the analysis of active and reactive response characteristics of PMSG lays the foundation for model simplification. Based on the unit model, a direct-drive wind farm aggregation equivalence method based on the excitation–response relationship is proposed. The equivalent model obtained by this method is suitable for the small disturbance stability analysis of direct-drive wind farms grid connected system, with good accuracy. The simulation verified the effectiveness of the aggregation model.
Rapid Beam Tracking Using Power Measurement for Terahertz Communications
Authors: He Xiaodan , Zhang Changming , Lu Chi , Yu Xianbin
Published: May 2024

With abundant bandwidth resources, terahertz communications are considered one of the key technologies to meet the requirement for high data-rate transmission in the future. In order to compensate for the severe propagation loss of terahertz communications, directional antennas with high gain and narrow beams are expected to be adopted, making beam tracking significant for robust communications. In this paper, a tracking method based on power measurement is proposed, consisting of beam status monitoring, recognition of the deviation direction, and movement toward the optimal angle. By observing the change in the received signal power, beam misalignment is first checked, and whether the misalignment is out of tracking range is also determined. Then, the deviation direction is recognized by comparing the received power variations in the candidate directions, and the beam angle is adjusted accordingly until it reaches the optimal angle. With a small scanning range, the deviation direction is recognized in a short duration, allowing for rapid beam tracking. Numerical results indicate that the alignment error is competitively low and stable in the proposed beam tracking method, and its technical superiority is particularly dominant in situations involving variable motion at high speeds.
Developing Different Test Conditions to Verify the Robustness and Versatility of Robotic Arms Controlled by Evolutionary Algorithms
Authors: Szabo Roland
Published: May 2024

In this paper, different test cases where robotic arms are tested will be presented. A robotic arm is tested for the gravity effects that can be observed on it. The other robotic arm is tested for how much precision it has by using it to learn to write. The other robotic arm is tested on how well it can function as a solar tracker and how precisely it can function as an energy harvester. On the basis of these tests, the robotic arm’s mechanical structure, electronics, and software are put to the test. The software is based on evolutionary software that implements genetic algorithms. The entire command system is also ported to FPGAs (to hardware) to increase speed and response time.
An Accurate Cooperative Localization Algorithm Based on RSS Model and Error Correction in Wireless Sensor Networks
Authors: Chang Bo , Zhang Xinrong , Bian Haiyi
Published: May 2024

Aiming at the problem that there is a big contradiction between accuracy and calculation and cost based on the RSSI positioning algorithm, an accurate and effective cooperative positioning algorithm is proposed in combination with error correction and refinement measures in each stage of positioning. At the ranging stage, the RSSI measurement value is converted to distance by wireless channel modeling and the dynamic acquisition of the power attenuation factor. Then, the ranging correction is carried out by using the known anchor node ranging error information. The Taylor series expansion least-square iterative refinement algorithm is implemented in the position optimization stage, and satisfactory positioning accuracy is obtained. The idea of cooperative positioning is introduced to upgrade the nodes that meet the requirements and are upgraded to anchor nodes and participate in the positioning of other nodes to improve the positioning coverage and positioning accuracy. The experimental results show that the localization effect of this algorithm is close to that of the Taylor series expansion algorithm based on coordinates but far higher than that of the basic least-squares localization algorithm. The positioning accuracy can be improved rapidly with the decrease in the distance measurement error.
Video Detection Method Based on Temporal and Spatial Foundations for Accurate Verification of Authenticity
Authors: Lin Chin-Yuan , Lee Jen-Chun , Wang Shuenn-Jyi , Chiang Chung-Shi , Chou Chao-Lung
Published: May 2024

With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research disciplines are actively engaged in developing deepfake video detection technologies to mitigate the risks associated with malicious deepfake content. Therefore, the importance of deepfake video detection technology cannot be overemphasized. This study addresses the challenge posed by images in nonexistent datasets by analyzing deepfake video detection methods. Using temporal and spatial detection techniques and employing 68 facial landmarks for alignment and feature extraction, this research integrates the attention-guided data augmentation (AGDA) strategy to enhance generalization capabilities. The detection performance is evaluated on four datasets: UADFV, FaceForensics++, Celeb-DF, and DFDC, with superior results compared to alternative approaches. To evaluate the study’s ability to accurately discriminate authenticity, detection experiments are conducted on both genuine and deepfake videos synthesized using the DeepFaceLab and FakeApp frameworks. The experimental results show better performance in detecting deepfake videos than other methods compared.
Failure Mechanism Information-Assisted Multi-Domain Adversarial Transfer Fault Diagnosis Model for Rolling Bearings under Variable Operating Conditions
Authors: Zhong Zhidan , Zhang Zhihui , Cui Yunhao , Xie Xinghui , Hao Wenlu
Published: May 2024

Deep transfer learning tackles the challenge of fault diagnosis in rolling bearings across variable operating conditions, which is pivotal for intelligent bearing health management. Traditional transfer learning may not be able to adapt to the specific characteristics of the target domain, especially in the case of variable working conditions or lack of annotated data for the target domain. This may lead to unstable training results or negative transfer of the neural network. This paper proposes a new method for enhancing unsupervised domain adaptation in bearing fault diagnosis, aimed at providing robust fault diagnosis for rolling bearings under varying operating conditions. It incorporates bearing fault finite element simulation data into the domain adversarial network, guiding adversarial training using fault evolution mechanisms. The algorithm establishes global and subdomain classifiers, with simulation signals replacing label predictions for target data in the subdomain, ensuring minimal information transfer. By reconstructing the loss function, we can extract the common features of the same type bearing under different conditions and enhance the domain antagonism robustness. The proposed method is validated using two sets of testbed data as target domains. The results demonstrate that the method can adequately adapt the deep feature distributions of the model and experimental domains, thereby improving the accuracy of fault diagnosis in unsupervised cross-domain scenarios.
Combined Use of Python and DIgSILENT PowerFactory to Analyse Power Systems with Large Amounts of Variable Renewable Generation
Authors: Jiménez-Ruiz Javier , Honrubia-Escribano Andrés , Gómez-Lázaro Emilio
Published: May 2024

Over the last decade considerable efforts have been made to reduce greenhouse gas emissions, leading to the significant development and implementation of renewable energy plants across all power systems in the world. Wind energy has consolidated its position as one of the two key energy sources (in conjunction with solar photovoltaics) to achieve completely green power systems. Integrating wind energy into power systems is a more complicated task compared to traditional generation systems, as wind energy relies on a variable energy source characterised by high variability. Several tools currently exist to simulate the effect of wind energy generation in power systems, but they often lack the versatility demanded by researchers. This paper analyses how both Python 3.11 and DIgSILENT PowerFactory 2024 can be used synergistically to assess the implementation of wind power plants, highlighting how the use of these two tools combined can be of great interest for both researchers and grid operators.
Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster Pairing
Authors: Sha Xiaobao , Sun Wenjian , Liu Xiang , Luo Yang , Luo Chunbo
Published: May 2024

Federated learning (FL) is widely regarded as highly promising because it enables the collaborative training of high-performance machine learning models among a large number of clients while preserving data privacy by keeping the data local. However, many existing FL frameworks have a two-layered architecture, thus requiring the frequent exchange of large-scale model parameters between clients and remote cloud servers over often unstable networks and resulting in significant communication overhead and latency. To address this issue, we propose to introduce edge servers between the clients and the cloud server to assist in aggregating local models, thus combining asynchronous client–edge model aggregation with synchronous edge–cloud model aggregation. By leveraging the clients’ idle time to accelerate training, the proposed framework can achieve faster convergence and reduce the amount of communication traffic. To make full use of the grouping properties inherent in three-layer FL, we propose a similarity matching strategy between edges and clients, thus improving the effect of asynchronous training. We further propose to introduce model-contrastive learning into the loss function and personalize the clients’ local models to address the potential learning issues resulting from asynchronous local training in order to further improve the convergence speed. Extensive experiments confirm that our method exhibits significant improvements in model accuracy and convergence speed when compared with other state-of-the-art federated learning architectures.
Efficient Gaussian Process Calculations Using Chebyshev Nodes and Fast Fourier Transform
Authors: Dudek Adrian , Baranowski Jerzy
Published: May 2024

Gaussian processes have gained popularity in contemporary solutions for mathematical modeling problems, particularly in cases involving complex and challenging-to-model scenarios or instances with a general lack of data. Therefore, they often serve as generative models for data, for example, in classification problems. However, a common problem in the application of Gaussian processes is their computational complexity. To address this challenge, sparse methods are frequently employed, involving a reduction in the computational domain. In this study, we propose an innovative computational approach for Gaussian processes. Our method revolves around selecting a computation domain based on Chebyshev nodes, with the optimal number of nodes determined by minimizing the degree of the Chebyshev series, while ensuring meaningful coefficients derived from function values at the Chebyshev nodes with fast Fourier transform. This approach not only facilitates a reduction in computation time but also provides a means to reconstruct the original function using the functional series. We conducted experiments using two computational methods for Gaussian processes: Markov chain Monte Carlo and integrated nested Laplace approximation. The results demonstrate a significant reduction in computation time, thereby motivating further development of the proposed algorithm.
Neural Chaotic Oscillation: Memristive Feedback, Symmetrization, and Its Application in Image Encryption
Authors: Huang Keyu , Li Chunbiao , Li Yongxin , Lei Tengfei , Fu Haiyan
Published: May 2024

The symmetry of neuron discharging has some relationship with the electrophysiological characteristics and dynamic behavior of a neuron, and has a close relation with the symmetry of ion channels, current balance, neuron type, synaptic transmission, and network effects. Among them, the feedback and interactions in the network have a particularly direct impact on the symmetrical discharge of a neuron element. This work introduces a memristor as a synapse into a neuron cell, taking the membrane potential back to ion channels, and therefore various symmetric firing behaviors of Hindmarsh–Rose (HR) neurons are observed, including chaos and various periodic firings. By further adjusting the feedback, coexisting symmetrical discharge of the neuron is achieved. Furthermore, the impact of frequency variations on the memristor synapse is analyzed, and thus the operating regimes of memristor and resistor are classified and discussed. Circuit simulations prove the neural chaotic firings along with their symmetrized discharging processes, demonstrating the effectiveness of symmetrical control of chaotic discharge. Finally, applying the symmetrical system to DNA image encryption can effectively protect the security of images.
A Bayesian Deep Unfolded Network for the Off-Grid Direction-of-Arrival Estimation via a Minimum Hole Array
Authors: Li Ninghui , Zhang Xiaokuan , Lv Fan , Zong Binfeng , Feng Weike
Published: May 2024

As an important research focus in radar detection and localization, direction-of-arrival (DOA) estimation has advanced significantly owing to deep learning techniques with powerful fitting and classifying abilities in recent years. However, deep learning inevitably requires substantial data to ensure learning and generalization abilities and lacks reasonable interpretability. Recently, a deep unfolding technique has attracted widespread concern due to the more explainable perspective and weaker data dependency. More importantly, it has been proven that deep unfolding enables convergence acceleration when applied to iterative algorithms. On this basis, we rigorously deduce an iterative sparse Bayesian learning (SBL) algorithm and construct a Bayesian deep unfolded network in a one-to-one correspondence. Moreover, the common but intractable off-grid errors, caused by grid mismatch, are directly considered in the signal model and computed in the iterative process. In addition, minimum hole array, little considered in deep unfolding, is adopted to further improve estimation performance owing to the maximized array degrees of freedom (DOFs). Extensive simulation results are presented to illustrate the superiority of the proposed method beyond other state-of-the-art methods.
Decentralized Virtual Impedance Control for Power Sharing and Voltage Regulation in Islanded Mode with Minimized Circulating Current
Authors: Khan Mubashir Hayat , Zulkifli Shamsul Aizam , Tutkun Nedim , Ekmekci Ismail , Burgio Alessandro
Published: May 2024

In islanded operation, precise power sharing is an immensely critical challenge when there are different line impedance values among the different-rated inverters connected to the same electrical network. Issues in power sharing and voltage compensation at the point of common coupling, as well as the reverse circulating current between inverters, are problems in existing control strategies for parallel-connected inverters if mismatched line impedances are not addressed. Therefore, this study aims to develop an improved decentralized controller for good power sharing with voltage compensation using the predictive control scheme and circulating current minimization between the inverters’ current flow. The controller was developed based on adaptive virtual impedance (AVI) control, combined with finite control set–model predictive control (FCS-MPC). The AVI was used for the generation of reference voltage, which responded to the parameters from the virtual impedance loop control to be the input to the FCS-MPC for a faster tracking response and to have minimum tracking error for better pulse-width modulation generation in the space-vector form. As a result, the circulating current was maintained at below 5% and the inverters were able to share an equal power based on the load required. At the end, the performance of the AVI-based control scheme was compared with those of the conventional and static-virtual-impedance-based methods, which have also been tested in simulation using MATLAB/Simulink software 2021a version. The comparison results show that the AVI FCS MPC give 5% error compared to SVI at 10% and conventional PI at 20%, in which AVI is able to minimize the circulating current when mismatch impedance is applied to the DGs.
Microcontroller Based Evaluation of Voltage Regulators Efficiency and Their Noise Performance Estimation by Fast Allan Variance Method
Authors: Matejček Miroslav , Šostronek Mikuláš
Published: May 2024

This article deals with power supply linear and switching regulators commonly used in various applications for stabilizing an output voltage and ensuring a necessary power input to the load. It describes their basic parameters, performances, advantages, and disadvantages according to their topologies. We design a measurement chain for efficiency evaluation based on power monitors INA219 connected to an embedded system, Arduino UNO. Measurements were focused on evaluation linear regulators MA7805, LM317, switching regulators SZBK07, LM2575, SCW05B-05, and XH-M401. The resulting efficiency of linear and switching regulators was analyzed and errors in the measurement chain were evaluated. The second contribution is an innovative way of carrying out regulator noise estimation using a fast Allan variance method, focused on white noise and flicker noise (bias instability). The main contribution is employing a fast Allan variance method algorithm that dramatically decreases computation time by up to 11 s for 72 million measured (or generated) samples. It enables the analysis of large data sets of various physical quantities (for example, regulator output voltages).
Nonfullerene Small Molecular Acceptor Acting as a Solid Additive Enables Highly Efficient Pseudo-Bilayer All-Polymer Solar Cells
Authors: Liu Jiayin , Ni Yuheng , Zhang Jiaqi , Zhao Yijun , Xu Wenjing , Ma Xiaoling , Zhang Fujun
Published: May 2024

In this work, pseudo-bilayer planar heterojunction (PPHJ) all-polymer solar cells (APSCs) were constructed on the basis of the commonly used PY-IT and PM6 as the acceptor and donor, respectively. A nonfullerene small molecular acceptor (NF-SMA) BTP-eC9 was incorporated into the PY-IT layer as the solid additive in consideration of its similar building block to PY-IT. BTP-eC9 can serve as a photon capture reinforcer and morphology-regulating agent to realize more adequate photon capture, as well as a more orderly molecular arrangement for effective carrier transport. By incorporating 2 wt% BTP-eC9, the efficiency of PM6/PY-IT-based PPHJ-APSCs was boosted from 15.11% to 16.47%, accompanied by a synergistically enhanced short circuit current density (JSC, 23.36 vs. 24.08 mA cm−2) and fill factor (FF, 68.83% vs. 72.76%). In another all-polymer system, based on PBQx-TCl/PY-DT as the active layers, the efficiency could be boosted from 17.51% to 18.07%, enabled by the addition of 2 wt% L8-BO, which further verified the effectiveness of using an NF-SMA as a solid additive. This work demonstrates that incorporating an NF-SMA as a solid additive holds great potential for driving the development of PPHJ-APSCs.
Embodied Carbon in New Zealand Commercial Construction
Authors: Finnie David A. , Masood Rehan , Goldsworthy Seth , Harding Benjamin
Published: May 2024

Decarbonization is gaining priority from the macro to the micro level. However, achieving this is a critical challenge, as industries are still immature. This study explores the practices used to calculate and reduce embodied carbon (EC) in New Zealand (NZ) commercial construction projects. In the Paris Agreement, NZ pledged to reduce its net GHG emissions to 50 percent below the gross 2005 levels by 2030. The built environment generates approximately 40% of global greenhouse gas (GHG) emissions, with 11% being generated by manufacturing materials. EC represents carbon dioxide (CO2) emitted into the atmosphere throughout the extraction, fabrication, transportation, and assembly of building materials. A survey questionnaire was distributed to stakeholders in commercial construction via the New Zealand Institute of Quantity Surveyors (NZIQS) open forum. Twenty-seven valid responses were analyzed. The survey tested and expanded on the interview findings. Calculating and reducing EC are not mandatory in NZ. Most industry professionals had yet to experience EC calculation in projects. Clients most commonly drive EC reduction in public projects with calculations that are often conducted during the concept or detailed design stages. The challenges in measuring and lowering EC include a lack of client willingness to fund EC calculation, lack of knowledge and experience, lack of previous cost data, lack of EC materials, and lack of fit-for-purpose EC calculation tools. These findings may inform NZ government policy initiatives supporting EC reduction to meet their 2050 target.
Techno-Economic Evaluation of CSP–PV Hybrid Plants with Heat Pump in a Temperature Booster Configuration
Authors: Iñigo-Labairu Javier , Dersch Jürgen , Hirsch Tobias , Giuliano Stefano , Loevenich Matthias , Córdoba Diego
Published: May 2024

Concentrated solar power (CSP)—photovoltaic (PV) hybrid power plants allow for the generation of cheap electrical energy with a high capacity factor (CF). A deep integration of both technologies offers synergies, using parts of the PV generated electricity for heating the thermal storage tank of the CSP unit. Such configurations have been previously studied for systems coupled by an electric resistance heater (ERH). In this work, the coupling of a CSP and a PV plant using a heat pump (HP) was analyzed due to the higher efficiency of heat pumps. The heat pump is used as a booster to lift the salt temperature in the storage system from 383 to 565 °C in order to reach higher turbine efficiency. A techno-economic analysis of the system was performed using the levelized cost of electricity (LCOE), the capacity factor and nighttime electricity fraction as variables for the representation. The CSP–PV hybrid with a booster heat pump was compared with other technologies such as a CSP–PV hybrid plant coupled by an electric heater, a standalone parabolic trough plant (PT), a photovoltaic system with battery storage (PV–BESS), and a PV thermal power plant (PVTP) consisting of a PV plant with an electric heater, thermal energy storage (TES) and a power block (PB).
Site Selection of Wind Farms in Poland: Combining Theory with Reality
Authors: Amsharuk Artur , Łaska Grażyna
Published: May 2024

With global shifts towards sustainable energy models, the urgency to address rising fossil fuel prices, military conflicts, and climate change concerns has become evident. The article aims to identify the development of wind energy in Poland. This study introduces an integrated methodology for enhancing renewable energy capacities by selecting new construction sites for onshore wind farms across Poland. The proposed methodology utilises a hybrid model incorporating multiple criteria decision-making methods, such as the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), alongside the semiautomated spatial analysis method using QGiS software (v. 3.32 Lima). The model considers economic, social, and environmental criteria and limitations, offering a comprehensive approach to the decision-making process. It was found that wind farms occupy 460.7 km2 in Poland, with a 250 m buffer around each turbine and a total power capacity of 5818 MW. The results show that an additional 7555.91 km2 of selected areas, 2.34% of the country’s area, theoretically offer significant opportunities for wind energy development. The spatial analysis identifies potential sites with promising opportunities for domestic and international renewable energy investors. The study’s findings contribute towards achieving national and EU renewable energy targets while offering a replicable framework for informed spatial planning decisions in other regions.
Sustainable Transport in the European Union: Exploring the Net-Zero Transition through Confirmatory Factor Analysis and Gaussian Graphical Modeling
Authors: Sichigea Mirela , Cîrciumaru Daniel , Brabete Valeriu , Barbu Cătălin Mihail
Published: May 2024

The sustainability of the transport sector is targeted by various policies adopted by the European Union, and their impact must be constantly monitored in order to maximize the desired objective. This paper, through a two-stage investigation, aims to present a systemic approach of the sustainability dimensions in transport and to introduce an innovative technique to analyze the interdependencies between them. In the first stage, relevant indicators were selected from the Eurostat database for the content of four dimensions: economic, environmental, social and technological. The robustness of the developed dimensions was assessed and validated through a confirmatory factor analysis. In the second stage, a Gaussian graphical model was estimated as a technique integrating graphical and statistical modeling to identify complex structures of linkages between variables (as components of each dimension of sustainability). The structure of the network clearly highlights the dependence of transport on fossil fuel consumption as the main determinant of pollution in the sector (CO2 emissions). In addition, the central role of railways in decarbonizing transport is highlighted, in contrast to the limited, and isolated at one end of the network, role of electric vehicles. The findings support that affordability of this new technology plays an important role in its impact on zero-emission transition. Concentrating on the period 2013–2022, at EU27 level, the results are relevant in the context of decarbonization policies, offering useful insights both for future research and policy makers.
Experimental Investigation on Heat Transfer Enhancement of Phase Change Materials by Fractal Fins
Authors: Guo Zishuo , Xu Li , Sun Feihu , Sun Si
Published: May 2024

The low thermal conductivity of phase change materials restricts their application fields such as thermal storage and electronic equipment cooling. In order to enhance the heat charging capacity of the phase change unit, fractal fins inspired by plant leaves were designed and manufactured. The changes in the solid–liquid interface, temperature distribution and liquid fraction in the phase change units with fractal fins during melting were investigated experimentally and compared units with the conventional rectangular fin. The results show that fractal fins have better heat transfer enhancement effects than rectangular fins because the enhancement of heat conduction exceeds the suppression of natural convection. Increasing the number of fins can also shorten the melting time and make the temperature distribution more uniform. Compared with the one rectangular fin unit, the full melting time of the unit with three fractal fins is reduced by 17.07%, and the bottom surface temperature is reduced by 27.47%. However, increasing the number of fins while using tree-like fractal fins may cause the fins to inhibit natural convection more than enhance heat conduction. The research in this paper will provide a better understanding of the melting process of phase change units with fins and provide data for future numerical simulations.
Combined Geometrical Optimisation of a Square Microchannel with Smoothed Corners
Authors: Lorenzini Marco , Suzzi Nicola
Published: May 2024

Several engineering systems currently use microchannel heat sinks. In order to increase the performance of these devices, optimisation according to the first and second law of thermodynamics is employed. One way to achieve the goal is to modify the geometry of the cross-section, as is done in this paper for square ducts, having the walls at a uniform temperature which is higher than that of the bulk fluid at the inlet. The effects of both the thermal entry region of the duct and the heat generation due to viscous dissipation are considered. The resulting Graetz–Brinkman problem is solved numerically to obtain the velocity and temperature fields. It is demonstrated that non-negligible viscous heating eventually causes the heat flux to reverse (from fluid to walls), and that, only after this condition is achieved, can the flow become fully developed, which makes the entry region the only useful stretch for real-life applications. The length after which the direction of the heat flux reverses due to viscous heating in the fluid is obtained as a function of the Brinkman number and of the smoothing radius. Optimisation with performance evaluation criteria and entropy generation minimisation was carried out separately, and the results were combined into a single objective function. A comparison with published models highlights how neglecting the entry region and viscous heating yields misleading results. It turns out that smoothing the corners is always profitable in the case of the constrained heated perimeter or area of the cross-section but seldom when the characteristic length or the hydraulic diameter is fixed. With few exceptions, viscous heating amplifies the trends experienced for zero-Brinkman flows. The results are in non-dimensional form, yet they have been obtained starting from plausible dimensional values and are applicable to real-life devices.
Optimization of the Load Command for a Coal-Fired Power Unit via Particle Swarm Optimization–Long Short-Term Memory Model
Authors: Hao Xiaoguang , Yang Chunlai , Chen Heng , Dong Jianning , Bao Jiandong , Wang Hui , Zhang Wenbin
Published: May 2024

This study addresses the challenges faced by coal-fired power plants in adapting to energy fluctuations following the integration of renewable energy sources into the power grid. The flexible operation of thermal power plants has become a focal point in academic research. A numerical model of a coal-fired power plant was developed in this study using the Long Short-Term Memory (LSTM) algorithm and the Particle Swarm Optimization (PSO) algorithm based on actual operation data analysis. The combined PSO-LSTM approach improved the accuracy of the model by optimizing parameters. Validation of the model was performed using a Dymola physical simulation model, demonstrating that the PSO-LSTM coupled numerical model accurately simulates coal-fired power plant operations with a goodness of fit reaching 0.998. Overall system performance for comprehensively evaluating the rate and accuracy of unit operation is proposed. Furthermore, the model’s capability to simulate the load variation process of automatic generation control (AGC) under different load command groups was assessed, aiding in optimizing the best load command group. Optimization experiments show that the performance index of output power is optimal within the experimental range when the set load starts and stops are the same and the power of load command γ = 1.8. Specifically, the 50–75% Turbine Heat Acceptance (THA) load rise process enhanced the overall system performance index by 55.1%, while the 75–50% THA load fall process improved the overall system performance index by 54.2%. These findings highlight the effectiveness of the PSO-LSTM approach in optimizing thermal power plant operations and enhancing system performance under varying load conditions.
Supercritical Extraction Techniques for Obtaining Biologically Active Substances from a Variety of Plant Byproducts
Authors: Herzyk Filip , Piłakowska-Pietras Dorota , Korzeniowska Małgorzata
Published: May 2024

Supercritical fluid extraction (SFE) techniques have garnered significant attention as green and sustainable methods for obtaining biologically active substances from a diverse array of plant byproducts. This paper comprehensively reviews the use of supercritical fluid extraction (SFE) in obtaining bioactive compounds from various plant residues, including pomace, seeds, skins, and other agricultural byproducts. The main purpose of supercritical fluid extraction (SFE) is the selective isolation and recovery of compounds, such as polyphenols, essential oils, vitamins, and antioxidants, that have significant health-promoting properties. Using supercritical carbon dioxide as the solvent, supercritical fluid extraction (SFE) not only eliminates the need for hazardous organic solvents, e.g., ethanol, and methanol, but also protects heat-sensitive bioactive compounds. Moreover, this green extraction technique contributes to waste valorisation by converting plant byproducts into value-added extracts with potential applications in the food, pharmaceutical, and cosmetic industries. This review highlights the advantages of SFE, including its efficiency, eco-friendliness, and production of residue-free extracts, while discussing potential challenges and future prospects for the utilisation of SFE in obtaining biologically active substances from plant byproducts.
Association between Young People’s Neighbourhoods’ Characteristics and Health Risk Factors in Saudi Arabia
Authors: Al-Nuaim Anwar , Bursais Abdulmalek K. , Hassan Marwa M. , Alaqil Abdulrahman I. , Collins Peter , Safi Ayazullah
Published: May 2024

Introduction: A neighbourhood’s environmental characteristics can positively or negatively influence health and well-being. To date, no studies have examined this concept in the context of Saudi Arabian youth. Therefore, this study aimed to evaluate the association between a neighbourhood’s environmental characteristics and health risk factors among Saudi Arabian youth. Methods: A total of 335 secondary-school students (175 males, 160 females), aged 15–19 years old, participated. Body mass index (BMI) and waist circumference measurements were taken, and physical activity (steps) was measured via pedometer. The perceived neighbourhood environment was assessed using the International Physical Activity Questionnaire Environment Module (IPAQ-E). Results: Significant differences were found between the youths from urban, rural farm, and rural desert locations in terms of BMI, waist circumference, daily steps, accessibility, infrastructure, social environment, household vehicles, safety, and access to facilities (p < 0.001). Rural desert youths were less active, and males (26.43 + 8.13) and females (24.68 + 5.03) had higher BMIs compared to the youths from other areas. Chi-square analysis revealed a significant difference (χ21 = 12.664, p < 0.001) between the genders as to social-environment perceptions. Males perceived their neighbourhood as a social environment more than was reported by females (68.39% and 50.28%, respectively). Pearson’s correlation revealed negative significant relationships between steps and both safety of neighbourhood (r = −0.235, p < 0.001) and crime rate (r = −0.281, p < 0.001). Discussion: Geographical location, cultural attitudes, lack of facilities, and accessibility impact youth physical-activity engagement and weight status; this includes environmental variables such as residential density, neighbourhood safety, household motor vehicles, and social environment. Conclusions: This is the first study examining associations with neighbourhood environments in the youths of the Kingdom of Saudi Arabia. Significant associations and geographical differences were found. More research and policy interventions to address neighbourhoods’ environmental characteristics and health risk factors relative to Saudi Arabian youth are warranted.
Understanding and Involving the Perspective of Pregnant Women as Users When Designing the Framework of e-Health and Exercise Interventions during Pregnancy: Preliminary Study
Authors: Santos-Rocha Rita , Ferreira Mariana , Pimenta Nuno , Branco Marco , Oviedo-Caro Miguel , Szumilewicz Anna
Published: May 2024

Health and exercise technology may promote a healthy lifestyle during pregnancy. The objective of this cross-sectional study was to understand and involve the perspective of pregnant women as users in the design of a framework for future e-health and exercise interventions during pregnancy. Pregnant women replied to a questionnaire aimed at understanding their physical activity patterns, needs, and preferences regarding the use of mobile applications (apps). The main results showed that one-third of the women did not practice any type of exercise during pregnancy. Women preferred to exercise in a gym, outdoors, or at home. The majority already had or were currently using a fitness app, but never used any pregnancy-specific app. Most women agreed that it was important to have a specific app for pregnancy to improve knowledge about recommendations on lifestyle, have direct contact with health and exercise professionals, have social interaction with other mothers, and have guidance on preparation for childbirth and postpartum recovery. Understanding and involving the perspective of pregnant women as users will allow researchers to improve the design of a pregnancy-specific app and future e-health and exercise interventions during pregnancy. These preliminary results will lead to the development of the “active pregnancy app” focused on the promotion of an active and healthy lifestyle during pregnancy and postpartum.
Supplementary Dam Site Selection Using a Geospatial Approach: A Case Study of Wivenhoe Dam
Authors: Zytoon Aseel , Gharineiat Zahra , Alajarmeh Omar
Published: May 2024

Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a geospatial approach to assess the feasibility of constructing a supplementary dam near Linville, Brisbane, Australia, with the aim of mitigating floods and preventing overtopping failure at Wivenhoe Dam. Using QGIS software and a 25 m resolution DEM from the Queensland Spatial Catalogue ‘QSpatial’ website, four potential dam sites were analysed, considering cross-sections, watershed characteristics, and water volume calculations. Systematic selection criteria were applied on several dam wall options to identify the cost-effective and optimal one based on the dam wall dimensions, volume-to-area, and volume-to-cost ratios. The selected option was further assessed against predefined criteria yielding the optimal choice. The study provides insights into the feasibility and effectiveness of supplementary dam construction for flood mitigation in the region, with recommendations for future research and implementation plans for the asset owners.
Annual and Seasonal Dynamics of CO2 Emissions in Major Cities of China (2019–2022)
Authors: Zhao Yue , Feng Yuning , Du Mingyi , Fraedrich Klaus
Published: May 2024

To control the growth of CO2 emissions and achieve the goal of carbon peaking, this study carried out a detailed spatio-temporal analysis of carbon emissions in major cities of China on a city-wide and seasonal scale, used carbon emissions as an indicator to explore the impact of COVID-19 on human activities, and thereby studied the urban resilience of different cities. Our research re-vealed that (i) the seasonal patterns of CO2 emissions in major cities of China could be divided into four types: Long High, Summer High, Winter High, and Fluctuations, which was highly related to the power and industrial sectors. (ii) The annual trends, which were strongly affected by the pan-demic, could be divided into four types: Little Impact, First Impact, Second Impact, and Both Impact. (iii) The recovery speed of CO2 emissions reflected urban resilience. Cities with higher levels of de-velopment had a stronger resistance to the pandemic, but a slower recovery speed. Studying the changes in CO2 emissions and their causes can help to make timely policy adjustments during the economic recovery period after the end of the pandemic, provide more references to urban resilience construction, and provide experience for future responses to large-scale emergencies.
Editorial: Special Issue, “Molecular Advances in Skin Diseases 2.0”
Authors: Kanda Naoko
Published: May 2024

Recently, the pathomechanisms of various skin diseases have been progressively elucidated [...]
Guardians at the Gate: Immune System in Gastrointestinal Diseases
Authors: Layunta Elena , Mesonero Jose Emilio , Latorre Eva
Published: May 2024

The immune system plays a key role in gastrointestinal (GI) pathologies, being responsible for protecting the body against infection, maintaining homeostasis, and regulating the inflammatory response in the GI tract [...]