bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Heat Treatment on the Microstructure and Properties of Ti–Al Composite Coatings with Formation of a TiAl3 Intermetallic Phase
Authors: Rutkowska-Gorczyca Małgorzata , Wiśniewski Marcin , Dziubek Mateusz , Kowalewski Piotr , Abdulina Saule , Winnicki Marcin
Published: November 2024

In response to environmental issues and the intensive degradation of parts, the civil and military aviation industries have shown increasing interest in developing more sustainable materials and technologies; therefore, this paper proposes the regeneration of structural components by cold spraying. As part of this research, Ti–Al composite powder was deposited by low-pressure cold spraying and then heat treated to obtain a Ti–Al3 intermetallic phase. The Ti–Al3 intermetallic phase is characterized by high hardness and abrasion resistance. The research has shown that at appropriately selected heat treatment parameters, this phase is formed in a certain area of the Ti–Al coating. The presence and morphology of the Ti–Al3 phase were confirmed by X-ray, scanning, and transmission electron microscopy. It has been found that the presence of this phase increases the hardness of coatings and reduces the friction coefficient.
Hydro Generator Stator Bar Modeling and Simulation Based on Finite Element Simulation
Authors: Yu Guang , Cheng Yujia
Published: November 2024

As motor capacity and rated voltage increase, the demand for motor insulation also increases. Additionally, the electric field distribution at the end of a large-scale hydro generator is extremely nonuniform, and corona discharge occurs. This destroys the main insulation, which significantly affects the service life of electrical machinery. The regulation of the electric field concentration at the end of a large-scale hydro generator needs to be addressed. In this manuscript, a large hydro generator with 120 MW rated capacity and 15.75 kV rated voltage was studied. For the purpose of electric field homogenization in the stator bar end, decrease of highest field strength, and surface loss, the computing methods of the electric field in the stator bar end were studied. The electric field distribution in the stator bar end was obtained. According to COMSOL 5.6 modeling software, the hydro generator stator bar is an accurate model. Using finite element analysis (FEA), the stator bar characteristics can be simulated. The different stator bars with no anti-corona structure, linear anti-corona structure, two-layer nonlinear anti-corona structure, and three-layer nonlinear anti-corona structure were compared, including the electric potential, electric field, and loss distribution under rated voltage. From the experimental results, with no anti-corona structure and linear anti-corona structure, the electric field concentration is present in the exit slot. The values of electric field and loss are both higher, which causes corona discharge easily. With the nonlinear anti-corona structure, the electric field concentration is improved significantly, effectively decreasing the highest electric field. The three-layer nonlinear anti-corona structure is the best. Compared with the two-layer nonlinear anti-corona structure, the values of the highest electric field and loss are 16% and 77% lower, respectively.
Polyelectrolyte-Surfactant Mixture Effects on Bulk Properties and Antibacterial, Cytotoxic Activity of Fine Sulfur Particles
Authors: Turganbay Seitzhan , Aidarova Saule , Issayeva Assem , Iskakbayeva Zhanar , Sabitov Aitugan , Turganbay Gulsinay , Babayev Alpamys A.
Published: November 2024

Elemental sulfur, commonly known for its wide range of biological activities, has a long history of use in protecting all garden and vegetable crops from a range of pests and diseases, including powdery mildew, ascochyta blight, clubroot, plant mites, oidium, anthracnose, and scab. In the present study, a quick and environmentally friendly approach has been developed for the synthesis of sulfur nanoparticles with antibacterial activity. Fine sulfur particles (FSPs) were prepared by modifying the surface of elemental sulfur using various polyelectrolyte–surfactant mixtures (PSMs) including sodium carboxymethyl cellulose–sodium dodecylbenzene sulfonate (NaCMC-SDBS) and polyhexamethylene guanidine hydrochloride–cetyltrimethylammonium bromide (PHMG-CTAB). The FSPs were characterized by UV–visible spectrophotometry, X-ray diffraction (XRD), thermogravimetric/differential scanning calorimetry analysis (TG/DSC), and scanning electron microscopy (SEM), with the FSPs showing an almost spherical shape with an average size in the range of 150–200 nm. The antibacterial activity of the FSPs was tested against Gram-positive Staphylococcus aureus and Enterococcus faecium and Gram-negative Escherichia coli and Pseudomonas aeruginosa bacteria and one fungus (Aspergillus brasiliensis ATCC 95 16404). Based on this, it could be seen that FSPs exhibited significant antimicrobial activity against Gram-positive bacteria, i.e., S. aureus and E. faecium. The in vitro cytotoxicity of the FSPs-1 and FSPs-2 studied in normal (MeT-5A) and tumorous (MCF-7) human cell lines was assessed in the concentration range from 500 μg/mL to 0.12 mg/mL, from which it was determined as being non-cytotoxic. The received products can be considered for potential application in agriculture and medicine.
Dynamic Cohort Formation with Hierarchical Blockchain Using GDP for Enhanced FL
Authors: Ahmad Sunila Fatima , Abbas Zahra , Syed Madiha Haider , Anjum Adeel , Rehman Semeen
Published: November 2024

Federated Learning (FL) can be defined as an effective solution for using the benefits of machine learning (ML) in distributed systems, in which the data of the clients remain protected. However overlaid challenges affecting today’s FL systems are centered on time optimization, scalability, and security. To these challenges, this paper responds with a new solution comprising the dynamic cohort creation via smart contracts and a hierarchical blockchain approach. Firstly, the research utilizes smart contracts for the dynamic tracking of cohorts in real time and addresses a two-tier blockchain structure for safety and efficiency in storage. In addition, Gaussian Differential Privacy (GDP) is used as a privacy-preserving mechanism that adds controlled noise to the data or model updates to protect individual data points from being inferred by adversaries. The method we are proposing includes four practical steps that include efficient update validation and aggregation; this will enhance training time, and increase model accuracy as well as data confidentiality. The standard dataset is used to show enhanced performance and scalability which validates this method. Based on the above investigations, it could be concluded that the proposed approach improves FL efficiency and creates a new direction in the development of secure, accurate, and scalable ML. The present study indicates that the implementation of blockchain with FL fortified by GDP will establish a novel innovation between intelligent and safe Artificial Intelligence (AI) architecture for safeguarding the privacy of ML system.
Active Polypropylene Fibers for Controlling Shrinkage Cracks in Cement-Stabilized Materials
Authors: Cao Haibo , Li Jing , Chen Tuanjie , Ren Haisheng , Qiao Zhu
Published: November 2024

Conventional polypropylene fibers, characterized by their smooth surfaces, exhibit relatively weak bonding with cement-based materials, limiting their effectiveness in enhancing these materials’ mechanical properties. This study investigates a graft-modified approach to activating polypropylene fibers, introducing amide groups onto their surfaces to improve fiber–matrix interaction. The active polypropylene fibers were produced using an ultraviolet (UV) grafting technique, where maleic anhydride was first used to graft carboxyl groups onto the fiber surfaces, followed by acylation with diethylenetriamine to introduce amide bonds. The optimal experimental conditions were identified by using the degree of amidation as the response metric. Fourier-transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) confirmed successful amination and surface activation, with a marked increase in specific surface area. The water contact angle of the active polypropylene fibers decreased significantly from 106.3° to 39.9°, indicating greatly improved wettability by the cement slurry and enhanced bonding strength between the fibers and the cement matrix. To evaluate the effects of the modified fibers, cement-stabilized macadam specimens incorporating various fiber contents were prepared and tested to determine their mechanical properties and shrinkage performance. The results indicated that, compared to conventional polypropylene fibers, the activated polypropylene fibers increased the 28-day compressive strength of CSM by 6.56%, enhanced tensile strength by 4.94%, reduced the rebound modulus by 7.42%, decreased the drying shrinkage coefficient by 25.55%, and lowered the thermal shrinkage coefficient by 13.16%. These findings demonstrate that the chemical bonding between the active polypropylene fibers and the cement matrix is significantly enhanced, leading to improved overall performance in crack resistance, material toughening, and shrinkage mitigation.
Three-Dimensional Green Tensor of One-Dimensional Hexagonal Quasicrystals
Authors: Lazar Markus , Michelitsch Thomas , Agiasofitou Eleni
Published: November 2024

In this work, the elastic 4×4 Green tensor of one-dimensional quasicrystals is given and has phonon, phason and phonon–phason coupling components. Using the residue method, a closed-form expression of the elastic 4×4 Green tensor for one-dimensional hexagonal quasicrystals of Laue class 10, which possess 10 independent material constants, is derived. The 10 independent components of the obtained 4×4 Green tensor are numerically presented in contour plots, revealing features of anisotropy as well as the interesting result that the phason component of the Green tensor has the strongest contribution in comparison with all the other components. In the case of vanishing phonon–phason coupling, the phonon part of the derived Green tensor reproduces Kröner’s well-known elastic 3×3 Green tensor for hexagonal crystals. The analytical closed-form expression of the derived Green tensor provides an advantage for efficient computational calculations in various applications.
Morphological Diversity of Desmiophyllum Lesquereux Fossil Leaves and Related Palaeoenvironmental Implications from the Early Cretaceous of Northeastern Spain
Authors: Sender Luis M. , García-Cobeña Josué , Diez José B. , Cobos Alberto
Published: November 2024

A variety of leaves of different morphological sizes and venation types corresponding to the gymnosperm genus Desmiophyllum have been found in five fossil sites originating from the Barremian to the Cenomanian periods in northeastern Spain over an interval comprising 25 million years that encompasses the Early Cretaceous–Late Cretaceous boundary. These leaves are preserved in various lithologies corresponding to different depositional environments such as lagoonal systems, coastal swamps, deltaic plains, lacustrine environments and fluvial-related deposits. These new data shed light on the morphological and paleoenvironmental variability of Desmiophyllum recorded in the Cretaceous deposits from southwestern Eurasia.
Critical Perspectives in Speech-Language Therapy: Towards Inclusive and Empowering Language Practices
Authors: Lamoureux Geneviève , Tessier Alexandra , Finlay Sébastien , Verduyckt Ingrid
Published: November 2024

This conceptual paper critically examines the use of traditional medicalized terminology in speech-language therapy, with a particular focus on the Quebec context. It highlights how current language practices, rooted in a medical model of disability, often marginalize individuals with communication differences such as stuttering, autism, and aphasia by pathologizing these variations. Drawing on contemporary frameworks such as the social model of disability, neurodiversity, and “diversité capacitaire” (a French term that translates to “capacity diversity” or “ability diversity”, emphasizing the richness of diverse abilities and communication styles), the article advocates for more inclusive and empowering language that respects and reflects communicative diversity. The authors emphasize the importance of participatory approaches, including consultation with the communities directly involved and the establishment of terminological committees, to develop respectful and affirming language. Ultimately, this paper calls for a shift in speech-language therapy practices to promote a more inclusive understanding of communication, enabling individuals with communication differences to fully participate in society.
Evaluating Water Turbidity in Small Lakes Within the Taihu Lake Basin, Eastern China, Using Consumer-Grade UAV RGB Cameras
Authors: Xie Dong , Qiu Yunjie , Chen Xiaojie , Zhao Yuchen , Feng Yuqing
Published: November 2024

Small lakes play an essential role in maintaining regional ecosystem stability and water quality. However, turbidity in these lakes is increasingly influenced by anthropogenic activities, which presents a challenge for traditional monitoring methods. This study explores the feasibility of using consumer-grade UAVs equipped with RGB cameras to monitor water turbidity in small lakes within the Taihu Lake Basin of eastern China. By collecting RGB imagery and in situ turbidity measurements, we developed and validated models for turbidity prediction. RGB band indices were used in combination with three machine learning models, namely Interpretable Feature Transformation Regression (IFTR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Results showed that models utilizing combinations of the R, G, B, and ln(R) bands achieved the highest accuracy, with the IFTR model demonstrating the best performance (R² = 0.816, RMSE = 3.617, MAE = 2.997). The study confirms that consumer-grade UAVs can be an effective, low-cost tool for high-resolution turbidity monitoring in small lakes, providing valuable insights for sustainable water quality management. Future research should investigate advanced algorithms and additional spectral features to further enhance prediction accuracy and adaptability.
An Enhanced Adaptive Ensemble Kalman Filter for Autonomous Underwater Vehicle Integrated Navigation
Authors: Liang Zeming , Fan Shuangshuang , Feng Jiacheng , Yuan Peng , Xu Jiangjiang , Wang Xinling , Wang Dongxiao
Published: November 2024

Autonomous Underwater Vehicles (AUVs) rely on integrated navigation systems and corresponding filtering algorithms to ensure mission success and the spatiotemporal accuracy of sampled data. Among these, the ensemble Kalman filter (EnKF) combines Monte Carlo methods with the Kalman filter, which is particularly suited for nonlinear systems. This study proposes an enhanced adaptive EnKF algorithm to improve the smoothness and accuracy of the filtering process. Instead of the conventional Gaussian distribution, this algorithm employs a Laplace distribution to construct the system state vector and observation vector ensembles, enhancing stability against non-Gaussian noise. Additionally, the algorithm dynamically adjusts the number of vector members in the ensemble using adaptive mechanisms by specifying thresholds during filtering to adapt the requirements of real-world observational settings. Using field trial data from DVL, GPS, and electronic compass measurements, we optimize the algorithm’s parameter settings and evaluate the overall performance of the algorithm. Results indicate that the proposed adaptive EnKF achieves superior accuracy and smoothness performance. Compared to the conventional EnKF and EKF, it not only reduces the average positioning error by 30% and 44%, respectively, but also significantly improves the filtering smoothness and stability, highlighting its advantages for AUV navigation.
Analysis of the Energy Storage Efficiency of a UAV-Mounted Sensor Launcher Built on Traditional Crossbow Launch Mechanisms
Authors: He Yanan , Xu Huiqi , Wang Lei , Di Lingsong , Liu Weihua , Liu Liang
Published: November 2024

Deploying sensors to target locations using UAV platforms can effectively address the issue of limited aerial endurance in micro-UAVs. This paper introduces a launch method based on the crossbow principle, which is capable of concealing the deployment of heavy sensors. Given that the size and mass of the launcher on the UAV should be minimized, optimizing the structural energy storage performance of the launcher is essential. Initially, a static energy storage model for the launcher was developed, and criteria for evaluating the energy storage coefficient were established. Using the control variable method, the impact of seven structural parameters on the energy storage performance was analyzed. Based on these findings, a particle swarm optimization algorithm was proposed to optimize six parameters, aiming to maximize the energy storage coefficient. Subsequently, the bending stiffness coefficient of the launcher’s bow limb can be adjusted according to the specific application scenario to manage the energy storage within a reasonable range. Calculations revealed that after optimization, the maximum draw force was only 0.675 times that of the original plan under the same energy storage conditions. With the same maximum draw force, the energy storage capacity was 1.5 times higher than the original plan, indicating a significant optimization effect. This optimization approach provides a theoretical foundation for the energy storage optimization analysis of UAV-launched crossbow-based systems.
Symmetries or Asymmetries: How MSCI Index Advanced European Markets’ Exchange Rates Respond to Macro-Economic Fundamentals
Authors: Tabash Mosab I. , AsadUllah Muhammad , Siddiq Quratulain , Mansour Marwan , Daniel Linda Nalini , Al-Absy Mujeeb Saif Mohsen
Published: November 2024

The purpose of this study is to find symmetries and asymmetries in the exchange rate and macroeconomic fundamentals of advanced European markets, namely Denmark, the Euro Area, and United Kingdom, for the period of 2011 to 2022 via application of the NARDL technique. The findings reveal that interest rate affects DKK exchange rate asymmetrically in the long and short run, whereas money supply affects it in the short run. Foreign reserves are found to be helpful for all three currencies in stabilizing the exchange rate. A decline in gold price weakens GBP, DKK, and EUR in the long run. Previous studies suggest that the existence of asymmetrical relationships justifies the selection of NARDL for empirical analysis. This study makes a contribution to the existing literature, as it proves that forecasting via NARDL is also robust for analysis. The findings have significant policy implications for financial applications.
Continuously Beam-Steered Phased Array Antenna Using GaN Varactors for Millimeter-Wave Applications
Authors: Hamdoun Abdelaziz , Medjdoub Farid , Himdi Mohamed , Zegaoui Malek , Lafond Olivier
Published: November 2024

A continuously steerable beam patch antenna array employing a classical phase shifter based on GaN HEMTs is presented. Here, the GaN HEMTs are used as varactor diodes to achieve the tunability purpose. By controlling the DC bias of these varactors from −2 V to 2 V, the proposed array antenna can provide continuous beam steering from 0° to +25° in the azimuth plane at 41.20 GHz, while achieving a low side-lobe level and good impedance matching performances. Using GaN HEMTs as varactors to achieve beam steering capability has never been tried before to the best of our knowledge. The measurement results agree well with the simulation results and validate the effectiveness of the proposed beam steering based on GaN technology. This proposed phased array antenna will find numerous applications within future wireless communications systems, especially for millimeter-wave applications.
CoCENet: Collaborative Channel Estimation Network for Rapidly Time-Varying UAV Systems
Authors: Zhou Fan , Zhang Jilong , Yang Ming , Xing Yixuan , Liu Kai , Zhang Peiying
Published: November 2024

Wireless communication capability is vital for remote transmission between unmanned aerial vehicle (UAV) coordination systems, but the communication link between highly dynamic UAV systems is unstable and severely disturbed due to its rapidly time-varying channel. Current channel estimation methods suffer from insufficient inter-symbol interference (ISI) and inter-carrier interference (ICI) suppression and inadequate noise filtering. Therefore, a collaborative channel estimation network (CoCENet) is proposed in this paper, and it can restrain the channel interference by capturing the amplitude–phase and time–frequency correlation at the same time. Moreover, CoCENet applies a multi-scale fusion strategy to optimize the purity of the estimated outcome. Our experiment results demonstrate that CoCENet has preferable performance in terms of the suppression of channel interference and noise in rapidly time-varying UAV systems in a complex environment without stationarity assumption. At a signal-to-noise ratio (SNR) of −10 dB, the mean square error (MSE) of CoCENet is improved by 1.7–2.3 dB compared to existing methods, and at a SNR of 20 dB, the MSE is improved by 1.1–2.2 dB.
Achieving High-Accuracy Target Recognition Using Few ISAR Images via Multi-Prototype Network with Attention Mechanism
Authors: Zhang Linbo , Zou Xiuting , Xu Shaofu , Ma Bowen , Lu Wenbin , Lv Zhenbin , Zou Weiwen
Published: November 2024

Inverse synthetic aperture radar (ISAR) is a significant means of detection in space of non-cooperative targets, which means that the imaging geometry and associated parameters between the ISAR platform and the detection targets are unknown. In this way, a large number of ISAR images for high-accuracy target recognition are difficult to obtain. Recently, prototypical networks (PNs) have gained considerable attention as an effective method for few-shot learning. However, due to the specificity of the ISAR imaging mechanism, ISAR images often have unknown range and azimuth distortions, resulting in a poor imaging effect. Therefore, this condition poses a challenge for a PN to represent a class through a prototype. To address this issue, we use a multi-prototype network (MPN) with attention mechanism for ISAR image target recognition. The use of multiple prototypes eases the uncertainty associated with the fixed structure of a single prototype, enabling the capture of more comprehensive target information. Furthermore, to maximize the feature extraction capability of MPN for ISAR images, this method introduces the classical convolutional block attention module (CBAM) attentional mechanism, where CBAM generates attentional feature maps along channel and spatial dimensions to generate multiple robust prototypes. Experimental results demonstrate that this method outperforms state-of-the-art few-shot methods. In a four-class classification task, it achieved a target recognition accuracy of 95.08%, representing an improvement of 9.94–17.49% over several other few-shot approaches.
Using LSTM with Trajectory Point Correlation and Temporal Pattern Attention for Ship Trajectory Prediction
Authors: Zhou Yi , Guo Haitao , Lu Jun , Gong Zhihui , Yu Donghang , Ding Lei
Published: November 2024

Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long-term and complex spatial–temporal features hidden within the data. Moreover, they often overlook correlations among multivariate dynamic features such as longitude (LON), latitude (LAT), speed over ground (SOG), and course over ground (COG), which are essential for precise trajectory forecasting. To address these pressing issues and fulfill the need for more accurate and comprehensive ship trajectory prediction, we propose a novel and integrated approach. Firstly, a Trajectory Point Correlation Attention (TPCA) mechanism is devised to establish spatial connections between trajectory points, thereby uncovering the local trends of trajectory point changes. Subsequently, a Temporal Pattern Attention (TPA) mechanism is introduced to handle the associations between multiple variables across different time steps and capture the dynamic feature correlations among trajectory attributes. Finally, a Great Circle Route Loss Function (GCRLoss) is constructed, leveraging the perception of the Earth’s curvature to deepen the understanding of spatial relationships and geographic information. Experimental results demonstrate that our proposed method outperforms existing ship trajectory prediction techniques, showing enhanced reliability in multi-step predictions.
UGVs’ Navigation Supported by Multi-UAVs in a ROS Environment
Authors: Yang Tingting , Bi Xiaoyu , Shi Yixin , Song Jian , Li Xinlin , Chen Yiyang
Published: November 2024

It is challenging for unmanned ground vehicles (UGVs) to navigate in an unknown environment without equipping them with sensors, such as LiDAR, cameras, etc. As a result, it is necessary to employ an unmanned aerial vehicle (UAV) to provide the UGVs with map information, obstacle information and relative position estimations. The UGVs receive the messages from the UAV and then automatically plan a global route. When approaching obstacles, UGVs release collision position messages to the UAV. This UAV calculates the optimal path point based on the collision position and the time to collision of the UGVs. However, the UAV’s relative position estimation equipment has a limited range, such that it cannot provide all UGVs with the information in time. This means that UGVs receive information late or even lose communication with the UAV. To address the above issues, this paper advocates for the use of multiple UAVs and the creation of different communication strategies. A variety of tests are conducted in a simulated environment built in ROS to pair each UAV with different numbers of UGVs and explore their initial positions to find the most suitable configuration.
Preventive Planning of ‘Product-as-a-Service’ Offers Using Genetic Population-Driven Stepping Crawl Threads
Authors: Niemiec Krzysztof , Szwarc Eryk , Bocewicz Grzegorz , Banaszak Zbigniew
Published: November 2024

Unlike the precise methods implemented in constrained programming environments, the proposed approach to preventive planning of Product-as-a-Service offers implements a competitive solution based on Genetic Population Stepping Crawl Threads (GPSCT).GPSCT techniques are used to determine the so-called stepping crawl threads (SCT) that recreate, in subsequent steps, variants of the allocation of sets of leased devices with parameters that meet the expectations of the customers ordering them by means of genetic algorithms. SCTs initiated at a selected point of the Cartesian product space of the functional repertoire of the equipment offered penetrate it in search of offer variants that meet the constraints imposed by the size of the budget and the risk level (i.e., expressed as the likelihood of damaging the device or losing part of its functionality) of individual customers. Two approaches of implementation techniques were used to determine the initial SCT population for the genetic algorithm—branch and bound (BBA) and linear programming (LPA). Many experiments assessed their impact on the computation time and the quality of the obtained solution. The performed computational experiments indicate that the effectiveness of both approaches depends on the specificity of the problem considered each time. Interestingly, for different instances of the problem, an alternative solution can always be selected that is competitive with the exact methods, allowing for a 10-fold increase in scalability.
An Anti-Interference Method Based on Energy Residual Searching in GNSS Positioning Applications
Authors: Jiang Xiaobing , Lei Ming , Niu Yimeng , Wan Jiashan , Xia Na
Published: November 2024

Addressing the issue of linear frequency modulation (LFM) interference in GNSS positioning, this paper proposes an interference suppression method based on energy residual searching, incorporating the fractional Fourier transform (FrFT) for improved performance. In GNSS systems, LFM interference can severely impair positioning accuracy and system robustness, potentially rendering the receiver incapable of performing accurate localization. To address this issue, an energy residual function is introduced to quantitatively assess the impact of interference on the original signal. This function combines differences in signal energy with signal quality after spectral line removal to achieve effective interference evaluation. By optimizing the parameters of the fractional Fourier transform to maximize the value of the energy residual function the proposed method significantly enhances interference suppression. Our experimental results demonstrate that the method performs exceptionally well on practical datasets, effectively removing interference and restoring relevant peaks. This approach mitigates the negative effects of LFM interference on GNSS positioning performance, substantially improving both positioning accuracy and system robustness.
The Impact of Mechanical Failure of 18650 Batteries on the Safety of Electric Transport Operations
Authors: Bąkowski Henryk , Przytuła Iga , Cebulska Wioletta , Hadryś Damian , Ćwiek Janusz
Published: November 2024

The safety of 18650 lithium-ion batteries is critical for the reliability and durability of electric vehicles, especially as interest in sustainable transportation grows. Battery failures, such as fires or explosions, pose significant risks to both users and manufacturers, highlighting the need for advanced power systems. This study used finite element method (FEM) simulations and crash tests to evaluate battery safety in accident scenarios. The results showed that mechanical damage, especially from collisions, can cause internal short circuits, increasing the risk of thermal runaway, especially when combined with high temperatures during normal operation or charging. This can be caused by mechanical damage to the battery causing a change in the distance inside the battery, causing it to short circuit. The results highlight the importance of designing battery systems that prevent internal short circuits, especially under extreme conditions, and the need for continuous monitoring of battery parameters to detect early signs of failure. In the context of improving battery safety, the battery not only saves lives, but also extends vehicle life, reduces electronic waste, and increases energy efficiency, which is consistent with global efforts to minimize the environmental impact of technology and promote safer transportation.
Comprehensive Analysis of Failures in Photovoltaic Installations—A Survey-Based Study
Authors: Kut Paweł , Pietrucha-Urbanik Katarzyna , Kurek Patryk
Published: November 2024

The increasing demand for renewable energy sources indicates that an increasing number of single-family homeowners are choosing photovoltaic installations. These systems facilitate self-sufficient electricity production, increasing the energy independence of households. Although the prevalence and advancements in this field are growing, users frequently experience technical problems and failures that can affect the efficiency of the installation and the satisfaction with their use. The aim of this paper is to analyze the problems encountered by users of photovoltaic installations in single-family buildings. The study relies on user feedback, enabling the identification of the most frequently occurring issues from their viewpoint and the assessment of the reliability of the systems. While the analysis does not allow for a direct technical diagnosis of faults, the results obtained from the surveys provide valuable information on the frequency of problems and the elements of the system that require special attention. On this basis, conclusions can be drawn that can support further work on improving the reliability of the installation. The presented analysis includes not only specific failures, but also the overall satisfaction of users.
Advances in the Prevention of Shipping-Related Air Pollution
Authors: Garbatov Yordan , Georgiev Petar
Published: November 2024

This review looks at the advancements in shipping-related air pollution prevention in the context of ship life cycle assessment and energy efficiency. It discusses which design option is best for implementing various strategies to lower greenhouse gas emissions. It covers logistics, digitization, environmental requirements, and the greenhouse gases produced. Among the issues for enhancing the propulsion system’s performance are air lubrication, ship hull optimization, and hull and propeller maintenance and cleaning. Alternative fuels, wind-assisted propulsion, and nuclear energy are given special attention. Energy-efficient design solutions, risk-based environmental ship design, and retrofitting older ships to improve energy efficiency are also covered. Several trends and recommendations for lowering shipping-related air pollution have been found in the review.
Multi-Time Optimization Scheduling Strategy for Integrated Energy Systems Considering Multiple Controllable Loads and Carbon Capture Plants
Authors: Han Zhe , Li Zehua , Wang Wenbo , Liu Wei , Ma Qiang , Sun Sidong , Liu Haiyang , Zhang Qiang , Cao Yue
Published: November 2024

In response to the dual carbon targets, it is necessary not only to reduce carbon emissions but also to increase the proportion of renewable energy generation capacity, thereby exacerbating the scarcity of flexible resources in the power system. Addressing these challenges, this study proposes an operational optimization framework for an integrated energy system. This system encompasses wind/solar power plants, coal-fired power plants, carbon capture power plants, gas turbines, energy storage systems, and controllable loads, including reducible power loads, transferable power loads, electrolytic aluminum loads, transferable heat loads, and reducible loads. This study employs a system combining carbon capture plants with thermal power stations to supply flexible resources to the integrated energy system while reducing carbon emissions during the generation process of the thermal power units. A multi-timescale optimization scheduling approach is adopted to manage the uncertainties in wind, photovoltaic, and electric/thermal loads within the integrated energy system. The operational costs of the integrated energy system consider the capacity degradation costs of energy storage systems, the solvent degradation costs of carbon capture, and carbon costs. Finally, the cplex solver was used to solve the above model. The simulation results show that the consideration of five controllable loads leads to an increase of 7.22% in the interactive benefits with the power grid; the difference between the complete cost model and the incomplete overall benefits is 94.35%. It can be seen that the dispatching method proposed in this study can take advantage of the dispatching advantages of source-load adjustable resources and achieve the goal of low-carbon economic dispatching of the power system.
Overview of Technologies for Solar Systems and Heat Storage: The Use of Computational Fluid Dynamics for Performance Analysis and Optimization
Authors: Janus Jakub , Filipowska Monika , Jabłoński Hubert , Wieliński Mateusz , Sornek Krzysztof
Published: November 2024

This article reviews selected solar energy systems that utilize solar energy for heat generation and storage. Particular attention is given to research on individual components of these systems, aimed at improving their efficiency and performance. It focuses on an analysis of the literature concerning the design of thermal storage units, with an emphasis on the use of computational fluid dynamics (CFD) as a research tool. Conclusions from scientists’ research regarding the impact of tank shape, thermal insulation, flow parameters, and the use of stratification partitions on heat storage efficiency have been presented. The literature review indicates that thermal storage units play a key role in the efficiency of solar systems, and thermal stratification within them can significantly improve their performance. The methodology was based on an analysis of journals, primarily from after 2008, focusing on articles related to the application of CFD methodology in the study of solar systems and heat storage.
Thermodynamic Analysis of the Second Fluid Brayton Cycle for Scramjet Engine
Authors: Luo Jiamao , Qi Xin , Jiao Si , Xiao Yunlei , Huang Shengfang , Yang Shunhua
Published: November 2024

The burning chamber wall of the ramjet engine is facing an extremely thermal environment during normal conditions. Thermal protection measures must be taken on the wall surface of the combustion chamber. At the same time, the aircraft faces high-power electrical demand problems under high-speed cruising states. To address these issues, a second fluid-closed Brayton cycle system was introduced in this paper. Helium was utilized as the secondary fluid medium, while kerosene was used as the final heat sink. The ramjet engine chamber wall was cooled by the helium cycle system. At the same time, part of the heat absorbed by the helium cycle was transformed into electric power by a generator. This work proposes a new method of thermal management in a closed cycle. Unlike traditional methods, this proposal can regulate the mass flow rate of helium based on the requirement of heat load. A zero-dimensional numerical calculation method was established for thermodynamic analysis. The results show that as the equivalence ratio of 0.8~1.5 for the kerosene flow rate, the system can suffer the thermal load of 200~350 kJ/kg on the combustion chamber wall at the maximum kerosene allowable temperature. To ensure the normal operation of the circulating system, the mass flow ratio between the helium and the air changes from 0.02 to 0.045. Compared with the direct kerosene cooling method, the second fluid circulation method leads to the kerosene equivalent saving ratio by 2% to 14%; at the same time, such a system could generate 160~500 kJ/kg of electrical energy. This new thermal management method can achieve kerosene saving, electric power generating and suffering more thermal loads under the premise of satisfying normal work.
Integration of Optical Remote Sensing and Laser Point Cloud for Forest Stock Estimation in Karst Mountainous Areas
Authors: Zheng Jiajia , Zhou Zhongfa , Zhu Meng , Wang Jiale , Wan Jiaxue , Long Yangyang
Published: November 2024

This study addresses the challenges posed by the complex topography and forest structure in karst mountainous areas, as well as the difficulties in estimating forest stock using traditional methods. We propose a method that integrates optical remote sensing data from Sentinel-2 into airborne LiDAR data to estimate forest stock in karst areas. First, an Allometric Growth Model correlating tree height and diameter at breast height (DBH) in karst areas was developed based on field measurements. Tree height information extracted from LiDAR data was then combined with the binary wood volume model specific to fir trees in Guizhou Province to calculate the individual tree biomass of fir trees. In addition, this study evaluated the robustness of three machine learning methods, the Random Forest Regression Model, K-Nearest Neighbors Regression Model, and Backpropagation Neural Network Model, in estimating forest stock in karst mountainous areas. The results indicate the following: (1) The Allometric Growth Model based on field data showed strong predictive power for DBH and can be used for large-scale estimation. (2) The distribution characteristics of individual tree biomass and plot biomass under different site conditions revealed the distribution pattern of fir trees in the study area, providing important information for understanding the growth status of forest stock in the region. (3) The Random Forest Regression Model demonstrated exceptional accuracy, generalization capability, and robustness in the estimation of forest stock within karst mountainous regions. This study provides an effective technical tool for estimating forest stock in karst areas and under complex terrain conditions and has significant scientific value and practical implications for the monitoring and management of forest ecosystem carbon sinks.
Optimizing Radio Access for Massive IoT in 6G Through Highly Dynamic Cooperative Software-Defined Sharing of Network Resources
Authors: Bouhafs Faycal , Raschella Alessandro , Mackay Michael , Hashem Eiza Max , den Hartog Frank
Published: November 2024

The Internet of Things (IoT) has been a major part of many use cases for 5G networks. From several of these use cases, it follows that 5G should be able to support at least one million devices per km2. In this paper, we explain that the 5G radio access schemes as used today cannot support such densities. This issue will have to be solved by 6G. However, this requires a fundamentally different approach to accessing the wireless medium compared to current generation networks: they are not designed to support many thousands of devices in each other’s vicinity, attempting to send/receive data simultaneously. In this paper, we present a 6G system architecture for trading wireless network resources in massive IoT scenarios, inspired by the concept of the sharing economy, and using the novel concept of spectrum programming. We simulated a truly massive IoT network and evaluated the scalability of the system when managed using our proposed 6G platform, compared to standard 5G deployments. The experiments showed how the proposed scheme can improve network resource allocation by up to 80%. This is accompanied by similarly significant improvements in interference and device energy consumption. Finally, we performed evaluations that demonstrate that the proposed platform can benefit all the stakeholders that decide to join the scheme.
Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market
Authors: Barua Moumita , Kumar Teerath , Raj Kislay , Roy Arunabha M.
Published: November 2024

This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. The study evaluates model performance using key regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results indicate that CNN and GRU models generally outperform the others, depending on the specific stock, and demonstrate superior capabilities in forecasting stock price movements. This investigation provides insights into the strengths and limitations of each model while highlighting potential avenues for improvement through feature engineering and hyperparameter optimization.
Evaluating the Impact of Recursive Feature Elimination on Machine Learning Models for Predicting Forest Fire-Prone Zones
Authors: Barzani Ali Rezaei , Pahlavani Parham , Ghorbanzadeh Omid , Gholamnia Khalil , Ghamisi Pedram
Published: November 2024

This study aimed to enhance the accuracy of forest fire susceptibility mapping (FSM) by innovatively applying recursive feature elimination (RFE) with an ensemble of machine learning models, specifically Support Vector Machine (SVM) and Random Forest (RF), to identify key fire factors. The fire zones were derived from MODIS satellite imagery from 2012 to 2017. Further validation of these data has been provided by field surveys and reviews of land records in rangelands and forests; a total of 326 fire points were determined in this study. Seventeen factors involving topography, geomorphology, meteorology, hydrology, and human factors were identified as being effective primary factors in triggering and spreading fires in the selected mountainous case study area. As a first step, the RFE models RF, Extra Trees, Gradient Boosting, and AdaBoost were used to identify important fire factors among all selected primary factors. The SVM and RF models were applied once on all factors and secondly on those derived from the RFE model as the key factors in FSM. Training and testing data were divided tenfold, and the model’s performance was evaluated using cross-validation. Various metrics, including recall, precision, F1 score, accuracy, area under the curve (AUC), Matthew’s correlation coefficient (MCC), and Kappa, were employed to measure the performance of the models. The assessments demonstrate that leveraging RFE models enhances the FSM results by identifying key factors and excluding unnecessary ones. Notably, the SVM model exhibits significant improvement, achieving an increase of over 10.97% in accuracy and 8.61% in AUC metrics. This improvement underscores the effectiveness of the RFE approach in enhancing the predictive performance of the SVM model.
The Ignition Phenomenon and Mechanism of Welding Spatters Under Different Current Intensities
Authors: Wang Feiyue , Wan Litian , Luo Jing , Tong Yanmin
Published: November 2024

The ignition of combustible materials by electric welding spatters represents a significant cause of fires in welding operations, and the current intensity is a sensitive factor that affects the ignition capacity of welding spatters. In this work, the influence of different current intensities on the physical properties and ignition capacity of welding spatters on common combustible materials was investigated, and the ignition mechanism of electric welding spatter was also explained by means of the hot-spot theory. The results indicated that the splash range, the total generated quantity, the maximum diameter, and the temperature of electric welding spatters increased with the enhancement in current intensity. Furthermore, a higher current intensity was associated with a greater likelihood of producing irregular spatter particles. The probability of ignition of electrode welding spatters was found to be sensitive to their physical properties, exhibiting a non-linear increase with increasing current intensity. At a current intensity of 360 A, a surge in both the physical properties and ignition capacity of the spatters was observed, which is attributed to the coupling of a reduction in the critical hot-spot radius and an unstable pulsation in the arc.
Study on the Mechanisms of Flavor Compound Changes During the Lactic Fermentation Process of Peach and Apricot Mixed Juice
Authors: Zhao Yao , Liu Ruoqing , Mu Ying , Lv Mingshan , Xing Jun , Zheng Li , Aihaiti Aihemaitijiang , Wang Liang
Published: November 2024

This study employed headspace solid-phase microextraction gas chromatography–mass spectrometry (HS-SPME-GC-MS) and liquid chromatography–mass spectrometry (LC-MS) for non-targeted metabolomics analyses to examine the impact of mixed fermentation with various lactic acid bacteria (LAB) on the flavor compounds and metabolites of peach and apricot mixed juice (PAMJ), specifically focusing on the alterations of volatile compounds and non-volatile metabolites, as well as their metabolic pathways during the fermentation process. A total of 185 volatiles were identified using HS-SPME-GC-MS analysis, revealing significant differential metabolites, including eugenol, benzaldehyde, and γ-decalactone etc. The results indicated that lactic fermentation significantly enhanced the overall flavor of the juice toward the end of the fermentation process. In the interim, untargeted metabolomics utilizing LC-MS identified 1846 divergent metabolites, with 564 exhibiting up-regulation and 1282 demonstrating down-regulation. The metabolic pathway study performed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed significant changes in the metabolic levels of amino acids and saccharides after the lactic fermentation of PAMJ. Primarily associated with amino acid metabolism and starch and sucrose metabolism pathways. This work establishes a theoretical foundation for advancing fermented fruit juices with superior quality.