bluuebunny/crossref_metadata_2025 huggingface dataset:
Heterogeneous graph neural network for modeling intelligent manufacturing systems
Authors: Zhu Guangyi , Wang Siyuan , Wang Lilin
Published: October 2024

Currently, manufacturing systems have become more and more complex, often involving multiple machines, systems and processes to produce workpieces. In order to facilitate comprehensive analysis and control of manufacturing processes, the integration of connections between machine level, system level, and process level in manufacturing process modeling is needed. However, traditional graph deep learning models are unable to take into account the heterogeneity of different machines in the system when modeling manufacturing systems. To address this problem, this paper proposes a new approach: modeling manufacturing systems using the heterogeneous graph neural network sample and aggregate algorithm based on cutting-edge Bayesian neural networks and graph deep learning. This method considers the connection between different manufacturing system levels, treats machine operations as nodes, and connects different nodes through material flow and operational similarity. The effectiveness of the method is demonstrated through its application to model an aero-engine blade production line. Extensive numerical experiments show that the proposed graph modeling method is effective in expressing the heterogeneity of different machines and multi-level manufacturing processes. By integrating machine heterogeneity into the modeling of a manufacturing system, it not only facilitates comprehensive analysis and control of the manufacturing process, but also lays the foundation for cost savings and productivity improvements in the manufacturing system.
Explainable AI for automated respiratory misalignment detection in PET/CT imaging
Authors: Salimi Yazdan , Mansouri Zahra , Amini Mehdi , Mainta Ismini , Zaidi Habib
Published: October 2024

Purpose. Positron emission tomography (PET) image quality can be affected by artifacts emanating from PET, computed tomography (CT), or artifacts due to misalignment between PET and CT images. Automated detection of misalignment artifacts can be helpful both in data curation and in facilitating clinical workflow. This study aimed to develop an explainable machine learning approach to detect misalignment artifacts in PET/CT imaging. Approach. This study included 1216 PET/CT images. All images were visualized and images with respiratory misalignment artifact (RMA) detected. Using previously trained models, four organs including the lungs, liver, spleen, and heart were delineated on PET and CT images separately. Data were randomly split into cross-validation (80%) and test set (20%), then two segmentations performed on PET and CT images were compared and the comparison metrics used as predictors for a random forest framework in a 10-fold scheme on cross-validation data. The trained models were tested on 20% test set data. The model’s performance was calculated in terms of specificity, sensitivity, F1-Score and area under the curve (AUC). Main results. Sensitivity, specificity, and AUC of 0.82, 0.85, and 0.91 were achieved in ten-fold data split. F1_score, sensitivity, specificity, and AUC of 84.5 vs 82.3, 83.9 vs 83.8, 87.7 vs 83.5, and 93.2 vs 90.1 were achieved for cross-validation vs test set, respectively. The liver and lung were the most important organs selected after feature selection. Significance. We developed an automated pipeline to segment four organs from PET and CT images separately and used the match between these segmentations to decide about the presence of misalignment artifact. This methodology may follow the same logic as a reader detecting misalignment through comparing the contours of organs on PET and CT images. The proposed method can be used to clean large datasets or integrated into a clinical scanner to indicate artifactual cases.
Box-counting measure of metric spaces
Authors: Huang Liang-yi , Rao Hui , Wen Zhiying , Xu Yan-li
Published: October 2024

In this paper, we introduce a new notion called the box-counting measure of a metric space. We show that for a doubling metric space, an Ahlfors regular measure is always a box-counting measure; consequently, if E is a self-similar set satisfying the open set condition, then the Hausdorff measure restricted to E is a box-counting measure. We show two classes of self-affine sets, the generalized Lalley-Gatzouras type self-affine sponges and Barański carpets, always admit box-counting measures; this also provides a very simple method to calculate the box-dimension of these fractals. Moreover, among others, we show that if two doubling metric spaces admit box-counting measures, then the multi-fractal spectra of the box-counting measures coincide provided the two spaces are Lipschitz equivalent.
Tailoring the coexistence of Ce(PO4)@Mn3(PO4)2/MXene for supercapacitive application
Authors: Obodo Raphael M , Nsude Hope E , Okereke Benjamin O , Eze Chimezie U , Duru Miletus O , Ezike Sabastine C , Ahmad Ishaq , Maaza M
Published: October 2024

Recently, supercapacitors have been considered an excellent option for improving electrical and electronic systems because of their huge potential as energy storage devices. In this work, two phases of the hydrothermal process and drop casting method were used to fabricate the synthesized composites of Cerium phosphate/MXene (MX) (Ce(PO4)/MXene), Manganese phosphate (Mn3(PO4)2/MXene), and Cerium@Manganese phosphates (Ce(PO4)@Mn3(PO4)2/MXene) electrodes. These electrodes’ structural, morphological, functional, optical and electrochemical characteristics were examined. The electrochemical studies were carried out in a 3-electrode system to determine their specific capacitance, energy and power densities. The maximum obtained specific capacitance of 1125 Fg−1 and 1350 Fg−1 from Ce(PO4)@Mn3(PO4)2/MXene electrode using 1.0 mVs−1 scan rate and 0.5 Ag−1 current density respectively. Using a 1.0 molar solution of Na2SO4 as the electrolyte, 10,000 continuous charge–discharge cycles were achieved with a cyclic stability of 75.7%.
Time-fractional of cubic-quartic Schrödinger and cubic-quartic resonant Schrödinger equations with parabolic law: various optical solutions
Authors: Ahmed Hakima Khudher , Ismael Hajar Farhan
Published: October 2024

Schrödinger's nonlinear equation is a fundamental model in fiber optics and many other areas of science. Using the Jacobi elliptic expansion function method, the time-fractional cubic-quartic nonlinear Schrödinger equation and cubic-quartic resonant nonlinear Schrödinger equation are investigated. By applying the effective Jacobi elliptic expansion function method, optical soliton solutions such as bright, dark, singular, periodic singular, exponential, and Jacobi elliptic function solutions have been obtained. The effect of the time-fractional derivative on the solutions is also revealed. Graphical representations are illustrated to showcase the physical properties of raised solutions, providing a comprehensive understanding of the solutions’ functionality.
High-quality multimodal MRI with simultaneous EEG using conductive ink and polymer-thick film nets
Authors: Cicero Nicholas G , Fultz Nina E , Jeong Hongbae , Williams Stephanie D , Gomez Daniel , Setzer Beverly , Warbrick Tracy , Jaschke Manfred , Gupta Ravij , Lev Michael , Bonmassar Giorgio , Lewis Laura D
Published: October 2024

Objective. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets. Approach. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality. Main results. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present. Significance. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.
Brain and muscle derived features to discriminate simple hand motor tasks for a rehabilitative BCI: comparative study on healthy and post-stroke individuals
Authors: de Seta Valeria , Colamarino Emma , Pichiorri Floriana , Savina Giulia , Patarini Francesca , Riccio Angela , Cincotti Febo , Mattia Donatella , Toppi Jlenia
Published: October 2024

Objective. Brain–Computer Interfaces targeting post-stroke recovery of the upper limb employ mainly electroencephalography to decode movement-related brain activation. Recently hybrid systems including muscular activity were introduced. We compared the motor task discrimination abilities of three different features, namely event-related desynchronization/synchronization (ERD/ERS) and movement-related cortical potential (MRCP) as brain-derived features and cortico-muscular coherence (CMC) as a hybrid brain-muscle derived feature, elicited in 13 healthy subjects and 13 stroke patients during the execution/attempt of two simple hand motor tasks (finger extension and grasping) commonly employed in upper limb rehabilitation protocols. Approach. We employed a three-way statistical design to investigate whether their ability to discriminate the two movements follows a specific temporal evolution along the movement execution and is eventually different among the three features and between the two groups. We also investigated the differences in performance at the single-subject level. Main results. The ERD/ERS and the CMC-based classification showed similar temporal evolutions of the performance with a significant increase in accuracy during the execution phase while MRCP-based accuracy peaked at movement onset. Such temporal dynamics were similar but slower in stroke patients when the movements were attempted with the affected hand (AH). Moreover, CMC outperformed the two brain features in healthy subjects and stroke patients when performing the task with their unaffected hand, whereas a higher variability across subjects was observed in patients performing the tasks with their AH. Interestingly, brain features performed better in this latter condition with respect to healthy subjects. Significance. Our results provide hints to improve the design of Brain–Computer Interfaces for post-stroke rehabilitation, emphasizing the need for personalized approaches tailored to patients’ characteristics and to the intended rehabilitative target.
Prediction of Tensile Properties of Natural Fiber Reinforced PP Hybrid Composites by Facca-Kortschot-Yan (FKY) and Palsule Equations
Authors: Pant Mridul , Palsule Sanjay
Published: October 2024

Natural fiber reinforced polymer hybrid composites are advanced materials for commercial and engineering applications. Rule of hybrid mixtures has been applied to develop Facca-Kortschot-Yan (FKY) Equation and Palsule equation for predicting tensile strength and modulus of these hybrid polymer composites. This study predicts the values of tensile strength and modulus of a few natural fibers reinforced polypropylene hybrid composites by these two equations and compares them with the reported experimental values.
Synthesis of nanochitosan from crab shells: an assessment of morphology, dye adsorption capability and antibacterial characteristics as future prospects for wastewater treatment
Authors: Panchal Manoj , Raghavendra G , Abhishek D , Om Prakash M , Ojha S , Yelamasetti Balram
Published: October 2024

In current experimental study, waste crab shells were used for the synthesis of chitosan nanoparticles and characterized. Preparation of chitosan nanoparticles were done through series of processes involving demineralization, deproteinization, decolouration, deacetylation, and ball milling. Chitosan nanoparticles thus obtained were characterized using X-ray diffraction (XRD) analysis, FTIR (Fourier transform infrared) spectroscopy, and Transmission Electron Microscopy (TEM). The methylene blue and methyl orange dye adsorption was conducted with the aid of UV-vis Spectroscopy. The antibacterial ability of chitosan nanoparticles were also assessed for four different bacterial strains. X-ray diffraction results support the chitosan production and the crystalline nature of the synthesized chitosan. The presence of amine and amide functional groups was verified through FTIR analysis. The degree of de-acetylation was found to be 70.24%, as calculated from FTIR analysis. Transmission Electron Microscopy images of chitosan nanoparticles revealed a fibrous and crystalline structure with molecules aligned in an antiparallel fashion. The dye adsorption test revealed the methylene dye adsorption capacity of 187.7 mg/g whereas for methyl orange it was found to be 296.40 mg/g. The chitosan nanoparticles showed good antibacterial ability against four different bacterial strain.
Experimentation Study of Sound and Vibration Absorption in Two-Wheeler Engines using Tea Waste and Nano-Clay Material
Authors: Senthil Kumar P , Nithiyarasan R , Kamalesh M D , Sudalai Muthu T , Vinoth P
Published: October 2024

Noise and vibration from vehicles is a growing problem in cities due to increased operation in transportation around. Prolonged exposure to loud noises can harm health, including hearing loss. Engine vbrations in vehicles, especially motocycles, worsen rides and pose health risks. A novel material using tea waste, nano clay, and additives like epoxy or polyester resin was developed. Tests showed that the composite material reduced vibrations, especially at lower speeds (10-25 km/h), promising smoother engine performance. Other tests, including sound absorption and SEM imaging, confirmed its effectiveness. Water absorption tests also showed it repelled water well. This novel composite material offers a solution to reduction in noise and vibrations in vechiles, improving comfort and potentially reducing health risks as biodegradable material.
Design of Unit Cells-Based Metamaterial Antenna
Authors: Nishanth Rao K , LaxmaReddy D , Monika S. , Sailaja M.
Published: October 2024

Artificial metallic structures with simultaneous negative permittivity (ɛ) and permeability (µ) that result in a negative refractive index are known as metamaterials. It supports backward waves, i.e., group velocities and inside Metamaterial phase velocities are antiparallel, because of its negative index. The shortcomings of a regular patch antenna, such as low gain and efficiency, can be overcome with the help of this metamaterial antenna, which is helpful for wireless communication. The shortcomings of a regular patch antenna, such as low gain and efficiency, can be overcome with the help of this metamaterial antenna, which is helpful for wireless communication. The effort is justified by the proposal to use DNG (Double Negative) Metamaterials to create compact planar antennas. Additionally, it has peculiar features when compared to materials that are easily obtained because of its negative permeability and permittivity, which results in a negative refractive index. Owing to the unique characteristics of Metamaterials, the antenna achieves 3.768 dB, 53.76 % overall efficiency, and a 1.6 VSWR at 5 GHz frequency using a single unit cell. These results are excellent for wireless LANs and point-to-point communication. Additionally, a different metamaterial antenna with three unit cells produces superior results at 5 GHz frequency, with a return loss of -13.51 dB, a gain of 5.19.17 dB, and an overall efficiency of 92.50% at 2.4 GHz, the results are 4.1830 dB, 60.78%, and -14.51 dB, respectively.
Experimental Testing of Rubber Materials for Enhancement of Suspension Bush Performance
Authors: Karthi Vinith K S , Sreenaveen S , Sarveshnarayanan G , Muthukumar S
Published: October 2024

In the ever-evolving automotive industry, the enhancement of vehicle performance hinges on fundamental components, with the rubber bush in vehicle suspension emerging as a critical element for transformative innovation. This research delves into the optimization of rubber bushes through vulcanization, a process renowned for augmenting rubber’s mechanical properties. The study scrutinizes the performance characteristics of various rubber materials, evaluating their suitability for incorporation into suspension rubber bushes. The research employs a rigorous three- stage testing methodology, including compression, tensile, and fatigue tests, utilizing a Universal Testing Machine (UTM). These stages provide comprehensive insights into the materials’ responses to compression forces, tensile strengths and weaknesses, and fatigue resistance. The findings aid in the informed selection of materials for suspension rubber bushes, contributing to the advancement of automotive suspension system technology. This study not only enhances our understanding of rubber material behaviour but also offers practical insights for applications in automotive engineering. Aligned with the goal of advancing suspension system technology, the research underscores the importance of material-specific properties in real-world scenarios. Ultimately, this research is poised to make a significant contribution to the continual evolution of automotive suspension systems, emphasizing durability, resilience, and overall performance. Neoprene rubber exhibits performance qualities in compression, tensile strength, and fatigue resistance that are equivalent to those of natural rubber, according to the extensive testing findings. This implies that neoprene rubber is a good substitute for using in suspension bushings, providing manufacturers and vehicle engineers with a tempting choice.
Structural, Optical, and Electrical Transformations in FexSi1−xO Thin Films: The Role of Iron and Annealing
Authors: Sudha M , Balamurugan A , Girisha L , Kalaiarasan S , Ravivarman G , Nanthakumar S , Girimurugan R
Published: October 2024

Thin films FexSi1−xO, with iron (Fe) content between 0 to 20% have been applied to substrates made of soda lime glass by means of the spray pyrolysis process. Annealing the films allowed us to test their thermal stability. Both the as-depositedFexSi1−xO thin film and annealed FexSi1−xOthin film were analyzed by. Researchers analyzed the structure of the films and its composition using these methods. The XRD study demonstrated that the FexSi1−xOthin films, even if they are deposited or annealed, have a wurtzite structure in the plane orientation. The result shows that the films’ crystalline structure is not affected by the heating process. In addition to that, the good indicator of film quality and consistency were no signs of pinholes or cracks in the films. An iron deficiency was identified by following the annealing process, according to the compositional analysis done by EDAX. Annealing affects the integration of Fe in the film’s matrix, according to these changes occur in the composition. The band gap changes to red for FexSi1−xOthin films was observed in the optical characteristics of these films while tested using Ultra Violet -Visible spectroscopy. Integrating Fe into films alters their electronic structure, and it is one important indicator for band gap change. The incorporation of Fe resulted in a reduction in resistance and it is measured by the electrical properties using the two-probe method which in turn indicates an improvement in the films’ electrical conductivity. In conclusion, the outcomes if this research supports the feasibility of incorporating iron into the silicon carbide thin films. There are major structural changes to the films’ electrical characteristics and optical properties due to this incorporation. The outcomes of this research have significant implications for the advancement of electronic and optoelectronic devices that could make use of these alterations to enhance their performance.
Multi-Objective Metaheuristic Optimization for Enhancing Wear Characteristics in Al6060/Si3N4/Bn Hybrid Composites
Authors: Jai Rajesh P , Balambica V. , Achudhan M.
Published: October 2024

This paper is devoted to research work on the tribological parameters of Si3N4/BN coating composites whose goal is to minimize the wear rate and COF (coefficient of friction). A variety of experimental studies were carried out employing several parameter variations such as various amplitudes of applied loading, sliding speed, distance, and Si3N4 and BN weight fractions. The experimental results were applied to make a sense of the models which expressed the COF and the wear rate with the Si3N4 and BN weight percentages; applied load; sliding velocity as well as sliding distance. Predictive models were calibrated on platform and experimental results and have shown good agreement. Furthermore, among cause identification was performed an in-depth mistake analysis to reveal affects on the accuracy of prediction. The paper will then branch out into multi-objective optimization through applying various algorithms, such as Particle Swarm (PSO), Firefly Algorithm, Cuckoo-Search, Grey Wolf Optimization (GWO), Multi-objective Teaching-Learning Based Optimization (MOTLBO), and Genetic Algorithm mixed with Non-dominated Sorting Genetic Algorithm II (GA-NSGA-II The optimized tribological parameters were figured out. For instance, it was revealed at what weight percentages Si3N4 and BN were optimal, how applied normal load and sliding velocity should be and how far sliding distance ought to be. The work is summarized in a part depicting the validation of models as predictive and the targeting of multiple objectives to further enhance the overall tribological results. The real world implications of enhancing abrasion resistance as regards composite coatings which consist of Si3N4/BN particles and the potential uses in different industries are described as well. Apart from this, it is also suggested to perform several additional experiments for this purpose, such as the testing different optimization algorithms and analyzing different impacting parameters. It contributes to the response of the tribological influence on composite coating revealing more complex data about molecular processes that are consequently useful for shell-like materials design.
Toolpath and Tool Design Innovations in Deformation Machining for Superior AA-7075 T6 Fins
Authors: Ponnusamy Prasanth , Seenivasan S , Loganathan Ganesh Babu , Magesh Babu D , Sivalingam A , Nanthakumar S , Girimurugan R
Published: October 2024

A hybrid manufacturing process, deformation machining (DM) combines subtractive manufacturing with incremental forming. Monolithic components with complex profiles are created through the hybridization of manufacturing technologies, which also reduces the wastage of raw materials. The properties of geometry and surface quality of the fin made from the aluminum alloy Al-7075 T6 using the DM process’s bending mode was examined in this research, as were the effects of various approaches, toolpath methods, and tool design. To achieve the desired shape for the machined fin, various combinations of arcuate and slanting fin toolpath techniques were explored utilizing both top-down and bottom-up methods. The bottom-up method and the arcuate toolpath strategy were used to investigate various tool profiles. Using the best combination of toolpath strategy and tool profile, named as the T3 tool profile, which has a 0.6 mm nose radius and a circular sector, bottom-to-top (BTT)technique, and arcuate toolpath method, components with better geometrical features and surface roughness (SR) were produced. The analysis of the forming force in the bending approach of the DM progression was further carried out using this optimal combination.
Performance Estimation of Self-Circulating Fluidized Bed Gasification with mixtures of Biomass
Authors: Manickam Selvaraj , Jaganraj R. , Ganesh R. , Srinivasa Rao N. , Pradeep A. , Anbuchezhiyan G.
Published: October 2024

The thermochemical conversion method is a well-proven technology for producing synthesis gas. Mixture of biomass in varied ratios is a novel strategy that has been tested in Self-Circulating Fluidized Bed gasification (SFBG). SFBG is designed and tested using the aforesaid biomass at a mixture ratio of 75%-25% for equivalency ratios of 0.3, 0.4, and 0.5. The gasification system’s overall effectiveness was assessed by considering crucial factors, including gas production, the heating value of the generated gas, carbon conversion, and the efficiency of the system in producing cold gas.
Artificial intelligence and machine learning for defect detection in castings.
Authors: Alamuru Sunanda , Reddy G. S. , Jagannadha Raju M.V.
Published: October 2024

This research delves into the intersection of artificial intelligence (AI) and quality control processes in the context of casting operations, presenting a multifaceted exploration of methodologies aimed at enhancing efficiency, accuracy, and overall quality in manufacturing. The study encompasses diverse approaches, ranging from defect prediction utilizing machine learning models to applying AI techniques in various casting methods. One pivotal contribution is the introduction of Smart Quality Inspection (SQI), an innovative AI-based approach. The study details the design and implementation of a custom Convolutional Neural Network (CNN) model for SQI, achieving an exceptional accuracy rate of 99.86% in inspecting casting products. The success of SQI not only transforms traditional inspection practices but also minimizes the impact of operator, social, organizational, task, and environmental factors. The research highlights the versatility of AI in defect identification, addressing challenges in visual inspection through proposed methods such as second-order derivative and morphology operations, row-by-row adaptive thresholding, and 2-D wavelet transform. Notably, the wavelet transform emerges as a versatile technique, effectively addressing various casting defects, marking a promising avenue for automatic defect detection in castings based on X-ray inspection. While celebrating accomplishments, the study recognizes ongoing challenges, emphasizing the need for continuous refinement and innovation. Future directions include formally classifying defects, localized defect detection, and exploring controlled environmental conditions for data collection, ensuring the adaptability and reliability of AI-based approaches. Looking ahead, the vision involves seamlessly integrating AI systems into assembly lines, automating inspections, and leveraging real-time accessibility for continual improvements. Furthermore, the potential synergy with Natural Language Processing (NLP) techniques on inspection log data presents exciting possibilities for a holistic understanding of manufacturing maintenance processes. This research not only contributes to the evolving landscape of quality control but also sets the stage for a dynamic and intelligent future in the manufacturing industry.
Development of Regression model and Optimization of Process Parameters for Wire Electrical Discharge of SAE 1010 Steel using Taguchi Grey Approach
Authors: Manikandan N , Thejasree P , Raghurami Reddy D , Kumar Peyyala Pramod
Published: October 2024

SAE 1010 is a predominant material that possesses exceptional qualities that make it highly suitable for automotive applications, especially for use in headed fasteners and bolts. Its primary usage is in the automotive industry, although it also exhibits significant potential in various other technical domains. Various challenges in conventional machining have been resolved by adopting alternative methodologies that entail the elimination of material. Wire electrical discharge machining (WEDM) is one of the advanced machining techniques. The primary objective of this study is to propose a Taguchi-based grey technique for optimizing multiple performance indicators in the Wire Electrical Discharge Machining (WEDM) process of SAE 1010 material. A regression model has been constructed to examine the correlation between the two sets of data. The experimental design employs Taguchi’s method, with the independent variables being the status of the pulse (on/off) and the magnitude of the applied current. Material removal rate, surface roughness, dimensional variation, and form/orientation tolerance errors are considered as performance measures in this investigation and these are optimized by Taguchi-Grey approach. Performing an effective investigation on developing systems assists in enhancing the efficiency and effectiveness of manufacturing processes.
3-Blade Propeller Design and Acoustical Analysis
Authors: Sura Sreekanth , Nagaraj Goud B , Santhani Raju , Sathwik  , Manideep
Published: October 2024

In the recent time, the deployment and usage of small scale unmanned aerial vehicles (UAVs) have grown rapidly for a wide range of civilian and military applications. These applications range from racing, delivery and photography for civilians to land based reconnaissance operations for military purposes. The noise produced by the propellers is one of the major factors which are stopping its application in delivery of goods. As a lot of drones around the houses making annoying noises will lead to the frustration and stress to local residents. Even in the application of drone in military purpose for reconnaissance the noise generated by the propeller should be decreased to make the drone stealthy. This projects goal is to design a 3-blade propeller for a small size unmanned aerial vehicle (UAV) using propeller theories and to analyse its acoustical behaviour. The propeller design is validated by using two methods they are – Analytical, Experimental. Blade Element Theory is used for analytical analysis whereas experimental validation has been done using thrust stand. For acoustical analysis a python code has been developed based on Gutin & Deming theory. The parameters of a propeller like Diameter, RPM (Revolutions per minute), and Load distribution are iterated to design an efficient, low noise propeller which is capable to produce required amount of thrust.
Advanced Machining Performance Through Optimization of Awjm Parameters using Metaheuristic Techniques
Authors: Jai Rajesh P , Balambica V. , Achudhan M.
Published: October 2024

This study comprehensively deals with the optimization of parameters in abrasive water jet machining (AWJM) with material associated factors giving special focus to machining performance. By fine-tuning of water pressure, abrasive flow rate, traverse speed and standoff distance, the work provides a multi-dimensional platform for comparative analysis. In other words, it provides the predictive models for MRR and DOC helped by the knowledge gained through these experiments. The efficiency of models is proved when compared to the measured data, thus their higher accuracy of prediction is displayed. Subsequently, different meta heuristics, i.e., Firefly, Particle Swarm, Cuckoo Search, Grey Wolf Optimization, and Teaching-Learning-Based Optimization algorithms, for AWJM parameters tuning to achieve material-specific machining performance. Results show some improvements in MRR and DOC over the experimental baseline which certify the effectiveness of the presented optimization method. The scope of this paper also covers the broader perspectives of machining applications in the future. This research brings us a step closer to the development of novel machining processes that are tailored for the characteristics inherent in any material through the utilization of the right machining parameter selection. The suggested method can be used as a reliable decision base by engineers for making choice on AWJM operations for more complex materials, thus institutionalizing foundation for future research and improvisations in related material cuttings etc. The results shown here are responsible for inaugurating a new era in the development of AWJM material-design methods with more accuracy and efficiency, applied across different industries.
Sensorless control of HSPMSM based on fuzzy feedforward phase locked loop
Authors: Fan Shengwen , Wang Yunqian , Tan Jingge
Published: October 2024

Aiming at the problem that the dynamic and steady-state performance of the traditional phase locked loop speed identification method will become worse with the increase of the speed after obtaining the information of Counter-electromotive force or flux linkage in the sensorless control of the motor, a sliding mode observer combining fuzzy PI and feedforward phase locked loop control is proposed to realize the sensorless control of the motor. To improve the steady-state performance of the phase-locked loop PI regulator, a fuzzy PI regulator is applied to feedforward phase-locked loop control, and the proportional integral coefficient of the phase-locked loop PI regulator is adjusted through a fuzzy controller; for the dynamic performance of the motor, a feedforward phase-locked loop is used to reduce steady-state position tracking errors under load disturbances. The simulation results demonstrate that the fuzzy feedforward phase-locked loop speed identification method is suitable for high-speed motor sensorless control, and its dynamic and steady-state performance is superior to traditional phase-locked loop methods.
Enhanced output performance of perovskite-based triboelectric nanogenerators devices by surface engineering
Authors: Li Jiajia , Qin Qi
Published: October 2024

Finding materials with excellent dielectric properties to increase the friction charges density is an effective method to improve the output performance of triboelectric nanogenerators (TENGs). As a star material in semiconductors, perovskite has remarkable dielectric properties and can generate considerable frictional charges, which is expected to enable TENGs to achieve high performance. In this study, the organic-inorganic hybrid (MAPbI3) perovskite is used as friction materials to prepare the TENGs device. The Poly (ethylene glycol) diacrylate (PEGDA) molecule is used to modify the surface of MAPbI3 to passivize the surface defects of the film, so as to improve the performance of the device greatly. At a mechanical operating frequency of 2 Hz, after PEGDA treatment, the open-circuit voltage, short-circuit current density and transfer charges of the champion device are 276.86 V, 68.61 mA m−2 and 56.08 nC, respectively, which are 54 %, 64 % and 39 % higher than those of the pristine device. And the highest output power density of the device is increased from 3.13 W m−2 to 9.17 W m−2. The device has excellent continuous operation stability, and the optimized device can light 147 commercial blue LEDs with a power of 0.06 W.
Short-term photovoltaic power prediction based on IMRFO-StemGNN
Authors: Han Changning , Wang Qing , Wang Pingxin , Li Congcong
Published: October 2024

To improve the accuracy of photovoltaic power generation prediction, this paper proposes a photovoltaic power generation prediction method based on the Improved Manta ray foraging optimization (IMRFO) algorithm and Spectral Temporal Graph Neural Network (StemGNN). Firstly, by collecting photovoltaic module data and weather forecast data, a multivariate photovoltaic feature database is constructed, and key meteorological feature quantities are selected as input data for the prediction model based on the Pearson correlation coefficient. Then, a spectral time graph neural network will be built, and the IMRFO algorithm will be used to optimize the hyperparameters of the StemGNN model to improve prediction accuracy. Simulation examples show that the proposed method can effectively improve the accuracy of photovoltaic power generation prediction.
Mask optimization for optical lithography based on UNet
Authors: Song Yabo , Xu Shuang , Lin Kefan
Published: October 2024

Traditional photolithography methods are increasingly unable to meet the ever-stringent demands for pattern resolution and alignment accuracy, and developing efficient mask optimization techniques has become an urgent need in the industry. This paper delves into the application of Inverse Lithography Technology (ILT) in nanoscale photolithography and proposes an improved mask optimization algorithm. The backbone network of this algorithm employs a UNet architecture, trained initially on a prepared dataset comprising original masks and the ones optimized through traditional methods. The pre-trained backbone network generates a coarse mask, which is then inputted into a correction layer to refine the mask, enhancing pattern accuracy and processing efficiency. Compared to traditional gradient-based mask optimization methods, neural ILT demonstrates superior effectiveness, which enhances the efficiency and pattern quality of the lithography process while reducing production costs.
An analog switch circuit structure suitable for low-distortion transmission
Authors: Lv Zhengze , Wei Hailong , Wu Qi
Published: October 2024

A MOSFET analog switch circuit structure suitable for low-distortion bidirectional signal transmission is proposed. By introducing a “threshold voltage matching MOSFET” into the MOSFET switch driver circuit structure, the driving voltage corrected by the threshold voltage is generated by the driving circuit, eliminating the problem of poor conduction on-resistance flatness in traditional analog switch circuit structure due to MOSFET second-order effects and non-constant overdrive voltage. The circuit completed schematic and layout design based on the 0.13 μm BCD process. Simulation results show that the switch has bidirectional transmission capability, with a typical on-resistance value of 115 Ω. It achieves less than 5% on-resistance non-flatness under resistive loads ranging from 500 Ω to 1 KΩ, which is more than 50% improvement compared to traditional analog switch structures. Moreover, the on-resistance flatness is insensitive to changes in load and temperature.
Bi-level planning of electric vehicle charging station considering energy consumption per unit mileage
Authors: Shen Kunpeng , Li Keyao , Yu Yibao , Yang Wei
Published: October 2024

This article addresses the phenomenon of neglecting traffic conditions and congestion levels in existing EV charging load modeling, and innovatively considers the energy consumption per unit mileage of electric vehicles, presents a bi-level planning model for EV charging stations. Firstly, based on the traffic network model, the speed flow model is introduced, and the mileage energy consumption model is established. Combined with the Dijkstra method, the driving path is planned, and the EV charging demand model is formed. Secondly, a bi-level programming model of the charging station, with the charging station side and user side as upper and lower layers is established, and the KKT (Karush-Kuhn-Tucker Conditions) condition is introduced to decouple the upper and lower layers. The Improved Whale Optimization Algorithm (IWOA) and Voronoi diagram are used to solve the model and determine the service range and capacity of the charging station. Finally, the proposed method’s rationality and effectiveness are confirmed through an example taken from a region in south.
Optimizing energy storage configuration scheme considering optimization of electricity prices, carbon emissions, and flexible load
Authors: Xu Jian , Du Changyu , Li Moujun
Published: October 2024

This paper presents a method for optimizing energy storage considering electricity prices, carbon emissions, and load flexibility. It uses an advanced K-means algorithm to cluster photovoltaic data, creating typical output scenarios. The study then explores how optimizing these factors affects EV charging, factoring in environmental temperature effects on EV power consumption to develop an EV charging model. Incorporating demand response, it establishes an economic model focusing on the costs of electricity carbon emissions and revenue from electricity sales. Results show that optimizing these elements in energy storage design can cut emissions and enhance economic viability.
A SOC balancing adaptive droop control scheme for multiple energy storage modules in DC microgrids
Authors: Li Yongxin , Wang Dazhi , Ni Yongliang , Song Keling
Published: October 2024

The droop technique has its limitations, including poor current-sharing accuracy and bus voltage deviations that result from its inherent characteristics. Based on traditional droop control, a more effective droop control approach scheme based on battery cells’ state of charge (SOC) is proposed. By adding SOC values to the droop coefficient, it is able to equalize both the SOC and the energy storage modules’ output power. Thus, it succeeds in achieving the objective of balancing the energy storage batteries’ SOC and output power. Furthermore, to compensate for the voltage deviation, an adaptive PI controller is incorporated into the secondary loop of the system. The effectiveness of the suggested control method is evaluated using the MATLAB/Simulink platform.
A single-frequency Doppler radar target tracking method based on the IPSO algorithm
Authors: Jiang Yaxuan , Ding Yipeng
Published: October 2024

In this paper, a target tracking algorithm, which is based on improved particle swarm optimization (IPSO) technology, is proposed for single-frequency Doppler radar (SF-DR). First, the target detection model corresponding to an SF-DR structure is constructed. In contrast to conventional Doppler radar systems, our proposed model achieves a more simplified radar design by decreasing the quantity of transmitting elements, which can not only contribute to the radar engineering parameter optimization, such as radar size, cost, and power consumption, but also improve the echo quality by avoiding the RF interference between transmitting components. Then, based on the target detection model, an IPSO-based target localization algorithm is proposed. To improve the computational efficiency and target positioning accuracy, the iterative criterion of the inertia weight parameter is modified. Finally, numerous experiments were carried out, and the outcomes confirmed the efficacy of the proposed approach.
Particle identification capability of a homogeneous calorimeter composed of oriented crystals
Authors: Monti-Guarnieri Pietro , Bandiera Laura , Canale Nicola , Carsi Stefano , De Salvador Davide , Guidi Vincenzo , Haurylavets Viktar , Lezzani Giulia , Longo Francesco , Malagutti Lorenzo , Mangiacavalli Sofia , Mazzolari Andrea , Moulson Matthew , Negrello Riccardo , Paternò Gianfranco , Perna Leonardo , Petroselli Christian , Prest Michela , Romagnoni Marco , Saibene Giosuè , Selmi Alessia , Sgarbossa Francesco , Soldani Mattia , Sytov Alexei , Tikhomirov Victor , Vallazza Erik
Published: October 2024

Recent studies have shown that the electromagnetic shower
induced by a high-energy electron, positron or photon incident along
the axis of an oriented crystal develops in a space more compact
than the ordinary. On the other hand, the properties of the hadronic
interactions are not affected by the lattice structure. This means
that, inside an oriented crystal, the natural difference between the
hadronic and the electromagnetic shower profile is strongly
accentuated. Thus, a calorimeter composed of oriented crystals could
be intrinsically capable of identifying more accurately the nature
of the incident particles, with respect to a detector composed only
of non-aligned crystals. Since no oriented calorimeter has ever been
developed, this possibility remains largely unexplored and can be
investigated only by means of numerical simulations. In this work,
we report the first quantitative evaluation of the particle
identification capability of such a calorimeter, focusing on the
case of neutron-gamma discrimination. We demonstrate through Geant4
simulations that the use of oriented crystals significantly improves
the performance of a Random Forest classifier trained on the
detector data. This work is a proof that oriented calorimeters could
be a viable option for all the environments where particle
identification must be performed with a very high accuracy, such as
future high-intensity particle physics experiments and
satellite-based γ-ray telescopes.
Mechanical and suture-holding properties of a UV-cured atelocollagen membrane with varied crosslinked architecture
Authors: Zhang Ruya , Brooker Charles , Whitehouse Laura L E , Thomson Neil H , Wood David , Tronci Giuseppe
Published: October 2024

The mechanical competence and suturing ability of collagen-based membranes are paramount in guided bone regeneration (GBR) therapy, to ensure damage-free implantation, fixation and space maintenance in vivo. However, contact with the biological medium can induce swelling of collagen molecules, yielding risks of membrane sinking into the bone defect, early loss of barrier function, and irreversibly compromised clinical outcomes. To address these challenges, this study investigates the effect of the crosslinked network architecture on both mechanical and suture-holding properties of a new atelocollagen (AC) membrane. UV-cured networks were obtained via either single functionalisation of AC with 4-vinylbenzyl chloride (4VBC) or sequential functionalisation of AC with both 4VBC and methacrylic anhydride. The wet-state compression modulus (E
c) and swelling ratio (SR) were significantly affected by the UV-cured network architecture, leading up to a three-fold reduction in SR and about two-fold increase in E
c in the sequentially functionalised, compared to the single-functionalised, samples. Electron microscopy, dimensional analysis and compression testing revealed the direct impact of the ethanol series dehydration process on membrane microstructure, yielding densification of the freshly synthesised porous samples and a pore-free microstructure with increased E
c. Nanoindentation tests via spherical bead-probe atomic force microscopy (AFM) confirmed an approximately two-fold increase in median (interquartile range (IQR)) elastic modulus in the sequentially functionalised (E
AFM = 40 (13) kPa), with respect to single-functionalised (E
AFM = 15 (9) kPa), variants. Noteworthy, the single-functionalised, but not the sequentially functionalised, samples displayed higher suture retention strength (SRS = 28 ± 2–35 ± 10 N∙mm−1) in both the dry state and following 1 h in phosphate buffered saline (PBS), compared to Bio-Gide® (SRS: 6 ± 1–14 ± 2 N∙mm−1), while a significant decrease was measured after 24 h in PBS (SRS= 1 ± 1 N∙mm−1). These structure-property relationships confirm the key role played by the molecular architecture of covalently crosslinked collagen, aimed towards long-lasting resorbable membranes for predictable GBR therapy.