bluuebunny/crossref_metadata_2025 huggingface dataset:
Clinicoradiopathologic Analysis of Odontomas: A Retrospective Study of 242 Cases
Authors: DeColibus Katherine A. , Rasner D. Shane , Okhuaihesuyi Osariemen , Owosho Adepitan A.
Published: October 2023

Odontomas are considered hamartomatous lesions and are one of the two most common odontogenic tumors of the jaw. Odontomas are classified as compound or complex. Recently, ameloblastic fibro-odontoma (AFO) and ameloblastic fibro-dentinoma were reclassified as developing odontomas. Though clinically odontomas are usually asymptomatic, they have adverse effects on adjacent teeth such as tooth impaction, delayed eruption, displacement of teeth, over-retention of teeth, and can give rise to odontogenic cysts within the jaw. We sought to evaluate the clinicoradiopathologic presentations of odontomas by collecting and analyzing the clinical, radiographic, and pathologic data of odontomas diagnosed in our institution from 2013 to 2022. Over this 10-year period, there were 242 patients with a histopathological and/or radiographic diagnosis of odontoma. There was no gender predilection and ages ranged from 3 to 101 years (median, 14 years). The second decade of life was the most prevalent (57.4%). There was no jaw predilection; however, the anterior jaw was the most common location. Ninety-four (38.8%) cases presented with clinical findings. The most common finding was tooth impaction (n = 83). Nine (3.7%) cases were histopathologically confirmed to be associated with other lesions such as dentigerous cysts (n = 8) and nasopalatine duct cyst (n = 1). The median age (25 years) of patients diagnosed with odontomas associated with cysts was older than patients with odontomas (14 years) without associated cysts. Compound odontomas were the most common type of odontoma compared to complex and AFOs with 71.4%, 26.6%, and 2%, respectively. The majority of compound odontomas involved the anterior jaw (69.3%) and mandible (54.9%) while the majority of complex odontomas involved the posterior jaw (59.6%) and maxilla (54.7%). The four AFOs were in the posterior jaw and 75% involved the maxilla. The median age (12 years) of patients diagnosed with AFO was the youngest compared to patients diagnosed with compound (13 years) and complex (16 years). In conclusion, we analyzed the clinical, radiographic, and pathologic features of 242 new cases of odontomas. Our study reaffirms that odontomas frequently affect the pediatric population and can disrupt their dentition. Based on the result of this study, our clinical recommendation to prevent problems to adjacent teeth from odontomas is for dentists to be apt in the diagnose of odontomas to ensure that they are surgically removed in a timely manner.
Refined Composite Multiscale Fuzzy Dispersion Entropy and Its Applications to Bearing Fault Diagnosis
Authors: Rostaghi Mostafa , Khatibi Mohammad Mahdi , Ashory Mohammad Reza , Azami Hamed
Published: October 2023

Rotary machines often exhibit nonlinear behavior due to factors such as nonlinear stiffness, damping, friction, coupling effects, and defects. Consequently, their vibration signals display nonlinear characteristics. Entropy techniques prove to be effective in detecting these nonlinear dynamic characteristics. Recently, an approach called fuzzy dispersion entropy (DE–FDE) was introduced to quantify the uncertainty of time series. FDE, rooted in dispersion patterns and fuzzy set theory, addresses the sensitivity of DE to its parameters. However, FDE does not adequately account for the presence of multiple time scales inherent in signals. To address this limitation, the concept of multiscale fuzzy dispersion entropy (MFDE) was developed to capture the dynamical variability of time series across various scales of complexity. Compared to multiscale DE (MDE), MFDE exhibits reduced sensitivity to noise and higher stability. In order to enhance the stability of MFDE, we propose a refined composite MFDE (RCMFDE). In comparison with MFDE, MDE, and RCMDE, RCMFDE’s performance is assessed using synthetic signals and three real bearing datasets. The results consistently demonstrate the superiority of RCMFDE in detecting various patterns within synthetic and real bearing fault data. Importantly, classifiers built upon RCMFDE achieve notably high accuracy values for bearing fault diagnosis applications, outperforming classifiers based on refined composite multiscale dispersion and sample entropy methods.
Constraints on Tsallis Cosmology from Big Bang Nucleosynthesis and the Relic Abundance of Cold Dark Matter Particles
Authors: Jizba Petr , Lambiase Gaetano
Published: October 2023

By employing Tsallis’ extensive but non-additive δ-entropy, we formulate the first two laws of thermodynamics for gravitating systems. By invoking Carathéodory’s principle, we pay particular attention to the integrating factor for the heat one-form. We show that the latter factorizes into the product of thermal and entropic parts, where the entropic part cannot be reduced to a constant, as is the case in conventional thermodynamics, due to the non-additive nature of Sδ. The ensuing two laws of thermodynamics imply a Tsallis cosmology, which is then applied to a radiation-dominated universe to address the Big Bang nucleosynthesis and the relic abundance of cold dark matter particles. It is demonstrated that the Tsallis cosmology with the scaling exponent δ∼1.499 (or equivalently, the anomalous dimension Δ∼0.0013) consistently describes both the abundance of cold dark matter particles and the formation of primordial light elements, such as deuterium 2H and helium 4He. Salient issues, including the zeroth law of thermodynamics for the δ-entropy and the lithium 7Li problem, are also briefly discussed.
A Note on the Mixing Factor of Polar Codes
Authors: Wei Keer , Jin Xiaoyu , Yang Weihua
Published: October 2023

Over binary-input memoryless symmetric (BMS) channels, the performance of polar codes under successive cancellation list (SCL) decoding can approach maximum likelihood (ML) algorithm when the list size L is greater than or equal to 2MF, where MF, known as mixing factor of code, represents the number of information bits before the last frozen bit. Recently, Yao et al. showed the upper bound of the mixing factor of decreasing monomial codes with length n=2m and rate R≤12 when m is an odd number; moreover, this bound is reachable. Herein, we obtain an achievable upper bound in the case of an even number. Further, we propose a new decoding hard-decision rule beyond the last frozen bit of polar codes under BMS channels.
Principal Component Analysis and t-Distributed Stochastic Neighbor Embedding Analysis in the Study of Quantum Approximate Optimization Algorithm Entangled and Non-Entangled Mixing Operators
Authors: Sarmina Brian García , Sun Guo-Hua , Dong Shi-Hai
Published: October 2023

In this paper, we employ PCA and t-SNE analyses to gain deeper insights into the behavior of entangled and non-entangled mixing operators within the Quantum Approximate Optimization Algorithm (QAOA) at various depths. We utilize a dataset containing optimized parameters generated for max-cut problems with cyclic and complete configurations. This dataset encompasses the resulting RZ, RX, and RY parameters for QAOA models at different depths (1L, 2L, and 3L) with or without an entanglement stage within the mixing operator. Our findings reveal distinct behaviors when processing the different parameters with PCA and t-SNE. Specifically, most of the entangled QAOA models demonstrate an enhanced capacity to preserve information in the mapping, along with a greater level of correlated information detectable by PCA and t-SNE. Analyzing the overall mapping results, a clear differentiation emerges between entangled and non-entangled models. This distinction is quantified numerically through explained variance in PCA and Kullback–Leibler divergence (post-optimization) in t-SNE. These disparities are also visually evident in the mapping data produced by both methods, with certain entangled QAOA models displaying clustering effects in both visualization techniques.
A Framework for Enhancing Stock Investment Performance by Predicting Important Trading Points with Return-Adaptive Piecewise Linear Representation and Batch Attention Multi-Scale Convolutional Recurrent Neural Network
Authors: Lin Yu , Liu Ben
Published: October 2023

Efficient stock status analysis and forecasting are important for stock market participants to be able to improve returns and reduce associated risks. However, stock market data are replete with noise and randomness, rendering the task of attaining precise price predictions arduous. Moreover, the lagging phenomenon of price prediction makes it hard for the corresponding trading strategy to capture the turning points, resulting in lower investment returns. To address this issue, we propose a framework for Important Trading Point (ITP) prediction based on Return-Adaptive Piecewise Linear Representation (RA-PLR) and a Batch Attention Multi-Scale Convolution Recurrent Neural Network (Batch-MCRNN) with the starting point of improving stock investment returns. Firstly, a novel RA-PLR method is adopted to detect historical ITPs in the stock market. Then, we apply the Batch-MCRNN model to integrate the information of the data across space, time, and sample dimensions for predicting future ITPs. Finally, we design a trading strategy that combines the Relative Strength Index (RSI) and the Double Check (DC) method to match ITP predictions. We conducted a comprehensive and systematic comparison with several state-of-the-art benchmark models on real-world datasets regarding prediction accuracy, risk, return, and other indicators. Our proposed method significantly outperformed the comparative methods on all indicators and has significant reference value for stock investment.
Improving Existing Segmentators Performance with Zero-Shot Segmentators
Authors: Nanni Loris , Fusaro Daniel , Fantozzi Carlo , Pretto Alberto
Published: October 2023

This paper explores the potential of using the SAM (Segment-Anything Model) segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by mainstream segmentators, and then merging the segmentation masks provided by these two networks. We examine the “oracle” method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from the ground truth. One of the main contributions of this work is the combination (fusion) of the logit segmentation masks produced by the SAM model with the ones provided by specialized segmentation models such as DeepLabv3+ and PVTv2. This combination allows for a consistent improvement in segmentation performance in most of the tested datasets. We exhaustively tested our approach on seven heterogeneous public datasets, obtaining state-of-the-art results in two of them (CAMO and Butterfly) with respect to the current best-performing method with a combination of an ensemble of mainstream segmentator transformers and the SAM segmentator. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method.
Ungrading: The Case for Abandoning Institutionalized Assessment Protocols and Improving Pedagogical Strategies
Authors: Crogman Horace T. , Eshun Kwame O. , Jackson Maury , TrebeauCrogman Maryam A. , Joseph Eugene , Warner Laurelle C. , Erenso Daniel B.
Published: October 2023

Our current grading system assumes everyone starts at approximately the same place. This assumption presumes uniformity and impartiality to be inherent in our school system. We argue that this is not the case. This work explores the evolution of grading systems and the integration of new technologies in education, focusing on the development of more inclusive, dynamic, and adaptable teaching and assessment strategies. Key methods include diversified assessments, experiential learning approaches such as problem-based learning and the generated question learning model, and the incorporation of artificial intelligence (AI) in hyperflex learning strategies. The proposed work astutely identifies the critical flaws within the modern grading system and puts forth a compelling solution: shifting the focus towards assessing students’ improvement scores. This approach not only offers a progressive path forward, but also significantly enhances equity by holding students accountable for their knowledge gaps while promoting a more comprehensive evaluation. Additionally, creative engagement techniques, such as mock banking reward systems, are employed to enhance student motivation and participation. AI-facilitated formative assessments and personalized learning plans are also discussed, emphasizing the importance of real-time insights into student progress and the provision of flexible personalized learning environments. This comprehensive approach to education fosters student ownership of learning, promotes active participation, and equips students with essential lifelong learning skills. Moreover, a more accurate assessment of student learning and progress would be fostered, thus creating a paradigm shift from the currently flawed grading system.
Science Skills Development through Problem-Based Learning in Secondary Education
Authors: Pozuelo-Muñoz Jorge , Calvo-Zueco Elena , Sánchez-Sánchez Ester , Cascarosa-Salillas Esther
Published: October 2023

We present a study carried out with 16-year-old students in Spain using a problem-based learning approach as a pedagogical mode to develop science skills. The main objective of this work was to analyze the development of science skills through an inquiry process in class. The data were collected through audio and video recordings. The students were given the freedom to choose a problem to solve, and they decided on a near-environmental problem to research. They suggested a research question, formulated a hypothesis, designed experiments, observed, collected data, and searched for information. The teacher acted as a facilitator of resources. Finally, the students communicated the results obtained in their inquiry process. They performed all the above while asking themselves questions they had to answer during the course of the project, which increased in depth as the work evolved. The results of this research present PBL as an optimal methodology to develop scientific skills, such as inquiry practice, by means of asking questions.
Doctoral Students’ Experiences, Self-Efficacy, and Sense of Belonging Related to Academic Writing in an Online Program
Authors: Naidoo Keshrie , Quaynor Laura , Shen Yuyang
Published: October 2023

Considering the expansion of online graduate education, higher education practitioners must support student belonging and academic writing in this growing context. The purpose of this mixed-methods study was to use social cognitive theory to explore how students in an online Doctor of Education program experience becoming academic writers and to examine trends in students’ belonging and writing self-efficacy. This explanatory sequential design study included quantitative data collection via an electronic survey followed by individual and focus group interviews with participants. Seventy-four students completed the survey (29% response rate) and eleven students participated in interviews. Survey results revealed that students had a moderate to high sense of belonging related to the writing process and a significant negative correlation between writing self-efficacy and belonging uncertainty. Qualitative analysis of the interviews resulted in five themes: (1) writing is experienced as a journey, (2) unclear expectations are painful, (3) English is viewed as a privilege, (4) peer support is needed, and cannot be forced, and (5) developing writers view writing as a craft and identified writing tools. This study provides supporting evidence that belonging is possible in online doctoral education. Recommendations for practice include the strategic engagement of peers and clear, timely notes from instructors.
Creating the Conditions for Geographic Conceptual Development in Post-Primary Students through Collaborative Guided Inquiry
Authors: Kriewaldt Jeana , Robertson Lucy , Ziebell Natasha
Published: October 2023

This paper explores the potential for a collaborative guided inquiry task to stimulate geographic thinking using core geographic concepts of ‘location’, ‘distance and direction’, ‘scale’, ‘symbols’, ‘relative location’ and ‘slope and topography.’ The guided inquiry began with a visit to a park, with students then applying geographic thinking to redesign the park in a way that optimised utility for various user groups. The data generated included student work samples and video recordings of student groups as they worked through the task. The results show that the task design facilitated a deeper understanding of geographic concepts, including spatial relationships, connections and interactions. Furthermore, the collaborative nature of the task prompted students to use skills of explaining, negotiating and justifying their decisions. A critical feature of this analysis is the role that the teacher has in providing specialised guidance to support geographic thinking based on the needs of each group. The study highlights the value of practical, real-world experiences in geography education to learn, discuss and explore geographic concepts, enabling development of critical thinking, reasoning and problem-solving skills.
Adaptive Satellite Navigation Anti-Interference Algorithm Based on Inverse Cosine Function
Authors: Qu Pingping , Yuan Zibo , Wang Ershen , Xu Song , Liu Tianfeng
Published: October 2023

Contrasting the dilemma that the traditional time-domain least mean square (LMS) algorithm in the existing satellite navigation receiver anti-interference system cannot satisfy the convergence time is short and maintain a low level of steady-state error at the same time, an inverse cosine variable step size LMS algorithm (ICVS-LMS) is proposed. To begin with, the LMS algorithm, with a fixed step size focuses on its effectiveness in attenuating and suppressing interference signals, is analyzed, and then the proposed ICVS-LMS algorithm is analyzed. In conclusion, both the ICVS-LMS algorithm and the traditional algorithm are simulated and compared in terms of their effectiveness in suppressing interference in satellite navigation signals. The experimental results demonstrate that the improved algorithm significantly reduces convergence time while maintaining a small steady-state error. The improved algorithm demonstrates high robustness and an obvious suppression effect on interference signals. The anti-interference performance is 8.41–12.22% higher than that of the proposed algorithm.
Verification of an Evolving Security Scheme in the Internet of Vehicles
Authors: Xin Weiyang , Zhang Hanning , Xu Chunxiang , Yao Jiangyuan , Li Deshun , Jin Ting
Published: October 2023

The security scheme of the Internet of Vehicles (IoV) protects the vehicles from network attacks. However, during the experimental deployment of the IoV, people usually pay attention to its function and performance, and only develop a security scheme empirically when security defects are found. When a security scheme becomes very complex, it is very difficult to find the security vulnerability and to modify it. In view of this, we propose a verification method to verify the security of an evolving security scheme. This method uses formal methods to verify the evolving security scheme, actively finds the security problems of the security scheme, and promotes the evolution of the security scheme accordingly. This method is applied to the scenario of the IoV and its security scheme—the method evolves the Internet of Vehicles configuration and the security properties, establishes a corresponding formal model, and then iteratively verifies this using a formal method. The approach can fully simulate the evolution process of a security scheme in the IoV during deployment, and can effectively find the corresponding security vulnerabilities, to promote the evolving security scheme in the IoV, which supports the feasibility and usability of the method.
A Multi-Band Circularly Polarized-Shared Aperture Antenna for Space Applications at S and X Bands
Authors: Kabir Syed Salman , Khan Mehedi Hassan , Latif Saeed I.
Published: October 2023

In this article, a compact multiband antenna design and analysis is presented with a view of ensuring efficient uplink/downlink communications at the same time from a single antenna for CubeSat applications. This design shares the aperture of an S-band slot antenna to accommodate a square patch antenna operating in the X-band. Shared aperture antennas, along with an air gap and dielectric loading, provided good gain in both frequency bands. The S-band patch had an S11 = −10 dB bandwidth of 30 MHz (2013–2043 MHz, 1.5%), and the X-band antenna demonstrated a bandwidth of 210 MHz (8320–8530 MHz, 2.5%). The Axial Ratio (<3 dB) bandwidth of the slot antenna in the S-band is 7 MHz (2013–2020 MHz, 0.35%), and it is 67 MHz (8433–8500 MHz, 0.8%) in the case of patch antenna in the X-band. While the maximum gain in the S-band reached 7.7 dBic, in the X-band, the peak gain was 12.8 dBic. This performance comparison study shows that the antenna is advantageous in terms of high gain, maintains circular polarization over a wideband, and can replace two antennas needed in CubeSats for uplink/downlink, which essentially saves space.
Research on Fast Multi-Threshold Image Segmentation Technique Using Histogram Analysis
Authors: Xu Mingjin , Chen Shaoshan , Gao Xiaopeng , Ye Qing , Ke Yongsheng , Huo Cong , Liu Xiaohong
Published: October 2023

This paper investigates a method for the multi-threshold segmentation of grayscale imaging using the local minimum points of a histogram curve as the segmentation threshold. By smoothing the histogram curve and judging the conditions, the expected peaks and valleys are identified, and the corresponding minimum points are used as segmentation thresholds to achieve fast multi-threshold image segmentation. Compared to the OTSU method (maximum between-class variance) for multi-threshold segmentation and the region growing method, this method has less computational complexity. In the recognition and segmentation process of solder pads with adhesion of underfill in LED Chips, the segmentation time is less than one percent of that of the OTSU method and the region growing method. The segmentation effect is better than the OTSU method and the region growing method, and it can achieve fast multi-threshold segmentation of images. Moreover, it has strong adaptability to the differences in the overall grayscale of images, meeting the requirements for high UPH (Units Per Hour) in industrial production lines.
Robust Feature Selection Method Based on Joint L2,1 Norm Minimization for Sparse Regression
Authors: Yang Libo , Zhu Dawei , Liu Xuemei , Cui Pei
Published: October 2023

Feature selection methods are widely used in machine learning tasks to reduce the dimensionality and improve the performance of the models. However, traditional feature selection methods based on regression often suffer from a lack of robustness and generalization ability and are easily affected by outliers in the data. To address this problem, we propose a robust feature selection method based on sparse regression. This method uses a non-square form of the L2,1 norm as both the loss function and regularization term, which can effectively enhance the model’s resistance to outliers and achieve feature selection simultaneously. Furthermore, to improve the model’s robustness and prevent overfitting, we add an elastic variable to the loss function. We design two efficient convergent iterative processes to solve the optimization problem based on the L2,1 norm and propose a robust joint sparse regression algorithm. Extensive experimental results on three public datasets show that our feature selection method outperforms other comparison methods.
Homogeneous Finite-Time Pose Tracking of Leader–Following Spacecraft Formation Using a Twistor-Based Model
Authors: Guo Yunhe , Chen Jing , Zhang Bo , Huang Hanqiao
Published: October 2023

For integrated pose control of six-degree-of-freedom (6-DOF) leader–following spacecraft formations, the coupled relative pose between two spacecraft is described by a unified model in the framework of twistors, based on which a finite-time control law is devised for the 6-DOF dynamic system. Firstly, some necessary coordinate frames are defined, and then, relative 6-DOF dynamics of the follower spacecraft with respect to the desired frame are modeled by using twistors. Secondly, an integrated pose controller is designed for the 6-DOF formation that takes full advantage of homogeneous theory to guarantee finite-time stability. Finally, numerical simulations are carried out to validate the effectiveness of the proposed controller. Developing integrated 6-DOF formation control law based on twistors is more straightforward than conventional methods, and the finite-time algorithm achieves stronger robustness than asymptotic ones.
An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm
Authors: Niu Shanwei , Nie Zhigang , Liu Jiayu , Chu Mingcao
Published: October 2023

This study aims to enhance the localization of the inner and outer circles of the iris while addressing issues of excessive invalid computations and inaccuracies. To achieve this objective, diverse methods are employed to improve the process to varying extents. Initially, the image undergoes pre-processing operations, including grayscale conversion, mathematical morphological transformation, noise reduction, and image enhancement. Subsequently, the accurate localization of the inner and outer edges is achieved by applying algorithms such as Canny edge detection and the Hough transform, allowing for the determination of their corresponding center and radius values within the iris image. Lastly, an improvement is made to the particle swarm optimization algorithm by combining various algorithms, namely LinWPSO, RandWPSO, contraction factor, LnCPSO, and AsyLnCPSO, employing mechanisms such as simulated annealing and the ant colony algorithm. Through dual validation on the CASIA-Iris-Syn dataset and a self-built CASIA dataset, this approach significantly enhances the precision of iris localization and reduces the required iteration count.
Comparison of the Ability of Neural Network Model and Humans to Detect a Cloned Voice
Authors: Milewski Krzysztof , Zaporowski Szymon , Czyżewski Andrzej
Published: October 2023

The vulnerability of the speaker identity verification system to attacks using voice cloning was examined. The research project assumed creating a model for verifying the speaker’s identity based on voice biometrics and then testing its resistance to potential attacks using voice cloning. The Deep Speaker Neural Speaker Embedding System was trained, and the Real-Time Voice Cloning system was employed based on the SV2TTS, Tacotron, WaveRNN, and GE2E neural networks. The results of attacks using voice cloning were analyzed and discussed in the context of a subjective assessment of cloned voice fidelity. Subjective test results and attempts to authenticate speakers proved that the tested biometric identity verification system might resist voice cloning attacks even if humans cannot distinguish cloned samples from original ones.
Micro-Expression Spotting Based on VoVNet, Driven by Multi-Scale Features
Authors: Yang Jun , Wu Zilu , Wu Renbiao
Published: October 2023

Micro-expressions are a type of real emotional expression, which are unconscious and difficult to hide. Identifying these expressions has great potential applications in areas such as civil aviation security, criminal interrogation, and clinical medicine. However, because of their characteristics such as short duration, low intensity, and sparse action units, this makes micro-expression spotting difficult. To address this problem and inspired by object detection methods, we propose a VoVNet-based micro-expression spotting model, driven by multi-scale features. Firstly, VoVNet is used to achieve the extraction and reuse of different scale perceptual field features to improve the feature extraction capability. Secondly, multi-scale features are extracted and fused using the Feature Pyramid Network module, incorporating optical flow features, and by realizing the interactive fusion of fine-grained feature information and semantic feature information. Finally, the model is trained and optimized on CAS(ME)2 and SAMM Long Video. The experimental results show that the F1 score of the proposed model is improved by 0.1963 and 0.2441 on the two datasets compared with the baseline method, which outperforms the most popular spotting methods.
Potato Malformation Identification and Classification Based on Improved YOLOv3 Algorithm
Authors: Wang Guanping , Yang Wanxia , Liu Yan , Yang Xiaoping , Wang Qi , Yang Sen , Feng Bin , Sun Wei , Li Hongling
Published: October 2023

Potato malformation seriously affects commercial value, and its removal has become one of the core steps in the post-harvest and pre-sales process of potatoes. At present, this work mainly relies on manual visual inspection, which requires a lot of labor and incurs high investment costs. Therefore, precise and efficient automatic detection technology urgently needs to be developed. Due to the efficiency of deep learning based on image information in the field of complex object feature extraction and pattern recognition, this study proposes the use of the YOLOv3 algorithm to undertake potato malformation classification. However, the target box regression loss function MSE of this algorithm is prone to small errors being ignored, and the model code is relatively large, which limits its performance due to the high demand for computing hardware performance and storage space. Accordingly, in this study, CIOU loss is introduced to replace MSE, and thus the shortcoming of the inconsistent optimization direction of the original algorithm’s loss function is overcome, which also significantly reduces the storage space and computational complexity of the network model. Furthermore, deep separable convolution is used instead of traditional convolution. Deep separable convolution first convolves each channel, and then combines different channels point by point. With the introduction of an inverted residual structure and the use of the h-swish activation function, deep separable convolution based on the MobileNetv3 structure can learn more comprehensive feature representations, which can significantly reduce the computational load of the model while improving its accuracy. The test results showed that the model capacity was reduced by 66%, mAP was increased by 4.68%, and training time was shortened by 6.1 h. Specifically, the correctness rates of malformation recognition induced by local protrusion, local depression, proportional imbalance, and mechanical injury within the test set range were 94.13%, 91.00%, 95.52%, and 91.79%, respectively. Misjudgment mainly stemmed from the limitation of training samples and the original accuracy of the human judgment in type labeling. This study lays a solid foundation for the final establishment of an intelligent recognition and classification picking system for malformed potatoes in the next step.
A Software Testing Workflow Analysis Tool Based on the ADCV Method
Authors: Mao Zijian , Han Qiang , He Yu , Li Nan , Li Cong , Shan Zhihui , Han Sheng
Published: October 2023

Based on two progressive aspects of the modeling problems in business process management (BPM), (1) in order to address the increasing complexity of user requirements on workflows underlying various BPM application scenarios, a more verifiable fundamental modeling method must be invented; (2) to address the diversification of software testing processes, more formalized advanced modeling technology must also be applied based on the fundamental modeling method. Aiming to address these modeling problems, this paper first proposes an ADCV (acquisition, decomposition, combination, and verification) method that runs through the core management links of four types of business processes (mining, decomposition, recombination, and verification) and then describes the compositional structure of the ADCV method and the design of corresponding algorithms. Then, the software testing workflow is managed and monitored using the method, and the corresponding analysis tool is implemented based on Petri nets. At the same time, the tool is applied to the case processing of the software testing workflow. Specifically, the workflow models are established successively through ADCV during the process of business iteration. Then, the analysis tool developed with the ADCV method, the model–view–controller (MVC) design pattern, and Java Swing technology are applied to instances of the software testing workflow to realize the modeling and management of the testing processes. Thus, the analysis tool can guarantee the accuracy of the parameter estimations of related software reliability growth models (SRGMs) and ultimately improve the quality of software products.
Testing Rogowski Coils with Merging Units for Smart Grids
Authors: Daboul Mayada , Orságová Jaroslava , Jurák Viktor , Vrtal Matěj
Published: October 2023

As smart grids incorporate renewable energy sources and advanced power electronics, ensuring accurate measurement systems becomes paramount due to the increased complexity and potential sources of disturbances. This article focuses on the laboratory calibration of Rogowski coils (RCs) and merging units (MUs), which are fundamental for measuring, controlling, and monitoring digital power systems. A comprehensive digital calibration system is introduced, utilizing precise, commercially available components such as a fluxgate current transducer and a National Instrument. The system assesses magnitude and phase displacement errors under various operating conditions, including abnormal scenarios. Additionally, the impact of uncertainty sources on the measurement chain analysis is discussed, with test results conforming to established standards. This research contributes to enhancing the accuracy and reliability of measurement systems in the context of evolving smart grids.
Material Selection Framework for Lift-Based Wave Energy Converters Using Fuzzy TOPSIS
Authors: Arredondo-Galeana Abel , Yeter Baran , Abad Farhad , Ordóñez-Sánchez Stephanie , Lotfian Saeid , Brennan Feargal
Published: October 2023

Material selection is a crucial aspect in the design of reliable, efficient and long-lasting wave energy converters (WECs). However, to date, the development of tailored methodologies applied to the material selection of WECs remains vastly unexplored. In this paper, a material selection framework for the case of lift-based WECs is developed. The application of the methodology is demonstrated with the hydrofoils of the device. Offshore steel, high-strength offshore steel, aluminium alloys, and carbon- and glass-fibre-reinforced composites are considered and evaluated subject to relevant criteria for wave energy converters, namely structural reliability, hydrodynamic efficiency, offshore maintainability, total manufacturing cost and environmental impact. Candidate materials are assessed via fuzzy TOPSIS for three scenarios of the life cycle of the WEC: conceptual, commercial and future projection stages. Results show that the choice of optimal materials could change from present to future and that multi-criteria decision-making tools aided by a fuzzy approach are useful design tools for novel WECs when field data are scarce. Hence, methodologies such as the ones presented in this work can help in reducing the probability of mechanical failures of emerging WEC technology.
Evaluating Traffic-Calming-Based Urban Road Design Solutions Featuring Cooperative Driving Technologies in Energy Efficiency Transition for Smart Cities
Authors: Tumminello Maria Luisa , Macioszek Elżbieta , Granà Anna , Giuffrè Tullio
Published: October 2023

Traffic-calming measures (TCMs) are non-invasive devices designed to improve road mobility and urban areas on a human scale. Despite their potential, they have been in use for a long time and now have to deal with the latest technological innovations in the automotive field, such as cooperative driving technologies (CDTs), to improve energy efficiency in cities. The goal of this study is to explore the safety and operational performances of TCMs featuring CDTs in urban areas. An urban-scale road network close to a seaside area in the City of Mazara del Vallo, Italy, was properly redesigned and simulated in AIMSUN to assess several design solutions, where connected and automated vehicles (CAVs) have been employed as a more energy-efficient public transportation system. Preliminarily, the fine-tuning process of model parameters included CAVs and human-operated vehicles (HOVs) flowing through the network up to saturation conditions. The safety of the planned solutions was tested by using surrogate measures. The micro-simulation approach allowed us to know in advance and compare the operational and safety performances of environmentally friendly solutions involving TCMs and CDTs. These results can also support urban road decision makers in pivoting urban-traffic-calming-based design solutions featuring cooperative driving technologies toward energy efficiency transitions for smart cities.
Research on Oil and Gas Energy Cooperation between China and Central-North Asian Countries under the “One Belt and One Road” Strategy
Authors: Duan Mingchen , Duan Yi
Published: October 2023

The “One Belt and One Road” strategy provides a new opportunity for deepening oil and gas energy cooperation between China and Central-North Asian countries. This paper systematically studies the supply and demand characteristics, interdependence and cooperation prospects of oil and gas between them using relevant oil and gas energy data of these countries from 2016 to 2021. The objective of this research is to provide a basis for the overall planning of oil and gas energy cooperation between China and Central-North Asian countries in the future. The results show that the proven reserves of oil and gas in Central-North Asian countries are high; particularly, the proven reserves of natural gas account for 30% of the world. These countries also have high oil and gas self-sufficiency rates. These have laid a material foundation for China to strive for the sustained and stable import of oil and gas energy from Central-North Asian countries. At the same time, there are great imbalances in oil and gas reserves, production, consumption, available exports and their growth rates among Central-North Asian countries, which provide a basis for China and Central-North Asian countries to make strategic decisions on oil and gas energy differential cooperation. China imports 80 × 106 tons of oil and 490 × 108 cubic meters of gas from Central-North Asian countries every year and Chinese dependence on oil and gas imports in this region is 16% and 37%, respectively. The study believes that China has certain potential to increase the import of oil and gas from Central-North Asian countries and has good prospects for oil and gas cooperation. It is proposed that China should give priority to increasing oil and gas exploration and development cooperation with Central-North Asian countries with high oil and gas reserve–production ratios, and should strengthen oil and gas resource trade cooperation with Central-North Asian countries with high oil and gas self-sufficiency rates. At the same time, China should increase investment and cooperation in the midstream and downstream business of the oil industry in major Central-North Asian countries through various financing channels. China should uphold the principle of win–win cooperation to promote the in-depth and steady development of oil and gas energy cooperation between China and Central-North Asian countries.
Waterborne LiNi0.5Mn1.5O4 Cathode Formulation Optimization through Design of Experiments and Upscaling to 1 Ah Li-Ion Pouch Cells
Authors: Lizaso Lander , Urdampilleta Idoia , Bengoechea Miguel , Boyano Iker , Grande Hans-Jürgen , Landa-Medrano Imanol , Eguia-Barrio Aitor , de Meatza Iratxe
Published: October 2023

High-voltage spinel LiNi0.5Mn1.5O4 (LNMO) is a promising candidate as a lithium-ion battery cathode material to fulfill the high-energy density demands of the electric vehicle industry. In this work, the design of the experiment’s methodology has been used to analyze the influence of the ratio of the different components in the electrode preparation feasibility of laboratory-scale coatings and their electrochemical response. Different outputs were defined to evaluate the formulations studied, and Derringer–Suich’s methodology was applied to obtain an equation that is usable to predict the desirability of the electrodes depending on the selected formulation. Afterward, Solver’s method was used to figure out the formulation that provides the highest desirability. This formulation was validated at a laboratory scale and upscaled to a semi-industrial coating line. High-voltage 1 Ah lithium-ion pouch cells were assembled with LNMO cathodes and graphite-based anodes and subjected to rate-capability tests and galvanostatic cycling. 1 C was determined as the highest C-rate usable with these cells, and 321 and 181 cycles above 80% SOH were obtained in galvanostatic cycling tests performed at 0.5 C and 1 C, respectively. Furthermore, it was observed that the LNMO cathode required an activation period to become fully electrochemically active, which was shorter when cycled at a lower C-rate.
A Bi-Level Optimization Model for Inter-Provincial Energy Consumption Transfer Tax in China
Authors: Zeng Lijun , Zhang Wencheng , Yang Muyi
Published: October 2023

The serious energy crisis and environmental problems resulting from fossil energy excessive consumption have caused severe challenges to the control of energy consumption and intensity (dual controls) and the sustainable development of China’s economy and society. The current territorial management model (TMM) of energy consumption “dual control” needs urgent improvement. Therefore, this study proposes an inter-provincial energy consumption transfer tax model (ECTTM) based on the Stackelberg game and bi-level optimization theory. In this model, the central government is the leader at the upper-level, and provincial governments are the lower-level followers. An optimization algorithm based on NSGA-II was designed to solve this model to obtain the optimal transfer tax rate and provincial energy consumption. The ECTTM aims to maximize the socioeconomic benefits of energy consumption overall and in each province under the premise of achieving the dual control target. The model’s effectiveness and superiority were illustrated through an empirical study of electricity consumption in Shanghai, Zhejiang, Shaanxi, and Guizhou. Compared with the TMM, the socioeconomic benefits under the ECTTM increased by 14.67%, and the electricity consumption per unit of gross domestic product decreased by 12.8%. Policy suggestions on the ECTTM’s implementation are proposed to promote further improvements in dual controls.
Simple Form of Compact Thermal Model of the Large LED Modules
Authors: Górecki Krzysztof , Ptak Przemysław
Published: October 2023

The paper proposes a simple compact thermal model of a large LED module. This model takes into account self-heating in each of the LEDs contained in such a module and mutual thermal couplings between each pair of these LEDs. It also allows determining the waveforms of the junction temperature of each of the LEDs. In each moment of time, the temperature distributions in the LED module in the form of values of the junction temperature of all the diodes contained in this module are calculated using the proposed model. The developed thermal model has the form of an electric network and it contains a much smaller number of components than thermal models given in the literature. The form of the developed model and the method of determining the values of its parameters as well as the results of calculations and measurements confirming the practical usefulness of this model are presented.
Effects of Parameter Scaling on Archimedes Screw Generator Performance
Authors: Simmons Scott , Dellinger Guilhem , Lubitz William David
Published: October 2023

Archimedes screws are an ancient pumping technology that has more recently found use as a technology for hydropower generation. Currently, the literature is lacking reliable data, performance predicting models, and design guidelines. Most performance models presented in the literature are theoretical or were developed and evaluated using laboratory-scale data. This paper presents novel experimental and numerical simulation data to the literature from screw generators with a wide range of sizes (laboratory to full-scale powerplant scale) and orientations. The data suggest that the components of power production (pressure-driven and viscous/friction) scale differently depending on system size, configuration, and operating conditions. So, for the robust validation of models and the development of reliable design guidance, data from a wide range of sizes and configurations are crucial. The paper presents data collected from laboratory experiments, field measurements from operating powerplants, and data from numerical simulations. The numerical simulations were evaluated for accuracy with experimental data, then used to collect performance data from a wide range of screw geometries and scales. The length-scale (diameter), number of blades, fill height of water, inclination angle, and surface roughness were all varied. The data gathered in these experiments were analyzed and used to develop back-of-the-envelope estimations for the effect of each parameter on overall system performance; the relationships are intended to serve as a useful reference for designers, though they should not be used in lieu of a design model. The length-scale and number of blades were related to power in a way that could be reasonably approximated with a constant value. The fill height, inclination angle, and surface roughness were related to power in a way that could be approximated reasonably with first-order polynomial fits. Altogether, this paper presents much-needed, novel data to the literature; the data are integral for future model development and evaluation.