bluuebunny/crossref_metadata_2025 huggingface dataset:
Liquid–Liquid Equilibrium Behavior of Ternary Systems Comprising Biodiesel + Glycerol and Triglyceride + Methanol: Experimental Data and Modeling
Authors: Yang Lingmei , Xing Shiyou , Teng Xianbin , Liu Rukuan , Wang Zhongming , Lin Baining , Lv Pengmei , Al-Haimi Akram Ali Nasser Mansoor , Yehia Fatma , Luo Wen
Published: May 2024

Having a comprehensive knowledge of phase equilibrium is advantageous for industrial simulation and design of chemical processes. For further acquisition of primary data to facilitate the separation and purification of waste oil biodiesel systems, a liquid–liquid equilibrium (LLE) tank is deployed for the ternary system of waste oil biodiesel + methanol + glycerin, thereby enhancing the precision and efficiency of the process. The phase equilibrium system was constructed under the influence of atmospheric pressure at precise temperatures of 303.15 K, 313.15 K, and 323.15 K. The equilibrium components of each substance were analyzed by employing high-temperature gas chromatography, a sophisticated analytical method that enables the identification and quantification of individual components of a sample. Moreover, the ternary liquid–liquid equilibrium data were correlated by implementing the NRTL and UNIQUAC activity coefficient models. Subsequently, the binary interaction parameters of the ternary system were derived by conducting regression analysis. The experimental data demonstrated that the presence of lower methanol content in the system resulted in nearly immiscible biodiesel and glycerol phases, which ultimately facilitated the separation of biodiesel and glycerol. Conversely, with the increase in methanol content, the mutual solubility of biodiesel and glycerol was observed to increase gradually. The results showed that the calculated values of the NRTL and UNIQUAC models aligned well with the experimental values. The root-mean-square deviations of the NRTL and UNIQUAC models at 313.15 K were 2.76% and 3.56%, respectively.
Detection of Crabs and Lobsters Using a Benchmark Single-Stage Detector and Novel Fisheries Dataset
Authors: Iftikhar Muhammad , Neal Marie , Hold Natalie , Gregory Dal Toé Sebastian , Tiddeman Bernard
Published: May 2024

Crabs and lobsters are valuable crustaceans that contribute enormously to the seafood needs of the growing human population. This paper presents a comprehensive analysis of single- and multi-stage object detectors for the detection of crabs and lobsters using images captured onboard fishing boats. We investigate the speed and accuracy of multiple object detection techniques using a novel dataset, multiple backbone networks, various input sizes, and fine-tuned parameters. We extend our work to train lightweight models to accommodate the fishing boats equipped with low-power hardware systems. Firstly, we train Faster R-CNN, SSD, and YOLO with different backbones and tuning parameters. The models trained with higher input sizes resulted in lower frames per second (FPS) and vice versa. The base models were highly accurate but were compromised in computational and run-time costs. The lightweight models were adaptable to low-power hardware compared to the base models. Secondly, we improved the performance of YOLO (v3, v4, and tiny versions) using custom anchors generated by the k-means clustering approach using our novel dataset. The YOLO (v4 and it’s tiny version) achieved mean average precision (mAP) of 99.2% and 95.2%, respectively. The YOLOv4-tiny trained on the custom anchor-based dataset is capable of precisely detecting crabs and lobsters onboard fishing boats at 64 frames per second (FPS) on an NVidia GeForce RTX 3070 GPU. The Results obtained identified the strengths and weaknesses of each method towards a trade-off between speed and accuracy for detecting objects in input images.
Caudal Regression Syndrome First Diagnosed in Adulthood: A Case Report and a Review of the Literature
Authors: Bulahs Intars , Teivāne Agnete , Platkājis Ardis , Balodis Arturs
Published: May 2024

Background: Caudal regression syndrome (CRS) is a rare congenital malformation characterized by incomplete development of the lower spine and spinal cord. Its estimated incidence ranges from 1 to 2 per 100,000 live births, leading to a spectrum of clinical presentations. Although most cases are diagnosed during childhood, only a small number of cases have been documented in adults in the medical literature. Case Report: A 27-year-old woman underwent an outpatient magnetic resonance imaging (MRI) of the thoracolumbar spine due to severe lower back pain experienced for the first time. Despite congenital leg abnormalities and multiple childhood surgeries, no further investigations were conducted at that time. MRI revealed congenital anomalies consistent with CRS, including coccygeal agenesis, L5 sacralization, and spinal cord defects. The patient also had a long-standing pilonidal cyst treated conservatively, now requiring operative treatment due to an abscess. Conclusions: This report underscores a rare case of CRS initially misdiagnosed and mistreated over many years. It emphasizes the importance of considering less common diagnoses, especially when initial investigations yield inconclusive results. This clinical case demonstrates a highly valuable and educative radiological finding. In the literature, such cases with radiological findings in adults are still lacking.
The Fate of RPE Cells Following hESC-RPE Patch Transplantation in Haemorrhagic Wet AMD: Pigmentation, Extension of Pigmentation, Thickness of Transplant, Assessment for Proliferation and Visual Function—A 5 Year-Follow Up
Authors: da Cruz Lyndon , Soomro Taha , Georgiadis Odysseas , Nommiste Britta , Sagoo Mandeep S. , Coffey Peter
Published: May 2024

(1) Background: We reviewed a stem cell-derived therapeutic strategy for advanced neovascular age-related macular degeneration (nAMD) using a human embryonic stem cell-derived retinal pigment epithelium (hESC-RPE) monolayer delivered on a coated, synthetic basement membrane (BM)—the patch—and assessed the presence and distribution of hESC-RPE over 5 years following transplantation, as well as functional outcomes. (2) Methods: Two subjects with acute vision loss due to sub-macular haemorrhage in advanced nAMD received the hESC-RPE patch. Systematic immunosuppression was used peri-operatively followed by local depot immunosuppression. The subjects were monitored for five years with observation of RPE patch pigmentation, extension beyond the patch boundary into surrounding retina, thickness of hESC-RPE and synthetic BM and review for migration and proliferation of hESC-RPE. Visual function was also assessed. (3) Results: The two study participants showed clear RPE characteristics of the patch, preservation of some retinal ultrastructure with signs of remodelling, fibrosis and thinning on optical coherence tomography over the 5-year period. For both participants, there was evidence of pigment extension beyond the patch continuing until 12 months post-operatively, which stabilised and was preserved until 5 years post-operatively. Measurement of hESC-RPE and BM thickness over time for both cases were consistent with predefined histological measurements of these two layers. There was no evidence of distant RPE migration or proliferation in either case beyond the monolayer. Sustained visual acuity improvement was apparent for 2 years in both subjects, with one subject maintaining the improvement for 5 years. Both subjects demonstrated initial improvement in fixation and microperimetry compared to baseline, at year 1, although only one maintained this at 4 years post-intervention. (4) Conclusions: hESC-RPE patches show evidence of continued pigmentation, with extension, to cover bare host basement membrane for up to 5 years post-implantation. There is evidence that this represents functional RPE on the patch and at the patch border where host RPE is absent. The measurements for thickness of hESC-RPE and BM suggest persistence of both layers at 5 years. No safety concerns were raised for the hypothetical risk of RPE migration, proliferation or tumour formation. Visual function also showed sustained improvement for 2 years in one subject and 5 years in the other subject.
Radiographical Diagnostic Evaluation of Mandibular Cortical Index Classification and Mandibular Cortical Width in Female Patients Prescribed Antiosteoporosis Medication: A Retrospective Cohort Study
Authors: Seki Keisuke , Nagasaki Maki , Yoshino Tona , Yano Mayuko , Kawamoto Aki , Shimizu Osamu
Published: May 2024

Osteoporosis is often detected late and becomes severe because of a lack of subjective symptoms. Digital panoramic radiography (DPR) has been reported to be useful for osteoporosis screening based on the morphological classification of the mandibular inferior cortex. The purpose of this study was to evaluate the sensitivity and specificity of the mandibular cortical index (MCI) in the diagnosis of osteoporosis in a group of patients who were and were not using antiosteoporosis medication (AOM). Three hundred and fifty female patients aged 40 years or older who had DPR imaging performed during a 6-year period from December 2015 to February 2022 met the selection criteria. Two examiners recorded mandibular cortical width and MCI from the images. These results were statistically examined together with the patients’ demographic data. Forty-nine patients were using AOM (13 nonbisphosphonate/denosumab and 36 bisphosphonate/denosumab). MCI type 3 was the most common in the AOM group. In the MCI classification, DPR imaging among the AOM group was more sensitive (0.95) than that of the control group. This method of estimating osteoporosis based on MCI classification using DPR images has high sensitivity, especially in patients using AOM, suggesting that this method is useful as a screening test.
A Measure That Really Works? Impact Evaluation of the Contribution for Self-Employment as a Tool of Active Labour Market Policy in Slovakia
Authors: Svabova Lucia , Gabrikova Barbora
Published: May 2024

Unemployment presents a significant challenge requiring attention not only in developing countries but also in economically developed ones. Active labour market policies offer a potential solution to address this issue. This study focuses on assessing the impact of the intervention called Contribution for Self-employment provided under the Act on Employment Services No. 5/2004 Coll in the Slovak Republic. This financial support is extended voluntarily to unemployed individuals seeking jobs and aims to partially defray the expenses associated with launching business ventures. The primary objective of this research is to quantify the effectiveness of the Contribution for Self-employment in enhancing the employment of its recipients, thereby gauging its efficacy in reducing unemployment. The evaluation employs a counterfactual impact assessment methodology, utilising propensity score matching for analysis, with propensity score estimated by the logistic regression. Data from the registry of jobseekers maintained by the Central Labour Office of Social Affairs and Family in Slovakia are utilised in this analysis. This study’s findings indicate a favourable impact of the contribution on the employment of its participants compared to the comparable non-participants. Consequently, this intervention emerges as a viable mechanism for supporting entrepreneurship and mitigating unemployment in Slovakia.
Security Threats, Requirements and Recommendations on Creating 5G Network Slicing System: A Survey
Authors: Gao Shujuan , Lin Ruyan , Fu Yulong , Li Hui , Cao Jin
Published: May 2024

Network slicing empowers 5G with enhanced network performance and efficiency, cost saving, and better QoS and customer satisfaction, and expands the commercial application scenarios of 5G networks. However, the introduction of new techniques usually raises new security threats. Most of the existing works on 5G security only focus on 5G itself and do not analyze 5G network slicing security in detail. We consider network slices as a virtual logical network that can unite the subnetwork parts of 5G. If a 5G network slice has security problems or has been attacked, the entire 5G network will have security risks. In this paper, after synthesizing the existing literature, we analyze the security threats step by step through the lifecycle of 5G network slices, analyzing and summarizing more than 70 security threats in three major categories. Based on the security issues investigated, from a viewpoint of building a secure 5G network slicing system, we compiled 24 security requirements and proposed the corresponding recommendations for different scenarios of 5G network slicing. Finally, we collated the future research trends of 5G network slicing security.
Advancements in PCB Components Recognition Using WaferCaps: A Data Fusion and Deep Learning Approach
Authors: Starodubov Dmitrii , Danishvar Sebelan , Abu Ebayyeh Abd Al Rahman M. , Mousavi Alireza
Published: May 2024

Microelectronics and electronic products are integral to our increasingly connected world, facing constant challenges in terms of quality, security, and provenance. As technology advances and becomes more complex, the demand for automated solutions to verify the quality and origin of components assembled on printed circuit boards (PCBs) is skyrocketing. This paper proposes an innovative approach to detecting and classifying microelectronic components with impressive accuracy and reliability, paving the way for a more efficient and safer electronics industry. Our approach introduces significant advancements by integrating optical and X-ray imaging, overcoming the limitations of traditional methods that rely on a single imaging modality. This method uses a novel data fusion technique that enhances feature visibility and detectability across various component types, crucial for densely packed PCBs. By leveraging the WaferCaps capsule network, our system improves spatial hierarchy and dynamic routing capabilities, leading to robust and accurate classifications. We employ decision-level fusion across multiple classifiers trained on different representations—optical, X-ray, and fused images—enhancing accuracy by synergistically combining their predictive strengths. This comprehensive method directly addresses challenges surrounding concurrency, reliability, availability, and resolution in component identification. Through extensive experiments, we demonstrate that our approach not only significantly improves classification metrics but also enhances the learning and identification processes of PCB components, achieving a remarkable total accuracy of 95.2%. Our findings offer a substantial contribution to the ongoing development of reliable and accurate automatic inspection solutions in the electronics manufacturing sector.
UNet-BiLSTM: A Deep Learning Method for Reconstructing Electrocardiography from Photoplethysmography
Authors: Guo Yanke , Tang Qunfeng , Chen Zhencheng , Li Shiyong
Published: May 2024

Electrocardiography (ECG) is generally used in clinical practice for cardiovascular diagnosis and for monitoring cardiovascular status. It is considered to be the gold standard for diagnosing cardiovascular diseases and assessing cardiovascular status. However, it is not always easy to obtain. Unlike ECG devices, photoplethysmography (PPG) devices can be placed on body parts such as the earlobes, fingertips, and wrists, making them more comfortable and easier to obtain. Several methods for reconstructing ECG signals using PPG signals have been proposed, but some of these methods are subject-specific models. These models cannot be applied to multiple subjects and have limitations. This study proposes a neural network model based on UNet and bidirectional long short-term memory (BiLSTM) networks as a group model for reconstructing ECG from PPG. The model was verified using 125 records from the MIMIC III matched subset. The experimental results demonstrated that the proposed model was, on average, able to achieve a Pearson‘s correlation coefficient, root mean square error, percentage root mean square difference, and Fréchet distance of 0.861, 0.077, 5.302, and 0.278, respectively. This research can use the correlation between PPG and ECG to reconstruct a better ECG signal from PPG, which is crucial for diagnosing cardiovascular diseases.
Sample Size Effect on Musculoskeletal Segmentation: How Low Can We Go?
Authors: Huysentruyt Roel , Van den Borre Ide , Lazendić Srđan , Duquesne Kate , Van Oevelen Aline , Li Jing , Burssens Arne , Pižurica Aleksandra , Audenaert Emmanuel
Published: May 2024

Convolutional Neural Networks have emerged as a predominant tool in musculoskeletal medical image segmentation. It enables precise delineation of bone and cartilage in medical images. Recent developments in image processing and network architecture desire a reevaluation of the relationship between segmentation accuracy and the amount of training data. This study investigates the minimum sample size required to achieve clinically relevant accuracy in bone and cartilage segmentation using the nnU-Net methodology. In addition, the potential benefit of integrating available medical knowledge for data augmentation, a largely unexplored opportunity for data preprocessing, is investigated. The impact of sample size on the segmentation accuracy of the nnU-Net is studied using three distinct musculoskeletal datasets, including both MRI and CT, to segment bone and cartilage. Further, the use of model-informed augmentation is explored on two of the above datasets by generating new training samples implementing a shape model-informed approach. Results indicate that the nnU-Net can achieve remarkable segmentation accuracy with as few as 10–15 training samples on bones and 25–30 training samples on cartilage. Model-informed augmentation did not yield relevant improvements in segmentation results. The sample size findings challenge the common notion that large datasets are necessary to obtain clinically relevant segmentation outcomes in musculoskeletal applications.
Reversible Image Fragile Watermarking with Dual Tampering Detection
Authors: Zhan Cai , Leng Lu , Chang Chin-Chen , Horng Ji-Hwei
Published: May 2024

The verification of image integrity has attracted increasing attention. Irreversible algorithms embed fragile watermarks into cover images to verify their integrity, but they are not reversible due to unrecoverable loss. In this paper, a new dual tampering detection scheme for reversible image fragile watermarking is proposed. The insect matrix reversible embedding algorithm is used to embed the watermark into the cover image. The cover image can be fully recovered when the dual-fragile-watermarked images are not tampered with. This study adopts two recovery schemes and adaptively chooses the most appropriate scheme to recover tampered data according to the square errors between the tampered data and the recovered data of two watermarked images. Tampering coincidence may occur when a large region of the fragile-watermarked image is tampered with, and the recovery information corresponding to the tampered pixels may be missing. The tampering coincidence problem is solved using image-rendering techniques. The experimental results show that the PSNR value of the watermarked image obtained using our scheme can reach 46.37 dB, and the SSIM value is 0.9942. In addition, high-accuracy tampering detection is achieved.
Probabilistic Task Offloading with Uncertain Processing Times in Device-to-Device Edge Networks
Authors: Shu Chang , Luo Yinhui , Liu Fang
Published: May 2024

D2D edge computing is a promising solution to address the conflict between limited network capacity and increasing application demands, where mobile devices can offload their tasks to other peer devices/servers for better performance. Task offloading is critical to the performance of D2D edge computing. Most existing works on task offloading assume the task processing time is known or can be accurately estimated. However, the processing time is often uncertain until it is finished. Moreover, the same task can have largely different execution times under different scenarios, which leads to inaccurate offloading decisions and degraded performance. To address this problem, we propose a game-based probabilistic task offloading scheme with an uncertain processing time in D2D edge networks. First, we characterize the uncertainty of the task processing time using a probabilistic model. Second, we incorporate the proposed probabilistic model into an offloading decision game. We also analyze the structural properties of the game and prove that it can reach a Nash equilibrium. We evaluate the proposed work using real-world applications and datasets. The experimental results show that the proposed probabilistic model can accurately characterize the uncertainty of completion time, and the offloading algorithm can effectively improve the overall task completion rate in D2D networks.
Simulation Analysis of Phase Jitter in Differential Sampling of AC Waveforms Based on the Programmable Josephson Voltage Standard
Authors: Wang Yanping , Sun Xiaogang , Zhao Jianting , Zhou Kunli , Lu Yunfeng , Qu Jifeng , Hu Pengcheng , He Qing
Published: May 2024

The effect of phase jitter on differential sampling using the programmable Josephson voltage standard (PJVS) system is studied in this paper. A phase jitter model is established for the measured signal, and compensation coefficients for phase jitter removal are derived for three different post-processing methods based on the discrete Fourier transform algorithm (DFT). Based on our analysis, the phase jitter compensation coefficients are determined by the phase jitter angle distribution and harmonic order. Furthermore, after analyzing and simulating various common distributions, the phase jitter compensation coefficients have been verified. The simulation shows that when the standard deviation of the phase jitter angle is 20 ns, and the frequency of the measuring waveform is 3.46 kHz, the influence of the phase jitter is 1 × 10−7. The results of the simulation indicate that, in the differential sampling of AC waveforms using a PJVS system, phase jitter is one of the error terms for an uncertainty budget that cannot be neglected, particularly as the frequency of the measured waveforms increases.
Optimizing an Autonomous Robot’s Path to Increase Movement Speed
Authors: Gorgoteanu Damian , Molder Cristian , Popescu Vlad-Gabriel , Grigore Lucian Ștefăniță , Oncioiu Ionica
Published: May 2024

The goal of this study is to address the challenges associated with identifying and planning a mobile land robot’s path to optimize its speed in a stationary environment. Our focus was on devising routes that navigate around obstacles in various spatial arrangements. To achieve this, we employed MATLAB R2023b for trajectory simulation and optimization. On-board data processing was conducted, while obstacle detection relied on the omnidirectional video processing system integrated into the robot. Odometry was facilitated by engine encoders and optical flow sensors. Additionally, an external video system was utilized to verify the experimental data pertaining to the robot’s movement. Last but not least, the algorithms and hardware equipment used enabled the robot to go along the path at greater speeds. Limiting the amount of time and energy required to travel allowed us to avoid obstacles.
Short-Term Air Traffic Flow Prediction Based on CEEMD-LSTM of Bayesian Optimization and Differential Processing
Authors: Zhou Rui , Qiu Shuang , Li Ming , Meng Shuangjie , Zhang Qiang
Published: May 2024

With the rapid development of China’s civil aviation, the flow of air traffic in terminal areas is also increasing. Short-term air traffic flow prediction is of great significance for the accurate implementation of air traffic flow management. To enhance the accuracy of short-term air traffic flow prediction, this paper proposes a short-term air traffic flow prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and long short-term memory (LSTM) of the Bayesian optimization algorithm and data differential processing. Initially, the model performs CEEMD on the short-term air traffic flow series. Subsequently, to improve prediction accuracy, the data differencing is employed to stabilize the time series. Finally, the smoothed sequences are, respectively, input into the LSTM network model optimized by the Bayesian optimization algorithm for prediction. After data reconstruction, the final short-term flow prediction result is obtained. The model proposed in this paper is verified by using the data from Shanghai Pudong International Airport. The results show that the evaluation indexes of the prediction accuracy and fitting degree of the model, RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R2 (Coefficient of Determination), are 0.336, 0.239, and 97.535%, respectively. Compared to other classical time-series prediction models, the prediction accuracy is greatly improved, which can provide a useful reference for short-term air traffic flow prediction.
On Embedding Implementations in Text Ranking and Classification Employing Graphs
Authors: Kalogeropoulos Nikitas-Rigas , Ioannou Dimitris , Stathopoulos Dionysios , Makris Christos
Published: May 2024

This paper aims to enhance the Graphical Set-based model (GSB) for ranking and classification tasks by incorporating node and word embeddings. The model integrates a textual graph representation with a set-based model for information retrieval. Initially, each document in a collection is transformed into a graph representation. The proposed enhancement involves augmenting the edges of these graphs with embeddings, which can be pretrained or generated using Word2Vec and GloVe models. Additionally, an alternative aspect of our proposed model consists of the Node2Vec embedding technique, which is applied to a graph created at the collection level through the extension of the set-based model, providing edges based on the graph’s structural information. Core decomposition is utilized as a method for pruning the graph. As a byproduct of our information retrieval model, we explore text classification techniques based on our approach. Node2Vec embeddings are generated by our graphs and are applied in order to represent the different documents in our collections that have undergone various preprocessing methods. We compare the graph-based embeddings with the Doc2Vec and Word2Vec representations to elaborate on whether our approach can be implemented on topic classification problems. For that reason, we then train popular classifiers on the document embeddings obtained from each model.
A High-Gain Metallic-via-Loaded Antipodal Vivaldi Antenna for Millimeter-Wave Application
Authors: Li Jun , Huang Junjie , He Hongli , Wang Yanjie
Published: May 2024

This paper presents a miniaturized-structure high-gain antipodal Vivaldi antenna (AVA) operating in the millimeter-wave (mm-wave) band. A gradient-length microstrip-patch-based director is utilized on the flares of the AVA to enhance gain. Additionally, an array of metallic vias is incorporated along the lateral and horizontal edges of the antenna for further gain enhancement and bandwidth extension. Based on the proposed structure, the AVA can achieve a peak gain of 11.9 dBi over a relative bandwidth of 71.24% within 16.5–36.6 GHz as measured, while the electrical dimension is only 1.54 × 2.69 × 0.07 λc3. The measured results show good agreement with the simulated ones. Owning the characteristics of being high-gain and ultra-wideband, and having a compact size, the proposed AVA can be a competitive candidate for future millimeter-wave communication.
Optimal Dispatching of Microgrids with Development of Prosumers Sharing Energy Storage
Authors: Li Fei , Su Kai , Li Xianshan , Zhang Binqiao
Published: May 2024

The charge/discharge operation of the prosumer’s energy storage and the energy interaction between prosumers and MGs are chaotic from the overall point of the MG’s operation. It causes considerable resource waste and reduces the overall benefits of the MG with multi-prosumers. Therefore, a game theory-based optimal scheduling strategy for the MG with multi-prosumers combined into a PRCO is proposed in this paper. According to the prosumers’ complementary characteristics of ES utilization and energy production, prosumers can be integrated into the PRCO to obtain energy reciprocity by sharing ES with an ordered charge–discharge operation. Meanwhile, to improve the collaboration of prosumers and the overall efficiency of the MG, a game scheduling model is established with the MG as the leader and the PRCO as the follower. The ToU price incentive policy is implemented in the MG to maximize the operational benefits and reduce the difference between the valley and peak load. Meanwhile, the PRCO responds to the price policy and implements an ordered charge–discharge strategy of ES to optimize each member’s energy scheduling strategy and minimize the total costs. The PRCO revenues are distributed to prosumers based on the Shapley value method. The uniqueness and existence of Stackelberg equilibrium in the game model are proved. The simulations of a community MG show that the ordered charge–discharge operation of ES is achieved and the overall benefits of the system are improved.
A Hybrid Model Based on CEEMDAN-GRU and Error Compensation for Predicting Sunspot Numbers
Authors: Yang Jianzhong , Liu Song , Xuan Shili , Chen Huirong
Published: May 2024

To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error compensation for predicting sunspot numbers. CEEMAND is applied to decompose the original sunspot number data into several components, which are then used to train and test the GRU for the optimal parameters of the corresponding sub-models. Error compensation is utilized to solve the delay phenomenon between the original sunspot number and the predictive result. We compare our method with the informer, extreme gradient boosting combined with deep learning (XGboost-DL), and empirical mode decomposition combined long short-term memory neutral network and attention mechanism (EMD-LSTM-AM) methods, and evaluation metrics, such as RMSE and MAE, are used to measure their performance. Our method decreases more than 2.2813 and 3.5827 relative to RMSE and MAE, respectively. Thus, the experiment can demonstrate that our method has an obvious advantage compared to others.
Searching for a Cheap Robust Steering Controller
Authors: Vidano Trevor , Assadian Francis
Published: May 2024

The study of lateral steering control for Automated Driving Systems identifies new control solutions more often than new control problems. This is likely due to the maturity of the field. To prevent repeating efforts toward solving already-solved problems, what is needed is a cohesive way of evaluating all developed controllers under a wide variety of environmental conditions. This work serves as a step in this direction. Four controllers are tested on five maneuvers representing highways and collision avoidance trajectories. Each controller and maneuver combination is repeated on five sets of environmental conditions or Operational Design Domains (ODDs). The design of these ODDs ensures the translation of these experimental results to real-world applications. The commercial software, CarSim 2020, is extended with Simulink models of the environment, sensor dynamics, and state estimation performances to perform highly repeatable and realistic evaluations of each controller. The results of this work demonstrate that most of the combinations of maneuvers and ODDs have existing cheap controllers that achieve satisfactorily safe performance. Therefore, this field’s research efforts should be directed toward finding new control problems in lateral path tracking rather than proposing new controllers for ODDs that are already solved.
HotCFuzz: Enhancing Vulnerability Detection through Fuzzing and Hotspot Code Coverage Analysis
Authors: Du Chunlai , Guo Yanhui , Feng Yifan , Zheng Shijie
Published: May 2024

Software vulnerabilities present a significant cybersecurity threat, particularly as software code grows in size and complexity. Traditional vulnerability-mining techniques face challenges in keeping pace with this complexity. Fuzzing, a key automated vulnerability-mining approach, typically focuses on code branch coverage, overlooking syntactic and semantic elements of the code. In this paper, we introduce HotCFuzz, a novel vulnerability-mining model centered on the coverage of hot code blocks. Leveraging vulnerability syntactic features to identify these hot code blocks, we devise a seed selection algorithm based on their coverage and integrate it into the established fuzzing test framework AFL. Experimental results demonstrate that HotCFuzz surpasses AFL, AFLGo, Beacon, and FairFuzz in terms of efficiency and time savings.
DPCalib: Dual-Perspective View Network for LiDAR-Camera Joint Calibration
Authors: Cao Jinghao , Yang Xiong , Liu Sheng , Tang Tiejian , Li Yang , Du Sidan
Published: May 2024

The precise calibration of a LiDAR-camera system is a crucial prerequisite for multimodal 3D information fusion in perception systems. The accuracy and robustness of existing traditional offline calibration methods are inferior to methods based on deep learning. Meanwhile, most parameter regression-based online calibration methods directly project LiDAR data onto a specific plane, leading to information loss and perceptual limitations. A novel network, DPCalib, a dual perspective view network that mitigates the aforementioned issue, is proposed in this paper. This paper proposes a novel neural network architecture to achieve the fusion and reuse of input information. We design a feature encoder that effectively extracts features from two orthogonal views using attention mechanisms. Furthermore, we propose an effective decoder that aggregates features from two views, thereby obtaining accurate extrinsic parameter estimation outputs. The experimental results demonstrate that our approach outperforms existing SOTA methods, and the ablation experiments validate the rationality and effectiveness of our work.
A Comparative Analysis of Distributor and Rotor Single Regulation Strategies for Low Head Mini Hydraulic Turbines
Authors: Barsi Dario , Satta Francesca , Ubaldi Marina , Zunino Pietro
Published: May 2024

Tubular axial turbines (TATs) play a crucial role in mini and micro hydropower setups that require simplified yet reliable solutions. In very low head scenarios, single regulation in TATs is common, due to economic impracticality of the sophisticated mechanisms involved in the conjugate distributor–rotor regulation typical of the Kaplan turbines. Distributor or rotor single regulation strategies offer operation flexibility, each with distinct advantages and disadvantages. Stator regulation is simpler, while rotor regulation is more complex but offers potential efficiency gains. The present paper analyzes energy losses associated with these regulation strategies using two approaches: 1D mean line turbomachinery equations and 3D Computational Fluid Dynamics (CFD). The 1D mean line approach is used for understanding the conceptual fluid dynamic aspects involved in the two different regulation approaches, thereby identifying the loss-generation mechanisms in off-design operation. Fully 3D CFD simulations allow for quantifying and deeply explaining the differences in the hydraulic efficiencies of the two regulation strategies. Attention is focused on the two main loss contributions: residual tangential kinetic energy at the rotor outlet and entropy generation. Rotor regulation, even if more complex, provides better results than distributor regulation in terms of both effectiveness (larger flow rate sensitivity to stagger angle variation) and turbine operating efficiency (lower off-design losses).
High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine
Authors: Du Wei , Peng Shi-Tao , Wu Pei-Sen , Tseng Ming-Lang
Published: May 2024

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.
Exploring Motion Stability of a Novel Semi-Submersible Platform for Offshore Wind Turbines
Authors: Zhao Hongxu , Wu Xiang , Zhou Zhou
Published: May 2024

The stability of offshore floating wind turbine foundation platforms is a fundamental requirement for the efficiency and safety of wind power generation systems. This paper proposes a novel small-diameter float-type semi-submersible platform to improve system stability. To evaluate the superior motion stability of the proposed floating platform, a comprehensive frequency–domain response analysis and experimental study were conducted in comparison with the OC4-DeepCwind platform developed by the National Renewable Energy Laboratory (NREL). The respective comparison of the frequency–domain response analysis and the experimental results demonstrated that the proposed floating wind turbine platform shows better hydrodynamic characteristics and resonance avoidance capability. This not only reduces the Response Amplitude Operators (RAOs), but also enhances the system stability, namely, effectively avoiding the regions of concentrated wave loading and low-frequency ranges. Furthermore, the proposed small-diameter semi-submersible platform has the potential to reduce manufacturing costs, providing valuable insights for the manufacturing of offshore floating wind turbine systems.
Numerical Analysis on Performance Improvement of a Vertical Plate Indirect Evaporative Cooler with Baffles
Authors: Zhou Wenhe , Cheng Shuo , Wang Jia , Liu Yong
Published: May 2024

The performance of the Plate Indirect Evaporative Cooler (PIEC) can be effectively improved by incorporating baffles in the dry channel. However, in the dimensional influence of the baffles on PIEC performance there remains a research gap. In order to investigate the impact of baffle dimensions on the wet bulb efficiency, namely the average heat transfer coefficient and the cooling capacity of the PIEC, this paper proposed and verified a three-dimensional numerical model and method based on the species transport model and the Euler wall film model. At the same time, in order to obtain the equilibrium point between the enhanced heat transfer performance and the additional resistance induced by baffles, a comprehensive performance evaluation index is introduced. The results indicate that, under the same conditions, (1) the baffle effect on PIEC performance is significant at a lower inlet air velocity, and the wet bulb efficiency of the PIEC with baffles can be improved by 22.8%; (2) the baffle effect on PIEC performance is negative if its relative length exceeds 60% or the primary air inlet velocity surpasses 4 m/s under the conditions specified in this paper; and (3) the baffle effect on PIEC performance is significant when its channel height is lower and its channel width is larger, and the wet bulb efficiency of the PIEC with baffles can be improved by 29.3%.
Optimizing High-Voltage Direct Current Transmission Corridors: Dynamic Thermal Line Rating for Enhanced Renewable Generation and Greenhouse Gas Emission Reductions
Authors: Pemachandra Veenavi , Musilek Petr , Kish Gregory
Published: May 2024

Recently, significant attention has been paid to the large-scale use of renewable energy through high-voltage direct current (HVDC) because of its economic feasibility. At the same time, the growing demand for electricity and the increasing penetration of renewable energy sources have prompted the electric power industry to explore methods to optimize the use of the existing grid infrastructure. Dynamic thermal line rating (DTLR) is a technique that allows transmission lines to operate at their maximum capacity, considering their real-time operating conditions. The majority of existing research on this topic has focused predominantly on employing DTLR in alternating current systems and exploring their applications. This study presents a novel approach by applying DTLR to HVDC transmission corridors, with the aim of maximizing the utilization of their capacity and facilitating increased integration of renewable energy. The performance of the proposed approach is evaluated by conducting a case study for an HVDC transmission line in Alberta, Canada. On average, the mean increase in ampacity above the static rating is 64% during winter and 34% during summer. This additional capacity can be used to integrate wind energy, replacing coal-fired generation. This leads to a significant reduction in greenhouse gas emissions, also quantified in this contribution.
A Study on the Techno-Economics Feasibility of a 19.38 KWp Rooftop Solar Photovoltaic System at Al-Abrar Mosque, Saudi Arabia
Authors: Alaboodi Abdulaziz S. , Alharbi Sultan J.
Published: May 2024

This research paper presents a comprehensive study on the implementation of photovoltaic (PV) energy systems at Al-Abrar Mosque in Saudi Arabia. The primary objective was to explore optimal regional solar power strategies. By synergistically integrating technical evaluations of the PV system with economic analyses, including the payback period and levelized cost of energy (LCOE), alongside an investigation of net metering and net billing scenarios, we delineated a pathway toward achieving net zero billing for the mosque’s energy requirements. This study examined two scenarios: Scenario I involved net metering, while Scenario II explored net billing. Our theoretical and simulation results, derived from detailed analyses conducted using PVsyst software, unequivocally demonstrated the superiority of net metering for this specific application. With net metering, the mosque’s energy needs can be efficiently met using minimal infrastructure—comprising only 34 photovoltaic modules and a single inverter. In contrast, net billing requires significantly higher resource demands, underscoring the economic and spatial advantages of net metering. Additionally, the payback period for Scenario I is 7.9 years, while for Scenario II, it extends to 87 years. Through rigorous simulations, this study reaffirmed the practicality and feasibility of the net metering approach within the context of Saudi Arabia. Furthermore, our research provides actionable insights for implementing sustainable solutions at specific sites, such as the Al-Abrar Mosque, and contributes to advancing renewable energy knowledge in the region.
Investigation on the Possibility of Improving the Performance of a Silicon Cell Using Selected Dye Concentrator
Authors: Brągoszewska Ewa , Milewicz Bartłomiej , Wajda Agata
Published: May 2024

There are many opportunities to increase the efficiency of photovoltaic cells. These include solutions such as tracking mechanisms, hybrid systems or dye concentrators. Importantly, their implementation can reduce the number of silicon cells in installations, leading to reduced environmental impact. The principle of a dye concentrator is to focus sunlight onto the surface of PV modules, increasing electricity production. In this study, the potential for increased PV cell efficiency is investigated using a selected dye concentrator—tinted and luminescent acrylic glass (polymethylmethacrylate, PMMA) in yellow and red colors. The experiment included multiple measurement calibrations, such as the temperature of the silicon cell under test and the irradiation, as well as different variants of PV systems consisting of a silicon cell and different types of PMMA. Overall, the results show an increase in PV cell performance and the dependence of the increase on the type of PMMA used. The most favorable of the PV systems tested appeared to be the combination of a PV cell with a red luminescent PV, for which an average efficiency improvement of 1.21% was obtained.
Development and Application of PIKH-Type Current Sensors to Prevent Improper Opening of Parallelly Connected DC Vacuum Circuit Breakers
Authors: Nowak Łukasz , Borkowski Piotr
Published: May 2024

This article describes the development of current sensors used in DCU-type high-speed circuit breakers. DCU-type circuit breakers use the principle of turning off a constant short-circuit current by means of countercurrent. The article presents a new current sensor design called PIKh, which uses Hall sensors to measure current and determine its direction. PIKh sensors allow parallel operation of high-speed circuit breakers during the “parking” operation. The article includes the algorithm and principle of operation of the PIKh sensor. The proposed solution was verified on an electric traction vehicle.
Combustion Analysis of Mixed Secondary Fuel Produced from Agro-Biomass and RDF in a Fluidized Bed
Authors: Kaczyński Konrad , Pełka Piotr
Published: May 2024

In recent decades, there has been growing interest in the thermal conversion of various alternative fuels, such as biomass and waste-derived fuels. One of the technological solutions for the so-called direct co-combustion of fuels is to create mixtures of various fuels, called mixed secondary fuel. However, mixed secondary fuel has different properties compared to primary fuels. Due to this, by properly selecting the types and proportions of mixtures, it is possible to eliminate their potentially negative impact on both combustion technology and the natural environment. In this paper, we decided to prepare mixed secondary fuel by mixing sunflower husk pellets with RDF (refuse-derived fuel) in a ratio of 1:1 and then analyze the combustion process of the produced fuel in fluidized bed conditions. The results obtained on the basis of the presented research indicate that the mixed secondary fuel eliminated the impact of alkali metal compounds on reducing the melting point of ash and, consequently, on the combustion process of the mixed secondary fuel. An additional benefit is the reduction of emissions of harmful compounds into the atmosphere occurring during the combustion of municipal waste and compliance with the concept of the circular economy.