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by Wentao Yu, Sudarshana Santhosh Kumar Kothapalli, Zhiyao Yang, Xuwen Guo, Xiaowei Li, Yimin Cai, Wen Feng and Lihua Yuan

Molecules 2024, 29(20), 4842; https://doi.org/10.3390/molecules29204842 (registering DOI) - 12 Oct 2024

Abstract

Achieving light-induced manipulation of controlled self-assembly in nanosized structures is essential for developing artificially dynamic smart materials. Herein, we demonstrate an approach using a non-photoresponsive hydrogen-bonded (H-bonded) macrocycle to control the self-assembly and disassembly of nanostructures in response to light. The present system comprises a photoacid (merocyanine, 1-MEH), a pseudorotaxane formed by two H-bonded macrocycles, dipyridinyl acetylene, and zinc ions. The operation of such a system is examined according to the alternation of self-assembly through proton transfer, which is mediated by the photoacid upon exposure to visible light. The host–guest complexation between the macrocycle and bipyridium guests was investigated by NMR spectroscopy, and one of the guests with the highest affinity for the ring was selected for use as one of the components of the system, which forms the host–guest complex with the ring in a 2:1 stoichiometry. In solution, a dipyridine and the ring, having no interaction with each other, rapidly form a complex in the presence of 1-MEH when exposed to light and thermally relax back to the free ring without entrapped guests after 4 h. Furthermore, the addition of zinc ions to the solution above leads to the formation of a polypseudorotaxane with its morphology responsive to photoirradiation. This work exemplifies the light-controlled alteration of self-assembly in non-photoresponsive systems based on interactions between the guest and the H-bonded macrocycle in the presence of a photoacid. Full article

(This article belongs to the Section Organic Chemistry)

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25 pages, 2413 KiB

Open AccessArticle

Object Detection Algorithm for Citrus Fruits Based on Improved YOLOv5 Model

by Yao Yu, Yucheng Liu, Yuanjiang Li, Changsu Xu and Yunwu Li

Agriculture 2024, 14(10), 1798; https://doi.org/10.3390/agriculture14101798 (registering DOI) - 12 Oct 2024

Abstract

To address the challenges of missed and false detections in citrus fruit detection caused by environmental factors such as leaf occlusion, fruit overlap, and variations in natural light in hilly and mountainous orchards, this paper proposes a citrus detection model based on an improved YOLOv5 algorithm. By introducing receptive field convolutions with full 3D weights (RFCF), the model overcomes the issue of parameter sharing in convolution operations, enhancing detection accuracy. A focused linear attention (FLA) module is incorporated to improve the expressive power of the self-attention mechanism while maintaining computational efficiency. Additionally, anchor boxes were re-clustered based on the shape characteristics of target objects, and the boundary box loss function was improved to Foal-EIoU, boosting the model’s localization ability. Experiments conducted on a citrus fruit dataset labeled using LabelImg, collected from hilly and mountainous areas, showed a detection precision of 95.83% and a mean average precision (mAP) of 79.68%. This research not only significantly improves detection performance in complex environments but also provides crucial data support for precision tasks such as orchard localization and intelligent picking, demonstrating strong potential for practical applications in smart agriculture. Full article

(This article belongs to the Section Digital Agriculture)

21 pages, 1304 KiB

Open AccessFeature PaperArticle

Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features

by Huanyin Su, Shanglin Mo, Huizi Dai and Jincong Shen

Mathematics 2024, 12(20), 3204; https://doi.org/10.3390/math12203204 (registering DOI) - 12 Oct 2024

Abstract

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (temporal hysteresis). Additionally, the distribution characteristics are also complex. Previous studies mainly focus on passenger flow prediction at metro stations, while few methods solve the OD passenger flow prediction problems of an urban rail transit system. In view of this, we propose a novel deep learning method fusing high-dimensional features (HDF-DL) with multi-source data. The HDF-DL method is combined with three modules. The temporal module incorporates the time-varying, trend, and cyclic characteristics of OD passenger flow, while the latest OD passenger flow time sequence (within 1 h) is excluded from the time-varying characteristics. In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. Weather factors are considered in the external feature module. The HDF-DL method is tested on a large-scale metro system in China, in which eight baseline models are designed. The results show that the HDF-DL method achieves high prediction accuracy across multiple time granularities, with a mean absolute percentage error of about 10%. OD passenger flow in every departure time interval can be predicted with high and stable accuracy, effectively capturing temporal characteristics. The modular design of HDF-DL, which fuses high-dimensional features and employs appropriate neural networks for different data types, significantly reduces prediction errors and outperforms baseline models. Full article

(This article belongs to the Special Issue Application of Mathematical Methods to Transportation: Modeling and Analysis)

22 pages, 7052 KiB

Open AccessArticle

Data-Driven Dynamic Security Partition Assessment of Power Systems Based on Symmetric Electrical Distance Matrix and Chebyshev Distance

by Hang Qi, Ruiyang Su, Runjia Sun and Jiongcheng Yan

Symmetry 2024, 16(10), 1355; https://doi.org/10.3390/sym16101355 (registering DOI) - 12 Oct 2024

Abstract

A rapid dynamic security assessment (DSA) is crucial for online preventive and restoration decision-making. The deep learning-based DSA models have high efficiency and accuracy. However, the complex model structure and high training cost make them hard to update quickly. This paper proposes a dynamic security partition assessment method, aiming to develop accurate and incrementally updated DSA models with simple structures. Firstly, the power grid is self-adaptively partitioned into several local regions based on the mean shift algorithm. The input of the mean shift algorithm is a symmetric electrical distance matrix, and the distance metric is the Chebyshev distance. Secondly, high-level features of operating conditions are extracted based on the stacked denoising autoencoder. The symmetric electrical distance matrix is modified to represent fault locations in local regions. Finally, DSA models are constructed for fault locations in each region based on the radial basis function neural network (RBFNN) and Chebyshev distance. An online incremental updating strategy is designed to enhance the model adaptability. With the simulation software PSS/E 33.4.0, the proposed dynamic security partition assessment method is verified in a simplified provincial system and a large-scale practical system in China. Test results demonstrate that the Chebyshev distance can improve the partition quality of the mean shift algorithm by approximately 50%. The RBFNN-based partition assessment model achieves an accuracy of 98.96%, which is higher than the unified assessment with complex models. The proposed incremental updating strategy achieves an accuracy of over 98% and shortens the updating time to 30 s, which can meet the efficiency of online application. Full article

(This article belongs to the Special Issue New Power System and Symmetry)

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11 pages, 472 KiB

Open AccessArticle

High-Data-Rate Modulators Based on Graphene Transistors: Device Circuit Co-Design Proposals

by Anibal Pacheco-Sanchez, J. Noé Ramos-Silva, Nikolaos Mavredakis, Eloy Ramírez-García and David Jiménez

Electronics 2024, 13(20), 4022; https://doi.org/10.3390/electronics13204022 (registering DOI) - 12 Oct 2024

Abstract

The multifunctionality feature of graphene field-effect transistors (GFETs) is exploited here to design circuit building blocks of high-data-rate modulators by using a physics-based compact model. Educated device performance projections are obtained with the experimentally calibrated model and used to choose an appropriate improved feasible GFET for these applications. Phase-shift and frequency-shift keying (PSK and FSK) modulation schemes are obtained with 0.6 GHz GFET-based multifunctional circuits used alternatively in different operation modes: inverting and in-phase amplification and frequency multiplication. An adequate baseband signal applied to the transistors’ input also serves to enhance the device and circuit performance reproducibility since the impact of traps is diminished. Quadrature PSK is also achieved by combining two GFET-based multifunctional circuits. This device circuit co-design proposal intends to boost the heterogeneous implementation of graphene devices with incumbent technologies into a single chip: the baseband pulses can be generated with CMOS technology as a front end of line and the multifunctional GFET-based circuits as a back end of line. Full article

(This article belongs to the Special Issue Selected Papers from XXXVIII Conference on Design of Circuits and Integrated Systems—DCIS 2023)

9 pages, 529 KiB

Open AccessArticle

Diagnostic Performance of Visionix VX120+ Platform for Dry Eye Screening

by Elena Martínez-Plaza, Laura Barberán-Bernardos, Ainhoa Molina-Martín and David P. Piñero

Diagnostics 2024, 14(20), 2276; https://doi.org/10.3390/diagnostics14202276 (registering DOI) - 12 Oct 2024

Abstract

Objective: To evaluate the accuracy of diagnosing dry eye disease (DED) by using the Visionix VX120+, to establish reference values for tear meniscus height (TMH) and non-invasive break-up time (NIBUT), and to compare the NIBUT measurements with the fluorescein tear break-up time (FBUT), Methods: fifty-eight subjects (34 dry eye and 24 control) were enrolled. The TMH, first NIBUT, and NIBUT50% were evaluated with the Visionix VX120+, and the FBUT was measured with a slit-lamp. The Receiver Operating Characteristic (ROC) curve was used to evaluate the diagnostic performance, and the Bland–Altman method was performed to analyze the agreement. Results: The areas under the curve were 0.62, 0.60, and 0.70 for the TMH, first NIBUT, and NIBUT50%, respectively. The optimal cut-off values (sensitivity, specificity) were 0.29 (0.62, 0.67), 5.05 (0.85, 0.46), and 7.35 (0.65, 0.79) for the TMH, first NIBUT, and NIBUT50%, respectively. The mean differences (lower, upper limits of agreement) were −1.10 (−8.78, 6.58) and 1.55 (−5.68, 8.78) for the first NIBUT vs. FBUT and the NIBUT50% vs. FBUT, respectively. Conclusions: In conclusion, the NIBUT50% can be a useful tool for dry eye screening, with acceptable values of sensitivity and specificity. First, the NIBUT and NIBUT50% should not be used interchangeably with the FBUT. Full article

(This article belongs to the Section Biomedical Optics)

12 pages, 522 KiB

Open AccessArticle

Distal Pancreatectomy with and without Celiac Axis Resection for Adenocarcinoma: A Comparison in the Era of Neoadjuvant Therapy

by Sara K. Daniel, Camille E. Hironaka, M. Usman Ahmad, Daniel Delitto, Monica M. Dua, Byrne Lee, Jeffrey A. Norton, Brendan C. Visser and George A. Poultsides

Cancers 2024, 16(20), 3467; https://doi.org/10.3390/cancers16203467 (registering DOI) - 12 Oct 2024

Abstract

Background: Distal pancreatectomy with celiac axis resection (DP-CAR) has been used for selected patients with pancreatic cancer infiltrating the celiac axis. We compared the short- and long-term outcomes between DP-CAR and distal pancreatectomy alone (DP) in patients receiving neoadjuvant therapy. Methods: Patients undergoing DP-CAR from 2013 to 2022 were retrospectively reviewed. Clinicopathologic features, post-operative morbidity, and survival outcomes were compared with patients undergoing DP after neoadjuvant chemotherapy. Results: Twenty-two DP-CAR and thirty-four DP patients who underwent neoadjuvant chemotherapy were identified. There were no differences in comorbidities or CA19-9 levels. OR time was longer for DP-CAR (304 vs. 240 min, p = 0.007), but there was no difference in the transfusion rate (22.7% vs. 14.7%). Vascular reconstruction was more common in DP-CAR (18.2% vs. 0% arterial, p = 0.05; 40.9% vs. 12.5% venous, p = 0.04). There was no difference in morbidity or mortality between the two groups. Although there was a trend towards larger tumors in DP-CAR (5.1 cm vs. 3.8 cm, P = 0.057), the overall survival from the initiation of treatment (32 vs. 28 months, p = 0.43) and surgery (30 vs. 24 months, p = 0.43) were similar. Discussion: DP-CAR is associated with similar survival and morbidity compared to DP patients requiring neoadjuvant chemotherapy and should be pursued in appropriately selected patients. Full article

(This article belongs to the Special Issue Recent Advances in Pancreatic Surgery for Pancreatic Ductal Adenocarcinoma)

27 pages, 3921 KiB

Open AccessArticle

Automatic High-Resolution Operational Modal Identification of Thin-Walled Structures Supported by High-Frequency Optical Dynamic Measurements

by Tongfa Deng, Yuexin Wang, Jinwen Huang, Maosen Cao and Dragoslav Sumarac

Materials 2024, 17(20), 4999; https://doi.org/10.3390/ma17204999 (registering DOI) - 12 Oct 2024

Abstract

High-frequency optical dynamic measurement can realize multiple measurement points covering the whole surface of the thin-walled structure, which is very useful for obtaining high-resolution spatial information for damage localization. However, the noise and low calculation efficiency seriously hinder its application to real-time, online structural health monitoring. To this end, this paper proposes a novel high-resolution frequency domain decomposition (HRFDD) modal identification method, combining an optical system with an accelerometer for measuring high-accuracy vibration response and introducing a clustering algorithm for automated identification to improve efficiency. The experiments on the cantilever aluminum plate were carried out to evaluate the effectiveness of the proposed approach. Natural frequency and damping ratios were obtained by the least-squares complex frequency domain (LSCF) method to process the acceleration responses; the high-resolution mode shapes were acquired by the singular value decomposition (SVD) processing of global displacement data collected by high-speed cameras. Finally, the complete set of the first nine order modal parameters for the plate within the frequency range of 0 to 500 Hz has been determined, which is closely consistent with the results obtained from both experimental modal analysis and finite element analysis; the modal parameters could be automatically picked up by the DBSCAN algorithm. It provides an effective method for applying optical dynamic technology to real-time, online structural health monitoring, especially for obtaining high-resolution mode shapes. Full article

22 pages, 10336 KiB

Open AccessArticle

Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout

by Junli Liu, Deyu Zhang, Zhongpeng Liu, Tianyu Guo and Yanyan Yan

Sustainability 2024, 16(20), 8851; https://doi.org/10.3390/su16208851 (registering DOI) - 12 Oct 2024

Abstract

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production. Full article

(This article belongs to the Section Sustainable Engineering and Science)

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17 pages, 950 KiB

Open AccessArticle

Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity

by Dinis Falcão, Francisco Reis, José Farinha, Nuno Lavado and Mateus Mendes

Algorithms 2024, 17(10), 455; https://doi.org/10.3390/a17100455 (registering DOI) - 12 Oct 2024

Abstract

Predictive maintenance has gained importance due to industrialization. Harnessing advanced technologies like sensors and data analytics enables proactive interventions, preventing unplanned downtime, reducing costs, and enhancing workplace safety. They play a crucial role in optimizing industrial operations, ensuring the efficiency, reliability, and longevity of equipment, which have become increasingly vital in the context of industrialization. The analysis of time series’ stationarity is a powerful and agnostic approach to studying variations and trends that may indicate imminent failures in equipment, thus contributing to the effectiveness of predictive maintenance in industrial environments. The present paper explores the use of the Augmented Dickey–Fuller p-value temporal variation as a possible method for determining trends in sensor time series and thus anticipating possible failures of a wood chip pump in the paper industry. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

16 pages, 1370 KiB

Open AccessArticle

A Martingale Posterior-Based Fault Detection and Estimation Method for Electrical Systems of Industry

by Chao Cheng, Weijun Wang, He Di, Xuedong Li, Haotong Lv and Zhiwei Wan

Mathematics 2024, 12(20), 3200; https://doi.org/10.3390/math12203200 (registering DOI) - 12 Oct 2024

Abstract

The improvement of information sciences promotes the utilization of data for process monitoring. As the core of modern automation, time-stamped signals are used to estimate the system state and construct the data-driven model. Many recent studies claimed that the effectiveness of data-driven methods relies greatly on data quality. Considering the complexity of the operating environment, process data will inevitably be affected. This poses big challenges to estimating faults from data and delivers feasible strategies for electrical systems of industry. This paper addresses the missing data problem commonly in traction systems by designing a martingale posterior-based data generation method for the state-space model. Then, a data-driven approach is proposed for fault detection and estimation via the subspace identification technique. It is a general scheme using the Bayesian framework, in which the Dirichlet process plays a crucial role. The data-driven method is applied to a pilot-scale traction motor platform. Experimental results show that the method has good estimation performance. Full article

(This article belongs to the Special Issue Finite-Time/Fixed-Time Stability and Control of Dynamical Systems)

19 pages, 2949 KiB

Open AccessArticle

Model Predictive Control of Aero-Mechanical Actuators with Consideration of Gear Backlash and Friction Compensation

by Qixuan Zuo, Bo Wang, Jingbo Chen and Haiying Dong

Electronics 2024, 13(20), 4021; https://doi.org/10.3390/electronics13204021 (registering DOI) - 12 Oct 2024

Abstract

To address the issues of low positional accuracy and significant torque pulsation caused by gear backlash and nonlinear friction in the mechanical transmission mechanism of aeronautical flap electromechanical actuators, we propose a model predictive control method for flap electromechanical actuator considering gear backlash and friction compensation. Firstly, we model the gear backlash in the electromechanical actuator’s mechanical transmission mechanism and design a corresponding torque current compensation method using a simplified dead zone model. Secondly, the LuGre compensation friction model is introduced, and a friction torque current compensation method is developed to address the nonlinear friction torque generated during system operation. Finally, the proposed current compensation strategies are employed to mitigate the adverse effects of gear backlash and nonlinear friction on system control performance. The simulation results demonstrate that the proposed method enhances position tracking accuracy, reduces torque pulsation, and significantly improves the overall control performance of the system. Full article

(This article belongs to the Special Issue Nonlinear Intelligent Control: Theory, Models, and Applications)

19 pages, 1078 KiB

Open AccessArticle

Is Carmustine Wafer Implantation in Progressive High-Grade Gliomas a Relevant Therapeutic Option? Complication Rate, Predictors of Complications and Onco-Functional Outcomes in a Series of 53 Cases

by Grigorios Gkasdaris, Julien Berthiller, Jacques Guyotat, Emmanuel Jouanneau, Clémentine Gallet, David Meyronet, Laure Thomas, Stéphanie Cartalat, Antoine Seyve, Jérôme Honnorat, François Ducray and Thiebaud Picart

Cancers 2024, 16(20), 3465; https://doi.org/10.3390/cancers16203465 (registering DOI) - 12 Oct 2024

Abstract

Background/Objectives: The aim was to determine the complication rate and the predictors of complications and survival in high-grade glioma surgically managed at progression with implantation of Carmustine wafers. Methods: A retrospective series of 53 consecutive patients operated on between 2017 and 2022 was built. Results: The median age was 55 ± 10.9 years. The rates of global and infectious complications were 35.8% and 18.9%, respectively. In multivariate analysis, patients with a preoperative neurological deficit were more prone to develop a postoperative complication (HR = 5.35 95%CI 1.49–19.26, p = 0.01). No predictor of infectious complication was identified. In the grade 4 glioma subgroup (n = 44), progression-free and overall survival (calculated starting from the reresection) reached 3.95 months, 95% CI 2.92–5.21 and 11.51 months, 95% CI 9.11–17.18, respectively. Preoperative KPS > 80% (HR = 0.97 95%CI 0.93–0.99, p = 0.04), Gross Total Resection (HR = 0.38 95%CI 0.18–0.80, p = 0.01), and 3-month postoperative KPS > 80% (HR = 0.35 95%CI 0.17–0.72, p = 0.004) were predictors of prolonged overall survival. Conclusions: Surgical resection is a relevant option in high-grade gliomas at progression, especially in patients with a preoperative KPS > 80%, without preoperative neurological deficit, and amenable to complete resection. In patients elected for surgery, Carmustine wafer implantation is associated with a high rate of complications. It is consequently critical to closely monitor the patients for whom this option is chosen. Full article

(This article belongs to the Section Cancer Therapy)

31 pages, 2260 KiB

Open AccessReview

Comprehensive Review of Biomass Pyrolysis: Conventional and Advanced Technologies, Reactor Designs, Product Compositions and Yields, and Techno-Economic Analysis

by Wojciech Jerzak, Esther Acha and Bin Li

Energies 2024, 17(20), 5082; https://doi.org/10.3390/en17205082 (registering DOI) - 12 Oct 2024

Abstract

Pyrolysis is an environmentally friendly and efficient method for converting biomass into a wide range of products, including fuels, chemicals, fertilizers, catalysts, and sorption materials. This review confirms that scientific research on biomass pyrolysis has remained strong over the past 10 years. The authors examine the operating conditions of different types of pyrolysis, including slow, intermediate, fast, and flash, highlighting the distinct heating rates for each. Furthermore, biomass pyrolysis reactors are categorized into four groups, pneumatic bed reactors, gravity reactors, stationary bed reactors, and mechanical reactors, with a discussion on each type. The review then focuses on recent advancements in pyrolysis technologies that have improved efficiency, yield, and product quality, which, in turn, support sustainable energy production and effective waste management. The composition and yields of products from the different types of pyrolysis have been also reviewed. Finally, a techno-economic analysis has been conducted for both the pyrolysis of biomass alone and the co-pyrolysis of biomass with other raw materials. Full article

(This article belongs to the Special Issue Prospects for Biomass Pyrolysis and Gasification Technologies into Bioenergy)

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26 pages, 8182 KiB

Open AccessArticle

A Data Mining-Based Method to Disclose Usage Behavior Patterns of Fresh Air Systems in Beijing Dwellings during the Heating Season

by Sijia Gao, Song Pan, Yiqiao Liu, Ning Zhu, Tong Cui, Li Chang, Xiaofei Han and Ying Cui

Buildings 2024, 14(10), 3235; https://doi.org/10.3390/buildings14103235 (registering DOI) - 12 Oct 2024

Abstract

As the popularity of fresh air systems (FAS) in residential buildings increases, exploring the behavioral characteristics of their use can help to provide a comprehensive understanding of the potential for demand flexibility in residential buildings. However, few studies in the past have focused on the personalized usage behavior of FAS. To fill this gap, this study proposes a method based on data mining techniques to reveal the behavioral patterns of FAS usage and the motivations behind them, including motivational patterns, operation duration patterns, and human–machine interaction patterns, for 13 households in Beijing. The simultaneously obtained behavioral patterns, in turn, form the basis of association rules, which can classify FAS usage behavior into two typical residential user profiles containing user behavioral characteristics. This study can not only provide more accurate assumptions and inputs for behavioral stochastic models but also provide data support for the development and optimization of demand response strategies. Full article

(This article belongs to the Special Issue Indoor Air Quality: Differences in Working Environments for Different Practitioners)

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