Applied Sciences最新文献

{"title":"A Novel Tracking Strategy Based on Real-Time Monitoring to Increase the Lifetime of Dual-Axis Solar Tracking Systems","authors":"Diego A. Flores-Hernández, Luis R. Islas-Estrada, Sergio I. Palomino-Resendiz","doi":"10.3390/app14188281","DOIUrl":"https://doi.org/10.3390/app14188281","url":null,"abstract":"Solar tracking systems allow an increase in the use of solar energy for its conversion with photovoltaic technology due to the alignment with the sun. However, there is a compromise between tracking accuracy and the energy required to perform the movement action. Consequently, the wear of the tracker components increases, reducing its useful lifetime and affecting the profitability of these systems. The present research develops a novel tracking strategy based on real-time measurements to increase the lifetime without reducing the energy productivity of the tracking systems. The proposed approach is verified experimentally by implementing the real-time decision-making algorithm and a conventional tracking algorithm in identical tracking systems under the same weather conditions. The proposed strategy reduces energy consumption by 14.18% due to the tracking action, maintaining a practically identical energy generation between both systems. The findings highlight a 53.33% reduction in the movements required for tracking and a 60.77% reduction in operation time, which translates into a 6.8-fold increase in the lifetime of the solar tracking system under the experimental conditions applied. The results are promising, so this research initiates and motivates the development of more complex models to increase the useful life of the tracking systems and their profitability and environmental impact concurrently.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-Precipitation Synthesized Ag-Doped Ceria Redox Material (ACRM) for the Thermochemical Conversion of CO2 into Solar Fuels","authors":"Gorakshnath Takalkar, Sayma Akhter, Rahul R. Bhosale","doi":"10.3390/app14188272","DOIUrl":"https://doi.org/10.3390/app14188272","url":null,"abstract":"In this investigation, an effort was made to introduce Ag into the CeO2 fluorite crystal lattice to form Ce0.99Ag0.01O2-δ (ACRM) using an ammonium hydroxide-assisted co-precipitation method. The resulting powder obtained after the co-precipitation reaction, filtration, and drying was annealed at 800 °C in a muffle furnace to obtain crystalline ACRM. The phase composition and microstructure of the synthesized ACRM were analyzed using a powder X-ray diffractometer (PXRD) and a scanning electron microscope (SEM). The characterized ACRM powder was then subjected to multiple thermochemical thermal reduction (TR) and CO2 splitting (CDS) cycles using a high-temperature thermogravimetric analyzer (TGA). The TR step was conducted using Ar gas as an inert atmosphere, maintaining the temperature at 1400 °C for 60 min. Subsequently, the same powder was subjected to the CDS step by treating it with a gaseous mixture of 50% CO2 and Ar gas at 1000 °C for 30 min. ACRM displayed stable redox reactivity towards thermochemical CDS cycles by generating an average of 50.9 μmol of O2/g·cycle and 101.6 μmol of CO/g·cycle, respectively, over 10 thermochemical cycles.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simplified Deep Learning for Accessible Fruit Quality Assessment in Small Agricultural Operations","authors":"Víctor Zárate, Danilo Cáceres Hernández","doi":"10.3390/app14188243","DOIUrl":"https://doi.org/10.3390/app14188243","url":null,"abstract":"Fruit quality assessment is vital for ensuring consumer satisfaction and marketability in agriculture. This study explores deep learning techniques for assessing fruit quality, focusing on practical deployment in resource-constrained environments. Two approaches were compared: training a convolutional neural network (CNN) from scratch and fine-tuning a pre-trained MobileNetV2 model through transfer learning. The performance of these models was evaluated using a subset of the Fruits-360 dataset chosen to simulate real-world conditions for small-scale producers. MobileNetV2 was selected for its compact size and efficiency, suitable for devices with limited computational resources. Both approaches achieved high accuracy, with the transfer learning model demonstrating faster convergence and slightly better performance. Feature map visualizations provided insight into the model’s decision-making, highlighting damaged areas of fruits which enhances transparency and trust for end users. This study underscores the potential of deep learning models to modernize fruit quality assessment, offering practical, efficient, and interpretable tools for small-scale farmers.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology","authors":"Lin Bi, Dewei Bai, Boxun Chen","doi":"10.3390/app14188273","DOIUrl":"https://doi.org/10.3390/app14188273","url":null,"abstract":"To address the problem of incomplete perception of limited viewpoints of ore blocks in future remote and intelligent shoveling-dominated mining scenarios, a method of using new view generation technology to predict ore blocks with limited view based on a latent diffusion model is proposed. Initially, an ore block image-pose dataset is created. Then, based on prior knowledge, the latent diffusion model undergoes transfer learning to develop an intelligent ore block shape prediction model (IOBSPM) for rock blocks. During training, structural similarity loss is innovatively introduced to constrain the prediction results and solve the issue of discontinuity in generated images. Finally, neural surface reconstruction is performed using the generated multi-view images of rock blocks to obtain a 3D model. Experimental results show that the prediction model, trained on the rock block dataset, produces better morphological and detail generation compared to the original model, with single-view generation time within 5 s. The average PSNR, SSIM, and LPIPS values reach 23.02 dB, 0.754, and 0.268, respectively. The generated views also demonstrate good performance in 3D reconstruction, highlighting significant implications for future research on remote and autonomous shoveling.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical Simulation of Seismic-Wave Propagation in Specific Layered Geological Structures","authors":"Chunyue Hao, Zhoupeng Gu, Kai Li, Xianqian Wu","doi":"10.3390/app14188278","DOIUrl":"https://doi.org/10.3390/app14188278","url":null,"abstract":"This study presents a numerical simulation approach used to investigate seismic-wave propagation in specific geological structures. Using the LS-DYNA software, the simulation incorporated a TNT explosion model to simulate seismic energy released during earthquakes. It provides a new method to investigate the propagation characteristics of seismic-waves within geological structures. Firstly, the measurement conditions and geological settings of the seismic event on 18 February 2012 in Northeast China are presented. Subsequently, a numerical simulation model of seismic-wave propagation is developed. The simulation result validates it by comparing it with recorded data from seismic stations, demonstrating a promising correspondence between the simulated and observed data. Additionally, the simulation simulates the seismic-wave propagation within water and layered geological structures, validating the numerical simulation model. The numerical model is an effective tool for simulating the propagation of seismic waves in geological structures. This study is important for evaluating seismic-wave propagation using the simulation method.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Influence of Vibration Frequency and Vibration Duration on the Mechanical Properties of Zhanjiang Formation Structural Clay","authors":"Yanhua Xie, Bin Tang, Yansong Shi, Shuaiyu Liu, Jiankun Hu, Binghui Zhang","doi":"10.3390/app14188262","DOIUrl":"https://doi.org/10.3390/app14188262","url":null,"abstract":"Vibration frequency and vibration duration are disturbance factors for the structural properties of clay. This study investigates how the vibration frequency and vibration duration from construction disturbances affect the mechanical properties of Zhanjiang Formation structural clay. An electric, frequency-adjustable vibration table applied varying frequencies and durations of vibration to undisturbed soil, creating structural clay samples with different disturbance degrees. Unconfined compressive strength tests and one-dimensional consolidation compression tests were conducted on these samples to obtain disturbance degrees RDq and RDS, defined by strength loss values and compression deformation characteristics, respectively. Orthogonal experiments and grey correlation analysis were used to explore the effects of vibration frequency and vibration duration on the mechanical properties of Zhanjiang Formation structural clay. The results indicated that disturbance degrees RDq and RDS increased linearly with increase in vibration frequency and vibration duration. Range analysis was conducted using two-factor three-level orthogonal experiment of disturbance degrees, and a grey relational analysis model was established to determine the primary and secondary effects of vibration duration and vibration frequency on the mechanical properties of Zhanjiang Formation structural clay. The results demonstrated that the findings from orthogonal experiments and grey relational analysis were consistent, showing that vibration duration had a more significant impact than vibration frequency on the mechanical properties of structural clay. The conclusion suggests that vibration disturbance manifests as a “fatigue damage effect”. Continuous vibration disturbance progressively weakens the cementation bonds between soil particles due to “accumulated” energy, leading to gradual fracture and destruction. With constant vibration frequency, longer durations, or with constant duration, higher frequencies intensify the “fatigue damage effect” of vibration disturbance. Furthermore, during vibration disturbance, Zhanjiang Formation structural clay shows a more pronounced “fatigue damage effect” from vibration duration than from vibration frequency, with cementation bonds between soil particles weakening more effectively due to “accumulated” energy. The research findings enhance the understanding of how vibration frequency and vibration duration from disturbance sources impact the mechanical properties of Zhanjiang Formation structural clay, offer theoretical guidance for using construction vibration machinery, and provide a reference for preventing and controlling soil disturbance.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WorkloadGPT: A Large Language Model Approach to Real-Time Detection of Pilot Workload","authors":"Yijing Gao, Lishengsa Yue, Jiahang Sun, Xiaonian Shan, Yihan Liu, Xuerui Wu","doi":"10.3390/app14188274","DOIUrl":"https://doi.org/10.3390/app14188274","url":null,"abstract":"The occurrence of flight risks and accidents is closely related to pilot workload. Effective detection of pilot workload has been a key research area in the aviation industry. However, traditional methods for detecting pilot workload have several shortcomings: firstly, the collection of metrics via contact-based devices can interfere with pilots; secondly, real-time detection of pilot workload is challenging, making it difficult to capture sudden increases in workload; thirdly, the detection accuracy of these models is limited; fourthly, the models lack cross-pilot generalization. To address these challenges, this study proposes a large language model, WorkloadGPT, which utilizes low-interference indicators: eye movement and seat pressure. Specifically, features are extracted in 10 s time windows and input into WorkloadGPT for classification into low, medium, and high workload categories. Additionally, this article presents the design of an appropriate text template to serialize the tabular feature dataset into natural language, incorporating individual difference prompts during instance construction to enhance cross-pilot generalization. Finally, the LoRA algorithm was used to fine-tune the pre-trained large language model ChatGLM3-6B, resulting in WorkloadGPT. During the training process of WorkloadGPT, the GAN-Ensemble algorithm was employed to augment the experimental raw data, constructing a realistic and robust extended dataset for model training. The results show that WorkloadGPT achieved a classification accuracy of 87.3%, with a cross-pilot standard deviation of only 2.1% and a response time of just 1.76 s, overall outperforming existing studies in terms of accuracy, real-time performance, and cross-pilot generalization capability, thereby providing a solid foundation for enhancing flight safety.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the Image Pre-Processing for Large Fleets Based on a Fuzzy Approach to Handle Multiple Resolutions","authors":"Ching-Yun Mu, Pin Kung","doi":"10.3390/app14188254","DOIUrl":"https://doi.org/10.3390/app14188254","url":null,"abstract":"Image pre-processing is crucial for large fleet management. Many traffic videos are collected by closed-circuit television (CCTV), which has a fixed area monitoring for image analysis. This paper adopts the front camera installed in large vehicles to obtain moving traffic images, whereas CCTV is more limited. In practice, fleets often install cameras with different resolutions due to cost considerations. The cameras evaluate the front images with traffic lights. This paper proposes fuzzy enhancement with RGB and CIELAB conversions to handle multiple resolutions. This study provided image pre-processing adjustment comparisons, enabling further model training and analysis. This paper proposed fuzzy enhancement to deal with multiple resolutions. The fuzzy enhancement and fuzzy with brightness adjustment produced images with lower MSE and higher PSNR for the images of the front view. Fuzzy enhancement can also be used to enhance traffic light image adjustments. Moreover, this study employed You Only Look Once Version 9 (YOLOv9) for model training. YOLOv9 with fuzzy enhancement obtained better detection performance. This fuzzy enhancement made more flexible adjustments for pre-processing tasks and provided guidance for fleet managers to perform consistent image-enhancement adjustments for handling multiple resolutions.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Versatile Video Coding-Post Processing Feature Fusion: A Post-Processing Convolutional Neural Network with Progressive Feature Fusion for Efficient Video Enhancement","authors":"Tanni Das, Xilong Liang, Kiho Choi","doi":"10.3390/app14188276","DOIUrl":"https://doi.org/10.3390/app14188276","url":null,"abstract":"Advanced video codecs such as High Efficiency Video Coding/H.265 (HEVC) and Versatile Video Coding/H.266 (VVC) are vital for streaming high-quality online video content, as they compress and transmit data efficiently. However, these codecs can occasionally degrade video quality by adding undesirable artifacts such as blockiness, blurriness, and ringing, which can detract from the viewer’s experience. To ensure a seamless and engaging video experience, it is essential to remove these artifacts, which improves viewer comfort and engagement. In this paper, we propose a deep feature fusion based convolutional neural network (CNN) architecture (VVC-PPFF) for post-processing approach to further enhance the performance of VVC. The proposed network, VVC-PPFF, harnesses the power of CNNs to enhance decoded frames, significantly improving the coding efficiency of the state-of-the-art VVC video coding standard. By combining deep features from early and later convolution layers, the network learns to extract both low-level and high-level features, resulting in more generalized outputs that adapt to different quantization parameter (QP) values. The proposed VVC-PPFF network achieves outstanding performance, with Bjøntegaard Delta Rate (BD-Rate) improvements of 5.81% and 6.98% for luma components in random access (RA) and low-delay (LD) configurations, respectively, while also boosting peak signal-to-noise ratio (PSNR).","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Insights into the Molecular Basis of HIV Glycoproteins","authors":"Amir Elalouf, Hanan Maoz, Amit Yaniv Rosenfeld","doi":"10.3390/app14188271","DOIUrl":"https://doi.org/10.3390/app14188271","url":null,"abstract":"Human Immunodeficiency Virus (HIV) is a diploid, C-type enveloped retrovirus belonging to the Lentivirus genus, characterized by two positive-sense single-stranded RNA genomes, that transitioned from non-human primates to humans and has become globally widespread. In its advanced stages, HIV leads to Acquired Immune Deficiency Syndrome (AIDS), which severely weakens the immune system by depleting CD4+ helper T cells. Without treatment, HIV progressively impairs immune function, making the body susceptible to various opportunistic infections and complications, including cardiovascular, respiratory, and neurological issues, as well as secondary cancers. The envelope glycoprotein complex (Env), composed of gp120 and gp41 subunits derived from the precursor gp160, plays a central role in cycle entry. gp160, synthesized in the rough endoplasmic reticulum, undergoes glycosylation and proteolytic cleavage, forming a trimeric spike on the virion surface. These structural features, including the transmembrane domain (TMD), membrane-proximal external region (MPER), and cytoplasmic tail (CT), are critical for viral infectivity and immune evasion. Glycosylation and proteolytic processing, especially by furin, are essential for Env’s fusogenic activity and capacity to evade immune detection. The virus’s outer envelope glycoprotein, gp120, interacts with host cell CD4 receptors. This interaction, along with the involvement of coreceptors CXCR4 and CCR5, prompts the exposure of the gp41 fusogenic components, enabling the fusion of viral and host cell membranes. While this is the predominant pathway for viral entry, alternative mechanisms involving receptors such as C-type lectin and mannose receptors have been found. This review aims to provide an in-depth analysis of the structural features and functional roles of HIV entry proteins, particularly gp120 and gp41, in the viral entry process. By examining these proteins’ architecture, the review elucidates how their structural properties facilitate HIV invasion of host cells. It also explores the synthesis, trafficking, and structural characteristics of Env/gp160 proteins, highlighting the interactions between gp120, gp41, and the viral matrix. These contributions advance drug resistance management and vaccine development efforts.","PeriodicalId":8224,"journal":{"name":"Applied Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142181129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}