bluuebunny/crossref_metadata_2025 huggingface dataset:
TCNet: Transformer Convolution Network for Cutting-Edge Detection of Unharvested Rice Regions
Authors: Yang Yukun , He Jie , Wang Pei , Luo Xiwen , Zhao Runmao , Huang Peikui , Gao Ruitao , Liu Zhaodi , Luo Yaling , Hu Lian
Published: July 2024

Cutting-edge detection is a critical step in mechanized rice harvesting. Through visual cutting-edge detection, an algorithm can sense in real-time whether the rice harvesting process is along the cutting-edge, reducing loss and improving the efficiency of mechanized harvest. Although convolutional neural network-based models, which have strong local feature acquisition ability, have been widely used in rice production, these models involve large receptive fields only in the deep network. Besides, a self-attention-based Transformer can effectively provide global features to complement the disadvantages of CNNs. Hence, to quickly and accurately complete the task of cutting-edge detection in a complex rice harvesting environment, this article develops a Transformer Convolution Network (TCNet). This cutting-edge detection algorithm combines the Transformer with a CNN. Specifically, the Transformer realizes a patch embedding through a 3 × 3 convolution, and the output is employed as the input of the Transformer module. Additionally, the multi-head attention in the Transformer module undergoes dimensionality reduction to reduce overall network computation. In the Feed-forward network, a 7 × 7 convolution operation is used to realize the position-coding of different patches. Moreover, CNN uses depth-separable convolutions to extract local features from the images. The global features extracted by the Transformer and the local features extracted by the CNN are integrated into the fusion module. The test results demonstrated that TCNet could segment 97.88% of the Intersection over Union and 98.95% of the Accuracy in the unharvested region, and the number of parameters is only 10.796M. Cutting-edge detection is better than common lightweight backbone networks, achieving the detection effect of deep convolutional networks (ResNet-50) with fewer parameters. The proposed TCNet shows the advantages of a Transformer combined with a CNN and provides real-time and reliable reference information for the subsequent operation of rice harvesting.
Design and Simulation of Intra-Row Obstacle Avoidance Shovel-Type Weeding Machine in Orchard
Authors: Jia Weidong , Tai Kaile , Wang Xiaowen , Dong Xiang , Ou Mingxiong
Published: July 2024

This paper presents the design of an intra-row obstacle avoidance shovel-type weeding machine. Theoretical analysis of intra-row weeding components guided the determination of the structures and parameters for key parts, including the signal acquisition mechanism, automatic obstacle avoidance mechanism, and weeding shovel. Furthermore, a hydraulic system was designed to support these functions. The design aims to optimize intra-row weeding operations, reduce labor costs, enhance weed control effectiveness, and prevent collisions between weeding equipment and grapevines. Through the construction of a mathematical model, the analysis determined the necessary minimum return speed of the hydraulic cylinder for the intra-row weeding shovel to avoid grapevines. We also established a reasonable range for the extension speed of the hydraulic cylinder to minimize areas missed during weeding. Further analysis showed that using the minimum return speed of the hydraulic cylinder effectively reduced missed weeding areas. A virtual prototype model of the weeding machine was created in ADAMS. Using the coverage rate of weeding operation as the evaluation index, single-factor simulation tests determined that the extension speed of the piston rod in the obstacle avoidance hydraulic cylinder and the forward speed of the weeding machine are the main influencing factors. The preset threshold of the control system, which triggered the automatic obstacle avoidance mechanism when the obstacle avoidance rod reached a specific angle (the “Angle Threshold”), was identified as a secondary influencing factor. Other factors were considered irrelevant. Hydraulic cylinder extension speed, weeding machine forward speed, and angle threshold were chosen as the influencing factors. Following the principles of a Box–Behnken experimental design, a quadratic regression combination experiment was designed using a three-factor, three-level response surface analysis method. The evaluation criterion focused on the coverage rate of weeding operation. A regression model was developed to determine the coverage rate of the weeding operation, identifying the optimal parameters as follows: obstacle avoidance hydraulic cylinder extension speed of 120 mm/s, forward speed of the weeding machine at 0.6 m/s, and an angle threshold of 18°. The optimized coverage rate of the weeding operation achieved 86.1%. This study serves as a reference for further optimization of intra-row weeding machines in vineyards and for other crops.
Recognition Method of Cabbage Heads at Harvest Stage under Complex Background Based on Improved YOLOv8n
Authors: Tian Yongqiang , Zhao Chunjiang , Zhang Taihong , Wu Huarui , Zhao Yunjie
Published: July 2024

To address the problems of low recognition accuracy and slow processing speed when identifying harvest-stage cabbage heads in complex environments, this study proposes a lightweight harvesting period cabbage head recognition algorithm that improves upon YOLOv8n. We propose a YOLOv8n-Cabbage model, integrating an enhanced backbone network, the DyHead (Dynamic Head) module insertion, loss function optimization, and model light-weighting. To assess the proposed method, a comparison with extant mainstream object detection models is conducted. The experimental results indicate that the improved cabbage head recognition model proposed in this study can adapt cabbage head recognition under different lighting conditions and complex backgrounds. With a compact size of 4.8 MB, this model achieves 91% precision, 87.2% recall, and a mAP@50 of 94.5%—the model volume has been reduced while the evaluation metrics have all been improved over the baseline model. The results demonstrate that this model can be applied to the real-time recognition of harvest-stage cabbage heads under complex field environments.
Research on Tobacco Field Semantic Segmentation Method Based on Multispectral Unmanned Aerial Vehicle Data and Improved PP-LiteSeg Model
Authors: Zhang Jun , Qiang Zhenping , Lin Hong , Chen Zhuqun , Li Kaibo , Zhang Shuang
Published: July 2024

In recent years, the estimation of tobacco field areas has become a critical component of precision tobacco cultivation. However, traditional satellite remote sensing methods face challenges such as high costs, low accuracy, and susceptibility to noise, making it difficult to meet the demand for high precision. Additionally, optical remote sensing methods perform poorly in regions with complex terrain. Therefore, Unmanned Aerial Vehicle multispectral remote sensing technology has emerged as a viable solution due to its high resolution and rich spectral information. This study employed a DJI Mavic 3M equipped with high-resolution RGB and multispectral cameras to collect tobacco field data covering five bands: RGB, RED, RED EDGE, NIR, and GREEN in Agang Town, Luoping County, Yunnan Province, China. To ensure the accuracy of the experiment, we used 337, 242, and 215 segmented tobacco field images for model training, targeting both RGB channels and seven-channel data. We developed a tobacco field semantic segmentation method based on PP-LiteSeg and deeply customized the model to adapt to the characteristics of multispectral images. The input layer’s channel number was adjusted to multiple channels to fully utilize the information from the multispectral images. The model structure included an encoder, decoder, and SPPM module, which used a multi-layer convolution structure to achieve feature extraction and segmentation of multispectral images. The results indicated that compared to traditional RGB images, multispectral images offered significant advantages in handling edges and complex terrain for semantic segmentation. Specifically, the predicted area using the seven-channel data was 11.43 m² larger than that obtained with RGB channels. Additionally, the seven-channel model achieved a prediction accuracy of 98.84%. This study provides an efficient and feasible solution for estimating tobacco field areas based on multispectral images, offering robust support for modern agricultural management.
A Deeper Insight into the Yield Formation of Winter and Spring Barley in Relation to Weather and Climate Variability
Authors: Yiğit Ali , Chmielewski Frank-M.
Published: July 2024

This study used descriptive statistical methods to investigate how the yield development of winter and spring barley was affected by annual weather variability within the vegetative, ear formation, anthesis, and grain-filling phases. Meteorological, phenological, and yield data from the agrometeorological field experiment in Berlin-Dahlem (Germany) between 2009 and 2022 were used. The results show that the lower yield variability in winter barley (cv = 18.7%) compared to spring barley (cv = 32.6%) is related to an earlier start and longer duration of relevant phenological phases, so yield formation is slower under generally cooler weather conditions. The significantly higher yield variability in spring barley was mainly the result of adverse weather conditions during ear formation and anthesis. In both phases, high temperatures led to significant yield losses, as has often been the case in recent years. In addition, a pronounced negative climatic water balance during anthesis was also a contributing factor. These meteorological parameters explained 82% of the yield variability in spring barley. New strategies for spring barley production are needed to avoid further yield losses in the future. Rising temperatures due to climate change could probably allow an earlier sowing date so that ear formation and anthesis take place in a generally cooler and wetter period, as shown for 2014.
Compatibility and Stability Analysis of Haploid Inducers under Different Source Germplasm and Seasons in Maize Using GGE Biplot
Authors: Dermail Abil , Lübberstedt Thomas , Suwarno Willy Bayuardi , Chankaew Sompong , Lertrat Kamol , Ruanjaichon Vinitchan , Suriharn Khundej
Published: July 2024

Multiple factors can affect the R1-nj purple kernel expression and seed set, reducing its efficiency in identifying haploids in maize. The complex interaction among the haploid inducer (HI), source germplasm (SG), and season (S) is inevitable in in vivo maize haploid induction but could be used through compatibility and stability tests. We tested five HI genotypes on 25 distinct source germplasm in two different seasons of tropical savanna in Thailand. The dry season was more suitable than the rainy season for haploid induction. We noticed varying degrees of R1-nj inhibition among the 25 tropical source germplasm, with some of them exhibiting significant issues with the R1-nj purple kernel expression. Therefore, using the R1-nj alone may not provide accurate ploidy identification in maize. Despite the intense R1-nj expression, haploid inducer BHI306 showed poor stability and compatibility with tropical source germplasm for pollination rate and seed set during the rainy season. The GGE biplot suggested KHI42 and KHI64 as the most compatible haploid inducers under their respective two different mega-source germplasm for the pollination rate and R1-nj seed set. These findings can guide breeders in selecting the most compatible and stable haploid inducers under varying conditions.
Antimicrobial Resistance Profile, Whole-Genome Sequencing and Core Genome Multilocus Sequence Typing of B. anthracis Isolates in Croatia from 2001 to 2022
Authors: Kompes Gordan , Duvnjak Sanja , Reil Irena , Mihaljević Željko , Habrun Boris , Benić Miroslav , Cvetnić Luka , Špičić Silvio , Bagarić Antonela
Published: July 2024

Bacillus anthracis, the causative agent of anthrax disease, is a worldwide threat to livestock, wildlife and public health. It is also considered one of the most important pathogens of bioterrorism. Rapid and reliable diagnosis and administration of antimicrobials are essential for effective anthrax treatment. In this study, we determined the in vitro susceptibilities of 40 isolates of B. anthracis isolated in Croatia over the recent two decades to 18 antimicrobials. Whole-genome sequencing was performed, and bioinformatics tools were used to determine virulence factors and antimicrobial resistance genes. Core genome-based multilocus sequence typing was used for isolate comparison and phylogenetic analysis. All isolates were susceptible to all antimicrobials recommended for post-exposure prophylaxis or anthrax therapy. Susceptibility was found to all other tested antimicrobials that are an alternative for primary therapy. We found two beta-lactamase genes, but their expression is not sufficient to confer resistance. In all isolates used in this study, we found 21 virulence genes, 8 of which are responsible for toxin and capsule production. As far as phylogenetic analysis is concerned, the B. anthracis isolates from Croatia are categorised into two clades. The first is clade A, subclade Trans Eurasia, and the other is clade B, subclade B2.
Role of parC Mutations at Position 84 on High-Level Delafloxacin Resistance in Methicillin-Resistant Staphylococcus aureus
Authors: Bolaños Silvia , Acebes Cesar , Martínez-Expósito Óscar , Boga José Antonio , Fernández Javier , Rodríguez-Lucas Carlos
Published: July 2024

High-level delafloxacin-resistant (H-L DLX-R) Staphylococcus aureus isolates (minimum inhibitory concentration ≥1 mg/L) associated with mutations affecting position 84 of ParC have emerged. We aimed to elucidate the role of these mutations as a mechanism of H-L DLX resistance in methicillin-resistant S. aureus (MRSA) isolates recovered from blood cultures. Susceptibility to DLX was determined in 75 MRSA isolates by E-test, and an rt-PCR was developed to detect mutations affecting position 84 of ParC to screen a further 185 MRSA isolates. The genomes of 48 isolates, including all DLX-R isolates or with alterations at position 84, and also a subset of DLX-susceptible isolates were analyzed. Among the 75 isolates studied, 77.34% were DLX-susceptible and only 4 H-L DLX-R isolates were found. Seven (3.8%) isolates with alterations at position 84 of ParC were detected by rt-PCR. Genomic analysis showed that 89.9% (8/9) of isolates with the substitution E84K/G in ParC, together with other mutations in gyrA and parC, were H-L DLX-R. However, the E84K substitution in ParC alone or with other alterations was found in two isolates without H-L DLX-R. Alterations at position 84 of ParC are rare but play a key role in H-L DLX resistance in MRSA but only when other alterations in GyrA are present.
Enhancing the Understanding of Subsurface Relations: Machine Learning Approaches for Well Data Analysis in the Drava Basin, Pannonian Super Basin
Authors: Brcković Ana , Orešković Jasna , Cvetković Marko , Marić-Đureković Željka
Published: July 2024

The aim of this study was to confirm if predictive regression algorithms can provide reliable results in missing geophysical logging data in the western and eastern parts of the Drava Super Basin, especially Gola Field, and to apply unsupervised machine learning methods for a better understanding of lithological subsurface relations. Numerous regression models have been used for the estimation of prediction accuracy, along with some clustering algorithms to support the estimation of lithology distribution estimations in well log datasets, consisting of 20 wells in total. Tree-based algorithms and the boosting algorithm have been optimized and proven valuable in predicting well log data when they are not measured or are unavailable at all depth intervals. For blind datasets, predictions become much less reliable. For this purpose, neural networks with at least one Long Short-Term Memory (LSTM) layer have significantly improved the accuracy and reliability of predictions, not in terms of absolute values but in the aspect of the trends in values that change with the depth and other well features, as well as in terms of the magnitudes. Trendlines can further be used for pattern recognition or as a newly engineered feature. Unsupervised learning has confirmed reliability in lithology recognition on validation sets and has proven to be a great asset in distinguishing variabilities in the petrophysical properties of sediments.
Environmental Protection in the Planning of Large Solar Power Plants
Authors: Josimović Boško , Manić Božidar , Niković Ana
Published: July 2024

The global trend of reducing the “carbon footprint” has influenced the dynamic development of projects that use renewable energy sources, including the development of solar energy in large solar power plants. Consequently, there is an increasingly pronounced need in scientific circles to consider the impact these projects have on space and the environment. The fact that international financial institutions consider environmental effect to be a significant factor when funding solar energy projects is one of the main reasons this topic is so important in professional circles, particularly among solar energy investors. This paper highlights the fact that solar power plants can have both positive and negative impacts on space and the environment. Those impacts need to be defined in order to choose optimal spatial and territorial solutions that ensure preventive planning and active environmental protection. In the process, the application of strategic environmental assessment (SEA) in the planning and spatial organization of solar power plants becomes important. SEA is characterized by a holistic approach where complex interactions and correlations in the location of planned implementation of the solar power plant can be understood at the earliest stage of project development. By doing this, it is possible to prevent all potential risks that may emerge in the project’s later stages of implementation, which is favorable both from the aspect of effective environmental protection and from the point of view of investors investing in solar power plant projects. Optimal solutions that bring about the basic role of SEA are sought primarily in the analysis of the spatial relations of the solar power plant with regard to land, biodiversity, landscape, and basic environmental factors, which is particularly highlighted in the paper. Also, the basic methodological concept applied in SEA is demonstrated, combining different methodological approaches and methods for impact assessment, as part of a unique semi-quantitative method of multi-criteria evaluation of planning solutions.
Turning Data Center Waste Heat into Energy: A Guide to Organic Rankine Cycle System Design and Performance Evaluation
Authors: Corigliano Orlando , Algieri Angelo , Fragiacomo Petronilla
Published: July 2024

This study delves into the adoption of the organic Rankine cycle (ORC) for recovering waste heat from data centers (DCs). Through a literature review, it examines energy reuse with a focus on electric power generation, the selection of working fluids, and system design principles. The objective is to develop a thorough framework for system design and analysis, beginning with a quantity and quality investigation of waste heat available. Air cooling systems, chosen often for their simplicity, account for about 70% of used cooling methods. Water cooling demonstrates greater effectiveness, albeit less commonly adopted. This study pays close attention to the selection of potential working fluids, meticulously considering the limitations presented by the available sources of heat and cold for vaporization and condensation, respectively. It reviews an ORC-based system setup, incorporating fluid streams for internal processes. The research includes a conceptual case study where the system is designed and simulations are conducted in the DWSIM environment. The simulation model considers hot air or hot liquid water returning from the data center cooling system for ORC working fluid evaporation. Ambient water serves for condensing, with pentane and isopentane identified as suitable organic fluids. Pentane assures ORC net electric efficiencies ranging between 3.1 and 7.1% when operating pressure ratios increase from 2.8 to 6.4. Isopentane systems, meanwhile, achieve efficiencies of 3.6–7.0% across pressure ratios of 2.7–6.0. Furthermore, the investigation provides key performance indicators for a reference data center in terms of power usage effectiveness (PUE), energy reuse factor (ERF), energy reuse effectiveness (ERE), and greenhouse gas (GHG) savings. This study concludes with guidelines for system analysis, including exergy considerations, and details the sizing process for evaporators and condensers.
Long 3D-POT: A Long-Term 3D Drosophila-Tracking Method for Position and Orientation with Self-Attention Weighted Particle Filters
Authors: Yin Chengkai , Liu Xiang , Zhang Xing , Wang Shuohong , Su Haifeng
Published: July 2024

The study of the intricate flight patterns and behaviors of swarm insects, such as drosophilas, has long been a subject of interest in both the biological and computational realms. Tracking drosophilas is an essential and indispensable method for researching drosophilas’ behaviors. Still, it remains a challenging task due to the highly dynamic nature of these drosophilas and their partial occlusion in multi-target environments. To address these challenges, particularly in environments where multiple targets (drosophilas) interact and overlap, we have developed a long-term Trajectory 3D Position and Orientation Tracking Method (Long 3D-POT) that combines deep learning with particle filtering. Our approach employs a detection model based on an improved Mask-RCNN to accurately detect the position and state of drosophilas from frames, even when they are partially occluded. Following detection, improved particle filtering is used to predict and update the motion of the drosophilas. To further enhance accuracy, we have introduced a prediction module based on the self-attention backbone that predicts the drosophila’s next state and updates the particles’ weights accordingly. Compared with previous methods by Ameni, Cheng, and Wang, our method has demonstrated a higher degree of accuracy and robustness in tracking the long-term trajectories of drosophilas, even those that are partially occluded. Specifically, Ameni employs the Interacting Multiple Model (IMM) combined with the Global Nearest Neighbor (GNN) assignment algorithm, primarily designed for tracking larger, more predictable targets like aircraft, which tends to perform poorly with small, fast-moving objects like drosophilas. The method by Cheng then integrates particle filtering with LSTM networks to predict particle weights, enhancing trajectory prediction under kinetic uncertainties. Wang’s approach builds on Cheng’s by incorporating an estimation of the orientation of drosophilas in order to refine tracking further. Compared with those methods, our method performs with higher accuracy on detection, which increases by more than 10% on the F1 Score, and tracks more long-term trajectories, showing stability.
Controlling the Mechanical Response of Stochastic Lattice Structures Utilizing a Design Model Based on Predefined Topologic and Geometric Routines
Authors: Krešić Inga , Kaljun Jasmin , Rašović Nebojša
Published: July 2024

The paper explores enhancing the mechanical behavior of stochastic lattice structures through a semi-controlled design approach. By leveraging the Gibson-Ashby model and predefined geometric routines, the study aims to optimize the mechanical response of lattice structures under compressive stress. Transitioning from stochastic to semi-controlled tessellation using Rhinoceros 7 software enables more predictable deformation behavior. Design parameters such as node formation, strut thickness, and lattice generation patterns are correlated with relative density to regulate stiffness and strength. Experimental validation using Acrylic Styrene Acrylonitrile (ASA) filament demonstrates the effectiveness of the proposed design model. The research emphasizes the importance of understanding internal mechanics by introducing a novel design approach to control geometry and topology arrangement in shaping lattice properties. By introducing a semi-controlled mechanism, the study seeks to improve the reliability and uniformity of mechanical responses in lattice structures. The findings highlighted the benefits of semi-controlled design approaches in achieving tailored mechanical properties. Specimens were compression tested in quasi-static uniaxial loading and showed that structures created with parabolic distribution dimensioned by hp=0.5hv originated the most reliable and most vital mechanical response compared with other design models, including typical Voronoi distribution. The improved mechanical response in between proposed design models constantly progressed by about 15% on average consecutively, starting from the parabolic distribution dimensioned by hp=1.0hv as the weakest ranked, up to the best one, dimensioned by hp=0.5hv, even better than the typical Voronoi distribution. The proposed design model has introduced an entirely novel approach that significantly enhances the product’s volume tessellation using routines that guarantee the validity of geometric and topologic entities. Uniaxial compression tests on lattice blocks highlighted the effect of the proposed approach on the mechanical properties of these structures, having shown particularly crucial repeatability and stability. Overall, the paper contributes to advancing the field of lightweight lattice structures through the novel design methodology and material characterization.
Experimental Performance Evaluation of a Thermoacoustic Stirling Engine with a Low-Cost Arduino-Based Acquisition System
Authors: Iniesta Carmen , Vinolas Jordi , Prieto Federico , Olazagoitia Jose Luis , Soliverdi Luis
Published: July 2024

Existing thermoacoustic dynamic measurement instrumentation is typically expensive and relies on proprietary designs, sensors, and acquisition equipment. However, this study presents a low-cost acquisition solution for sampling pressure and calculating instantaneous power measurements from a thermoacoustic engine using cost-effective sensors and control boards based on the Arduino platform. While the platform was designed for thermoacoustic engines with extra security for safe, high-speed data transfer, it could also suit other applications with similar needs. This study details the implementation of the sampling system and sensor arrangement, employing Python 3.10.2 algorithms based on the two-pressure sensor method to process and present the data. The sampling system is evaluated against a high-performance sound acquisition system. The accuracy of the low-cost system is found to be satisfactory for expected thermoacoustic experimental conditions in terms of pressure resolution and accuracy, with no data loss. These results allow for significantly increased accuracy at low cost in determining the maximum power extracted from any thermoacoustic device.
Digital Image Processing: Advanced Technologies and Applications
Authors: Mahmood Zahid
Published: July 2024

A few decades ago, conventional image processing methods mostly focused on basic tasks such as image enhancement, registration, or edge detection [...]
Zircon U-Pb Age, Sr-Nd-Hf Isotopic Characteristics of Baiyinhushuo Adakite in Inner Mongolia: Implications for Tectonic Evolution
Authors: Fan Yuxu , Xiao Qinghui , Cheng Yang , Li Yan
Published: July 2024

The petrogenesis and geodynamic implications of the Early Paleozoic adakitic rocks in the east of Inner Mongolia remain topics of debate. This study presents new petrology data through zircon U-Pb age and Lu-Hf isotopic composition, whole-rock major-trace element geochemistry, and Sr-Nd isotopes from adakitic rocks. The zircon U-Pb dating results demonstrate that the formation age is 242.8 ± 1.0 Ma, which is the product of Early Triassic magmatism. Petrology and geochemical study have shown that the granodiorite have high SiO2 (66.93~69.40%), Al2O3 (15.37~15.43%), and MgO (1.35~1.55%), with LREE enrichment and HREE deficit, and they have high Sr, low Y, and high Sr/Y ratios, showing typical signatures of adakitic rocks. The εHf(t) values of zircon vary between +11.3 and +13.8, with low whole-rock (87Sr/86Sr)i (0.703382) and positive εNd(t) values, and the average Mg# of the rock is 56.14, suggesting that adakite derived from partial melting of MORB materials and magma interaction with the mantle. Comprehensive analysis suggests that during the Late Permian to Early Triassic, the subducted slab of the Paleo-Asian Ocean broke off, and the residual oceanic slab preserved in the mantle beneath the subduction zone underwent partial melting, generating adakitic magma.
Non-Invasive Continuous Blood Pressure Estimation from Single-Channel PPG Based on a Temporal Convolutional Network Integrated with an Attention Mechanism
Authors: Dai Dong , Ji Zhaohui , Wang Haiyan
Published: July 2024

Traditional cuff-based blood pressure measurement methods suffer from issues such as intermittency and applicability, while cuff-less continuous blood pressure estimation techniques are increasingly gaining attention due to their non-invasive and continuous monitoring advantages. In this paper, aiming at the challenges faced by existing cuff-less continuous blood pressure estimation models in terms of accuracy, data requirements, and generalization ability, a series of innovative approaches are proposed. Deep learning techniques are introduced to design an end-to-end blood pressure estimation model with high accuracy, ease of training, and strong generalization ability. To address the insufficient accuracy of traditional neural networks in cuff-less continuous blood pressure estimation, we propose an end-to-end, beat-to-beat blood pressure estimation model that combines the temporal convolutional network (TCN) and convolutional block attention module (CBAM). By enhancing the model’s ability to process time series data and focus on key features of photoplethysmography (PPG), the blood pressure estimation accuracy during the resting state is significantly improved. The absolute mean error and standard deviation of systolic blood pressure (SBP) estimation using the algorithm in this chapter on the University of California, Irvine (UCI) physiological signal dataset are 5.3482 mmHg and 8.3410 mmHg, respectively, which are superior to other deep learning models based on convolutional neural network and recurrent neural network architectures.
IR and Raman Dual Modality Markers Differentiate among Three bis-Phenols: BPA, BPS, and BPF
Authors: Chao Kuanglin , Schmidt Walter , Qin Jianwei , Kim Moon , Tao Feifei
Published: July 2024

bis-Phenol A (BPA), bis-Phenol S (BPS), and bis-Phenol F (BPF) are important polymer industry plasticizers. Regulatory measures have restricted the use of BPA in plastic formulations, especially for those which come in contact with food products. Rapid, accurate spectroscopic measurements are required for distinguishing which of the three are present. The bis-phenol groups are structurally identical. The second set of bis-groups (CH3-C-CH3, O=S=O, and H-C-H, respectively) are discretely different chemically, but vibrational modes corresponding to these groups are not unique identifiers, routinely overlapping with wavenumbers present in other members of the set. The dual modality method identifies the specific wavenumbers in which the Infrared (IR) signal is near zero and the Raman relative intensity is maximum, and those in which the Raman signal is minimum and the IR signal is maximum. The normalized intensity ratio between IR and Raman enhances the signal [BPA 10.6 (1508 cm−1); BPS 7.4 (751 cm−1); BPF 5.1 (1100 cm−1)]. The ratio between Raman and IR in BPF is also enhanced: 6.3 (845 cm−1). Discerning which specific wavenumbers are most enhanced is experimentally feasible, though not necessarily at present theoretically predictable. This study demonstrates that IR and Raman spectra are not just complimentary, but together they are confirmatory even when the normalized intensity ratios of corresponding wavenumbers are most different.
Model-Based Design and Testbed for CubeSat Attitude Determination and Control System with Magnetic Actuation
Authors: Ticona Coaquira Franklin Josue , Wang Xinsheng , Vidaurre Torrez Karen Wendy , Mamani Quiroga Misael Jhamel , Silva Plata Miguel Angel , Luna Verdueta Grace Abigail , Murillo Quispe Sandro Estiven , Auza Banegas Guillermo Javier , Antezana Lopez Franz Pablo , Rojas Arturo
Published: July 2024

This study introduces a robust model-based framework designed for the verification and validation (V&V) of Attitude Determination and Control Systems (ADCSs) in nanosatellites, focusing on magnetic actuation while still being applicable to larger spacecraft platforms. By employing Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and Hardware-in-the-Loop (HIL) methodologies, this framework enables a thorough and systematic approach to testing and validation. The framework facilitates the assessment of long-term maneuvers, addressing challenges such as initial small-attitude errors and restricted 3D movements. Two specific maneuvers are evaluated: detumbling and nadir pointing, utilizing quaternions and a comprehensive suite of sensors, including six sun sensors, a three-axis magnetometer, a three-axis gyroscope, GPS, and three magnetorquers. The methodologies—MIL, SIL, PIL, and HIL—integrate the behaviors of digital sensors, analog signals, and astrodynamic perturbations. Based on an optimized SIL environment, Monte Carlo simulations were performed to optimize control gains for nadir pointing, achieving a mean pointing accuracy of 11.69° (MIL) and 18.22° (PIL), and an angular velocity norm of 0.0022 rad/s for detumbling. The HIL environment demonstrated a mean pointing accuracy of 9.96° and an angular velocity norm of 0.0024 rad/s. This comprehensive framework significantly advances the design and verification processes for nanosatellite ADCSs, enhancing the reliability and performance of nanosatellite missions.
A Turning Brake System for Motorcycles via an Autoregulative Optimal Slip Ratio
Authors: Zhang Xiao-Dong , Chen Chih-Keng , Ma Yu-Jie
Published: July 2024

Motorcycles are efficient and flexible tools for short-trip transportation, but they feature static instability and lean while cornering. This characteristic increases the danger of overturning. This study proposes a system to brake a motorcycle safely in a turn. The optimal slip ratio decision model is used to generate the optimal value according to roll angle and vertical force. Given that the roll angle cannot be measured directly, a Kalman filter is used to estimate the roll angle via kinematic parameters, measured by inertial measurement unit. The PID controller adjusts the current slip ratio to follow the optimal slip ratio. Using the motorcycle dynamics model from BikeSim, a co-simulation platform is constructed in MATLAB/Simulink to verify the reliability of the designed brake system. The results show that, compared with a traditional brake controller, the proposed brake system can control the motorcycle braking process by autoregulating the optimal slip ratio in time, according to the kinematic parameters. Both brake performance and stability are well considered, which contributes to improving the safety of the motorcycle. This research work has certain reference value for the development of motorcycle active safety systems.
Enhanced Photoacoustic Spectroscopy Integrated with a Multi-Pass Cell for ppb Level Measurement of Methane
Authors: Wang Lihui , Zhang Tie , Huang Yan , Zheng Yuhao , Wang Gaoxuan , He Sailing
Published: July 2024

A compact photoacoustic spectroscopy system integrated with a non-coaxial multi-pass cell was developed for improving the instrument performance in the measurement of methane. The multi-pass cell with compact light spot mode was proposed for concentrating the light radiation within a limited space, which effectively reduces the instrument dimension. A distributed feedback (DFB) laser with a central wavelength of 1653 nm was employed to excite the photoacoustic signal of methane. A total of 21 round trips of reflection were achieved in an acoustic resonant cavity with a radius of 4 mm and a length of 36 mm. Four microphones were installed around the cavity to collect the signal. An 11-fold enhancement of the photoacoustic signal was achieved through the multi-pass cell, compared to a single-pass cell with dimension of 10 cm. The system was used to measure different concentrations of methane, which showed good linearity. The continuous detection of 10 ppm methane gas was carried out for 6000 s. The Allan standard deviation analysis indicates that the limit of detection of the system was 5.7 ppb with an optimum integration time of 300 s.
Right Quantum Calculus on Finite Intervals with Respect to Another Function and Quantum Hermite–Hadamard Inequalities
Authors: Cuntavepanit Asawathep , Ntouyas Sotiris K. , Tariboon Jessada
Published: July 2024

In this paper, we study right quantum calculus on finite intervals with respect to another function. We present new definitions on the right quantum derivative and right quantum integral of a function with respect to another function and study their basic properties. The new definitions generalize the previous existing results in the literature. We provide applications of the newly defined quantum calculus by obtaining new Hermite–Hadamard-type inequalities for convex, h-convex, and modified h-convex functions.
Integral Equations: New Solutions via Generalized Best Proximity Methods
Authors: Albargi Amer Hassan , Ahmad Jamshaid
Published: July 2024

This paper introduces the concept of proximal (α,F)-contractions in F-metric spaces. We establish novel results concerning the existence and uniqueness of best proximity points for such mappings. The validity of our findings is corroborated through a non-trivial example. Furthermore, we demonstrate the applicability of these results by proving the existence of solutions for Volterra integral equations related to population growth models. This approach not only extends best proximity theory, but also paves the way for further research in applied mathematics and beyond.
Pole Analysis of the Inter-Replica Correlation Function in a Two-Replica System as a Binary Mixture: Mean Overlap in the Cluster Glass Phase
Authors: Frusawa Hiroshi
Published: July 2024

To investigate the cluster glass phase of ultrasoft particles, we examine an annealed two-replica system endowed with an attractive inter-replica field similar to that of a binary symmetric electrolyte. Leveraging this analogy, we conduct pole analysis on the total correlation functions in the two-replica system where the inter-replica field will eventually be switched off. By synthesizing discussions grounded in the pole analysis with a hierarchical view of the free-energy landscape, we derive an analytical form of the mean overlap between two replicas within the mean field approximation of the Gaussian core model. This formula elucidates novel numerical findings observed in the cluster glass phase.
The Evolution of Three Schizothoracinae Species from Two Major River Systems in Northwest China Based on Otolith Morphology and Skeletal Structure
Authors: Wang Chengxin , Hu Linghui , Song Yong , Xie Haoyang , Yang Liting , Serekbol Gulden , Huo Bin , Chen Shengao
Published: July 2024

Schizothoracinae species are the largest group of Cypriniformes that readily adapt to the natural conditions of the Qinghai-Tibet Plateau. This group has habitat characteristics and distribution patterns centered on the Qinghai-Tibet Plateau. To study the evolution of three Schizothoracinae species in Northwest China, the evolutionary characteristics of these species were explored based on differences in otolith morphology and skeletal morphology. From 2020 to 2022, 138 samples (63 Aspiorhynchus laticeps, 35 Diptychus maculatus and 40 Schizothorax pseudaksaiensis) were collected from the Tarim River and Ili River, 6 basic morphological parameters of otoliths were measured and converted into 6 morphological factors and 7 morphological indices. A total of 77 Fourier transform coefficients of each otolith were selected The first three principal components accounted for 92.834% of the total variation in 13 otolith morphological indices of the three Schizothoracinae species, and the overall discrimination rate was 94.20%. According to the principal component analysis of 77 Fourier harmonic values of otoliths, the first 20 principal components explained 97.233% of the total variation, and the overall discrimination rate was 100%. The results of the cluster analysis directly reflected the relationships between related species. The differences in the bone morphology of the three Schizothoracinae species were particularly reflected in the number of whiskers, pharyngeal teeth and vertebrae, and there were also significant differences in the shapes of the sphenotic (SP), pterotic (PTE), preoperculum (PO), branchiostegal ray (BRA) and basibranchial (BB) bones. Their unique morphological and skeletal characteristics are closely related to geological changes and water system evolutionary trends. This study contributes to the understanding of species identification and the evolutionary status of plateau fishes, provides a reference for further evolutionary classification and for assessing the evolutionary mechanisms of plateau fishes, and provides a scientific basis for phylogeny and germplasm resource protection.
Experimental Insights on the Use of Secukinumab and Magnolol in Acute Respiratory Diseases in Mice
Authors: Vicovan Andrei Gheorghe , Petrescu Diana Cezarina , Constantinescu Daniela , Iftimi Elena , Cernescu Irina Teodora , Ancuta Codrina Mihaela , Caratașu Cezar-Cătălin , Șorodoc Laurențiu , Ceasovschih Alexandr , Solcan Carmen , Ghiciuc Cristina Mihaela
Published: July 2024

This study investigates the combined treatment of secukinumab (SECU) and magnolol (MAGN) in a mouse model of LPS-induced ALI overlapped with allergic pulmonary inflammation, aiming to better understand the mechanism behind this pathology and to assess the therapeutic potential of this novel approach in addressing the severity of ALI. The combined treatment reveals intricate immunomodulatory effects. Both treatments inhibit IL-17 and promote M2 macrophage polarization, which enhances anti-inflammatory cytokine production such as IL-4, IL-5, IL-10, and IL-13, crucial for lung repair and inflammation resolution. However, the combination treatment exacerbates allergic responses and increases OVA-specific IgE, potentially worsening ALI outcomes. MAGN pretreatment alone demonstrates higher potency in reducing neutrophils and enhancing IFN-γ, suggesting its potential in mitigating severe asthma symptoms and modulating immune responses. The study highlights the need for careful consideration in therapeutic applications due to the combination treatment’s inability to reduce IL-6 and its potential to exacerbate allergic inflammation. Elevated IL-6 levels correlate with worsened oxygenation and increased mortality in ALI patients, underscoring its critical role in disease severity. These findings offer valuable insights for the advancement of precision medicine within the realm of respiratory illnesses, emphasizing the importance of tailored therapeutic strategies.
Strategies for Increasing the Throughput of Genetic Screening: Lessons Learned from the COVID-19 Pandemic within a University Community
Authors: Miguel Fernanda , Baleizão A. Raquel , Gomes A. Gabriela , Caria Helena , Serralha Fátima N. , Justino Marta C.
Published: July 2024

Amidst the COVID-19 pandemic, the Polytechnic University of Setúbal (IPS) used its expertise in molecular genetics to establish a COVID-19 laboratory, addressing the demand for community-wide testing. Following standard protocols, the IPS COVID Lab received national accreditation in October 2020 and was registered in February 2021. With the emergence of new SARS-CoV-2 variants and safety concerns for students and staff, the lab was further challenged to develop rapid and sensitive diagnostic technologies. Methodologies such as sample-pooling extraction and multiplex protocols were developed to enhance testing efficiency without compromising accuracy. Through Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) analysis, the effectiveness of sample pooling was validated, proving to be a clear success in COVID-19 screening. Regarding multiplex analysis, the IPS COVID Lab developed an in-house protocol, achieving a sensitivity comparable to that of standard methods while reducing operational time and reagent consumption. This approach, requiring only two wells of a PCR plate (instead of three for samples), presents a more efficient alternative for future testing scenarios, increasing its throughput and testing capacity while upholding accuracy standards. The lessons learned during the SARS-CoV-2 pandemic provide added value for future pandemic situations.
The Adaptation of the Communicative Effectiveness Index (CETI) into Greek: A Reliability and Validity Study
Authors: Charalambous Marina , Phylactou Phivos , Antoniou Eleftheria , Christodoulou Maria , Kambanaros Maria
Published: July 2024

The Communicative Effectiveness Index (CETI) is an informant rating scale designed to measure changes in functional communication in people with aphasia (PWA) from the carer’s perspective. It offers a comprehensive view of aphasia’s impact on everyday communication situations, aiding clinicians in designing personalized intervention plans. The aim of this study was to translate and adapt the CETI into Greek (CETI-GR) and validate its psychometric properties. The CETI-GR was translated into Greek using back-translation. A pilot and a content validity study ensured its acceptability. The study involved 30 people with aphasia and 30 carers. The CETI-GR’s psychometric properties were evaluated, including internal consistency, test–retest reliability, inter-rater reliability, and validity measures. The CETI-GR demonstrated excellent internal consistency (Cronbach’s α > 0.95) and excellent inter-rater reliability (ICC ≥ 0.93). Excellent consistency was revealed when testing the CETI responses given only by PWA (α = 0.91) versus their carers (α = 0.97). Test–retest reliability was high (ICC = 0.88). Significant correlations between the CETI-GR and measures of language severity, functional communication, and quality of life supported convergent validity. The CETI-GR is a reliable tool for assessing functional communication in chronic aphasia. Its Greek adaptation enhances aphasia rehabilitation, enabling person-centered care and improving the quality of life for people with aphasia and carers.
The Impact of the Cost of Travel Time and Feedback Type on Green Travel
Authors: Zhong Bowei , Tan Min , Zhong Wen , Fan Wei
Published: July 2024

Green travel is a special type of pro-environmental behavior, which requires people to pay a time cost to reduce carbon emissions. This study explored the impact of the cost of travel time and feedback types on green travel. To verify the change of travel choice under different time costs, experiment 1 explored the impact of different costs of travel time on green travel. The results showed that with the increase in time cost, green travel behavior gradually decreased. This suggests that time costs can hinder green travel behavior. To intervene in this negative effect, experiment 2 explored the effects of different types of feedback intervention. The results showed that both environmental and health feedback could only intervene with green travel behavior when the time costs were low. This indicates that health and environmental feedback can intervene in the negative effects of travel time cost, but the cost range of the intervention is limited. This study has implications for promoting green travel behavior.