bluuebunny/crossref_metadata_2025 huggingface dataset:
Hyperspectral Classification of Frost Damage Stress in Tomato Plants Based on Few-Shot Learning
Authors: Ruan Shiwei , Cang Hao , Chen Huixin , Yan Tianying , Tan Fei , Zhang Yuan , Duan Long , Xing Peng , Guo Li , Gao Pan , Xu Wei
Published: September 2023

Early detection and diagnosis of crop anomalies is crucial for enhancing crop yield and quality. Recently, the combination of machine learning and deep learning with hyperspectral images has significantly improved the efficiency of crop detection. However, acquiring a large amount of properly annotated hyperspectral data on stressed crops requires extensive biochemical experiments and specialized knowledge. This limitation poses a challenge to the construction of large-scale datasets for crop stress analysis. Meta-learning is a learning approach that is capable of learning to learn and can achieve high detection accuracy with limited training samples. In this paper, we introduce meta-learning to hyperspectral imaging and crop detection for the first time. In addition, we gathered 88 hyperspectral images of drought-stressed tomato plants and 68 images of freeze-stressed tomato plants. The data related to drought serve as the source domain, while the data related to frost damage serve as the target domain. Due to the difficulty of obtaining target domain data from real-world testing scenarios, only a limited amount of target domain data and source domain data are used for model training. The results indicated that meta-learning, with a minimum of eight target domain samples, achieved a detection accuracy of 69.57%, precision of 59.29%, recall of 66.32% and F1-score of 62.61% for classifying the severity of frost stress, surpassing other methods with a target domain sample size of 20. Moreover, for determining whether the plants were under stress, meta-learning, with a minimum of four target domain samples, achieved a detection accuracy of 89.1%, precision of 89.72%, recall of 93.08% and F1-score of 91.37% outperforming other methods at a target domain sample size of 20. The results show that meta-learning methods require significantly less data across different domains compared to other methods. The performance of meta-learning techniques thoroughly demonstrates the feasibility of rapidly detecting crop stress without the need for collecting a large amount of target stress data. This research alleviates the data annotation pressure for researchers and provides a foundation for detection personnel to anticipate and prevent potential large-scale stress damage to crops.
Analysis of Clinical Phenotypes through Machine Learning of First-Line H. pylori Treatment in Europe during the Period 2013–2022: Data from the European Registry on H. pylori Management (Hp-EuReg)
Authors: Nyssen Olga P. , Pratesi Pietro , Spínola Miguel A. , Jonaitis Laimas , Pérez-Aísa Ángeles , Vaira Dino , Saracino Ilaria Maria , Pavoni Matteo , Fiorini Giulia , Tepes Bojan , Bordin Dmitry S. , Voynovan Irina , Lanas Ángel , Martínez-Domínguez Samuel J. , Alfaro Enrique , Bujanda Luis , Pabón-Carrasco Manuel , Hernández Luis , Gasbarrini Antonio , Kupcinskas Juozas , Lerang Frode , Smith Sinead M. , Gridnyev Oleksiy , Leja Mārcis , Rokkas Theodore , Marcos-Pinto Ricardo , Meštrović Antonio , Marlicz Wojciech , Milivojevic Vladimir , Simsek Halis , Kunovsky Lumir , Papp Veronika , Phull Perminder S. , Venerito Marino , Boyanova Lyudmila , Boltin Doron , Niv Yaron , Matysiak-Budnik Tamara , Doulberis Michael , Dobru Daniela , Lamy Vincent , Capelle Lisette G. , Nikolovska Trpchevska Emilija , Moreira Leticia , Cano-Català Anna , Parra Pablo , Mégraud Francis , O’Morain Colm , Ortega Guillermo J. , Gisbert Javier P. ,   on behalf of the Hp-EuReg Investigators
Published: September 2023

The segmentation of patients into homogeneous groups could help to improve eradication therapy effectiveness. Our aim was to determine the most important treatment strategies used in Europe, to evaluate first-line treatment effectiveness according to year and country. Data collection: All first-line empirical treatments registered at AEGREDCap in the European Registry on Helicobacter pylori management (Hp-EuReg) from June 2013 to November 2022. A Boruta method determined the “most important” variables related to treatment effectiveness. Data clustering was performed through multi-correspondence analysis of the resulting six most important variables for every year in the 2013–2022 period. Based on 35,852 patients, the average overall treatment effectiveness increased from 87% in 2013 to 93% in 2022. The lowest effectiveness (80%) was obtained in 2016 in cluster #3 encompassing Slovenia, Lithuania, Latvia, and Russia, treated with 7-day triple therapy with amoxicillin–clarithromycin (92% of cases). The highest effectiveness (95%) was achieved in 2022, mostly in Spain (81%), with the bismuth–quadruple therapy, including the single-capsule (64%) and the concomitant treatment with clarithromycin–amoxicillin–metronidazole/tinidazole (34%) with 10 (69%) and 14 (32%) days. Cluster analysis allowed for the identification of patients in homogeneous treatment groups assessing the effectiveness of different first-line treatments depending on therapy scheme, adherence, country, and prescription year.
Recovery of Staphylococci from Teatcups in Milking Parlours in Goat Herds in Greece: Prevalence, Identification, Biofilm Formation, Patterns of Antibiotic Susceptibility, Predictors for Isolation
Authors: Michael Charalambia K. , Lianou Daphne T. , Tsilipounidaki Katerina , Gougoulis Dimitris A. , Giannoulis Themistoklis , Vasileiou Natalia G. C. , Mavrogianni Vasia S. , Petinaki Efthymia , Fthenakis George C.
Published: September 2023

The objectives of this work are (a) to describe staphylococci on the teatcups of milking parlours in goat farms and identify predictors for the presence of staphylococcal isolates on the teatcups, (b) to evaluate relationships with total bacterial counts and somatic cell counts in bulk-tank milk, and (c) to establish patterns of susceptibility to antibiotics for the staphylococcal isolates and identify predictors for the recovery of resistant isolates. In a cross-sectional study of 66 goat farms across Greece, swab samples were collected from 303 teatcups (upper and lower part) for staphylococcal recovery, identification, and assessment of biofilm formation. Details regarding health management on the farms (including conditions in the milking parlour) and the socio-demographic characteristics of farmers were collected by means of a structured questionnaire. A total of 87 contaminated teatcups (28.7%) were found on 35 goat farms (53.0%). Staphylococci were more frequently recovered from the upper than the lower part of teatcups: 73 versus 43 teatcups, respectively. After identification, 67 staphylococcal isolates (i.e., excluding similar isolates) were recovered from the teatcups; Staphylococcus aureus, Staphylococcus capitis, and Staphylococcus equorum predominated. Of these isolates, 82.1% were biofilm-forming. In multivariable analysis, the annual incidence of clinical mastitis in the herd emerged as the only significant factor associated with the isolation of staphylococci from the teatcups. Of the 67 isolates, 23 (34.3%) were resistant to at least one antibiotic, and 14 (22.4%) were multi-resistant. Resistance was found most commonly against penicillin and ampicillin (22.4% of isolates), fosfomycin (17.9%), clindamycin (14.9%), erythromycin, and tetracycline (13.4%). In multivariable analysis, the annual incidence of clinical mastitis in the herd and the use of detergent for parlour cleaning emerged as significant factors associated with the isolation of staphylococci resistant to antibiotics.
Risk Factors and Pathogens of Wound Infection in Burn Inpatients from East China
Authors: Zhou Siqi , Xiao Shuzhen , Wang Xuedong , Wang Xuefeng , Han Lizhong
Published: September 2023

Background: Infection is the predominant contributor to morbidity and mortality in burn patients, and burn wound infection (BWI) is the most common reason. The objective of this research was to analyze the incidence, factors and progression of BWI, in terms of events and bacteria. Methods: Clinical variables of all qualified patients admitted to burn wards were analyzed retrospectively in 2021 at a tertiary hospital in eastern China through univariate analysis and multivariate logistic regression. The Kaplan–Meier method was also used for plotting survival curves. Isolates and resistance data were evaluated to demonstrate the evolution of targeted antibiotics of strains from BWI. Results: A total of 580 (median age, 39.5 years (23–56 years); 372/580 (64.14%) male) patients were evaluated, 348 (60.0%) of whom experienced BWI. A variety of factors are associated with BWI. Multivariate logistic regression analysis showed that depth and area of burn and duration from burn to first hospitalization are independent risk factors for BWI. For BWI onset in these patients, 47.24% (274/580) occurred in the first week. The most frequently isolated causative organism was Staphylococcus aureus (15.7%) in patients with BWI. The duration of transition from Gram-positive strains (median 3 days, (2–7 days)) to Gram-negative (median 10 days, (4–17 days)) ones isolated from burn wound shrunk. Hospital length of stay was considered as a protective factor for BWI. Conclusion: The precise assessment of factors affecting BWI in burn patients enhances prompt and suitable management. Swab cultures for surveillance could be utilized to monitor the microbiological status of burn patients.
In Vitro Impact of Fluconazole on Oral Microbial Communities, Bacterial Growth, and Biofilm Formation
Authors: Dornelas-Figueira Louise Morais , Ricomini Filho Antônio Pedro , Junges Roger , Åmdal Heidi Aarø , Cury Altair Antoninha Del Bel , Petersen Fernanda Cristina
Published: September 2023

Antifungal agents are widely used to specifically eliminate infections by fungal pathogens. However, the specificity of antifungal agents has been challenged by a few studies demonstrating antibacterial inhibitory effects against Mycobacteria and Streptomyces species. Here, we evaluated for the first time the potential effect of fluconazole, the most clinically used antifungal agent, on a human oral microbiota biofilm model. The results showed that biofilm viability on blood and mitis salivarius agar media was increased over time in the presence of fluconazole at clinically relevant concentrations, despite a reduction in biomass. Targeted PCR revealed a higher abundance of Veillonella atypica, Veillonella dispar, and Lactobacillus spp. in the fluconazole-treated samples compared to the control, while Fusobacterium nucleatum was reduced and Streptococcus spp were not significantly affected. Further, we tested the potential impact of fluconazole using single-species models. Our results, using Streptococcus mutans and Streptococcus mitis luciferase reporters, showed that S. mutans planktonic growth was not significantly affected by fluconazole, whereas for S. mitis, planktonic growth, but not biofilm viability, was inhibited at the highest concentration. Fluconazole’s effects on S. mitis biofilm biomass were concentration and time dependent. Exposure for 48 h to the highest concentration of fluconazole was associated with S. mitis biofilms with the most increased biomass. Potential growth inhibitory effects were further tested using four non-streptococcal species. Among these, the planktonic growth of both Escherichia coli and Granulicatella adiacens was inhibited by fluconazole. The data indicate bacterial responses to fluconazole that extend to a broader range of bacterial species than previously anticipated from the literature, with the potential to disturb biofilm communities.
Development and Verification of a Simulation Model of an Automated Assembly Line
Authors: Bendowska Karolina , Zawadzki Przemysław
Published: September 2023

Simulation models are integral to IT solutions built according to the Industry 4.0 concept. They are often the basis for developing digital twin solutions, which is why development environments are constantly developed and improved to meet the growing requirements of manufacturing enterprises. This work presents the development and verification of a simulation model of an automated assembly line located in the Smart Factory laboratory of the Poznań University of Technology. One of the criteria for designing the assembly process on this line is the time it takes for transport pallets to pass through individual stations. The work is a response to the need to quickly analyze the designed product assembly scenarios to choose the best one. The operation of the automated line with six assembly stations and allowing for the simultaneous transport of six pallets were considered. The main purpose of the work was to build a simulation model that would allow us to obtain assembly times close to real. The model was created using the Tecnomatix Plant Simulation 16.0 software, and verification work was carried out through a comparative analysis of the results of the simulation time of various assembly scenarios in relation to the actual operation of the line.
Proof of Optimum (PoO): Consensus Model Based on Fairness and Efficiency in Blockchain
Authors: Gündüz Fatih , Birogul Serdar , Kose Utku
Published: September 2023

Blockchain systems are popular technologies that have recently emerged. As a decentralized system, blockchain technology has provided many solutions and many problems associated with these solutions. One of its most important problems is that while performing hash calculations very intensively to create a new consensus block, it reduces its efficiency depending on the duration. In this study, a new model to avoid Proof of Work (PoW), which directs the computations made to create blocks to optimization algorithms, is proposed. The proof mechanism proposed in this study is called Proof of Optimization (PoO). A traveling salesman problem (TSP) is entered into the designed system to solve the optimization algorithms. Nodes are asked to solve the TSP in certain iterations and populations. As a result, nodes are asked to create blocks with the fitness, density and time values obtained. PoO and PoW consensus algorithms were subjected to an experimental comparison in the system. The test results show that the block generation time of the PoO consensus model varies between 2 s according to the dataset solution with the least cities (ulysses22) and 60 s according to the dataset solution with the most cities (gr666). Additionally, as a result of experimental analyses, it was determined that decentralization, which is the percentage of block creation among miners in the blockchain, reached a more stable value and the fairness index rose above 0.90 on average. When the obtained values were compared with PoW, it was observed that the block time was more stable and the decentralization of the blockchain was higher. In this way, high-equipped nodes in the blockchain system are prevented from dominating the network. Thus, it is ensured that low-equipped nodes have the right to create blocks in the blockchain. The difficulty levels of the problems can be adjusted by changing the number of cities in the TSP evaluated in this study. In this way, the problem of creating blocks in the network can be made more difficult or easier at any time.
Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
Authors: Liu Hongjie , Wu Keshu , Fu Sicheng , Shi Haotian , Xu Hongzhe
Published: September 2023

In the rapidly advancing domain of vehicular traffic management and autonomous driving, accurate lane change predictions are paramount for ensuring safety and optimizing traffic flow. This study introduces a comprehensive two-stage prediction model that harnesses the capabilities of long short-term memory (LSTM) for anticipating vehicular lane changes. Initially, we employed a variety of models, such as regression methods, SVMs, and a multilayer perceptron, to categorize lane change behaviors. The dataset was then segmented based on vehicle trajectories and lane change patterns. In the subsequent phase, we utilized the superior classification outcomes from LinearSVC to curate our training data. We developed two dedicated LSTM networks tailored to specific datasets: the lane-keeping LSTM (LK-LSTM) and the lane-changing LSTM (LC-LSTM). By integrating insights from both models, we achieved a comprehensive prediction of vehicular lane changes. Our results indicate that the unified prediction model markedly enhances prediction precision. Accurate lane change predictions offer valuable contributions to advanced driver-assistance systems (ADAS), with the potential to minimize traffic mishaps and enhance traffic fluidity. As we transition to a more autonomous automotive era, refining these predictions becomes essential in seamlessly merging human and automated driving experiences.
Tuning of Two Sub-Mounts in Mass-Block Integrated Mount Module over Self-Excitation and Basement Input
Authors: Kim Changwon , Kim Chan-Jung
Published: September 2023

If an unexpected vibration or force is expected to be generated during operation, a vibration mount must be installed beneath the support system. A mass-block can be considered to reduce the structure basement fundamental frequency of the basement. Thus, the efficiency of a single-mount or mass-block can be enhanced by implementing a single-mount module called a mass-block integrated mount module (MIMM). This study addressed the tuning process of multiple mount modules in a supported system by evaluating the mount module using the proposed total performance index, building on a previous evaluation method comprising the first and second performance indices. This index was subsequently applied to assess the control capability of MIMM concerning both the self-excitation of an electric power plant and basement input. The extended 3-degrees-of-freedom (3-d.o.f.) electric power generator model was applied to determine the best selection of the mechanical properties for the MIMM over the various mechanical parameter settings (ranging between 10 and 1000% of the original parameters) of the two sub-mounts. The simulation results demonstrate a reduction in the total performance index from 8.2, as calculated in a previous study, to 0.2. The novelty of the optimal MIMM conditions is confirmed by the simultaneous enhancement of both performance indices.
Fully Automatic Thoracic Cavity Segmentation in Dynamic Contrast Enhanced Breast MRI Using Deep Convolutional Neural Networks
Authors: Berchiolli Marco , Wolfram Susann , Balachandran Wamadeva , Gan Tat-Hean
Published: September 2023

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is regarded as one of the main diagnostic tools for breast cancer. Several methodologies have been developed to automatically localize suspected malignant breast lesions. Changes in tissue appearance in response to the injection of the contrast agent (CA) are indicative of the presence of malignant breast lesions. However, these changes are extremely similar to the ones of internal organs, such as the heart. Thus, the task of chest cavity segmentation is necessary for the development of lesion detection. In this work, a data-efficient approach is proposed, to automatically segment breast MRI data. Specifically, a study on several UNet-like architectures (Dynamic UNet) based on ResNet is presented. Experiments quantify the impact of several additions to baseline models of varying depth, such as self-attention and the presence of a bottlenecked connection. The proposed methodology is demonstrated to outperform the current state of the art both in terms of data efficiency and in terms of similarity index when compared to manually segmented data.
Joint Location–Allocation Model for Multi-Level Maintenance Service Network in Agriculture
Authors: Li Jinliang , Ren Weibo , Wang Xibin
Published: September 2023

The maintenance service network is always designed as a multi-level service network to provide timely maintenance service for failed machinery, and is rarely studied in agriculture. Thus, this paper focuses on a three-level maintenance service network location–allocation problem in agriculture, which contains several spare part centres, service stations, and service units. This research aims to obtain the optimal location of spare part centres and service stations while determining service vehicle allocation results for service stations, and the problem can be called a multi-level facility location and allocation problem (MLFLAP). Considering contiguity constraints and hierarchical relationships, the proposed MLFLAP is formulated as a mixed-integer linear programming (MILP) model integrating with P-region and set covering location problems to minimize total service costs, including spare part centre construction costs, service vehicle usage costs, and service mileage costs of service stations. The Benders decomposition-based solution method with several improvements is then applied to decompose the original MLFLAP into master problem and subproblems to find the optimal solutions effectively. Finally, a real-world case in China is proposed to evaluate the performance of the model and algorithm in agriculture, and sensitivity analysis is also conducted to demonstrate the impact of several parameters.
Attention-Guided Network Model for Image-Based Emotion Recognition
Authors: Arabian Herag , Battistel Alberto , Chase J. Geoffrey , Moeller Knut
Published: September 2023

Neural networks are increasingly able to outperform traditional machine learning and filtering approaches in classification tasks. However, with the rise in their popularity, many unknowns still exist when it comes to the internal learning processes of the networks in terms of how they make the right decisions for prediction. As a result, in this work, different attention modules integrated into a convolutional neural network coupled with an attention-guided strategy were examined for facial emotion recognition performance. A custom attention block, AGFER, was developed and evaluated against two other well-known modules of squeeze–excitation and convolution block attention modules and compared with the base model architecture. All models were trained and validated using a subset from the OULU-CASIA database. Afterward, cross-database testing was performed using the FACES dataset to assess the generalization capability of the trained models. The results showed that the proposed attention module with the guidance strategy showed better performance than the base architecture while maintaining similar results versus other popular attention modules. The developed AGFER attention-integrated model focused on relevant features for facial emotion recognition, highlighting the efficacy of guiding the model during the integral training process.
The Effects of the Variable-Pressure Rolling of a Wire Arc Additively Manufactured Inconel625-SS308L Bimetallic Structure
Authors: Hwang Ye-Han , Lee Choon-Man , Kim Dong-Hyeon
Published: September 2023

The superior deposition rates of Wire Arc Additive Manufacturing (WAAM) allow for the production of a substantial number of parts while reducing the time and cost associated with machining, and have attracted significant attention. This study focused the microstructural and mechanical properties of a bimetallic thin-wall composed of SS308L and Inconel625, manufactured using variable-pressure rolling WAAM. A detailed analysis of the cross-sectional microstructure of the bi-metallic wall of non-pressure cases revealed a columnar solidification structure. In contrast, the pressured case was dominated by equiaxed structures. The microhardness tests conducted on the bimetallic interface revealed a hardness range of 139–188 HV within the SS308L region, and a 187–222 HV hardness spectrum within the Inconel625 region. The bimetallic interface displayed no unexpected hardness variations. Tensile tests carried out in the vertical direction across the bi-metallic interface resulted in a localized failure at the 308L region. The range of ultimate tensile strength was between 539 and 585 MPa, the yield strength varied from 344 to 377 MPa, and the elongation measurements were found to be within the range of 37 to 44%. All test specimens showed a consistent pattern of ductile rupture upon fractographic inspection, with fracture occurring in the SS308L region.
CFD Assessment of Car Park Ventilation System in Case of Fire Event
Authors: Rahif Ramin , Attia Shady
Published: September 2023

This scientific article presents the results of computational fluid dynamics (CFD) simulations conducted using OpenFOAM to evaluate the effectiveness of a jet fan ventilation system in managing the dispersion of smoke resulting from a car fire incident within an underground car park spanning a total area of 21,670 m2, situated in Tabriz, Iran. The primary objective of the study is to determine the velocity fields and evaluate visibility conditions within a 10 m radius to gauge the efficiency and effectiveness of the system. The study employs a smoke concentration production rate of 5.49 × 10−4 kg/m3s for simulations involving fire scenarios. A total of 17 fire scenarios are examined, each extending 30 m in all directions from the initial location. The research findings demonstrate that the placement of jet fan components plays a significant role in the system’s efficiency, with fans positioned near the ceiling leading to back-layering. To mitigate this issue, the recommended design solution involves the strategic installation of multiple jet fan arrays in specific zones with the addition of 10 extra jet fans, effectively curbing lateral smoke dispersion. Furthermore, the analysis of air flow rates shows that when jet fans direct an excessive airflow towards the exhaust shafts (which have a designated flow rate of 22.5 m3/s), recirculating flows occur, leading to the dispersion of smoke throughout the car park. Consequently, the utilization of low-velocity jet fans (11.2 m/s) proves to be more effective in clearing smoke compared to high-velocity jet fans (22.3 m/s). The study also emphasizes the importance of optimal positioning of supply and exhaust shafts to achieve effective smoke control, highlighting the need for placing them on opposite walls or minimizing airflow turns. Additionally, the research underscores the significance of fire resistance in jet fan units, as their failure during fire incidents can have severe consequences.
A Multi-Story Expandable Systematic Hierarchical Construction Information Classification System for Implementing Information Processing in Highway Construction
Authors: Chung Taewon , Bok Jin Hoon , Ji Hyon Wook
Published: September 2023

In the field of infrastructure construction, progress in digital transformation remains limited; this is particularly true in road construction, an infrastructure facility involving design, construction, and operation stages. Many construction subjects are involved at each stage of this cycle, generating substantial construction information. To drive the digital transformation of the construction industry, a construction information classification system is necessary for the development of a systematic construction information model. This study focuses on categorizing construction information into objects and activities, defining unit work by combining these two categories, and allowing for the flexible processing of construction information. A construction information classification system was developed, representing the evolving construction-related methodological information throughout the project’s lifecycle. Applying this multi-story expandable systematic hierarchical system to a highway project demonstrates the representation of key tasks in each construction phase, enabling the future stage-specific expansion of construction information for individual tasks. The proposed system could advance the digital transformation in infrastructure construction.
Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
Authors: Meng Qingsheng , Chen Yuhong , Sha Fei , Liu Tao
Published: September 2023

The inversion of the Rayleigh wave dispersion curve is a crucial step in obtaining the shear wave velocity (VS) of near-surface structures. Due to the characteristics of being ill-posed and nonlinear, the existing inversion methods presented low efficiency and ambiguity. To address these challenges, we describe a six-layer deep neural network algorithm for the inversion of 1D VS from dispersion curves of the fundamental mode Rayleigh surface waves. Our method encompasses several key advancements: (1) we use a finer layer to construct the 1-D VS model of the subsurface, which can describe a more complex near-surface geology structure; (2) considering the ergodicity and orderliness of strata evolution, the constrained Markov Chain was employed to reconstruct the complex velocity model; (3) we build a practical and complete dispersion curve inversion process. Our model tested the performance using a random synthetic dataset and the influence of different factors, including the number of training samples, learning rate, and the selection of optimal artificial neural network architecture. Finally, the field test dispersion data were used to further verify the method’s effectiveness. Our synthetic dataset proved the diversity and rationality of the random VS model. The results of training and predicting showed higher accuracy and could speed the inversion process (only ~15 s), and we proved the important effect of different factors. The outcomes derived from the application of this technique to the measured dispersion data in the Yellow River Delta exhibit a strong correlation with the outcomes obtained from the integration of the very fast simulated annealing method and the downhill simplex method, as well as the statistically derived shear wave velocity data of the sedimentary layers in the Yellow River Delta. From a long-term perspective, our method can provide an alternative for deriving VS models for complex near-surface structures.
Research on Variable-Swept Hybrid Aerial Underwater Vehicle Plunge-Diving Control Based on Adaptive Dynamic Surface Control
Authors: Xing Beibei , Wei Zhaoyu , Lu Mingqing , Liao Fei
Published: September 2023

The variation in aerodynamic parameters during the process of a variable sweepback hybrid aerial underwater vehicle (HAUV) affects flight stability. During the air–water trans-media locomotion, there are medium mutations and solid–liquid gas coupling phenomena, resulting in the complex dynamic process of HAUV. To ensure stable control during the trans-media process of a variable sweepback vehicle, this study proposes a neural-network-based adaptive dynamic surface control method for aircraft flight-path angle. This method aims to establish an effective control model for the entire process of air to media transition, in response to the characteristics of uncertainty and external disturbances in the process of variable backsweeping in the air and media transition. By utilizing the multibody dynamics method, the dynamic equations for variable-swept vehicles are established and transformed into a rigorous feedback system with model uncertainty. The adaptive dynamic surface method in this paper introduces a first-order filter, which overcomes the “differential explosion” problem in traditional backstepping control design through differential filtering; the unknown parameters present in the model are estimated online through adaptive laws, and the uncertain parts of the system are overcome through nonlinear damping items. By analyzing Lyapunov stability, the semi-global stability of the required closed-loop system can be obtained, and adjusting the controller parameters can make the tracking error infinitely small. Numerical simulations are conducted to illustrate the tracking control of flight-path angles for different plunge-diving angle rates and strategy of ingress. The results show that HAUV with variable-swept configuration with different strategy has a great effect on the stability of plunge-diving locomotion; the designed controller can effectively track the target trajectory and has a certain degree of robustness and adaptability.
Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes
Authors: Zhang Jun , Zhang Rongxi , Shu Xinming , Yu Lulu , Xu Xuanning
Published: September 2023

The identification of trolley codes poses a challenge in engineering, as there are often situations where the accuracy requirements for their detection cannot be met. YOLOv7, being the state-of-the-art target detection method, demonstrates significant efficacy in addressing the challenge of trolley coding recognition. Due to the substantial dimensions of the model and the presence of numerous redundant parameters, the deployment of small terminals in practical applications is constrained. This paper presents a real-time approach for identifying trolley codes using a YOLOv7 deep learning algorithm that incorporates channel pruning. Initially, a YOLOv7 model is constructed, followed by the application of a channel pruning algorithm to streamline its complexity. Subsequently, the model undergoes fine-tuning to optimize its performance in terms of both speed and accuracy. The experimental findings demonstrated that the proposed model exhibited a reduction of 32.92% in the number of parameters compared to the pre-pruned model. Additionally, it was observed that the proposed model was 24.82 MB smaller in size. Despite these reductions, the mean average precision (mAP) of the proposed model was only 0.03% lower, reaching an impressive value of 99.24%. We conducted a comparative analysis of the proposed method against five deep learning algorithms, namely YOLOv5x, YOLOv4, YOLOv5m, YOLOv5s, and YOLOv5n, in order to assess its effectiveness. In contrast, the proposed method considers the speed of detection while simultaneously ensuring a high mean average precision (mAP) value in the detection of trolley codes. The obtained results provide confirmation that the suggested approach is viable for the real-time detection of trolley codes.
Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning
Authors: Akbulut Uğur , Cifci Mehmet Akif , Aslan Zafer
Published: September 2023

In the face of mounting global challenges stemming from population growth and climate fluctuations, the sustainable management of water resources emerges as a paramount concern. This scientific endeavor casts its gaze upon the Upper Euphrates basin, homing in on the Tunceli Munzur water sub-basin and the Sakarya Basin’s Kütahya Porsuk Stream Beşdeğirmen rivers. The investigation unfolds through the intricate analysis of daily average flow data, total daily precipitation, and daily average air temperature values, with the objective of unraveling the complexities of future water potential estimation. Central to our exploration are a series of well-established techniques including linear regression (LR), support vector regression (SVR), decision tree (DT), random forest (RF), and extra trees regression (ETR). We employ these methodologies diligently to decipher patterns woven within the dataset, fostering an informed understanding of water dynamics. To ascend the pinnacle of estimation accuracy, we introduce a groundbreaking hybrid approach, wherein the enigmatic wavelet transform (WT) technique assumes a pivotal role. Through systematic stratification of our dataset into training, validation, and test sets, comprising roughly 65%, 15%, and 20% of the data, respectively, a comprehensive experiment takes shape. Our results unveil the formidable performance of the ETR method, achieving a striking 88% estimation accuracy for the Porsuk Stream Beşdeğirmen, while the RF method garners a commendable 85.2% success rate for the Munzur water Melekbahçe. The apex of innovation unfolds within our hybrid model, a harmonious fusion of methodologies that transcends their individual capacities. This composite entity elevates estimation success rates by a remarkable 20% for the Munzur water Melekbahçe and an appreciable 11% for the Porsuk Stream Beşdeğirmen. This amalgamation culminates in an extraordinary overall success rate of 97.7%. Our findings transcend mere insights, resonating as guiding beacons for navigating the intricate maze of water resource management in an era marked by uncertainties. This study underscores the indispensability of advanced mathematical paradigms and machine learning frontiers, fortifying the bedrock of sustainable water resource management for the generations to come. By harnessing the fusion of federated learning and a constellation of innovative techniques, we endeavor to illuminate the path towards deciphering the complex tapestry of water resource estimation and management, facilitating a resilient and enduring aquatic world.
Utilizing Empirical Bayes Estimation to Assess Reliability in Inverted Exponentiated Rayleigh Distribution with Progressive Hybrid Censored Medical Data
Authors: Hashem Atef F. , Alyami Salem A. , Yousef Manal M.
Published: September 2023

This study addresses the issue of estimating the shape parameter of the inverted exponentiated Rayleigh distribution, along with the assessment of reliability and failure rate, by utilizing Type-I progressive hybrid censored data. The study explores the estimators based on maximum likelihood, Bayes, and empirical Bayes methodologies. Additionally, the study focuses on the development of Bayes and empirical Bayes estimators with balanced loss functions. A concrete example based on actual data from the field of medicine is used to illustrate the theoretical insights provided in this study. Monte Carlo simulations are employed to conduct numerical comparisons and evaluate the performance and accuracy of the estimation methods.
Built-Up Fiber-Reinforced Polymers (FRP) Profiles with Improved Shear Performance for FRP–Concrete Hybrid Section
Authors: Zhang Qifeng , Xu Zuohong , Hu Yuqing
Published: September 2023

Fiber-reinforced polymer (FRP)–concrete hybrid sections, composed of FRP profiles and a concrete slab, have gathered attention in construction due to their lightweight, easy installation, and high durability. However, the low shear strength and brittle behavior of commercially available pultruded FRP profiles often leads to brittle shear failure at low load levels. To enhance the shear strength and ductility, this study proposes a novel H-shaped FRP profile that is built from two U-shaped pultruded FRP profiles and a hand lay-up sandwiched core of multi-directional fibers. Direct shear tests showed that the built-up FRP profiles failed in pseudo-ductile mode while the U-shaped pultruded FRP profiles failed in brittle mode. Built-up FRP had 1.5 times the capacity and 2.8 times the ultimate redundancy compared to pultruded FRP. Additionally, flexural tests of FRP–concrete hybrid beams revealed that the webs of the built-up FRP profiles failed in a higher shear capacity with smeared cracks.
Optimizing Treatment for Relapsed/Refractory Classic Hodgkin Lymphoma in the Era of Immunotherapy
Authors: Randall Michael P. , Spinner Michael A.
Published: September 2023

Most patients with classic Hodgkin lymphoma (cHL) are cured with combination chemotherapy, but approximately 10–20% will relapse, and another 5–10% will have primary refractory disease. The treatment landscape of relapsed/refractory (R/R) cHL has evolved significantly over the past decade following the approval of brentuximab vedotin (BV), an anti-CD30 antibody-drug conjugate, and the PD-1 inhibitors nivolumab and pembrolizumab. These agents have significantly expanded options for salvage therapy prior to autologous hematopoietic cell transplantation (AHCT), post-transplant maintenance, and treatment of relapse after AHCT, which have led to improved survival in the modern era. In this review, we highlight our approach to the management of R/R cHL in 2023 with a focus on choosing first salvage therapy, post-transplant maintenance, and treatment of relapse after AHCT. We also discuss the management of older adults and transplant-ineligible patients, who require a separate approach. Finally, we review novel immunotherapy approaches in clinical trials, including combinations of PD-1 inhibitors with other immune-activating agents as well as novel antibody-drug conjugates, bispecific antibodies, and cellular immunotherapies. Ongoing studies assessing biomarkers of response to immunotherapy and dynamic biomarkers such as circulating tumor DNA may further inform treatment decisions and enable a more personalized approach in the future.
Silver Nanoparticles’ Biogenic Synthesis Using Caralluma subulata Aqueous Extract and Application for Dye Degradation and Antimicrobials Activities
Authors: Alamier Waleed M. , Hasan Nazim , Syed Imam Saheb , Bakry Ayyob M. , Ismail Khatib Sayeed , Gedda Gangaraju , Girma Wubshet Mekonnen
Published: September 2023

The issue of organic contaminants in water resulting from industrial, agricultural, and home activities makes it necessary to effectively address the problems of water scarcity. Using modern technologies that can effectively remove pollutants from wastewater is the way to address this key problem. The use of nanoparticles (NPs) has been advocated due to their unique physical and chemical characteristics and advantageous applications. NPs’ surface stability and synthesis routes are core concerns for environmental remediation and biological applications. In this work, we demonstrated the biogenic synthesis of silver NPs (Ag-CS NPs) by using Caralluma subulata (CS) aqueous extract as a reducing and capping/template agent. The synthesized Ag-CS NPs were characterized by UV-visible absorbance spectroscopy, Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, powdered X-ray diffractometer (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), transmission electron microscopy (TEM), and Zeta potential. The performance of Ag-CS NPs was evaluated on methylene blue (MB) dye degradation and antibacterial activity tests against bacterial and fungal isolates. The results showed that Ag-CS NPs (0.05%, 20.0 μL) reduced MB by 95.52% within 28 min in the presence of NaBH4 (10.0 mM, 0.980 μL). The degradation of MB followed pseudo zero-order chemical kinetics (R2 = 0.9380), with the reaction rate constant 0.0508 mol L−1 min−1. In addition, Ag-CS NPs were applied as antibacterial agents against 19 bacterial isolates. Ag-CS NPs showed inhibition in both Gram-positive and Gram-negative bacterial, as well as fungal isolates. As a greener ecofriendly approach, multifunctional Ag-CS NPs make a promising candidate for the remediation of contaminated water, as well as for important bioapplications.
Metabolomics Profile of the Secretome of Space-Flown Oligodendrocytes
Authors: Vergnes Laurent , Foucaud Bernard , Cepeda Carlos , Espinosa-Jeffrey Araceli
Published: September 2023

Intracranial hypertension (ICP) and visual impairment intracranial pressure (VIIP) are some of the sequels of long-term space missions. Here we sought to determine how space microgravity (µG) impacts the metabolomics profile of oligodendrocyte progenitors (OLPs), the myelin-forming cells in the central nervous system. We report increased glutamate and energy metabolism while the OLPs were in space for 26 days. We also show that after space flight, OLPs (SPC OLPs) display significantly increased mitochondrial respiration and glycolysis. These data are in agreement with our previous work using simulated microgravity. In addition, our global metabolomics approach allowed for the discovery of endogenous metabolites secreted by OLPs while in space that are significantly modulated by microgravity. Our results provide, for the first time, relevant information about the energetic state of OLPs while in space and after space flight. The functional and molecular relevance of these specific pathways are promising targets for therapeutic intervention for humans in long-term space missions to the moon, Mars and beyond.
Spontaneous Confinement of mRNA Molecules at Biomolecular Condensate Boundaries
Authors: Perelman Rebecca T. , Schmidt Andreas , Khan Umar , Walter Nils G.
Published: September 2023

Cellular biomolecular condensates, termed ribonucleoprotein (RNP) granules, are often enriched in messenger RNA (mRNA) molecules relative to the surrounding cytoplasm. Yet, the spatial localization and diffusion of mRNAs in close proximity to phase separated RNP granules are not well understood. In this study, we performed single-molecule fluorescence imaging experiments of mRNAs in live cells in the presence of two types of RNP granules, stress granules (SGs) and processing bodies (PBs), which are distinct in their molecular composition and function. We developed a photobleaching- and noise-corrected colocalization imaging algorithm that was employed to determine the accurate positions of individual mRNAs relative to the granule’s boundaries. We found that mRNAs are often localized at granule boundaries, an observation consistent with recently published data. We suggest that mRNA molecules become spontaneously confined at the RNP granule boundary similar to the adsorption of polymer molecules at liquid–liquid interfaces, which is observed in various technological and biological processes. We also suggest that this confinement could be due to a combination of intermolecular interactions associated with, first, the screening of a portion of the RNP granule interface by the polymer and, second, electrostatic interactions due to a strong electric field induced by a Donnan potential generated across the thin interface.
ERBB2-Mutant Gastrointestinal Tumors Represent Heterogeneous Molecular Biology, Particularly in Microsatellite Instability, Tumor Mutation Burden, and Co-Mutated Genes: An In Silico Study
Authors: Uchida Shiro , Sugino Takashi
Published: September 2023

During recent years, activating mutations in ERBB2 have been reported in solid tumors of various organs, and clinical trials targeting ERBB2-mutant tumors have been conducted. However, no effective treatment has been established for gastrointestinal tumors targeting ERBB2 mutations. ERBB2-mutant tumors have a higher tumor mutation burden (TMB) and microsatellite instability (MSI) than ERBB2 non-mutant tumors, but not all ERBB2-mutant tumors are TMB- and MSI-high. Thus, a more detailed classification of ERBB2-mutant tumors based on the underlying molecular mechanisms is required. Herein, we classified ERBB2 mutations into three groups—group 1: both ERBB2 mutations and amplifications; group 2: ERBB2 mutations annotated as putative driver mutations but without amplifications; group 3: ERBB2 mutations annotated as non-driver mutations (passenger mutations or unknown significance) and those that were not amplified in gastrointestinal tumors. Esophageal adenocarcinoma, gastric cancer, and colorectal cancer presented significantly higher MSI and TMB in the ERBB2-mutant group than in the ERBB2-wild-type group. The proportions of TMB- and MSI-high tumors and frequency of co-mutated downstream genes differed among the groups. We identified TMB- and MSI-high groups; this classification is considered important for guiding the selection of drugs for ERBB2-mutant tumors with downstream genetic mutations.