bluuebunny/crossref_metadata_2025 huggingface dataset:
Typology of Business Incubators in Spain According to the Stages of Startups Incubation
Authors: Asensio-Ciria Ana , De-Pablos-Heredero Carmen , Blanco Jiménez Francisco José , García Martínez Antón
Published: November 2024

The aim of this work was to classify the business incubators in Spain according to the four phases of the startup’s incubation process. Considering that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the business incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis has been applied. All the incubation variables were used simultaneously, reducing their number and grouping them into factors. Finally, the cases were grouped according to these latent variables. Principal component analysis reduced dimensionality to eight factors with a 74% explained variance. Factor 1 was positively related to pre-incubation variables; factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3, named activity monitoring and control, was related to phase 3, or basic incubation variables. Cluster analysis facilitates the grouping of business incubators into three clusters: Group 1 (16% of the total), incubators with strong deficits in incubation phases 1, 2, and 3. They are small-sized business incubators, often located in rural areas or cities, with a low graduation rate. Group 2 (30%), business incubators with a very high graduation rate and strongly positive values in factors 1 and 2. Factor 3, although positive, is susceptible to improvement. They are the largest group of business incubators and usually located in industrial and technological parks. Group 3 (54%) is the majority, with values close to clusters 2 and 3.
A Physics- and Data-Driven Study on the Ground Effect on the Propulsive Performance of Tandem Flapping Wings
Authors: Duan Ningyu , Wang Chao , Zhou Jianyou , Jia Pan , Zhong Zheng
Published: November 2024

In this paper, we present a physics- and data-driven study on the ground effect on the propulsive performance of tandem flapping wings. With numerical simulations, the impact of the ground effect on the aerodynamic force, energy consumption, and efficiency is analyzed, revealing a unique coupling effect between the ground effect and the wing–wing interference. It is found that, for smaller phase differences between the front and rear wings, the thrust is higher, and the boosting effect due to the ground on the rear wing (maximum of 12.33%) is lower than that on a single wing (maximum of 43.83%) For a larger phase difference, a lower thrust is observed, and it is also found that the boosting effect on the rear wing is above that on a single wing. Further, based on the bidirectional gate recurrent units (BiGRUs) time-series neural network, a surrogate model is further developed to predict the unsteady aerodynamic characteristics of tandem flapping wings under the ground effect. The surrogate model exhibits high predictive precision for aerodynamic forces, energy consumption, and efficiency. On the test set, the relative errors of the time-averaged values range from −4% to 2%, while the root mean squared error of the transient values is less than 0.1. Meanwhile, it should be pointed out that the established surrogate model also demonstrates strong generalization capability. The findings contribute to a comprehensive understanding of the ground effect mechanism and provide valuable insights for the aerodynamic design of tandem flapping-wing air vehicles operating near the ground.
Numerical and Experimental Simulation of Supersonic Gas Outflow into a Low-Density Medium
Authors: Dubrovin Kirill , Yarkov Lev , Zarvin Alexandr , Zaitsev Alexander , Kalyada Valeriy , Yaskin Alexandr , Bondar Yevgeniy
Published: November 2024

This study is aimed at developing methods for the experimental and numerical simulation of the outflow of underexpanded gas jets into a rarefied medium. The numerical method is based on using Navier–Stokes equations in the continuum flow regime and the direct simulation Monte Carlo method in the transitional flow regime. The experimental method includes the modeling of jet flows in the LEMPUS-2 gas-dynamic setup with electron beam diagnostics for the jet density measurements. The results of the experimental modeling for the nozzles of various diameters confirm that a key parameter determining the jet structure is the Reynolds number based on the characteristic length ReL. The results of the numerical simulations agree well with the experimental data both for the maximum values of the ReL considered (approximately 30) when a barrel jet structure with Mach disks is formed and for the minimum values (approximately 4) when no Mach disks are formed. In the entire range of parameters, significant thermal nonequilibrium is observed at all jet segments where the measurements are performed.
Foliar Fertilization Improves the Nitrogen Nutrition of Sugarcane
Authors: Andrade Joel José de , Oliveira Emídio Cantídio Almeida de , Lima Amanda Michele dos Santos , Amorim Gabriela Priscila Sena , Oliveira Ester Souza , Freire Fernando José , Adelino Wagner Sandro de Moura , Oliveira Filho Emídio Cantídio Almeida de
Published: November 2024

Increasing the recovery of N fertilizer (RNf) is the main challenge in managing nitrogen fertilization in sugarcane. This study aimed to evaluate the efficiency of complementary foliar fertilization in managing nitrogen nutrition in sugarcane. Four fertilization managements, combining soil (5.0 and 4.5 g plot−1 of N) and foliar (1.0 and 1.5 g plot−1 of N) fertilization in up to two application events (0.5 + 0.5 and 0.75 + 0.75 g plot−1 of N), were compared with conventional fertilization (6.0 g plot−1 of N in soil). The change from 6.0 g plot−1 to 4.5 g plot−1 of N reduced the RNf by 46% before the first foliar fertilization. The RNf (26%) was similar between managements after the first foliar fertilization. After the second foliar fertilization, the RNf was 38% higher than that for conventional management. The accumulation of N in the aerial part of sugarcane was similar between managements until the first foliar fertilization. After the second foliar fertilization, the accumulation of N increased by 3.5% with foliar fertilization. The biomass accumulated by the managements was similar before and after the first and second foliar fertilization. The splitting of foliar fertilization increased the accumulation of N and RNf by 22% and 24%, respectively. The fertilization management with 4.5 g plot−1 of N applied to the soil, with two applications of 0.75 g plot−1 of N on the leaf, obtained greater accumulations of N and RNf. Foliar fertilization increases the efficiency of fertilization and improves the N nutrition of sugarcane.
Human-Centered Robotic System for Agricultural Applications: Design, Development, and Field Evaluation
Authors: Seol Jaehwi , Park Yonghyun , Pak Jeonghyeon , Jo Yuseung , Lee Giwan , Kim Yeongmin , Ju Chanyoung , Hong Ayoung , Son Hyoung Il
Published: November 2024

This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture.
Responses of N2O, CO2, and NH3 Emissions to Biochar and Nitrification Inhibitors Under a Delayed Nitrogen Application Regime
Authors: Wu Haizhong , Wang Daichang , Zhang Dengxiao , Rao Wei , Yuan Qingsong , Shen Xiaobo , Ma Guozhen , Jie Xiaolei , Liu Shiliang
Published: November 2024

Greenhouse gas and NH3 emissions are exacerbated by the inappropriate timing and excessive application of nitrogen (N) fertilizers in wheat cultivation in China. In this study, the impacts on N2O, CO2, and NH3 emissions of a delayed and reduced N application regime on the Huang-Huai-Hai Plain were investigated. The treatments comprised the control (N0), conventional N at 270 kg N ha−1 (N270) and optimized N application of 180 kg N ha−1 (N180), N180 + biochar at 7.5 t ha−1 (N180B7.5), N180 + biochar at 15 t ha−1 (N180B15), N180 + DMPP (a nitrification inhibitor; N180D), N180D + biochar at 7.5 t ha−1 (N180DB7.5), and N180D + biochar at 15 t ha−1 (N180DB15). Reduced N application (N180) lowered N2O and NH3 emissions. Biochar application resulted in a 4–25% and 12–16% increase in N2O and NH3 emissions, respectively. Application of DMPP significantly decreased N2O emissions by 32% while concurrently inducing a 9% increase in NH3 emissions. Co-application of DMPP and biochar significantly reduced the activity of nitrification enzymes (HAD, NOO), resulting in a reduction of 37–38% in N2O emissions and 13–14% in NH3 emissions. No significant differences in CO2 emissions were observed among the various N treatments except the N0 treatment. Application of DMPP alone did not significantly affect grain yield. However, biochar, in combination with DMPP, effectively increases grain yield. The findings suggest that the N180DB15 treatment has the potential to reduce emissions of N2O and NH3 while concurrently enhancing soil fertility (pH, SOC) and wheat yield.
Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
Authors: Zanchin Alessandro , Cogato Alessia , Sozzi Marco , Tomasi Diego , Marinello Francesco
Published: November 2024

Sentinel-2 (S2) is widely considered a reliable satellite constellation for monitoring several crops, such as grapevine (Vitis vinifera L.). A large dataset of Italian vineyards randomly chosen was monitored with S2 from 2017 to 2022. Two vegetation indices (VIs) and their statistics were calculated from each vineyard. In addition, structural features and topographic information were assessed using Google Earth and national databases. The research study aims to identify the most relevant drivers of spatial variability by assessing the VIs among the whole dataset and the within-vineyard variability. The latitude and the vintage showed the most relevant effect on spatial variability, depicting the effect of daylight hours, climate conditions and weather events. However, the vintage did not affect the patterns of the within-field variability. Regarding grapevine management, training systems and the rows’ orientation were relevant boosters of variability. While the vineyards planted with north–south-oriented rows reached the highest VIs values, the east–west-oriented ones showed the highest variability. Finally, an interaction effect was detected between hill or plain plantation and the terrain slope on both the average and variability of the VIs. The conclusions from the present study suggest the relevance of clustering vineyards under remote supervision according to the structural features to reduce data variability. Further studies should investigate other structural features or managerial properties.
Performance of Different Cool-Season Species and Cultivars Overseeded on Bermudagrass and Managed with Autonomous Mower
Authors: Sciusco Giuliano , Caturegli Lisa , Magni Simone , Federighi Tommaso , Fontanelli Marco , Robert Noemie , Alderton Stephen R. , Volterrani Marco
Published: November 2024

With global warming, the cultivation area of bermudagrass is moving northwards in the Mediterranean area despite its winter dormancy and loss of green color. The most common solution is overseeding with perennial ryegrass, which can be complicated to remove in spring. DLF breeders have released a new cultivar of annual ryegrass that is stress tolerant and easy to transition in spring. A trial was carried out on a sod farm in Pisa, Italy. Twenty-nine varieties of cool-season grasses, encompassing both forage and turf species, were overseeded on a hybrid bermudagrass variety ‘Tifway’ (Cynodon dactylon x transvaalensis). The aim of this trial was to compare the overseeding potential of various cool-season turfgrass species and cultivars and to identify which ones perform best in terms of cleanness of cut when overseeded on hybrid bermudagrass in autumn and maintained at a mowing height of 25 mm. Moreover, the following parameters were also assessed: actual turf height (cm); cumulated height (cm); turf visual color and quality (1–9 scale); and visual ground cover (%). Plots were managed with an autonomous mower, which adopted a daily mowing frequency and was set to work with systematic trajectories. The results showed differences between cultivars, and the annual ryegrass showed the best spring transition (scoring a mean value of 96% green cover in July 2023) compared to the other entries. The best result of CoC was measured for rough-stalked meadow grass ‘Sabrena 1’ and tall fescue ‘Turfway’ with 0.9 mm.
Optimizing Steering Angle Prediction in Self-Driving Vehicles Using Evolutionary Convolutional Neural Networks
Authors: Khawaldeh Bashar , Mora Antonio M. , Faris Hossam
Published: November 2024

The global community is awaiting the advent of a self-driving vehicle that is safe, reliable, and capable of navigating a diverse range of road conditions and terrains. This requires a lot of research, study, and optimization. Thus, this work focused on implementing, training, and optimizing a convolutional neural network (CNN) model, aiming to predict the steering angle during driving (one of the main issues). The considered dataset comprises images collected inside a car-driving simulator and further processed for augmentation and removal of unimportant details. In addition, an innovative data-balancing process was previously performed. A CNN model was trained with the dataset, conducting a comparison between several different standard optimizers. Moreover, evolutionary optimization was applied to optimize the model’s weights as well as the optimizers themselves. Several experiments were performed considering different approaches of genetic algorithms (GAs) along with other optimizers from the state of the art. The obtained results demonstrate that the GA is an effective optimization tool for this problem.
OTM-HC: Enhanced Skeleton-Based Action Representation via One-to-Many Hierarchical Contrastive Learning
Authors: Usman Muhammad , Cao Wenming , Huang Zhao , Zhong Jianqi , Ji Ruiya
Published: November 2024

Human action recognition has become crucial in computer vision, with growing applications in surveillance, human–computer interaction, and healthcare. Traditional approaches often use broad feature representations, which may miss subtle variations in timing and movement within action sequences. Our proposed One-to-Many Hierarchical Contrastive Learning (OTM-HC) framework maps the input into multi-layered feature vectors, creating a hierarchical contrast representation that captures various granularities within a human skeleton sequence temporal and spatial domains. Using sequence-to-sequence (Seq2Seq) transformer encoders and downsampling modules, OTM-HC can distinguish between multiple levels of action representations, such as instance, domain, clip, and part levels. Each level contributes significantly to a comprehensive understanding of action representations. The OTM-HC model design is adaptable, ensuring smooth integration with advanced Seq2Seq encoders. We tested the OTM-HC framework across four datasets, demonstrating improved performance over state-of-the-art models. Specifically, OTM-HC achieved improvements of 0.9% and 0.6% on NTU60, 0.4% and 0.7% on NTU120, and 0.7% and 0.3% on PKU-MMD I and II, respectively, surpassing previous leading approaches across these datasets. These results showcase the robustness and adaptability of our model for various skeleton-based action recognition tasks.
Aspects of Tuberculosis in Greece over the Last Century: Historical Perspectives and Today’s Challenges
Authors: Pliatsikas Aimilios , Vrioni Georgia , Papaparaskevas Joseph , Tsiamis Constantinos , Tsakris Athanasios
Published: November 2024

Tuberculosis has had a long-standing prevalence in Greece over the last century, going hand in hand with the country’s overall socioeconomic situation. The current study explores aspects of tuberculosis in Greece since the interwar period and up to the present. Apparently, ignorance of the nature of the disease coupled with false medical perceptions sealed the fate of patients while unwittingly influencing public opinion and making societies hostile toward patients. Such paradigms are of great and timeless value for the cohesion and functioning of societies, not excluding modern ones. Although the present-day situation in Greece is utterly different from that in the previous century, considering the limited number of cases and minimal impact of the disease, new challenges have emerged, such as the under-reporting of cases, the emergence of multidrug-resistant tuberculosis, latent tuberculosis or the management of large refugee flows in the Eastern Mediterranean involving citizens from countries with a high prevalence of the disease. On the other hand, the study of tuberculosis is nowadays more urgent than ever, given the global concerns regarding the future of the disease in the midst of the COVID-19 pandemic, which has put enormous pressure on national health systems.
Nanoparticle-Based Nitric Oxide Donors: Exploring Their Antimicrobial and Anti-Biofilm Capabilities
Authors: Tortella Fuentes Gonzalo , Fincheira Paola , Rubilar Olga , Leiva Sebastian , Fernandez Ivette , Schoebitz Mauricio , Pelegrino Milena T. , Paganotti André , dos Reis Roberta Albino , Seabra Amedea B.
Published: November 2024

Background: Nitric oxide (NO) is an antimicrobial and anti-biofilm agent with significant potential for combating biofilm-associated infections and antibiotic resistance. However, owing to its high reactivity due to the possession of a free radical and short half-life (1–5 s), the practical application of NO in clinical settings is challenging. Objectives: This review explores the development of NO-releasing nanoparticles that provide a controlled, targeted delivery system for NO, enhancing its antimicrobial efficacy while minimizing toxicity. The review discusses various NO donors, nanoparticle platforms, and how NO disrupts biofilm formation and eradicates pathogens. Additionally, we examine the highly encouraging and inspiring results of NO-releasing nanoparticles against multidrug-resistant strains and their applications in medical and environmental contexts. This review highlights the promising role of NO-based nanotechnologies in overcoming the challenges posed by increasing antibiotic resistance and biofilm-associated infections. Conclusions: Although NO donors and nanoparticle delivery systems show great potential for antimicrobial and anti-biofilm uses, addressing challenges related to controlled release, toxicity, biofilm penetration, resistance, and clinical application is crucial.
Spectrogram-Based Arrhythmia Classification Using Three-Channel Deep Learning Model with Feature Fusion
Authors: Eleyan Alaa , Bayram Fatih , Eleyan Gülden
Published: November 2024

This paper introduces a novel deep learning model for ECG signal classification using feature fusion. The proposed methodology transforms the ECG time series into a spectrogram image using a short-time Fourier transform (STFT). This spectrogram is further processed to generate a histogram of oriented gradients (HOG) and local binary pattern (LBP) features. Three separate 2D convolutional neural networks (CNNs) then analyze these three image representations in parallel. To enhance performance, the extracted features are concatenated before feeding them into a gated recurrent unit (GRU) model. The proposed approach is extensively evaluated on two ECG datasets (MIT-BIH + BIDMC and MIT-BIH) with three and five classes, respectively. The experimental results demonstrate that the proposed approach achieves superior classification accuracy compared to existing algorithms in the literature. This suggests that the model has the potential to be a valuable tool for accurate ECG signal classification, aiding in the diagnosis and treatment of various cardiovascular disorders.
Aging Performance and an Improved Evaluation Method for PE80 and PE100 Pipelines for Urban Gas
Authors: Lin Nan , Sun Xingze , Sun Ming , Li Xiaolong , Liu Changzheng , Chen Shan , Meng Tao
Published: November 2024

Polyethylene (PE) pipes are widely used in urban gas transportation due to their good toughness and corrosion resistance. Currently, the designed service life of a PE pipeline is 50 years, and some urban gas PE pipelines are approaching their service life. However, research on the aging assessment of PE pipelines is not complete, and it is impossible to effectively predict their aging status in service. Once urban gas PE pipelines are damaged, serious accidents may be caused. PE80 and PE100 pipelines are commonly used for urban gas, and improved accelerated aging tests were conducted considering different conditions of pressure, pipe diameter, and temperature. According to the experimental results, an aging life prediction method for PE pipes was constructed based on the Arrhenius formula considering multiple effect factors.
Acceleration of Green Transitions on the Base of Port Organizational Ecosystem’s Resilience Enhancement: A Socio-Technological Approach
Authors: Valioniene Elena , Zuperkiene Erika , Placiene Birute
Published: November 2024

Seaports are pivotal nodes in global trade, under increasing pressure to expedite green transitions while navigating the complexities of modern economic and environmental challenges. This research investigates the intricate relationship between the resilience of port organizational ecosystems and the successful implementation of green transitions. Employing a combination of focus group interviews and participatory observation, the study explores the primary obstacles, particularly the insufficiency of managerial capacity, which hampers the integration of technological innovations. Challenges identified include outdated infrastructure, cybersecurity vulnerabilities, and the imperative for sustained investment in new technologies, along with the need to promote a culture of sustainability. Experts highlight the crucial role of managerial competencies and continuous learning in overcoming these barriers. Furthermore, the research underscores the importance of fostering strong partnerships among stakeholders, including government bodies, industry associations, and environmental organizations. The findings offer valuable insights for policymakers, port managers, and other stakeholders seeking to navigate the challenges for ensuring a sustainable future for the maritime sport organizational ecosystems and maritime industry.
Differences in Body Fat in Athletes Categorized by Resting Metabolic Rate
Authors: Fields Jennifer B. , Askow Andrew T. , Jones Margaret T. , Jagim Andrew R.
Published: November 2024

The purpose of the study was to examine differences in body fat percentage (BF%) across groups stratified by resting metabolic rate (RMR) when normalized to body weight. National Collegiate Athletic Association Division III athletes (n = 190; Age: 19.8 ± 1.4 year; Body Mass: 79.3 ± 20.2 kg; Height: 175.0 ± 9.3 cm, Body Mass Index: 25.6 ± 4.9 kg/m2) participated in this cross-sectional mixed cohort study. Body composition was assessed using air displacement plethysmography. RMR was assessed using indirect calorimetry. For each sex, tertiles were determined and used to create low, moderate, and high relative RMR groups as follows: low (M: <26 kcal/kg; F: <24 kcal/kg), moderate (M: 26.1–29.0 kcal/kg; F: 24.1–27.0 kcal/kg), and high (M: >29.1 kcal/kg; F: >27.1 kcal/kg). The mean ± standard deviation RMR for male and female athletes was 27.9 ± 3.2 and 25.9 ± 2.8 kcals/kg when expressed relative to body weight. When stratified by sex, males in the low RMR group had significantly higher BF% values than those in the moderate (mean difference, [95% confidence intervals]) (7.2, [2.4, 12.0] kcal/kg; p < 0.01) and high RMR groups (7.7, [2.9, 12.5] kcal/kg; p < 0.001). Female athletes in the moderate RMR group had higher body fat percentages than those in the high RMR group (mean difference, [95% confidence intervals]) (5.8, [2.4, 9.2] kcal/kg; p < 0.01). Female athletes in the moderate relative RMR group had higher BF% values than those in the higher relative RMR group (3.3, [−0.1, 6.7] kcal/kg; p = 0.049). Both male and female athletes with a low relative RMR had a higher BF%.
Exploring Evolutionary Algorithms for Multi-Objective Optimization in Seismic Structural Design
Authors: Göktepe Körpeoğlu Seda , Yılmaz Süleyman Mesut
Published: November 2024

The seismic design of structures is an emerging practice in earthquake-resistant construction. Therefore, using energy-dissipation devices and optimizing these devices for various purposes are important. Evolutionary computation, nature-inspired, and meta-heuristic algorithms have been studied more in recent years for the optimization of these devices. In this study, the development of evolutionary algorithms for seismic design in the context of multi-objective optimization is examined through bibliometric analysis. In particular, evolutionary algorithms such as genetic algorithms and particle swarm optimization are used to optimize the performance of structures to meet seismic loads. While genetic algorithms are used to improve both the cost and seismic performance of the structure, particle swarm optimization is used to optimize the vibration and displacement performance of structures. In this study, a bibliometric analysis of 661 publications is performed on the Web of Science and Scopus databases and on how the research in this field has developed since 1986. The R-studio program with the biblioshiny package is used for the analyses. The increase in studies on the optimization of energy dissipation devices in recent years reveals the effectiveness of evolutionary algorithms in this field.
Heart Rate and Rhythm Changes in Dogs Treated in a Hyperbaric Oxygen Chamber
Authors: Graczyk Szymon , Łunkiewicz Wojciech , Grzeczka Arkadiusz , Zyśko Dorota , Pasławski Robert , Pasławska Urszula
Published: November 2024

In veterinary medicine, hyperbaric oxygen chamber treatment (HBOT) is gaining popularity. Therefore, an increasing number of patients referred for this therapy are being recorded, mainly due to ischemic events, wound healing support, and a high risk of reperfusion damage. During the HBOT procedure, several changes occur in the body’s micro- and macroenvironments. This study involved 34 dogs of various ages and health statuses. The atmospheric conditions in the test hyperbaric chamber included a pressure of 1.5 atmosphere absolute (ATA) at 100% oxygenation. The individuals were divided into three groups: (1) individuals with degenerative mitral valve disease (DMVD), (2) individuals with diseases other than cardiac issues, and (3) healthy individuals who qualified for the HBOT procedure. The period of measurement using the Holter apparatus was divided into four stages: 30 min before the subject’s placement in the hyperbaric chamber; a 4-min compression period (setting chamber conditions); a 90 min HBOT period; and a 1 min decompression period of the hyperbaric chamber. During the HBOT, there was a statistically significant decrease in heart rate in groups 2 and 3 compared to group 1. The heart rate in group 1 remained unchanged through every period of the study. In addition, some of the dogs developed respiratory arrhythmia; in two dogs, premature ventricle beats occurred. The changes observed during the ventricular period indicate that the HBOT procedure causes a significant change in heart rate in dogs without cardiac diseases.
Research on Nanometer Precision Measurement Method of High Order Even Aspheres
Authors: Xu Hao , Liu Junfeng , Chen Shanyong
Published: November 2024

Optical aspheres are demanded with extremely high precision to meet functional requirements in space telescopes, extreme ultraviolet lithography, and other modern large optical systems. The nano-precision fabrication of optical aspheres requires high-precision measurements to guide deterministic optical processing. Null test is the preferred method for high-precision measurements. Null optics are required to compensate for the incident wavefront in the null test of optical aspheres. However, wavefront aberrations caused by the transmission flat or transmission sphere of interferometer and null optics can limit measurement accuracy and need to be separated. A nano-precision measurement method is proposed for the even optical aspheres of high order in this paper. A computer-generated hologram is used as a null optic to realize a null test on optical aspheres. Mapping distortion correction is performed on the measurement results to ensure that the transverse coordinates of the measurement results correspond correctly to those of the test surface. Absolute testing is applied to separate the wavefront aberrations caused by the computer-generated hologram and interferometer optics. Finally, the results obtained by this method were used to guide deterministic optical processing, enabling the nano-precision fabrication of optical aspheres.
Understanding the Flows of Signals and Gradients: A Tutorial on Algorithms Needed to Implement a Deep Neural Network from Scratch
Authors: Klęsk Przemysław
Published: November 2024

Theano, TensorFlow, Keras, Torch, PyTorch, and other software frameworks have remarkably stimulated the popularity of deep learning (DL). Apart from all the good they achieve, the danger of such frameworks is that they unintentionally spur a black-box attitude. Some practitioners play around with building blocks offered by frameworks and rely on them, having a superficial understanding of the internal mechanics. This paper constitutes a concise tutorial that elucidates the flows of signals and gradients in deep neural networks, enabling readers to successfully implement a deep network from scratch. By “from scratch”, we mean with access to a programming language and numerical libraries but without any components that hide DL computations underneath. To achieve this goal, the following five topics need to be well understood: (1) automatic differentiation, (2) the initialization of weights, (3) learning algorithms, (4) regularization, and (5) the organization of computations. We cover all of these topics in the paper. From a tutorial perspective, the key contributions include the following: (a) proposition of R and S operators for tensors—rashape and stack, respectively—that facilitate algebraic notation of computations involved in convolutional, pooling, and flattening layers; (b) a Python project named hmdl (“home-made deep learning”); and (c) consistent notation across all mathematical contexts involved. The hmdl project serves as a practical example of implementation and a reference. It was built using NumPy and Numba modules with JIT and CUDA amenities applied. In the experimental section, we compare hmdl implementation to Keras (backed with TensorFlow). Finally, we point out the consistency of the two in terms of convergence and accuracy, and we observe the superiority of the latter in terms of efficiency.
A Transferable Meta-Learning Phase Prediction Model for High-Entropy Alloys Based on Adaptive Migration Walrus Optimizer
Authors: Hou Shuai , Zhou Minmin , Bai Meijuan , Liu Weiwei , Geng Hua , Yin Bingkuan , Li Haotong
Published: November 2024

The phases of high-entropy alloys (HEAs) are crucial to their material properties. Although meta-learning can recommend a desirable algorithm for materials designers, it does not utilize the optimal solution information of similar historical problems in the HEA field. To address this issue, a transferable meta-learning model (MTL-AMWO) based on an adaptive migration walrus optimizer is proposed to predict the phases of HEAs. Firstly, a transferable meta-learning algorithm frame is proposed, which consists of meta-learning based on adaptive migration walrus optimizer, balanced-relative density peaks clustering, and transfer strategy. Secondly, an adaptive migration walrus optimizer model is proposed, which adaptively migrates walruses according to the changes in the average fitness value of the population over multiple iterations. Thirdly, balanced-relative density peaks clustering is proposed to cluster the samples in the source and target domains into several clusters with similar distributions, respectively. Finally, the transfer strategy adopts the maximum mean discrepancy to find the most matching historical problem and transfer its optimal solution information to the target domain. The effectiveness of MTL-AMWO is validated on 986 samples from six datasets, including 323 quinary HEAs, 366 senary HEAs, and 297 septenary HEAs. The experimental results show that the MTL-AMWO achieves better performance than other algorithms.
Natural Inhibitors of the Polyphenol Oxidase Activity Isolated from Shredded Stored Iceberg Lettuce (Lactuca sativa L.)
Authors: Sierocka Małgorzata , Świeca Michał
Published: November 2024

Polyphenol oxidase (PPO) is the key enzyme responsible for enzymatic browning. To extend the shelf life of shredded lettuce, knowledge about biochemical PPO properties is required. The characterization of the enzyme from shredded, cold-stored lettuce was performed using pyrocatechol and the endogenous substrate (ES) (lettuce phenolics). The optimum pH and temperature for PPO activity were 5 and 50 °C, respectively. Natural infusions used as the PPO inhibitors (IC50) were ranked as follows: lovage (0.09%), marjoram (0.13%), orange peel (0.14%), oregano (0.15%), basil (0.22%), lemon peel (0.24%), parsley leaves (0.58%), and wheat bran (1.06%). Among well-recognized PPO inhibitors, kojic acid (0.00043%), ascorbic acid (0.00053%), and L-cysteine (0.00085%) were the most effective. Among the metal ions, MgCl2, FeCl2, and CaCl2 at 0.5 mM inhibited the PPO activity most effectively (by 28%, 27%, and 21%, respectively). The substrate used (pyrocatechol/ES) significantly influenced the enzyme inhibition. Using pyrocatechol, the lovage extract acted in a mixed mode (Kmi = 27.8 mM, Vmaxi = 2.03 mU), while the ES acted according to the non-competitive mode (Kmi= 0.57 mg GAE/mL, Vmax = 0.0046 U). The study confirms that natural extracts are more effective than L-cysteine when the ES is used. A pre-storage treatment with an infusion may be potentially used to improve the quality of shredded lettuce.
Speech Emotion Recognition Using Transfer Learning: Integration of Advanced Speaker Embeddings and Image Recognition Models
Authors: Jakubec Maros , Lieskovska Eva , Jarina Roman , Spisiak Michal , Kasak Peter
Published: November 2024

Automatic Speech Emotion Recognition (SER) plays a vital role in making human–computer interactions more natural and effective. A significant challenge in SER development is the limited availability of diverse emotional speech datasets, which hinders the application of advanced deep learning models. Transfer learning is a machine learning technique that helps address this issue by utilizing knowledge from pre-trained models to improve performance on a new task in a target domain, even with limited data. This study investigates the use of transfer learning from various pre-trained networks, including speaker embedding models such as d-vector, x-vector, and r-vector, and image classification models like AlexNet, GoogLeNet, SqueezeNet, ResNet-18, and ResNet-50. We also propose enhanced versions of the x-vector and r-vector models incorporating Multi-Head Attention Pooling and Angular Margin Softmax, alongside other architectural improvements. Additionally, reverberation from the Room Impulse Response datasets was added to the speech utterances to diversify and augment the available data. Notably, the enhanced r-vector model achieved classification accuracies of 74.05% Unweighted Accuracy (UA) and 73.68% Weighted Accuracy (WA) on the IEMOCAP dataset, and 80.25% UA and 79.81% WA on the CREMA-D dataset, outperforming the existing state-of-the-art methods. This study shows that using cross-domain transfer learning is beneficial for low-resource emotion recognition. The enhanced models developed in other domains (for non-emotional tasks) can further improve the accuracy of SER.
Statistical Properties of SIS Processes with Heterogeneous Nodal Recovery Rates in Networks
Authors: Guo Dongchao , Jiao Libo , Feng Wendi
Published: November 2024

The modeling and analysis of epidemic processes in networks have attracted much attention over the past few decades. A major underlying assumption is that the recovery process and infection process are homogeneous, allowing the associated theoretical studies to be conducted in a convenient manner. However, the recovery and infection processes usually exhibit heterogeneous rates in the real world, which makes it difficult to characterize the general relations between the dynamics and the underlying network structure. In this work, we focus on the susceptible–infected–susceptible (SIS) epidemic process with heterogeneous recovery rates in a finite-size complete graph. Specifically, we study the metastable-state statistical properties of SIS epidemic dynamics with two different nodal recovery rates in complete graphs. We propose approximate solutions to the metastable-state expectation and the variance in the number of infected nodes within the framework of the mean-field approximation method. We also derive several upper and lower bounds of the steady-state probability that a node is in the infected state. We verify the proposed approximate solutions of the mean and variance via simulations. This work provides insights into the fluctuations in the statistical properties of epidemic processes with complex dynamical behaviors in networks.
A Comprehensive Evaluation of Machine Learning Algorithms for Digital Soil Organic Carbon Mapping on a National Scale
Authors: Radočaj Dorijan , Jug Danijel , Jug Irena , Jurišić Mladen
Published: November 2024

The aim of this study was to narrow the research gap of ambiguity in which machine learning algorithms should be selected for evaluation in digital soil organic carbon (SOC) mapping. This was performed by providing a comprehensive assessment of prediction accuracy for 15 frequently used machine learning algorithms in digital SOC mapping based on studies indexed in the Web of Science Core Collection (WoSCC), providing a basis for algorithm selection in future studies. Two study areas, including mainland France and the Czech Republic, were used in the study based on 2514 and 400 soil samples from the LUCAS 2018 dataset. Random Forest was first ranked for France (mainland) and then ranked for the Czech Republic regarding prediction accuracy; the coefficients of determination were 0.411 and 0.249, respectively, which was in accordance with its dominant appearance in previous studies indexed in the WoSCC. Additionally, the K-Nearest Neighbors and Gradient Boosting Machine regression algorithms indicated, relative to their frequency in studies indexed in the WoSCC, that they are underrated and should be more frequently considered in future digital SOC studies. Future studies should consider study areas not strictly related to human-made administrative borders, as well as more interpretable machine learning and ensemble machine learning approaches.
Contrastive Learning for Morphological Disambiguation Using Large Language Models in Low-Resource Settings
Authors: Tolegen Gulmira , Toleu Alymzhan , Mussabayev Rustam
Published: November 2024

In this paper, a contrastive learning approach for morphological disambiguation (MD) using large language models (LLMs) is presented. A contrastive loss function is introduced for training the approach, which reduces the distance between the correct analysis and contextual embeddings while maintaining a margin between correct and incorrect embeddings. One of the aims of the paper is to analyze the effects of fine-tuning an LLM on MD in morphologically complex languages (MCLs) with special reference to low-resource languages such as Kazakh, as well as Turkish. Another goal of the paper is to consider various distance measures for this contrastive loss function, aiming to achieve better results when performing disambiguation by computing the distance between the context and the analysis embeddings. The existing approaches for morphological disambiguation, such as HMM-based and feature-engineering approaches, have limitations in modeling long-term dependencies and in the case of large, sparse tagsets. These challenges are mitigated in the proposed approach by leveraging LLMs, thus achieving better accuracy in handling the cases of ambiguity and OOV tokens without the need to rely on other features. Experiments were conducted on three datasets for two MCLs, Kazakh and Turkish—the former is a typical low-resource language. The results revealed that the proposed approach with contrastive loss improves MD performance when integrated with knowledge from large language models.
SimCDL: A Simple Framework for Contrastive Dictionary Learning
Authors: Ilie-Ablachim Denis C. , Dumitrescu Bogdan
Published: November 2024

In this paper, we propose a novel approach to the dictionary learning (DL) initialization problem, leveraging the SimCLR framework from deep learning in a self-supervised manner. Dictionary learning seeks to represent signals as sparse combinations of dictionary atoms, but effective initialization remains challenging. By applying contrastive learning, we encourage similar representations for augmented versions of the same sample while distinguishing between different samples. This results in a more diverse and incoherent set of atoms, which enhances the performance of DL applications in classification and anomaly detection tasks. Our experiments across several benchmark datasets demonstrate the effectiveness of our method for improving dictionary learning initialization and its subsequent impact on performance in various applications.
Pesticide Residue Coverage Estimation on Citrus Leaf Using Image Analysis Assisted by Machine Learning
Authors: Basavaraju Adarsh , Davidson Edwin , Diracca Giulio , Chen Chen , Santra Swadeshmukul
Published: November 2024

Globally, the agricultural industry has benefited from using pesticides to minimize crop losses. Nevertheless, the indiscriminate overuse of pesticides has led to significant risks associated with a detrimental impact on the environment and human health. Therefore, emerging concerns of pesticide residue found in crops, food, and livestock are a pressing issue. To address the above challenges, there have been many efforts made towards implementing machine learning to enable precision agricultural practices to reduce pesticide overuse. As of today, there are no guiding digital tools available for citrus growers to provide pesticide residue leaf coverage analysis after foliar applications. Herein, we are the first to report software assisted by lightweight machine learning (ML) to determine the Kocide 3000 and Oxytetracycline (OTC) residue coverage on citrus leaves based on image data analysis. This tool integrates a foundational Segment Anything Model (SAM) for image preprocessing to isolate the area of interest. In addition, Kocide 3000 and Oxytetracycline (OTC) residue coverage analysis was carried out using a specialized Mask Region-Based Convolutional Neural Network (CNN). This CNN was pre-trained on the MS COCO dataset and fine-tuned by training with acquired datasets in laboratory and field conditions. The developed software demonstrated excellent performance on both pesticides’ accuracy, precision, and recall, and F1 score metrics. In summary, this tool has the potential to assist growers with the decision-making process for controlling pesticide use rate and frequency, minimizing pesticide overuse.
Hovering Control on a Variable-Parameters Model of a Small Unmanned Helicopter Based on a Backstepping Sliding Mode Method
Authors: Zhang Hongmei , Sun Zhiyuan , Xu Jin , Han Rongming
Published: November 2024

In order to strengthen the hovering precision of a small unmanned helicopter with a manipulator for completing a hovering operation, in this study, a double-loop control strategy of position and attitude is designed. In the position loop, a sliding mode controller is proposed. It attains high precision and strong robustness under continuous disturbance. With the manipulator continuously operating during helicopter hovering, the overall center of gravity and the moment of inertia of the system will also change. Therefore, a backstepping sliding mode controller is proposed in the attitude loop for controlling the hovering attitude of the helicopter. The high precision and strong robustness of the control system are proven. The digital simulation presents a position steady-state error of less than 2% under constant disturbance, and the hovering attitude angle fluctuation amplitude of the helicopter is less than 0.05 rad.
Strategic Excellence in Padel: Design and Validation of Key Tactical Principles
Authors: Conde-Ripoll Rafael , Caldeira Paulo , Sánchez-Alcaraz Bernardino J. , Muñoz Diego , Bustamante-Sánchez Álvaro , Escudero-Tena Adrián
Published: November 2024

The purpose of this study was to design and validate a guide to strategic principles in padel (SPP), offering practical recommendations for both players and coaches. Once the first version of the guide was developed by six relevant researchers in padel, 11 padel experts (all with a federation title as coaches and with experience as a coach and player) conducted a qualitative and quantitative assessment of the guide. Aiken’s V coefficient and confidence intervals were used to calculate content validity, and Cronbach’s α coefficient to analyze reliability. The adequacy and wording of the 15 principles initially designed were evaluated. One principle was eliminated for obtaining values < 0.91 in Aiken’s V coefficient regarding adequacy. The wording of the remaining principles was modified according to the qualitative assessments of the experts. The reliability of the instrument was acceptable, (α = 0.90). This guide provides a foundational list of strategic principles for padel, offering practical recommendations for players and coaches. It could also serve as a useful resource for federations to incorporate into coach education programs, supporting the development of tactical knowledge in padel.