Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Key Nodes in Complex Networks Based on Network Representation Learning

Recently, some research has utilized machine learning methods to identify critical nodes in complex networks. However, existing approaches often lack a comprehensive consideration of network structural features during node feature extraction. Benefiting from the powerful feature extraction capability of network representation learning methods, a simple and effective algorithm for identifying key nodes in complex networks, termed Network Representation Learning and Key Node Identification (NRL_KNI), is proposed. The NRL_KNI algorithm utilizes network embedding techniques for learning node feature representations, followed by clustering and the utilization of quota-based limited sampling to obtain sampled nodes. Subsequently, these sampled nodes are employed to train a regression model for predicting the diffusion capability of unsampled nodes. To rank node influences, a Local Structure Influence Score (LSIS) based on the local structure is introduced to evaluate nodes’ final impact. Experimental results on eight real-world datasets demonstrate that the NRL_KNI algorithm generally outperforms traditional centrality methods and network representation learning-based methods in terms of the Jaccard similarity coefficient and Kendall’s Tau correlation coefficient evaluation metrics.
A Multiparameter RUL Prediction Method for UAV Lithium-Ion Battery Based on Physical Information

With the rapid development of unmanned aerial vehicles (UAVs), low-cost electrically driven UAVs have been widely used in various fields, and the battery module of UAVs is a critical component that directly affects their operational safety. The actual UAV operation scenario’s working condition mode is more complex. It is difficult to realize the full charge and discharge of the lithium-ion battery, so extracting the battery degradation characteristics is challenging. Moreover, the flight endurance and power parameters of UAVs are mainly determined by the capacity and internal resistance (IR) of Li-ion batteries. For UAVs, flight endurance and load power are equally important parameters. For UAVs in operation, it is crucial to simultaneously predict the capacity and IR, which is also a challenge. This article introduces the incremental capacity analysis (ICA) method to extract the half-peak width integral area and peak voltage as health indicators (HI) as degradation characteristics to characterize battery performance degradation. It establishes the multitask learning (MTL) model for indirect life prediction to address these issues. It builds an MTL model for indirect life prediction to solve these problems. The proposed MTL indirect prediction model is compared with the direct prediction and the single-task learning (STL) models. The results show that the MTL indirect prediction model has excellent prediction performance, with the relative periodic error (RUL_err) less than 1.5%, the root-mean-square error (RMSE), and the mean absolute percentage error (MAPE) held below 1.5% and 1.0%, respectively, and saves 55% of the computational time cost. Therefore, this approach is more suitable for embedded retrofit and applied to UAV-like working environments.
Design of PI sliding mode control for Zeta DC-DC converter in PV system

Solar energy has become one of the most potential alternative energies in the world. To convert solar energy into electricity, a photovoltaic (PV) system can be utilized. However, the ﬂuctuation of sunlight intensity throughout the day greatly affects the generated energy in the PV system. A battery may be beneﬁcial to store the generated energy for later use. A DC–DC converter is commonly exploited to produce a constant output voltage during the battery charging process. A Zeta converter is a DC–DC converter which can be used to produce output values above or below the input voltage without changing the polarity. To deal with the inherent non-linearity and time-varying properties of the converter, in this paper the sliding mode control (SMC) is ﬁrst analyzed and exploited before being integrated with a proportional-integral (PI) control to regulate the output voltage of the PV system. Disturbances are given in the form of changes in input voltage, reference voltage, and load. Voltage deviation and recovery time to reach a steady-state condition of the output voltage after disturbances are investigated and compared to the results using a proportional-integral-differential (PID) controller. The results show that the proposed control design performs faster than the compared PID control method.
Euclid preparation. XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential candidates. In this context, deep learning techniques are particularly suitable for the finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong lensing systems on the basis of their morphological characteristics. We train and test our models on different subsamples of a data set of forty thousand mock images, having characteristics similar to those expected in the wide survey planned with the ESA mission \Euclid, gradually including larger fractions of faint lenses. We also evaluate the importance of adding information about the colour difference between the lens and source galaxies by repeating the same training on single-band and multi-band images. Our models find samples of clear lenses with $\gtrsim 90\%$ precision and completeness, without significant differences in the performance of the three architectures. Nevertheless, when including lenses with fainter arcs in the training set, the three models' performance deteriorates with accuracy values of $\sim 0.87$ to $\sim 0.75$ depending on the model. Our analysis confirms the potential of the application of CNNs to the identification of galaxy-scale strong lenses. We suggest that specific training with separate classes of lenses might be needed for detecting the faint lenses since the addition of the colour information does not yield a significant improvement in the current analysis, with the accuracy ranging from $\sim 0.89$ to $\sim 0.78$ for the different models.
Canopy‐atmosphere interactions under foggy condition—Size‐resolved fog droplet fluxes and their implications

Microphysical processes of fog and their spatial and temporal pattern are a challenge to study under natural conditions. This work focuses on the development of bidirectional fluxes of fog droplets above a forest canopy in northeastern Taiwan. Bidirectional fluxes occurred regularly, start from the smallest droplet class (<2.66 µm diameter), and subsequently extend to larger droplets up to 7.41 µm diameter. The development of the bidirectional fluxes with positive (upward) fluxes of smaller droplets and downward fluxes of larger fluxes is associated with a temperature gradient and with the activation of fog droplets according to the Köhler theory. Small fog droplets develop close to the canopy as result of evapotranspiration and subsequent condensation. The rapid growth of small fog droplets and the accelerated growth of activated droplets, a process which is more likely to occur at higher levels of the fog layer, lead to a sink of small droplets and a source of larger droplets within the fog. This is in accordance with the observation that positive droplet number fluxes of small fog droplets outnumber the negative fluxes from the larger fog droplets. For liquid water, the net flux is negative.
The abundance of parasitic nematodes Halicephalobus species (Nematoda: Rhabditida) invading humans and animals in national parks of Vietnam

Halicephalobus nematodes are parasites in horses, ruminants, and humans. They with the most demonstrated H. gingivalis infect the hosts and cause granulomas to fatalities. Anthelmintic treatments are not effective in many cases, so precautions against Halicephalobus infection based on the understanding of life history may help. So far, ten species have been globally identified. We isolated many wild-type Halicephalobus sp. strains in vegetation samples collected from two national parks (Cat Tien and Cuc Phuong National Parks) in Vietnam and cultured them on bacteria. Thus, Halicephalobus nematodes could live in the wild and feed on bacteria. In previous studies, horses were predominantly found to be hosts of Halicephalobus sp. nematodes, but in this study they are not presented in the parks, suggesting that other wild animals such as ruminants in the parks should be their hosts. In addition, we analyzed the 18S rDNA sequences of the isolated nematodes and found that they are likely divergent between the two ecological systems, suggesting the two nematode communities have evolved differently.
Aphid Detection Network from Global to Local

Common aphids on crops are not suitable for general-purpose object detection frameworks due to their small size and the presence of occlusion. Taking this as a starting point, we fully consider the characteristics of aphid targets and propose a network called Overall-Specific Net (OS-Net) for detecting aphid targets in a manner that goes from overall to specific in a single-stage detection network. This network consists of two detection head modules. The first detection head module is responsible for detecting aphids in densely populated areas, while the second module, based on the first module, detects aphids in densely populated areas by deploying denser anchor boxes. Experimental results on our dataset show that the average accuracy can be improved by approximately 5.1% compared to the baseline network.
Expertise, trading zones and the planning system: A case study of an energy-from-biomass plant

Planning disputes are sites of contestation in which science-based regulations come into conflict with the place-based knowledge of local communities. The procedural and often technical nature of these regulations means that these controversies are marked by an asymmetry of resources that is often experienced by community groups as an asymmetry in credibility. In short, the expertise of developers is generally accepted as such, whilst the knowledge claimed by citizens is dismissed as ‘anecdotal’ or ‘NIMBYism’. In this article, we make the argument that the asymmetries of expertise are less stark than the current system typically allows and that recognising and accommodating this would improve the planning system by enhancing the representation and inclusion of community voices. We explore this position by using a case study of the construction of a Energy-from-Biomass plant in South Wales. Drawing from 30 qualitative interviews, we maintain that the planning process has the potential to function as one of a network of ‘trading zones’ in which different communities enact their rights and have their claims to knowledge and expertise recognised. Crucial to this argument is understanding that the levels and kinds of expertise that different parties bring to the interactions are more than just matters of attribution: community groups can have genuine expertise.
Translating Nature-Based Solutions for Water Resources Management to Higher Educational Programs in Three European Countries

Climate change has increasing impacts of hydro-meteorological extremes on water resources. Projections indicate a similar trend and challenge in the effectiveness of conventional engineering solutions in climate change adaptation (CCA) and disaster risk reduction (DRR) strategies. Nature-based solutions (NbSs) have been promoted as viable approaches and measures that complement engineering solutions. While the effects of NbSs have been increasingly demonstrated, their broader implementation can be favoured by NbSs knowledge integration in higher education curricula. Knowledge on how the research practice is translated into the educational landscape is missing. This paper adopts the concept of knowledge translation and analyses the integration of NbSs in the study programs of higher education institutions in three European countries (Croatia, Czechia, and Slovenia). Specifically, it explores the extent, thematic areas, and curricular settings of NbSs related to water resources management in implemented curricula at public universities. The results show that NbSs are integrated in a limited number of courses within the relevant study programs (in the fields of, e.g., natural sciences, geography, and engineering and technology) and represent rather an extension of compulsory curricula. Bibliometric analysis revealed that most courses involving the NbSs approach still represent a personalized knowledge, i.e., developed by professors during their research activities. The barriers impairing a broader integration of NbSs in the studied programs are then discussed. Our results therefore indicate that NbSs do not represent a mainstream knowledge that would proliferate into higher education curricula through accreditations procedures, but that the knowledge that is mostly integrated through direct incremental implementation of NbSs in the individual compulsory lessons or facultative courses. We assert that without broader and systematic NbSs knowledge translation to study programs, the effectiveness of climate change adaptation and disaster risk reduction strategies cannot be fully achieved.
UAV Cluster Mission Planning Strategy for Area Coverage Tasks

In the context of area coverage tasks in three-dimensional space, unmanned aerial vehicle (UAV) clusters face challenges such as uneven task assignment, low task efficiency, and high energy consumption. This paper proposes an efficient mission planning strategy for UAV clusters in area coverage tasks. First, the area coverage search task is analyzed, and the coverage scheme of the task area is determined. Based on this, the cluster task area is divided into subareas. Then, for the UAV cluster task allocation problem, a step-by-step solution is proposed. Afterward, an improved fuzzy C-clustering algorithm is used to determine the UAV task area. Furthermore, an optimized particle swarm hybrid ant colony (PSOHAC) algorithm is proposed to plan the UAV cluster task path. Finally, the feasibility and superiority of the proposed scheme and improved algorithm are verified by simulation experiments. The simulation results show that the proposed method achieves full coverage of the task area and efficiently completes the task allocation of the UAV cluster. Compared with related comparison algorithms, the method proposed in this paper can achieve a maximum improvement of 21.9% in balanced energy consumption efficiency for UAV cluster task search planning, and the energy efficiency of the UAV cluster can be improved by up to 7.9%.
Discovering Intraday Tail Dependence Patterns via a Full-Range Tail Dependence Copula

In this research, we employ a full-range tail dependence copula to capture the intraday dynamic tail dependence patterns of 30 s log returns among stocks in the US market in the year of 2020, when the market experienced a significant sell-off and a rally thereafter. We also introduce a model-based unified tail dependence measure to directly model and compare various tail dependence patterns. Using regression analysis of the upper and lower tail dependence simultaneously, we have identified some interesting intraday tail dependence patterns, such as interactions between the upper and lower tail dependence over time among growth and value stocks and in different market regimes. Our results indicate that tail dependence tends to peak towards the end of the regular trading hours, and, counter-intuitively, upper tail dependence tends to be stronger than lower tail dependence for short-term returns during a market sell-off. Furthermore, we investigate how the Fama–French five factors affect the intraday tail dependence patterns and provide plausible explanations for the occurrence of these phenomena. Among these five factors, the market excess return plays the most important role, and our study suggests that when there is a moderate positive excess return, both the upper and lower tails tend to reach their lowest dependence levels.
Does Subjective Well-Being and Perceived Organizational Can Support Startup Employees’ Performance During Hybrid Workforce Era?

: The hybrid workforce is a combined work from an office and remote working implemented by many companies after the pandemic subsided. This study analyzed the subjective well-being and perceived organizational support in the hybrid workforce era on startup employees' performance in Indonesia. The total of the research samples was 110 samples, which the researcher used convenience sampling to obtain and SEM-PLS analysis to analyze the data. The results showed differences between permanent and temporary employees that stem from differences in perceptions between the two groups of employees. In the effect of perceived organizational support on subjective well-being, the researcher obtained the same results between temporary and permanent employees, in which perceived organizational support affected their well-being. The result obtained for the effect of subjective well-being on employee performance showed that it did not affect temporary employees but permanent employees. In the effect of perceived organizational support on employee performance, temporary employees have a more significant influence on permanent employees. In the influence of the hybrid workforce era on employee performance, the results obtained did not affect temporary but affect permanent employees.
Spatial Characteristics of Interannual Variability in Wind Stress over the Western North Pacific.

Abstract About 4.5 million wind data observations reported from ships during 1961–83 are used to elucidate the spatial structure of interannual variations of the wind stress over the western North Pacific (0°–0°N, 120°E–170°W). Spatially coherent features are described in terms of time series and cross-spectral analyses of monthly data of the zonal and meridional components in 2° lat-5° long quadrangles. In a large portion of the study area, the interannual variation of the zonal stress dominates that of the meridional stress. In the region of the westerlies north of 28°N, the strength of the wintertime maximum in the eastward stress changes from year to year, and the interannual variation of the zonal stress is not coherent over the whole region. The interannual energy in the region is highest in the western North Pacific and is not concentrated in any particular period bands. In the doldrums, located in the western portion of the region between the equator and 10°N, where the stress magnitude is smaller.
Brilliant bird brains: object recognition in Herring Gulls ( Larus argentatus smithsonianus )

The ability to recognize and associate objects with prior experiences has been demonstrated in several avian taxa. Corvids, for example, recognize visual stimuli associated with negative previous experiences and will modify their behavioral responses according to the level of perceived threat. However, the extent to which similar memory-based behaviors exist in seabirds is poorly understood. Our study investigated object recognition by Herring Gulls ( Larus argentatus smithsonianus ) on their breeding grounds, the site of a long-term monitoring project. When handling gulls and conducting field research, researchers wear standard bicycle helmets as a protective measure against physical aggression by nesting gulls. We tested whether previously banded gulls vary their behavioral responses based on the headgear worn by human researchers. Herring Gulls were significantly more aggressive when approached by helmeted observers compared to those wearing neutral headwear or even holding a helmet, thus indicating a potential association between wearing bike helmets and the negative experience with researchers. Our study demonstrates that Herring Gulls are capable of object memory and object recognition within context, and display different behaviors toward different objects according to their levels of perceived threat. This information is key when designing studies with nesting gulls to ensure that behavioral biases are not unintentionally created due to gull prior experiences or researcher safety gear.
Modified Direct Torque Control of Solar Fed Sensorless Switched Reluctance Motor Drive for Electric Vehicle With Regenerative Braking

In this article, a Photovoltaic (PV) fed Zeta converter based Switched Reluctance Motor (SRM) drive is demonstrated for light electric vehicle (LEV) application. An improved direct torque control (DTC) scheme is implemented on a 12/8 SRM. The flux loop is eliminated, and switching sectors are formed, based on rotor position. Turn-off angle control makes sectors' span adjustable. By carefully choosing switching vectors, innate restriction on creation of negative torque is removed. Implemented algorithm provides a reduced torque ripple with phase current profiling at turn-on instants. With a use of discrete rotor position estimates, rotor position is determined. Through a comparison between phase flux and stored flux, discrete rotor position is computed. A flux polynomial is formed to store magnetic data of SRM. A lead acid battery is connected to DC-link directly. To enhance range and reliability, a PV panel is connected to the system. A zeta DC-DC converter is employed with a variable step size P&O MPPT (maximum power point tracking) control algorithm to extract maximum power from PV panel. A regenerative braking strategy is also integrated with the system to extract kinetic energy stored in inertial system. This energy is fed-back to source and enhances range of vehicle with a same battery pack. Validation of algorithm is demonstrated on a prototype.
Sonothrombolysis for novel adjuvant therapy in ST-elevation myocardial infarction: a systematic review

Current standard ST-segment elevation myocardial infarction (STEMI) treatments, such as percutaneous coronary intervention (PCI) and thrombolytic agent administration, still have some limitations. Sonothrombolysis is a new therapeutic modality that utilizes ultrasound energy to break up thrombus and is currently considered a promising alternative therapeutic strategy to treat STEMI. This systematic review aims to further review the clinical application of sonothrombolysis as an adjuvant therapy modality in STEMI based on the evidence available in the last ten years. We systematically searched studies on PubMed, Cochrane Library, and ScienceDirect databases. After removing duplicates and screening studies based on inclusion and exclusion criteria, data extraction and critical appraisal were performed by three independent reviewers. Study search and selection resulted in five studies with 614 patients and an additional 273 patients as a reference group. Results showed that patients treated with an intermittent, short pulse, and high mechanical index sonothrombolysis with microbubble infusion had significantly better ST-segment resolution and vessel recanalization rates than the other treatment groups. In addition, there was no significant difference in the safety level among all intervention groups. In conclusion, sonotrombolysis is a promising novel therapy that is an adjunct to PCI, which is effective and safe for treating STEMI.