Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanistic Study on the Corrosion of (La,Sr)(Co,Fe)O3-δ Cathodes Induced by CO2

Solid Oxide Fuel Cell (SOFC) cathodes operating in ambient atmospheric conditions inevitably encounter CO2 contamination, leading to sustained performance deterioration. In this investigation, we examined the impact of CO2 on three variants of (La,Sr)(Co,Fe)O3-δ cathodes and employed the distribution of relaxation times method to distinguish distinct electrochemical processes based on impedance spectra analysis. We meticulously analyzed and discussed the corrosion resistance of these (La,Sr)(Co,Fe)O3-δ cathodes under high CO2 concentrations, relying on the experimental data. Electrochemical impedance spectroscopy results revealed that La0.6Sr0. 4Co0.2Fe0. 8O3-δ (LSCF−6428), La0.4Sr0. 6Co0.2Fe0. 8O3-δ (LSCF−4628), and La0.4Sr0. 6Co0.2Fe0. 7Nb0.1O3-δ (LSCFN−46271) cathodes exhibited persistent degradation when exposed to CO2 at temperatures of 650 °C or 800 °C during the durability-testing period. An increase in electrode polarization resistance was observed upon CO2 introduction to the electrode, but electrode performance recovered upon returning to a pure air environment. Furthermore, X-ray diffraction and scanning electron microscopy analyses confirmed that CO2 did not cause permanent damage to the (La,Sr)(Co,Fe)O3-δ cathodes. These findings indicate that the (La,Sr)(Co,Fe)O3-δ cathodes exhibit excellent resistance to CO2-induced corrosion.
Deneb: An Open-source High-performance Flow Solver based on DRM-DG Method

: High-order methods are being recognized as powerful tools for handling scale-resolving simulations over complex geometry. However, several obstacles still block their complete applications to practical engineering problems: a compromise between accuracy and eﬃciency on mixed-curved meshes, inherent vulnerability to numerical oscillations, and lack of open-source high-performance solvers for researchers. To address these issues, we present Deneb, an open-source high-order accurate numerical solver that enables high-performance scale-resolving simulations on PDE-based ﬂow systems. Deneb uses the physical domain-based modal discontinuous Galerkin (DG) method; thus, it can provide an arbitrary high-order accurate solution on mixed-curved meshes and has the potential for handling polyhedral meshes as well. However, the DG method becomes signiﬁcantly expensive on high-order curved elements when using quadrature rules, blocking its applicability to practical engineering problems. To avoid this, the direct reconstruction method (DRM) is applied to the DG volume and surface integrals to perform eﬃcient numerical integration on non-aﬃne elements without accuracy loss. The resulting DRM-DG method eliminates the severe cost of a quadrature-based approach on mixed-curved meshes. Deneb oﬀers explicit and implicit Runge–Kutta methods as well to achieve high-order accuracy in time. In addition, Krylov subspace methods and preconditioners are available for high-performance linear system solving in parallel. Deneb possesses a strong capability to resolve multi-physical shocks without numerical instabilities with the aid of multi-dimensional limiting and artiﬁcial viscosity methods. In particular, the hierarchical multi-dimensional limiting process enables eﬃcient computations of supersonic ﬂows without time-step restriction. The current release of Deneb covers the simulations of hypersonic equilibrium and magneto-hydrodynamic ﬂows as well as compress-ible Navier–Stokes equations, but it has the potential to solve any PDE-based multi-physical ﬂow systems. Several benchmark problems are presented to highlight Deneb’s capability to perform scale-resolving and multi-physical ﬂow simulations. A scalability test is also presented to verify the scaling characteristics of Deneb for high-performance computing.
Aerodynamic model comparison for an X-shaped vertical-axis wind turbine

Abstract. This article presents a comparison study of different aerodynamic models for an X-shaped vertical-axis wind turbine and offers insight into the 3D aerodynamics of this rotor at fixed pitch offsets. The study compares six different numerical models: a double-multiple streamtube (DMS) model, a 2D actuator cylinder (2DAC) model, an inviscid free vortex wake model (from CACTUS), a free vortex wake model with turbulent vorticity (from QBlade), a blade-resolved unsteady Reynolds-averaged Navier–Stokes (URANS) model, and a lattice Boltzmann method (from PowerFLOW). All models, except URANS and PowerFLOW use the same blade element characteristics other than the number of blade elements. This comparison covers the present rotor configuration for several tip-speed ratios and fixed blade pitch offsets without unsteady corrections, except for the URANS and PowerFLOW which cover a single case. The results show that DMS and 2DAC models are inaccurate – especially at highly loaded conditions, are unable to predict the downwind blade vortex interaction, and do not capture the vertical/axial induction this rotor exhibits. The vortex models are consistent with each other, and the differences when compared against the URANS and PowerFLOW mostly arise due to the unsteady and flow curvature effects. Furthermore, the influence of vertical induction is very prominent for this rotor, and this effect becomes more significant with fixed pitch offsets where the flow at the blade root is considerably altered.
A Multi-Fidelity Gaussian Process Regression Method for Probabilistic Wind Farm Power Curve Estimation

Accurate estimation of the power curve for wind turbines or wind farms is crucial to ensure their efficient operation and management. However, conventional methods for power curve estimation rely either on expensive and infrequent measurements or on low-quality numerical simulations. Moreover, the majority of previous studies on power curve estimation for wind turbines or wind farms focused on deterministic estimation, which provides a point estimate of the relationship between wind speed and power generation. Nevertheless, the deterministic approach fails to consider the inherent uncertainty associated with wind energy production resulting from varying turbine characteristics. This can lead to inaccurate power generation estimation and suboptimal decisions regarding energy management. In this paper, a kernel density estimation (KDE) based Multi-Fidelity Gaussian Process Regression (MFGPR) model is proposed to fuse theoretical power curve data and the ground true measurements to create a mapping of wind speed and wind power. By conducting a case study on an actual wind farm in China, the efficacy of the proposed MFGPR model was demonstrated in characterizing the variability of wind power. The probabilistic MFGPR model was also able to generate confidence intervals that encompassed the measured power, thereby improving the accuracy and confidence in wind power estimation or wind resource assessment. Overall, the proposed MFGPR model offers a reliable approach to integrate high-fidelity ground measurements and theoretical power curve data, resulting in precise wind resource assessment and power estimation.
Calcium regulates cortex contraction in Physarum polycephalum

The tubular network-forming slime mold Physarum polycephalum is able to maintain long-scale contraction patterns driven by an actomyosin cortex. The resulting shuttle streaming in the network is crucial for the organism to respond to external stimuli and reorganize its body mass giving rise to complex behaviors. However, the chemical basis of the self-organized flow pattern is not fully understood. Here, we present ratiometric measurements of free intracellular calcium in simple morphologies of Physarum networks. The spatiotemporal patterns of the free calcium concentration reveal a nearly anti-correlated relation to the tube radius, suggesting that calcium is indeed a key regulator of the actomyosin activity. We compare the experimentally observed phase relation between the radius and the calcium concentration to the predictions of a theoretical model including calcium as an inhibitor. Numerical simulations of the model suggest that calcium indeed inhibits the contractions in Physarum, although a quantitative difference to the experimentally measured phase relation remains. Unraveling the mechanism underlying the contraction patterns is a key step in gaining further insight into the principles of Physarum’s complex behavior.
Homogenisation of cystic ﬁ brosis sputum by sonication — An essential step for Aspergillus PCR

The importance of Aspergillus as a lung pathogen in cystic ﬁ brosis (CF) is becoming increasingly recognised. However, fungal culture of CF sputum is unreliable and there is no consensus for identifying phenotypes beyond ABPA that may bene ﬁ t from antifungal therapy. There are no published studies using real-time PCR to detect Aspergillus in CF sputum. The major barrier to sensitive detection of Aspergillus using PCR is sputum homogenisation. This study aimed to optimise sputum homogenisation utilising sonication to improve Aspergillus DNA extraction. Sonication amplitude and duration that enabled sputum homogenisation but ensured preservation of DNA integrity were ﬁ rst determined. 160 sputum samples were collected from CF patients. 49 of the sputum samples were split, one half was used for standard culture and the other half was homogenised with NALC – NaOH before undergoing DNA extraction. The subsequent 111 samples were homogenised with dithiothreitol plus sonication prior to culture and DNA extraction. Real-time PCR targeting a portion of the 18S rDNA of Aspergillus was performed on all DNA extractions. In the 49 samples with no sonication 8 (16%) were culture positive but only 4 of these were PCR positive. However, PCR was positive in 11 culture negative samples. PCR after sonication showed a signi ﬁ cant improvement in sensitivity: 33 (30%) were culture and PCR positive, 48 (43%) were culture negative, but PCR positive (p b 0.0001) and 30 (27%) were culture and PCR negative. The combination of dithiothreitol and sonication to homogenise sputum increases PCR yield, with PCR being substantially more sensitive than culture.
Internship proposal

Additive Manufacturing (AM) technologies are now capable of fabricating microstructures at the scale of microns, therefore enabling to precise control of the macroscopic physical behavior. This control empowers a wide range of industrial applications by bringing high-performance customized materials. The design of microstructures with tailored macroscopic properties was introduced in the 1990s [12]. A key challenge is to relate the parameters of the microscopic structure to the macroscopic physical behavior. Homogenization methods [1] have been extensively used to predict the homogeneous physical behavior of heterogeneous materials (see Figure 1). Conversely, inverse homogenization [14] methods seek to find a microstructure with a prescribed macroscopic physical behavior. State of the art methods consider periodic microstructures [14, 12, 13] (see Figure 2), offering compact storage, efficient display, and simulation of the macroscopic physical behavior.
Azo-Linkage Redox Metal–Organic Framework Incorporating Carbon Nanotubes for High-Performance Aqueous Energy Storage

The design of well-defined hierarchical free-standing electrodes for robust high-performance energy storage is challenging. We report herein that azo-linkage redox metal–organic frameworks (MOFs) incorporate single-walled carbon nanotubes (CNTs) as flexible electrodes. The in situ-guided growth, crystallinity and morphology of UiO-66-NO2 MOFs were finely controlled in the presence of CNTs. The MOFs’ covalent anchoring to CNTs and solvothermal grafting anthraquinone (AQ) pendants endow the hybrid (denoted as CNT@UiO-66-AQ) with greatly improved conductivity, charge storage pathways and electrochemical dynamics. The flexible CNT@UiO-66-AQ displays a highest areal specific capacitance of 302.3 mF cm−2 (at 1 mA cm−2) in −0.4~0.9 V potential window, together with 100% capacitance retention over 5000 cycles at 5 mA cm−2. Its assembled symmetrical supercapacitor (SSC) achieves a maximum energy density of 0.037 mWh cm−2 and a maximum power density of 10.4 mW cm−2, outperforming many MOFs-hybrids-based SSCs in the literature. Our work may open a new avenue for preparing azo-coupled redox MOFs hybrids with carbaneous substrates for high-performance robust aqueous energy storage.
Assessment and Diagnostic Classification Using DC:0-5 in Early Childhood Mental Health Clinics: The Protocol for the Developmental Psychiatry Diagnostic Challenges Study (DePsy)

Mental health problems in early childhood are common, but there is a lack of psychiatric research on this age group. DC:0-5 is a multiaxial classification system for mental disorders in early childhood, providing a framework for standardizing clinical practice and research. However, research on the validity of DC:0-5 is scarce. The Developmental Psychiatry Diagnostic Challenges Study (DePsy) is a multi-site, prospective clinical study including six German early childhood mental health (ECMH) clinics. The main objective of the study is to contribute to the validation of Axis I and Axis II of DC:0-5. A second aim of the study is to describe the population of the participating clinics regarding diagnoses, family context, and treatment outcomes. Additionally, the impact of environmental risk factors, including parental Adverse Childhood Experiences (ACEs) and media use, on child psychopathology and caregiver–child relationships will be examined. Over two years, patients aged 0.0–5.9 years old will be enrolled in the study. Assessments include ICD-10 and DC:0-5 diagnoses, developmental tests, video-based observations of caregiver—child interactions, and questionnaires on child psychopathology, media use, parental stress, and treatment satisfaction. Study results will promote the standardization of assessment and treatment in ECMH clinics aiming to improve the development of patients and their families.
Characterization of the Chemopreventive Properties of Cannabis sativa L. Inflorescences from Monoecious Cultivars Grown in Central Italy

Hemp bioproducts hold great promise as valuable materials for nutraceutical and pharmaceutical applications due to their diverse bioactive compounds and potential health benefits. In line with this interest and in an attempt to valorize the Lazio Region crops, this present study investigated chemically characterized hydroalcoholic and organic extracts, obtained from the inflorescences of locally cultivated Felina 32, USO 31, Ferimon and Fedora 17 hemp varieties. In order to highlight the possible chemopreventive power of the tested samples, a bioactivity screening was performed, which included studying the antimutagenic activity, radical scavenging power, cytotoxicity in human hepatoma HepG2 cells, leakage of lactate dehydrogenase (LDH) and modulation of the oxidative stress parameters and glucose-6-phosphate dehydrogenase (G6PDH) involved in the regulation of the cell transformation and cancer proliferation. Tolerability studies in noncancerous H69 cholangiocytes were performed, too. The organic extracts showed moderate to strong antimutagenic activities and a marked cytotoxicity in the HepG2 cells, associated with an increased oxidative stress and LDH release, and to a G6PDH modulation. The hydroalcoholic extracts mainly exhibited radical scavenging properties with weak or null activities in the other assays. The extracts were usually well-tolerated in H69 cells, except for the highest concentrations which impaired cell viability, likely due to an increased oxidative stress. The obtained results suggest a possibility in the inflorescences from the Felina 32, USO 31, Ferimon and Fedora 17 hemp varieties as source of bioactive compounds endowed with genoprotective and chemopreventive properties that could be harnessed as preventive or adjuvant healing strategies.
Lateral Flow Immunoassay Based on Quantum-Dot Nanobeads for Detection of Chloramphenicol in Aquatic Products

Quantum dot nanobeads (QBs) were used as signal source to develop competitive lateral flow immunoassay (LFIA) for the detection of chloramphenicol (CAP). The quantitative detection of CAP was achieved by calculating the total color difference (∆E) values of the test line (T line) using the images of test strips. QB-based LFIA (QBs-LFIA) allowed the effective dynamic linear detection of CAP in the range of 0.1–1.5 ng/mL. The limit of detection (LOD) was 3.0 ng/mL, which was 50 and 667 times lower than those achieved for two different brands of colloidal gold kits. The recoveries of CAP during real-sample detection were 82.82–104.91% at spiked levels of 0.1, 0.7, and 1.5 ng/mL. These results indicate that the developed QBs-LFIA facilitates the sensitive detection of CAP.
Well-being in the Paris region: widening regional disparities despite overall improvement

Has the socio-spatial divide widened over the past 10 years in the Paris region? Using an original method inspired by the work of Amartya Sen to measure the distribution of well-being in the Paris region between 1999 and 2006, Lise Bourdeau-Lepage and Elisabeth Tovar come to a rather pessimistic conclusion: the general rise in well-being actually masks widening regional disparities and setbacks in the northern suburbs of Paris, which are increasingly disadvantaged.
Water exploitation-induced climate change

Downstream countries can experience a range of direct, indirect, and feedback effects from upstream water piracy. These consequences can range from economic decline due to decreased availability of water for agriculture and fisheries, to the disruption of ecosystems from alterations to water flows and ecosystems, to the potential for political tensions that arise due to unequal access to water resources. The upstream country backs the downstream country's government to serve its own interests. The loss of the heat-storing water medium of the Aral Sea due to the former Soviet Union pirating water from the basin's feeding rivers for cotton production has resulted in warmer summers and cooler winters in the region than before. India is turning the basins of the Ganges and the Teesta into another Aral Sea basin, while deluging the basins with recurrent floods through the sudden release of water from the Farakka and Teesta barrages, and other trans-border river barrages. India's threat to revoke the more than six-decade-old Indus River water treaty has put Pakistan at a severe disadvantage, as extreme climatic events and an increase in irrigation progression have been linked to an increase in lightning-related fatalities. Some Nobel Laureate Professors have mistakenly identified energy insolvency as the cause of water piracy amid the competing plans of building dams and barrages by India and China, which threatens to turn the lower Brahmaputra and Mekong basins into another Aral Sea-like disaster Climatologists should soon embark on a holistic study of CO 2 emissions and surface water exploitation in order to keep the planet livable, and use water for necessity rather than
PACKET THRESHOLD ALGORITHM COUPLED WITH MACHINE LEARNING FOR DDoS CLASSIFICATION ATTACKS

Today, DDoS attacks are the most common Internet threats. DDoS attacks are generated by attackers from anywhere to disable a company's servers from being accessed by users worldwide. An attacker can easily launch one or more types of DDoS attacks at a time. DDoS attacks that can be generated by attackers include Slowloris, UDP flood, Smurf, HTTP flood, TCP SYN flood and more. Therefore, we have proposed a technique called the Packet Threshold Algorithm (PTA) in this paper, where it is combined with several machine learning to classify normal packet and DDoS attacks, namely UDP flood, Smurf, TCP SYN flood and Ping of Death. There are four machine learning, which are K-Nearest Neighbor (KNN), Naïve Bayes, Logistic Regression and Support Vector Machine (SVM) combined with the Packet Threshold Algorithm (PTA) to reduce the false positive rate to obtain high detection accuracy. Among the four combinations of techniques, PTA-KNN has been considered the best technique in the context of the reduction of false positive rates. The determination of this best technique is based on the PTA-KNN has achieved the highest detection accuracy (99.83%) compared to the other three techniques with only a 0.02% false positive rate. The determination of this best technique is based on the PTA-KNN has achieved the highest detection accuracy (99.83%) compared to the other three techniques with only a 0.02% false positive rate.
Daily Activity Budget of Nicobar Long-tailed Macaque (Macaca fascicularis umbrosa) in Great Nicobar Island, India

Nicobar long-tailed macaques (Macaca fascicularis umbrosa Miller, 1902) are distributed in three Islands of Nicobar namely Great Nicobar, Little Nicobar and Katchal. Their insular population requires special attention from research and management perspectives. Daily activity budget of M.f. umbrosa in the Great Nicobar Island was studied from October 2011 to September 2013 by intensive direct observation method. Study revealed that Nicobar long-tailed macaque, undergoes most of the time for Locomotion (36.07%), followed by feeding (22.35%), resting or being inactive (15.74%), grooming (11.14%), vocalization (7.03%), playing (5.64%), sexual arousal (1.46%) and agonistic (0.56%). All daily activities have significant difference (χ2 = 1156.22; df = 7, P = 0.05). Chi-square test demonstrated that the daily activity budget differed significantly among the behaviours. Qualitative results found that the interaction within the group was fighting and grabbing food. The significant observation of disability in their legs was noticed in Nicobar Long-tailed Macaque. The relation between their behaviour and disability is also discussed.
Cryptocurrency factor momentum

Is there a momentum effect in cryptocurrency anomalies? To answer this, we analyze data from over 3900 coins spanning the years 2014 to 2022 and replicate 34 anomalies in the cross-section of cryptocurrency returns. We document a discernible pattern in factor premia: past winners consistently outperform losers. The effect persists across subperiods, withstands various methodological approaches, and its magnitude parallels that of its stock market counterpart. However, the autocorrelation in factor returns is not widespread and primarily stems from size and volatility anomalies. Additionally, unlike in stocks, cryptocurrency factor momentum originates from price momentum, which subsequently transfers to the factor level.