Miracle-dz/UniFAR-FTUs huggingface dataset:
Grazing incidence X-ray scattering from magnetic thin films and nanostructures

Grazing incidence scattering of synchrotron x-rays has been used to characterize the structure of magnetic thin films and periodic nanostructures. The combined metal and metal oxide films have been chosen to clarify the effects of growth processing techniques in technologically important magnetic and magnetoresistive thin film materials, and have particular relevance to the magnetic tunnel junction (MTJ) class of magnetic sensor. Co/Al2O3 thin films and Co/MgO multilayer thin films have been characterized using x-ray reflectivity and diffuse scatter analysis to explain how preparatory oxidation of the lower ferromagnet in an MTJ can reduce Neel interlayer coupling and improve the consistency of magnetoresistance. Measurements reveal differing effects for Al2O3 and MgO tunnel barrier materials. In Co/Al2O3 systems, preoxidation is found to reduce significantly chemical interdiffusion at the interface between the two layers, implying a more uniform oxidation of the barrier layer. In Co/MgO multilayers, an increased in-plane correlation length of the inherited interface roughness is seen after preoxidation. This implies that preoxidation suppresses the short wavelength undulations on both sides of the tunnel barrier that cause Neel coupling. Grazing incidence in-plane diffraction measurements on epitaxial Fe/MgO/Fe [001] and Fe/Au/MgO/Fe [001] films during annealing to 600 K have shown that, in both cases, the MgO lattice is initially strained towards being commensurate with the iron and gold layers, but relaxes after annealing towards a typical bulk MgO lattice. The iron and gold layers display linear thermal expansion at rates consistent with the bulk material. These in-plane lattice measurements demonstrate how the strain and strain dispersion in an epitaxial MgO barrier layer can be relieved under controlled annealing conditions. Finally, patterned thin film surfaces with submicron periodic symmetries have been studied by grazing incidence x-ray scattering. A novel semi-kinematical theory has been developed into a numerical algorithm capable of simulating the scatter from a wide range of arbitrary and disordered nanoscale arrays. This has allowed key structural parameters including array periodicity, symmetry and array coherence lengths to be extracted from experimental data.
Diagnosis of Water-Influx Locations of Horizontal Well Subject to Bottom-Water Drive through Well-Testing Analysis

Horizontal well (HW) has been widely applied to enhance well productivity and prevent water coning in the anisotropic reservoir subject to bottom-water drive. However, the water-cut increases quickly after only one or two years’ production in China while oil recovery still keeps at a very low level. It becomes a major challenge to effectively estimate production distribution and diagnose water-influx locations. Ignoring the effect of nonuniform production distribution along wellbore on pressure response may cause erroneous results especially for water-influx location determination. This paper developed an analytical method to determine nonuniform production distribution and estimate water-influx sections through well-testing analysis. Each HW is divided into multiple producing segments (PS) with variable parameters (e.g., location, production, length, and skin factor) in this model. By using Green’s functions and the Newman-product method, the novel transient pressure solutions of an HW can be obtained in the anisotropic reservoir with bottom-water drive. Secondly, the influences of nonuniform production-distribution on type curves are investigated by comparing the multisegment model (MSM) with the whole-segment model (WSM). Results indicate that the method proposed in this paper enables petroleum operators to interpret parameters of reservoir and HW more accurately by using well-testing interpretation on the basis of bottom-hole pressure data and further estimate water-influx sections and nonproducing segments. Additionally, relevant measures can be conducted to enhance oil production, such as water controlling for water-breakthrough segments and stimulation treatments for nonproducing locations.
Investigating the Dynamics of Interdisciplinary Evolution in Technology Developments

In a global economy where technology plays a vital role, technology fusion is important for developing designs of outstanding innovations. These designs often involve the transfer of knowledge between different technological industries, a term known as “interdisciplinarity”. This paper aims to contribute to the literature of interdisciplinary innovation by using patent indicators to explore how conventional technologies have evolved into interdisciplinary technologies in different industries. Patent citation analysis and negative binomial regression were conducted to: 1) observe the dynamics and evolution of interdisciplinary technologies and, 2) explore how interdisciplinary knowledge influences technology developments. The study found that interdisciplinary knowledge plays a significant role in the development of valuable technologies in all investigated industries. An important managerial implication from this is that firms should consider developing interdisciplinary technologies whenever possible.
Seismological Implication to the Tectonic Evolution of the Lützow-Holm Bay Region (East Antarctica)

Passive source studies using teleseismic events demonstrated heterogeneous structure in the Lützow-Holm Bay (LHB) region, East Antarctica. Depth variations of upper mantle discontinuities (410 and 660 km) were derived from long-period receiver functions by local array stations. Shallow depths in topography of upper mantle discontinuity were cleared beneath the continental ice sheet back azimuth. These results reflect a paleo- upwelling of the mantle plume associated with Gondwana breakup. Lithospheric mantle anisotropy derived by shear waves’ (SKS) splitting anticipated a relationship between “fossil” anisotropy and the past tectonics in NE-SW orientation. Origin of mantle anisotropy was assumed to be caused by supercontinent assembly rather than present asthe- nospheric flow parallel with absolute plate motion. The deep seismic surveys by active sources, moreover, were carried out over continental ice sheet and provided clear infor- mation on crust-mantle boundary, together with inner lithospheric mantle reflections. The extracted lithospheric cross-sectional images by seismic reflection analyses implied tectonic influence of compressive stress during Pan-African age.
Mycoplasma bovis mastitis in dairy cattle

Mycoplasma bovis has recently been identified increasingly in dairy cows causing huge economic losses to the dairy industry. M. bovis is a causative agent for mastitis, pneumonia, endometritis, endocarditis, arthritis, otitis media, and many other clinical symptoms in cattle. However, some infected cows are asymptomatic or may not shed the pathogen for weeks to years. This characteristic of M. bovis, along with the lack of adequate testing and identification methods in many parts of the world until recently, has allowed the M. bovis to be largely undetected despite its increased prevalence in dairy farms. Due to growing levels of antimicrobial resistance among wild-type M. bovis isolates and lack of cell walls in mycoplasmas that enable them to be intrinsically resistant to beta-lactam antibiotics that are widely used in dairy farms, there is no effective treatment for M. bovis mastitis. Similarly, there is no commercially available effective vaccine for M. bovis mastitis. The major constraint to developing effective intervention tools is limited knowledge of the virulence factors and mechanisms of the pathogenesis of M. bovis mastitis. There is lack of quick and reliable diagnostic methods with high specificity and sensitivity for M. bovis. This review is a summary of the current state of knowledge of the virulence factors, pathogenesis, clinical manifestations, diagnosis, and control of M. bovis mastitis in dairy cows.
Detection of phenoxy herbicide dosage in cotton crops through the analysis of hyperspectral data

ABSTRACT Although herbicide drifts are known worldwide and recognized as one of the major risks for crop security in the agriculture sector, the traditional assessment of damage in cotton crops caused by herbicide drifts has several limitations. The aim of this study was to assess proximal sensor and modelling techniques in the detection of phenoxy herbicide dosage in cotton crops. In situ hyperspectral data (400–900 nm) were collected at four different times after ground-based spraying of cotton crops in a factorial randomized complete block experimental design with dose and timing of exposure as factors. Three chemical doses: nil, 5% and 50% of the recommended label rate of the herbicide 2,4-D were applied to cotton plants at specific growth stages (i.e. 4–5 nodes, 7–8 nodes and 11–12 nodes). Results have shown that yield had a significant correlation (p-values <0.05) to the green peak (~550 nm) and NIR range, as the pigment and cell internal structure of the plants are key for the assessment of damage. Prediction models integrating raw spectral data for the prediction of dose have performed well with classification accuracy higher than 80% in most cases. Visible and NIR range were significant in the classification. However, the inclusion of the green band (around 550 nm) increased the classification accuracy by more than 25%. This study shows that hyperspectral sensing has the potential to improve the traditional methods of assessing herbicide drift damage.
Prospects for pretreatment methods of lignocellulosic waste biomass for biogas enhancement: opportunities and challenges

ABSTRACT Lignocellulose biomass/lignocellulosic waste biomass (LCB/LCWB) represents the largest renewable pool for potentially fermentable carbohydrates, which provides a good solution for bioenergy production. Although it is assumed to have a lower theoretical yield for biogas than waste material made of sugar or starch, it is free from the problems associated with other generations of biofuels. An inexpensive and efficient pretreatment method of LCB/LCWB is highly desired to achieve an economical biogas production process. This paper reviews the conventional, advanced and infant (i.e. under development) pretreatment methods that have been studied for enhancement of biogas production. In addition to various pretreatment methods, this article also reviews further aspects of the conventional, advanced and infant methods (nanotechnolgy) for pretreatment of LCB/LCWB. Thus, the article provides systematic technological strategies and new pretreatment approaches for sustainable bioprocessing of LCB/LCWB into value-added product.
A Comparative Study of Hedging Functions of Chinese and U.S. Bond Futures

: With the gradual deepening of China's interest rate marketization reform, the management of interest rate risk is becoming more and more important. Treasury bond futures are the main standardized interest rate risk management tools in the world. This paper conducts a systematic research and comparison of the hedging function of the treasury bond futures markets in China and the United States. The VAR model and DCC-GARCH model are used to study the hedging function of the treasury bond futures market in China and the United States. It is found that the hedging efficiency of the U.S. treasury bond futures market is better than that of China's treasury bond futures market, and China's treasury bond futures market still needs further reform and development, and this paper puts forward some corresponding suggestions.
Near‐continuous tremor and low‐frequency earthquake activities in the Alaska‐Aleutian subduction zone revealed by a mini seismic array

Tectonic tremor and low‐frequency earthquakes (LFEs) are relatively poorly studied in the Alaska‐Aleutian subduction zone due to the limited data availability, difficult logistics, and rugged terrain. Using 2 months of continuous data recorded by a mini seismic array in the Akutan Island, we detect near‐continuous tremor activity with an average of 1.3 h of tectonic tremor per day using a beam backprojection method. Tremor sources are clustered in two patches with an ~25 km gap in between them. In addition, we visually identify three low‐frequency earthquakes, and using them as templates, we detect ~1300 additional LFEs applying a matched‐filter method. Tremor and LFE activities agree well in space and time, and LFEs show a much smaller recurrence interval during tremor than during non‐tremor time periods. Tremor sources propagate both along the strike and dip directions of the subduction fault with velocities ranging between 13 and 110 km/h. Prolific patchy tremor and LFE activities suggest lateral heterogeneity in the locked to freely slipping transition zone, indicating that slow earthquakes may play an important role in the earthquake cycles in this subduction zone.
Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing

Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult to overlook. Knowledge distillation provides a means for practical model compression to alleviate this delay. Nevertheless, the prevailing knowledge distillation approaches are not explicitly designed for semantic hashing. They ignore the unique search paradigm of semantic hashing, the inherent necessities of the distillation process, and the property of hash codes. In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. To ensure the effectiveness of two kinds of search paradigms in the context of semantic hashing, BRCD first aligns the semantic spaces between the teacher and student models through a contrastive knowledge distillation objective. Additionally, to eliminate noisy augmentations and ensure robust optimization, a cluster-based method within the knowledge distillation process is introduced. Furthermore, through a bit-level analysis, we uncover the presence of redundancy bits resulting from the bit independence property. To mitigate these effects, we introduce a bit mask mechanism in our knowledge distillation objective. Finally, extensive experiments not only showcase the noteworthy performance of our BRCD method in comparison to other knowledge distillation methods but also substantiate the generality of our methods across diverse semantic hashing models and backbones. The code for BRCD is available at https://github.com/hly1998/BRCD.
Aerosol concentration and airflow distribution assessment in a multi-zone indoor space with mechanical ventilation: field measurements and CFD simulations

In the past decades, numerous studies were performed to investigate the effects of mechanical ventilation system (MV) on aerosol concentration in indoor environments. However, few attention was given to the prediction of aerosol concentration in multi-zone indoor spaces, where the distribution and migration of aerosol particles are significantly influenced by airflow patterns, room dimensions and geometries, supply and exhaust locations, internal partitions, and so on. This study aims at analyzing the aerosol concentration and airflow pattern in a players’ dressing room (composed of three-connected spaces) of a stadium by means of Computational Fluid Dynamcs (CFD) simulations. The simulations are performed with Reynolds-averaged Navier-Stokes (RANS) approach by means of passive scalar (PS), drift flux (DF), and Eulerian-Lagrangian (EL) methods. The aerosol concentration ( c ) from field measurements, previously performed in the same indoor environment, is then compared to simulated (CFD) data. The comparison shows that the deviation between the measured and simulated data is of about 37% for the PS method, 24.3% for the DF method, and 18% for the EL method.
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data

While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over. In this work, we address this concern for tabular data. Starting with simple qualitative tests for whether an LLM knows the names and values of features, we introduce a variety of different techniques to assess the degrees of contamination, including statistical tests for conditional distribution modeling and four tests that identify memorization. Our investigation reveals that LLMs are pre-trained on many popular tabular datasets. This exposure can lead to invalid performance evaluation on downstream tasks because the LLMs have, in effect, been fit to the test set. Interestingly, we also identify a regime where the language model reproduces important statistics of the data, but fails to reproduce the dataset verbatim. On these datasets, although seen during training, good performance on downstream tasks might not be due to overfitting. Our findings underscore the need for ensuring data integrity in machine learning tasks with LLMs. To facilitate future research, we release an open-source tool that can perform various tests for memorization \url{https://github.com/interpretml/LLM-Tabular-Memorization-Checker}.
f(R,T) Analogue Gravity in (2+1) D graphene sheet

We examine the analogue gravity model within the context of f(R,T) gravity applied to graphene. The derivation of the Lagrangian density in two dimensions (2D) is undertaken, accounting for the altered gravitational effects as characterized by the function f(R,T). The Lagrangian encompasses the quasiparticle field $\psi(x)$, its adjoint $\overline{\psi}$, the effective metric tensor $g^{\mu\nu}$, and the gauge field $A_{\nu}$. The equations of motion are established through variational principles applied to the Lagrangian, resulting in modified Dirac equations. We discuss the interpretation of the additional terms in the equations of motion and their significance in capturing the modified gravitational dynamics in the graphene system. Our findings contribute to the understanding of analogue gravity models and their applications in condensed matter systems.
PR-SLAM: Parallel Real-Time Dynamic SLAM Method Based on Semantic Segmentation

SLAM (Simultaneous Localization and Mapping) is the core technology enabling autonomous exploration by mobile robots in unknown environments. While there have been numerous impressive SLAM systems developed, many of them are primarily based on the assumption of static environments, limiting their applicability in real-world settings. In order to enhance the robustness and accuracy of systems in dynamic real-world scenarios, we have introduced a parallelized real-time SLAM system called PR-SLAM, building upon the foundation of ORB-SLAM3. This algorithm introduces a dynamic probability update strategy within the semantic segmentation thread, completely decoupling the semantic segmentation thread from the tracking thread. Theoretically, the processing time per frame is solely dependent on the runtime of the tracking thread. Furthermore, we employ a geometric approach based on reprojection error to compensate for semantic gaps generated during semantic segmentation model inference. We have also designed a semantic optimization thread based on the dynamic probability of map points to optimize camera poses during semantic gaps. Finally, to reduce semantic gaps, we have performed lightweight modifications to SOLOV2. Comparative experiments were conducted against the state-of-the-art SLAM systems using the TUM dataset. The results indicate significant improvements in both accuracy and real-time performance for PR-SLAM. When compared to ORB-SLAM3, PR-SLAM achieved a remarkable 97.83% improvement in absolute trajectory accuracy and demonstrated an impressive 86.71% increase in runtime speed compared to DynaSLAM.
Predicting postinterventional rupture of intracranial aneurysms using arteriography-derived radiomic features after pipeline embolization

Background and purpose Postinterventional rupture of intracranial aneurysms (IAs) remains a severe complication after flow diverter treatment. However, potential hemodynamic mechanisms underlying independent predictors for postinterventional rupture of IAs remain unclear. In this study, we employed arteriography-derived radiomic features to predict this complication. Methods We included 64 patients who underwent pipeline flow diversion for intracranial aneurysms, distinguishing between 16 patients who experienced postinterventional rupture and 48 who did not. We performed propensity score matching based on clinical and morphological factors to match these patients with 48 patients with postinterventional unruptured IAs at a 1:3 ratio. Postinterventional digital subtraction angiography were used to create five arteriography-derived perfusion parameter maps and then radiomics features were obtained from each map. Informative features were selected through the least absolute shrinkage and selection operator method with five-fold cross-validation. Subsequently, radiomics scores were formulated to predict the occurrence of postinterventional IA ruptures. Prediction performance was evaluated with the training and test datasets using area under the curve (AUC) and confusion matrix-derived metrics. Results Overall, 1,459 radiomics features were obtained, and six were selected. The resulting radiomics scores had high efficacy in distinguishing the postinterventional rupture group. The AUC and Youden index were 0.912 (95% confidence interval: 0.767–1.000) and 0.847 for the training dataset, respectively, and 0.938 (95% confidence interval, 0.806–1.000) and 0.800 for the testing dataset, respectively. Conclusion Radiomics scores generated using arteriography-derived radiomic features effectively predicted postinterventional IA ruptures and may aid in differentiating IAs at high risk of postinterventional rupture.
Effect of hydroperiod on CO2 fluxes at the air‐water interface in the Mediterranean coastal wetlands of Doñana

Wetlands are productive ecosystems that play an important role in the Earth's carbon cycle and thus global carbon budgets. Climate variability affects amount of material entering and the metabolic balance of wetlands, thereby modifying carbon dynamics. This study presents spatiotemporal changes in air‐water CO2 exchange in the vast wetlands of Doñana (Spain) in relation to different hydrological cycles. Water sources feeding Doñana, including groundwater and streams, ultimately depend on the fluctuating balance between annual precipitation and evapotranspiration. Hence, in order to examine the contribution of the rainfall pattern to the emission/capture of CO2 by a range of aquatic habitats in Doñana, we took monthly measurements during severely wet, dry, and normal hydrological years (2010–2013). During wet hydrological cycles, CO2 outgassing from flooded marshes markedly decreased in comparison to that observed during subsequent dry‐normal cycles, with mean values of 25.84 ± 19 and 5.2 ± 8 mmol m−2 d−1, respectively. Under drier meteorological conditions, air‐water CO2 fluxes also diminished in permanent floodplains and ponds, which even behaved as mild sinks for atmospheric CO2 during certain periods. Increased inputs of dissolved CO2 from the underground aquifer and the stream following periods of high rainfall are believed to be behind this pattern. Large lagoons with a managed water supply from an adjacent estuary took up atmospheric CO2 nearly permanently. Regional air‐water carbon transport was 15.2 GgC yr−1 under wet and 1.24 GgC yr−1 under dry meteorological conditions, well below the estimated net primary production for Doñana wetlands, indicating that the ecosystem acts as a large CO2 sink.