Miracle-dz/UniFAR-FTUs huggingface dataset:
Vertical accretion versus elevational adjustment in UK saltmarshes: an evaluation of alternative methodologies

Abstract Simultaneous measurements of vertical accretion from marker horizons and marsh-elevation change from sedimentation-erosion tables (SET) were made in selected marshes along the East Anglian coast of the UK in order to address the following objectives: (1) to ascertain the validity of treating accretion measurements obtained within tidally dominated, minerogenic saltmarshes as equivalent to surface elevation changes; (2) to explore the implications, in terms of physical and biological processes, of discrepancies between separately measured vertical accretion and elevation change within contrasting marsh types. Data were collected from several marsh environments at Scolt Head Island and Stiffkey on the North Norfolk coast and at an experimental managed realignment project near Tollesbury, Essex. Scolt Head Island was selected for its long-term datasets of marsh accretion, Stiffkey for its contrasting open coast-back barrier settings, and Tollesbury for its experimental management, in order to illustrate the potential application of the SET method and evaluate the relationship between vertical accretion and elevation change in a variety of marsh settings. The relationship between vertical accretion and elevation change varied widely among marsh settings of different age and height (within the tidal frame) at Scolt Head Island and Stiffkey. Rates of vertical accretion and elevation change were similar in the older and midheight settings on Scolt Head Island, indicating control of elevation change by surface accretionary processes (e.g. sediment deposition). However, subsurface processes controlled elevation at three of the marsh sites. Spartina Marsh, the youngest and lowest of the back barrier settings at Scolt Head Island, exhibited continuous shallow subsidence (vertical accretion greater than elevation change) over a 4-year period, implying that compaction controls elevation change. In the upper part of Hut Marsh and the interior of the Stiffkey marshes, elevation change exceeded vertical accretion suggesting that subsurface processes (e.g. organic accumulation) controlled elevation in these settings. Surface accretionary processes control elevation change in both the highly dynamic, outer marsh at Stiffkey and the low, restored marsh at Tollesbury. Despite the occurrence of shallow subsidence, all sites gained elevation at an annual rate comparable to that of sea-level rise. In summary, the SET provides the means to critically evaluate the influence of vertical accretion measures on elevation and represents an improved method by which to evaluate the vulnerability of a marsh to sea-level rise.
Detecting and tracking small moving target in infrared image sequence

Nowadays, infrared imaging systems play important roles in the field of civil and military. Especially small infrared target detecting and recognizing is one of the most widely use. The capability of target-detection algorithm is an important index of the system. This paper presents a novel algorithm for detecting a small moving target in infrared (IR) image sequences and finding its mass center, and recording the target moving track. In the target searching and recognizing algorithm of infrared image sequences, infrared image sequence is broken into frames, filtered by spatial filter algorithm, which helped to reduce granular noise. We use the Canny algorithm factor to find the edge of the target, and the result of detecting target edge is process by ecological open-loop filter method, including erosion and dilation algorithm with a same scale. Then, the candidate targets are recognized and saved temporarily. In order to get the mass centers of the candidate targets, the valid area of the candidate targets is defined by different weight valves, and then the mass centers are calculated by weighted average algorithm, and record per frame. After got several frames mass centers of the candidate targets, we get rid of the non-target mass centers by frame difference algorithm, and get the real mass center of the small moving infrared target. If the background is observed for enough time, the effect of frame difference algorithm is more efficiency. Finally, the moving track of the target is found out. The infrared (IR) image sequences used here are obtained through an IR camera in the laboratory, which uses a 288*384 silicon infrared image sensor produced by ULIS company. The methods referred above are realized and simulated on compute with Matlab. Theory analysis and experiments prove the method is reasonable and efficient.
Experimental and DFT Atomistic Insights into the Mechanism of Corrosion Protection of Low-Carbon Steel in an Acidic Medium by Polymethoxyflavones from Citrus Peel Waste

Developing green anticorrosive films is gaining great attention in science and engineering. Citrus fruit peels are mainly discarded as waste, although they can be an excellent repository of phytochemicals, that can be exploited as mitigating agents for corrosion. Herein, we report the high anticorrosion activity of a citrus extract for low-carbon steel in 1 M HCl solution at different temperatures. The main extract constituents were identified by MS and NMR. Two polymethoxyflavones (PMFs), namely nobiletin and heptamethoxyflavone, were identified as major constituents of the extract and the crude PMFs-based extract was investigated for corrosion protection. Using potentiodynamic polarization, weight loss and electrochemical impedance spectroscopy (EIS) methods, this extract revealed improved inhibition efficiency of 94%. The inhibition mechanism was elucidated by considering electrochemical kinetics and adsorption thermodynamics. SEM and UV–vis supported the electrochemical results. PMFs-based extract acted as a mixed-type inhibitor with a Langmuir model of adsorption. Importantly, DFT simulations provided atomic-level insights into the inhibition mechanism and unraveled donor-acceptor interactions between the methoxy groups of PMFs and iron atoms, facilitating the formation of a stable inhibition adsorption layer, and thus supporting the experimental findings. In addition to the physical barrier effect of PMF inhibitor, π-back bonding effect between PMF and steel was suggested.
Dense attention network identifies EEG abnormalities during working memory performance of patients with schizophrenia

Introduction Patients with schizophrenia typically exhibit deficits in working memory (WM) associated with abnormalities in brain activity. Alterations in the encoding, maintenance and retrieval phases of sequential WM tasks are well established. However, due to the heterogeneity of symptoms and complexity of its neurophysiological underpinnings, differential diagnosis remains a challenge. We conducted an electroencephalographic (EEG) study during a visual WM task in fifteen schizophrenia patients and fifteen healthy controls. We hypothesized that EEG abnormalities during the task could be identified, and patients successfully classified by an interpretable machine learning algorithm. Methods We tested a custom dense attention network (DAN) machine learning model to discriminate patients from control subjects and compared its performance with simpler and more commonly used machine learning models. Additionally, we analyzed behavioral performance, event-related EEG potentials, and time-frequency representations of the evoked responses to further characterize abnormalities in patients during WM. Results The DAN model was significantly accurate in discriminating patients from healthy controls, ACC = 0.69, SD = 0.05. There were no significant differences between groups, conditions, or their interaction in behavioral performance or event-related potentials. However, patients showed significantly lower alpha suppression in the task preparation, memory encoding, maintenance, and retrieval phases F(1,28) = 5.93, p = 0.022, η2 = 0.149. Further analysis revealed that the two highest peaks in the attention value vector of the DAN model overlapped in time with the preparation and memory retrieval phases, as well as with two of the four significant time-frequency ROIs. Discussion These results highlight the potential utility of interpretable machine learning algorithms as an aid in diagnosis of schizophrenia and other psychiatric disorders presenting oscillatory abnormalities.
An assessment of surge-induced crevassing and the formation of crevasse squeeze ridges

[1] Evidence for extensive crevassing is preserved on the deglaciated forelands of many surging glaciers as crevasse squeeze ridges (CSRs). At some point these crevasses make direct connection with the bed in order to become sediment filled, and full-depth connections have been inferred from turbid water up-wellings in crevasses and the formation of concertina eskers. The dynamics of seven surging glaciers are assessed, using a linear elastic fracture mechanics approach, to determine the likely directions of fracture and controlling parameters for Mode I crevasses. Extensional surface strain rates are insufficient to promote top-down full-depth penetration. For small crevasse spacing (<5 m), surface strain rates are sufficient for top-down crevassing to depths of 4–12 m, explaining the extensive surface crevassing associated with glacier surging. As has been shown in other settings, top-down, full-depth crevassing is only possible when water is added and approaches 97% of the crevasse depth. The provision of sufficient meltwater to facilitate this is problematic due to the extensive surface crevassing, unless water can move along connected crevasses to a dominant water capturing crevasse. For ice thicknesses greater than ∼200 m, basal water pressures in excess of 80–90% of flotation are required for full-depth, bottom-up crevassing. Field evidence suggests that this is the default for surging glaciers and that, on occasion, water pressures may even become artesian. CSRs, found across many surging glacier forelands and ice margins, most likely result from the infilling of basal crevasses, driven for the most part, bottom-up, by high basal water pressures.
Primary bioreceptivity of limestones used in southern European monuments

Abstract Different Mediterranean Basin limestones, like Calcário Ançã (Portugal), Calcário Lioz (Portugal), Piedra San Cristobal (Spain), Piedra Escúzar (Spain) and Pietra di Lecce (Italy), have been widely used as building materials in the European architecture. The aim of this study was focused on biodeterioration, mainly on evaluation of the primary bioreceptivity of those materials. A set of samples was inoculated with a cultured photosynthetic biofilm under laboratory conditions. Several assessment tools were applied to monitor the colonization overtime of the different lithotypes. After 3 months of incubation the colonization occurred endolithically in some lithotypes, namely Piedra San Cristobal and Piedra Escúzar. Spectrophotometric determination of chlorophyll a was a useful analytical technique to achieve the total amount of photosynthetic biomass on rock substrates, demonstrating that Piedra Escúzar and Calcário Lioz were the highest and lowest bioreceptive lithotypes, respectively. Microscopic and image analyses were essential to understand the stone colonization process and its pattern of distribution. Physical stone parameters and exposure conditions were shown to play an important role in the establishment and development of photosynthetic colonization.
Tidal Constituents in the Shallow Waters of the Southwest Indian Coast

A two-dimensional hydrodynamic model was set up and validated for the west coast of India. The spatio-temporal variation of the tidal constituents for a 110 km stretch of the southwest coast of India was then studied by setting up a fine grid model. The study brings out variability in the shallow water constituents and their selective amplification. Within the Cochin estuary, the amplitudes of almost all the major tidal constituents show a gradual reduction upstream compared to other locations. The shallow water constituents show significant amplification and Z0, the constituent related to mean sea level shows five-fold amplification within the estuary.
BADANet: Boundary Aware Dilated Attention Network for Face Parsing

Over the past few years, deep learning techniques have revolutionized the field of face parsing by utilizing massive datasets to generate high-level features and achieve outstanding performance. Usually, these techniques involve Convolutional Neural Networks (CNNs) to derive features from the input image and then a decoder network to forecast the semantic labels for every pixel. Even with complex deep convolutional neural networks (DCNN), incorrect parsing leads to a semantic gap between identical features, especially at boundary levels. Face parsing has encountered additional challenges due to the inclusion of pose, illumination and facial expressions. In order to address these issues, a Boundary Aware Dilated Attention Network (BADANet) is introduced which explores the use of multi-scale techniques to improve the accuracy and robustness of the per-pixel frames. BADANet’s dilated attention module has been combined with a lightweight backbone to achieve exceptional results on LaPa, CelebAMask-HQ, and iBugMask. Extensive evaluations of the proposed method demonstrate its performance to be on par with various state-of-the-art techniques. The pipeline for the BADANet is available at https://github.com/abhigoku10/BADANet
Regional model simulations of New Zealand climate

Simulation of New Zealand climate is examined through the use of a regional climate model nested within the output of the Commonwealth Scientific and Industrial Research Organisation nine-level general circulation model (GCM). R21 resolution GCM output is used to drive a regional model run at 125 km grid spacing over the Australasian region. The 125 km run is used in turn to drive a simulation at 50 km resolution over New Zealand. Simulations with a full seasonal cycle are performed for 10 model years. The focus is on the quality of the simulation of present-day climate, but results of a doubled-CO2 run are discussed briefly. Spatial patterns of mean simulated precipitation and surface temperatures improve markedly as horizontal resolution is increased, through the better resolution of the country's orography. However, increased horizontal resolution leads to a positive bias in precipitation. At 50 km resolution, simulated frequency distributions of daily maximum/minimum temperatures are statistically similar to those of observations at many stations, while frequency distributions of daily precipitation appear to be statistically different to those of observations at most stations. Modeled daily precipitation variability at 125 km resolution is considerably less than observed, but is comparable to, or exceeds, observed variability at 50 km resolution. The sensitivity of the simulated climate to changes in the specification of the land surface is discussed briefly. Spatial patterns of the frequency of extreme temperatures and precipitation are generally well modeled. Under a doubling of CO2, the frequency of precipitation extremes changes only slightly at most locations, while air frosts become virtually unknown except at high-elevation sites.
In Vitro Culture Studies for the Mitigation of Heavy Metal Stress in Plants

Heavy metals are among the most common and dangerous contaminants; their action on plants, as well as the possibility for plants to effectively absorb and translocate them, have been studied for several years, mainly for exploitation in phytoremediation, an environmentally friendly and potentially effective technology proposed and studied for the recovery of contaminated soils and waters. In this work, the analysis has focused on the studies developed using in vitro techniques on the possibilities of mitigating, in plants, the stress due to the presence of heavy metals and/or improving their absorption. These objectives can be pursued with the use of different substances and organisms, which have been examined in detail. The following are therefore presented in this review: an analysis of the role of metals and metalloids; the use of several plant growth regulators, with their mechanisms of action in different physiological phases of the plant; the activity of bacteria and fungi; and the role of other effective compounds, such as ascorbic acid and glutathione.
GPS analyses of the Sumatra-Andaman earthquake

The Sumatra, Indonesia, earthquake on 26 December 2004 was one of the most devastating earthquakes in history. With a magnitude of Mw = 9.3 (revised based on normal-mode amplitudes by Stein and Okal, http://www.earth.northwestern.edu/people/seth/research/sumatra.html), it is the second largest earthquake recorded since 1900. It occurred about 100 km off the west coast of northern Sumatra, where the relatively dense Indo-Australian plate moves beneath the lighter Burma plate, resulting in stress accumulation. The average relative velocity of the two plates is about 6 cm/yr. On 26 December 2004, however, the two plates moved by a distance of several meters, releasing the stress accumulated over hundreds of years. The result was a devastating tsunami that hit coastlines across the Indian Ocean, killing about 300,000 people in Indonesia, Sri Lanka, India, Thailand, Burma, Malaysia, Somalia, and other countries (Guardian, 29 January 2005, http://www.guardian.co.uk/tsunami/story/0,15671,1380895,00.html).
Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: Analysis of the climate change signal

[1] A statistical bias correction technique is applied to twelve high-resolution climate change simulations of temperature and precipitation over Europe, under the SRES A1B scenario, produced for the EU project ENSEMBLES. The bias correction technique is based on a transfer function, estimated on current climate, which affects the whole Probability Distribution Function (PDF) of variables, and which is assumed constant between the current and future climate. The impact of bias correction on 21st Century projections, their inter-model variability, and the climate change signal is investigated, with focus being on discrepancies between the original and the bias-corrected results. As assessing the impact of climate change is significantly dependent on the frequency of extreme events, we also analyze the evolution of the shape of the PDFs, and extreme events indices. Results show that the ensemble mean climate change signal and its inter-model variability are generally conserved. However, the impact of the bias correction varies amongst regions, seasons and models, and differences up to 0.5°C for the summer temperature climate change signal are found in Southern Europe. Finally the bias correction is found to influence the probability of extreme events like extremely hot or frost days, which also impacts the climate change signal.
DeepComboSAD: Spectro-Temporal Correlation Based Speech Activity Detection for Naturalistic Audio Streams

Speech activity detection (SAD) serves as a crucial front-end system to several downstream Speech and Language Technology (SLT) tasks such as speaker diarization, speaker identification, and speech recognition. Recent years have seen deep learning (DL)-based SAD systems designed to improve robustness against static background noise and interfering speakers. However, SAD performance can be severely limited for conversations recorded in naturalistic environments due to dynamic acoustic scenarios and previously unseen non-speech artifacts. In this letter, we propose an end-to-end deep learning framework designed to be robust to time-varying noise profiles observed in naturalistic audio. We develop a novel SAD solution for the UTDallas Fearless Steps Apollo corpus based on NASA's Apollo missions. The proposed system leverages spectro-temporal correlations with a threshold optimization mechanism to adjust to acoustic variabilities across multiple channels and missions. This system is trained and evaluated on the Fearless Steps Challenge (FSC) corpus (a subset of the Apollo corpus). Experimental results indicate a high degree of adaptability to out-of-domain data, achieving a relative Detection Cost Function (DCF) performance improvement of over 50% compared to the previous FSC baselines and state-of-the-art (SOTA) SAD systems. The proposed model also outperforms the most recent DL-based SOTA systems from FSC Phase-4. Ablation analysis is conducted to confirm the efficacy of the proposed spectro-temporal features.
Long-lead forecasting of precipitation and wheat yields in Saskatchewan using teleconnection indices

Teleconnections among the central, east equatorial Pacific, the Pacific/North American (PNA) pattern, and Southern Oscillation (SO) and monthly precipitation, monthly temperature, and spring wheat yields in Saskatchewan are examined. When sea surface temperatures in the central equatorial Pacific are warmer than the air temperatures (strong El Nifio) there is an upward flux ofwater vapour into the atmosphere, convection, heat released by condensation, a strengthening of the westerlies and a vitalization of the Hadley circulation. When sea surface temperatures are colder than the air temperatures (strong La Nifia) in the central equatorial Pacific it produces the opposite influence on atmospheric processes. Composite analysis reveals that El Nifio and La Nifia are the primary modulators of the Pacific/North American pattern and movement of surface cyclones across the western continent. Correlation and composite analyses indicate that between 1950 and 1998 warmer than normal sea surface temperatures iIi the central and east equatorial Pacific during the winter and early spring (El Nifio) are associated with cooler and wetter conditions during the May through July period in Saskatchewan and higher wheat yields. Conversely cooler than normal sea surface temperatures in the central and east equatorial Pacific during the winter and early spring (La Nifia) are associated with hotter and drier conditions during the May through July period in Saskatchewan and lower wheat yields. The relationship appears to be strongest for the Brown soil zone and weakest for the Black soil zone in Saskatchewan.
Cognitive functions and sense of coherence in patients with carotid artery stenosis—Preliminary report

Background There is scarcely any data in the available literature on the relationship between sense of coherence (SOC) and cognitive functioning, and no information on the relationship between SOC and cognitive parameters in patients with carotid atherosclerosis. Aim The aim of this paper was to determine the relationship of SOC measured prior to carotid artery stenting (CAS) with neurocognitive functioning in patients with carotid atherosclerosis 12 months after CAS. Methods A total of 35 patients with carotid atherosclerosis completed the SOC-29 Orientation to Life Questionnaire (SOC-29) and completed a cognitive test battery twice, i.e., before (baseline–T1) and 12 months after stent implantation (follow-up–T2). Selected cognitive functions such as memory, attention, perception, visuospatial and executive functions and non-verbal fluency were assessed. Results One year after the procedure, patients with a higher SOC and sense of manageability present better performance in visual memory. Higher SOC and sense of meaningfulness are positively related to the speed of understanding the changing rules of card sorting (WCST). Conclusion Higher overall SOC and a component of sense of meaningfulness and manageability may be related to better cognitive functioning 1 year after the procedure.