Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization and Validation of Soil Frozen‐Thawing Parameterizations in Noah‐MP

Water‐heat transport in frozen soil influences the energy and water exchanges between land and atmosphere. However, frozen soil water‐heat transport simulation biases remaining in land surface model (LSM) result in uncertainties of climate model performance. In this study, the biases of the Noah with multi‐parameterization (Noah‐MP) land surface model in simulating soil water‐heat transport in freeze‐thaw (FT) process were investigated. On this basis, three parameterizations that were effective in other LSMs were implemented into Noah‐MP to optimize its performance. The results show that Noah‐MP still exhibits biases in simulating soil temperature and moisture in FT process. Even with various combinations of current parametrizations, the soil water–heat coupling relationship simulated by Noah‐MP does not align with observations. Nevertheless, the implementation of a virtual temperature parameterization, a liquid water‐dependent soil permeability parameterization, and modifications to soil hydraulic and thermal properties through considering the influences of organic matter and gravel, led to improvements in soil moisture and temperature simulations during both completely frozen and thawing periods. These enhancements resulted in a reduction of bias by approximately 20%–50%, and the simulated soil water–heat coupling relationship with implemented parameterizations closely matches observations. Global simulations further validate the improvements brought about by the implemented frozen‐thawing parameterizations in Noah‐MP.
Arabic Regional Dialect Identification (ARDI) using Pair of Continuous Bag-of-Words and Data Augmentation

—Author profiling is the process of finding characteristics that make up an author’s profile. This paper presents a machine learning-based author profiling model for Arabic users, considering the author’s regional dialect as a crucial characteristic. Various classification algorithms have been implemented: decision tree, KNN, multilayer perceptron, random forest, and support vector machines. A pair of Continuous Bag-of-Word (CBOW) models has been used for word representation. A well-known data set has been used to evaluate the proposed model and a data augmentation process has been implemented to improve the quality of training data. Support vector machines achieved a 50.52% f1-score, outperforming other models.
Modeling glacial meltwater plume dynamics and sedimentation in high‐latitude fjords

[1] A model, SedPlume, has been developed to simulate marine sediment deposited by glacial meltwater plumes emerging from tidewater glaciers. Turbid meltwater emerging from beneath a glacier into a fjord rises as a buoyant forced plume due to density contrasts with the ambient fjord water. SedPlume assumes that meltwater discharge flows at a constant rate for long enough periods that the plume reaches a steady state. Entrainment of ambient fluid into the turbulent plume is assumed to occur at a rate proportional to the local velocity of the plume. Plume motion is considered in two dimensions: one horizontal dimension (perpendicular to the glacier front) and the vertical dimension. An integral model is formulated for the conservation equations of volume, momentum, buoyancy, and sediment flux along the path of a turbulent plume injected into stably stratified ambient fluid. Sedimentation occurs from the plume when the radial component of the sediment fall velocity exceeds the entrainment velocity. When the plume reaches the surface, it is treated as a radially spreading surface gravity current, for which exact solutions exist for the sediment deposition rate. Flocculation of silt and clay particles is modeled using empirical measurements of particle settling velocities in fjords to adjust the settling velocity of fine-grained sediments. SedPlume has been applied to McBride Inlet, Alaska, a temperate glaciated fjord where the majority of sedimentation originates from meltwater sources. SedPlume produces rates and patterns of sedimentation in good agreement with observations, with calculated peak ice-proximal annual sedimentation rates of approximately 22 m yr−1.
Experimental investigation of CO2 breakthrough and flow mechanisms in shale

Supercritical CO2 breakthrough and flow mechanisms in shale have been investigated in laboratory experiments using a high pressure flow cell and cylindrical samples of shale from the Draupne formation in the North Sea. The main objective is to study the basic mechanisms involved in the breakthrough process and define the controlling parameters for supercritical CO2 flow in a low permeable shale. Experimental testing provides new insight into the CO2 breakthrough process through simultaneous measurements of deformation and ultrasonic velocities in the sample. A marked sample dilation associated with the CO2 breakthrough is identified accompanied with a pronounced drop in ultrasonic velocities. X-ray images of the sample using a high resolution 3D computer tomography (CT) scanner provide information on macroscopic fracture distribution inside the sample before and after testing. The CO2 breakthrough pressure for the Draupne material seems to depend on confining pressure and effective pressure rather than pore pressure difference across the sample. After breakthrough the effective CO2 permeability was found to follow a simple model for permeability in fractured rock. The drop in ultrasonic velocity was associated with mechanical changes and possible micro fracturing inside the sample. Based on our observations we conclude that pressure-induced opening of micro-fractures during the breakthrough process is an important mechanism for flow in addition to capillary displacement. Our findings may have important consequences for later testing and estimation of CO2 breakthrough pressure and flow in shale.
Cilnidipine Nanocrystals, Formulation and evaluation for Optimization of solubility and Dissolution Rate

Cilnidipine CLD fourth generation Ca+2 channel blockers, since cilnidipine has a very low solubility (BCS Class-II drug Low solubility High permeability) and extremely low medication compliance, it has been used to treat hypertension and hypertensive-associated vascular disorders.so cilnidipine can be formulated as nanocrystals NCs using solvent anti-solvent technology which can improve the solubility and bioavailability . Two different stabilizers(poloxamer 188 and Tween20) were utilized to prepare nine formula with different ratio 1:0.25,1:0.5 and 1:1. Study and evaluation the formulation factors which may effect on particle size and Polydispersity index PDI. The prepared CLD NCs were evaluated for particle size and dissolution study ,F4 was the smallest size 152 nm ,PDI (0.161 )and the saturated solubility was increased about ten folds, this formula was subjected for freeze drying by adding 3 % mannitol as cryoprotectant . A complete dissolusion was established in about 20 minutes which confer the DSC and PXRD results in conversion from crystalline into amorphous state . It can conclude that solvent anti-solvent technique was a useful in preparation of nanocrystals suspension .
Dynamical causes for incipient magma chambers above slabs

Using a regional upper-mantle model with an unprecedented spatial resolution of ∼100 m, we have investigated at multiple resolutions the character of incipient magma chambers forming under oceanic arcs. The magma chambers are formed from wave-like structures propagating upward along descending slabs and consist of compositionally buoyant, hydrated, partially molten subducted crustal and mantle material. These wave structures are 300–500 °C colder than the mantle wedge and may have an upward velocity of >1 m/yr. Inverted temperature structures and transitory bimodal magmatism are plausible consequences of finger-like penetration of relatively cold, hydrated material of the incipient magma chambers into the hot mantle wedge. Apart from forming and periodic feeding of the magma chambers, “cold” waves may also transport upward thousands of cubic kilometers of subducted material and may cause the rapid exhumation of ultrahigh-pressure rocks along slabs.
Examining Student Gendered Experiences with Parent Involvement in The Bahamas

Within English-speaking Caribbean K-12 schooling systems, there has been a consistent trend of boys performing at a lower academic standard than girls. Caribbean scholars suggest approaching gendered achievement patterns from multiple perspectives, dimensions, and methodologies to help clarify the inequities that are prevalent in Caribbean schooling. To address this call, I narrow attention onto the Bahamian family context and raise the question as to whether parent involvement with schooling in The Bahamas differs for boys versus girls. Survey data (parents, n = 377) came from a comprehensive study on parent involvement with schooling that took place in Grand Bahama during summer of 2018. Multivariate analysis of factor scores using two scales, Basic Needs Involvement (11-item scale) and Academic Involvement (3-item scale), showed that parental involvement was similar for boys and girls. Like previous findings in The Bahamas, there were more girls enrolled in private schools than boys. Findings are discussed in terms of societal perceptions about whose education is valued, mandates for gender parity, the impact global trends have on local trends, and moving towards empirical-based theories for gendered achievement patterns within Caribbean societies.
Speech Enhancement using Fully Convolutional UNET and Gated Convolutional Neural Network

—Speech Enhancement aims to enhance audio intelligibility by reducing background noises that often degrade the quality and intelligibility of speech. This paper brings forward a deep learning approach for suppressing the background noise from the speaker's voice. Noise is a complex nonlinear function, so classical techniques such as Spectral Subtraction and Wiener filter approaches are not the best for non-stationary noise removal. The audio signal was processed in the raw audio waveform to incorporate an end-to-end speech enhancement approach. The proposed model's architecture is a 1-D Fully Convolutional Encoder-to-Decoder Gated Convolutional Neural Network (CNN). The model takes the simulated noisy signal and generates its clean representation. The proposed model is optimized on spectral and time domains. To minimize the error among time and spectral magnitudes, L1 loss is used. The model is generative, denoising English language speakers, and capable of denoising Urdu language speech when provided. In contrast, the model is trained exclusively on the English language. Experimental results show that it can generate a clean representation of a clean signal directly from a noisy signal when trained on samples of the Valentini dataset. On objective measures such as PESQ (Perceptual Evaluation of Speech Quality) and STOI (Short-Time Objective Intelligibility), the performance evaluation of the research outcome has been conducted. This system can be used with recorded videos and as a preprocessor for voice assistants like Alexa, and Siri, sending clear and clean instructions to the device
Who is afraid of “light”?

This study investigates the effects of outdoor lighting on people’s perceptions of nighttime landscapes. More specifically, it examines the relationship between lighting and specific physical features of nighttime landscapes in participants’ reports of fear. To accomplish this, a combination of eye-tracking and self-reported data were collected. This study ultimately presents design guidelines intended to reduce fear levels experienced in the nighttime landscape.
Application of a Multiscale Physiologically-Based Bone and Calcium Systems Model to Guide the Development of GnRH receptor modulators for the Management of Endometriosis

compared to the E2 decrease. In addition, within the range of acceptable 6-month BMD changes (e.g., 0.8% up toward 1.6%), there was minimal early differentiation of BM across doses. An extension of the model prediction across a continuous range of 10‐80 pg/mL adequately predicted the broader literature database, indicating that E2 was a reliable predictor of 6-month BMD. The logistic regression indicated that approximately 15% of patients with E2 > 80 pg/mL were expected to have an ESSS=0, whereas P(ESSS=0) increased to 26% and 29% at E2 values of 40 and 20 pg/mL, respectively. Conclusions: E2, measured as early as 1‐2 months after treatment initiation, was shown to be a reliable predictor of 6-month BMD change, whereas bone markers, as affected through GnRH inhibition, were projected to change too slowly to provide reliable dose differentiation earlier than at least 3-month study duration. Doses within a GnRH antagonist development program that target E2 in the range of > 20= to 40 pg/mL are expected to provide efficacious EM pain response while minimizing BMD effects. Overall, our model-based approach provides a quantitative framework for preclinical and clinical research efforts focused on mechanisms that modulate E2 levels.
A rare great earthquake on an oceanic fossil fracture zone

first motion solution found in this study and has a strike consistent with the fracture zone north of the bend. The southern fault plane, with strike 178 ◦ ,d ip 54 ◦ and rake 65 ◦ , has a strike consistent with the portion of the fracture zone to the south of the bend and has a dip which can be explained by the style of deformation that the region is undergoing. The centroid moment tensor solution of the broad-band model is calculated and found to be consistent with the region of low misfit in mantle wave solution space. The broad-band solution has a moment of 1.53 × 10 21 Nm( Mw 8.1), again, consistent with the mantle wave data. Slip propagated bilaterally with an approximate rupture velocity of ∼ 3k ms −1 ∼80 per cent of local shear wave speed. The rupture front is less well resolved to the south of the epicentre than to the north. The majority (∼75 per cent) of moment originated from slip on the northern fault. The broad-band data requires significant slip below the oceanic Moho with as much as 70 per cent of moment due to slip in the brittle uppermost mantle in the preferred model.
Variation of streamflow and sediment discharge rate and relationship with temperature change in Yellow River source area

The hydrological data of Huangheyan,Jimai,Maqu and Tangnaihai hydrological stations,and the meteorological data of ten representative weather stations on the Yellow River source area are selected as data source. The variation of annual mean runoff,annual mean sediment discharge rate,temperature and precipitation changes during 56 years is analyzed and the runoff and sediment change at Tangnaihai hydrological station in response to temperature is studied. Results show that the annual runoff and sediment discharge rate at the four stations in 1955- 1990 presented a trend of increase,but Tangnaihai station( 1991- 2011) comparing to 1956- 1990 decreased by 15. 9% and 28. 5%,respectively. Except Henan and Tongde weather stations,the temperature at other eight weather stations in 1953- 2011 kept an upward trend,increased by 0. 31 to 0. 41℃ /( 10a). The temperature rising in the Yellow River source area is a consistent trend,but there is no consistency of the precipitation change. The rainfall change in response to temperature rising had a weak relationship in the 1953- 1990,but this relationship gradually increased in 1991- 2011. The annual runoff and sediment discharge rate increased or decreased in response to the temperature dropping or rising.
Diagnostic Accuracy and Performance Analysis of a Scanner-Integrated Artificial Intelligence Model for the Detection of Intracranial Hemorrhages in a Traumatology Emergency Department

Intracranial hemorrhages require an immediate diagnosis to optimize patient management and outcomes, and CT is the modality of choice in the emergency setting. We aimed to evaluate the performance of the first scanner-integrated artificial intelligence algorithm to detect brain hemorrhages in a routine clinical setting. This retrospective study includes 435 consecutive non-contrast head CT scans. Automatic brain hemorrhage detection was calculated as a separate reconstruction job in all cases. The radiological report (RR) was always conducted by a radiology resident and finalized by a senior radiologist. Additionally, a team of two radiologists reviewed the datasets retrospectively, taking additional information like the clinical record, course, and final diagnosis into account. This consensus reading served as a reference. Statistics were carried out for diagnostic accuracy. Brain hemorrhage detection was executed successfully in 432/435 (99%) of patient cases. The AI algorithm and reference standard were consistent in 392 (90.7%) cases. One false-negative case was identified within the 52 positive cases. However, 39 positive detections turned out to be false positives. The diagnostic performance was calculated as a sensitivity of 98.1%, specificity of 89.7%, positive predictive value of 56.7%, and negative predictive value (NPV) of 99.7%. The execution of scanner-integrated AI detection of brain hemorrhages is feasible and robust. The diagnostic accuracy has a high specificity and a very high negative predictive value and sensitivity. However, many false-positive findings resulted in a relatively moderate positive predictive value.
Clinical Outcomes Following Use of Tranexamic Acid in High Tibial Osteotomy: A Systematic Review

This study aimed to evaluate the clinical outcomes following administration of tranexamic acid (TXA) in patients undergoing high tibial osteotomy (HTO) through a systematic review of current available evidence. A systematic database search of PubMed, Embase and Cumulative Index of Nursing and Allied Health Literature (CINAHL) was performed from inception up to December 2022, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Inclusion criteria were (i) randomised control trials, cohort studies or case-control studies that had more than 10 patients; (ii) studies reporting outcomes after TXA administration, of any route, before or after HTO, compared to placebo, control and different doses or routes; and (iii) studies reporting blood loss, including haemoglobin (Hb) drop, estimated blood loss, transfusion requirement and complications. Case reports, reviews, abstracts, non-HTO studies, non-human studies and duplicates were excluded. A synthesized comparison of drain output, wound complications, transfusion requirement and pooled analyses of blood loss and Hb drop was performed. Eleven studies involving 974 patients were included. Nine studies had placebo comparison, and two used single-dose TXA versus multiple doses. All studies reported on postoperative hemoglobin and nine on blood loss. In the six TXA versus placebo studies reporting on total blood loss, the TXA group had a pooled, estimated standardised mean difference (SMD) in blood loss of -2.37 (95% confidence interval (CI) -3.67, -1.07; P = 0.0004). For the Hb drop, on postoperative days (PODs) one, two, and five, the SMDs were -0.97 (95% CI -1.19, -0.75; P < 0.00001) for POD1, -0.74 (95% CI -1.03, -0.46; P < 0.00001) for POD2 and -0.87 (95% CI -1.10, -0.64; P < 0.00001) for POD5. TXA administration in HTO significantly reduces perioperative blood loss. This can greatly improve recovery, reduce complications and shorten length of stay. This is especially pertinent given supply shortages of NHS blood resources.
Reconstruction of early spring temperature for central Japan from the tree‐ring widths of Hinoki cypress and its verification by other proxy records

We describe the results of a dendroclimatic study in central Japan using ring width data from living trees of Hinoki cypress. February–April temperatures were reconstructed for the period AD 1719–1961. This reconstruction, from which five cooling events were determined for the late Little Ice Age, was validated against other climate proxies. There is a reasonable agreement with cold periods previously estimated from documentary records in Japan and China. A notably good agreement is also found for a dendroclimatic reconstruction for Korea, suggesting that the annual activity of the winter monsoon in the western Pacific Rim persisted longer in the 19th century than at present. Records of prolonged famines and unusual climatic phenomena support the reconstruction of the cold periods in the 1780s, 1810s and 1830s. It is thus revealed that the Hinoki chronology has enough potential to reconstruct the climatic variability further into the past.
Detection of counterfeit holograms using hyperspectral imaging

Distinguishing an authentic hologram from a replicated one presents a notable obstacle, chiefly attributable to the factor of reflectivity. A hologram's reflection pattern can undergo a significant transformation due to even a minor variation in lighting conditions. As a result, the creation of a standardized duplicate hologram detection device has proven to be difficult to achieve. The present study proposes a new methodology for distinguishing between genuine and replicated holograms. This is achieved through the utilization of a portable and economical housing module that employs snapshot hyperspectral imaging (HSI) algorithm. The proposed module comprises a Raspberry Pi 4 processor, a Raspberry Pi camera, a display, and a light-emitting diode lighting system equipped with a dimmer. By employing a visible HSI algorithm, the RGB image captured by the Raspberry Pi camera can be converted into a hyperspectral image. Following this, a particular area of focus is designated within the spectral image, and assessments are conducted to determine the average gray value (MGV) and reflectivity. The experimental results indicate that shorter wavelengths are most suitable for differentiating holograms when utilizing MGV as the classification parameter. On the other hand, the utilization of reflectivity for discrimination is more efficient with longer wavelengths. The distinctive attributes of this design encompass its affordability, simplicity, absence of moving parts and the absence of a requirement for an additional decoding key. Subsequent research endeavors may delve into additional improvements and modifications aimed at maximizing the efficiency of the housing module and broadening its scope of potential implementations.
Fusion of Surface Soil Moisture Data for Spatial Downscaling of Daily Satellite Precipitation Data

Remote sensing satellites provide an effective solution for obtaining large-scale precipitation data. However, the spatial resolution of satellite-based precipitation products is often too coarse for hydrological applications at the regional scale. As a solution, spatial downscaling has been increasingly investigated to increase the spatial resolution of satellite-based precipitation. The selection of effective explanatory variables at fine spatial resolution has become a crucial concern in precipitation downscaling. Generally, surface soil moisture (SSM) has a strong physical relation with precipitation (especially at the regional scale), but this relationship has rarely been considered. In this article, we proposed to fuse SSM in precipitation downscaling. Specifically, the 3 km SSM data (i.e., SPL2SMAP_S) were incorporated to downscale the 10 km Integrated Multisatellite Retrievals for Global Precipitation Measurement daily precipitation data to 3 km. Based on case studies in southeastern China, the proposed strategy was compared with the existing scheme fusing digital elevation model or normalized difference vegetation index data as an alternative. The results demonstrated that, compared to the original precipitation product, all downscaling results can provide richer spatial details. The proposed scheme outperformed the other schemes, with a correlation coefficient of 0.63, a root-mean-square error of 15.7 mm, and a mean absolute error of 8.74 mm. Furthermore, the proposed scheme is more sensitive to precipitation events of different intensities. In addition, when the historical precipitation is discontinuous, the advantages of the proposed scheme are more apparent.