Miracle-dz/UniFAR-FTUs huggingface dataset:
Holocene multidecadal- to millennial-scale variations in Iceland-Scotland overflow and their relationship to climate

The Nordic Seas overflows are an important part of the Atlantic thermohaline circulation. While there is growing evidence that the overflow of dense water changed on orbital time scales during the Holocene, less is known about the variability on shorter time scales beyond the instrumental record. Here we reconstruct the relative changes in flow strength of Iceland-Scotland Overflow Water (ISOW), the eastern branch of the overflows, on multidecadal-millennial time scales. The reconstruction is based on mean sortable silt ( SS¯) from a sediment core on the Gardar Drift (60°19′N, 23°58′W, 2081 m). Our SS¯ record reveals that the main variance in ISOW vigor occurred on millennial time scales (1–2 kyr) with particularly prominent fluctuations after 8 kyr. Superimposed on the millennial variability, there were multidecadal-centennial flow speed fluctuations during the early Holocene (10–9 kyr) and one prominent minimum at 0.9 kyr. We find a broad agreement between reconstructed ISOW and regional North Atlantic climate, where a strong (weak) ISOW is generally associated with warm (cold) climate. We further identify the possible contribution of anomalous heat and freshwater forcing, respectively, related to reconstructed overflow variability. We infer that ocean poleward heat transport can explain the relationship between regional climate and ISOW during the middle to late Holocene, whereas freshwater input provides a possible explanation for the reduced overflow during early Holocene (8–10 kyr).
Mechanistic Insight into Poly-Reactivity of Immune Antibodies upon Acid Denaturation or Arginine Mutation in Antigen-Binding Regions

The poly-reactivity of antibodies is defined as their binding to specific antigens as well as to related proteins and also to unrelated targets. Poly-reactivity can occur in individual molecules of natural serum antibodies, likely due to their conformation flexibility, and, for therapeutic antibodies, it plays a critical role in their clinical development. On the one hand, it can enhance their binding to target antigens and cognate receptors, but, on the other hand, it may lead to a loss of antibody function by binding to off-target proteins. Notably, poly-reactivity has been observed in antibodies subjected to treatments with dissociating, destabilizing or denaturing agents, in particular acidic pH, a common step in the therapeutic antibody production process involving the elution of Protein-A bound antibodies and viral clearance using low pH buffers. Additionally, poly-reactivity can emerge during the affinity maturation in the immune system, such as the germinal center. This review delves into the underlying potential causes of poly-reactivity, highlighting the importance of conformational flexibility, which can be further augmented by the acid denaturation of antibodies and the introduction of arginine mutations into the complementary regions of antibody-variable domains. The focus is placed on a particular antibody’s acid conformation, meticulously characterized through circular dichroism, differential scanning calorimetry, and sedimentation velocity analyses. By gaining a deeper understanding of these mechanisms, we aim to shed light on the complexities of antibody poly-reactivity and its implications for therapeutic applications.
Self-Supervised Variational Graph Autoencoder for System-Level Anomaly Detection

Unsupervised anomaly detection (AD) methods, either reconstruction based or prediction based, determine anomalies based on residuals. Occasional mutations in a single variable can cause the residuals to exceed the limits. Indeed, such mutations are not variations in the operating mechanism of the system. Thus, system-level anomalies are challenging to characterize. Complex networks (or “graphs”) are well adapted for modeling and characterizing the laws of evolution of complex systems. However, most industrial scenarios are without graphs. Hence, a self-supervised variational graph autoencoders (SS-VGAE) method is proposed. First, the multisource sensor dynamic graph is constructed through detrended cross-correlation analysis (DCCA). Second, target and self-supervised learning tasks are designed. The target task is to reconstruct the input graph structure to minimize the reconstruction loss. The self-supervised task is to learn the optimal center of the hypersphere in the latent space such that the mean features are gathered toward the center as much as possible. Multitask joint optimization allows high- and low-dimensional space features to be considered simultaneously, thereby improving the reliability of anomaly scores. Then, the distribution of anomaly scores is calculated and integrated into a system health indicator (HI). The system HI is more applicable to assist decision making. Finally, the superiority of the proposed method, namely, better detection accuracy and robustness, is demonstrated by nuclear personal computer transient analyzer (PCTRAN) simulation data and Skoltech anomaly benchmark (SKAB) data. Last but not least, systematic anomalies are found to make the correlation between the variables stronger.
The Lower Eastern Layered Series of Rhum

Abstract The Lower Eastern Layered Series (LELS) comprises approximately units 1–5 of previous workers. Remapping has revealed considerable along-strike lithological variation in the units of the LELS. It is suggested, on the basis of field and geochemical evidence, that two layers formerly regarded as ‘conformable intrusive sheets of fine-grained olivine gabbro’, may be evolved alii valite layers rather than later intrusions. Xenolith suites, in these layers and elsewhere, indicate a component derived from the roof or walls of the magma chamber. Cryptic variation is more extensive in the LELS than in other parts of Rhum: olivine forsterite content varies from 85.6 to 70, and clinopyroxene Mg×100/(Mg+Fe) varies from 88 to 74. Postcumulus effects and subsolidus re-equilibration have altered the initial compositions of the mineral phases. The data are consistent with a model of repeated replenishment by picritic magma, although the replenishing liquids may have been slightly less magnesian than those subsequently available, during the formation of the upper ELS.
Premotor and Posterior Parietal Cortex Activity is Increased for Slow, as well as Fast Walking Poststroke: An fNIRS Study

Methods Twenty individuals in the chronic stage of stroke walked: (1) at their normal pace, (2) slower than normal, and (3) as fast as possible. Functional near-infrared spectroscopy was used to assess bilateral prefrontal, premotor, sensorimotor, and posterior parietal cortices during walking. Results No significant differences in laterality were observed between walking speeds. The ipsilesional prefrontal cortex was overall more active than the contralesional prefrontal cortex. Premotor and posterior parietal cortex activity were larger during slow and fast walking compared to normal-paced walking with no differences between slow and fast walking. Greater increases in brain activation in the ipsilesional prefrontal cortex during fast compared to normal-paced walking related to greater gait speed modulation. Conclusions Brain activation is not linearly related to gait speed. Ipsilesional prefrontal cortex, bilateral premotor, and bilateral posterior parietal cortices are important areas for gait speed modulation and could be an area of interest for neurostimulation.
NUTRITIVE SCREENING AND FORAGE YIELD OF GUINEA AND HYBRID NAPIER GRASS VARIETIES UNDER DENSE SHADE OF MATURE OIL PALM PLANTATION

Cattle grazing and sparse weed flora due to poor light infiltration in mature oil palm plantations have created a need for the identification of shade tolerant fodder grass varieties. A total of 14 varieties of Guinea grass and four varieties of hybrid Napier were screened for 4 years under dense shade of mature oil palm plantation. Significant differences were observed among the fodder grass varieties evaluated for fodder yield and nutritive quality attributes. Of all the varieties, Sampoorna (24.55 t/ha), CO-4 (22.52 t/ha) and KKM-1 (22.34 t/ha) among hybrid Napier grass and BG-1 (23.12 t/ha), DGG-1 (22.00 t/ha), CO-3 (21.38 t/ha) and Grazing Guinea-1 (20.28 t/ha) among Guinea grass have been identified as promising fodder yielders. Overall, better nutritive levels in fodder quality attributes like crude protein (11.88%), crude fat (2.15%), neutral detergent fibre (66.75%), acid detergent fibre (44.37%), lignin (8.91%) and acid insoluble ash (6.00%) were observed in Sampoorna. Because of unique/sturdy growth habit, better adaptation and optimum fodder yield, Sampoorna along with Grazing Guinea-1 can be the most suitable option for a sustainable oil palm-based farming system.
Linking variability in soil solution dissolved organic carbon to climate, soil type, and vegetation type

Lateral transport of carbon plays an important role in linking the carbon cycles of terrestrial and aquatic ecosystems. There is, however, a lack of information on the factors controlling one of the main C sources of this lateral flux, i.e., the concentration of dissolved organic carbon (DOC) in soil solution across large spatial scales and under different soil, vegetation, and climate conditions. We compiled a database on DOC in soil solution down to 80 cm and analyzed it with the aim, first, to quantify the differences in DOC concentrations among terrestrial ecosystems, climate zones, soil, and vegetation types at global scale and second, to identify potential determinants of the site‐to‐site variability of DOC concentration in soil solution across European broadleaved and coniferous forests. We found that DOC concentrations were 75% lower in mineral than in organic soil, and temperate sites showed higher DOC concentrations than boreal and tropical sites. The majority of the variation (R2 = 0.67–0.99) in DOC concentrations in mineral European forest soils correlates with NH4+, C/N, Al, and Fe as the most important predictors. Overall, our results show that the magnitude (23% lower in broadleaved than in coniferous forests) and the controlling factors of DOC in soil solution differ between forest types, with site productivity being more important in broadleaved forests and water balance in coniferous stands.
Forests of the Northern United States

Bounded by Maine, Maryland, Missouri, and Minnesota, the 20 Northern States have a larger population and a higher proportion of forest cover than other comparably sized U.S. regions. Forest-associated issues across the North include insect and disease pests, invasive species, forest management capacity, management standards, biodiversity, forest fragmentation, water quality, water quantity, output of forest products, recreation, and environmental literacy - all related to sustainability at local, State, and regional scales. This report uses the Montreal Process to summarize current conditions and recent trends in seven categories - biodiversity; forest productive capacity; forest ecosystem health; soil and water resources; forest carbon and biomass; long-term socioeconomic benefits; and the legal, institutional, and economic framework for sustainable management - and adds an eighth category to reflect the importance of urban and community forests to the Northern States. Since 1953, population in the North increased by 40 percent, forest area by 28 percent, and timber volume by 140 percent. The increases in forest area appear to be leveling off as urban expansion subsumes about 1.5 million acres of forest land per decade. Seventy-four percent of forests are privately owned, yet one acre in six is in some category of protected status. Forests are aging; and although total mortality for the region has been relatively stable in recent years, emerald ash borer and other invasive species are now poised to kill billions of trees. Forests supply 48 percent of the region's water needs and employ 441,000 in its forest products sector. Participation in a wide range of nature-based recreation activities is increasing at 10 to 20 percent per decade. These and many other characteristics of northern forests summarized in this report become interrelated on the North's forest landscapes, sometimes in complex ways. The information in this report provides a basis for ongoing, detailed discussions about these large-scale interactions and how they affect the sustainability of northern forests.
Actividad física en Diabetes Mellitus tipo II, un elemento terapéutico eficaz: revisión del impacto clínico

Se realizo una revision en bases de datos (PubMed, PEDro) de estudios tipo ensayo clinico, tipo cohorte, revisiones sistematicas, meta analisis y guias de practica clinica basadas en evidencia que hubieran estudiado los beneficios que tiene la actividad fisica en la prevencion, tratamiento y disminucion del riesgo de presentar complicaciones y muerte en pacientes con diabetes mellitus Tipo II. La realizacion de actividad fisica de forma regular se asocio con la disminucion del riesgo de desarrollar diabetes mellitus; de igual manera se asocio a disminucion en los valores porcentuales de hemoglobina glucosilada A1C. Los pacientes diabeticos que realizan altos niveles de actividad fisica presentaron disminucion del riesgo de presentar complicaciones y muerte por enfermedad cardiovascular y por cualquier causa. En la actualidad la evidencia cientifica sobre el impacto de la actividad fisica en la prevencion y tratamiento de la diabetes mellitus es solida, por lo que se debe enfatizar la promocion de la actividad fisica como parte fundamental de los esquemas terapeuticos para dicha enfermedad
Physicochemical characteristics of homemade ice cream with mustard green (Brassica juncea) powder or puree

Ice cream is a product of high sensory acceptance because of the countless flavor options and their combination. It has become one of the most popular desserts and its production is increasing year by year and highly accepted by children, adolescents, adults, as well as the elderly public. A number of studies have been carried out in developing new functional ice creams with ingredients such as fruits like avocado, dragon fruit, banana, passion, guava, lemon and soursop. The present study was, therefore, carried out to assess the possibility of producing homemade ice cream with enhanced nutritional value with mustard green leaves. This research explored the utilization of powdered and fresh mustard green leaves in the form of puree as nutritional enrichment for homemade ice cream. Homemade ice cream ingredients in this experiment consisted of 100 g solids and 150 ml water. The solids consisted of 74 g full cream milk powder, 15 g sugar, 10 g skimmed milk powder, 0.5 g emulsifier, and 0,5 g Carboxy Methyl Cellulose. Mustard green powder was added in accordance to treatments, namely 3%, 6%, 9% and 12% from the 100 g solids. Whereas mustard green puree were added as replacement of 100 ml water from the total of 150 ml water in the formula. Control treatment was made with no mustard green powder or puree. The chemical and physical characteristics were determined in three replicates using randomized completely blocked design. Data was submitted to Analysis of Variance (ANOVA) and further tested using Honestly Significant Difference (HSD). Based on the results of this study, the addition of mustard green powder and puree on homemade ice cream had significant increase in ash content, total chlorophyll, melting rate, viscosity, and yellowness, but a decrease in overrun, lightness, and redness. The higher the concentration of mustard green powder or puree, the higher the increase of ash content, total chlorophyll, melting rate, viscosity and yellowness. On the other hand, the higher the concentration of mustard green powder or puree the higher the decrease of overrun, lightness, and redness. Mustard green powder gave higher ash content and total chlorophyll than the puree, but mustard green puree gave brighter colour indicated by higher lightness, greenness, and yellowness. Key words: ice cream, homemade, mustard green, powder, puree, enrichment
Crypto-Ransomware Detection and Prevention Techniques and Tools A Survey

: Crypto-ransomware is among the extremely prevalent malware cyberattacks worldwide. it is typically spread using phishing emails and compromised websites, where crypto-ransomware is downloaded stealthily after luring users to click on malicious links. Additionally, attackers may take advantage of available vulnerabilities in software installed on the victim’s device or use a zero-day vulnerability in the operating system. Crypto-ransomware employs encryption techniques against users’ data and resources, rendering them inaccessible and demanding a cryptocurrency ransom for decryption. Crypto-ransomware attacks have witnessed massive growth within the past few years, resulting in massive financial loss across the globe. Di ff erent detection and prevention techniques have been proposed to overcome crypto-ransomware attacks, and many tools have been implemented. In this work the authors present a summary of the most recently used techniques and tools, highlighting di ff erent employed strategies that address crypto-ransomware attacks in the di ff erent stages of the attack chain.
Gradient Boosting Models for Cybersecurity Threat Detection with Aggregated Time Series Features

The rapid proliferation of Internet of Things (IoT) devices has revolutionized the way we interact with and manage our surroundings. However, this widespread adoption has also brought forth significant cybersecurity challenges. IoT devices, with their interconnectedness and varying functionalities, present a unique threat landscape that requires tailored detection techniques. Traditional approaches to cybersecurity, primarily focused on network monitoring and anomaly detection, often fall short in effectively identifying threats originating from IoT devices due to their dynamic and complex behaviors. This paper addresses our solution for FedCSIS 2023 Challenge: Cybersecurity Threat Detection in the behavior of IoT Devices. First, we aggregated time series features, and then at the feature selection stage, we filtered and combined different categorical and numerical features to generate four different feature sets. The Gradient boosting models, i.e. lightgbm, catboost and xgboost, are applied and trained individually with hyper-parameter tuning. The final three submissions are two best individual lightgbm models with the AUC scores of 0.9999 and 0.9998, respectively on the different feature sets, which secured the 4th place with a final score of 0.9993, and one ensemble result with a AUC score of 0.9998 from combination of xgboost, catboost and lightgbm, which has the final score of 0.9997 while unluckily was missing in the final three evaluation entries.
Localization for Intelligent Vehicles in Underground Car Parks Based on Semantic Information

Global navigation satellite system (GNSS) signals cannot be received indoors, thus to deploy intelligent vehicles in underground car parks other localization methods are needed. In this paper, we use various carpark signs that are widely and uniformly distributed in underground parking lots as localization references. We propose a coarse-to-fine multiscale localization method that relies solely on vision sensors for underground parking lot localization based on a preconstructed lightweight node map. In coarse localization, we propose a semantic keyframe topological localization method to predict the localization range (candidate set of nodes). In node-level localization, we extract features by learning-based neural networks and construct a hybrid k-nearest neighbor (H-KNN) model to search for the closest node within the coarse localization results. In metric localization, we construct plane homography and perspective-n-point (PnP) models, allowing the vehicle’s pose (rotation and translation relative to the closest node) to be computed for refined localization. The proposed method has been tested in two underground parking lots of an office building and a shopping mall with different characteristics. Experimental results demonstrate that root mean square error (RMSE) is 0.38 m and the proposed method exhibits strong robustness in various scenarios.
Oil spill detection using satellite based SAR: experience from a field experiment

During the commissioning phase of the ERS-1, C-band, synthetic aperture radar (SAR) images were obtained regularly over international waters for a range of operational, real-time uses. Among other capabilities, SAR is expected to be able to detect oil slicks on the sea surface. A project was defined with the aim of developing and demonstrating a system for early oil spill detection using satellite based SAR. During the Dedicated Oil Spill Experiment August 1991 (DOSE-91) three slicks, each 20 metric tons of stabilized crude oil, were released in phase with the passages of the ERS-1 satellite. The experiment included sea truthing, oil slick tracking using drifting buoys, and aircraft surveillance using various remote sensing techniques to compare and verify satellite observations. This paper presents the observations gathered during the experiment with emphasis on the SAR imagery of oil slicks under certain environmental conditions. Image enhancements techniques used to process the imagery are also discussed. 7 refs.
A comparative review of radiation aerosol models

Aerosol characteristics to be introduced into a set of models within a framework of a Standard Radiation Atmosphere (SRA) are reviewed, and a reconciliation of the Toon and Pollack (TP) global model with the SRA models is attempted. Among the topics discussed are physical and radiative characteristics, radiation aerosol models, albedo for single scattering, and phase function and spectral variation of extinction. Size distribution models are also covered, inclusive the Junge power law, and the modified gamma-, log normal-, and zold-distribution. Recommendations include modifying the SRA profile VI to resemble the Elterman profile modified in the TP model, improving discontinuity at the tropopause level for all SRA models, and retaining the same log-normal size distribution for all SRA components.