Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Variability of Retroflection Structures and Transports in the Atlantic Ocean as Inferred from Satellite-Derived Salinity Maps

Three of the world’s most energetic regions are in the tropical and South Atlantic: the North Brazil Current Retroflection, the Brazil-Malvinas Confluence, and the Agulhas Current Retroflection. All three regions display offshore diversions of major boundary currents, which define the intensity of the returning limb of the Atlantic meridional overturning circulation. In this work, we use a sea-surface salinity (SSS) satellite product, combined with a high-resolution numerical model and in situ measurements, in order to explore the seasonal variation of the surface currents and transports in these three regions. The analysis of the model output shows that the SSS patterns reflect the surface velocity structure, with the largest horizontal SSS gradients coinciding with those areas of highest velocity and the most predominant velocity vector being 90° anticlockwise (clockwise) from the horizontal SSS gradient in the northern (southern) hemisphere. This information is then applied to the SSS satellite product to obtain maps of water velocity and salt transports, leading to a quantitative tool to estimate both water and salt transports in key regions of the world ocean.
Measuring from Space the Efficiency of Local Forest Management: The Successful Case of the Indigenous Community of Cherán, Mexico

Remote sensing-based forest cover change maps are usually elaborated for national level baseline information towards environmental conservation schemes. Community forest management is acknowledged as a key component of sustainable use of natural resources. In this research, we provide a means of technological appropriation for the community of Cherán, state of Michoacán, Mexico, to complement their local forest management initiative since 2012, after severe illegal logging occurred all along the previous decade. Participatory mapping, collaborative online cartography and global forest cover products were shared with the Community Council of the Commons. The environmental effect of the governance change occurred in 2012 was assessed using the Global Forest Cover Change (GFCC) product from 2000 to 2016. We find a significant reduction of deforestation in Cherán, due to local empowerment, which contrasts with overall trend in the region. We discuss the potential benefits of an alliance between communities and the academy through the construction of decentralized spatial data infrastructures in Mexico.
Multitask Learning-Based Quality Assessment and Denoising of Electrocardiogram Signals

In recent years, there has been a surge in applying deep learning (DL) methods for signal quality assessment (SQA) and denoising of electrocardiogram (ECG) signals. However, solely focusing on denoising can lead to situations, where models trained on severely contaminated signals perform poorly on higher quality signals. Likewise, carrying out SQA in isolation may be constrained by the number of training data and susceptible to label noise. In this study, we leverage the correlation between denoising and SQA and propose a multitask learning network (MTL-NET) that addresses both tasks in parallel to mitigate the challenges. MTL-NET employs a stacked bidirectional LSTM network, combined with an attention module, as a shared backbone to extract common features for both SQA and denoising tasks. Weighted loss functions are utilized to balance the performance of these two tasks. Within MTL-NET, we classify ECG signal quality into three categories and compare MTL-NET’s performance with baseline methods dedicated solely to denoising or three-class SQA. The results demonstrate that MTL-NET outperforms other denoising methods across all metrics obviously, including the corresponding single-task denoising model. Specifically, MTL-NET significantly enhances the denoising performance of the “intermediate” quality category and the SQA performance in identifying “unacceptable” signal segments. It provides a novel perspective on ECG signal preprocessing by implementing a balance rule through loss functions for both tasks within a model. With 0.28 M parameters and 27.35 M floating-point operations per second (FLOPs), the MTL-NET is not only efficient but also exhibits excellent generalization performance on an independent dataset, making it suitable for deployment in resource-constrained environments.
Regional Convergence in the European Union, new Member States and Croatia

Abstract Over the past two decades, the issue of regional convergence in the European Union has been the subject of a wide range of empirical research. This paper aims to provide more information on the differences in regional growth patterns of new member states (NMS), as well as Croatia, in addition to the factors influencing regional disparities within each country. This research provides an analysis of regional convergence in the period 2001-2008 at the NUTS II and NUTS III level. The most widely used model for testing convergence hypotheses is beta-convergence analysis. Other factors commonly included in the econometric modelling of convergence are demographic variables, labour market conditions, industrial structure, institutional factors and overall government policy. The main hypothesis is that the process of regional convergence in NMS and Croatia is not strong enough to dominate over other factors, influencing regional potential growth (mainly industry structure and quality of human capital). Absolute β-convergence can be found at the national level for EU countries. Convergence also can be found for NMS regions, but the pace of convergence on the regional level is lower in comparison to the national level and the estimated β-convergence parameter is less significant.
Capital Structure, Financial Performance, Investment Decision and Firm Value

This study aims to determine the effect of Capital Structure, Financial Performance, and Investment Decisions on Firm Value. Capital Structure is measured using the Debt to Equity Ratio (DER), Financial Performance is measured using Return On Assets (ROA), and Investment Decisions are measured using the Price Earning Ratio (PER). Meanwhile, firm value is measured using Tobin's Q. The population in this study are all infrastructure sector companies listed on the Indonesia Stock Exchange for the 2017-2021 period. The number of samples in this study was 9 companies obtained through purposive sampling, and sample data as much as 45 was obtained. This study's data type is secondary data with quantitative research methods. The analysis technique used is panel data regression analysis with a significance level of 5%. This research was processed using Eviews 9 software. It showed that (1) Capital Structure has a significant effect on Firm Value, (2) Financial Performance has a significant effect on Firm Value, and (3) Investment Decisions have a significant effect on Firm Value.
From hyperglycemia to intervertebral disc damage: exploring diabetic-induced disc degeneration

The incidence of lumbar disc herniation has gradually increased in recent years, and most patients have symptoms of low back pain and nerve compression, which brings a heavy burden to patients and society alike. Although the causes of disc herniation are complex, intervertebral disc degeneration (IDD) is considered to be the most common factor. The intervertebral disc (IVD) is composed of the upper and lower cartilage endplates, nucleus pulposus, and annulus fibrosus. Aging, abnormal mechanical stress load, and metabolic disorders can exacerbate the progression of IDD. Among them, high glucose and high-fat diets (HFD) can lead to fat accumulation, abnormal glucose metabolism, and inflammation, which are considered important factors affecting the homeostasis of IDD. Diabetes and advanced glycation end products (AGEs) accumulation- can lead to various adverse effects on the IVD, including cell senescence, apoptosis, pyroptosis, proliferation, and Extracellular matrix (ECM) degradation. While current research provides a fundamental basis for the treatment of high glucose-induced IDD patients. further exploration into the mechanisms of abnormal glucose metabolism affecting IDD and in the development of targeted drugs will provide the foundation for the effective treatment of these patients. We aimed to systematically review studies regarding the effects of hyperglycemia on the progress of IDD.
Impacts of climate change on streamflow in the Upper Mississippi River Basin: A regional climate model perspective

[1] Impact of climate change on streamflow in the Upper Mississippi River Basin is evaluated by use of a regional climate model (RCM) coupled with a hydrologic model, Soil and Water Assessment Tool (SWAT). The RCM we used resolves, at least partially, some fine-scale dynamical processes that are important contributors to precipitation in this region and that are not well simulated by global models. The SWAT model was calibrated and validated against measured streamflow data using observed weather data and inputs from the U.S. Environmental Protection Agency Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) geographic information systems/ database system. Combined performance of SWAT and RCM was examined using observed weather data as lateral boundary conditions in the RCM. The SWAT and RCM performed well, especially on an annual basis. Potential impacts of climate change on water yield and other hydrologic budget components were then quantified by driving SWAT with current and future scenario climates. Twenty-one percent increase in future precipitation simulated by the RCM produced 18% increase in snowfall, 51% increase in surface runoff, and 43% increase in groundwater recharge, resulting in 50% net increase in total water yield in the Upper Mississippi River Basin on an annual basis. Uncertainty analysis showed that the simulated change in streamflow substantially exceeded model biases of the combined modeling system (with largest bias of 18%). While this does not necessarily give us high confidence in the actual climate change that will occur, it does demonstrate that the climate change ‘‘signal’’stands out from the climate modeling (global plus regional) and impact assessment modeling (SWAT) ‘‘noise. ’’ INDEX TERMS: 1655 Global Change: Water cycles (1836); 1860 Hydrology: Runoff and streamflow; 1866 Hydrology: Soil moisture; KEYWORDS: climate change, streamflow, SWAT Citation: Jha, M. , Z. Pan, E. S. Takle, and R. Gu (2004), Impacts of climate change on streamflow in the Upper Mississippi River Basin: A regional climate model perspective, J. Geophys. Res., 109, D09105, doi:10.1029/2003JD003686.
First record of the genus Staurocladia (Hydrozoa: Cladonematidae) in the Gulf of Mexico with the description of Staurocladia dzilamensis sp. nov., a new species of crawling hydromedusa

The Cladonematidae are a family of hydrozoans with a worldwide distribution and morphological adaptations for a benthic mode of life. Species of this family are characterized by high morphological variability, which has caused many taxonomical debates, mainly for the species of the genera Eleutheria Quatrefages, 1842 and Staurocladia Hartlaub, 1917. Herein, we describe Staurocladia dzilamensis sp. nov., a new species of crawling hydromedusa from the southern Gulf of Mexico. This finding also constitutes the first record of the genus Staurocladia for the Gulf of Mexico. The presence of additional nematocyst clusters, supplementing the apical one on the upper branch of the tentacles, places it within Staurocladia. The presence of exumbrellar buds, a conspicuous marginal ring of nematocysts, 6–11 bifid tentacles with lower branches longer than their upper counterpart, the cnidome with stenoteles of two size classes, and two nematocyst clusters on the upper branch supplementing the apical one, opposite placed alternately on its aboral and oral sides permits to differentiate S. dzilamensis from its congeners. A taxonomic key for the species of Staurocladia is provided.
On the thermal impact of gas venting and hydrate crystallization

[1] At the gas venting rates observed in some seafloor localities, heat advection and the latent heat of hydrate crystallization could significantly warm the subsurface, reduce the hydrate stability zone thickness, and decompose previously crystallized hydrate. Effects of heat advection and the latent heat of hydrate crystallization are limited by the duration of time over which venting occurs and ultimately by the time it takes to plug vents with hydrate, however. We examine the connection between gas venting, hydrate crystallization, and subsurface temperature using a new one-dimensional analytical solution to the steady state heat flow equation with constant hydrate crystallization, and we examine the effects of the duration of venting employing one-dimensional finite element solutions to the transient heat flow equation that include space- and time-dependent hydrate dissolution and crystallization. We show that if lateral losses of heat are negligible, hydrate crystallization and the advection of heat by gas flow at the more vigorous rates of gas discharge observed on the seafloor could increase the temperature gradient near the surface by more than an order of magnitude and decrease the feed gas hydrate stability zone thickness from ∼570 to <200 m before the vents plug with hydrate. We compute how the chemistry of the venting gas and crystallizing hydrate evolve as a vent plugs with hydrate.
Phytosteroids from Roots of Psammosilene tunicoides W.C.Wu et C.Y.Wu

: To explore structurally and pharmacologically interesting secondary metabolites from Psammosilene tunicoides , a crude extract of this plant was investigated, leading to the isolation of a new phytoecdysteroid, furoecdysterone ( 1 ) and four known phytosteroid compounds, ecdysterone ( 2 ), 22-deoxyecdysterone ( 3 ), β -sitosteryl D -glucoside ( 4 ), and  -sitosterol ( 5 ). Their structures were identified based on a detailed analysis of NMR spectra and MS data. Those compounds, except for 2
A Survey on Integrating Edge Computing With AI and Blockchain in Maritime Domain, Aerial Systems, IoT, and Industry 4.0

In terms of digital transformation, organizations today are aware of the critical role that data and information play in their expansion and development in light of the Internet of Things. To increase network performance and stability, many applications are moving from cloud computing to edge computing (EC). However, in order to satisfy customers, applications like intelligent transportation systems, smart grids, smart cities, and healthcare call for even more effective services. This survey addresses extensive research on two aspects: firstly, we present the advancements of two application domains namely maritime areas and aerial systems in terms of integration with EC architecture. Secondly, we cover the most recent technologies, artificial intelligence (AI) and blockchain, combined into the EC paradigm by discussing several experiments conducted in various fields to demonstrate the value of utilizing them in the edge computing architecture. We analyze the results of eleven experiments in each technology from 2015 to 2023.
Agile Cache Replacement in Edge Computing via Offline-Online Deep Reinforcement Learning

One fundamental problem of content caching in edge computing is how to replace contents in edge servers with limited capacities to meet the dynamic requirements of users without knowing their preferences in advance. Recently, online deep reinforcement learning (DRL)-based caching methods have been developed to address this problem by learning an edge cache replacement policy using samples collected from continuous interactions (trial and error) with the environment. However, in practice, the online data collection phase is often expensive and time-consuming, thus hindering the practical deployment of online DRL-based methods. To bridge this gap, we propose a novel Agile edge Cache replacement method based on Offline-online deep Reinforcement learNing (ACORN), which can efficiently learn an edge cache replacement policy offline from a training dataset collected by a behavior policy (e.g., Least Recently Used) and then improve it with fast online fine-tuning. We also design a specific convolutional neural network structure with multiple branches to effectively extract content popularity knowledge from the dataset. Experimental results show that the offline policy generated by ACORN outperforms the behavior policy by up to 38%. Through online fine-tuning, ACORN also achieves the number of cache hits as good as that of several advanced DRL-based methods while significantly reducing the number of training epochs by up to 40%.
Clinical genomic profiling of malignant giant cell tumor of bone: A retrospective analysis using a real‑world database

Malignant giant cell tumor of bone (GCTB) is identified by the presence of multinucleated giant cells, with an aggressive behavior and a high risk of metastasis, which has not been genetically characterized in detail. H3 histone family member 3A (H3F3A) gene mutations are highly recurrent and specific in GCTB. The present study analyzed the clinical information and genomic sequencing data of eight cases of malignant GCTB (out of 384 bone sarcoma samples) using an anonymized genomic database. There were 5 males and 3 females among the cases, with a median age of 33 years at the time of the initial diagnosis. H3F3A G34W and G34L mutations were detected in 3 patients and 1 patient, respectively. In 75% of cases without H3F3A mutation, mitogen-activated protein kinase (MAPK) signaling pathway gene alterations were found (KRAS single nucleotide variant, KRAS amplification, nuclear respiratory factor 1-BRAF fusion). Moreover, the collagen type I alpha 2 chain-ALK fusion was detected in remaining one case. The most frequent gene alterations were related to cell cycle regulators, including TP53, RB1, cyclin-dependent kinase inhibitor 2A/B and cyclin E1 (75%, 6 of 8 cases). On the whole, the present study discovered recurrent MAPK signaling gene alterations or other gene alterations in cases of malignant GCTB. Of note, two fusion genes should be carefully validated following the pathology re-review by sarcoma pathologists. These two fusion genes may be detected in resembling tumors, which contain giant cells, apart from malignant GCTB. The real-world data used herein provide a unique perspective on genomic alterations in clinicopathologically diagnosed malignant GCTB.
Performance of Quantity Discount Contract Under Supply and Demand Disruptions

The quantity discount contract has long been one of the most frequently employed basic supply contracts. However, we must reexamine whether this contract is still effective under supply and demand disruptions that have been frequent recently. We compare three contracts: one quantity discount contract advantageous to the supplier, one quantity discount contract advantageous to the retailer, and one wholesale price contract as a basic case for comparison. We consider various disruption characteristics, such as the occurrence and timing of disruption, and four different disruption types. We find that the quantity discount contract is not always the right choice, especially in an unexpected disruption situation, but it still performs well in not only no disruption but also long-term disruption situations. Even if a disruption occurs, the supplier can be better off through a quantity discount contract, even though the contract choice might devastate the retailer's profit and harm the supply chain's long-term health. Thus, for the profit maximization of the entire supply chain or the retailer, both the internal and external business conditions need to be carefully considered. We provide in detail the conditions in terms of disruption size and loss as well as the market and production characteristics.
Sustainability Report and Financial Performance

environmental performance in the sustainability report and social performance in the sustainability report on the financial performance of mining companies in the coal sub-sector for the 2018-2020 period. The population in this study were 35 companies. The sample selection in this study used a purposive sampling technique, in order to obtain 14 companies that fit into the criteria. The data analysis technique used is panel data regression using EViews 9.0 software. The results of this study indicate that economic performance in the sustainability report, environmental performance in the sustainability report and social performance in the sustainability report together have an effect on financial performance, economic performance in the sustainability report and environmental performance in the sustainability report have no partial effect on financial performance. While social performance in the sustainability report has a partial influence on financial performance.
Demineralized Bone Matrix Fibers plus Allograft Bone for Multilevel Posterolateral Spine Fusion: A Game Changer?

Introduction: While autograft bone is the gold standard for multilevel posterolateral lumbar fusion, bone substitutes and graft extenders such as allograft bone, ceramics and demineralized bone matrix (DBM) have been used to avoid the morbidity and insufficient quantity associated with harvesting autologous bone. The primary objective of this retrospective study was to determine whether, in patients with increased risk of operative nonunion re-lated to multilevel fusion, adding DBM fibers to mineralized bone allograft resulted in better fusion than using allograft alone. The secondary objectives were to evaluate how adding DBM fibers affects functional disability, low back pain, intraoperative blood loss and the nonunion rate. Methods: This retrospective study involved a chart review of consecutive patients who underwent multilevel lumbar spinal fusion and were operated on by a single surgeon. The patients were divided into two groups: 14 patients received mineralized bone allograft (control group) and 14 patients received a combination of mineralized bone allograft and DBM (experimental group). Patients were reviewed at a mean of 16.
TECTONIC EVOLUTION OF THE NORTHERN MARGIN OF THE CENOZOIC ARARAT BASIN, LESSER CAUCASUS, ARMENIA

This paper investigates the structure of the northern margin of the Ararat depression in a study area in SE Armenia. The depression is a Cenozoic intermontane basin located to the south of the Lesser Caucasus. The purpose is to improve understanding of the basin's structure and origin within a regional tectonic framework which has been dominated since the Late Cretaceous by the closure of Neotethys and the Arabia‐Eurasia collision. We suggest that the depression is not a graben controlled by normal faults; rather, based on detailed observations, structures in the study area are interpreted as oblique‐slip reverse and thrust faults activated in post Oligocene‐Miocene times. These compressional faults resulted in the formation of asymmetric fold structures including the Lanjanist and Urts anticlines which are well expressed in the surface relief to the north of the Ararat depression. In general the structural pattern is complicated by secondary normal faults which resulted in superimposed gravitational slope processes and erosion.