Miracle-dz/UniFAR-FTUs huggingface dataset:
Four Years of Earthquake Early Warning in Southern Iberia: 2016–2019

The performance of an earthquake early warning system (EEWS) for southern Iberia during the period of 2016–2019 is analyzed. The software PRESTo (PRobabilistic and Evolutionary early warning SysTem; the University of Naples Federico II, Italy) operating at the Universidad Complutense de Madrid has detected 728 events (2 < Mw < 6.3), with 680 earthquakes occurring in southern Iberia. Differences between the EEWS origin time and epicenter and those of the Instituto Geográfico Nacional (IGN) catalog are less than 2 s and 20 km, respectively, for 70% of the detected earthquakes. The main differences correspond to the EEWS magnitude that is underestimated for earthquakes that occurred at the west of the Gibraltar Strait (Mw differences larger than 0.3 for 70%). To solve this problem, several relationships have been tested, and a modification to those that currently use PRESTo is proposed. Other improvements, such as to densify the network or to use 3D Earth models, are proposed to decrease the time needed to issue the alert and avoid the false alerts (19 events over a total of 728 events). The EEWS has estimated the depth for 680 events and compared to those from the IGN (491 events). The performance of PRESTo during the 2020–2021 Granada swarm is analyzed. The hypocentral locations for the three largest earthquakes are close to those from the IGN (differences from 1 to 7 km for the epicenter and 0 s for the time origin), although there are some differences in their magnitude estimations that varies from 0.2 to 0.5. The PRESTo first times are 17, 25, and 41 s after the origin time. This study shows that the actual PRESTo EEWS configured for the southern Iberia may generate effective warnings despite the low seismicity rate in this region. To decrease the warning time, the geometry and density of the seismic network must be improved together with the use of 3D Earth models and on-site system approaches.
A Novel Mechanism for Gravity-Balancing of Serial Robots With High-Dexterity Applications

Counterbalancing mechanisms considerably reduce the size of actuators in serial robots. However, by limiting the joints’ angles, imposing dead-point configurations, adding extra links, and attaching heavy/bulky balancing springs/counterweights, they sacrifice the dexterity of serial robots. Such limitations prevent serial robots from being implemented in significant robotic applications including biomedical robotic tasks. This paper proposes a novel cable-driven mechanism which addresses all of these limitations and simultaneously moves all actuators to the base. Via a kinematic and potential energy analysis, effectiveness of the proposed mechanism is proven. In order to experimentally evaluate the proposed approach, a fully-balanced four-link serial arm is fabricated and tested which shows 81.7% reduction in the average torque of the actuators. In order to provide maximum dexterity level with the minimum number of components in the proposed mechanism, an optimal design approach is proposed and studied. In this approach, the number of links to reach a desired target along a border-collision-free motion is minimized. Via different experimental and simulation examples, effectiveness of the proposed approach is demonstrated.
Design of FM-DCSK Waveform for Underwater Covert Communication System

The bit energy of chaotic signal in underwater covert communication system is not stable, which will cause the estimation interference and the bit rate decline. Therefore, the frequency-modulated differential chaos shift keying (FMDCSK) covert communication system was proposed which was combined the FM modulation with the differential chaos shift keying (DCSK) system. The bit energy of signals remained stable by using FM modulation so that the BER performance can be improved. Meanwhile, the system was also combined with the modified DCSK system which uses a two-channel scheme, therefore it can improve the transmission efficiency. In this paper, the feasibility of FM-DCSK was analyzed first. Meanwhile, the Matlab simulation contrasted the two different systems of FM-DCSK and DCSK. The simulation results show that the relative performance in this proposed system can be worked well.
Carrier lifetime as a function of Se content for CdSexTe1-x films grown on Al2O3 and MgZnO

Time-resolved photoluminescence (TRPL) with two excitation wavelengths – 670 (standard) and 405 nm – was used to examine the effect on carrier lifetime of two significant recent advances in CdTe: the incorporation of Se to form graded CdSe<inf>x</inf>Te<inf>1-x</inf> and the use of Mg<inf>y</inf>Zn<inf>1-y</inf>O buffers. The two excitation wavelengths probe depths of approximately 130 and 35 nm, respectively, and their comparison helps differentiate interface and bulk contributions to carrier lifetime. It was found that x = 0.2 Se was required to obtain lifetime improvements, primarily in the bulk. Additionally, TRPL traces for Mg<inf>y</inf>Zn<inf>1-y</inf>O/CdSe<inf>x</inf>Te<inf>1-x</inf> samples showed fast initial decay followed by a long-lived tail, which may be indicative of trap-dominated recombination. This behavior was not present for CdSe<inf>x</inf>Te<inf>1-x</inf> films grown on Al<inf>2</inf>O<inf>3</inf>, which is currently state-of-the-art for surface passivation in CdTe. This indicates that further work is required to sufficiently passivate the front interface.
Robustness-Aware Real-Time SFC Routing Update in Multi-Tenant Clouds

In multi-tenant clouds, requests need to traverse a set of network functions (NFs) in a specific order, referred to as a service function chain (SFC), for security and business logic issues. Due to workload dynamics, the central controller of a multi-tenant cloud needs to frequently update the SFC routing, so as to optimize various network performance, such as load balancing. To achieve effective SFC routing update, we should consider two critical requirements: system robustness and real-time update. Without considering these two requirements, prior works either result in fragile clouds or suffer from large update delay. In this paper, we propose a robustness-aware real-time SFC routing update (R3-UA) scheme which takes both requirements into consideration. R3-UA pursues robustness-aware real-time routing update through two phases: robust NF instance assignment and real-time SFC routing update. Two algorithms with bounded approximation ratios are proposed for these two phases, respectively. The large-scale simulation results show the superior performance of R3-UA compared with other alternatives.
Enhancing Crisis Management because of Deep Learning, Big Data and Parallel Computing Environment: Survey

The broad dissemination and scope of social media enables individuals to exchange information in realtime. This active involvement of societies plays a major role in reducing disaster risk and alleviating at-risk populations. While any operation needs accurate information in crisis management to enable a rapid response to decrease the potential loss of life. Timely retrieval of information from regions of an area affected by disaster is a demanding task. A catastrophe relief and response method's effectiveness depends largely on a prompt and accurate crisis assessment. This knowledge is primarily collected on site by first responders and can be updated later on. Several technics have been builts to automate this need through the extraction and analysis of appropriate content from social media. These approaches are not, however, well incorporated into the mechanism of relief. For more advancement, it would be important to reveal them. The era of big data and analytics is giving crisis management new possibilities (DM). Big data is fundamentally transforming humanitarian operations and crisis management due to its ability to simulate, evaluate, and forecast disasters. However, the related literature is complex and fragmented, necessitating a study in order to determine its progression. Big data and its uses for disaster mitigation have been the subject of many publications. This thesis explores big data in DM based on a systematic literature review to current key contributions, holes, threats, and future research agenda. The results are presented in terms of yearly dissemination, key articles, and-most cited papers in the review. The results also reveal a classifiable pattern.
Adversarial Attack and Defense on Graph-based IoT Botnet Detection Approach

To reduce the risk of botnet malware, methods of detecting botnet malware using machine learning have received enormous attention in recent years. Most of the traditional methods are based on supervised learning that relies on static features with defined labels. However, recent studies show that supervised machine learning-based IoT malware botnet models are more vulnerable to intentional attacks, known as an adversarial attack. In this paper, we study the adversarial attack on PSI-graph based researches. To perform the efficient attack, we proposed a reinforcement learning based method with a trained target classifier to modify the structures of PSI-graphs. We show that PSI-graphs are vulnerable to such attack. We also discuss about defense method which uses adversarial training to train a defensive model. Experiment result achieves 94.1% accuracy on the adversarial dataset; thus, shows that our defensive model is much more robust than the previous target classifier.
Pegylated-interferon-alpha treatment modulating the immune response of cytotoxic lymphocytes in cervical intraepithelial neoplasia

Background: Interferons are inducible secretory glycoproteins with immunomodulators, antiviral, antiangiogenic and antiproliferative e fects. Evaluate the mechanisms responsible by regression of patients diagnosed with Cervical Intraepithelial Neoplasia (CIN) and treated with IFN-α, systemically and locally, by Interferon-α (IFNα) receptor 1 (IFNR1) and IFN-α receptor 2 (IFNR2) and transcription factors STAT-1 (Signal Transducers and Activators of Transcription 1) and IRF-7 (Interferon Regulatory Factor 7), as well as the endogenous produced IFN-α by total (CD3+), Helper (CD4+), cytotoxic (CD8+) T lymphocytes and monocytes (CD14+). Methods: A prospective study was developed in which eighteen patients diagnosed with CIN II/III in treatment protocolwith Peginterferon-α. Cellswere evaluated using Real-Time and low cytometry, and the data were analyzed using Kruskal-Wallis and ANOVA tests, considering p≤ 0.05. Results: Eight patients obtained regression of the lesion, and ten did not obtain the regression. Patientswhodid respondpositively to the treatmentpresented a CD8+ T lymphocyte with IFN-α increase when compared to patientswho not responded positively. When analyzing CD8+ T lymphocytes during the stages of treatment in lesion regression, it is observed a significant IFNR1 (p = 0.0391) decrease in patients who did not achieve lesion regression. CD3 and CD14 data was not significant. Discussion: Immunomodulation by Interferon-alpha seems to depend on the systemic expression of IFN receptors. Our data suggest thatpatientswhocanrespondto immunotherapyalreadyhavea patternof IFN receptor expression in lymphocytes,which contributes to successful treatment.
Review of Cancer - Related Cachexia: Definition, Physiopathology and Treatment

Patricia Rioja, Department of Medical Oncology, Instituto Nacional de Enfermedades Neoplasicas, Angamos Este Avenue 2520, Surquillo, Lima, Peru, Tel: +51 2016500x2208, E-mail: patyriojavi@gmail.com Received: 11 May 2021 Accepted: 04 June 2021 Published: 11 June 2021 Copyright: ©2021 Rioja P, This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and build upon your work non-commercially.
REVISTA DE CONTABILIDAD SPANISH Determinantes del periodo medio de pago municipal y eﬁcacia del principio de sostenibilidad de la deuda comercial Determinants of the municipal average payment period and effectiveness of the commercial debt sustainability

The policies implemented to improve the liquidity of Spanish municipalities and the introduction of the commercial debt sustainability principle reveal the importance of controlling late payment by public sector. In this context, the purpose of this paper is to analyse the effect of the ﬁscal rule directed to control the average payment periods of public administrations and whether the political environment, socioeconomic characteristics and ﬁnancial situation inﬂuence on the average payment period of municipalities. In order to achieve this goal, an exploratory analysis is carried oud and panel data methodology (2009-2015) is applied. The paper contributes to provide evidence of the effectiveness of the commercial debt sustainability principle and improves the knowledge for the analysis of short-term ﬁnancial position.
Agronomic and nutraceutical behavior of F2 populations developed of interracial crosses of pepper

Every day it is more difficult to satisfy the world food demand, for this reason the importance of working in the development of new varieties of high yield and fruit quality, taking advantage of the genetic resources of Mexico, for the development and use of superior varieties that allow to reduce the production costs of protected agriculture systems since, in Mexico in these systems a significant percentage comes from foreign companies at high costs. However, small producers sow creole seed of low yield and fruit quality. In this work, the RTF and its components (NFP, PPF), in addition to some quality characters (CAA, CT and CAPs) and agronomic (ADP, DBT, DAF, and DAC) of nine pepper hybrids in their F2 generations and their respective parents, which were established in a greenhouse, under an experimental design complete random blocks with three replications. The variance analyzes exhibited significant differences (p< 0.01) between parents and F2 populations in all the variables studied. In RTF, the F2 populations (P1,7 and P3,4) exceeded their F1 by 7 and 5% respectively, the latter being the one that presented the highest RTF in this generation with 2 764 g plant. All F2 populations presented intermediate amounts and positive values in DEP in CT, CAA and CAPs. Concluding that in the F2 generation there were promising populations for the development of new varieties, since they increased yield and fruit quality from F1 to F2, with an estimated yield greater than 100 t ha. Reception date: November 2020 Acceptance date: January 2021 Rev. Mex. Cienc. Agríc. vol. 12 num. 1 January 01 February 14, 2021
3D Densealexnet Model for Brain Tumour Segmentation

Background: The collection of anomalous cells within or around the brain is stated as a brain tumour. Automatic brain tumour segmentation is considered a challenging task due to complexity and gradient diffusion. To improve the segmentation of 3D brain MRI images Deep Neural Network (DNN) is evolved. However, it is subjected to the drawback of training computational power and complexity. Objective: In this paper, proposed a 3D Dense AlexNet model with backpropagation for segmentation of tumour in brain MRI images. The developed architecture consists of Neural Network for processing input 3D images. This paper focused on improving the overall segmentation process with the Alexnet model for 3D brain images for performance improvement. Method: Based on the training and validation test self-constrained 3D Dense ALexNet model is developed. Within the 3D Dense AlexNet backpropagation is adopted for removing complexity in the testing process and accuracy improvement. Based on the training and testing process 3D MRI image sequences are trained and processed for segmentation on the tumour. Result: The analysis of results expressed that the proposed 3D Dense AlexNet exhibits improved segmentation performance. Based on the proposed 3D AlexNet architecture MRI images are segmented with minimal time. The performance of the proposed 3D Dense AlexNet model exhibited the improved accuracy of tumour detection with reduced computational complexity.
Exploring the impact of organizational context on budgeting

Most organizations use budgeting and describe it as an institutionalized management accounting system, but it is also criticized for being unsuitable in today’s uncertain business environment. This paper explores how sudden changes in organizational context impact the importance of budgeting. Earlier studies have reported less importance given to budgeting following a crisis and a need for more interactive approaches. Our study is based on a survey of CFOs of the 300 largest companies in Iceland, according to the dataset Frjáls Verslun, following the financial crisis of 2008. A total of 191 (63.6%) responded to the survey. The results show widespread use of budgeting, regardless of the size of the organization. The results also show that uncertainty and organizational complexity do not impact the perceived importance of budgeting. Contrary to prior studies, this indicates that budgeting remains an important management tool during a sudden change in the organizational context
Risk Factors of Coronary Calcium Onset in Healthy Japanese Individuals: A Longitudinal Study Using Low-Dose Chest CT

Low-dose CT lung cancer screening has been reported to reduce mortality by 20% compared to single-view posteroanterior chest radiography [1]. Also, the amount of coronary artery calcification (CAC) measured by electrocardiography (ECG)gated CT reflects arteriosclerosis [2] and is a strong predictor of coronary artery disease (CAD), cardiovascular events, and allcause mortality, above and beyond the Framingham risk score [3-5]. In a recent study, CAC on standard chest CT correlated well with that on ECG-gated CT and was linked to mortality risk [6]. In addition, in patients undergoing CT lung cancer screening, the assessment of coronary calcification has been successfully performed using low-dose chest CT. Results had excellent correlation with ECG-gated coronary calcium score [7-9]. The procedure had good sensitivity and specificity and showed overall accuracy [10]. The amount of CAC is an independent prognostic factor for heart disease [4,11-13]. If the amount of CAC is 0, atherosclerotic lesions including vulnerable plaques can be ruled out with a high probability [12,14]. However, the onset of CAC and the related factors in healthy individuals are not well understood. The aim of this study was to quantify CAC using low-dose chest CT, longitudinally performed cc This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/bync/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. CVIA Risk Factors of Coronary Calcium Onset in Healthy Japanese Individuals: A Longitudinal Study Using Low-Dose Chest CT
Analyzing Impact of Social Media Sentiments on Financial Markets

This paper analyzes the impact of continuously changing sentiments on apparently unstable stock exchange. Right when a monetary supporter decides to buy or sell stock, his decision is very much dependent on to rise or fall in price of the stock. In this paper, we look at the possibility of using notion attitudes (good versus negative) and moreover sentiments (delight, feel sorry for, etc) isolated from finance related news or tweets to help predict stock worth turns of events. This examination uses a model-self-ruling approach to manage uncover the mysterious components of stock exchange data using distinctive significant learning techniques like Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU).
Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning

A core question in multi-agent systems is understanding the motivations for an agent's actions based on their behavior. Inverse reinforcement learning provides a framework for extracting utility functions from observed agent behavior, casting the problem as finding domain parameters which induce such a behavior from rational decision makers. We show how to efficiently and scalably extend inverse reinforcement learning to multi-agent settings, by reducing the multi-agent problem to N single-agent problems while still satisfying rationality conditions such as strong rationality. However, we observe that rewards learned naively tend to lack insightful structure, which causes them to produce undesirable behavior when optimized in games with different players from those encountered during training. We further investigate conditions under which rewards or utility functions can be precisely identified, on problem domains such as normal-form and Markov games, as well as auctions, where we show we can learn reward functions that properly generalize to new settings.
Epigallocatechin-3-Gallate Protects against Aluminum-Induced Neurotoxicity in Rats under Social Isolation, Electric Shock and Inadequate Protein Malnutrition

Epigallocatechin-3-gallate (EGCG) is the most abundant catechin in green tea. This study aims to investigate the influence of EGCG on different stressful conditions as social isolation, Electric Shock (EC) and inadequate nutritional conditions as Protein Malnutrition (PM) on neurotoxicity induced by Al in rats. Rats were randomly categorized into 10 groups. One normal control received saline, and four Al toxicity groups injected daily for three weeks by AlCl3 (70 mg/kg, IP). One of them served as Al toxicity model, two groups subjected to different stresses either by isolation as mild stressful condition or by Electric Shock (EC) as high stressful condition. The last group was maintained on 10% casein diet, a group recieved EGCG (10 mg/kg, IP) and other 4 groups treated with EGCG and different stress conditions employed in the study. Rats exposed to Al showed brain neurotoxicity and neuronal degenerations. Both mild (SI) and high (EC) stressful conditions as well as inadequate nutrition (PM) enhanced Al-induced neurotoxicity and brain neuronal degenerations. The enhancement induced by stresses especially in its higher conditions (ES) was more pronounced than that of inadequate nutritional conditions (PM) as indicated by the significant increase in Aβ, AChE, MDA, TNF-α, IL-1β together with the significant decrease in SOD, TAC, BDNF. EGCG showed more pronounced protection against hazards of Al in both stressful conditions (SI and EC) rather than in PM. The protective effects of EGCG were indicated by the significant decrease in Aβ, AChE, MDA, TNF-α, IL-1β together with the increase in SOD, TAC, BDNF and confirmed by brain histopathological examinations. EGCG can protect against Al-induced brain neuronal degenerations in all conditions. Consequently, administration of EGCG together with socialization as well as adequate protein nutrition is advised especially on excessive Al-exposure to avoid the severity of its neuronal toxicity.
CHANGES IN PROPERTIES OF A CLAYEY SOIL AFTER ADDING COMPOSTED AND UNCOMPOSTED GYTTJA

This study presents an incubation experiment that investigates the effects of adding composted and uncomposted gyttja on a clayey soil’s structural stability and other properties. For this purpose, 3% (w/w) of the composted and uncomposted gyttja was added to pots with and without grass pea (Lathyrus sativus L.) seeds and incubated for nine months under greenhouse conditions. In the third, sixth, and ninth months of the experiment, soil samples were tested. The results show that the time elapsed during the incubation period greatly affected the physical (average weighted diameter, aggregate stability, and volume weight) and chemical (pH, EC, organic matter, N, P, K, Ca, Mg, and Na) properties of the soil. The input of composted and uncomposted gyttja caused significant changes in soil properties, but these were less pronounced in soils with composted gyttja.