Miracle-dz/UniFAR-FTUs huggingface dataset:
anticipos en la contratación pública durante el COVID- 19 en Colombia y España

Debido a la pandemia del COVID-19 los mandatarios a nivel internacional a través de la administración actuaron de una manera atípica debido a la emergencia sanitaria. Gracias a esta situación, se gestó la contratación de emergencia sanitaria pública en Colombia y España, con el objetivo de recuperar la economía y contener el contagio del virus. En consecuencia, se afectó la reglamentación de la figura normativa del anticipo en España en los contratos celebrados por las instituciones públicas, mientras que en Colombia no se alteró, es por esto que, el presente artículo se realizó por medio del método cualitativo-descriptivo, en el cual se identificaron los cambios sustanciales que sufrieron las normativas, al generar estas contrataciones de emergencia y como establecieron excepciones por los mismos gobiernos como resultado de la aplicación de nuevas normas jurídicas. Por ende, pretendemos dar respuesta a la siguiente pregunta ¿Cuáles fueron los cambios normativos más importantes derivados de la pandemia sobre el anticipo en Colombia y España y su relación con la corrupción? De acuerdo con el análisis realizado, se toman en cuenta casos de corrupción de la contratación pública y los anticipos, se determinó que el mal manejo de la figura ha desencadenado, varios hechos relevantes donde se produce este fenómeno, sin embargo, no es el generador absoluto de la problemática. En cuanto al comparativo entre los países, se identifica que Colombia utiliza el anticipo con mayor proporción, contrario sensu a lo establecido en España, incluso antes de la pandemia.
Implementation of Okun Law Before Autonomy and After Regional Autonomy 1979-2022

Regional Autonomy is carried out by many countries, including Indonesia. Autonomy is intended to provide better public services. The freedom to take initiative on the basis of granting regional autonomy opens up opportunities for regions to develop their potential to increase economic growth. High economic growth should be able to reduce unemployment. High unemployment rates have an impact on slowing the rate of economic growth. This research uses a paired t-test to see the differences between before and after regional autonomy and the analytical method used is the Vector Error Correction Model (VECM) to analyze the relationship between variables in the long and short term. The results of data analysis show that there is a one-way causal relationship between economic growth and unemployment which is significant and in the short term, the relationship between the unemployment rate variable and economic growth is only found in lag 3. In the long term the unemployment rate variable has a negative relationship with the unemployment rate. It can be concluded that Okun’s law only exists in the long term
Uranium-lead and potassium-argon ages from eastern Bella Coola and adjacent parts of Anahim Lake and Mount Waddington map areas, west-central British Columbia

Foliated rocks from the Atnarko complex yield 159 to 157 Ma metamorphic and plutonic U-Pb ages and 148 to 128 Ma hornblende K-Ar ages in the south, and 130 to 115 Ma plutonic U-Pb ages in the north; mica K-Ar cooling ages range from 113 to 97 Ma. The undeformed Firvale pluton, firmly dated by 134 to 132 Ma U-Pb ages, is nonconformably overlain by the Lower Cretaceous Monarch assemblage and by distinct felsic volcanic rocks with a 112 Ma U-Pb age. The Hotnarko volcanic rocks, with a 154 Ma U-Pb age, are inferred to be part of this basement. West of the Talchako River, the Monarch assemblage is deposited on plutonic rocks with 160 to 155 Ma U-Pb ages. The Atnarko complex represents a mid-Cretaceous structural culmination that exposes the deeper levels of this heterogeneous basement. Discrete stocks of the Four Mile suite, which yield 63 Ma U-Pb ages, intrude most older units and plug prominent north-northwest-trending brittle faults. 1 Contribution to the Targeted Geoscience Initiative (TGI) 2001-2003 Page 1 Résumé : Des roches foliées du complexe d’Atnarko ont livré des âges U-Pb de 159 à 157 Ma pour les roches métamorphiques et plutoniques et des âges K-Ar sur hornblende de 148 à 128 Ma dans le sud, ainsi que des âges U-Pb de 130 à 115 Ma pour les roches plutoniques dans le nord; les âges de refroidissement par K-Ar sur mica vont de 113 à 97 Ma. Le pluton non déformé de Firvale, dont l’âge U-Pb est de 134 à 132 Ma, est recouvert en discordance par l’assemblage de Monarch du Crétacé inférieur et par des roches volcaniques felsiques distinctives datées à 112 Ma par la méthode U-Pb. Les roches volcaniques de Hotnarko, dont l’âge U-Pb est de 154 Ma, feraient partie de ce socle. À l’ouest de la rivière Talchako, l’assemblage de Monarch repose sur des roches plutoniques datant de 160 à 155 Ma (U-Pb). Le complexe d’Atnarko représente une culmination structurale du Crétacé moyen qui expose les niveaux plus profonds de ce socle hétérogène. Des stocks distincts de la suite de Four Mile, dont les âges U-Pb sont de 63 Ma, ont recoupé la plupart des anciennes unités et ont injecté d’importantes failles cassantes à direction nord-nord-ouest.
Response surface methodology mediated optimization of serine protease by Salinicola tamaricis BGN2 isolated from the West Coast of India and its application in culturing of MCF7 cell line

Proteases are the type of hydrolytic enzymes which have potential biotechnological applications. We report the isolation, optimized production, purification, and cell culture applications for halo-tolerant protease from Salinicola tamaricis BGN2, isolated from the marine environment on the West coast of India. Media optimization for protease production was done using a central composite design by response surface methodology. The initial screening and Plackett–Burman design revealed that glucose, fish glue, soybean flour, CaCl 2, and NaCl were significant factors for protease production. A fivefold increase (55.2 U/ml) in protease production was observed with an optimized medium comprised of glucose (0.5%), fish glue (0.75%), and NaCl (10%) after 96 hours of incubation. Acetone precipitation and gel-filtration chromatography followed by sodium dodecyl sulfate–polyacrylamide gel electrophoresis and zymography analysis showed a 43 KDa molecular weight for the purified protease. Application studies demonstrated significant trypsin-like activity of protease in sub-culturing MCF7 cell-line in both time and dose-dependent manner. Effective time and quantity of protease for cell dislodging activity were found to be one minute and 200 µl, respectively. It is a rare report demonstrating the trypsin-like
Rainfall Intensity and Frequency Explain Production Basis Risk in Cumulative Rain Index Insurance

With minimal moral hazard and adverse selection, weather index insurance promises financial resilience to farmers struck by harsh weather conditions through swift compensation at affordable premium. Despite these advantages, the very nature of indexing gives rise to production basis risk as the selected weather indexes do not sufficiently correspond to actual damages. To address this problem, we develop a stochastic yield model, built upon a stochastic soil moisture model driven by marked Poisson rainfall. Our analysis shows that even under similar temperature and rainfall amount yields can differ significantly; this was empirically supported by a 2‐year field experiment in which rain‐fed maize was grown under very similar total rainfall. Here, the year with more intense, less‐frequent rainfall produces a better yield—a rare counter evidence to most climate change projections. Through a stochastic yield model, we demonstrate the crucial roles of rainfall intensity and frequency in determining the yield. Importantly, the model allows us to compute rainfall pattern‐related basis risk inherent in cumulative rain index insurance. The model results and a case study herein clearly show that total rainfall is a poor indicator of yield, imposing unnecessary production basis risk on farmers and false‐positive payouts on insurers. Incorporating rainfall intensity and frequency in the design of rain index insurance can offer farmers better protection, while maintaining the attractive features of the weather index insurance and thus fulfilling its promise of financial resilience.
Effect of Drip Fertigation on Growth, Yield and Economics of Pigeonpea

A field investigation entitled "Response of pigeonpea to different drip fertigation levels” was carried out at Department of Agronomy Farm, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola during the kharif season of 2016-17. The experiment was laid out in Randomised Block Design with four replications and eight different irrigation and fertigation treatments imposed for pigeonpea crop with an objective to study the efficacy of split application of N,P and K on growth, yield and economics of Pigeonpea. The experimental site was established with inline drip irrigation system (16 mm) lateral laid out at 90 cm with 30 cm dripper spacing. Irrigation water was applied through drip irrigation system on every alternate day at the rate of 100 per cent crop evapotranspiration level. Experiment results revealed that, in pigeonpea all the growth parameters viz. plant height, number of branches plant -1 , root nodules plant -1 , dry matter accumulation plant -1 , yield attributes viz. Pod plant -1 and seed yield were substantially enhanced by drip fertigation levels at 125:100:100 per cent recommended NPK than lower fertigation levels (75 and 100 %), drip irrigation with 100 per cent recommended NPK and over conventional soil application in furrow with 100 per cent RDNPK kg ha -1 . As a consequence of better growth and yield attributes, drip fertigation at 125:100:100 per cent recommended dose of NPK ha -1 had recorded higher pigeon-pea seed yield of 3866 kg ha -1 . The GMR, NMR and B:C ratio was found maximum with 125:100:100 per cent RDNPK treatment. Drip fertigation as well as soil application of fertilizer had no significant impact on seed index, protein content and all the quality parameters of pigeonpea. It could be concluded that application of 125:100:100 per cent recommended dose of N,P and K in five splits found to be best for maximizing the yield and beneficial in increasing the productivity and economic returns of pigeonpea.
EEG-Based Multimodal Emotion Recognition: A Machine Learning Perspective

Emotion, a fundamental trait of human beings, plays a pivotal role in shaping aspects of our lives, including our cognitive and perceptual abilities. Hence, emotion recognition also is central to human communication, decision-making, learning, and other activities. Emotion recognition from electroencephalography (EEG) signals has garnered substantial attention due to advantages such as noninvasiveness, high speed, and high temporal resolution; driven also by the complementarity between EEG and other physiological signals at revealing emotions, recent years have seen a surge in proposals for EEG-based multimodal emotion recognition (EMER). In short, EEG-based emotion recognition is a promising technology in medical measurements and health monitoring. While reviews exist, which explore emotion recognition from multimodal physiological signals, they focus mostly on general combinations of modalities and do not emphasize studies that center on EEG as the fundamental modality. Furthermore, existing reviews take a methodology-agnostic perspective, primarily concentrating on the biomedical basis or experimental paradigms, thereby giving little attention to the methodological characteristics unique to this field. To address these gaps, we present a comprehensive review of current EMER studies, with a focus on multimodal machine learning models. The review is structured around three key aspects: multimodal feature representation learning, multimodal physiological signal fusion, and incomplete multimodal learning models. In doing so, the review sheds light on the advances and challenges in the field of EMER, thus offering researchers who are new to the field a holistic understanding. The review also aims to provide valuable insight that may guide new research in this exciting and rapidly evolving field.
Understanding fracture distribution and its relation to knickpoint evolution in the Rio Icacos watershed (Luquillo Critical Zone Observatory, Puerto Rico) using landscape‐scale hydrogeophysics

The Rio Icacos watershed in the Luquillo Mountains (Puerto Rico) is unique due to its extremely rapid weathering rates. The watershed is incised into a quartz diorite that has developed a large knickzone defining the river profile. Regolith thickness within the watershed generally decreases from 20 to 30 m at the ridges to several meters in the quartz diorite‐dominated valley to tens of centimeters near the major river knickpoint, as determined from previous studies. Above the knickzone, we observe spheroidal corestones, but below this weathering is much less apparent. Measured erosion rates from previous studies are also high in the knickzone compared with upper elevations within the river profile. A suite of near‐surface geophysical methods (i.e. ground penetrating radar and terrain conductivity) capable of fast data acquisition in rugged landscapes, was deployed at kilometer scales to characterize critical zone structure. Concentrations of chaotic ground penetrating radar (GPR) reflections and diffraction hyperbolas with low electrical conductivity were observed in vertical zones that outcrop at the land surface as areas of intense fracturing and spheroidally weathered corestones. The width of these fractured and weathered zones showed an increase with proximity to the knickpoint, and was attributed to dilation of these sub‐vertical fractures near the knickpoint, as postulated theoretically by a stress model calculated for the topographic variability across the knickzone in the Rio Icacos, and that shows a release of compressive stress near the knickpoint. We hypothesize that erosion rates increase in the knickzone because of this inferred dilation of fractures. Specifically, opened fractures could enhance access of water and in turn promote spalling, erosion, and spheroidal weathering. This study shows that ground‐based hydrogeophysical methods used at the landscape‐scale (traditionally applied at smaller scales) can be used to explore critical zone architecture at the scales needed to explain the extreme variability in erosion rates across river profiles. © 2018 John Wiley & Sons, Ltd.
Hospitalization forecast to inform COVID-19 pandemic planning and resource allocation using discrete event simulation

Purpose: This study aims to address the pressing need for accurate forecasting of healthcare resource demands during the COVID-19 pandemic. It presents a novel approach that combines a stochastic Markov model and a discrete event simulation model to dynamically predict hospital admissions and daily occupancy of hospital and ICU beds.Design/methodology/approach: The research builds upon existing work related to predicting COVID-19 spread and patient influx to hospital emergency departments. The proposed model was developed and validated at San Juan de Alicante University Hospital from July 10, 2020, to January 10, 2022, and externally validated at Hospital Vega Baja. The model involves an admissions generator based on a stochastic Markov model, feeding data into a discrete event simulation model in the R programming language. The probabilities of hospital admission were calculated based on age-stratified positive SARS-COV-2 results from the health department's catchment population. The discrete event simulation model simulates distinct patient pathways within the hospital to estimate bed occupancy for the upcoming week. The performance of the model was measured using the median absolute difference (MAD) between predicted and actual demand.Findings: When applied to data from San Juan hospital, the admissions generator demonstrated a MAD of 6 admissions/week (interquartile range [IQR] 2-11). The MAD between the model's predictions and actual bed occupancy was 20 beds/day (IQR 5-43), equivalent to 5% of total hospital beds. For ICU occupancy, the MAD was 4 beds/day (IQR 2-7), constituting 25% of ICU beds. Evaluation with data from Hospital Vega Baja showcased an admissions generator MAD of 2.42 admissions/week (IQR 1.02-7.41). The MAD between the model's predictions and actual bed occupancy was 18 beds/day (IQR 19.57-38.89), approximately 5.1% of hospital beds. The ICU occupancy MAD was 3 beds/day (IQR 1-5), making up 21.4% of ICU beds.Practical implications: The dynamic predictions of hospital admissions, ward beds, and ICU occupancy for COVID-19 patients proved highly valuable to hospital managers, facilitating early and informed planning of resource allocation. Additionally, this study underscores the importance of utilizing simulation techniques to predict and manage hospital occupancy levels, thereby enhancing decision-making in hospital bed management, not only during pandemics but also during regular periods.Originality/value: This study introduces a novel hybrid approach that combines stochastic modeling and discrete event simulation to forecast healthcare resource demands during the COVID-19 pandemic. The methodology's effectiveness in predicting admissions and bed occupancy contributes to improved resource planning and situational awareness.
Neural network-based estimation of biomechanical vocal fold parameters

Vocal fold (VF) vibrations are the primary source of human phonation. High-speed video (HSV) endoscopy enables the computation of descriptive VF parameters for assessment of physiological properties of laryngeal dynamics, i.e., the vibration of the VFs. However, underlying biomechanical factors responsible for physiological and disordered VF vibrations cannot be accessed. In contrast, physically based numerical VF models reveal insights into the organ’s oscillations, which remain inaccessible through endoscopy. To estimate biomechanical properties, previous research has fitted subglottal pressure-driven mass–spring–damper systems, as inverse problem to the HSV-recorded VF trajectories, by global optimization of the numerical model. A neural network trained on the numerical model may be used as a substitute for computationally expensive optimization, yielding a fast evaluating surrogate of the biomechanical inverse problem. This paper proposes a convolutional recurrent neural network (CRNN)-based architecture trained on regression of a physiological-based biomechanical six-mass model (6 MM). To compare with previous research, the underlying biomechanical factor “subglottal pressure” prediction was tested against 288 HSV ex vivo porcine recordings. The contributions of this work are two-fold: first, the presented CRNN with the 6 MM handles multiple trajectories along the VFs, which allows for investigations on local changes in VF characteristics. Second, the network was trained to reproduce further important biomechanical model parameters like VF mass and stiffness on synthetic data. Unlike in a previous work, the network in this study is therefore an entire surrogate of the inverse problem, which allowed for explicit computation of the fitted model using our approach. The presented approach achieves a best-case mean absolute error (MAE) of 133 Pa (13.9%) in subglottal pressure prediction with 76.6% correlation on experimental data and a re-estimated fundamental frequency MAE of 15.9 Hz (9.9%). In-detail training analysis revealed subglottal pressure as the most learnable parameter. With the physiological-based model design and advances in fast parameter prediction, this work is a next step in biomechanical VF model fitting and the estimation of laryngeal kinematics.
Comparison of geodetic slip-deficit and geologic fault slip rates reveals that variability of elastic strain accumulation and release rates on strike-slip faults is controlled by the relative structural complexity of plate-boundary fault systems

Comparison of geodetic slip-deficit rates with geologic fault slip rates on major strike-slip faults reveals marked differences in patterns of elastic strain accumulation on tectonically isolated faults relative to faults that are embedded within more complex plate-boundary fault systems. Specifically, we show that faults that extend through tectonically complex systems characterized by multiple, mechanically complementary faults (that is, different faults that are all accommodating the same deformation field), which we refer to as high-Coefficient of Complexity (or high-CoCo) faults, exhibit ratios between geodetic and geologic rates that vary and that depend on the displacement scales over which the geologic slip rates are averaged. This indicates that elastic strain accumulation rates on these faults change significantly through time, which in turn suggests that the rates of ductile shear beneath the seismogenic portion of faults also vary through time. This is consistent with models in which mechanically complementary faults trade off slip in time and space in response to varying mechanical and stress conditions on the different component faults. In marked contrast, structurally isolated (or low-CoCo) faults exhibit geologic slip rates that are similar to geodetic slip-deficit rates, regardless of the displacement and time scales over which the slip rates are averaged. Such faults experience relatively constant geologic fault slip rates as well as constant strain accumulation rate (aside from brief, rapid post-seismic intervals). This suggests that low-CoCo faultsd "keep up" with the rate imposed by the relative plate-boundary condition, since they are the only structures in their respective plate-boundary zone that can effectively accommodate the imposed steady plate motion. We hypothesize that the discrepancies between the small-displacement average geologic slip rates and geodetic slip-deficit rates may provide a means of assessing a switch of modes for some high-CoCo faults, transitioning from a slow mode to a faster mode, or vice versa. If so, the differences between geologic slip rates and geodetic slip-deficit rates on high-CoCo faults may indicate changes in a fault's behavior that could be used to refine next-generation probabilistic seismic hazard assessments.
Stem Cells and Bone Tissue Engineering

Segmental bone defects that are caused by trauma, infection, tumor resection, or osteoporotic fractures present significant surgical treatment challenges. Host bone autograft is considered the gold standard for restoring function but comes with the cost of harvest site comorbidity. Allograft bone is a secondary option but has its own limitations in the incorporation with the host bone as well as its cost. Therefore, developing new bone tissue engineering strategies to treat bone defects is critically needed. In the past three decades, the use of stem cells that are delivered with different scaffolds or growth factors for bone tissue engineering has made tremendous progress. Many varieties of stem cells have been isolated from different tissues for use in bone tissue engineering. This review summarizes the progress in using different postnatal stem cells, including bone marrow mesenchymal stem cells, muscle-derived stem cells, adipose-derived stem cells, dental pulp stem cells/periodontal ligament stem cells, periosteum stem cells, umbilical cord-derived stem cells, peripheral blood stem cells, urine-derived stem cells, stem cells from apical papilla, and induced pluripotent stem cells, for bone tissue engineering and repair. This review also summarizes the progress using exosomes or extracellular vesicles that are delivered with various scaffolds for bone repair. The advantages and disadvantages of each type of stem cell are also discussed and explained in detail. It is hoped that in the future, these preclinical results will translate into new regenerative therapies for bone defect repair.