Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Sample Properties and Pretest Estimation of a Spatial Hausman–Taylor Model

This paper considers a Hausman and Taylor (1981) panel data model that exhibits a Cliff and Ord (1973) spatial error structure. We analyze the small sample properties of a generalized moments estimation approach for that model. This spatial Hausman-Taylor estimator allows for endogeneity of the time-varying and time-invariant variables with the individual effects. For this model, the spatial effects estimator is known to be consistent, but its disadvantage is that it wipes out the effects of time-invariant variables, which are important for most empirical studies. Monte Carlo results show that the spatial Hausman-Taylor estimator performs well in small samples. Key Words: Hausman-Taylor estimator; Spatial random effects; Small sample properties JEL No. C23, 31
Mòdul pedagògic en un sistema tutor intel·ligent per a predir l'evolució de l'alumnat

Intelligent Tutoring Systems are systems based on Artificial Intelligence that can analyze models of knowledge to get to understand the learner’s state. Its function is trying to help him or her through emulating the role of a human tutor. In this project we’ll implement the Pedagogical Module of an Intelligent Tutoring System. Specifically, we’ll implement a prediction system that tries to estimate the grades students will have at the end of the academic year. For this we’ll incorporate methods of Machine Learning. Machine Learning is a discipline from the field of Mathematics and Computer Science that studies algorithms to predict future outcomes and act accordingly, and, if applicable, implement an automated behaviour that tries to aim for the best possible result. Specifically, the algorithms we’ll use are called classifiers, a kind of Supervised Learning algorithm. We’ll implement a library with different kinds of classifiers, test their functionality using Ten-Fold Cross-Validation and simulate its usage with different datasets that come from the UCI repository and from 4 subjects that are taught in grades at the University of Barcelona, to analyze their accuracy and efficiency. Furthermore, this library will need to be usable by teachers. Using it, they will be able to input their history from previous years and their current course to get an estimate of that year’s results. By doing this, they will be able to detect a deviation from previous courses and work on getting it back on track. The implementation will have the options to either go for the most theoretically precise classifier or use the pre-set option for a fast execution. The most precise option will be determined by testing every single classifier using the train set, while the pre-set classifier will be chosen based on this project’s tests and the algorithm’s consistency. These functions will work through a user interface, which will be used by teachers from different fields of the University of Barcelona. This means the interface will need to be user-friendly and easy to learn.
Determining Phosphorus Loading Rates Based on Land Use in an Urban Watershed

Non-point phosphorus (P) loading to surface water can degrade water quality and impair habitat. As urban areas continue to grow in the United States, critical P source loading areas need to be identified to assess their impact on water quality. A 332 ha urban watershed in Ithaca, NY was selected and monitored for two years, with above average precipitation. Runoff collected from 98 precipitation events and three land uses was analyzed for dissolved P (DP), particulate P (PP), and total P (TP), with mass losses calculated. Monitored land uses included fertilized lawns (FL), urban barren (UB) areas and wooded (FR) areas. Stream gauges were installed at the stream entrance to the urban area and the watershed outlet to monitor the impact of the urban area on stream water quality. A multivariate analysis of the data revealed that the FL land use had higher DP losses than the other land uses on the shallow, low storage, runoff prone urban soils. If runoff volumes were low, the FL land use had similar or considerably lower DP losses. Particulate P mass losses were highest from the FR and UB land uses due to little or no ground cover to prevent erosive P losses. Total P losses from the land uses were highest on the shallow, low storage soils and surprisingly similar among land uses. As the stream flowed through the urban area, there was a two fold increase in P loads when compared to the undeveloped upper area of the watershed. This indicated that the urban area was contributing P to the stream. Best management practices in this and other similar watersheds should focus on reducing runoff and P loss from source areas to realize the largest reduction in P loading to surface water. Ultimately, it is imperative to assess land use performance under varying conditions in order to reduce P loads in surface water.
Long-distance intelligent liquid supply for coal mining faces based on liquid demand prediction

The coal mine pump station liquid supply system adapts to changes in the main pressure for supply, but under conditions of long-distance liquid supply beyond 2000 m, there is a problem with the difficult system response to the hydraulic support liquid demand, resulting in significant pressure fluctuations in the hydraulic system and serious performance degradation of the supply system. To solve this problem, a long-distance intelligent liquid supply method based on liquid demand prediction for coal mining faces is proposed to ensure stable liquid supply. The method integrates parameters such as the mining machine’s position, the hydraulic support’s column pressure before and after the mining machine, the forward travel distance, and action codes to construct a feature vector. Based on the Transformer Model, a Liquid Demand Prediction Model for hydraulic support is established. Attention mechanisms are combined to solve the problem of time prediction for long sequences. The model is validated using real measured data from a hydraulic system in a coal mine, and the experimental results show that the model can accurately predict liquid demand and achieve long-distance intelligent liquid supply for coal mining faced by combining pump station control strategies.
Deep learning to design Z-FFR device models

Z-Pinch fusion centre, encased by a fission envelope, serves as an individual neutron source. It can expeditiously catalyze fission reactions in 238U and 232Th nuclear materials, which are hard to use in current commercial nuclear reactors. This is the essence of the Z-Pinch Driven Fusion-Fission Hybrid Reactor (Z-FFR). The fusion core acts as a stand-alone neutron source, efficiently driving fission reactions in nuclear energy materials that are difficult to use in existing commercial nuclear reactors, such as 238U and 232Th. Then it can deliver enormous amounts of energy in a stable and controlled manner. This new type of reactor uses the fact that the fission discharges energy (∼200 Megaelectronvolts) and the neutrons’ number released is much greater than that released when the fusion discharge energy (∼17 Megaelectronvolts). Moreover, the neutrons’ number is released to achieve energy amplification and neutron amplification, significantly makes it less difficult in implementing fusion technology applications, and increases the utilisation of nuclear energy resources by more than one order of magnitude. The Z-FFR has a complex design and covers a wide range of physical processes. The use of deep learning to design the device model allows for a more closely engineered model. Deep learning allows the model design to be decomposed, the Z-FFR design data flow to be analysed and optimised, and the tedious physical process to be turned into a deep learning network layering so that we can obtain an accurate physical model. The deuterium-tritium combustion depth parameters obtained by deep learning reach around 30%, demonstrating the ability to achieve fusion self-sustained combustion.
An Automatic and Objective Vision Screening System Based on EEG Signals

Vision screening cannot be popularized due to the uneven distribution of medical resources and subjective limitations of traditional detection. This paper proposes an automatic and objective vision screening system, which uses Electroencephalogram (EEG) signals to analyze people’s visual state, avoiding subjective errors. Meanwhile, this system just relies on personal operation without the involvement of medical staff, reducing the cost of time and medical resources. The experimental results show that the classification accuracy of the visual state can be increased to 80%, with the combined use of signal processing, relevant feature extraction, and optimization measures in this system. It indicates that the system is feasible and can play a role in visual screening based on EEG signals in the future.
DWT-BSS: Blind Source Separation applied to EEG signals by extracting wavelet transform’s approximation coefficients

The Electroencephalogram (EEG) signal is widely contaminated by a physiological artifact, such as muscle activity, heart rhythm, and eye movement. The researcher has proposed a number of methods to clean the EEG signal. A group of these methods is called Blind Source Separation (BSS). In this paper, we suggest an approach that combines the BSS methods and the Discrete Wavelet Transform (DWT) algorithm, in order to evaluate the BSS methods after applying them to the approximation coefficients extracted using the DWT. The aim of this work is to identify which BSS algorithms, using which family of wavelet and at which decomposition level, would provide excellent performance. We used the Spearman Correlation Coefficient (SCC) to rate our methods. The technique that performs the best, as evaluated by the SCC between the generated component and the approximation coefficient obtained from the Horizontal EOG results, is AMICA, which obtains a value of 0.81 for levels 2 while using the wavelet symlet at scales 7 and 11. With a value of 0.70 and the use of the wavelet Daubechies at scale 9 and Coiflets at scales 2 and 5 for level 1, AMICA also has the best SCC value calculated between the separated component and the approximation coefficient recovered from the Vertical EOG. While employing the wavelet symlets at scales 5, 7, 8, and 11. for level 2, and level 3 when using the wavelet symlets at scales 1 and 2.
Exact dynamics of multimode periodic input states in coupled waveguide arrays

In this work, we investigate the quantum state reconstruction of the periodic input state in a 1-D waveguide array. In particular, we consider a single-photon multimode entangled W-state with different periodicities as an input to the array and study the effect of periodicity in the output. For comparison, we also study separable single photon periodic input states. We study the evolution of average photon number and the fidelity of the periodic input state and give the exact solution to investigate the revival of single photon multimode periodic input. Our solution is valid for any number of waveguides in the array. Our findings show the revival of the average photon number and almost complete quantum state reconstruction of the initial multimode entangled state for certain periodicities. The results reported here are significant because multimode-entangled states are essential resources for various applications in the physical implementation of photonic quantum technologies.
Study Protocol for the Intraoperative Complications Assessment and Reporting with Universal Standards (ICARUS) Global Cross-Specialty Surveys Among Surgeons, Anesthesiologists, Nurses, Interventional Cardiologists, and Interventional Radiologists

Every year, approximately 200 million surgeries are performed worldwide, and intraoperative adverse events (iAEs) have a significant impact on patients and surgeons. Despite their importance, the true scale of iAEs remains underestimated due to inadequate methods for assessment, collection, grading, and reporting. Various grading systems have been introduced over the past decade, but their adoption has been limited, leading to inconsistencies in reporting. Furthermore, a lack of standardized frameworks for defining, assessing, and collecting iAEs, coupled with litigation concerns, contributes to underreporting. Only half of surgery and anesthesiology journals provide guidance on reporting perioperative adverse events, and recommendations for reporting iAEs are notably lacking in surgical literature. To address these issues, the Intraoperative Complications Assessment and Reporting with Universal Standard (ICARUS) Global Surgical Collaboration was established in 2022. The initiative involves conducting global surveys and a Delphi consensus to understand the barriers for poor reporting of iAEs, validate shared criteria for reporting, define iAEs according to surgical procedures, evaluate the existing grading systems' reliability, and identify strategies for enhancing the collection, reporting, and management of iAEs. A sample size of 2,398 respondents was calculated for the study, with invitations extended to 86,574 healthcare providers. This effort represents an essential step towards improved patient safety and the well-being of healthcare professionals in the surgical field.
Climate influences on infectious diseases in Nigeria, West Africa

Meningitis and cholera have remained major health burden in Nigeria, especially in the heavily populated northwest region – which is identified as one of the “hotspots” of climate change. The strong sensitivity that both diseases exhibit to climate is raising concern that future anthropogenic climate change may exacerbate the occurrence of the diseases. This thesis aimed at modelling the influences of climate on the incidence of the selected diseases, and assessing their future risk in northwest Nigeria. The aim is achieved by first, investigating and understanding the spatial and time characteristics of both meteorological and diseases conditions in the region. This was followed by developing and validating suites of empirical statistical models capable of explaining and predicting both diseases. Models that are specifically designed for climate change studies were applied to estimate the future impact of climate change, by forcing them with simulations from an ensemble of statistically downscaled Atmosphere-ocean Global Climate Models (AOGCMs), for three different scenarios in the early and late 21st century. Results from developed models indicate the significant roles of both meteorological and socioeconomic factors on incidence of diseases. Evaluation of models developed with 1-month lagged explanatory variables suggest the potential to predict both diseases cases up to a month to aid decision making. Projection results suggest that future temperature increases due to climate change has the potential to significantly increase diseases cases in all scenarios and time slices. It is noteworthy that the projections result represents only the climatological potential for increased cases due to climate change, assuming that the present prevention strategies remain similar in the future.
The utility of the cropland data layer for Forest Inventory and Analysis

The Forest Service, U.S. Department of Agriculture's (USDA's) Northern Research Station Forest Inventory and Analysis program (NRS-FIA) uses digital land cover products derived from remotely sensed imagery, such as the National Land Cover Dataset (NLCD), for the purpose of variance reduction via postsampling stratification. The update cycle of the NLCD product is infrequent; NLCD 2001 was the first update since the release of NLCD 1992, and was not yet fully completed as of late 2006. Consequently, FIA field data collected as recently as 2005 are being poststratified with land cover data collected more than a decade before. In addition, NRS-FIA has performed its own land cover classification of remotely sensed imagery for use in the stratification of some States, a time-consuming process. Alternative sources of information need to be evaluated both to eliminate the temporal mismatch between land cover data and FIA plot information and to reduce the amount of analyst time required to perform the stratification process.
A novel method for measuring acute thermal tolerance in fish embryos

Abstract Aquatic ectotherms are vulnerable to thermal stress, with embryos predicted to be more sensitive than juveniles and adults. When examining the vulnerability of species and life stages to warming, comparable methodology must be used to obtain robust conclusions. Critical thermal methodology is commonly used to characterize acute thermal tolerances in fishes, with critical thermal maximum (CTmax) referring to the acute upper thermal tolerance limit. At this temperature, fish exhibit loss of controlled locomotion due to a temperature-induced collapse of vital physiological functions. While it is relatively easy to monitor behavioural responses and measure CTmax in larval and adult fish, this is more challenging in embryos, leading to a lack of data on this life stage, or that studies rely on potentially incomparable metrics. Here, we present a novel method for measuring CTmax in fish embryos, defined by the temperature at which embryos stop moving. Additionally, we compare this measurement with the temperature of the embryos’ last heartbeat, which has previously been proposed as a method for measuring embryonic CTmax. We found that, like other life stages, late-stage embryos exhibited a period of increased activity, peaking approximately 2–3°C before CTmax. Measurements of CTmax based on last movement are more conservative and easier to record in later developmental stages than measurements based on last heartbeat, and they also work well with large and small embryos. Importantly, CTmax measurements based on last movement in embryos are similar to measurements from larvae and adults based on loss of locomotory control. Using last heartbeat as CTmax in embryos likely overestimates acute thermal tolerance, as the heart is still beating when loss of response/equilibrium is reached in larvae/adults. The last movement technique described here allows for comparisons of acute thermal tolerance of embryos between species and across life stages, and as a response variable to treatments.
Evaluation of sea turtle morbidity and mortality within the Indian Ocean from 12 years of data shows high prevalence of ghost net entanglement

Anthropogenic activities can negatively affect sea turtle populations. Quantifying the effect of human actions on these threatened species can help guide management strategies to reduce adverse impacts. However, such assessments require extensive effort and resources and as such have not been carried out in many areas of important sea turtle habitat, including the Republic of the Maldives (Maldives). Here, we utilise 12 years of data (2010–2022) collected from marine turtle stranding and rehabilitation cases from across the Maldives to identify the key threats in this region. Olive ridley turtles were found stranded or injured most frequently (74.7% of total cases), along with hawksbill (15.2%), and green (10.1%) turtles. Anthropogenic factors were the primary cause of injury or stranding in 75.2% of cases with entanglement in ghost fishing gear being the most common (66.2% of all cases). Other causes of morbidity, such as from turtles being kept as pets (5.6%), boat strikes (<1%), bycatch (<1%), and poaching (<1%) were recorded less frequently. Olive ridley turtles were more likely to have injuries associated with entanglement than other species and showed a peak in admissions during the northeast monsoon, in the period following the known arribada nesting season in nearby India. Turtles admitted to rehabilitation following entanglement were released a mean of 70 days sooner and had 27.5% lower mortality rates than for other causes of admission. This study highlights the high prevalence of ghost net entanglement of sea turtles within the Maldives. The topic of ghost fishing is of global importance and international cooperation is critical in tackling this growing issue.
Syntaxin-6 promotes the progression of hepatocellular carcinoma and alters its sensitivity to chemotherapies by activating the USF2/LC3B axis

Syntaxin-6 (STX6), a protein of the syntaxin family, is located in the trans-Golgi network and is involved in a variety of intracellular membrane transport events. STX6 is overexpressed in different human malignant tumors. However, little is known about its exact function and molecular mechanism in hepatocellular carcinoma (HCC). In this study, we found that the expression of STX6 was significantly increased in HCC tissues and was associated with poor survival. Gain- and loss-of-function experiments showed that STX6 promotes cell proliferation and metastasis of HCC cells both in vitro and in vivo. Mechanistically, STX6 was negatively regulated by the upstream stimulatory factor 2 (USF2). In addition, STX6 facilitates the association of autophagosomes with lysosomes. Importantly, we demonstrated that STX6 overexpression, despite enhanced resistance to lenvatinib, sensitizes HCC cells to the autophagy activator rapamycin. This study revealed that, under the control of USF2, STX6 accelerates the degradation of microtubule-associated protein 1 light chain 3 beta (LC3) by promoting autophagic flux, ultimately promoting HCC progression. Collectively, we suggest that the USF2-STX6-LC3B axis is a potential therapeutic target in liver cancer.
Mycobacteroides abscessus infection in a patient with tetralogy of Fallot

A 58‐year‐old woman with tetralogy of Fallot was referred to our hospital with a 2‐month history of hemoptysis and low‐grade fever. The sputum smear on admission showed positive results for acid‐fast bacilli, and comparative genomic analysis of the cultured sputum confirmed Mycobacteroides abscessus subsp. abscessus infection. Long‐term combination antimicrobial therapy gradually improved the patient's symptoms. Although non‐tuberculous mycobacteria infection is rarely observed in patients with cyanotic congenital heart disease, a worldwide increase in non‐tuberculous mycobacteria infections may increase the incidence of this rare combination.