Miracle-dz/UniFAR-FTUs huggingface dataset:
DESIGUALDAD, CRECIMIENTO Y DESARROLLO ECONÓMICO EN AMÉRICA LATINA INEQUALITY, GROWTH AND ECONOMIC DEVELOPMENT IN LATIN AMERICA

The objective of the paper is to identify the causal relationships between income inequality, levels of democracy and economic growth in Latin America from 1996 to 2014. With this objective in mind, fixed and random effects models are estimated with a periodicity of growth episodes of two, five and ten years to analyze the impact of the variables of interest in different time horizons. The countries in the sample are Argentina, Bolivia, Brazil, Chile, Colombia, Dominican Republic, Ecuador, El Salvador, Jamaica, Mexico, Panama, Peru, and Venezuela. The results indicate that increments in the levels of democracy have a negative effect on the growth of GDP per capita in the two-year growth episodes, to later become positive in the fifth year and reach its maximum effect ten years later. This highlights the importance of the stability of political regimes for a country to reach a state of stationary growth. The results of the Gini variable show that higher levels of income inequality lead to lower economic growth. Its negative impact reaches its maximum five years after the shock that triggered the increase in inequality.
DETECTION OF ANTIBIOTICS RESISTANCE GENES IN STAPHYLOCOCCUS AUREUS ISOLATED FROM POULTRY FARMS

Poultry industry in Egypt was subjected to various problems one of them was the early chick mortalities that cause great economic losses and its investigation is the request of many poultry scientists and men. In poultry staphylococci, including Staphylococcus aureus are known to cause various diseases from acute septicemia to chronic osteomyelitis. Two hundred and sixty samples of chickens, ducks and turkeys were collected from different poultry farms. A survey on Staphylococcal infection among chickens, ducks and turkeys was carried out. It was found that out of 260 infected and dead chickens, ducks and turkeys suspected to be infected with Staphylococcosis, 54 cases (20.76%) revealed Staphylococcus micro-organisms. The isolated strains were typed as: Staphylococcus aureus (74.07%), Staphylococcus epidermidis (14.8 %) and Staphylococcus saprophyticus (11%). The results of sensitivity biogram revealed that S. aureus were highly sensitive to Amoxicillin, Ampicillin, Ciprofloxacine, Enrofloxacine and lincomycin and resistant to Erythromycin, Streptomycine and Chloramphenicol. The results of MIC of 6 representative coagulase positive Staphylococci isolates against 6 selected antibiotics commonly used in poultry farms showed that 100% of isolates were sensitive to amoxicillin, enrofloxacin, ciprofloxacin and oxytetracyclin, while 100% of 6 isolates were resistant to streptomycin and lincomycin. The incidence of isolation of Staphylococci from the internal organs of examined birds and from unabsorbed yolk sac, joints, liver, intestinal content and heart blood was 36.3%, 20%, 19.2%, 17.3% and 16.9% respectively. Ten of each detected isolates were examined by cPCR for resistance genes blaZ and aac(6’) aph (2’’). The isolates harbored these resistance genes with percentage of 100% for S. aureus. In experiment, the pathogenicity of the isolated strains of S. aureus for 7 dayold chicks was studied. S. aureus injected subcutaneously, oral and intranasal caused death of 100%, 100% and 26.7% of the used chicks respectively.
Molecular characterization of Panton-Valentine Leukocidin positive
 Staphylococcus aureus isolates obtained from clinical sample in Isfahan,
 Iran

Staphylococcus aureus is one of the main significant human pathogens which can produce various toxins such as Panton-Valentine Leukocidin (PVL) which is known as a prominent toxin associated with S. aureus infections. PVL-positive strains can cause a wide variety of skin, soft tissue, necrotizing pneumonia, fasciitis and life-threatening infections. Therefore, the aim of this study was evaluating the molecular characteristics of PVL-positive strains such as the presence of mecA , SCC mec types, agr types and exfoliative toxin genes. In this study, a total of 152 S. aureus strains were collected from clinical samples of patients who referred to Isfahan’s Alzahra hospital (Iran). The isolates were confirmed phenotypically by conventional methods and then PVL-positive isolates were identified by PCR molecular test. Thereafter, antibiotic resistance pattern, agr groups (I, II, III, and IV), exfoliative toxins ( eta and etb ), mecA gene and SCCmec various types were carried out. Totally, 52 (34.2%) of strains were positive for PVL. Six PVL-positive strains harbored mecA gene, one strain had SCC mec I, and 5 strains SCC mec type IV. The highest ratio of agr groups belonged to group (I) and the ( eta) gene was also detected in 18 isolates. The PVL-positive S. aureus strains can cause more serious infections, so identification of the genetic characteristics and antibiotic resistance monitoring of these strains is necessary.
A Grid Connected PV System Using Bifred Converter With Neuro Fuzzy Based MPPT Algorithm

: The implementation of a Fuzzy based maximum power point tracking in Grid-connected PV system alongside Reactive power compensation. For array, a single diode model is used, and MATLAB is used to perform the simulation analysis. In the fuzzy logic controller, voltage and current are taken as inputs and the effective value of AC current corresponding to the maximum power point is the output. Thus the reactive power doesn't outperform its power rating in addition to providing voltage by the inverter with transformer to compensate for. This results in the use of a PV system around evening time and at times of low irradiation. Rules relating to Fuzzy logic controller input and output are composed and simulation is done a DC-DC Bifred converter is utilized for keeping up DC input to the inverter at different states of irradiations and temperature. Gating pulses to the inverter are created by the PI (Proportional controller) controller. Solar panel simulation model is developed and results for various irradiation and temperature conditions are acquired with the Fuzzy logic controller. Results show the viability of the technique proposed for utilizing the PV system. This project is implemented in MATLAB simulation and experimental results.
Lung organoids: target cells for understanding respiratory diseases

The lung is a highly complex and dynamic organ. In its proximal part, the bronchiolar airways are connected to a single tracheal tube. Moving distally, the bronchiolar airways gradually narrow and branch into bronchioles. The terminal bronchioles, as the most distal segments, are the last conducting airway and give rise to two or more respiratory bronchioles. Each respiratory bronchiole gives rise to three alveolar ducts, which are lined with alveoli, and the alveolar ducts terminate in alveolar sacs and eISSN 2765-205X Organoid 2021;1:e4 • https://doi.org/10.51335/organoid.2021. 1.e4 Review Article
Potential of Peptide-Based Non-Structural Protein 1 (NS1) Inhibitor in Obstructing Dengue Virus (DENV) Replication

Introduction: Dengue Virus (DENV) is the pathogen for human dengue fever and is responsible for 390 million infections per year. The viral genome produces about 10 viral protein products, one of them being NS1. The NS1 protein plays a key role in viral replication and stimulation of humoral immune cells, thus being the perfect candidate to create an effective antiviral drug or vaccine for dengue Methods: Dengue Virus (DENV) is the pathogen for human dengue fever and is responsible for 390 million infections per year. The viral genome produces about 10 viral protein products, one of them being NS1. The NS1 protein plays a key role in viral replication and stimulation of humoral immune cells, thus being the perfect candidate to create an effective antiviral drug or vaccine for dengue Conclusion: The review established promising results of using peptide-based intervention on NS1. Further in vivo and randomized controlled trials are advised to solidify the applicability and biosafety of the intervention
Machine Vision based Computer-Aided Detection of Pulmonary Tuberculosis using Chest X-Ray Images

Tuberculosis (TB) is a lethal disease and developing countries are struggling to overcome this health hazard especially in rural areas and faced globally. Therefore, serious measures are required to reduce this global health hazard. Millary and pulmonary are the most common types of tuberculosis occurring globally. X-ray is the preliminary method to detect tuberculosis; however, the diagnosis is quite often subject to human error. In contrast, the chances of curing Tuberculosis depend on its timely and accurate diagnosis. Therefore, an intelligent machine learning algorithm is developed in this study to assist the clinician in an accurate TB identification in x-ray images. The proposed method pre-processes the X-ray image, enhances its quality and extracts the features of each class which are further passed on to a Deep Convolutional Neural Network-based design for the X-ray image classification, followed by the identification of the tuberculosis type i.e. Millary, Cavitary, Healthy. The classification accuracy for the developed method resulted in 88% and 89% for millary and cavitary TB diseases in x ray images.
ASSESSMENT OF FEAR LEVEL IN COMMERCIAL BROILER CHICKEN STRAINS WHEN EXPOSED TO HUMAN CONTACT

ةٌثحبلا ةٌفلخلا : جاجدلا ًف ةٌجاتنلإا ةءافكلاو ةحارلإا ةلاح ىلع بلسلاب رثوٌ فوخلل ضرعتلا ةملقلأا ةجرد فعض ىلإ يدؤٌ دق امم ةرٌبكلا دادعلأا رفاوت ثٌح ةفثكملا ةٌبرتلا ماظن لخاد ناسنلإاب كاكتحلااو ةثٌدحلا ةنكٌملا ةمظنلأ . ةساردلا نم ضرغلا : فوخلا ىوتسم ساٌق وه تلالاس ًف ةٌجاتنلإا ةءافكلاب اهطابتراو ةعرزملا لخاد لامعلاب كاكتحلال جاجدلا ضرعت دنع ناٌفٌلإا ًهو رصم دٌعص ًف ًاعوٌش رثكلأاو ةٌراجتلا نٌمستلا جاجد ىرادب 43 دربهلاو بوكلاو سورلاو . جئاتنلاو ثحبلا قرط : كلذكو ةلماكب عٌطقلا ىوتسم ىلع فوخلا ساٌق مت ساٌق ىلإ ةفاضلإابو ًلاوتلا ىلع توشو لورتس يرابتخاب فرعٌ امب يدرفلا ىوتسملا ىلع للاخ تاٌفولا ةبسنو ًئاذغلا لٌوحتلاو ومنلا لدعم لثم ةٌجاتنلإا تافصلا 7 ةٌبرتلا عٌباسأ . ًئاذغلا لٌوحتلا ةءافكو ومنلا تلادعم تاذ بوكلاو سورلا تلالاس نأ ةساردلا هذه تحضوأ فوخلا نم ىلعأ تاٌوتسم ًاضٌأ اهٌدل ىلعلأا , فوخلا ىوتسم نٌب طابترا دوجو ىلع لدٌ امم تلالاسلا هذه ًف ةٌجاتنلإا ةءافكلاو . جاتنتسلاا : دق ةٌجاتنلإا ةءافكلا ةداٌزل هجوملا باختنلاا بحطصٌ ه ىلع دكؤٌ امم ةٌراجتلا نٌمستلا جاجد ىرادب تلالاس ًف فوخلا ىوتسم ًف ةداٌز ا امهنٌب ًباجٌإ طابترا دوجو .
Extensive type IIIB bone loss of the acetabulum and suitability of 3D printings during planning

Introduction. Despite the advances made in medicine, arthroplasty performed in young patients is still associated with an increased risk of complications. Massive destruction of the acetabular ring is a challenge, requires careful planning, and the use of various surgical techniques and implants. Case Report. The case is presented of a young woman with a Paprosky type IIIB defect of the acetabular rin who underwent a revision total hip arthroplasty. During planning, 3D printed bone models were used because the choice of surgical technique was not obvious. The 3D prints, together with the radiological diagnostics performed, provided a real picture of the problem, and a better understanding of the pathoanatomy. Conclusions. Multiple, unpunished trialing on printed models by using different trial implants make it possible to test the feasibility and practice the techniques. It may prevent making the mistake of choosing wrong the surgical technique. Additionally, the models give better visualization of the patient’s anatomy during the surgery lifetime.
A Review of Multiple-Person Abnormal Activity Recognition

In recent years, computer vision technology capable of detecting human behavior has attracted more and more attention. Although it has been widely used in many applications, accurate and effective human motion recognition is still a challenge in the field of computer vision. This paper presents a review of the latest research methods for multi-person human anomalous action recognition. The article deeply analyzes the calculation method of activities and briefly describes popular datasets. Discussing the unresolved issues, it provides new ideas for future research.
Impact of Extreme Temperatures on Emergency Hospital Admissions by Age and Socio-Economic Deprivation in England

Climate change poses an unprecedented challenge to population health and health systems' resilience, with increasing fluctuations in extreme temperatures through pressures on hospital capacity. While earlier studies have estimated morbidity attributable to hot or cold weather across cities, we provide the first large-scale, population-wide assessment of extreme temperatures on inequalities in excess emergency hospital admissions in England. We used the universe of emergency hospital admissions between 2001 and 2012 combined with meteorological data to exploit daily variation in temperature experienced by hospitals (N = 29,371,084). We used a distributed lag model with multiple fixed-effects, controlling for seasonal factors, to examine hospitalisation effects across temperature-sensitive diseases, and further heterogeneous impacts across age and deprivation. We identified larger hospitalisation impacts associated with extreme cold temperatures than with extreme hot temperatures. The less extreme temperatures produce admission patterns like their extreme counterparts, but at lower magnitudes. Results also showed an increase in admissions with extreme temperatures that were more prominent among older and socioeconomically-deprived populations - particularly across admissions for metabolic diseases and injuries.
Market Distraction and Near-Zero Volatility Persistence

This study shows that during the FIFA World Cups, the Olympic Games, and Christmas and New Year, the average daily volatility persistence is near zero across 17 equity indices in 14 developed economies. During the World Cups, the volatility persistence decreases with proxies for the intensity of distraction and negative sentiment. The evidence indicates reduced information production by distracted financial analysts and journalists. In addition, volatility persistence is low in June and high in January and October, consistent with reduced attention during holidays and heightened attention afterwards. The seasonal variations in volatility persistence explain 8.7% of variations in volatility level across global markets.
Dividend Restrictions and Asymmetric Information

We develop a dynamic model of banks whose insiders have superior information about the impact of a pending shock to the bank’s cash holdings and can signal the bank’s type through its dividend policy. Banks that will be adversely affected by the shock have incentives to pool with unaffected banks to increase their market value. To avoid being mimicked, the unaffected banks can credibly signal via a more aggressive payout strategy. Dividend payout restrictions have the potential to prevent a separating equilibrium from forming. This leads to the bad type adopting a more aggressive payout policy with a higher risk of default but mitigates the distortion of the good type's policy. We identify a number of scenarios where this trade-off presents an opportunity for regulatory intervention and some where it does not.
IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion

We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output. We leverage two quality estimation (QE) models at different granularities: sentence-level QE, to predict the quality of each machine-translated sentence, and word-level QE, to locate the parts of the machine-translated sentence that need correction. Additionally, we introduce a novel translation suggestion model conditioned on both the left and right contexts, providing alternatives for specific words or phrases for correction. Finally, with word alignments, IntelliCAT automatically preserves the original document’s styles in the translated document. The experimental results show that post-editing based on the proposed QE and translation suggestions can significantly improve translation quality. Furthermore, a user study reveals that three features provided in IntelliCAT significantly accelerate the post-editing task, achieving a 52.9% speedup in translation time compared to translating from scratch. The interface is publicly available at https://intellicat.beringlab. com/.
From Motor Control to Team Play in Simulated Humanoid Football

Learning to combine control at the level of joint torques with longer-term goal-directed behavior is a long-standing challenge for physically embodied artificial agents. Intelligent behavior in the physical world unfolds across multiple spatial and temporal scales: Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals that are defined on much longer time scales and that often involve complex interactions with the environment and other agents. Recent research has demonstrated the potential of learning-based approaches applied to the respective problems of complex movement, long-term planning, and multiagent coordination. However, their integration traditionally required the design and optimization of independent subsystems and remains challenging. In this work, we tackled the integration of motor control and long-horizon decision-making in the context of simulated humanoid football, which requires agile motor control and multiagent coordination. We optimized teams of agents to play simulated football via reinforcement learning, constraining the solution space to that of plausible movements learned using human motion capture data. They were trained to maximize several environment rewards and to imitate pretrained football-specific skills if doing so led to improved performance. The result is a team of coordinated humanoid football players that exhibit complex behavior at different scales, quantified by a range of analysis and statistics, including those used in real-world sport analytics. Our work constitutes a complete demonstration of learned integrated decision-making at multiple scales in a multiagent setting.
Gapless Spin Liquid and Non-local Corner Excitation in the Spin-1/2 Heisenberg Antiferromagnet on Fractal

Motivated by the mathematical beauty and the recent experimental realizations of fractal systems, we study the spin-1/2 antiferromagnetic Heisenberg model on a Sierpiński gasket. The fractal porous feature generates new kinds of frustration to exhibit exotic quantum states. Using advanced tensor network techniques, we identify a quantum gapless-spin-liquid ground state in fractional spatial dimension. This fractal spin system also demonstrates nontrivial non-local properties. While the extremely short-range correlation causes a highly degenerate spin form factor, the entanglement in this fractal system suggests a long-range scaling behavior. We also study the dynamic structure factor and clearly identify the gapless excitation with a stable corner excitation emerged from the ground-state entanglement. Our results unambiguously point out multiple essential properties of this fractal spin system, and open a new route to explore spin liquid and frustrated magnetism.
Weak-value amplification and the lifetime of a decaying particle

We study the possibility of varying the measured lifetime of a decaying particle based on the technique of weak value amplification in which an additional filtering process called postselection is performed. Our analysis made in a direct measurement scheme presented here shows that, for simple two-level systems, the lifetime may be prolonged more than three times compared to the original one, while it can also be shortened arbitrarily by a proper choice of postselection. This result is consistent with our previous analysis on the possible prolongation of the lifetime of B mesons that may be observed in laboratories, and suggests room for novel applications of weak value amplification beyond precision measurement conventionally considered.
From operator statistics to wormholes

For a generic quantum many-body system, the quantum ergodic regime is defined as the limit in which the spectrum of the system resembles that of a random matrix theory (RMT) in the corresponding symmetry class. In this paper we analyse the time dependence of correlation functions of operators. We study them in the ergodic limit as well as their approach to the ergodic limit which is controlled by non-universal massive modes. An effective field theory (EFT) corresponding to the causal symmetry and its breaking describes the ergodic phase. We demonstrate that the resulting Goldstone-mode theory has a topological expansion, analogous to the one described in arXiv:2008.02271 with added operator sources, whose leading non-trivial topologies give rise to the universal ramp seen in correlation functions. The ergodic behaviour of operators in our EFT is seen to result from a combination of RMT-like spectral statistics and Haar averaging over wave-functions. Furthermore we analytically capture the plateau behaviour by taking into account the contribution of a second saddle point. Our main interest are quantum many-body systems with holographic duals and we explicitly establish the validity of the EFT description in the SYK-class of models, starting from their microscopic description. By studying the tower of massive modes above the Goldstone sector we get a detailed understanding of how the ergodic EFT phase is approached and derive the relevant Thouless time scales. We point out that the topological expansion can be reinterpreted in terms of contributions of bulk wormholes and baby-universes.
Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey

The proliferation of services and service interactions within microservices and cloud-native applications, makes it harder to detect failures and to identify their possible root causes, which is, on the other hand crucial to promptly recover and fix applications. Various techniques have been proposed to promptly detect failures based on their symptoms, viz., observing anomalous behaviour in one or more application services, as well as to analyse logs or monitored performance of such services to determine the possible root causes for observed anomalies. The objective of this survey is to provide a structured overview and qualitative analysis of currently available techniques for anomaly detection and root cause analysis in modern multi-service applications. Some open challenges and research directions stemming out from the analysis are also discussed.