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In this paper we propose a new strategy, based on anomaly detection methods, to search for new physics phenomena at colliders independently of the details of such new events. For this purpose, machine learning techniques are trained using Standard Model events, with the corresponding outputs being sensitive to physics beyond it. We explore three novel AD methods in HEP: Isolation Forest, Histogram-Based Outlier Detection, and Deep Support Vector Data Description; alongside the most customary Autoencoder. In order to evaluate the sensitivity of the proposed approach, predictions from specific new physics models are considered and compared to those achieved when using fully supervised deep neural networks. A comparison between shallow and deep anomaly detection techniques is also presented. Our results demonstrate the potential of semi-supervised anomaly detection techniques to extensively explore the present and future hadron colliders’ data.

In this paper, we expand on the previously proposed concept of energy Mover’s distance. The resulting observables are shown to provide a way of identifying rare processes in proton–proton collider experiments. It is shown that different processes are grouped together differently and that this can contribute to the improvement of experimental analyses. The tt¯Z production at the Large Hadron Collider is used as a benchmark to illustrate the applicability of the method. Furthermore, we study the use of these observables as new features which can be used in the training of deep neural networks.

This paper evaluates the development impacts of migration and remittances in migrant source communities by applying insights from the New Economics of Labor Migration (NELM) theory to Ethiopia's migration. Using household survey data, we empirically evaluate how household participation in migration arises and so that the subsequent labor losses and the influx of remittances affect income sources and asset accumulation of smallholder farm households. To account several econometric issues and consistently estimate the impacts of migration and remittances, we adopted three-stage least-squares method complemented with endogeneity and multicollinearity test. Besides, using logistic and multinomial logistic regressions respectively, we estimate the determinants of the household migration decision to have migrants, as well as the probability of the household to send out temporary or permanent migrants. Findings suggest that larger and wealthier households are less likely to have migrant family members, while households living below the poverty line, as well as villages with the highest unemployment rate, are the most likely to have both temporary and permanent migrants. However, a rise in months spent out of agriculture has a significant negative effect on crop income and asset accumulation, but only for permanent migration. By contrast, the influx of remitted income from migrants has led to increased crop income and asset values in the form of land and livestock holdings. Finally, this manuscript provides more comprehensive evidence by showing the net-returns of migration in terms of initial lost-labor effects and the positive developmental impacts that it produces varied for households with different types of migration and production conditions.

The intrinsic quantum nature of jets and the Quark-Gluon Plasma makes the study of jet quenching a promising candidate to benefit from quantum computing power. Standing as a precursor of the full study of this phenomenon, we study the propagation of SU(3) partons in Quark-Gluon Plasma using quantum simulation algorithms. The algorithms are developed in detail, and the propagation of both quarks and gluons is analysed and compared with analytical expectations. The results, obtained with quantum simulators, demonstrate that the algorithm successfully simulates parton propagation, yielding results consistent with analytical baseline calculations.

A bstract An important aspect of the study of Quark-Gluon Plasma (QGP) in ultrarelativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP. In this work, we propose studying Deep Learning techniques for this purpose. Samples of Z +jet events were simulated in vacuum (pp collisions) and medium (PbPb collisions) and used to train Deep Neural Networks with the objective of discriminating between medium - and vacuum-like jets within the medium (PbPb) sample. Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance was studied. Our results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP.

Pre-operative administration of benzodiazepines can cause hypoventilation-a decrease in minute ventilation (MV)-commonly referred to as \"respiratory compromise or respiratory depression.\" Respiratory depression can lead to hypercarbia and / or hypoxemia, and may heighten the risk of other respiratory complications. Current anesthesia practice often places patients at risk for respiratory complications even before surgery, as respiratory monitoring is generally postponed until the patient is in the operating room. In the present study we examined and quantified the onset of respiratory depression following the administration of a single dose of midazolam in pre-operative patients, using a non-invasive respiratory volume monitor that reports MV, tidal volume (TV), and respiratory rate (RR). Impedance-based Respiratory Volume Monitor (RVM) data were collected and analyzed from 30 patients prior to undergoing orthopedic or general surgical procedures. All patients received 2.0 mg of midazolam intravenously at least 20 minutes prior to the induction of anesthesia and the effects of midazolam on the patient's respiratory function were analyzed. Within 15 minutes of midazolam administration, we noted a significant decrease in both MV (average decrease of 14.3% ± 5.9%, p<0.05) and TV (22.3% ± 4.5%, p<0.001). Interestingly, the corresponding RR increased significantly by an average of 10.3% ± 4.7% (p<0.05). Further analysis revealed an age-dependent response, in which elderly patients (age≥65 years, n = 6) demonstrated greater reductions in MV and TV and a lack of compensatory RR increase. In fact, elderly patients experienced an average decrease in MV of 34% ± 6% (p<0.05) compared to an average decrease of 9% ± 6% (p<0.05) in younger patients. We were able to quantify the effects of pre-operative midazolam administration on clinically significant respiratory parameters (MV, TV and RR) using a non-invasive RVM, uncovering that the respiratory depressive effect of benzodiazepines affect primarily TV rather than RR. Such respiratory monitoring data provide the opportunity for individualizing dosing and adjustment of clinical interventions, especially important in elderly patients. With additional respiratory data, clinicians may be able to better identify and quantify respiratory depression, reduce adverse effects, and improve overall patient safety.

Ghana and Côte d’Ivoire are the world’s leading cocoa ( Thebroma cacao ) producing countries; together they produce 53 % of the world’s cocoa. Cocoa contributes 7.5 % of the Gross Domestic Product (GDP) of Côte d’Ivoire and 3.4 % of that of Ghana and is an important cash crop for the rural population in the forest zones of these countries. If progressive climate change affected the climatic suitability for cocoa in West Africa, this would have implications for global cocoa output as well as the national economies and farmer livelihoods, with potential repercussions for forests and natural habitat as cocoa growing regions expand, shrink or shift. The objective of this paper is to present future climate scenarios for the main cocoa growing regions of Ghana and Côte d’Ivoire and to predict their impact on the relative suitability of these regions for growing cocoa. These analyses are intended to support the respective countries and supply chain actors in developing strategies for reducing the vulnerability of the cocoa sector to climate change. Based on the current distribution of cocoa growing areas and climate change predictions from 19 Global Circulation Models, we predict changes in relative climatic suitability for cocoa for 2050 using an adapted MAXENT model. According to the model, some current cocoa producing areas will become unsuitable (Lagunes and Sud-Comoe in Côte d’Ivoire) requiring crop change, while other areas will require adaptations in agronomic management, and in yet others the climatic suitability for growing cocoa will increase (Kwahu Plateu in Ghana and southwestern Côte d’Ivoire). We recommend the development of site-specific strategies to reduce the vulnerability of cocoa farmers and the sector to future climate change.

Mean sea level (MSL) behavior is a relevant indicator for monitoring climate change and coastal processes. Historically, its fluctuation has been studied based on tide gauge and altimetric observations. However, local and regional variations, such as land subsidence, rainfall patterns, and air temperature, can significantly influence the interpretation of these measurements. In this context, the main objective of this research is to measure the correlation between the MSL time series (dependent variable) and three independent variables (GNSS altimetry, precipitation, and air temperature) along the Brazilian coast, using the (Detrended Cross-Correlation Analysis) and the (Detrended Multiple Cross-Correlation Coefficient) coefficients. was applied to measure the level of cross-correlation between pairs of time series, while the assessed the joint influence of the independent variables on MSL (Multiple Correlation). Our findings identified that GNSS altimetry showed stronger and more stable correlations with MSL, especially in Salvador (EMSAL) and Santana (EMSAN), suggesting concordance with vertical crustal movements. In contrast, correlations with precipitation were weaker and showed greater fluctuations over time, possibly influenced by local hydrological factors. Air temperature showed more persistent patterns of positive correlation, particularly in Arraial do Cabo (EMARC) and Belém (EMBEL), consistent with the effect of ocean thermal expansion. In general, the multiple cross-correlation ( ), with the exception of EMIMB, showed higher values for larger scales (n>100). Sliding window analysis allowed the identification of dynamic regional patterns and seasonal extreme events, as observed in Fortaleza (EMFOR) in 2021. These findings reinforce the complexity of the factors controlling the MSL and demonstrate the effectiveness of the methods used in identifying multivariate patterns, offering important insights for coastal planning and the assessment of risks related to climate change.