ethicalabs/Research-Intent-Collab huggingface dataset:
Projections of Precipitation and Temperature Changes and Trends Using                     <scp>CMIP6</scp>                     Global Climate Models in the Eastern Amhara, Northeastern, Ethiopia

ABSTRACT                   Climate change is one of the biggest challenges of the 21st century. It severely affects many developing countries whose economy depends on climate‐sensitive sectors with low adaptive capacity. Studies in northeastern Ethiopia have not addressed the future climate conditions well, using the recently released CMIP6 global climate models. This study focused on projections of precipitation and temperature changes and trends using CMIP6 GCMs in the eastern Amhara, Northeastern, Ethiopia. The gridded temperature and precipitation data were extracted from the Climatic Research Unit (CRU TS4.07) and Global Precipitation Climatology Centre (GPCCv2020) for 1984–2014, respectively. The historical and projected data were retrieved from the Earth Systems Grid Federation (ESGF). The projections were computed under SSP2‐4.5 and SSP5‐8.5 scenarios for two future periods: 2040s (2030–2060) and 2080s (2070–2100). The modified Mann–Kendall's test and Sen's slope were used to detect precipitation and temperature trends. The annual and seasonal projected precipitation and temperature results showed significant increasing trends at a 5% probability level. The annual precipitation will increase by 7.77% and 13.74% under the SSP2‐4.5 scenario and by 14.02% and 28.48% under the SSP5‐8.5 scenario for the 2040s and 2080s, respectively. The annual maximum temperature will increase by 0.92°C and 1.86°C under SSP2‐4.5 and by 1.25°C and 3.39°C under the SSP5‐8.5 scenario. Likewise, the annual minimum temperature will increase by 1.62°C and 1.97°C in the 2040s and by 2.56°C and 4.48°C in the 2080s under SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Regarding spatial distribution, the most significant precipitation and temperature changes are projected in the west and central parts of the study area. Increasing precipitation trends and temperature changes are projected under both scenarios and periods. Thus, an analysis of the impacts of climate change and the design of solutions would be very relevant.
High‐Resolution Observations of the Cosmic Microwave Background Power Spectrum with ACBAR

We report the first measurements of anisotropy in the cosmic microwave background (CMB) radiation with the Arcminute Cosmology Bolometer Array Receiver (ACBAR). The instrument was installed on the 2.1 m Viper telescope at the South Pole in 2001 January; the data presented here are the product of observations up to and including 2002 July. We present two deep differential maps produced by subtracting leading and trailing observations from the main field. The differential maps cover approximately 24 deg2 of sky selected for low dust contrast. These results represent the highest signal-to-noise ratio observations of CMB anisotropy to date; in the deepest 150 GHz band map, we reached an rms of ~8.0 ?K per 5' beam. The 3
Living with uncertainty: from the precautionary principle to the methodology of ongoing normative assessment

The analysis of our epistemic situation regarding singular events, such as abrupt climate change, shows essential limitations in the traditional modes of dealing with uncertainty. Typical cognitive barriers lead to the paralysis of action. What is needed is taking seriously the reality of the future. We argue for the application of the methodology of                     ongoing normative assessment                     . We show that it is, paradoxically, a matter of forming a project on the basis of a fixed future                     which one does not want                     , and this in a coordinated way at the level of social institutions.                     Ongoing                     assessment may be viewed as a prescription to                     live with                     uncertainty, in a particular sense of the term, in order for a future catastrophe not to occur. The assessment is necessarily                     normative                     in that it must include the anticipation of a retrospective ethical judgment on present choices (notion of moral luck).
Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions

Abstract         Serverless computing has revolutionized cloud services by abstracting infrastructure management, enabling developers to focus on application logic. This research examines the security and compliance features of three major serverless platforms: AWS Lambda, Azure Functions, and Google Cloud Functions. By evaluating authentication mechanisms, data encryption practices, vulnerability management, and compliance certifications, we aim to provide a comparative analysis that informs businesses and developers on the most secure and compliant platform for their needs.
Natural language processing of multi-hospital electronic health records for public health surveillance of suicidality

AbstractThere is an urgent need to monitor the mental health of large populations, especially during crises such as the COVID-19 pandemic, to timely identify the most at-risk subgroups and to design targeted prevention campaigns. We therefore developed and validated surveillance indicators related to suicidality: the monthly number of hospitalisations caused by suicide attempts and the prevalence among them of five known risks factors. They were automatically computed analysing the electronic health records of fifteen university hospitals of the Paris area, France, using natural language processing algorithms based on artificial intelligence. We evaluated the relevance of these indicators conducting a retrospective cohort study. Considering 2,911,920 records contained in a common data warehouse, we tested for changes after the pandemic outbreak in the slope of the monthly number of suicide attempts by conducting an interrupted time-series analysis. We segmented the assessment time in two sub-periods: before (August 1, 2017, to February 29, 2020) and during (March 1, 2020, to June 31, 2022) the COVID-19 pandemic. We detected 14,023 hospitalisations caused by suicide attempts. Their monthly number accelerated after the COVID-19 outbreak with an estimated trend variation reaching 3.7 (95%CI 2.1–5.3), mainly driven by an increase among girls aged 8–17 (trend variation 1.8, 95%CI 1.2–2.5). After the pandemic outbreak, acts of domestic, physical and sexual violence were more often reported (prevalence ratios: 1.3, 95%CI 1.16–1.48; 1.3, 95%CI 1.10–1.64 and 1.7, 95%CI 1.48–1.98), fewer patients died (p = 0.007) and stays were shorter (p &lt; 0.001). Our study demonstrates that textual clinical data collected in multiple hospitals can be jointly analysed to compute timely indicators describing mental health conditions of populations. Our findings also highlight the need to better take into account the violence imposed on women, especially at early ages and in the aftermath of the COVID-19 pandemic.
A Location-Based Movie Recommender System Using Collaborative Filtering

Available recommender systems mostly provide recommendations based on the users preferences by utilizing traditional methods such as collaborative filtering which only relies on the similarities between users and items. However, collaborative filtering might lead to provide poor recommendation because it does not rely on other useful available data such as users locations and hence the accuracy of the recommendations could be very low and inefficient. This could be very obvious in the systems that locations would affect users preferences highly such as movie recommender systems. In this paper a new location-based movie recommender system based on the collaborative filtering is introduced for enhancing the accuracy and the quality of recommendations. In this approach, users locations have been utilized and take in consideration in the entire processing of the recommendations and peer selections. The potential of the proposed approach in providing novel and better quality recommendations have been discussed through experiments in real datasets.
A Bayesian deep learning pipeline for lithium‐ion battery SOH estimation with uncertainty quantification

AbstractIn recent years, deep learning (DL) methods for state of health (SOH) estimation of lithium‐ion (Li‐ion) batteries have attracted great attention. However, most existing DL‐based methods for SOH estimation were designed using specific battery dataset. To gain better performance, the procedure can be labor‐intensive, involving designing intricate features and fine‐tuning complex DL model, which significantly limits the applications of these methods. Moreover, uncertainty quantification, that is how much uncertainty these DL‐based methods can have on their SOH estimation, cannot be addressed with only point estimation. To this end, this paper proposes a Bayesian DL pipeline for Li‐ion battery SOH estimation targeting at automatic feature extraction and uncertainty‐aware DL‐model developing for general application to batteries under different working conditions. The proposed pipeline is composed of three modules accounting for common feature extraction, automatic feature selection, and Bayesian neural network (BNN) model construction. Specifically, a frequently‐used feature set is designed using the voltage, current and time data of constant current and constant voltage (CC‐CV) charging mode, while the recursive feature elimination (RFE) with cross validation (RFECV) algorithm is used to automatically select features based on relevance. Based on the automatically selected features, DL model is automatically constructed without too much human intervention. The uncertainty of SOH estimation is quantified by BNN model using Monte Carlo (MC) dropout method, which is subsequently used to calculate the confidence of the estimation results. Four battery datasets, covering regular charging, fast charging, and second‐use applications, are used to demonstrate the performance of the proposed method. The proposed pipeline shows superior performance to state‐of‐art DL‐based methods in terms of application capability to different working conditions and uncertainty quantification for SOH estimation.
Designing a Stock Recommender System Using the Collaborative Filtering Algorithm for the Tehran Stock Exchange

ObjectiveWith the increasing volume of information and the complexity of financial markets, investors are increasingly seeking innovative financial tools to make more informed decisions. These tools should help investors choose the right stocks and achieve better returns. In this regard, stock recommendation systems are becoming increasingly important. Stock recommendation systems can assist investors in achieving superior returns by selecting the right stocks. However, traditional stock recommendation systems often lack the necessary accuracy and efficiency. This research aims to develop a novel approach called Stock-based Collaborative Filtering to design a stock recommendation system for the Tehran Stock Exchange. This method is founded on two key assumptions: first, market inefficiency, meaning the stock market does not completely and accurately reflect all available information; and second, the presence of hidden information in stock movements, indicating that these movements contain valuable insights that can influence the prices of other stocks in the market. In this research, assuming the existence of the transmission effect on the Tehran Stock Exchange, we used the collaborative filtering technique, a common algorithm in recommender systems, to design a stock recommender system. The purpose is to help investors select the best-performing stocks to outperform the market.  MethodsThis study uses historical stock price data of 145 firms listed on the Tehran Stock Exchange from 2012 to 2021. The collaborative filtering algorithm was implemented in two stages: training and testing. In the training stage, the algorithm was trained using data from 2012 to 2016, and in the testing stage, its performance was evaluated on data from 2016 to 2021. Following, buy and sell signals were generated using the stock-based collaborative filtering algorithm during the same period. Finally, the strategy was evaluated. ResultsThe algorithm was tested as an investment strategy in both in-sample and out-of-sample periods. The results obtained from the algorithm for the out-of-sample periods show that this strategy can achieve a 25-fold return. The overall index returned 16 times during this period, indicating the excellent performance of the strategy over time. Additionally, the value at risk (VaR) for the selected method during the study period stood at -12.8%, indicating the lower risk of this method. ConclusionStock-based collaborative filtering is an active investment strategy. This intelligent algorithm aims to identify undervalued stocks and achieve higher returns than the market. This algorithm can serve as a valuable tool for active investors seeking to identify valuable stocks and achieve higher returns than the market. Therefore, further research is necessary to examine the performance of this algorithm in different markets and diverse economic conditions. Also, it is recommended to implement risk control strategies and optimize the system's efficacy further.
A machine-learning potential-based generative algorithm for on-lattice crystal structure prediction

We propose a method for crystal structure prediction based on a new structure generation algorithm and on-lattice machine learning interatomic potentials. Our algorithm generates the atomic configurations assigning atomic species to sites of the given lattice, and uses cluster expansion or low-rank potential to evaluate their energy. We demonstrate two benefits of such approach. First, our structure generation algorithm offers a ``smart'' configurational space sampling, targeting low-energy structures which significantly reduces computational costs. Second, the application of machine learning interatomic potentials significantly reduces the number of DFT calculations. We discuss how our algorithm resembles the latent diffusion models for image generation. We demonstrate the efficiency of our method by constructing the convex hull of Nb-Mo-Ta-W system, including binary and ternary Nb-W and Mo-Ta-W subsystems. We found new binary, ternary, and quaternary stable structures that are not reported in the AFLOW database which we choose as our baseline. Due to the computational efficiency of our method we anticipate that it can pave the way towards efficient high-throughput discovery of multicomponent materials.
The Precautionary Principle in Context: U.S. and E.U. Scientists' Prescriptions for Policy in the Face of Uncertainty<sup>*</sup>

Objective.  Our objective is to explain how scientists interpret less‐than‐certain scientific findings to inform policymakers' choices on controversial science policy issues. We focus on two particularly difficult policy cases concerning global climate change and low‐dose radiation protection.Methods.  Our method is to analyze data from a unique multination survey of scientists to analyze the ways their views about what is scientifically correct are translated into judgments about appropriate policy. The surveys asked scientists, randomly drawn from U.S. and E.U. subscribers to the journal Science, to indicate the “most likely” relationships between greenhouse gas emissions and average global temperatures and between radiation dose and incidence of cancer in humans. Follow‐up questions asked for their judgments about appropriate policy targets for reductions in greenhouse gas emission and safety standards for radiation exposure. The data permit analysis of the relationships between scientific certainty and policy judgments in these two cases.Results.  Our results shed light on when and how scientists reach precautionary policy conclusions, demonstrating that scientists' application of precaution is dependent on context. In the case of radiation protection, greater certainty is associated with less precaution. But with respect climate change, we found the opposite relationship.Conclusions.  We conclude with a discussion of the implications for the role of scientists, and scientific advice, in the policy process.
Fluctuation spectroscopy of surface melting of ice without, and with impurities

Water, in its three phases, is ubiquitous, and the surface properties of ice is important to clarifying the process of melting, as well as to various other fields, including geophysics. As such, the subject has been studied both theoretically and experimentally, for over a hundred years, while being an active field of research today. It has been established that surface melting, or premelting, exists below the melting point, and a `liquid-like layer' (LLL) exists on the surface of ice. Here, we use the surface thermal fluctuation spectra to study the properties of LLL, including its thickness, for pure ice, and for ice with impurities. We find that the properties of LLL are consistent with those of bulk liquid water, and for layers thicker than 10\,nm, their properties are experimentally indistinguishable from those of liquid water. Measured thicknesses are found to be much smaller than the previous experimental measurements close to the bulk melting temperature. We find that the additions of impurities at ppm levels cause LLL to be thicker, as well to be quite inhomogeneous, with properties depending on the dopant. This is revealed by scanning the surface at $μ$m level resolution, and can contribute to the slipperiness of ice in natural settings.
Sensitivity of rough differential equations: an approach through the Omega lemma

The It{ô} map assigns the solution of a Rough Differential Equation, a generalization of an Ordinary Differential Equation driven by an irregular path, when existence and uniqueness hold. By studying how a path is transformed through the vector field which is integrated, we prove that the It{ô} map is H{ö}lder or Lipschitz continuous with respect to all its parameters. This result unifies and weaken the hypotheses of the regularity results already established in the literature.
Technological advancements in occupational health: enhancing workplace safety and well-being

The advancement of technology has significantly impacted occupational health, improving the monitoring, prevention, and management of work-related health issues. Technologies such as wearable devices, artificial intelligence (AI), telemedicine, and electronic health records (EHRs) have enabled occupational health professionals to enhance workplace safety and employee well-being. Wearable devices, such as smartwatches and biometric sensors, monitor vital signs and physical activity, alerting workers to risks of fatigue or exposure to hazardous conditions, helping to prevent accidents and injuries, especially in high-risk sectors such as construction and manufacturing. AI, in turn, analyzes large datasets to predict health risks and recommend preventive measures, as well as chatbots and virtual assistants providing real-time guidance. Telemedicine, allowing remote consultations, has been essential for workers in remote locations, ensuring quick access to medical care and early intervention. Electronic health records (EHRs) centralize workers' medical histories, facilitating monitoring and communication between healthcare professionals and employers. Additionally, technologies such as virtual reality (VR) and augmented reality (AR) have been used for training and safety simulations, preparing workers for risky situations. Recent research highlights the importance of these technologies in improving working conditions and promoting occupational health, while also pointing out challenges such as privacy concerns and increased psychological pressure for adopting these solutions.
TeV gamma-ray observations from nearby AGNs by Utah Seven Telescope Array

We have investigated TeV gamma-ray emissions from nearby X-ray selected BL Lac objects using the Utah Seven Telescope Array for more than two years. These objects can be considered as potential sources of TeV gamma rays with inverse compton model. The gamma-ray flares from Mrk421 and Mrk501 found in these observation will be reported.
In someone else’s words: Judicial borrowing and the semantic authority of the African Court of Human and Peoples’ Rights

AbstractSince its first judgment on the merits in 2013, the African Court of Human and Peoples’ Rights (the African Court or ACtHPR) jurisprudence has bourgeoned. In building this jurisprudence, the African Court has borrowed significantly from the case law of the European Court of Human Rights and the Inter-American Court of Human Rights. This article empirically maps judicial borrowing in the jurisprudence of the African Court and connects this practice to the theoretical framing of the semantic authority of interpretive actors in international law. The article argues that judicial borrowing allows the African Court to borrow the semantic authority of these more established actors in the field of international human rights law. The practice has allowed the Court to boost its interpretive claims. The article posits that the Court is simultaneously internalizing external references: it transforms them into an internal part of its jurisprudence. Therefore, the African Court is transforming what was initially the semantic authority of its homologues in Strasbourg and San José, into assertions of its own semantic authority. This transformation allows the Court to assert itself as the central authority for the interpretation of human rights in Africa. These findings shed new light onto wider scholarly debates on the characteristics of African human rights jurisprudence in the field of international human rights law.
Additional file 5: of Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis

The P-value of the Cox univariate regression analysis using different gene sets. (A) The P-values of the Cox univariate regression analysis for pathway-based genes (blue) and top mutated genes (orange). (B) The P-values of univariant test and multivariant test for pathway-based genes (blue and grey) and top mutated genes (orange and purple). (PDF 344Â kb)
Nonlocal stochastic-partial-differential-equation limits of spatially correlated noise-driven spin systems derived to sample a canonical distribution

For a noisy spin system, we derive a nonlocal stochastic version of the overdamped Landau-Lipshitz equation designed to respect the underlying Hamiltonian structure and sample the canonical or Gibbs distribution while being driven by spatially correlated (colored) noise that regularizes the dynamics, making this Stochastic partial differential equation mathematically well-posed. We begin from a microscopic discrete-time model motivated by the Metropolis-Hastings algorithm for a finite number of spins with periodic boundary conditions whose values are distributed on the unit sphere. We thus propose a future state of the system by adding to each spin colored noise projected onto the sphere, and then accept this proposed state with probability given by the ratio of the canonical distribution at the proposed and current states. For uncorrelated (white) noise this process is guaranteed to sample the canonical distribution. We demonstrate that for colored noise, the method used to project the noise onto the sphere and conserve the magnitude of the spins impacts the equilibrium distribution of the system, as coloring projected noise is not equivalent to projecting colored noise. In a specific scenario we show this break in symmetry vanishes with vanishing proposal size; the resulting continuous-time system of Stochastic differential equations samples the canonical distribution and preserves the magnitude of the spins while being driven by colored noise. Taking the continuum limit of infinitely many spins we arrive at the aforementioned version of the overdamped Landau-Lipshitz equation. Numerical simulations are included to verify convergence properties and demonstrate the dynamics.