ethicalabs/Research-Intent-Collab huggingface dataset:
The Digital Currency of China's Central Bank: Digital Currency Electronic Payment(DCEP)

DCEP is the Chinese version of Central Bank Digital Currency (CBDC). It is the only legal digital currency in China and meets four conditions: (a) it is issued by the central bank; (b) it is digitized; (c) it is account and wallet based; (d) it is oriented towards the general public. As a retail central bank digital currency, it has three main technical features: a “tiered limit arrangement” (small-scale payments can be made anonymously while largescale payments cannot), a “two-tier operating system” (as with the central bank-commercial bank traditional model), and a “dual offline payment system” (supporting both parties of the transaction). Compared with CBDCs in other countries, China’s DCEP has smaller economic impacts, more obscure strategic goals, and more scarce technical details. But its progress in testing is ahead of central banks of other countries. This article is based on public information and is intended to explain what DCEP is and why and how it was developed. It also offers suggestions for future research.
Biometric Data and Privacy Rights: Registration Systems, Surveillance, and Legal Protections

This article examines Biometric Data and Privacy Rights: Registration Systems, Surveillance, and Legal Protections with a focused emphasis on Uganda within the field of Computer Science. It is structured as a replication study that organises the problem, the strongest verified scholarship, and the main analytical implications in a concise publication-ready format. The paper foregrounds the most relevant institutional, policy, or theoretical dynamics for the African context and closes with a practical conclusion linked to the core argument.
Artificial Intelligence in Endodontics: Transforming Diagnosis, Prognosis and Treatment Outcomes. A Narrative Review

Abstract  Endodontics is a critical branch of dentistry that focuses on diagnosing and treating diseases of the dental pulp and surrounding tissues. It is gradually incorporating artificial intelligence (AI) to improve diagnostics. AI employs algorithms and machine learning to process vast data sets, discern patterns, and deliver precise predictions. Accurate diagnosis and treatment planning depend on a precise understanding of diseases affecting the pulp and periapical tissues. AI models, such as convolutional neural networks and artificial neural networks, are employed to analyze the anatomy of the root canal system, determine working lengths, detect periapical lesions and root fractures, predict the success of retreatment procedures, and estimate the survival of stem cells in dental pulp. The purpose of this review is to assess the precision of artificial intelligence in endodontic dentistry in terms of diagnosis and prognosis. To review the literature, Studies were selected from PubMed, Scopus, Web of Science, and Google Scholar published over the last five decades (January 1975 to March 2024) by using key-words such as artificial intelligence, machine learning, deep learning, Convolutional Neural Networks, Artificial Neural Networks, application, endodontics, and dentistry. Although, it was concluded that the clinical application of AI in dentistry is in the nascent phase, further research is essential to verify its efficacy and precision. Present AI models show potential in endodontics, particularly in identifying periapical pathosis, diagnosing root fractures, determining working lengths, and predicting treatment outcomes.
Additional file 1: of Human rights’ interdependence and indivisibility: a glance over the human rights to water and sanitation

Interview Guide. Questions made during the interviews. (DOCX 19 kb)
VideoFrom3D: 3D Scene Video Generation via Complementary Image and Video Diffusion Models

Project page: https://kimgeonung.github.io/VideoFrom3D/
Chemical Reaction Networks Learn Better than Spiking Neural Networks

We mathematically prove that chemical reaction networks without hidden layers can solve tasks for which spiking neural networks require hidden layers. Our proof uses the deterministic mass-action kinetics formulation of chemical reaction networks. Specifically, we prove that a certain reaction network without hidden layers can learn a classification task previously proved to be achievable by a spiking neural network with hidden layers. We provide analytical regret bounds for the global behavior of the network and analyze its asymptotic behavior and Vapnik-Chervonenkis dimension. In a numerical experiment, we confirm the learning capacity of the proposed chemical reaction network for classifying handwritten digits in pixel images, and we show that it solves the task more accurately and efficiently than a spiking neural network with hidden layers. This provides a motivation for machine learning in chemical computers and a mathematical explanation for how biological cells might exhibit more efficient learning behavior within biochemical reaction networks than neuronal networks.
Evolution of accretion disk structure of the black hole X-ray binary MAXI J1820+070 during the rebrightening phase

Abstract                To understand the evolution of global accretion disk structure in the “rebrightening” phase of MAXI J1820+070, we perform a comprehensive analysis of its near infrared/optical/UV to X-ray spectral energy distribution (SED) utilizing data obtained by OISTER, Las Cumbres Observatory (LCO), Swift, NICER, and NuSTAR in 2019. Optical spectra observed with Seimei telescope in 2019 and 2020 are also analyzed. On the basis of the optical and X-ray light curves and their flux ratios, we divide the whole phase into three periods, Periods I (flux rise), II (decay), and III (dim). In the first two periods, the source stayed in the low/hard state (LHS), where the X-ray (0.3–30 keV) and optical/UV SED can both be fitted with power-law models. We interpret that the X-ray emission arises from hot corona via Comptonization, whereas the optical/UV flux is dominated by synchrotron radiation from the jets, with a partial contribution from the irradiated disk. The optical/UV power-law component smoothly connects to a simultaneous radio flux, supporting its jet origin. Balmer line profiles in the optical spectra indicate that the inner radius of an irradiated disk slightly decreased from ∼2 × 105rg (Period I) to ∼1 × 105rg (Period II), where rg is the gravitational radius, implying a change of the hot corona geometry. In Period III, the SED can be reproduced by an advection-dominated accretion flow and jet emission. However, the double-peaked Hα emission line indicates that a cool disk remained at large radii.
The MetaSUB Microbiome Core Analysis Pipeline Enables Large Scale Metagenomic Analysis

Motivation: Accurate data analysis and quality control is critical for metagenomic studies. Though many tools exist to analyze metagenomic data there is no consistent framework to integrate and run these tools across projects. Currently, computational analysis of metagenomes is time consuming, often misses potentially interesting results, and is difficult to reproduce. Further, comparison between metagenomic studies is hampered by inconsistencies in tools and databases.   Results: We present the MetaSUB Core Analysis Pipeline (CAP) a comprehensive tool to analyze metagenomes and summarize the results of a project. The CAP is designed in a bottom up fashion to perform QC, preprocessing, analysis and even to build relevant databases and install necessary tools.   Availability and Implementation: The CAP is available under an MIT License on GitHub at https://github.com/MetaSUB/CAP2 and on the Python Package Index. Documentation and examples are available on GitHub.
Dosage of resistance exercises in fibromyalgia: evidence synthesis for a systematic literature review up-date and meta-analysis

Several studies have examined the effect of different types of exercise on people with fibromyalgia. The aim of this study was to identify which dosage of resistance training is effective to reduce pain in women with fibromyalgia. Two authors independently selected studies included in a Cochrane Systematic Review and from an updated search up to May 2021 using the following databases: Embase, Central, Lilacs, PEDro, Current Controlled Trials, and WHO International Clinical Trials Registry Platform, Scopus and ISI Web of Science. Inclusion criteria were randomized clinical trials (RCTs) with female patients (18 years of age or older) diagnosed with fibromyalgia according to the American College of Rheumatology (ACR) criteria and the intervention, resistance exercises for pain reduction. The pain outcome value was extracted from studies for meta-analysis. Nine RCTs were included. Compared to the control groups, resistance exercise groups demonstrated a clinically and statistically significant effect on pain reduction when each exercise was performed in 1-2 sets or 3-5 sets of 4-12 or 5-20 repetitions twice a week, for 8-12 weeks, at intensities of 40-80% with one repetition maximum or perceived exertion. Resistance training exercises are effective to reduce pain in women with fibromyalgia when performed at moderate-to-high intensity in 1-2 sets of 4-20 repetitions twice a week, for 8-12 weeks. The protocol was registered on the International Prospective Register of Systematic Reviews (PROSPERO) platform, CRD42018095205.
“Single‐cell multimodal chromatin profiles revealing epigenetic regulations of cells in hepatocellular carcinoma”

AbstractHepatocellular carcinoma (HCC), with its increasing prevalence globally, is emerging as a major health challenge. Hepatocellular carcinoma cells exhibit both significant homogeneity and heterogeneity, governed by complex epigenomic regulations. Recently, numerous single‐cell unimodal techniques have been used to study HCC at various levelswhich have already revealed a complex interplay at genomic, transcriptomic, spatial and epigenomic levels. However, the use of single‐cell multimodal techniques combining different unimodal layers in HCC remains quite limited, necessitating further studies focusing on these methods to uncover novel markers, and mechanisms at epigenetic levels. In this commentary, we highlight how integrating multimodal approaches with epigenetic modifications can provide new insights into HCC and foster future therapeutic advancements.
Climate change projections of medicanes with a large multi-model ensemble of regional climate models

Cyclones with tropical characteristics, usually called medicanes, occasionally develop over the Mediterranean Sea.
Music Information Retrieval and Intelligent Genre Classification

Music is a sound that arises sensations in human mind and body. Music, since the beginning of time, has been present in every culture and life form, in audio and symbolic form, and in physical or digital mode of communication. While availability and scope for advancements increase exponentially, so does the need to search, compare, and organise music. Music industry has been striving towards finding the best possible approach to categorise music whether classification on the basis of emotions, instrumentation, genres, or any other music information will be most efficient as well as useful to listeners and music service providers. With the aim to support the best music experience, the current study statistically shows, with the help of prior research in music information retrieval and implementation of several powerful machine learning-based technologies, that genre classification, that too, ensemble-based, can be as accurate as 73.17%. The study analyses the performances of all models towards genre classification and concludes by proving max-voting ensemble-based models to be more accurate than each component classifier and advanced ensemble models and also optimal for real-world music genre classification as compared to prior experiments on GTZAN database, which is the novel contribution of the study.
Assessing the vulnerability of Timor Leste's agricultural sector to climate change with recommended options for adaptation and increased resilience of dependent communities

This study aims to develop a framework for strengthening farmers’ adaptive capacity and suggesting a transition mechanism to convert strengthened capacity into action within Timor-Leste’s agricultural context. The study first assesses the vulnerability of the agricultural sector to climate change. Secondly, factors that generate farmers’ adaptive capacity and its transitional process into action are examined. High agricultural vulnerability indices are associated with high exposure and low adaptive capacity. Policies, regulations, infrastructure, innovation, institutional arrangements, financial, knowledge and skills, behavior and attitudes influence capacity development. The transition mechanism consists of four stages: initiation; take-off; transition; and stabilization with specific characteristics in every stage of the transition flow.
The Effects of Mass Culture on the Loman Family : The Frankfurt School Critical Theory Applied to Arthur Miller’s Death of a Salesman

This essay discusses the effects of mass culture on the Loman family in Arthur Miller’s play, Death of a Salesman. The focus is to identify whether conformation to mass culture and alienation have caused the characters to be lonely and to give up what they enjoy and to fall in line with a system where individuality is replaced by totality. The theoretical framework used is the Frankfurt School’s critical theory which further developed Karl Marx’s socialist economic theory by putting an emphasis on the role of media and culture. The Frankfurt School critical theory purports that people in a mass culture society are estranged from themselves and others as large corporations and machines eradicate individual skills. Consumerism and meaningless media control and keep fatigued workers happy in their leisure time and thus distract them from starting a revolution. Advertising and entertainment create heroes to be revered which leads to a society where people strive for success, but this also results in competition and further estrangement. The conclusion is that the behaviour of the Loman family members is a result of conforming to the expectations of a mass culture society. Willy and Biff fail in their professional fields as they are not engaged in the manual work they are qualified for and that would fulfil their true needs. The constant effort to live up to expectations leads to immoralities, lies, contradictions, and loneliness. A false consciousness conveyed through media has indoctrinated a belief that someone who is not successful should be excluded from society. Willy’s escape after his own and his sons’ failures is suicide.
Contraction and expansion effects on the substitution-defect properties of thirteen alloying elements in bcc Fe

Proposed as blanket structural materials for fusion power reactors, reduced activation ferritic/martensitic (RAFM) steel undergoes volume expanding and contracting in a cyclic mode under service environment. Particularly, being subjected to significant fluxes of fusion neutrons RAFM steel suffers considerable local volume variations in the radiation damage involved regions. It is necessary to study the structure properties of the alloying elements in contraction and expansion states. In this paper we studied local substitution structures of thirteen alloying elements Al, Co, Cr, Cu, Mn, Mo, Nb, Ni, Si, Ta, Ti, V, and W in bcc Fe and calculated their substitutional energies in the volume variation range from -1.0% to 1.0%. From the structure relaxation results of the first five neighbor shells around the substitutional atom we find the relaxation in each neighbor shell keeps approximately uniform within the volume variation from -1.0% to 1.0% except those of Mn and the relaxation of the fifth neighbor shell is stronger than that of the third and forth, indicating that the lattice distortion due to the substitution atom is easier to spread in <111> direction than in other direction. The relaxation pattern and intensity are related to the size and electron structure of the substitutional atom. For some alloying elements, such as Mo, Nb, Ni, Ta, Ti and W, the substitutional energy decreases noticeably when the volume increases. Further analysis show that the substitutional energy comprises the energy variation originated from local structure relaxation and the chemical potential difference of the substitutional atom between its elemental crystalline state and the solid solution phase in bcc Fe. We think the approximately uniform relaxation of each neighbor shell around a substitutional atom give rise to a linear decrease in the substitutional energy with the increasing volume.
Measurement Non-invariance in Machine Learning: An Intersection of Machine Learning Bias and Test Bias

Algorithmic and machine learning bias have stirred concern in society as machine learning continues to channel into sensitive and high-stakes applications, including in healthcare, hiring, and criminal justice. While research surrounding machine learning bias may be relatively new, psychometricians have for decades researched a closely paralleled topic of test bias in psychological and educational testing. Leveraging the connection between these two fairness domains, this thesis studies the problem of machine learning bias from a measurement perspective, specifically focusing on measurement non-invariance in outcome variables as a source of machine learning bias. A framework is introduced, which conceptualizes machine learning bias in a psychometric sense and allows for tests of measurement invariance in machine learning. Using a Monte Carlo simulation study, the consequences of measurement bias on machine learning bias are demonstrated, as well as the effectiveness of a proposed bias mitigation technique to address these effects of measurement bias, which also follows from the proposed framework. The application of the proposed methods is illustrated with data from a large-scale health survey. Broader implications of the relevance of fairness in measurement for fairness in machine learning are discussed.
Climate change and Arctic ecosystems: 2. Modeling, paleodata‐model comparisons, and future projections

Large variations in the composition, structure, and function of Arctic ecosystems are determined by climatic gradients, especially of growing‐season warmth, soil moisture, and snow cover. A unified circumpolar classification recognizing five types of tundra was developed. The geographic distributions of vegetation types north of 55°N, including the position of the forest limit and the distributions of the tundra types, could be predicted from climatology using a small set of plant functional types embedded in the biogeochemistry‐biogeography model BIOME4. Several palaeoclimate simulations for the last glacial maximum (LGM) and mid‐Holocene were used to explore the possibility of simulating past vegetation patterns, which are independently known based on pollen data. The broad outlines of observed changes in vegetation were captured. LGM simulations showed the major reduction of forest, the great extension of graminoid and forb tundra, and the restriction of low‐ and high‐shrub tundra (although not all models produced sufficiently dry conditions to mimic the full observed change). Mid‐Holocene simulations reproduced the contrast between northward forest extension in western and central Siberia and stability of the forest limit in Beringia. Projection of the effect of a continued exponential increase in atmospheric CO2 concentration, based on a transient ocean‐atmosphere simulation including sulfate aerosol effects, suggests a potential for larger changes in Arctic ecosystems during the 21st century than have occurred between mid‐Holocene and present. Simulated physiological effects of the CO2 increase (to &gt;700 ppm) at high latitudes were slight compared with the effects of the change in climate.
Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions

Background: Many mathematical models have now been employed across every area of systems biology. These models increasingly involve large numbers of unknown parameters, have complex structure which can result in substantial evaluation time relative to the needs of the analysis, and need to be compared to observed data. The correct analysis of such models usually requires a global parameter search, over a high dimensional parameter space, that incorporates and respects the most important sources of uncertainty. This can be an extremely difficult task, but it is essential for any meaningful inference or prediction to be made about any biological system. It hence represents a fundamental challenge for the whole of systems biology.   Results: Bayesian statistical methodology for the uncertainty analysis of complex models is introduced, which is designed to address the high dimensional global parameter search problem. Bayesian emulators that mimic the systems biology model but which are extremely fast to evaluate are embedded within an iterative history match: an efficient method to search high dimensional spaces within a more formal statistical setting, while incorporating major sources of uncertainty. The approach is demonstrated via application to two models of hormonal crosstalk in Arabidopsis root development, which have 32 rate parameters, for which we identify the sets of rate parameter values that lead to acceptable matches to observed trend data. The biological consequences of the resulting comparison, including the evaluation of gene functions, are described.
The Role of the Registry and Legal Division of the African Court of Human and People's Rights in Dispute Settlement

This Essay explores whether the African Court of Human and People's Rights’ (African Court) Registry and Legal Division have a similar expansive role in the dispute settlement mechanism as the World Trade Organization's (WTO) Secretariat. The African Court is the African Union's regional body for enforcing human rights. This Essay contributes to the scholarship on African international courts by testing the central arguments in Pauwelyn and Pelc's “Who Guards the ‘Guardians of the System’? The Role of the Secretariat in WTO Dispute Settlement”1 through a comparative analysis of the role of the Secretariat within the African Court.2 Despite the growing jurisprudence and influence of Africa's international courts, they continue to be neglected by mainstream scholarship of international courts and tribunals. This is evident in Pauwelyn and Pelc's article, which does not refer to any of Africa's seven international courts and tribunals. For that matter, the article makes no reference to any international tribunal outside Europe or the United States.3 Our analysis illustrates the fact that the influence of the African Court's Registry and Legal Division (Registry) in the process for dispute settlement is not as significant and concerning as that of the WTO Secretariat. As such, the influence of the Registry on the outcome of a case is significantly limited and does not raise any legitimacy crisis like the WTO. We attribute this disparity to the operational and structural disparities between the African Court and the WTO. In the following paragraphs, we examine each of the factors that Pauwelyn and Pelc raise concerning the influence of the WTO staff in WTO panel and Appellate Body proceedings4 in the context of the functions of the African Court Registry.