ethicalabs/Research-Intent-Collab huggingface dataset:
Casting Light on Central Bank Digital Currency

Abstract                Several central banks have begun actively investigating the possibility of issuing central bank digital currency (CBDC). This new central bank liability would be a widely accessible digital form of fiat money, intended as legal tender. This chapter aims to answer a simple question: Does CBDC offer benefits? On the demand side, would it satisfy end user needs better than other forms of money? And on the supply side, would issuing CBDC allow central banks to more effectively satisfy public policy goals, including financial inclusion, operational efficiency, financial stability, monetary policy effectiveness, and financial integrity? In short, is CBDC a desirable form of money given existing and rapidly evolving alternatives? The chapter includes a summary of pilot projects and studies from central banks exploring the possibility of issuing CBDC. The analysis is based on publicly issued materials and discussions with staff members at central banks and technology providers around the world.
From Standards of International Law for Outer Space Resources Exploitation to Sustainable Mining

As space activities continue to develop and increase in number, so do environmental concerns in outer space. For decades now, humanity has continuously sent satellites into Earth orbits without caring for potential environmental consequences in outer space. Ultimately, these actions have proven to raise issues regarding the sustainability of the activity; issues which are now being addressed legally. Satellites were the first venture of humanity into space, and it is fair to admit we did not know better at the time. We do now. With the development of new types of space missions, such as space resources-related activities, it is safe to assume that new serious environmental problems will arise as well. Based on previous experience both on Earth and in outer space, it is logical, but also imperative, to question the environmental impact of these space resources activities and to consider legal solutions to promote and facilitate their sustainability. Accordingly, this research assesses the applicability of existing rules and mechanisms promoting environmental protection and sustainability in outer space to the case of the exploitation of space resources. To that end, an array of mechanisms is considered such as the framework of the UN Space Treaties, international environmental law, non-legally binding instruments, such as the space debris mitigation guidelines and COSPAR’s planetary protection policy, as well as national space legislations. Ultimately, this work aims at drafting the roadmap for the environmentally sustainable exploitation of space resources from a legal standpoint. It recommends the adoption of a mix of interdisciplinary approaches which balances a national effective approach with international guiding rules.
Special Focus: Implementation of Decisions of The African Court on Human and Peoples' Rights

This Special Focus is dedicated to the implementation of the decisions of the African Court on Human and Peoples' Rights (African Court). The seven articles included in this Special Focus were developed from papers presented at the Conference on Implementation and Domestic Impact of the Decisions of the African Court on Human and Peoples' Rights, held from 21 to 22 June 2024 in Arusha, Tanzania. The Conference was convened by the Centre for Human Rights, Faculty of Law, University of Pretoria (Centre), in collaboration with the African Court and the Coalition for an Effective African Court on Human and Peoples' Rights.
Transforming into a Lean Six Sigma Enterprise Through ISO 9001 Standard-Based Quality Management System

Lean Six Sigma is very effective when implemented as an integrated approach rather than implementing Six Sigma or lean separately. For this reason, the contemporary enterprises are taking steps to evolve as Lean Six Sigma enterprises for waste elimination and quality improvement. It has become essential for all the enterprises to obtain ISO 9001:2008 certification to sustain their growth in the open market. Under this scenario, it is more advantageous to implement Lean Six Sigma through ISO 9001:2008 standard based Quality Management System. With the aid of a recently developed model L6QMS-2008 for transforming enterprise into Lean Six Sigma enterprise through ISO 9001 standard based Quality Management System, this research paper draws comparison between two enterprises that have taken efforts to apply the L6QMS-2008 model.
metaSPAdes: a new versatile de novo metagenomics assembler

While metagenomics has emerged as a technology of choice for analyzing bacterial populations, assembly of metagenomic data remains difficult thus stifling biological discoveries. metaSPAdes is a new assembler that addresses the challenge of metagenome analysis and capitalizes on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes. We benchmark metaSPAdes against other state-of-the-art metagenome assemblers across diverse da-tasets and demonstrate that it results in high-quality assemblies.
Investigation of Lab Fire Prevention Management System of Combining Root Cause Analysis and Analytic Hierarchy Process with Event Tree Analysis

This paper proposed a new approach, combining root cause analysis (RCA), analytic hierarchy process (AHP), and event tree analysis (ETA) in a loop to systematically evaluate various laboratory safety prevention strategies. First, 139 fire accidents were reviewed to identify the root causes and draw out prevention strategies. Most fires were caused due to runaway reactions, operation error and equipment failure, and flammable material release. These mostly occurred in working places of no prompt fire protection. We also used AHP to evaluate the priority of these strategies and found that chemical fire prevention strategy is the most important control element, and strengthening maintenance and safety inspection intensity is the most important action. Also together with our surveys results, we proposed that equipment design is also critical for fire prevention. Therefore a technical improvement was propounded: installing fire detector, automatic sprinkler, and manual extinguisher in the lab hood as proactive fire protections. ETA was then used as a tool to evaluate laboratory fire risks. The results indicated that the total risk of a fire occurring decreases from 0.0351 to 0.0042 without/with equipment taking actions. Establishing such system can make Environment, Health and Safety (EH&amp;S) office not only analyze and prioritize fire prevention policies more practically, but also demonstrate how effective protective equipment improvement can achieve and the probabilities of the initiating event developing into a serious accident or controlled by the existing safety system.
Risk-Sensitive Reinforcement Learning via Policy Gradient Search

The objective in a traditional reinforcement learning (RL) problem is to find a policy that optimizes the expected value of a performance metric such as the infinite-horizon cumulative discounted or long-run average cost/reward. In practice, optimizing the expected value alone may not be satisfactory, in that it may be desirable to incorporate the notion of risk into the optimization problem formulation, either in the objective or as a constraint. Various risk measures have been proposed in the literature, e.g., exponential utility, variance, percentile performance, chance constraints, value at risk (quantile), conditional value-at-risk, prospect theory and its later enhancement, cumulative prospect theory. In this book, we consider risk-sensitive RL in two settings: one where the goal is to find a policy that optimizes the usual expected value objective while ensuring that a risk constraint is satisfied, and the other where the risk measure is the objective. We survey some of the recent work in this area specifically where policy gradient search is the solution approach. In the first risk-sensitive RL setting, we cover popular risk measures based on variance, conditional value-at-risk, and chance constraints, and present a template for policy gradient-based risk-sensitive RL algorithms using a Lagrangian formulation. For the setting where risk is incorporated directly into the objective function, we consider an exponential utility formulation, cumulative prospect theory, and coherent risk measures. This non-exhaustive survey aims to give a flavor of the challenges involved in solving risk-sensitive RL problems using policy gradient methods, as well as outlining some potential future research directions.
Adding Long-Wavelength Power to N-body Simulations

Tormen and Bertschinger have presented an algorithm which allows the dynamic range of N-body simulations to be extended by adding long-wavelength power to an evolved N-body simulation. This procedure is of considerable interest as it will enable mock galaxy catalogues to be constructed with volumes as large as those of the next generation of galaxy redshift surveys. Their algorithm, however, neglects the coupling between long-wavelength linear modes and short-wavelength non-linear modes. The growth of structure on small scales is coupled to the amplitude of long-wavelength density perturbations via their effect on the local value of the density parameter Omega_0.The effect of neglecting this coupling is quantified using a set of specially tailored N-body simulations. It is shown that the large-scale clustering of objects defined in the evolved density field such as galaxy clusters is strongly underestimated by their algorithm. An adaptation to their algorithm is proposed that, at the expense of additional complexity, remedies the shortcomings of the original one. Methods of constructing biased mock galaxy catalogues which utilise the basic algorithm of Tormen and Bertschinger, but avoid the pitfalls are discussed.
Text mining and social network analysis on computer science and engineering theses in Turkey

In this study, we examined 6,834 master's and PhD theses conducted on computer science and engineering between 1994 and 2013 in Turkey. Thesis data were compiled from the YOK national thesis database web portal. We used text mining techniques to extract research concepts and their co-occurrence data from graduate thesis abstracts. Then, we applied social network analysis techniques on the concept co-occurrence networks to visually explore core research concepts, and connections and relationships among them. We showed that text mining and social network analysis techniques together were very effective for knowledge discovery in scientific documents in computer science and engineering domain.
Architectures and Protocols for Secure and Energy-Efficient Integration of Wireless Sensor Networks with the Internet of Things. (Architectures et protocoles pour une intégration sécurisée et économe en énergie des réseaux de capteurs dans l'Internet des objets).

Our research explores the intersection of academic, industrial and standardization spheres to enable secure and energy-efficient Internet of Things. We study standards-based security solutions bottom-up and first observe that hardware accelerated cryptography is a necessity for Internet of Things devices, as it leads to reductions in computational time, as much as two orders of magnitude. Overhead of the cryptographic primitives is, however, only one of the factors that influences the overall performance in the networking context. To understand the energy - security tradeoffs, we evaluate the effect of link-layer security features on the performance of Wireless Sensors Networks. We show that for practical applications and implementations, link-layer security features introduce a negligible degradation on the order of a couple of percent, that is often acceptable even for the most energy-constrained systems, such as those based on harvesting.Because link-layer security puts trust on each node on the communication path consisted of multiple, potentially compromised devices, we protect the information flows by end-to-end security mechanisms. We therefore consider Datagram Transport Layer Security (DTLS) protocol, the IETF standard for end-to-end security in the Internet of Things and contribute to the debate in both the standardization and research communities on the applicability of DTLS to constrained environments. We provide a thorough performance evaluation of DTLS in different duty-cycled networks through real-world experimentation, emulation and analysis. Our results demonstrate surprisingly poor performance of DTLS in networks where energy efficiency is paramount. Because a DTLS client and a server exchange many signaling packets, the DTLS handshake takes between a handful of seconds and several tens of seconds, with similar results for different duty cycling protocols.But apart from its performance issues, DTLS was designed for point-to-point communication dominant in the traditional Internet. The novel ...
The Relative Effects of Logistics, Coordination and Human Resource on Humanitarian Aid and Disaster Relief Mission Performance

Most studies on humanitarian aid and disaster relief (HADR) missions suggest that the quality of logistics, coordination and human resource management will affect their performance. However, studies in developing countries are mainly conceptual and lack the necessary empirical evidence to support these contentions. The current paper thereby aimed to fill this knowledge gap by sta- tistically examining the effects of the abovementioned factors on such missions. Focusing on the Malaysian army due to its extensive experience in HADR operations, the paper opted for a quantita- tive approach to allow for a more objective analysis of the issues. The results show that there are other potential determinants of mission success which deserve due attention in future studies. They also suggest that human resource is not easily measured as a construct, and that this limitation in methodology must be overcome to derive more accurate conclusions regarding its effect on HADR mission performance.
A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.
Combining policy gradient and Q-learning

Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer. This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values. This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates. We refer to the new technique as 'PGQL', for policy gradient and Q-learning. We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms. We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQL. In particular, we tested PGQL on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.
Analysis of risk-forming factors and risk assessment in the management of an educational organization

статья посвящена анализу и оценке рисков, с которыми сталкивается современная образовательная организация в условиях динамично меняющейся внешней среды и внутренней структуры. Автором показаны методы анализа рискообразующих факторов, оказывающих существенное влияние на деятельность образовательной организации. Проведен обзор основных методов, используемых для оценки рисков, способных негативно повлиять на функционирование образовательной организации.                   the article is devoted to the analysis and assessment of the risks faced by a modern educational organization in a dynamically changing external environment and internal structure. The author shows methods for analyzing risk-generating factors that have a significant impact on the activities of an educational organization. The review of the main methods used to assess risks that can negatively affect the functioning of an educational organization is carried out.
Model Uncertainty, Ambiguity and the Precautionary Principle: Implications for Biodiversity Management

We analyze ecosystem management under ‘unmeasurable’ Knightian uncertainty or ambiguity which, given the uncertainties characterizing ecosystems, might be a more appropriate framework relative to the classic risk case (measurable uncertainty). This approach is used as a formal way of modelling the precautionary principle in the context of least favorable priors and maxmin criteria. We provide biodiversity management rules which incorporate the precautionary principle. These rules take the form of either safety margins and minimum safety standards or optimal harvesting under precautionary approaches.
Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality

This work contributes to the development of neural forecasting models with novel randomization-based learning methods. These methods improve the fitting abilities of the neural model, in comparison to the standard method, by generating network parameters in accordance with the data and target function features. A pattern-based representation of time series makes the proposed approach useful for forecasting time series with multiple seasonality. In the simulation study, we evaluate the performance of the proposed models and find that they can compete in terms of forecasting accuracy with fully-trained networks. Extremely fast and easy training, simple architecture, ease of implementation, high accuracy as well as dealing with nonstationarity and multiple seasonality in time series make the proposed model very attractive for a wide range of complex time series forecasting problems.
Contact inhibition of locomotion and mechanical cross-talk between cell-cell and cell-substrate adhesion determines the pattern of junctional tension in epithelial cell aggregates

We generated a computational approach to analyze the biomechanics of epithelial cell aggregates, either island or stripes or entire monolayers, that combines both vertex and contact-inhibition-of-locomotion models to include both cell-cell and cell-substrate adhesion. Examination of the distribution of cell protrusions (adhesion to the substrate) in the model predicted high order profiles of cell organization that agree with those previously seen experimentally. Cells acquired an asymmetric distribution of basal protrusions, traction forces and apical aspect ratios that decreased when moving from the edge to the island center. Our in silico analysis also showed that tension on cell-cell junctions and apical stress is not homogeneous across the island. Instead, these parameters are higher at the island center and scales up with island size, which we confirmed experimentally using laser ablation assays and immunofluorescence. Without formally being a 3-dimensional model, our approach has the minimal elements necessary to reproduce the distribution of cellular forces and mechanical crosstalk as well as distribution of principal stress in cells within epithelial cell aggregates. By making experimental testable predictions, our approach would benefit the mechanical analysis of epithelial tissues, especially when local changes in cell-cell and/or cell-substrate adhesion drive collective cell behavior.
Additional file 1 of A review of smartphone applications designed to improve occupational health, safety, and well-being at workplaces

Additional file 1. Overview of included apps.
The right to water possible. Social dimension of the Right to drinking water and sanitation.

The Human Right to water is indispensable for a worthy life and the accomplishment of other Human Rights. Almost 900 million persons lack access to the drinkable and  sure water and 2500 millions, 40 % of the world population, has not  a basic support to acces to sure water. The lack of access to the water and to the reparation has an enormous human cost, in social and economic terms. The Human right to the water is included implicitly or explicitly in diverse agreements and international declarations. This way the Human right establishes that every person has rightly to a suitable standard of living that assures the health and the well-being him, included the supply and the housing. The accomplishment of this right is impossible without the access to a minimal quantity of water. Numerous Agreements, Conventions and Charts protect this right on which there depends the effective accomplishment of other rights, though we are far from his effective application.
A Study On The Optimal Process For Efficient Logistical Transport

With the advent of various online to offline (O2O) services, the transportation volume of logistics companies is increasing. However, since vehicles and car owners are limited, if the shipper does not present an appropriate fare, the rate of allocation will decrease and making it difficult to secure car owners. It is also not easy for car owners to find the most suitable logistics at once amid numerous requests for logistics transportation in real time. For efficient dispatch, we study the dispatch contract process according to each position of the shipper and the car owner. For shippers, we suggest optimal fares with high dispatch probability according to fare decision factors, and check the probability of execution when the optimal fare is presented. For car owners, a personal model using random forest is built to narrow the their choice amid numerous shippers' cargo transport requests, and a customized cargo recommendation list is provided. New car owners use a group model built by grouping car owners with similar tendencies. In addition, in order to reduce the waiting time for car owners, we analyze a method of minimizing the distance that runs without loading. As a result, it is found that the average 29.771 km can be reduced.