ethicalabs/Research-Intent-Collab huggingface dataset:
Algorithmic Price Personalization and the Limits of Anti-Discrimination Law

As much attention is turned to regulating AI systems to minimize the risk of harm, including the one caused by discriminatory biased outputs, a better understanding of how commercial practices may contravene anti-discrimination law is critical. This article investigates the instances in which algorithmic price personalization, (i.e., setting prices based on consumers’ personal information with the objective of getting as close as possible to their maximum willingness to pay (APP)), may violate anti-discrimination law. It analyses cases whereby APP could constitute prima facie discrimination, while acknowledging the difficulty to detect this commercial practice. It discusses why certain commercial practice differentiations, even on prohibited grounds, do not necessarily lead to prima facie discrimination, offering a more nuanced account of the application of anti-discrimination law to APP. However once prima facie discrimination is established, APP will not be easily exempted under a bona fide requirement, given APP’s lack of a legitimate business purpose under the stringent test of anti-discrimination law, consistent with its quasi-constitutional status. This article bridges traditional anti-discrimination law with emerging AI governance regulation. Pointing to identified gaps in anti-discrimination law, it analyses how AI governance regulation could enhance anti-discrimination law and improve compliance.
Legal Regulation of the Protection of the Rights of Minors on the Internet in the People’s Republic of China

Children are one of the most vulnerable populations in cyberspace. As they gain more access to the internet and social media, which in turn increases the risk of cyber threats that can have serious unintended consequences. The relevance of the study is determined by the fact that despite the fact that the Internet appeared a long time ago, and children have always been its active users, today the protection of their rights on the Internet is not sufficiently regulated from the point of view of law.
Agricultural Production in Egypt: Assessing Vulnerability and Enhancing Adaptive Capacity and Resilience to Climate Change

The international community has developed the 2030 Agenda and the Sustainable Development Goals (SDGs) that include specific elements to enhance sustainable agricultural production and food security. SDG 13 “take urgent actions to combat climate change and its impacts” calls for enhancing the adaptive capacity and build resilience to climate change as well as mainstreaming climate change measures into policies and planning processes of the countries. According to the IPCC assessment, Near East North Africa is one of the world regions that are most vulnerable to climatic change and consequently, agriculture is the most affected sector. It is anticipated that such a climate change impact would reduce crop yield up to 30%. Within the ‘Regional Initiative for the Assessment of the Impact of Climate Change on Water Resources and Socio-Economic Vulnerability in the Arab Region’ (RICCAR), the impact of climate change on specific agriculture sectors was projected using the two considered climate scenarios, based on selected Representative Concentration Pathways (RCP), specifically RCP 4.5 (moderate-case scenario) and RCP 8.5 (worst-case scenario). Based on the RICCARD projections, the vulnerability of the agricultural sectors in the Arab Region to climate change have few aspects. Environmental challenges in the Arab world include water scarcity, with the lowest freshwater resource endowment in the world and very low precipitation with excessive exposure to extreme events. The Arab Region is one of the most impacted regions by climate change with half of its area’s cropland systems producing wheat, maize, olives, potatoes, olives and vegetables are considered high vulnerable to climate change. Livestock production as well is expected to decline due to drought, increase in degradation of rangelands and desertification. Expected more drought cycles in addition to increasing deforestation rate add to the challenges facing the forestry sector in the region. Egypt is considered as one of the countries that are greatly impacted by the climate change. The vulnerability of Egypt to climate change is significant in all agriculture sectors: cropping, fishery and livestock. Developing strategy to enhancing adaptive capacity and resilience to climate change in Egypt requires coordination and integration between the main related national strategies such as Egypt’s Vision 2030 [1], the Sustainable Agricultural Development Strategy [2], and Egypt’s National Strategy for Adaptation to Climate Change and Disaster Risk Reduction [3]. Climate change adaptation is of paramount importance to agriculture, given the reliance of the sector on climate. Climate change adaptation policies should be based on science, and incorporate knowledge of indigenous peoples and traditional practices.
Verification, calibration, and validation of stall delay models using NREL phase VI and MEXICO data

An accurate estimation of rotational effects is critical during the preliminary design of wind turbines. For this purpose, different stall delay models were developed based on the centrifugal pumping mechanism. However, their generality is not yet thoroughly evaluated. In this work, we investigated the causal relationship between the radial flow, the pressure reduction, and forces augmentation. Three stall delay models, which represent different solutions of the centrifugal pumping mechanism, were verified and modified to accurately predict the radial flow, the pressure coefficient, and normal and tangential force coefficients. Then, the three modified stall delay models were calibrated using radial flow data available from the literature. Finally, they were validated against the experimental data of the NREL phase VI and MEXICO rotors. The results showed that the centrifugal pumping produces a small chordwise pressure gradient in the separated boundary layer, which produces a small augmentation in the normal and tangential force coefficients. In contrast, the measured pressure coefficient and the normal and tangential force coefficients showed a large augmentation compared to the three modified stall delay models. Consequently, the lack of generality of current stall delay models is mainly due to the centrifugal pumping assumption. Furthermore, the verification and calibration of these stall delay models allowed us to isolate large errors in the model's output due to the model's assumptions. Thus, the importance of a rigorous verification and calibration before performing the validation.
2346 Development of toolkits to support for researchers integrating dissemination and implementation science into their translational research

OBJECTIVES/SPECIFIC AIMS: To use a systematic and iterative process to develop and refine toolkits to support dissemination and implementation (D&amp;I) research. METHODS/STUDY POPULATION: Participants included research staff from the Dissemination and Implementation Research Core (DIRC), a research methods core from the Institute of Clinical and Translational Science at Washington University in St. Louis, other D&amp;I experts from the University, and national experts from the D&amp;I field. This project used education design research methodology and a systematic and iterative process involving several phases. The first phase (preliminary research and initial development) consisted of analysis of the educational problem and its context, and led to the development of toolkit prototypes and plans for their implementation. In the second phase (development and formative evaluation), toolkits were iteratively evaluated with emphasis on content validity and consistency and effectiveness as perceived by the users. Finally, in the summative evaluation, the toolkits were evaluated based on their use as intended. RESULTS/ANTICIPATED RESULTS: Our team identified the target audience as DIRC customers and investigators from disciplines across the University, and found that resources for beginners to D&amp;I were lacking. The team developed 8 toolkits: (1) Introduction to D&amp;I; (2) How to develop D&amp;I Aims; (3) D&amp;I Designs; (4) Implementation Outcomes; (5) Implementation Organizational Measures; (6) Assessing Barriers and Facilitators; (7) D&amp;I Designs; and (8) Guideline research. These prototypes were iteratively revised for content validity and consistency. Finally, each toolkit was evaluated by two national experts in D&amp;I science, and further refined. DISCUSSION/SIGNIFICANCE OF IMPACT: This systematic and cyclical process led to the development of 8 toolkits to support researchers in D&amp;I science, which are now available on the DIRC Web site. This set the stage for development of new toolkits as additional needs are identified.
Logistics network topology optimization and transportation scheduling using graph neural networks

Abstract                   As the logistics business encounters increasing challenges, the proficient use of innovative intelligence algorithms to enhance logistics network structures and transport scheduling is essential. The adaptive Attention Mechanism for Deep Reinforcement Learning (AAM-DRL) algorithm is introduced in this study. AAM-DRL effectively simulates node-edge interactions in logistics networks by combining Graph Neural Networks (GNNs) and reinforcement learning. It also speeds up learning and improves policy performance by using an attention mechanism to change the value of state-action pairings. Experimental results demonstrate that AAM-DRL outperforms traditional algorithms, including DQN, DDPG, A3C, and PPO. In maze navigation tasks, it gets a mean reward of 98.5 ± 1.5 and converges in 260 ± 8 episodes, which is 8%–15% better than the others and takes 40 episodes less time to converge. AAM-DRL increases target balance by 5%–15% compared to benchmarks in multi-objective logistics optimization. It also exhibits greater resilience with adaptability scores of 0.95 ± 0.01. It demonstrates the importance of the attention mechanism for policy optimization, contributing to the theoretical study on logistics optimization of networks and reinforcement learning in challenging situations. In practice, AAM-DRL successfully optimizes network topologies, reduces transportation costs, enhances scheduling efficiency, and effectively manages complex logistical dynamics, making it highly useful in real-world applications.
COURT INTERVENTION IN ARBITRATION AND THE NATIONAL POLICY ON ARBITRATION AND ALTERNATIVE DISPUTE RESOLUTION 2024 VIS-À-VIS THE ARBITRATION AND MEDIATION ACT 2023

In order to bring the law and practice of arbitration in Nigeria to the level of contemporary international best practice, the Federal Government of Nigeria recently passed into law the Arbitration and Mediation Act 2023 and formulated the National Policy on Arbitration and Alternative Dispute Resolution 2024. This paper examines Nigeria's national policy approach to Alternative Dispute resolution (ADR) mechanisms through the lens of the Arbitration and Mediation Act of 2023. It analyzes how this legislation represents a significant shift in Nigeria's dispute resolution framework, aligns with international best practices, and supports Nigeria's economic development goals. The paper further explores the policy implications of this Act on Nigeria's justice system, business environment, and international trade relations, while also examining the socio-political context that necessitated this legislative reform and the anticipated impact on various stakeholders in the Nigerian legal and business landscape. The paper made recommendations on how to efficiently use these legislative and executive interventions in arbitration as a tool to ensure there is reduction in use of court intervention to frustrate the law and practice of arbitration in Nigeria and engender economic growth.
Neutron Physics for Nuclear Reactors: Unpublished Writings by Enrico Fermi, edited by S. Esposito and O. Pisanti

Neutron Physics for Nuclear Reactors: Unpublished Writings by Enrico Fermi, edited by S. Esposito and O. Pisanti, Singapore, World Scientific Publishing, 2010, 704 pp., 230 illustrations and 23 tab...
A RISC-V Simulator and Benchmark Suite for Designing and Evaluating Vector Architectures

Vector architectures lack tools for research. Consider the gem5 simulator, which is possibly the leading platform for computer-system architecture research. Unfortunately, gem5 does not have an available distribution that includes a flexible and customizable vector architecture model. In consequence, researchers have to develop their own simulation platform to test their ideas, which consume much research time. However, once the base simulator platform is developed, another question is the following: Which applications should be tested to perform the experiments? The lack of Vectorized Benchmark Suites is another limitation. To face these problems, this work presents a set of tools for designing and evaluating vector architectures. First, the gem5 simulator was extended to support the execution of RISC-V Vector instructions by adding a parameterizable Vector Architecture model for designers to evaluate different approaches according to the target they pursue. Second, a novel Vectorized Benchmark Suite is presented: a collection composed of seven data-parallel applications from different domains that can be classified according to the modules that are stressed in the vector architecture. Finally, a study of the Vectorized Benchmark Suite executing on the gem5-based Vector Architecture model is highlighted. This suite is the first in its category that covers the different possible usage scenarios that may occur within different vector architecture designs such as embedded systems, mainly focused on short vectors, or High-Performance-Computing (HPC), usually designed for large vectors.
Economic Community of West African States, Regional security and the Implementation of Humanitarian Intervention and the Responsibility to Protect: Rhetoric or Reality?

The last 25 years have seen Economic Community of West African States, through the use of various norms, structures and protocols, make the promotion of security and the implementation of humanitarian intervention and the responsibility to protect (R2P) important aspects of the political landscape in the sub-region. The article argues that despite the great strides made by the Economic Community of West African States (ECOWAS) in the implementation of R2P, there are not only challenges (inadequate funds available for peace and security missions, conflicting interests and lack of agreement, poor co-ordination, inadequate human and logistics capacity) with its application in the sub-region, but also concerns about its future. Thus, to promote security and realise the goals of implementing humanitarian intervention and R2P in the ECOWAS sub-region, not only should the actors involved have the requisite capacity but also political will and commitment, citizen awareness, and co-operation among ECOWAS member-states and with the international community should remain crucial to the process.
Fast error-safe MOID computation involving hyperbolic orbits

We extend our previous algorithm computing the minimum orbital intersection distance (MOID) to include hyperbolic orbits, and mixed combinations ellipse--hyperbola. The MOID is computed by finding all stationary points of the distance function, equivalent to finding all the roots of an algebraic polynomial equation of 16th degree. The updated algorithm carries about numerical errors as well, and benchmarks confirmed its numeric reliability together with high computing performance.
Modern Legal Mechanisms to Guarantee the Rights of Everyone to Nutrition and Food Security in the Russian Federation

Право каждого на достаточное питание — это необходимая основа для нормальной, экономически активной жизни человека. Продовольственная безопасность как состояние экономической и социальной сфер государства в целом является условием,  прямо определяющим степень реализации гражданами права на питание, защиты  данного права. В статье рассмотрено содержание основных категорий понятий, относящихся  к сфере обеспечения права на питание в отечественном и международном  праве; обозначены основные направления развития правового обеспечения продовольственной безопасности в современных условиях.                                                                                             Everyone’s right to adequate food is an essential foundation for a normal, economically  active life. Food security as the state of the economic and social sphere of the state  as a whole is a condition that directly determines the degree of realization by citizens  of the right to food. The article examines the content of the main categories of concepts  in the field of ensuring the right to food; the main directions of development of legal support  of food security in modern conditions are outlined.
Potential biodiversity map of understory plants for Nothofagus forests in Southern Patagonia: Analyses of landscape, ecological niche and conservation values

The role of understory plants in native forests is critical for ecosystem function, wildlife protection and ecosystem productivity. The interest to estimate biodiversity increased during the last decades at landscape level. The objective was to elaborate a map of potential biodiversity (MPB) of understory species of Nothofagus forest using potential habitat suitability maps (PHS) of 15 plants in Santa Cruz province, Argentina. Additionally, we asked the following questions: (i) Were plant species differentially distributed according to the forest types?, (ii) do forest types represent different plant species assemblage with specific ecological niche requirements?, and (iii) is it possible to detect hotspots in the MBP according to the forest types? We used 721 plots database of vascular plants, from where 15 indicator species were identified. The assemblage species for different forests (Nothofagus antarctica, N. pumilio and evergreen mixed) were analysed using a detrended correspondence analysis. Also, we explored 41 potential explanatory variables to develop PHS, and combined these maps to obtain one MPB (1-100%). Finally, we analysed the outputs into a GIS through different landscapes alternatives to detect hotspot areas. Marginality and specialization values allowed identifying species assemblage that presented similar variability in the habitat requirements. MPB varied across the landscape, with higher values in the south and lower values near glaciers. MPB had the highest values in N. antarctica forest with >50% cover at landscape level. N. antarctica present more hotspots than N. pumilio forests, mainly in the south, compared to mixed evergreen forests which present few hotspots near glaciers. These results can be used as a tool to design new management and conservation strategies at landscape level.
Exploring the Nexus between Geopolitics and Intervention for Human Protection Purposes: Lessons from the Southern African Development Community (SADC) Responses to the Humanitarian Crisis in Zimbabwe

This article seeks to discuss the connection between geopolitics and the new United Nations (UN) norm for intervention for human protection purposes, known as the Responsibility to Protect (R2P), by examining the Southern African Development Community's (SADC's) responses to the humanitarian crisis in Zimbabwe. Factoring a geopolitical dimension to the Zimbabwe crisis is necessary to understand why the country's economic and political turmoil became internationalised and, by implication, a policy responsibility for SADC. Zimbabwe represents a case where the SADC was/is still reluctant and hesitant to lead the international community into fully operationalising the R2P. It is argued that the lack of implementation (or only partial implementation) of R2P by the SADC bloc is a result of geopolitical imperatives such as economic interests, shared liberation war experiences, unresolved colonial inequ alities and the fact that the region's governments venerate the principles of state sovereignty and non-interference to the detriment of good governance and human rights observation. It is further argued that SADC's blind solidarity with the government of Zimbabwe has misled the international community into believing that the crisis is a geopolitical matter that can be contained and resolved by regional solutions.
Deep learning enhanced early detection of pancreatic cancer using optical neural networks and photonic crystals

Pancreatic cancer currently holds the lowest five-year survival rate and is projected to become the second leading cause of cancer-related deaths in the U.S. by 2030. In response to the urgent need for improved diagnostic methods, on-chip photonic devices utilizing waveguides have garnered significant attention for their exceptional performance, high integration density, and the slow light effect. This effect, achieved through photonic crystal structures, slows down the speed of light within the device, increasing the interaction time between light and biomolecules. This enhancement improves the sensitivity of biomarker detection, making it possible to identify low concentrations essential for early diagnosis. These devices are particularly suited for developing portable biosensing systems that offer high sensitivity, rapid throughput, and real-time monitoring. Complementing these advancements, deep learning techniques enable more sophisticated analyses, such as precise tumor localization, further enhancing the overall diagnostic capabilities. In this dissertation, I focus on defect-engineered photonic crystal (PC) microcavities, utilizing ultra-high sensitivity microcavities with nanoholes to experimentally detect various biomarkers from pancreatic cancer patients. Building on this foundation, I enhance the dynamic range of our biosensing platform through the implementation of these ultra-sensitive microcavities and the multiplexing of PC sensors. Additionally, I explore deep learning techniques, investigating the prediction of answer diversity in visual question answering (VQA) systems to optimize data collection. I further apply these techniques to analyze CT scans for early-stage pancreatic cancer detection. While PC microcavities can identify the presence of cancer, deep learning enables more advanced analysis, allowing for precise tumor localization using image semantic segmentation. This deep learning process leverages Integrated Photonic Chip Based Optical Neural Networks (ONNs), which provide a promising platform due to their high parallelism, low latency, and low energy consumption. By harnessing the advantages of ONNs, I aim to enhance the accuracy and efficiency of diagnosing early-stage pancreatic cancer.