ethicalabs/Research-Intent-Collab huggingface dataset:
INDIGENOUS PEOPLES AND ANCESTRAL LAND RIGHTS IN PAKISTAN: A CRITICAL EVALUATION OF LAWS AND POLICIES

This paper has critically looked at the legal and policy framework of Indigenous peoples and ancestral land rights in Pakistan. It asserts that the Pakistan is simply providing partial and scattered security to the communities, which have their identity, culture and means of livelihood attached to the ancestral territories. Despite the significant guarantees entailed in the Constitution of Pakistan, on property and culture as well as minority protection, Indigenous peoples are not identified as a distinct legal category and are not offered a complete framework of collective rights to territory. The given paper discloses that this gap is justified with references to the fact that, still existing laws of the colonial period, namely, the Land Acquisition Act, 1894, the Forest Act, 1927, concern state power, the official registration and administrative administration of the customary possession and communal tenure. Its significance to recent judicial authorities in Balochistan, where customary tribal possession has received increased acknowledgement, and where domestic law can be therefore regarded as moving towards a more rights-sensitive direction, is also highlighted in the discussion. We use the example of the Kalash people to see how there is a disjuncture between enshrinement and lived practice of dispossession, insecurity and cultural vulnerability. The paper also makes comparisons of the Pakistani situation with other international standards, especially the United Nations Declaration of the Rights of Indigenous Peoples and finds that the country is not completely compliant with the current standards on the collective land righting, participation, and consent. The paper will conclude that the existing constitutional and legal framework within Pakistani territory might possess some protective factors but it is still inadequate to an extent that should acknowledge and protect the historical land rights. Significant changes would need legal acknowledgment of indigenous and other disadvantaged groups, against colonial land laws, and reformation of the colonial land rules as well as introduction of consultation as well as consent processes in land management.
Substrate Release Mechanisms for Gas Metal Arc 3-D Aluminum Metal Printing. 3D Printing &Additive Manufacturing

Limited material options, prohibitively expensive equipment, and high production costs currently limit the ability of small and medium enterprises to use 3-D printing to prototype and manufacture metallic goods. A low-cost open-source 3-D metal printer that utilizes gas metal arc welding technology has been developed that could make metal printing accessible to the average consumer. Unfortunately, this technology would demand access to expensive cutting tools for part removal from the substrate. This paper investigates several substrate treatments to provide a low-cost method to easily remove 3-D printed 1100 aluminum parts from a reusable substrate. Coatings of aluminum oxide and boron nitride on 1100 aluminum and A36 low carbon steel substrates were tested. Lap shear tests were performed to assess the interlayer adhesion between the printed metal part and the print substrate. No warping of the substrate was observed during printing. It was determined that boron nitride coated low carbon steel provided the lowest adhesion strength. Printing aluminum on uncoated low carbon steel also allowed easy removal of the aluminum part with the benefit of no additional coating steps or costs.
Enabling Digital Health by Automatic Classification of Short Messages

In response to the growing HIV/AIDS and other health-related issues, UNICEF through their U-Report platform receives thousands of messages (SMS) every day to provide prevention strategies, health case advice, and counsel- ing support to vulnerable population. Due to a rapid increase in U-Report usage (up to 300% in last 3 years), plus approximately 1,000 new registrations each day, the volume of messages has thus continued to increase, which made it impossible for the team at UNICEF to process them in a timely manner. In this paper, we present a platform designed to perform automatic classification of short messages (SMS) in real-time to help UNICEF categorize and prioritize health-related messages as they arrive. We employ a hybrid approach, which combines human and machine intelligence that seeks to resolve the information overload issue by introducing processing of large-scale data at high-speed while maintaining a high classification accuracy. The system has recently been tested in conjunction with UNICEF in Zambia to classify short messages received via the U-Report platform on various health related issues. The system is designed to enable UNICEF make sense of a large volume of short messages in a timely manner. In terms of evaluation, we report design choices, challenges, and performance of the system observed during the deployment to validate its effectiveness.
The African Court on Human and Peoples’ Rights: assessing its effectiveness

This article examines the effectiveness of the African Court on Human and Peoples’ Rights using the compliance rate, the usage rate and the goal-based approaches. It uses a qualitative research method combined with purposive sampling and it consults the jurisprudence of the African Court and secondary sources to assess the effectiveness of the Court. The article submits that taking into consideration its relatively short period of operation and the volatile political environment in which it has been functioning, the African Court has been effective in presiding over sensitive cases involving issues such as freedom of expression, press freedom, and election disputes. Compliance with some of the Court’s sensitive judgments also attests to some degree of its effectiveness. It is also reasoned that the African Court should in the future pay more close attention to factors that are capable of affecting its effectiveness. These factors include the soundness of the legal reasoning employed in delivering judgments, the composition of the African Court, the enforceability of reparation orders, and a holistic understanding of the socio-economic and political context of the state parties.
Magmatic evolution during proto-oceanic rifting at Alu, Dalafilla and Borale Volcanoes (Afar) determined by trace element and Sr-Nd-Pb isotope geochemistry

Continental rifting and associated magmatism can eventually result in the formation of new ocean basins. However, the characteristics of magmatism in the latest stages of rifting are poorly understood. The Erta-Ale volcanic segment (EAVS) in the Danakil Depression of Afar, Ethiopia, provides a unique natural laboratory in which to investigate how magma generation evolves during the shift from continental rifting to oceanic spreading. Here we present new trace element data combined with Sr-Nd-Pb isotope ratios for three volcanoes, Alu, Dalafilla and Borale, in the north of the EAVS. These data shed light on the changes in melt production and storage that occur at this late stage in the rifting cycle. Elevated Ce/Pb and &amp; UDelta;Nb (33-48, 0.25-0.47 respectively) of the basalts, alongside Sr-Nd-Pb isotope geochemistry indicate the presence of a HIMU component, supplied by the Afar plume, together with contamination by the crust. Melting conditions, estimated using the trace element ratios, Smn/Ybn, Dyn/Ybn and Cen/Smn, indicate that magmas were primarily derived from spinel lherzolite (85-90%) with minor garnet lherzolite (10-15%) with a melt fraction of -4%. Melt-mantle equilibrium depths are estimated to be on the order of 64 to 83 km, shallower than that previously inferred within Afar. We suggest that this is likely a result of the increased plate thinning beneath the EAVS compared to other parts of Afar. Basaltic volcanics are found to exhibit heterogeneous Sr-Nd-Pb isotope compositions whilst more evolved rocks (i.e., SiO2 &amp; GE;52 wt%) exhibit consistent radiogenic compositions. This indicates that homogenisation of all melt compositions occurs prior to or during melt differentiation, with the latter process occurring rapidly in the upper crust with minimal crustal contamination. Overall whilst the Afar plume appears to be the dominant mantle component in the volcanic rocks, the melt characteristics and magmatic storage conditions beneath the EAVS shows variability that is likely controlled by a dynamic interplay between rifting and mantle processes.
Study on properties of liquid ammonia via molecular dynamics simulation based on ABEEMσπ polarisable force field

The molecular dynamics simulation has been performed to investigate the charge distribution, structural and dynamical properties of liquid ammonia at 273 K using a polarisable force field of the atom-bond electronegativity equalisation method (ABEEMσπ). One ammonia molecule in this model has eight charge sites, one N atomic site, three H atomic sites, three N–H bond sites and one lone-pair electron site. ABEEMσπ model can present the quantitative site charges of molecular ammonias in liquid and their changing in response to their surroundings. The radial distribution functions and dynamical properties are in fair agreement with the available experimental data. The first peak of gNN(r) appears at N–N distance of ~3.50 ± 0.05 Å where most hydrogen bonds are formed. The average coordination number of the first shell is 13.0 ± 0.1 among which a central ammonia molecule intimately connects 3 ~ 4 ammonia molecules by hydrogen bonds. The power spectrum shows the vibrations of hydrogen bonds. For a reference, a simple estimation of the average hydrogen bonding energy in liquid ammonia is 6.5 ± 0.1 kcal/mol larger than 3.8 ± 0.3 kcal/mol in dimer ammonia. Our simulation results provide more detailed information about liquid ammonia.
Impact of AlphaFold on structure prediction of protein complexes: The CASP15?CAPRI experiment

Abstract We present the results for CAPRI Round 54, the 5th joint CASP?CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo?trimers, 13 heterodimers including 3 antibody–antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High?quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2?Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2?Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
RAG-Enhanced Collaborative LLM Agents for Drug Discovery

Recent advances in large language models (LLMs) have shown great potential to accelerate drug discovery. However, the specialized nature of biochemical data often necessitates costly domain-specific fine-tuning, posing major challenges. First, it hinders the application of more flexible general-purpose LLMs for cutting-edge drug discovery tasks. More importantly, it limits the rapid integration of the vast amounts of scientific data continuously generated through experiments and research. Compounding these challenges is the fact that real-world scientific questions are typically complex and open-ended, requiring reasoning beyond pattern matching or static knowledge retrieval.To address these challenges, we propose CLADD, a retrieval-augmented generation (RAG)-empowered agentic system tailored to drug discovery tasks. Through the collaboration of multiple LLM agents, CLADD dynamically retrieves information from biomedical knowledge bases, contextualizes query molecules, and integrates relevant evidence to generate responses - all without the need for domain-specific fine-tuning. Crucially, we tackle key obstacles in applying RAG workflows to biochemical data, including data heterogeneity, ambiguity, and multi-source integration. We demonstrate the flexibility and effectiveness of this framework across a variety of drug discovery tasks, showing that it outperforms general-purpose and domain-specific LLMs as well as traditional deep learning approaches. Our code is publicly available at https://github.com/Genentech/CLADD.
Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures.
Activating the Unemployed or Liberating the Employed? Universal Basic Income in the French Welfare Reform Debate

Universal Basic Income has attracted growing interest in France over the last decade, most notably in 2017, when the socialist candidate Benoit Hamon made it a centrepiece of his unsuccessful campaign for the presidency. French UBI supporters see it not only as a response to persistent poverty and alarming waves of automation, but as a ‘new pillar’ of the welfare state. This chapter sets France’s contemporary debate over UBI in historical context, and shows how the process of welfare reform which has taken place since the 1980s—associated particularly with the development of guaranteed income schemes to alleviate poverty and encourage work—along with influential narratives of state crisis and job scarcity, progressively made it possible for UBI to appear as a radical but legitimate alternative. In doing so, it aims to shed light on one of UBI’s decisive paradox, that is, that it is proposed as a way to both activate the unemployed and liberate the employed.
Detecting Explanatory Insufficiency in Learned Representations: A Framework for Representational Vigilance

Learned representations are central to modern machine learning and are commonly evaluated through predictive performance, robustness, uncertainty estimation, or generalization. However, a learned representation may remain operationally successful while progressively failing to organize persistent residual structures that are not fully captured by conventional evaluation metrics. This article introduces VER, the Vigilant Evaluator of Representations, a conceptual framework for monitoring representational adequacy in learned representations. VER does not propose a new learning algorithm, loss function, or model architecture. Instead, it formalizes a diagnostic process through which persistent residual structures may be identified, analyzed, and interpreted as potential indicators of explanatory insufficiency. The framework distinguishes representational inadequacy from ordinary prediction error, uncertainty, noise, and distribution shift. It introduces a monitoring sequence based on representation identification, explanatory-domain delimitation, residual-structure detection, explanatory-resistance evaluation, and vigilance signaling. VER is intended as a contribution to representation diagnostics in machine learning. Its objective is not to replace existing evaluation methods but to complement them by treating representational adequacy as an explicit object of inquiry. A path toward empirical evaluation through representational-vigilance benchmarks is also outlined.
TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications

Time series databases are essential for the large-scale deployment of many critical industrial applications. In infrastructure monitoring, for instance, a database system should be able to process large amounts of sensor data in real-time, execute continuous queries, and handle complex analytical queries such as anomaly detection or forecasting. Several benchmarks have been proposed to evaluate and understand how existing systems and design choices handle specific use cases and workloads. Unfortunately, none of them fully covers the peculiar requirements of monitoring applications. Furthermore, they fall short of providing an automated way to generate representative real-world data and workloads for testing and evaluating these systems.           We present TSM-Bench, a benchmark tailored for time series database systems used in monitoring applications. Our key contributions consist of (1) representative queries that meet the requirements that we collected from a water monitoring use case, and (2) a new scalable data generator method based on Generative Adversarial Networks (GAN) and Locality Sensitive Hashing (LSH). We demonstrate, through an extensive set of experiments, how TSM-Bench provides a comprehensive evaluation of the performance of seven leading time series database systems while offering a detailed characterization of their capabilities and trade-offs.
Benchmark Instances and Optimal Solutions for the Traveling Salesman Problem with Drone

The use of drones in logistics is gaining more and more interest, and drones are becoming a more viable and common way of distributing parcels in an urban environment. As a consequence, there is a flourishing production of articles in the field of operational optimization of the combined use of trucks and drones for fulfilling customers requests. The aim is minimizing the total time required to service all the customers, since this has obvious economical impacts. However in the literature there is not yet a widely recognized basic model, and there are not well assessed sets of instances and optimal solutions that can be considered as a benchmark to prove the effectiveness of new solution methods. The aim of this paper is to fill this gap. On one side we will clearly describe some of the most common components of the truck/drone routing problems and we will define nine basic problem settings, by combining these components. On the other side we will consider some of the instances used by many researchers and we will provide optimal solutions for all the problem settings previously identified. Instances and detailed solutions are then organized into benchmarks made publicly available as validation tools for future research methods.
Morphological classification of galaxies with deep learning: comparing 3-way and 4-way CNNs

ABSTRACT                Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological classification. We train and test several convolutional neural network (CNN) architectures to classify the morphologies of galaxies in both a 3-class (elliptical, lenticular, and spiral) and a 4-class (+irregular/miscellaneous) schema with a data set of 14 034 visually classified SDSS images. We develop a new CNN architecture that outperforms existing models in both 3-way and 4-way classifications, with overall classification accuracies of 83 and 81 per cent, respectively. We also compare the accuracies of 2-way/binary classifications between all four classes, showing that ellipticals and spirals are most easily distinguished (&amp;gt;98 per cent accuracy), while spirals and irregulars are hardest to differentiate (78 per cent accuracy). Through an analysis of all classified samples, we find tentative evidence that misclassifications are physically meaningful, with lenticulars misclassified as ellipticals tending to be more massive, among other trends. We further combine our binary CNN classifiers to perform a hierarchical classification of samples, obtaining comparable accuracies (81 per cent) to the direct 3-class CNN, but considerably worse accuracies in the 4-way case (65 per cent). As an additional verification, we apply our networks to a small sample of Galaxy Zoo images, obtaining accuracies of 92, 82, and 77 per cent for the binary, 3-way, and 4-way classifications, respectively.
Epigenetic regulation of cilia stability and kidney by the chromatin remodeling SWI/SNF complexes

Abstract                                    Cilia are important subcellular organelles, whose assembly are regulated by master regulator transcription factors including Foxj1 and Rfx proteins. However, whether and how cilia are regulated at epigenetic level remains unknown. We addressed this question by knocking down or knocking out of chromatin remodeling genes. Notably, depletion of multiple components of the switch/sucrose non-fermentable (SWI/SNF) complexes led to ciliopathy-like phenotypes in zebrafish embryos. Specifically, loss of Actl6a, an essential components of the SWI/SNF complexes, resulted in cilia disassembly and cystic kidney, without affectting cilia motility. Omics analyses revealed that in Actl6a-depleted pronephros or embryos, a set of cilia genes—including master regulators                   foxj1a                   and                   rfx2                   —were downregulated at transcriptional level, chromatin accessibility level and SWI/SNF binding level. Depletion of                   foxj1a                   or                   rfx2                   in zebrafish also caused cilia disassembly and cystic kidney. Furthermore, overexpression of                   foxj1a                   or                   rfx2                   mRNA partially rescued the cystic kidney and cilia disassembly phenotypes in                   actl6a                   mutants. Taken together, our study reveals that the SWI/SNF complexes maintain cilia stability and kidney homeostasis by directly modulating the expression of                   foxj1a                   or                   rfx2                   .