ethicalabs/Research-Intent-Collab huggingface dataset:
Bimolecular chemistry in the ultracold regime

Advances in atomic, molecular, and optical (AMO) physics techniques allowed the cooling of simple molecules down to the ultracold regime ($\lesssim$ 1 mK), and opened the opportunities to study chemical reactions with unprecedented levels of control. This review covers recent developments in studying bimolecular chemistry at ultralow temperatures. We begin with a brief overview of methods for producing, manipulating, and detecting ultracold molecules. We then survey experimental works that exploit the controllability of ultracold molecules to probe and modify their long-range interactions. Further combining the use of physical chemistry techniques, such as mass spectrometry and ion imaging, significantly improved the detection of ultracold reactions and enabled explorations of their dynamics in the short-range. We discuss a series of studies on the reaction KRb + KRb $\rightarrow$ K$_2$ + Rb$_2$ initiated below 1 $μ$K, including the direct observation of a long-lived complex, the demonstration of product rotational state control via conserved nuclear spins, and a test of the statistical model using the complete quantum state distribution of the products.
Synthesis, Characterization and Electrochemistry of Cathode Material Li [Li&lt;sub&gt;0.2&lt;/sub&gt;Mn&lt;sub&gt;0.54&lt;/sub&gt;Ni&lt;sub&gt;0.13&lt;/sub&gt;Co&lt;sub&gt;0.13&lt;/sub&gt;]O&lt;sub&gt;2&lt;/sub&gt; Nanofibers Prepared by Electrospinning for Lithium Ion Batteries

Nanofibers of Li [Li0.2Mn0.54Ni0.13Co0.13]O2 with high crystallinity were easily synthesized by electrospinning. Scanning electron microscopy (SEM) showed that precursor wires were glossy, and the diameters of oxide calcined at 800°C were 50-200 nm. And X-ray diffraction (XRD) exhibited perfect crystal structure. The electrochemical properties of materials with the specific morphology were significantly improved. The first charge/discharge capacity was around 309.3 and 270.2 mAh·g-1 at 25 mA·g-1, and the first coulomb efficiency (87.4%) represented a good efficiency for Li2MnO3-type electrodes. After 20 cycles, discharge capacity was over 200 mAh·g-1 at 25 mA·g-1, and capacity retention was 79.8% at 250 mA·g-1.Owing to its simplicity and applicability, the electrospinning method is an promising method to fabricate high performance Li [Li0.2Mn0.54Ni0.13Co0.13]O2 materials for lithium ion batteries.
Knowledge Discovery in the Environmental Sciences: Visual and Automatic Data Mining for Radon Problems in Groundwater

AbstractEfficiently exploring a large dataset with the aim of forming a hypothesis is one of the main challenges in environmental research. The exploration of georeferenced environmental data is usually performed by established statistical methods. This paper presents an alternative approach. The aim of this study was to see if a visual data mining system and an integrated visual‐automatic data mining system could be used for data exploration for a particular environmental problem: the occurrence of radon in groundwater. In order to demonstrate this, two data mining systems were built, one consisting of visualisations and the other including an automatic data mining method – a Self‐Organising Map (SOM). The systems were designed for exploration of a large multidimensional dataset representing wells in Stockholm County.
Surgical simulation in robotic-assisted thoracic surgery: training

Surgical training in thoracic surgery now challenges trainees to master open and minimally invasive approaches. Strategies to improve training, specifically among robotic-assisted thoracic surgery, remain an area of interest needing continued advancement and standardization. Simulation has been proposed as a catalyst for such skill proficiency and training. With recent time constraints on surgical education, the role of simulation has become paramount to provide the additional exposure and opportunities to hone skill and performance levels to achieve the necessary proficiencies and learning curve. A vast array of robotic simulation curriculums and modules are cited in the literature, however, calls for universal standardized curriculums are resounding. A standardized curriculum will measure proficiency on any robotic simulator, standardize fundamentals in training, and provide more clear criteria for certification and credentialing. The curriculum should be robust and multifaceted, including didactics, observation, simulation, and hands-on performance.
Adaptive Designs in Fast-Track Registration Processes for Digital Health Applications

Fast-track procedures play an important role in the context of conditional registration of medical devices, such as listing processes for digital health applications. They offer the potential for earlier patient access to innovative products and involve two registration steps. The applicants can apply first for conditional registration. A successful conditional registration provides a limited funding or approval period and time to prepare the application for permanent registration (the second registration step). For conditional registration, products have to fulfill only a part of the requirements necessary for permanent registration. There is interest in valid and efficient study designs for fast-track procedures. This will be addressed in this paper. A motivating example is the German fast-track registration process of digital health applications (DiGA) for reimbursement by statutory health insurances. The main focus of the paper is the systematic statistical investigation of the utility of adaptive designs in the context of fast-track registration processes like the DiGA fast-track. We demonstrate that, in most cases, such designs are much more efficient than the current standard of two separate studies. A careful statistical discussion of the registration requirements and their consequences is also included. The results are based on numerical calculations supported by mathematical arguments.
Lattice dynamics and statistical mechanics of the Fm3m → I4/m structural phase transition in Rb2KInF6

The static and dynamic properties of cubic Rb2KInF6 crystals with elpasolite structure are calculated using a nonempirical method. Calculations are performed within a microscopic ionic-crystal model taking into account the deformation and polarization of ions. The deformation parameters of ions are determined by minimizing the total energy of the crystal. The calculated equilibrium lattice parameters agree satisfactorily with the experimental data. It is found that in the cubic phase there are vibrational modes that are unstable everywhere in the Brillouin zone. The eigenvectors of the unstablest mode at the center of the Brillouin zone of the cubic phase are associated with the displacements of F ions and correspond to rotations of InF6 octahedra. Condensation of this mode leads to a tetragonal distortion of the structure. In order to describe the Fm3m → I4/m phase transition, an effective Hamiltonian is constructed under the assumption that the soft mode whose eigenvector corresponds to octahedron rotation is local and coupled with homogeneous elastic strains. The parameters of the effective Hamiltonian are determined using the calculated crystal energy for the distorted structures due to soft-mode condensation. The thermodynamic properties of the system with this model Hamiltonian are investigated using the Monte Carlo method. The phase transition temperature is calculated to be 550 K, which is twice its experimental value (283 K). The tetragonal phase remains stable down to T=0 K; the effective Hamiltonian used in this paper thus fails to describe the second phase transition (to the monoclinic phase). Thus, the transition to the tetragonal phase occurs for the most part through octahedron rotations; however, additional degrees of freedom, first of all, the displacements of Rb ions, should be included into the effective Hamiltonian in order to describe the transition to the monoclinic phase.
From Backup Restoration to Minimum Viable Factory Recovery: A Systematization of Ransomware Recovery in Manufacturing Systems

Ransomware recovery in critical manufacturing infrastructure is not only a backup-restoration problem. Production capability depends on coupled information-technology, operational-technology, physical-process, quality, logistics, identity, and supplier systems. After ransomware, a plant may rebuild servers yet remain unable to schedule work, authenticate operators, trust engineering workstations, release product, reconnect OT assets, or coordinate suppliers. This paper reframes manufacturing ransomware recovery as a critical-infrastructure continuity and interdependency problem. We conduct a PRISMA-guided multivocal review of academic literature, standards and government guidance, threat frameworks, public incident material, and verified full-text/source-page evidence anchors. The review identifies nine evidence-backed recovery failure modes: dependency blindness, untrusted restore point and backup over-trust, identity trust collapse, lack of proof-of-recovery, unsafe OT reconnection, segmentation assumption failure, capability mismatch, unmanaged degraded operation, and supplier dependency failure. We then introduce Minimum Viable Factory Recovery (MVF Recovery): the smallest safe, trusted, and operationally meaningful production capability that can be resumed under current dependency, evidence, identity, data, network, OT, and supplier constraints. MVF Recovery is an analytical objective rather than a claim of full recovery, implementation, or safety certification. The paper derives a recovery lifecycle and benchmarking directions as secondary outputs. The contribution is an evidence-calibrated foundation for capability-centric ransomware recovery in critical manufacturing infrastructure.
An IoT-Based Architecture for Edge Computing to Reduce the Latency and Bandwidth for Streaming Data Application

In this paper, we propose an IoT-based architecture in an edge computing environment to stream real-time data to the user. The data has been considered to be multimedia data. In this case, users require to receive the data with minimum latency and a limited number of bandwidths. Data latency and jitter may vary in Cloud-enabled Multimedia Server due to several factors such as bandwidth availability, poor connectivity in constant mile connection. These phenomena make an overall effect on the QoS of the network. The result we obtained through simulation shows that the proposed architecture enhanced the existing cloud-based system with respect to better QoS. Opnet modeler (presently known as the Riverbed academic modeler) is used as a simulation tool to reduce the latency and bandwidth. The simulation result is analyzed using MATLAB for finding the variation of the latency and bandwidth using the datasets in the edge computing environment.
Visualizing Gene Ontology annotations

Contemporary techniques in biology produce readouts for large numbers of genes simultaneously, the typical example being differential gene expression measurements. Moreover, those genes are often richly annotated using GO terms that describe gene function and that can be used to summarize the results of the genome-scale experiments. However, making sense of such GO enrichment analyses may be challenging. For instance, overrepresented GO functions in a set of differentially expressed genes are typically output as a flat list, a format not adequate to capture the complexities of the hierarchical structure of the GO annotation labels.   In this chapter, we survey the various methods to visualize large, difficult-to-interpret lists of GO terms. We catalogue their availability (web-based or standalone), the main principles they employ in summarizing large lists of GO terms, and the visualization styles they support. These brief commentaries on each software are intended as a helpful inventory, rather than comprehensive descriptions of the underlying algorithms. Instead, we show examples of their use and suggest that the choice of an appropriate visualization tool may be crucial to the utility of GO in biological discovery.
Radar Sensing of Ultra-High Energy Cosmic Ray Showers

The intent of this paper is to review the history and potential importance of the use of radar techniques in detecting the ionization columns of ultra-high energy cosmic ray showers and give a short overview of a currently planned radar experiment at the Telescope Array. There is much activity in cosmic ray research to study the composition and source locations of this phenomena. Radar would be an important addition to fluorescence and scintillation detection as it theoretically could attain greater volume coverage and nearly the accuracy of fluorescence systems with less infrastructure and much longer running times. The currently estimated mean echo lifetime is on the order of 50 µs for a cosmic ray of energy 10^19 eV. It is shown that a continuous wave bi-directional radar system transmitting in the low-VHF with a large obstruction between receiver and transmitter (such as a mountain or earth curvature), which assures direct transmission is reduced, is the nominal configuration and the planned radar experiment at the Telescope Array satisfies these requirements.
FAULT AND EVENT-TREE TECHNIQUES IN OCCUPATIONAL HEALTH-SAFETY SYSTEMS - PART II: STATISTICAL ANALYSIS

Fault-Trees (FT) and Event-Trees (ET) are useful analytic tools for the assessment of reliability and safety of complex technical systems, and occupational health-safety systems (OHSS), as well. In this work we broaden and expand our previous study regarding the features of FT/ET methods. To clarify this further, in the present article we statistically analyzed the results of a literature survey, concentrated on FT/ET techniques applied in risk assessment (RA) of OHSSs, in order to (i) depict the subsistent situation of their application in various occupational fields, and (ii) enhance their handling and usage in RA of OHSS. The paper consists of two parts, including: (i) a literature survey (for years 2000-2012), concentrated on the main categories of FT/ET techniques concerning OHSS RA, and (ii) an examination and statistical analysis of the corresponding scientific papers published by thirteen representative scientific journals of Elsevier_B.V. and IEEE_Inc. The review shows that: (a) FT/ET techniques are classified into three basic categories (qualitative, quantitative, hybrid), (b) in risk assessment of occupational worksites, FT/ET application is not quite expanded and has not been extensively incorporated in the main RA methodologies of OHSSs, despite their significance, (c) the papers with FT/ETs constitute a very small part of the literature (~0.71%), (d) the qualitative methods present the highest relative occurrence-frequency (59%), and (e) the field of “Industry” concentrates the highest percentage of the papers (34%).
Advanced Techniques for Error Reduction, Mitigation and Their Applications. An Approach Towards Fault-Tolerant Quantum Computing

Quantum computing promises to address problems in simulation, optimization, and chemistry, but current hardware operates in the noisy intermediate-scale (NISQ) regime where coherence limits, gate imperfections, and readout errors restrict performance. Improving calibration and mitigating errors therefore remain essential. This thesis analyzes error reduction on superconducting transmon qubits through calibration, gate analysis, and circuit-level mitigation. It compares single-qubit amplitude calibration methods and analyzes two-qubit gates based on the cross-resonance interaction, identifying differences in precision, convergence, and performance. In addition, it develops Inverted-Circuit Zero-Noise-Extrapolation (IC-ZNE) which measures effective error strength using inverted circuits. Compared to standard ZNE, IC-ZNE achieves results closer to ideal values and reduces observable errors. Overall, the presented methods improve the reliability of near-term quantum processors.
Analytical study of technology enhanced learning in Arabic Language in primary level education in Government and Private schools of Oman

Education technology is one of the key application areas of Multimedia during these days. In the early stage, technology-based learning was limited to only those educational institutions which could afford it. But in this era of information technology, it became very important to incorporate various means of making the learning process more interesting and engaging not only in the higher education sector but also in school level. One similar application of the above context is defined in the proposed project which is the design of the educational application in Arabic for the children of age group 3-5 years in Al Ajial private school, Oman. This application is the solution or the problems and learning difficulties suffered by children in school. This educational application can influence how to learn basic skills such as reading, writing and mathematics and in a way to learn high- level skills such as organization and time planning, abstract thinking, and the development of long memory or short-term and interest in school and subject. This application will also be easy to use to the students in terms of the allocation of their subjects and materials required. I chose this project through continuous research for all schools and suggested my idea to some schools. This research paper will also critically evaluate various existing systems available for the same purpose. This paper will also provide the result of surveys and interviews conducted among the staff members of the various primary school, both in Government and Private sector. The critical evaluation includes results of various literature study along with a study of various features and their comparison result of which will draw some conclusion. As a result of this research paper, we will find a more effective solution and requirement specifications for the proposed project.
On the Specifics of the Content of Judicial Review Implementation by the Constitutional Court of the Russian Federation

Comparing practice of general jurisdiction courts and arbitral jurisdiction courts the author considers the influence of the norms-controlling powers of the Constitutional Court of the Russian Federation to legal regulation as by pronouncement of non-constitutional legal norms as repealed or by adjusting it to constitutional notion in practice. The author notes positive role over elimination of legal gaps and conflicts; grounds for adopting of oncoming legal acts. The author suggests introduction of compulsory requirement of carrying the Plenum the Supreme Court of the Russian Federation due to submission of the Constitutional Court of the Russian Federation to clarify lower courts conditions and procedural rules of existing legislation based on explained constitutional sense of its application in a specific case.
Book Reviews Compilation: Open Access Books on Open Scholarly Communications

Book Reviews Compilation: Open Access Books on Open Scholarly Communications
Agricultural monitoring by wireless sensor network, an application of Internet of Things (IoT)

Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats Sostenibles
Diffusion Models Generalize but Not in the Way You Might Think

Standard evaluation metrics suggest that Denoising Diffusion Models based on U-Net or Transformer architectures generalize well in practice. However, as it can be shown that an optimal Diffusion Model fully memorizes the training data, the model error determines generalization. Here, we show that although sufficiently large denoiser models show increasing memorization of the training set with increasing training time, the resulting denoising trajectories do not follow this trend. Our experiments indicate that the reason for this observation is rooted in the fact that overfitting occurs at intermediate noise levels, but the distribution of noisy training data at these noise levels has little overlap with denoising trajectories during inference. To gain more insight, we make use of a 2D toy diffusion model to show that overfitting at intermediate noise levels is largely determined by model error and the density of the data support. While the optimal denoising flow field localizes sharply around training samples, sufficient model error or dense support on the data manifold suppresses exact recall, yielding a smooth, generalizing flow field. To further support our results, we investigate how several factors, such as training time, model size, dataset size, condition granularity, and diffusion guidance, influence generalization behavior.
Using Topological Data Analysis (TDA) and Persistent Homology to Analyze the Stock Markets in Singapore and Taiwan

In recent years, persistent homology (PH) and topological data analysis (TDA) have gained increasing attention in the fields of shape recognition, image analysis, data analysis, machine learning, computer vision, computational biology, brain functional networks, financial networks, haze detection, etc. In this article, we will focus on stock markets and demonstrate how TDA can be useful in this regard. We first explain signatures that can be detected using TDA, for three toy models of topological changes. We then showed how to go beyond network concepts like nodes (0-simplex) and links (1-simplex), and the standard minimal spanning tree or planar maximally filtered graph picture of the cross correlations in stock markets, to work with faces (2-simplex) or any k-dim simplex in TDA. By scanning through a full range of correlation thresholds in a procedure called filtration, we were able to examine robust topological features (i.e. less susceptible to random noise) in higher dimensions. To demonstrate the advantages of TDA, we collected time-series data from the Straits Times Index and Taiwan Capitalization Weighted Stock Index (TAIEX), and then computed barcodes, persistence diagrams, persistent entropy, the bottleneck distance, Betti numbers, and Euler characteristic. We found that during the periods of market crashes, the homology groups become less persistent as we vary the characteristic correlation. For both markets, we found consistent signatures associated with market crashes in the Betti numbers, Euler characteristics, and persistent entropy, in agreement with our theoretical expectations.
Future-Proofing Global Health Governance Through the Proposed Pandemic Treaty

Verfassungsblog: On Matters Constitutional
CRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendation

Generative recommendation (GeneRec) has introduced a new paradigm that represents items as discrete semantic tokens and predicts items in a generative manner. Despite its strong performance across multiple recommendation tasks, existing GeneRec approaches still suffer from severe popularity bias and may even exacerbate it. In this work, we conduct a comprehensive empirical analysis to uncover the root causes of this phenomenon, yielding two core insights: 1) imbalanced tokenization inherits and can further amplify popularity bias from historical item interactions; 2) current training procedures disproportionately favor popular tokens while neglecting semantic relationships among tokens, thereby intensifying popularity bias. Building on these insights, we propose CRAB, a post-hoc debiasing strategy for GeneRec that alleviates popularity bias by mitigating frequency imbalance among semantic tokens. Specifically, given a well-trained model, we first rebalance the codebook by splitting over-popular tokens while preserving their hierarchical semantic structure. Based on the adjusted codebook, we further introduce a tree-structured regularizer to enhance semantic consistency, encouraging more informative representations for unpopular tokens during training. Experiments on real-world datasets demonstrate that CRAB significantly improves recommendation performance by effectively alleviating popularity bias.