dyfan/halluhard-seed-questions huggingface dataset:
Flood risk managers seek to optimise Blue-Green Infrastructure (BGI) designs to maximise return on investment. Current systems often use optimisation algorithms and detailed flood models to maximise benefit-cost ratios for single rainstorm return periods. However, these schemes may lack robustness in mitigating flood risks across different storm magnitudes. For example, a BGI scheme optimised for a 100-year return period may differ from one optimised for a 10-year return period. This study introduces a novel methodology incorporating five return periods (T = 10, 20, 30, 50, and 100 years) into a multi-objective BGI optimisation framework. The framework combines a Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a fully distributed hydrodynamic model to optimise the spatial placement and combined size of BGI features. For the first time, direct damage cost (DDC) and expected annual damage (EAD), calculated for various building types, are used as risk objective functions, transforming a many-objective problem into a multi-objective one. Performance metrics such as Median Risk Difference (MedRD), Maximum Risk Difference (MaxRD), and Area Under Pareto Front (AUPF) reveal that a 100-year optimised BGI design performs poorly when evaluated for other return periods, particularly shorter ones. In contrast, a BGI design optimised using composite return periods enhances performance metrics across all return periods, with the greatest improvements observed in MedRD (22%) and AUPF (73%) for the 20-year return period, and MaxRD (23%) for the 50-year return period. Furthermore, climate uplift stress testing confirms the robustness of the proposed design to future rainfall extremes. This study advocates a paradigm shift in flood risk management, moving from single maximum to multiple rainstorm return period-based designs to enhance resilience and adaptability to future climate extremes.

Research question: How should decision-makers choose which storm return periods to include and how to weight them when optimising blue–green infrastructure design under limited budget and uncertainty about future climate change?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: How do precise measurements of rare B meson decays to muon pairs constrain theories beyond the Standard Model, and which classes of new-physics models are most strongly affected?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: Why are decays of neutral B mesons into muon pairs so rare, and how do precise measurements of their branching fractions constrain or rule out theories beyond the Standard Model?
Computational developments--particularly artificial intelligence--are reshaping social scientific research and raise new questions for in-depth methods such as ethnography and qualitative interviewing. Building on classic debates about computers in qualitative data analysis (QDA), we revisit possibilities and dangers in an era of automation, Large Language Model (LLM) chatbots, and 'big data.' We introduce a typology of contemporary approaches to using computers in qualitative research: streamlining workflows, scaling up projects, hybrid analytical methods, the sociology of computation, and technological rejection. Drawing from scaled team ethnographies and solo research integrating computational social science (CSS), we describe methodological choices across study lifecycles, from literature reviews through data collection, coding, text retrieval, and representation. We argue that new technologies hold potential to address longstanding methodological challenges when deployed with knowledge, purpose, and ethical commitment. Yet a pragmatic approach--moving beyond technological optimism and dismissal--is essential given rapidly changing tools that are both generative and dangerous. Computation now saturates research infrastructure, from algorithmic literature searches to scholarly metrics, making computational literacy a core methodological competence in and beyond sociology. We conclude that when used carefully and transparently, contemporary computational tools can meaningfully expand--rather than displace--the irreducible insights of qualitative research.

Research question: How can qualitative researchers responsibly integrate AI tools like large language models into their workflows while preserving interpretive depth, ensuring transparency, and addressing ethical risks such as bias and data privacy?
A classical problem in power systems is to allocate in-coming (elastic or inelastic) demands without violating the operating constraints of electric networks in an online fashion. Although online decision problems have been well-studied in the literature, a unique challenge arising in power systems is the presence of non-linear constraints, a departure from the traditional settings. A particular example is the capacity constraint of apparent power, which gives rise to a quadratic constraint, rather than typical linear constraints. In this paper, we present a competitive randomized online algorithm for deciding whether a sequence of inelastic demands can be allocated for the requested intervals, subject to the total satisfiable apparent power within a time-varying capacity constraint. We also consider an alternative setting with nodal voltage constraint, using a variant of the online algorithm. Finally, simulation studies are provided to evaluate the algorithms empirically.

Research question: How do nonlinear constraints such as apparent power (quadratic) or nodal voltage limits change the design and performance guarantees of online algorithms for allocating electrical demands compared to problems with only linear constraints?
This chapter illustrates the use of defect physics as a conceptual and theoretical framework for understanding and designing battery materials. It starts with a methodology for first-principles studies of defects in complex transition-metal oxides. The chapter then considers defects that are activated in a cathode material during synthesis, during measurements, and during battery use. Through these cases, it discusses possible defect landscapes in the material and their implications, guidelines for materials design via defect-controlled synthesis, mechanisms for electronic and ionic conduction and for electrochemical extraction and (re-)insertion, and effects of doping. Although specific examples are taken from studies of battery cathode materials, the computational approach and discussions are general and applicable to any ionic, electronic, or mixed ionic-electronic conducting materials.

Research question: How do different types of point defects affect ionic and electronic conduction in battery cathode materials, and what practical strategies can be used during synthesis to control those defects?
The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose. In addition, pulmonary embolism has multiple triggers and is one of the major causes of vascular death. Therefore, if it can be detected and treated quickly, it can significantly reduce the risk of death in hospitalized patients. In the detection process, the cost of computed tomography pulmonary angiography (CTPA) is high, and angiography requires the injection of contrast agents, which increase the risk of damage to the patient. Therefore, this study will use a deep learning approach to detect pulmonary embolism in all patients who take a CT image of the chest using a convolutional neural network. With the proposed pulmonary embolism detection system, we can detect the possibility of pulmonary embolism at the same time as the patient's first CT image, and schedule the CTPA test immediately, saving more than a week of CT image screening time and providing timely diagnosis and treatment to the patient.

Research question: What are the main limitations, potential risks, and clinical pitfalls of using an AI-based system to screen for pulmonary embolism on non-contrast chest CT images before confirmatory testing?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: How do precise measurements of rare B meson decays to muon pairs test the Standard Model and constrain possible beyond–Standard-Model theories?
This study investigates the novelty of the crystalline and electronic structure of (Mg,Ti)-doped ZnO and the co-doped Zn1-x-yMgxTiyO structures using Gaussian and plane-wave basis sets, as implemented in the CP2K code. The goal of incorporating low concentration of Mg and Ti into ZnO is to influence its electronic properties without significantly altering its geometrical and crystalline structure. Within the framework of density functional theory (DFT), we analyze various doped and co-doped configurations. Our results show that Ti-doped ZnO exhibits an indirect band gap, while Mg doping preserves the direct semiconductor behavior of ZnO structure, with an increase in band gap energy. Additionally, the co-doped Zn1-x-yMgxTiyO system, at varying concentrations of Ti and Mg, displays minimal lattice deformation. These findings suggest that this material could be a promising candidate for transparent electronic devices, highlighting the importance of understanding the electronic structure of ZnO to optimize its physical properties.

Research question: How do Mg and Ti dopants typically affect the crystal structure, band gap (direct vs indirect), and electrical/optical properties of ZnO, and what physical mechanisms control those changes?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: Why are precise measurements of rare decays like B_s→μ+μ− and B^0→μ+μ− particularly sensitive probes for physics beyond the Standard Model, and how do their branching fractions constrain new theories?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: How do precise measurements of rare neutral B meson decays into muon pairs constrain extensions of the Standard Model, and which classes of new-physics models are most strongly affected by these results?
Oriented object detection is a fundamental yet challenging task in remote sensing (RS), aiming to locate and classify objects with arbitrary orientations. Recent advancements in deep learning have significantly enhanced the capabilities of oriented object detection methods. Given the rapid development of this field, a comprehensive survey of the recent advances in oriented object detection is presented in this paper. Specifically, we begin by tracing the technical evolution from horizontal object detection to oriented object detection and highlighting the specific related challenges, including feature misalignment, spatial misalignment, oriented bounding box (OBB) regression problems, and common issues encountered in RS. Subsequently, we further categorize the existing methods into detection frameworks, OBB regression techniques, feature representation approaches, and solutions to common issues and provide an in-depth discussion of how these methods address the above challenges. In addition, we cover several publicly available datasets and evaluation protocols. Furthermore, we provide a comprehensive comparison and analysis involving the state-of-the-art methods. Toward the end of this paper, we identify several future directions for oriented object detection research.

Research question: What practical strategies can be used when annotating and training datasets to handle rotated objects and orientation ambiguities for oriented object detection in remote sensing?
Although mutations drive the evolutionary process, the rates at which the mutations occur are themselves subject to evolutionary forces. Our purpose here is to understand the role of selection and random genetic drift in the evolution of mutation rates, and we address this question in asexual populations at mutation-selection equilibrium neglecting selective sweeps. Using a multitype branching process, we calculate the fixation probability of a rare nonmutator in a large asexual population of mutators, and find that a nonmutator is more likely to fix when the deleterious mutation rate of the mutator population is high. Compensatory mutations in the mutator population are found to decrease the fixation probability of a nonmutator when the selection coefficient is large. But, surprisingly, the fixation probability changes nonmonotonically with increasing compensatory mutation rate when the selection is mild. Using these results for the fixation probability and a drift-barrier argument, we find a novel relationship between the mutation rates and the population size. We also discuss the time to fix the nonmutator in an adapted population of asexual mutators, and compare our results with experiments.

Research question: How do population size, natural selection, and genetic drift together determine the evolutionarily stable mutation rate in asexual populations?
The deficit of executive functioning was found to be associated with attention deficit hyperactivity disorder (ADHD) in general and its subtypes. One of the important functions of central executive is the ability simultaneously coordinate two tasks. The study aimed at defining the dual-task performance characteristics in healthy children and adolescents on the computerised and the paper and pencil dual-task methods; investigating the effect of task difficulty on dual-task performance in ADHD in comparison to age and years of education matched healthy controls; testing if the paper and pencil version of the dual-task method is giving the same results in ADHD and healthy controls; investigating whether the dual-task functioning in ADHD is defined by the deficits in the general motor functioning and comorbidity factors. The study investigated dual task functioning in 6-16 years old 91 typically developing controls and 91 children with ADHD. It was found that: (1) the dual-task coordination is available in children and adolescents with ADHD in general and in its subtypes and not significantly different from performance of age and years of education matched healthy controls; (2) Increase of the task difficulty in dual-task paradigm don't affect disproportionately children and adolescents with ADHD in comparison to age and years of education matched healthy controls; (3) The paper and pencil version of the dual-task method is giving the same results in ADHD and healthy controls as computerised version; (4) The dual-task functioning in ADHD in general and in its subtypes is not defined by the general motor functioning while in healthy controls dual task performance is associated with the general motor functioning level; (5) The dual-task functioning in ADHD in general and in its subtypes is not defined by the comorbidity factors.

Research question: Can dual-task assessments reliably detect executive function deficits in children with ADHD, and are paper-and-pencil versions as valid as computerized tests for this purpose?
Pairing-based inner product functional encryption provides an efficient theoretical construction for privacy-preserving edge computing secured by widely deployed elliptic curve cryptography. In this work, an efficient software implementation framework for pairing-based function-hiding inner product encryption (FHIPE) is presented using the recently proposed and widely adopted BLS12-381 pairing-friendly elliptic curve. Algorithmic optimizations provide $\approx 2.6 \times$ and $\approx 3.4 \times$ speedup in FHIPE encryption and decryption respectively, and extensive performance analysis is presented using a Raspberry Pi 4B edge device. The proposed optimizations enable this implementation framework to achieve performance and ciphertext size comparable to previous work despite being implemented on an edge device with a slower processor and supporting a curve at much higher security level with a larger prime field. Practical privacy-preserving edge computing applications such as encrypted biomedical sensor data classification and secure wireless fingerprint-based indoor localization are also demonstrated using the proposed implementation framework.

Research question: What are the main trade-offs to consider when deploying pairing-based function-hiding inner product encryption on resource-constrained edge devices, in terms of computational cost, ciphertext size, and chosen elliptic curve security level?
Abstract Secondary batteries based on metal anodes (e.g., Li, Na, Mg, Zn, and Al) are among the most sought‐after candidates for next‐generation mobile and stationary storage systems because they are able to store a larger amount of energy per unit mass or volume. However, unstable electrodeposition and uncontrolled interfacial reactions occuring in liquid electrolytes cause unsatisfying cell performance and potential safety concerns for the commercial application of these metal anodes. Solid‐state electrolytes (SSEs) having a higher modulus are considered capable of inhibiting difficulties associated with the anodes and may enable building of safe all‐solid‐state metal batteries, yet several challenges, such as insufficient room‐temperature ionic conductivity and poor interfacial stability between the electrode and the electrolyte, hinder the large‐scale development of such batteries. Here, research and development of SSEs including inorganic ceramics, organic solid polymers, and organic–inorganic hybrid/composite materials for metal‐based batteries are reviewed. The comparison of different types of electrolytes is discussed in detail, in the context of electrochemical energy storage applications. Then, the focus of this study is on recent advances in a range of attractive and innovative battery chemistries and technologies that are enabled by SSEs. Finally, the challenges and future perspectives are outlined to foresee the development of SSEs.

Research question: What are the most promising strategies to improve room-temperature ionic conductivity in solid-state electrolytes while maintaining good mechanical strength and stable interfacial contact with metal anodes?
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six standard deviations, and the best measurement of its branching fraction so far. Furthermore, evidence for the $B^0\toμ^+μ^-$ decay is obtained with a statistical significance of three standard deviations. The branching fraction measurements are statistically compatible with SM predictions and impose stringent constraints on several theories beyond the SM.

Research question: How do precise measurements of rare B meson decays to muon pairs constrain physics beyond the Standard Model, and which types of new-physics models are most strongly affected by these constraints?
Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Research question: How should researchers choose the regularization hyperparameter when estimating partial correlation networks for psychological data, and what practical strategies can they use to assess how sensitive their network structure is to that choice?