Open Research Questions Research Articles

Position paper on high fidelity simulations for coupled processes, multi-physics and chemistry in geological disposal of nuclear waste

This opinion paper describes the major coupled T(Thermal)-H(Hydro)-M(Mechanical)-C(Chemical) processes in geological repository systems and the frontier of related model development. Particular focus is made on the analysis of existing approaches and open research questions with respect to the further development of coupled codes and models for realistic multi-scale simulations of repository systems. These include the use of machine learning and artificial intelligence in acceleration of computer codes; sensitivity analysis, inverse modelling and optimisation; software engineering and collaborative platforms for model development.

BioASQ Synergy: a dialogue between question-answering systems and biomedical experts for promoting COVID-19 research.

This article presents the novel BioASQ Synergy research process which aims to facilitate the interaction between biomedical experts and automated question-answering systems. The proposed research allows systems to provide answers to emerging questions, which in turn are assessed by experts. The assessment of the experts is fed back to the systems, together with new questions. With this iteration, we aim to facilitate the incremental understanding of a developing problem and contribute to solution discovery. The results suggest that the proposed approach can assist researchers to navigate available resources. The experts seem to be very satisfied with the quality of the ideal answers provided by the systems, suggesting that such systems are already useful in answering open research questions. BioASQ Synergy aspires to provide a tool that gives the experts easy and personalized access to the latest findings in a fast-growing corpus of material. In this article, we envisioned BioASQ Synergy as a continuous dialogue between experts and systems to issue open questions. We ran an initial proof-of-concept of the approach, in order to evaluate its usefulness, both from the side of the experts, as well as from the side of the participating systems.

Adaptive horizon size moving horizon estimation with unknown noise statistical properties

Abstract Moving horizon estimation (MHE) is an effective technique for state estimation. It formulates state estimation as an optimization problem over a finite time interval and is characterized by inherent robustness, flexibility, and explicit constraint handling capabilities. The horizon size is a crucial parameter influencing the estimation performance of MHE. However, the selection of the horizon size remains an open research question in the field of MHE. In this paper, we propose a novel adaptive horizon size MHE strategy that dynamically adjusts the horizon size based on the value of the objective function. This approach aims to improve the state estimation performance of MHE in real-time applications. Unlike conventional MHE methods that rely on a fixed horizon size, our adaptive strategy enhances robustness against unknown noise statistics by adjusting the horizon size. We analyze the convergence property of the estimation error and provide guidelines for parameter design to ensure optimal performance. The effectiveness and superiority of the proposed method are demonstrated through simulations involving an oscillatory system and a target tracking application under non-stationary noise conditions.

On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models

Objective:Traditional knowledge-based and machine learning diagnostic decision support systems have benefited from integrating the medical domain knowledge encoded in the Unified Medical Language System (UMLS). The emergence of Large Language Models (LLMs) to supplant traditional systems poses questions of the quality and extent of the medical knowledge in the models’ internal knowledge representations and the need for external knowledge sources. The objective of this study is three-fold: to probe the diagnosis-related medical knowledge of popular LLMs, to examine the benefit of providing the UMLS knowledge to LLMs (grounding the diagnosis predictions), and to evaluate the correlations between human judgments and the UMLS-based metrics for generations by LLMs. Methods:We evaluated diagnoses generated by LLMs from consumer health questions and daily care notes in the electronic health records using the ConsumerQA and Problem Summarization datasets. Probing LLMs for the UMLS knowledge was performed by prompting the LLM to complete the diagnosis-related UMLS knowledge paths. Grounding the predictions was examined in an approach that integrated the UMLS graph paths and clinical notes in prompting the LLMs. The results were compared to prompting without the UMLS paths. The final experiments examined the alignment of different evaluation metrics, UMLS-based and non-UMLS, with human expert evaluation. Results:In probing the UMLS knowledge, GPT-3.5 significantly outperformed Llama2 and a simple baseline yielding an F1 score of 10.9% in completing one-hop UMLS paths for a given concept. Grounding diagnosis predictions with the UMLS paths improved the results for both models on both tasks, with the highest improvement (4%) in SapBERT score. There was a weak correlation between the widely used evaluation metrics (ROUGE and SapBERT) and human judgments. Conclusion:We found that while popular LLMs contain some medical knowledge in their internal representations, augmentation with the UMLS knowledge provides performance gains around diagnosis generation. The UMLS needs to be tailored for the task to improve the LLMs predictions. Finding evaluation metrics that are aligned with human judgments better than the traditional ROUGE and BERT-based scores remains an open research question.

Definition and management of right ventricular injury in adult patients receiving extracorporeal membrane oxygenation for respiratory support using the Delphi method: a PRORVnet study. Expert position statements.

Veno-venous extracorporeal membrane oxygenation (VV-ECMO) is an integral part of the management algorithm of patients with severe respiratory failure refractory to evidence-based conventional treatments. Right ventricular injury (RVI) pertaining to abnormalities in the dimensions and/or function of the right ventricle (RV) in the context of VV-ECMO significantly influences mortality. However, in the absence of a universally accepted RVI definition and evidence-based guidance for the management of RVI in this very high-risk patient cohort, variations in clinical practice continue to exist. Following a systematic search of the literature, an international Steering Committee consisting of eight healthcare professionals involved in the management of patients receiving ECMO identified domains and knowledge gaps pertaining to RVI definition and management where the evidence is limited or ambiguous. Using a Delphi process, an international panel of 52 Experts developed Expert position statements in those areas. The process also conferred RV-centric overarching open questions for future research. Consensus was defined as achieved when 70% or more of the Experts agreed or disagreed on a Likert-scale statement or when 80% or more of the Experts agreed on a particular option in multiple-choice questions. The Delphi process was conducted through four rounds and consensus was achieved on 31 (89%) of 35 statements from which 24 Expert position statements were derived. Expert position statements provided recommendations for RVI nomenclature in the setting of VV-ECMO, a multi-modal diagnostic approach to RVI, the timing and parameters of diagnostic echocardiography, and VV-ECMO settings during RVI assessment and management. Consensus was not reached on RV-protective driving pressure thresholds or the effect of prone positioning on patient-centric outcomes. The proposed definition of RVI in the context of VV-ECMO needs to be validated through a systematic aggregation of data across studies. Until further evidence emerges, the Expert position statements can guide informed decision-making in the management of these patients.

Multidisciplinary Complex Treatment of Parkinson's disease : Cornerstone of an individualized treatment

The inpatient Parkinson's disease multimodal complex treatment (PD-MCT) was applied more than 15,000 times in 2022, in Germany. This number is increasing as is Parkinson's disease (PD), which affects more than 400,000 people in Germany and leads to 100,000 disability-adjusted life years. In recent years, several observational studies have been conducted on the effectiveness of this kind of multidisciplinary care. To summarize and discuss the evidence on the nature, benefits and potential of PD-MCT. Anarrative review of selected empirical findings was carried out. The PD-MCT frequently lasts for 2-3 weeks and aims to maintain the quality of life of people with PD. Disease symptoms and activities of daily living are jointly improved by pharmacological strategies and activating therapies (physiotherapy, occupational therapy, speech and language therapy, physical training, art therapy). The PD-MCT is auseful measure to avoid or mitigate crisis situations in the course of the disease. A total of eight observational studies (n = 1246) have shown good effectiveness with a total mean improvement of the International Parkinson and Movement Disorder Society unified Parkinson's disease rating scaleIII (MDS-UPDRSIII) by 7.8points. The transfer of effects into everyday life through intensive and specialized community-based care must be ensured in order to achieve sustained effects on the quality of life. Ideally, this transfer can be supported by integrated PD networks and digital technologies in the future. There is potential for development in the standardization, patient selection and quality assurance of PD-MCT as well as in the embedding in care structures such as PD networks. Open research questions include aprecise definition of the target group and higher quality evidence of short-term and long-term effectiveness.

Curved vortex surfaces in four-dimensional superfluids. I. Unequal-frequency double rotations

The study of superfluid quantum vortices has long been an important area of research, with previous work naturally focusing on two-dimensional and three-dimensional systems, where rotation stabilizes point vortices and line vortices respectively. Interestingly, this physics generalizes for a hypothetical four-dimensional (4D) superfluid to include vortex planes, which can have a much richer phenomenology. In this paper we study the possibility of skewed and curved vortex planes, which have no direct analog in lower dimensions. By analytically and numerically studying the 4D Gross-Pitaevskii equation, we show that such vortex surfaces can be stabilized and favored by double rotation with unequal rotation frequencies. Our work raises open questions for further research into the physics of these vortex surfaces and suggests interesting future extensions to tilted vortex surfaces under equal-frequency double rotation and to more realistic 4D models. Published by the American Physical Society 2024

Plants as Designers of Better Futures

This research explores the idea of plants as designers and discusses approaches that humans can use to support plant’s productive agencies. It argues that plants have unique and valuable capabilities for creating and caring for their environments. Human interventions often overlook or constrain such capabilities. In response, the article proposes to use numerical modelling to better understand plants better while challenging the anthropocentric assumptions that are common in design. It focuses on large old trees in Tasmania as examples of outstanding plant-designers that need more recognition and protection. The article also raises open questions for further research on the ethical, ecological, and aesthetic implications of vegetal design.

Software-Defined Named Data Networking in Literature: A Review

This paper presents an in-depth review of software-defined named data networking (SD-NDN), a transformative approach in network architectures poised to deliver substantial benefits. By addressing the limitations inherent in traditional host-centric network architectures, SD-NDN offers improvements in network performance, scalability, and efficiency. The paper commences with an overview of named data networking (NDN) and software-defined networking (SDN), the two fundamental building blocks of SD-NDN. It then explores the specifics of integrating NDN with SDN, illustrating examples of various SD-NDN models. These models are designed to leverage SDN for NDN routing, caching, and forwarding. The paper concludes by proposing potential strategies for further integration of SDN and NDN and some open research questions. These proposed strategies aim to stimulate further exploration and innovation in the field of SD-NDN.

A survey on node localization technologies in UWSNs: Potential solutions, recent advancements, and future directions

SummaryLocation‐based underwater communication applications such as strategic surveillance, disaster prevention, marine research, and mine detection have given the field of underwater wireless sensor networks (UWSN) a head start. Node localization is a prerequisite for accurate data collection, target monitoring, and network management in UWSNs. However, the unique characteristics of the underwater environment, such as signal attenuation, multipath propagation, and variable acoustic properties, pose a major challenge to effective node localization. Accurate sensor node location data is essential for successful underwater data collection, but difficult to achieve as the GPS system cannot be used in an underwater environment. In this paper, existing node localization techniques such as ALS, SLUM, MASL, SLMP, UDB, USP, etc., and recent advances such as the fusion of range‐based and range‐free techniques, the fusion of RSSI and AoA to improve localization accuracy by using directional information in addition to signal strength, and the use of optimization techniques such as PSO, COA, and WOA algorithms to improve the accuracy of the applied node localization algorithm, e.g., TP‐TSFLA, and challenges related to UWSN are discussed. Also, different localization algorithms that affect the accuracy of UWSN localization techniques have been evaluated and compared with NS2 in terms of localization error, localization coverage, energy consumption, and average communication cost metrics. In addition, this paper also provides an up‐to‐date investigation of localization techniques. Finally, the tools available for simulation are presented, followed by open research questions that need to be addressed in the localization of nodes.

Lighting the way: Compelling open questions in photosynthesis research.

Photosynthesis-the conversion of energy from sunlight into chemical energy-is essential for life on Earth. Yet there is much we do not understand about photosynthetic energy conversion on a fundamental level: how it evolved and the extent of its diversity, its dynamics, and all the components and connections involved in its regulation. In this commentary, researchers working on fundamental aspects of photosynthesis including the light-dependent reactions, photorespiration, and C4 photosynthetic metabolism pose and discuss what they view as the most compelling open questions in their areas of research.

Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey.

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously, consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance, was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.

AI-based avatars are changing the way we learn and teach: benefits and challenges

Advancements in the generative AI field have enabled the development of powerful educational avatars. These avatars embody a human and can, for instance, listen to users’ spoken input, generate an answer utilizing a large-language model, and reply by speaking with a synthetic voice. A theoretical introduction summarizes essential steps in developing AI-based educational avatars and explains how they differ from previously available educational technologies. Moreover, we introduce GPTAvatar, an open-source, state-of-the-art AI-based avatar. We then discuss the benefits of using AI-based educational avatars, which include, among other things, individualized and contextualized instruction. Afterward, we highlight the challenges of using AI-based educational avatars. Major problems concern incorrect and inaccurate information provided, as well as insufficient data protection. In the discussion, we provide an outlook by addressing advances in educational content and educational technology and identifying three crucial open questions for research and practice.

The Effects of Osteopathic Manipulative Treatment on Brain Activity: A Scoping Review of MRI and EEG Studies.

Osteopathic manipulative treatment (OMT) is a hands-on therapy aiming to achieve the global homeostasis of the patient. OMT focuses on treating the somatic dysfunctions characterized by tissue modifications, body asymmetry, and range-of-motion restrictions. The benefits related to OMT are thought to be associated with the interconnectedness of the body's systems and the inherent capacity for self-healing. However, whether OMT can influence brain activity, and, consequently, neurophysiological responses is an open research question. Our research investigates the literature to identify the effects of OMT on brain activity. The main purpose of the research question is: can OMT influence brain activity and consequently neurophysiological responses? A scoping review was conducted, searching the following databases: PubMed, Google Scholar, and OSTEOMED.DR (Osteopathic Medical Digital Repository), Scopus, Web of Science (WoS), and Science Direct. The initial search returned 114 articles, and after removing duplicates, 69 were considered eligible to be included in the final sample. In the end, eight studies (six randomized controlled trials, one pilot study, and one cross-over study) were finally included and analyzed in this review. In conclusion, OMT seems to have a role in influencing functional changes in brain activity in healthy individuals and even more in patients with chronic musculoskeletal pain. However, further RCT studies are needed to confirm these findings. Registration protocol: CRD42024525390.

The Decoherent Arrow of Time and the Entanglement Past Hypothesis

If an asymmetry in time does not arise from the fundamental dynamical laws of physics, it may be found in special boundary conditions. The argument normally goes that since thermodynamic entropy in the past is lower than in the future according to the Second Law of Thermodynamics, then tracing this back to the time around the Big Bang means the universe must have started off in a state of very low thermodynamic entropy: the Thermodynamic Past Hypothesis. In this paper, we consider another boundary condition that plays a similar role, but for the decoherent arrow of time, i.e. the subsystems of the universe are more mixed in the future than in the past. According to what we call the Entanglement Past Hypothesis, the initial quantum state of the universe had very low entanglement entropy. We clarify the content of the Entanglement Past Hypothesis, compare it with the Thermodynamic Past Hypothesis, and identify some challenges and open questions for future research.

Understanding the Genetic Basis of Variation in Meiotic Recombination: Past, Present, and Future.

Meiotic recombination is a fundamental feature of sexually reproducing species. It is often required for proper chromosome segregation and plays important role in adaptation and the maintenance of genetic diversity. The molecular mechanisms of recombination are remarkably conserved across eukaryotes, yet meiotic genes and proteins show substantial variation in their sequence and function, even between closely related species. Furthermore, the rate and distribution of recombination shows a huge diversity within and between chromosomes, individuals, sexes, populations, and species. This variation has implications for many molecular and evolutionary processes, yet how and why this diversity has evolved is not well understood. A key step in understanding trait evolution is to determine its genetic basis-that is, the number, effect sizes, and distribution of loci underpinning variation. In this perspective, I discuss past and current knowledge on the genetic basis of variation in recombination rate and distribution, explore its evolutionary implications, and present open questions for future research.

SoK: Can Trajectory Generation Combine Privacy and Utility?

While location trajectories represent a valuable data source for analyses and location-based services, they can reveal sensitive information, such as political and religious preferences. Differentially private publication mechanisms have been proposed to allow for analyses under rigorous privacy guarantees. However, the traditional protection schemes suffer from a limiting privacy-utility trade-off and are vulnerable to correlation and reconstruction attacks. Synthetic trajectory data generation and release represent a promising alternative to protection algorithms. While initial proposals achieve remarkable utility, they fail to provide rigorous privacy guarantees. This paper proposes a framework for designing a privacy-preserving trajectory publication approach by defining five design goals, particularly stressing the importance of choosing an appropriate Unit of Privacy. Based on this framework, we briefly discuss the existing trajectory protection approaches, emphasising their shortcomings. This work focuses on the systematisation of the state-of-the-art generative models for trajectories in the context of the proposed framework. We find that no existing solution satisfies all requirements. Thus, we perform an experimental study evaluating the applicability of six sequential generative models to the trajectory domain. Finally, we conclude that a generative trajectory model providing semantic guarantees remains an open research question and propose concrete next steps for future research.

The efficiency assessment of using hybrid neural networks for the detection of forged audio data in socially oriented systems

The subject of the research is the problem of detecting falsified data, in particular in audio format, in socially oriented systems. The goal of the work is to develop an effective model based on recurrent and convolutional neural networks for determining the fact of forgery of sound data, using MapReduce technology for parallelization. The article addresses the following tasks: determining the features of audio in socially-oriented systems, conducting an analysis of algorithms for processing audio information both in the form of text and in the form of a signal, forming a list of target architectures of neural networks and revealing the features of their implementation, conducting an experimental test of effectiveness selected approaches. The following methods used are – analytical and inductive method for determining the target set of neural network architectures; expert assessment for the formation of the most influential efficiency factors; experimental, multi-criteria evaluation and statistical methods of data augmentation to determine the most effective model. The following results were obtained: an audio data reprocessing algorithm was developed for the possibility of using recurrent and convolutional networks. Several approaches to data classification using augmentation based on vector autoregression and MapReduce parallelization technology have been implemented. It was determined that the most effective model for the multi-criteria selection problem is a combination of a bidirectional recurrent neural network with support for short- and long-term memory with several convolutional networks. The advantages of using MapReduce technology to optimize training time and data processing are shown, and a set of open questions for further research and applied implementation is defined. Conclusions: he application of an analytical and inductive approach followed by experimental verification made it possible to develop an effective (with an accuracy of more than 96%) a mechanism for detecting fabricated data both in the form of a signal and in text form. The obtained result makes it possible to assert the feasibility of implementing the proposed approach, and, accordingly, makes it possible to reduce the influence of such information in socially oriented systems, especially during crisis events.

Neurodevelopmental disorders and family quality of life: emerging trends and future research directions.

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are the most prevalent neurodevelopmental disorders. There is a growing body of literature investigating factors affecting quality of life in families (FQoL) with a child with these disorders. However, there are no studies that trace their knowledge anatomy. Thus, we conducted a scientometric analysis to describe this literature, detect certain variables that could be related to FQoL, and identify tendencies and open questions for future research. A literature search in the Web of Science, PubMed, and Scopus was run and identified 3281 publications published between 1975 and 2022. The results suggest an increase in the quantity of publications on FQoL in ASD and ADHD over the last few years (14% and 12%, respectively). For both research fields, the USA published the highest number of documents, showing that the production related to ADHD and FQoL is concentrated in just a few countries. Thematic analysis revealed several clusters, considering quality of life and children as core themes that are still setting trend lines. Moreover, it would be worthwhile to describe and analyze FQoL not only during the childhood of children with ASD and ADHD but also during their adolescence. IMPACT: Although the relationship between family quality of life and neurodevelopmental disorders could be considered novel, there is a growing interest from an interdisciplinary perspective. Family quality of life should be monitored not only during the childhood of children with ASD and ADHD, but also during their adolescence and adulthood. The analysis of the family quality of life in first-degree relatives and its relationship with protective factors (e.g., resilience and social support) should be explored in future studies.

Speech emotion recognition based on bi-directional acoustic–articulatory conversion

Acoustic and articulatory signals are naturally coupled and complementary. The challenge of acquiring articulatory data and the nonlinear ill-posedness of acoustic–articulatory conversions have resulted in previous studies on speech emotion recognition (SER) primarily relying on unidirectional acoustic–articulatory conversions. However, these studies have ignored the potential benefits of bi-directional acoustic–articulatory conversion. Addressing the problem of nonlinear ill-posedness and effectively extracting and utilizing these two modal features in SER remain open research questions. To bridge this gap, this study proposes a Bi-A2CEmo framework that simultaneously addresses the bi-directional acoustic–articulatory conversion for SER. This framework comprises three components: a Bi-MGAN that addresses the nonlinear ill-posedness problem, KCLNet that enhances the emotional attributes of the mapped features, and ResTCN-FDA that fully exploits the emotional attributes of the features. Another challenge is the absence of a parallel acoustic–articulatory emotion database. To overcome this issue, this study utilizes electromagnetic articulography (EMA) to create a multi-modal acoustic–articulatory emotion database for Mandarin Chinese called STEM-E2VA. A comparative analysis is then conducted between the proposed method and state-of-the-art models to evaluate the effectiveness of the framework. Bi-A2CEmo achieves an accuracy of 89.04% in SER, which is an improvement of 5.27% compared with the actual acoustic and articulatory features recorded by the EMA. The results for the STEM-E2VA dataset show that Bi-MGAN achieves a higher accuracy in mapping and inversion than conventional conversion networks. Visualization of the mapped features before and after enhancement reveals that KCLNet reduces the intra-class spacing while increasing the inter-class spacing of the features. ResTCN-FDA demonstrates high recognition accuracy on three publicly available datasets. The experimental results show that the proposed bi-directional acoustic–articulatory conversion framework can significantly improve the SER performance.