Key Research Challenges Research Articles

Towards the Development of a Copyright Risk Checker Tool for Generative Artificial Intelligence Systems

Generative Artificial Intelligence (GAI) is fundamentally changing the ways of working and blurring the boundaries between human and machine-generated contents. While there is an increasing interest in the adoption of GAI systems, such as ChatGPT and DALL-E, there are also serious concerns about the copyright of the contents – the inputs or generated as outputs by the GAI systems. Such concerns need to be identified and assessed to ensure the ethical and responsible use of GAI systems. Thus, this paper aims to address the key research challenge: "how to identify and assess GAI system's copyright concerns"? In response, we propose the development of a Copyright Risk Checker (CRC) Tool. This tool has been formulated and evaluated using a recognised design science research methodology, drawing on an analysis of ten legal cases across Australia, the United Kingdom, the United States, and Europe. The CRC Tool has undergone evaluation through an experimental scenario, and the results suggest that it is suitable for conducting an indicative copyright risk check of GAI systems. The outcomes of this preliminary assessment can be further examined by expert legal advisors for an in-depth analysis. The development of the CRC Tool provides a foundation for continued research and advancement in this significant area of study.

Sustainable Biopolymers in Eco-Friendly Triboelectric Energy Harvesting.

Biopolymer-based triboelectric nanogenerators (B-TENGs) represent an innovative fusion of eco-friendly, sustainable energy-harvesting technology with renewable and environmentally benign biopolymer material. This integration not only introduces novel pathways for advancing green energy solutions but also offers a critical approach to addressing contemporary environmental challenges and fostering sustainable progress. Over the past few years, B-TENGs have seen rapid and remarkable growth in the realm of biopolymers, device architecture, and their applications (e.g., implantable power source, electronic medicine, human anatomical and physiological movements monitoring sensors, etc.). In this review article, the promising developments in harnessing triboelectric biopolymers are encapsulated, enumerate their representative applications, evaluate the pros and cons of these biopolymers, highlight key challenges for future research, and offer strategic recommendations for innovating and realizing advanced B-TENGs.

A practitioner-centered policy roadmap for ethical computational social science in Germany, Austria, and Switzerland.

Background: Computational Social Science (CSS) utilizes large digital datasets and computational methods to study human behavior, raising ethical concerns about data privacy, informed consent, and potential misuse.Methods: This study employs a constructivist grounded theory approach, analyzing 15 in-depth interviews with CSS practitioners in Germany, Austria, and Switzerland. These countries share a European legal context regarding data privacy and hereby provide a comparable regulatory environment for examining ethical considerations.Results: Findings highlight key challenges in CSS research, including power imbalances with data providers, uncertainties around surveillance and data privacy (especially with longitudinal data), and limitations of current ethics frameworks. Researchers face tensions between established ethical principles and practical realities, often feeling disempowered and lacking support from ethics boards due to their limited CSS expertise. Regulatory ambiguity further discourages research due to fear of sanctions.Conclusions: To foster responsible CSS practices, this paper recommends establishing specialized ethics boards with CSS expertise. It also advocates for acknowledging CSS's unique nature in research policy by developing tailored data guidelines and providing legal certainty through clear guidelines. Grounding recommendations in practitioners' experiences, this study offers actionable steps to help enable ethical CSS research.

Linear and nonlinear causality in financial markets.

Identifying and quantifying co-dependence between financial instruments is a key challenge for researchers and practitioners in the financial industry. Linear measures such as the Pearson correlation are still widely used today, although their limited explanatory power is well known. In this paper, we present a much more general framework for assessing co-dependencies by identifying linear and nonlinear causalities in the complex system of financial markets. To do so, we use two different causal inference methods, transfer entropy and convergent cross-mapping, and employ Fourier transform surrogates to separate their linear and nonlinear contributions. We find that stock indices in Germany and the U.S. exhibit a significant degree of nonlinear causality and that correlation, while a very good proxy for linear causality, disregards nonlinear effects and hence underestimates causality itself. The presented framework enables the measurement of nonlinear causality, the correlation-causality fallacy, and motivates how causality can be used for inferring market signals, pair trading, and risk management of portfolios. Our results suggest that linear and nonlinear causality can be used as early warning indicators of abnormal market behavior, allowing for better trading strategies and risk management.

ACMamba: A State Space Model-Based Approach for Multi-Weather Degraded Image Restoration

In computer vision, eliminating the effects of adverse weather conditions such as rain, snow, and fog on images is a key research challenge. Existing studies primarily focus on image restoration for single weather types, while methods addressing image restoration under multiple combined weather conditions remain relatively scarce. Furthermore, current mainstream restoration networks, mostly based on Transformer and CNN architectures, struggle to achieve an effective balance between global receptive field and computational efficiency, limiting their performance in practical applications. This study proposes ACMamba, an end-to-end lightweight network based on selective state space models, aimed at achieving image restoration under multiple weather conditions using a unified set of parameters. Specifically, we design a novel Visual State Space Module (VSSM) and a Spatially Aware Feed-Forward Network (SAFN), which organically combine the local feature extraction capabilities of convolutions with the long-range dependency modeling capabilities of selective state space models (SSMs). This combination significantly improves computational efficiency while maintaining a global receptive field, enabling effective application of the Mamba architecture to multi-weather image restoration tasks. Comprehensive experiments demonstrate that our proposed approach significantly outperforms existing methods for both specific and multi-weather tasks across multiple benchmark datasets, showcasing its efficient long-range modeling potential in multi-weather image restoration tasks.

Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics.

Analyzing somatic evolution within a tumor over time and across space is a key challenge in cancer research. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genomic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and reconstruct spatial tumor evolution, or phylogeography, from SRT data. CalicoST identifies important classes of CNAs-including copy-neutral loss of heterozygosity and mirrored subclonal CNAs-that are invisible to total copy number analysis. Using nine patients' data from the Human Tumor Atlas Network, CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. CalicoST reconstructs a tumor phylogeography in three-dimensional space for two patients with multiple adjacent slices. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals mirrored subclonal CNAs on the two sides of the prostate, forming a bifurcating phylogeography in both genetic and physical space.

Fair Energy Trading in Blockchain-Inspired Smart Grid: Technological Barriers and Future Trends in the Age of Electric Vehicles

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption.

Isolation of Methane from Ambient Water and Preparation for Source-Diagnostic Natural Abundance Radiocarbon Analysis.

A key challenge in climate change research is apportioning the greenhouse gas methane (CH4) between various natural and anthropogenic sources. Isotopic source fingerprinting of CH4 releases, particularly with radiocarbon analysis, is a promising approach. Here, we establish an analytical protocol for preparing CH4 from seawater and other aqueous matrices for high-precision natural abundance radiocarbon measurement. Methane is stripped from water in the optionally field-operated system (STRIPS), followed by shore-based purification and conversion to carbon dioxide (CO2) in the CH4 Isotope Preparation System (CHIPS) to allow Accelerator Mass Spectrometry analysis. The blank (±1σ) of the combined STRIPS and CHIPS is low (0.67 ± 0.12 μg C), allowing natural sample sizes down to 10 μg C-CH4 (i.e., 30 L samples of 40 nM CH4). The full-system yield is >90% for both CH4-spiked seawater and ambient samples from CH4 hotspots in the Baltic Sea and the Arctic Ocean. Furthermore, the radiocarbon isotope signal of CH4 remains constant through the multistage processing in the STRIPS and the CHIPS. The developed method thus allows for in-field sampling and sample size reduction followed by precise and CH4-specific radiocarbon analysis. This enables powerful source apportionment of CH4 emitted from aquatic systems from the tropics to the polar regions.

Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due to its non-invasive nature and cost-effectiveness. With the rise of computational psychiatry, the integration of EEG with artificial intelligence has yielded remarkable results in diagnosing depression. This review offers a comparative analysis of two predominant methodologies in research: traditional machine learning and deep learning methods. Furthermore, this review addresses key challenges in current research and suggests potential solutions. These insights aim to enhance diagnostic accuracy for depression and also foster further development in the area of computational psychiatry.

Guiding treatment decisions in renal cell carcinoma: the role of biomarkers and clinical factors.

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for metastatic renal cell carcinoma (mRCC), significantly improving overall survival and achieving durable responses. This review is timely due to the increasing number of ICI-based regimens now considered standard care for RCC. There is an urgent need to identify reliable biomarkers that can predict therapeutic responses and resistance, a key challenge in current research. While tumor-specific factors such as pathological characteristics, genomic mutations, and transcriptional profiles have been extensively studied, no definitive predictive biomarker has yet emerged. Additionally, advanced technologies are being explored to address tumor heterogeneity. Recent research has focused on novel areas such as the microbiome, radiomics, and spatial transcriptomics, which show promise as potential biomarkers. The translation of these emerging biomarker findings into clinical practice is essential to improving personalized treatment strategies for RCC. Until reliable biomarkers are clinically available, clinical factors may play a pivotal role in guiding individualized treatment decisions to optimize patient outcomes.

Challenges and Approaches in the Study of Neural Entrainment.

When exposed to rhythmic stimulation, the human brain displays rhythmic activity across sensory modalities and regions. Given the ubiquity of this phenomenon, how sensory rhythms are transformed into neural rhythms remains surprisingly inconclusive. An influential model posits that endogenous oscillations entrain to external rhythms, thereby encoding environmental dynamics and shaping perception. However, research on neural entrainment faces multiple challenges, from ambiguous definitions to methodological difficulties when endogenous oscillations need to be identified and disentangled from other stimulus-related mechanisms that can lead to similar phase-locked responses. Yet, recent years have seen novel approaches to overcome these challenges, including computational modeling, insights from dynamical systems theory, sophisticated stimulus designs, and study of neuropsychological impairments. This review outlines key challenges in neural entrainment research, delineates state-of-the-art approaches, and integrates findings from human and animal neurophysiology to provide a broad perspective on the usefulness, validity, and constraints of oscillatory models in brain-environment interaction.

Internet of Things and the Evolution of Human 2.0: An Analytical Study on Enhancing Human Capabilities

The Internet of Things (IoT) technology has introduced new tools for enhancing human capabilities, promising improved quality of life and professional efficiency. The concept of "Augmented Human" (AH) refers to using technology to enhance and improve natural human abilities, whether physical or cognitive, through devices and technologies that boost sensory perception, physical strength, and cognitive performance. However, the integration of IoT with human augmentation technology has expanded the scope of IoT-related challenges beyond current boundaries. So far, these potential challenges associated with IoT-enabled human augmentation have not investigated. Therefore, this article aims to introduce readers to the concept of human augmentation (AH) and to summarize the key research challenges posed by such systems, thereby encouraging readers' interest in this topic. The article explores emerging IoT applications in human augmentation, along with devices and design principles, connectivity requirements, and security aspects. Keywords: Internet of Things, human capabilities, flexible hybrid electronics.

Intelligent Prediction of Rate of Penetration Using Mechanism-Data Fusion and Transfer Learning

Rate of penetration (ROP) is crucial for evaluating drilling efficiency, with accurate prediction essential for enhancing performance and optimizing parameters. In practice, complex and variable downhole environments pose significant challenges for mechanistic ROP equations, resulting in prediction difficulties and low accuracy. Recently, data-driven machine learning models have been widely applied to ROP prediction. However, these models often lack mechanistic constraints, limiting their performance to specific conditions and reducing their real-world applicability. Additionally, geological variability across wells further hinders the transferability of conventional intelligent models. Thus, combining mechanistic knowledge with intelligent models and enhancing model stability and transferability are key challenges in ROP prediction research. To address these challenges, this paper proposes a Mechanism-Data Fusion and Transfer Learning method to construct an intelligent prediction model for ROP, achieving accurate ROP predictions. A multilayer perceptron (MLP) was selected as the base model, and training was performed using data from neighboring wells and partial data from the target well. The Two-stage TrAdaBoost.R2 algorithm was employed to enhance model transferability. Additionally, drilling mechanistic knowledge was incorporated into the model’s loss function as a constraint to achieve a fusion of mechanistic knowledge and data-driven approaches. Using MAPE as the measure of accuracy, compared with conventional intelligent models, the proposed ROP prediction model improved prediction accuracy on the target well by 64.51%. The model transfer method proposed in this paper has a field test accuracy of 89.71% in an oilfield in China. These results demonstrate the effectiveness and feasibility of the proposed transfer learning method and mechanistic–data integration approach.

Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap

Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this article aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI’s within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies. †

Computationally efficient deep learning models for diabetic retinopathy detection: a systematic literature review

Diabetic retinopathy, often resulting from conditions like diabetes and hypertension, is a leading cause of blindness globally. With diabetes affecting millions worldwide and anticipated to rise significantly, early detection becomes paramount. The survey scrutinizes existing literature, revealing a noticeable absence of consideration for computational complexity aspects in deep learning models. Notably, most researchers concentrate on employing deep learning models, and there is a lack of comprehensive surveys on the role of vision transformers in enhancing the efficiency of these models for DR detection. This study stands out by presenting a systematic review, exclusively considering 84 papers published in reputable academic journals to ensure a focus on mature research. The distinctive feature of this Systematic Literature Review (SLR) lies in its thorough investigation of computationally efficient approaches and models for DR detection. It sheds light on the incorporation of vision transformers into deep learning models, highlighting their significant contribution to improving accuracy. Moreover, the research outlines clear objectives related to the identified problem, giving rise to specific research questions. Following an assessment of relevant literature, data is extracted from digital archives. Additionally, in light of the results obtained from this SLR, a taxonomy for the detection of diabetic retinopathy has been presented. The study also highlights key research challenges and proposes potential avenues for further investigation in the field of detecting diabetic retinopathy.

Physical Cell Disruption Technologies for Intracellular Compound Extraction from Microorganisms

This review focuses on the physical disruption techniques in extracting intracellular compounds, a critical step that significantly impacts yield and purity. Traditional chemical extraction methods, though long-established, face challenges related to cost and environmental sustainability. In response to these limitations, this paper highlights the growing shift towards physical disruption methods—high-pressure homogenization, ultrasonication, milling, and pulsed electric fields—as promising alternatives. These methods are applicable across various cell types, including bacteria, yeast, and algae. Physical disruption techniques achieve relatively high yields without degrading the bioactivity of the compounds. These techniques, utilizing physical forces to break cell membranes, offer promising extraction efficiency, with reduced environmental impacts, making them attractive options for sustainable and effective intracellular compound extraction. High-pressure homogenization is particularly effective for large-scale extracting of bioactive compounds from cultivated microbial cells. Ultrasonication is well-suited for small to medium-scale applications, especially for extracting heat-sensitive compounds. Milling is advantageous for tough-walled cells, while pulsed electric field offers gentle, non-thermal, and highly selective extraction. This review compares the advantages and limitations of each method, emphasizing its potential for recovering various intracellular compounds. Additionally, it identifies key research challenges that need to be addressed to advance the field of physical extractions.

A scientometrics review of conventional and soft computing methods in the slope stability analysis

Predicting slope stability is important for preventing and mitigating landslide disasters. This paper examines the existing approaches for analyzing slope stability. There are several established conventional approaches for slope stability analysis that can be applied in this context. However, in recent decades, soft computing methods has been extensively developed and employed in stochastic slope stability analysis, notably as surrogate models to improve computing efficiency in contrast to traditional approaches. Soft computing methods can deal with uncertainty and imprecision, which may be quantified using performance indices like coefficient of determination, in regression and accuracy in classification. This review study focuses on conventional methods such as the Bishop’s method and Janbu’s method, as well as soft computing models such as support vector machine, artificial neural network, Gaussian process regression, decision tree, etc. The advantages and limitations of soft computing techniques in relation to conventional methods have also been thoroughly covered in this paper. The achievements of soft computing methods are summarized from two aspects—predicting factor of safety and classification of slope stability. Key potential research challenges and future prospects are also given.

Riotous methodological and methodointuitive reflections on (non)religion, spirituality and the multispecies turn

The ‘multispecies turn’ in the social sciences and humanities is informing many disciplines including animal studies, anthropology, Indigenous studies and, more recently, sociology of religion. Scholars working on multispecies relations employ various methods and methodologies, many of which are challenging modern, Western, Christian paradigms and practices that are anthropocentric and focus on logos/words/texts/beliefs. This discussion examines new and multiple ways of conducting multispecies focused research, that is critical, reflexive, embodied, affective and intuitive. It begins with an overview of the methods applied by notable scholars – Kimmerer, Tsing and Beaman – researching multispecies relations, and then includes four personal ‘riotous’ reflections by this article’s co-authors, on their own positionality and experiences of conducting such research. The discussion concludes by identifying key challenges in research on diverse worldviews and multispecies relations, and aims to generate creative and scientific responses to further decenter anthropocentrism in academia.

Relative sea-level sensitivity in the Eurasian region to Earth and ice-sheet model uncertainty during the Last Interglacial

Fingerprinting the source and rate of the melt of polar ice sheets during the Last Interglacial is a key research challenge. This is reliant on high-quality relative sea-level constraints, and the correction of this data for the effects of glacial isostatic adjustment driven by ice sheet cover changes prior to the interglacial. However, both the spatial and temporal evolution of past ice sheets and the Earth’s rheological structure that serve as inputs to glacial isostatic adjustment predictions are significantly uncertain. This study sets out to determine the relative influence of each of these inputs on modelled values of Last Interglacial relative sea levels and how this influence varies spatially. To answer this question, we use a palaeo ice-sheet model and a gravitationally consistent glacial isostatic adjustment model. We develop new numerical tools to generate plausible ice-sheet extent and histories, quantify relative sea-level uncertainty, and perform a Sobol sensitivity analysis facilitated by the use of Gaussian process emulation. We find that Earth model parameters are the dominant contributors to relative sea-level uncertainty in most Eurasian regions, but that relative sea-level values in the Barents-Kara Sea are most influenced by ice-sheet loading, while the timing of the deglaciation has the greatest impact in the Baltic Sea. Our results show that the magnitude and rate of relative sea-level change is relatively insensitive to the specific timing of ice-sheet retreat, as well as the configuration of the far-field North American ice sheet. Overall, our work suggests that the coastlines of the southern North Sea and the English Channel are least influenced by relative sea-level uncertainty and are the most suitable for future data collection studies aiming to limit the influence of glacial isostatic adjustment.

Trade‐Offs and Optimization of Ecosystem Services in the Plain Terminal Lake Basin: A Case Study of Xinjiang

ABSTRACTInfluenced by climate change and human activities, plain terminal lake basins (PTLB) in arid regions experience significant shrinkage, resulting in a decline in ecosystem services (ESs) such as windbreak and sand fixation (SF) and habitat quality (HQ). However, systematic assessments of these services in such basins are limited. A key research challenge is to define and enhance the contribution of plain terminal lakes within the ESs framework of their respective basins. Therefore, this study focused on the Ebinur Lake Basin (ELB), Manas River Basin (MRB), and Tarim River Basin (TRB) in Xinjiang. We adopted both the InVEST and RWEQ models to assess ESs in these regions, by employing the production possibility frontier (PPF) to optimize various ESs, including carbon storage (CS), water yield (WY), HQ, and SF. The results indicated a general upward trend in ESs across these key basins, with a particularly significant increase in the CS. Spatial analysis revealed that regions with improved ESs were mainly concentrated in the mountainous and oasis areas of the basins, with the most pronounced improvement observed in the TRB. Within these basins, the interactions between the different ESs mainly exhibited synergistic relationships. However, notable concave trade‐off relationships existed between WY and CS, WY and HQ, and HQ and SF, particularly in the TRB. This was largely attributed to increased trade‐offs between WY and other ESs, resulting from the widespread agricultural expansion in the TRB. We plotted the optimal Pareto efficiency curves for ESs across the three basins to obtain the corresponding optimization results, and made recommendations for the enhancement of ESs. In summary, this study lays a solid scientific foundation for establishing ecological protection boundaries and promoting high‐quality sustainable development in the PTLB of arid areas.