Limited Data Availability Research Articles

A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning

As sound speed is a fundamental parameter of ocean acoustic characteristics, its prediction is a central focus of underwater acoustics research. Traditional numerical and statistical forecasting methods often exhibit suboptimal performance under complex conditions, whereas deep learning approaches demonstrate promising results. However, these methodologies fall short in adequately addressing multi-spatial coupling effects and spatiotemporal weighting, particularly in scenarios characterized by limited data availability. To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. Experimental results indicate that STA-ConvLSTM provides precise predictions of sound speed. Specifically, relative to the measured data, it achieved a root mean square error (RMSE) of approximately 0.57 m/s and a mean absolute error (MAE) of about 0.29 m/s. Additionally, when compared to single-dimensional spatial analysis, incorporating multi-spatial scale considerations yielded superior predictive performance.

Intelligent Fault Diagnosis of Planetary Gearbox Across Conditions Based on Subdomain Distribution Adversarial Adaptation.

Sensory data are the basis for the intelligent health state awareness of planetary gearboxes, which are the critical components of electromechanical systems. Despite the advantages of intelligent diagnostic techniques for detecting intricate fault patterns and improving diagnostic speed, challenges still persist, which include the limited availability of fault data, the lack of labeling information and the discrepancies in features across different signals. Targeting this issue, a subdomain distribution adversarial adaptation diagnosis method (SDAA) is proposed for faults diagnosis of planetary gearboxes across different conditions. Firstly, nonstationary vibration signals are converted into a two-dimensional time-frequency representation to extract intrinsic information and avoid frequency overlapping. Secondly, an adversarial training mechanism is designed to evaluate subclass feature distribution differences between the source and target domain. A conditional distribution adaptation is employed to account for correlations among data from different subclasses. Finally, the proposed method is validated through experiments on planetary gearboxes, and the results demonstrate that SDAA can effectively diagnose faults under crossing conditions with an accuracy of 96.7% in diagnosing gear faults and 95.2% in diagnosing planet bearing faults. It outperforms other methods in both accuracy and model robustness. This confirms that this approach can refine domain-invariant information for transfer learning with less information loss from the sub-class level of fault data instead of the overall class level.

AI-driven business analytics for SMES: Unlocking value through predictive and prescriptive analytic

Introduction: With an emphasis on predictive and prescriptive analytics, this study examines the revolutionary implications of AI-driven analytics on small and medium-sized organizations (SMEs). SMEs play a crucial role in the global economy and require advanced solutions to improve decision-making and operational efficiency. The research aims to explore how AI-powered analytics, particularly in predictive and prescriptive forms, can add value to SMEs by enhancing demand forecasting, customer behavior insights, and financial planning. To determine how AI-driven analytics might affect SMEs, a thorough assessment of the literature was undertaken. The study reveals that SMEs implementing predictive analytics experience notable improvements in areas such as inventory management, revenue generation, and overall operational efficiency. Furthermore, businesses that leverage prescriptive analytics benefit from optimized resource allocation, enhanced marketing strategies, and better risk management practices. These findings highlight the potential for AI to overcome key challenges faced by SMEs, including budget constraints and limited data availability. AI-driven analytics can provide valuable insights that allow SMEs to streamline operations and foster growth. With future trends pointing to greater accessibility and developments in machine learning, natural language processing, and the integration of AI with other cutting-edge technologies, like blockchain, AI-powered analytics offers substantial prospects for small and medium-sized enterprises.

Novel graph convolutional network for geological profile prediction using non-equidistant borehole data

ABSTRACT Accurately predicting geological profiles from sparse borehole data is challenging due to limited data availability and complex spatial correlations within strata. This study presents an advanced graph convolutional network designed to model flexible graph structures and predict geological profiles using unequally spaced borehole data. In this framework, each graph node corresponds to a stratum unit which represents the transformed coordinate features, with edges indicating spatial relationships among these units. Initially, the method was applied to a case study in Australia, demonstrating a 30% improvement in boundary accuracy compared to traditional methods. Subsequently, the approach was employed in a foundation project in Hangzhou, achieving mean measurement accuracies above 0.8 for validating boreholes. The study further explored the impact of borehole quantity and unequal spacing on prediction accuracy. Results indicate that significant accuracy gains occur only when new boreholes add critical spatial features; otherwise, the improvement remains minimal. Additionally, flexible borehole positioning results in a 44% disparity between the most and least accurate predictions.

Does Peer Socialization Within Cohorts Foster Political Attitudes? A Longitudinal Study of Elite Business Students

AbstractThe association between higher education and political attitudes is well-recognized, and research suggests that socialization amongst peers is one of the most probable mechanisms explaining a possible attitudinal change. However, identifying a socialization effect is difficult due to self-selection mechanisms and limitations in data availability. This article empirically investigates the underlying peer socialization by employing a longitudinal network study of undergraduate students in Swedish and Finnish top-ranked business schools (N = 2651). The study tests the hypotheses that (1) attitudes within a cohort converge over time, and (2) socially embedded students experience more attitudinal change during their studies than students who do not engage with their peers. The paper leverages the Covid-19 pandemic as an exogenous shock that severely affected the networking dynamics of specific cohorts since it moved their education off campus. It is thereby among the first studies to directly test the relationship between socializing behaviors within higher education and political attitudes using both subjective and objective measurements. Contrary to previous conjectures, the findings challenge a general socialization mechanism to foster political attitudes. Instead, they point toward the importance of self-selection mechanisms in studies of higher education and for the specific student composition.

Advancing data-driven decision-making for human papillomavirus (HPV)

Abstract Background HPV is among the most important infections associated with cancers in men and women, including cervical cancer which is the second most common cancer affecting women aged 15-44 years in the EU. Yet, data on vaccine uptake, HPV infection rates and incidence of related cancers is lacking across Europe, obstructing policymakers’ ability to build adequate prevention strategies towards the EU’s goal of eliminating HPV-related cancers. Methods Vaccines Europe analysed the landscape of data collection practices for HPV, highlighting discrepancies across Europe as well as successful initiatives. Results Our analysis reveals stark gaps in data collection across Europe. Some countries do not report disease levels and vaccination coverage for HPV. When reporting does occur, methods and timelines often differ between countries. Limited data availability exists for vaccination uptake in boys and young adults. This lack of standardisation hinders the comparability and reliability of collected data. It also impedes the integration and analysis of data within and across countries, hindering the development of effective elimination strategies. Concrete actions are needed at EU and national level to further develop and strengthen data collection, sharing and use in decision-making. Our research calls on strengthening ECDC’s role, promoting best practice sharing between countries, ensuring sustained resource allocation for data systems/surveillance, establishing electronic vaccination registries, an EU implementation roadmap to support countries in achieving the objectives of Europe’s Beating Cancer Plan, and translating data into policy recommendations. The Council Recommendation on vaccine-preventable cancers provides a framework for EU Member States to establish electronic vaccination registries and the ECDC to develop an HPV dashboard by the end of 2024. Key messages • Current data collection practices leave Europeans more vulnerable in the fight against cancer. • Robust data can guide policy and support elimination strategies for vaccine-preventable cancers.

Forecasting Green Sea Turtle (Chelonia mydas) Landing in Sarawak Using Grey Model

Green sea turtles, known scientifically as Chelonia mydas, prefer to nest on specific sandy beaches in Sarawak, particularly within the Sarawak Turtle Islands (STI). The number of turtles landing, among other variables (number of eggs collected, eggs incubated, and eggs hatched) is an important element in assessing the population size in Sarawak. However, modeling and predicting the number of turtles landing presents challenges due to limited data availability, resulting in less accurate forecasts for medium and long-term periods. To overcome this problem, this study presents a Grey Model (GM) approach, leveraging its capacity to effectively model systems with limited data, irregular patterns, and a lack of prior knowledge. Using data from 1949 to 2016, GM (1,1) was found to be the most suitable model for the given dataset, exhibiting the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as compared to other statistical models such as Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) and Exponential Smoothing. The model also suggested that the current conditions will likely increase turtle landings. This approach will find useful applications in evaluating the conservation status of the species.

Use of Artificial Intelligence tools in supporting decision-making in hospital management

BackgroundThe use of Artificial Intelligence (AI) tools in hospital management holds potential for enhancing decision-making processes. This study investigates the current state of decision-making in hospital management, explores the potential benefits of AI integration, and examines hospital managers’ perceptions of AI as a decision-support tool.MethodsA descriptive and exploratory study was conducted using a qualitative approach. Data were collected through semi-structured interviews with 15 hospital managers from various departments and institutions. The interviews were transcribed, anonymized, and analyzed using thematic coding to identify key themes and patterns in the responses.ResultsHospital managers highlighted the current inefficiencies in decision-making processes, often characterized by poor communication, isolated decision-making, and limited data access. The use of traditional tools like spreadsheet applications and business intelligence systems remains prevalent, but there is a clear need for more advanced, integrated solutions. Managers expressed both optimism and skepticism about AI, acknowledging its potential to improve efficiency and decision-making while raising concerns about data privacy, ethical issues, and the loss of human empathy. The study identified key challenges, including the variability in technical skills, data fragmentation, and resistance to change. Managers emphasized the importance of robust data infrastructure and adequate training to ensure successful AI integration.ConclusionsThe study reveals a complex landscape where the potential benefits of AI in hospital management are balanced with significant challenges and concerns. Effective integration of AI requires addressing technical, ethical, and cultural issues, with a focus on maintaining human elements in decision-making. AI is seen as a powerful tool to support, not replace, human judgment in hospital management, promising improvements in efficiency, data accessibility, and analytical capacity. Preparing healthcare institutions with the necessary infrastructure and providing specialized training for managers are crucial for maximizing the benefits of AI while mitigating associated risks.

Scalability and Replicability Analysis in Smart Grid Demonstration Projects: Lessons Learned and Future Needs

This paper compares various approaches to the scalability and replicability analysis (SRA) of smart grid pilot projects, highlighting the need for a comprehensive SRA methodology as called for by the European Commission and International Energy Agency. This study addresses the need for a standardized SRA methodology and explores how three EU-funded projects—Platone, EUniversal, and IElectrix—adapted the general guidelines developed by the BRIDGE initiative. These guidelines provide recommendations for developing a comprehensive large-scale deployment analysis. The results show that while the guidelines are usable and flexible, project-specific conditions and data availability limitations—particularly in regulatory and technical analysis—can pose challenges. Some key recommendations to overcome these and facilitate future applications are identified. These include defining SRA methodologies and securing data-sharing agreements early. The lack of standardized approaches for presenting SRA results hampers cross-project comparison. Thus, creating an open-use case repository and updating the BRIDGE guidelines with more detailed examples, benchmarks, and reference networks is recommended. Additionally, linking SRA with cost–benefit analysis (CBA) is suggested in order to evaluate the commercial viability of smart grid solutions. The paper concludes that while the BRIDGE guidelines have proven to be fit for purpose, further developments are needed to facilitate their practical application in real-world projects.

Development of physics-guided neural network framework for acid-base treatment prediction using carbon dioxide-based tubular reactor

Accurate acid-base treatment prediction is necessary to achieve the required yield, given the inherent complexity, high nonlinearity, and restricted availability of data samples; to address this challenge, a data-driven approach was developed. However, the technique is constrained by the need for sufficient data to construct an accurate model and lacks both process insight and physical consistency. Therefore, this study introduces a physics-guided neural network model for acid-base treatment prediction in a dynamic tubular reactor using the fundamental physical intermediate variables obtained through the derivation process of the reaction schematic. By integrating batch experimental data, which provides key intermediate variables such as residence time and hydroxide ion concentration, the model addresses the challenge of high nonlinearity and limited data availability. The result shows that the physics-guided potential of a hydrogen predictor had outstanding performance in terms of prediction accuracy (greatest coefficient of determination value of 0.9381). The proposed model demonstrated an average improvement of 24.92% in pH prediction accuracy compared to traditional models without physical guidance, with a maximum improvement of up to 64.95% under limited data conditions. Moreover, downsampling tests revealed that the proposed model maintained robust performance with minimal accuracy reduction even when data was limited without overfitting implication.

Machine learning‐aided process design using limited experimental data: A microwave‐assisted ammonia synthesis case study

AbstractAn open research question lies in how machine learning (ML) can accelerate the design optimization of chemical processes which are at very early experimental development stage with limited data availability. As an example, this article investigates the design of an intensified microwave‐assisted ammonia production reactor with 46 experimental data. We present an integrated approach of neural networks and synthetic minority oversampling technique to quantify the nonlinear input‐output relationships of this process. For ammonia concentration predictions at discrete operating conditions, the approach demonstrates 96.1% average accuracy over other ML methods (e.g., support vector regression 84.2%). The approach has also been applied for continuous optimization, identifying the optimal synthesis conditions at 597.37 K, 0.55MPa with feed flow rate of 1.67 ×10−3 m3/s kg and hydrogen to nitrogen ratio of 1 which is consistent with experimental observations. The data‐driven model enables to integrate this reactor with existing ammonia production infrastructure and benchmark with conventional techniques.

Multi-domain improves classification in out-of-distribution and data-limited scenarios for medical image analysis

Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. In this work, we show that employing models that incorporate multiple domains instead of specialized ones significantly alleviates the limitations observed in specialized models. We refer to this approach as multi-domain model and compare its performance to that of specialized models. For this, we introduce the incorporation of diverse medical image domains, including different imaging modalities like X-ray, MRI, CT, and ultrasound images, as well as various viewpoints such as axial, coronal, and sagittal views. Our findings underscore the superior generalization capabilities of multi-domain models, particularly in scenarios characterized by limited data availability and out-of-distribution, frequently encountered in healthcare applications. The integration of diverse data allows multi-domain models to utilize information across domains, enhancing the overall outcomes substantially. To illustrate, for organ recognition, multi-domain model can enhance accuracy by up to 8% compared to conventional specialized models.

Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship.

Nosocomial infections and Antimicrobial Resistance (AMR) stand as formidable healthcare challenges on a global scale. To address these issues, various infection control protocols and personalized treatment strategies, guided by laboratory tests, aim to detect bloodstream infections (BSI) and assess the potential for AMR. In this study, we introduce a machine learning (ML) approach based on Multi-Objective Symbolic Regression (MOSR), an evolutionary approach to create ML models in the form of readable mathematical equations in a multi-objective way to overcome the limitation of standard single-objective approaches. This method leverages readily available clinical data collected upon admission to intensive care units, with the goal of predicting the presence of BSI and AMR. We further assess its performance by comparing it to established ML algorithms using both naturally imbalanced real-world data and data that has been balanced through oversampling techniques. Our findings reveal that traditional ML models exhibit subpar performance across all training scenarios. In contrast, MOSR, specifically configured to minimize false negatives by optimizing also for the F1-Score, outperforms other ML algorithms and consistently delivers reliable results, irrespective of the training set balance with F1-Score.22 and.28 higher than any other alternative. This research signifies a promising path forward in enhancing Antimicrobial Stewardship (AMS) strategies. Notably, the MOSR approach can be readily implemented on a large scale, offering a new ML tool to find solutions to these critical healthcare issues affected by limited data availability.

A comparative study of crop evapotranspiration estimation in maize using empirical methods, pan evaporation and satellite-based remote sensing technique

A research study was conducted at the Agricultural College and Research Institute, Coimbatore to estimate the evapotranspiration (ET) of maize crop (Zea mays) over 2 consecutive seasons in 2022-2023. Among the different methods used to estimate crop evapotranspiration, the Food and Agricultural Organization Penman-Monteith model (FAO P-M) is widely recognized as the standard approach for ET estimation. This study aimed to compare the effectiveness of three alternative methods - Thornthwaite (TW), NDVI-based and pan methods against the FAO Penman-Monteith (P-M) model in estimating maize evapotranspiration. Meteorological data were collected from the TNAU weather station spanning the period from 2022 to 2023.The performance of the estimation methods was assessed using statistical metrics such as coefficient of determination (R2), root mean squared error (RMSE), percentage error and mean bias error. The findings revealed that the NDVI-based method, relying on satellite data, provided higher accuracy in estimating maize evapotranspiration compared to the FAO PM method. Specifically, the NDVI-based method achieved the highest coefficient of determination (R2) of 0.87 and 0.89, the lowest RMSE of 12.44 mm/month and 15.5 mm/month, the lowest percentage error of 4.8 % and 9.00 % and the lowest mean bias error of 5.5 and 7.85 for the first and second seasons respectively. This study highlights the effectiveness of the NDVI-based ET estimation method for accurately assessing maize evapotranspiration. While the FAO-56 Penman-Monteith method is highly regarded for its accuracy in both theoretical and practical contexts, the comparative evaluation presented in this paper offers valuable insights for selecting alternative methods that require less data, particularly in regions with limited data availability.

Multi-fidelity Gaussian process surrogate modeling for regression problems in physics

Abstract One of the main challenges in surrogate modeling is the limited availability of data due to resource constraints associated with computationally expensive simulations. Multi-fidelity methods provide a solution by chaining models in a hierarchy with increasing fidelity, associated with lower error, but increasing cost. In this paper, we compare different multi-fidelity methods employed in constructing Gaussian process surrogates for regression. Non-linear autoregressive methods in the existing literature are primarily confined to two-fidelity models, and we extend these methods to handle more than two levels of fidelity. Additionally, we propose enhancements for an existing method incorporating delay terms by introducing a structured kernel. We demonstrate the performance of these methods across various academic and real-world scenarios. Our findings reveal that multi-fidelity methods generally have a smaller prediction error for the same computational cost as compared to the single-fidelity method, although their effectiveness varies across different scenarios.

Behavior Change Techniques to Reduce Sugars Intake by Adolescents: A Systematic Review.

The adolescent diet is high in sugars compared with other age groups. Effective approaches to support sugar reduction by adolescents are needed as part of caries prevention. To systematically review peer-reviewed evidence (1990 to 2023) to identify effective behavior change techniques (BCTs) for sugars reduction in adolescents aged 10 to 16 y. Nine databases (CINAHL, Cochrane, Dental and Oral Sciences Source, EMBASE, MEDLINE, PubMed, PsycINFO, Scopus, and Web of Science) were searched. Identified articles were screened independently in duplicate for eligibility. Interventions were eligible if they aimed to change adolescent dietary behavior(s) and reported pre- and postsugar-relevant outcome measures. Interventions from included studies were coded using a 93-item BCT Taxonomy (Michie Taxonomy v1). Risk of bias was assessed using the Effective Public Health Practice Project Quality Assessment Tool for Quantitative Studies. Evidence synthesis by vote counting (number of studies showing positive versus null or negative effects) was applied to BCTs that were present in more than 5 interventions. Of 16,271 articles identified, 764 were screened in full, yielding 35 studies (in 43 papers), of which 3 were uncodeable. BCTs coded in interventions covered 11 of 16 BCT clusters and 25 of 93 individual BCTs in the BCT taxonomy. The median number of BCTs applied per study was 3 (interquartile range 2-6). Evidence synthesis indicated that the BCTs most positively associated with a positive reduction in sugars were (with the percentage of strong-/moderate-quality studies applying these techniques that successfully reduced sugars intake in brackets) feedback on behavior (100%), information on social and environmental consequences (100%), problem solving (75%), and social comparison (75%). Notwithstanding limitations in available data, the current evidence most strongly supports the use of BCTs relating to feedback on behavior, providing information on the social and environmental consequences, include problem solving and making social comparisons, to lower sugars intake in adolescents. The results of this study will enable clinicians to provide more effective dietary advice when supporting dietary behavior change to reduce sugars intake in adolescents. The results may also be used by researchers to guide future directions for research into effective sugars reduction in adolescents.

Gridded Grazing Intensity Based on Geographically Weighted Random Forest and Its Drivers: A Case Study of Western Qinghai–Tibetan Plateau

ABSTRACTOvergrazing affects the grass‐livestock balance and endangers grassland ecological security. Despite extensive studies conducted on identifying and quantifying grazing intensity, there is still room for improvement in the research on gridding grazing intensity, particularly in areas with limited data on the Qinghai–Tibet Plateau. Therefore, we proposed a grazing intensity spatialization method using geographically weighted random forest (GWRF) to gain further insights into the spatial heterogeneity of alpine grassland grazing intensity. This method incorporates multiple remote sensing data related to human activities and natural factors, as well as annual livestock statistics at the township level over several years, while adequately considering the spatial autocorrelation of grazing intensity. Additionally, we employed Lindeman Merenda Gold (LMG), the geographical detector model, and the structural equation model (SEM) to assess the contribution and influence path of driving factors to grazing intensity. We also utilize partial correlation analysis and dual‐phase mapping to examine the impact of natural and human activities on the spatial distribution of grazing intensity. The results demonstrate that the GWRF‐based grazing intensity spatial model accurately predicts grazing intensity by demonstrating its consistency with township‐scale livestock data (R 2 = 0.92 (p < 0.01), RMSE = 1.07). This provides valuable technical support for quantifying grazing intensity in alpine pastoral areas with limited data availability. We evaluate trends in grazing intensity and observe an increase in Gar and Purang counties. Furthermore, population density, normalized difference vegetation index (NDVI), and temperature are identified as three influential factors affecting grazing intensity in alpine pastoral areas. Additionally, other factors indirectly impact grazing intensity by influencing population density and NDVI levels, while their interactions amplify their overall influence. The dual‐phase mapping technique has demonstrated a significant impact of population density on 45.92% (p < 0.01) of the study area, emphasizing the substantial influence of human activities on grazing intensity. Our study provides a novel framework for spatially analyzing grazing intensity and unraveling the intricated driving mechanisms behind spatiotemporal changes, particularly in areas with limited data availability.

Determinants of Tax Regulations Referring to ESG Principles on Company Performance in Indonesia

Purpose: This study examines ESG integration in Indonesia's tax regulations and their impact on financial performance, focusing on green investments.Method: This study uses a qualitative case study method to analyze ESG integration in Indonesia's tax regulations, focusing on policies, carbon tax effectiveness, and emissions disclosure. The research is based on corporate legitimacy and institutional theories to explore the relationship between tax regulations and sustainable practices.Findings: The study's findings reveal significant growth in green investments in Indonesia, largely driven by supportive ESG-oriented tax policies. Despite this progress, challenges such as limited corporate awareness and data availability remain barriers to fully realizing the potential of these policies. The analysis also highlights the importance of these regulations in fostering sustainable business practices, particularly in industries like palm oil that are crucial for addressing climate change.Novelty: This research uniquely examines the impact of ESG-focused tax regulations on green investments and sustainable practices in Indonesia, using corporate legitimacy and institutional theories. It fills a literature gap by exploring how tax regulations drive ESG integration in the business sector of a developing country.Implications: The study has important implications for policymakers, businesses, and stakeholders in Indonesia and similar economies. It shows that ESG-focused tax policies positively impact green investments, suggesting that refining and implementing these policies could strengthen sustainable business practices and support environmental goals. The study also emphasizes the need for greater corporate awareness and better data collection to address challenges and ensure effective ESG integration in the corporate sector.

Semantic Web and its role in facilitating ICT data sharing for the circular economy: An ontology survey

The environmental pressure, CO2 emissions (including embodied energy) and delivery risks of our digital infrastructures are increasing. The exponentially growing digitisation of services that drive the transition from industry 4.0 to industry 5.0 has resulted in a rising materials demand for ICT hardware manufacturing. ICT devices such as laptops and data servers are being used on average for 3 and 4–5 years respectively (van Driel (2020)), while research shows that they should last 7 years before replacement (Journal of Cleaner Production 69 (2014), 10–16). A solution is to transition from a linear to a circular economy (CE), through which materials that were previously disposed of as waste are re-entered back into product life-cycles through processes such as reuse, recycling, remanufacturing, repurposing. However, the adoption of the CE in the ICT sector is currently limited due to the lack of tools that support knowledge exchange between sustainability, ICT and technology experts in a standardised manner and the limited data availability, accessibility and interoperability needed to build such tools. Further, the already existing knowledge of the domain is fragmented into silos and the lack of a common terminology restricts its interoperability and usability. These also lead to transparency and responsibility issues along the supply chain. For many years now, the Semantic Web has been known to provide solutions to such issues in the form of ontologies. Several ontologies for the ICT, materials and CE domains have been build and successfully utilised to support processes such as predictive maintenance. However, there is a lack of a systematic analysis of the existing ontologies in these domains. Motivated by this, we present a literature survey and analysis of, but not limited to, existing ontologies for ICT devices such as laptops, materials and the CE. In addition, we discuss the need for findable, accessible, interoperable, reusable (FAIR) data in the CE, different factors such as data privacy and security that affect this and the role of ontologies.

A comparative study of total and partial ionization cross sections, branching ratios and fragmentation patterns of XH4 (X = C, Si, Ge, Sn, Pb)

The formation and decomposition of SnH4 has been recently reported using electron ionization mass spectrometry (J. Am. Soc. Mass Spectrom. 35 (2024) 1523 and J. Vac. Sci. Technol. A 41 (2023) 063209) due to its importance in tin contamination cleaning in extreme ultraviolet lithography. A comprehensive understanding of the fragment patterns of SnH4 requires total and partial ionization cross sections of SnH4. However, there is limited availability of experimental and theoretical ionization data for SnH4. We report the electron-impact ionization fragmentation patterns of XH4 (X = C, Si, Ge, Sn, Pb) and should provide missing data, especially for SnH4 and PbH4. Total ionization cross sections are calculated using the Binary Encounter Bethe (BEB) method. Partial ionization cross sections and branching ratios are determined using mass spectrum data (MSD) and Huber et al’s method. Finally, we compare the calculated percentage abundances for the fragments of XH4 with experimental measurements.