Scientific Domains Research Articles

Lessons for Theory from Scientific Domains Where Evidence is Sparse or Indirect

AbstractIn many scientific fields, sparseness and indirectness of empirical evidence pose fundamental challenges to theory development. Theories of the evolution of human cognition provide a guiding example, where the targets of study are evolutionary processes that occurred in the ancestors of present-day humans. In many cases, the evidence is both very sparse and very indirect (e.g., archaeological findings regarding anatomical changes that might be related to the evolution of language capabilities); in other cases, the evidence is less sparse but still very indirect (e.g., data on cultural transmission in groups of contemporary humans and non-human primates). From examples of theoretical and empirical work in this domain, we distill five virtuous practices that scientists could aim to satisfy when evidence is sparse or indirect: (i) making assumptions explicit, (ii) making alternative theories explicit, (iii) pursuing computational and formal modelling, (iv) seeking external consistency with theories of related phenomena, and (v) triangulating across different forms and sources of evidence. Thus, rather than inhibiting theory development, sparseness or indirectness of evidence can catalyze it. To the extent that there are continua of sparseness and indirectness that vary across domains and that the principles identified here always apply to some degree, the solutions and advantages proposed here may generalise to other scientific domains.

Advances in the Light-Promoted Transformations of N-Heterocyclic Carbene Ligated Boryl Radicals

AbstractOrganoboron compounds are integral to modern life, with extensive applications in synthesis, materials science, medicine, and various other domains closely linked to human endeavors. NHC-BH3, noted for its stability, ease of synthesis, and high reactivity as a boryl radical precursor, has emerged as a key focus in boryl radical chemistry. Recently, the visible-light-induced single electron transfer (SET) and hydrogen atom transfer (HAT) processes have garnered considerable interest, presenting innovative strategies for generating boryl radicals from NHC-BH3. In the context of this review, our focus is on the synthesis of C–B and X–B bonds under visible light irradiation, facilitated by NHC-BH3. Furthermore, we explored the role of NHC-BH3 as a hydrogen donor or halogen atom transfer reagent in the construction of C–C bonds.1 Introduction2 Hydroboration3 Borylation4 Construction of X–B Bonds (X = N, O, S)5 Halogen Atom Transfer Reagent and Hydrogen Donor6 Conclusion

Advances in artificial intelligence-based technologies for increasing the quality of medical products.

Artificial intelligence (AI) is a technology that combines machine learning (ML) and deep learning. It has numerous usages in the domains of medicine and other sciences. Artificial intelligence can forecast the behavior of a drug's target protein and predict its desired physicochemical qualities. AI's potential to enhance healthcare services offerings formerly unheard-of opportunities for cost reserves, enhanced overall clinical and patient outcomes. The recent development of research in the biomedical field, encompassing fields such as genomics, computational medicine, AI, and algorithms for learning, has led to the demand for novel technology, a fresh workforce, and new standards of practice set the stage for the revolution in healthcare. By connecting these health statistics with cutting-edge AI technologies, precise insights into patient treatment can be obtained. Moreover, AI can aid in the search for new drugs by foretelling the target protein's two-dimensional structure. In the current review, an overview of the latest AI-based technologies and how they may be employed to reduce product development time to market and snowballing product quality, cost-effectiveness, as well as security throughout the manufacturing process is detailed.

Efficient small fragment sequencing of human, cattle, and bison miRNA, small RNA, or csRNA-seq libraries using AVITI

BackgroundNext-Generation Sequencing (NGS) catalyzed breakthroughs across various scientific domains. Illumina’s sequencing by synthesis method has long been central to NGS, but new sequencing methods like Element Biosciences’ AVITI technology are emerging. AVITI is reported to offer improved signal-to-noise ratios and cost reductions. However, its reliance on rolling circle amplification, which can be affected by polymer size, raises questions about its effectiveness in sequencing small RNAs (sRNAs) such as microRNAs (miRNAs), small nucleolar RNAs (snoRNAs), and many others. These sRNAs are crucial regulators of gene expression and involved in various biological processes. Additionally, capturing capped small RNAs (csRNA-seq) is a powerful method for mapping active or “nascent” RNA polymerase II transcription initiation in tissues and clinical samples.ResultsHere, we report a new protocol for seamlessly sequencing short fragments on the AVITI and demonstrate that AVITI and Illumina sequencing technologies equivalently capture human, cattle (Bos taurus), and bison (Bison bison) sRNA or csRNA sequencing libraries, increasing confidence in both sequencing approaches. Additionally, analysis of generated nascent transcription start site (TSS) data for cattle and bison revealed inaccuracies in their current genome annotations, underscoring the potential and necessity to translate small and nascent RNA sequencing methodologies to livestock.ConclusionsOur accelerated and optimized protocol bridges the advantages of AVITI sequencing with critical methods that rely on sequencing short fragments. This advance bolsters the utility of AVITI technology alongside traditional Illumina platforms, offering new opportunities for NGS applications.

Applications of Vedic Mathematics in Computer Science

The convergence of ancient wisdom and modern computation is an intriguing frontier explored in this research paper, which investigates the potential application of Vedic Mathematics in the domain of Computer Science. Vedic Mathematics, originating from ancient Indian texts known as the Vedas, offers a distinct approach to mathematical problem - solving that emphasizes mental agility, pattern recognition, and elegant algorithms. This paper delves into the historical context of Vedic Mathematics, tracing its origins and key principles, and highlights its relevance in contemporary Computer Science. The intersection of Vedic Mathematics and Computer Science is illuminated through the identification of shared principles, such as pattern recognition and optimization. The paper explores specific Vedic Mathematics techniques that have the potential to enhance computational efficiency, including the "Nikhilam" method for subtraction and the "Vertically and Crosswise" method for multiplication. Case studies are presented to demonstrate the practical application of Vedic Mathematics techniques in Computer Science, spanning areas such as data processing, algorithmic optimization, and error detection and correction. However, this integration is not without its challenges and ethical considerations. The paper addresses concerns about accuracy and reliability, particularly when integrating unconventional mathematical techniques into modern computational paradigms. Cultural sensitivity and proper acknowledgment of the origins of Vedic Mathematics are paramount, as is the need to ensure that the techniques are accessible to a diverse audience. The ethical implications of cross - cultural knowledge exchange are also explored, emphasizing the importance of collaboration and respect. Looking ahead, the paper outlines potential research directions and implications for education in Computer Science. The integration of Vedic Mathematics offers opportunities for developing novel algorithms, enhancing problem - solving skills, and fostering cross - cultural understanding. Collaborations between mathematicians and computer scientists are highlighted as a promising avenue for innovation in algorithmic design. In conclusion, the fusion of Vedic Mathematics and Computer Science represents a dynamic interplay of ancient traditions and modern advancements. As we navigate the complexities of integration, we embark on a journey that transcends disciplinary boundaries, fosters cultural appreciation, and holds the potential to reshape computational methodologies in the digital age.

Automated microfluidic system for rapid, multiplexed nucleic acid extraction and detection of pathogens

Nucleic acid (NA) detection is essential across biological research, clinical diagnostics, and industrial applications, yet the complexity of traditional extraction, amplification, and detection processes limits their efficiency and scalability. This study presents an integrated, and fully automated microfluidic system for NA detection, designed to operate a seamless 'sample-in, result-out' workflow. By incorporating two rotary shafts and pre-loaded reagents on-chip, this system reduces human intervention, minimizes contamination risks, and accelerates the process of multiplex pathogen detection. Equipped with 12 reaction chambers and a four-color fluorescence detection module, the system enables simultaneous detection of up to 48 unique targets under 80 min, achieving detection sensitivity as low as 10 CFU/mL in spiked samples. This system’s efficiency and robustness indicate promising applications in medicine, public health, agriculture, forensic science, and other research domains.

Human limits in machine learning: prediction of potato yield and disease using soil microbiome data

BackgroundThe preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potential of machine learning models for understanding the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant performance from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network.ResultsPrediction improves when we add environmental features, such as soil properties and microbial density, along with microbiome data. Different preprocessing strategies show that human decisions significantly impact predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is one of the optimal strategies to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level, or model characteristics. ML performance is limited when humans can’t classify samples accurately. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power.ConclusionsOur study highlights the importance of incorporating diverse environmental features and careful data preprocessing in enhancing the predictive power of machine learning models for soil and biological phenotype connections. This approach can significantly contribute to advancing agricultural practices and soil health management.

Enhancing retrieval performance of embedding models via fine-tuning on synthetic data in RAG chatbot for Vietnamese military science domain

Retrieval-Augmented Generation (RAG) is a technology that combines information retrieval with large language models, enabling chatbots to provide accurate answers by querying relevant documents from a data repository before generating responses. While RAG chatbot has demonstrated effectiveness in many applications, there are still limitations in specialized Vietnamese data domains, particularly in the military science field. To address this challenge, this paper proposes a framework for fine-tuning embedding models using synthetic datasets generated by ChatGPT to enhance retrieval performance in a Q&A application focused on the history of the Institute of Information Technology (IoIT). Our approach evaluates 11 popular embedding models and shows a significant average improvement of 18.15% in the MAP@K metric. The resulting IoIT history Q&A chatbot, developed with fine-tuned embedding models and the Vietnamese language model Vistral-7B, outperforms chatbots utilizing OpenAI's embedding models and ChatGPT. These findings highlight the potential of RAG chatbot technology for advancing information retrieval applications in specialized fields like military science.

CoNST: Code Generator for Sparse Tensor Networks

Sparse tensor networks represent contractions over multiple sparse tensors. Tensor contractions are higher-order analogs of matrix multiplication. Tensor networks arise commonly in many domains of scientific computing and data science. Such networks are typically computed using a tree of binary contractions. Several critical inter-dependent aspects must be considered in the generation of efficient code for a contraction tree, including sparse tensor layout mode order, loop fusion to reduce intermediate tensors, and the mutual dependence of loop order, mode order, and contraction order. We propose CoNST, a novel approach that considers these factors in an integrated manner using a single formulation. Our approach creates a constraint system that encodes these decisions and their interdependence, while aiming to produce reduced-order intermediate tensors via fusion. The constraint system is solved by the Z3 SMT solver and the result is used to create the desired fused loop structure and tensor mode layouts for the entire contraction tree. This structure is lowered to the IR of the TACO compiler, which is then used to generate executable code. Our experimental evaluation demonstrates significant performance improvements over current state-of-the-art sparse tensor compiler/library alternatives.

Symmetric Tridiagonal Eigenvalue Solver Across CPU Graphics Processing Unit (GPU) Nodes

In this work, an improved and scalable implementation of Cuppen’s algorithm for diagonalizing symmetric tridiagonal matrices is presented. This approach uses a hybrid-heterogeneous parallelization technique, taking advantage of GPU and CPU in a distributed hardware architecture. Cuppen’s algorithm is a theoretical concept and a powerful tool in various scientific and engineering applications. It is a key player in matrix diagonalization, finding its use in Functional Density Theory (FDT) and Spectral Clustering. This highly efficient and numerically stable algorithm computes eigenvalues and eigenvectors of symmetric tridiagonal matrices, making it a crucial component in many computational methods. One of the challenges in parallelizing algorithms for GPUs is their limited memory capacity. However, we overcome this limitation by utilizing multiple nodes with both CPUs and GPUs. This enables us to solve subproblems that fit within the memory of each device in parallel and subsequently combine these subproblems to obtain the complete solution. The hybrid-heterogeneous approach proposed in this work outperforms the state-of-the-art libraries and also maintains a high degree of accuracy in terms of orthogonality and quality of eigenvectors. Furthermore, the sequential version of the algorithm with our approach in this work demonstrates superior performance and potential for practical use. In the experiments carried out, it was possible to verify that the performance of the implementation that was carried out scales by 2× using two graphic cards in the same node. Notably, Symmetric Tridiagonal Eigenvalue Solvers are fundamental to solving more general eigenvalue problems. Additionally, the divide-and-conquer approach employed in this implementation can be extended to singular value solvers. Given the wide range of eigenvalue problems encountered in scientific and engineering domains, this work is essential in advancing computational methods for efficient and accurate matrix diagonalization.

MXene: A new revolution in the world of 2-D materials

MXenes have imposed a profound effect on materials science and nanotechnology fields after their discovery in 2011. Theoretical models have predicted more than 100 potential compositions of MXene whereas laboratory-scale synthesis reflects their success of over 40 distinct structures till date. The distinctive properties of MXenes have led to their use for a diverse range of applications, such as energy storage, environmental remediation, electronics, communications, gas and liquid separation and adsorption, biomedical fields, and optoelectronics. The increased interest of researchers in MXenes has led to a wide rise in research publications, showing their growing importance in different scientific domains. In 2024, MXenes had shown wide potential in various areas, including energy storage devices, electromagnetic interference shielding, nanocomposites, and hybrid materials. However, the variations in the choice of precursors, reactor design, cost, synthesis parameters pose several challenges in ensuring the production of high-quality MXenes. The applicability of MXenes continues to broaden as its compositions are continuously accelerating. This review aims is to provide a comprehensive overview of MXene history, its properties, challenges, latest trends, and different applications to highlight its potential and gather new audiences towards this family of two-dimensional materials.

Pollution Crime Scenario Reconstruction for Effective Environmental Redress

Dealing pollution cases as criminal offences requires proof beyond reasonable doubt for conviction. To meet this highest standard of evidentiary requirement, forensic professionals need to adopt the best tools and methods available to them. “Crime Scene Reconstruction” is a method adopted in traditional crimes to recreate the exact sequence of events leading to the crime from the physical evidences collected at the crime scene. This paper presents the adoption of the method of crime reconstruction to environmental crimes. All stages of traditional crime scene reconstruction, starting from recognition of physical evidence from the scene, its identification and individualization, collection of data, hypothesis formulation, it’s testing and final reconstruction are explained in the context of environmental crimes. An effective method to apply in the reconstruction of an environmental crime scene must rely on several experts of different scientific domains who are united in defining, through the search for evidences, the causal link that connects the “victim”, the “guilt” and the “perpetrator”. Often, the difficulty in such reconstruction is because the perpetrator’s motive is not really connected to environmental aspects, but only the consequential effects are revealed in the environment. However, even if the actions that caused the damage to the environment were not the real motive of the perpetrator, this does not redeem him from the responsibility of associated environmental crimes, but makes the process of reconstructing the crime more complex, especially in the search for the cause-effect relationship. The activity related to the analysis of an environmental crime scene is to be considered a situational awareness activity that aims to evaluate not only a specific action in a scene but the entire dynamics of a complex scenario. Considering the complexities that are characteristic of environmental crimes, we propose one more stage in addition to the general stages of traditional crime reconstruction, that of presenting the reconstructed scenario in a way that is easy for the legal fraternity to comprehend. Finally, the entire procedure of reconstruction is demonstrated using an actual case.

Climate responsive innovations at Karachi steel

Learning outcomes The learning outcomes are as follows: to identify and analyze the importance of operations management in a situation demanding minimizing environmental impact and maintaining operational momentum; access the risks faced during project executions and apply project management concepts to facilitate Karachi Steel in implementing indigenous technological solutions; and evaluate the importance of adaptability, continuous improvement and innovation in creating sustainable solutions to address complex challenges. Case overview/synopsis Javaid Iqbal, CEO of Karachi Steel, was the case’s protagonist. With capacity expansion, Javaid relocated the steel facility from Rawalpindi to Islamabad, Pakistan. The company encountered several difficulties because of the air emissions’ inconvenience to nearby residents and the strict environmental regulations. To push the emissions into the air, the company first installed a locally fabricated chimney. Later, they hired a foreign Pakistani engineering firm to install air filters, but the project proved unsuccessful. To control emissions, the company developed a Wet Particulate Control (WPC) system based on a water-sprinkling mechanism. The endeavor was successful, but it resulted in water pollution. As a result, Karachi Steel signed a contract with a local engineering company that invented and effectively installed an air filtration system. Karachi Steel not only devised solutions for their predicaments but also made significant contributions toward achieving the Sustainable Development Goals (SDGs). However, the emissions reporting and monitoring mechanism continued to cause inconvenience for regulators. In addition, the filtration facility encountered a blocked duct conveying zinc sulfate from smoke, resulting in the periodic suspension of operations. As Karachi Steel seek long-term solutions to current challenges, it is critical to examine the relationship between internal circumstances and external forces and stimulate a holistic approach to resolving issues within the realms of operations management and project management. Complexity academic level The case study is suitable for students pursuing their undergraduate degree programs in business studies or management sciences. This case can be taught in specific subjects in the domain of management sciences, including project management and operations management. Furthermore, undergraduate students pursuing degrees in environmental sciences, specializing in environmental impact assessment and sustainable development, can also learn from this case study. These subjects have the potential to provide students with a detailed understanding of the dynamic relationship between environmental problems caused by business activities, and how to address these challenges using principles of project management and operations management. There is no pre-requisite for this case study, and the level of difficulty is moderate. The recommended teaching pedagogy for this multidisciplinary case study includes role-playing exercises, simulations to replicate real-world situations and the Socratic method, which encourages critical thinking. Supplementary material Teaching notes are available for educators only. Subject code CSS 7: Management Science.

Design-assisted HPLC-UV method for therapeutic drug monitoring of pholcodine, ephedrine, and guaifenesin in biological fluids

Drug-drug interactions may amplify or diminish their intended effects, or even produce entirely new effects. Multicomponent mixture HPLC analysis offers a thorough and effective method for comprehending the makeup and behavior of complicated materials, advancing research and development across a range of scientific and industrial domains. A novel experimental design-assisted HPLC methodology for the concurrent investigation of the drug-drug interaction of pholcodine, ephedrine, and guaifenesin in biological fluids has been established. Rather than the routine methodology, the application of the factorial design-HPLC method offers a powerful and efficient tool for the analysis of these compounds. Both mixed and full factorial designs were employed to assess the impact of variable factors on chromatographic results. Utilizing an isocratic elution mode on a C18 column, the chromatographic separation was carried out. 15% Methanol, 5% acetonitrile, and 80% phosphate buffer with 0.1%(v/v) triethylamine set to pH 3 make up the mobile phase flowing at rate 1.0 mL/min. The calibration curves of the drugs show excellent linearity over a concentration ranges: 0.20–13.0 µg/mL for PHO, 0.50–20.0 µg/mL for EPH and 0.70–20.0 µg/mL for GUA with LOQ values of 0.18, 0.38 and 0.50. The fast separation and quantitation in less than 6 min is an advantage. Also, the method includes a robust sample preparation protocol for the analysis of complex biological samples, ensuring high selectivity and precision. The ease, speed and cost-effectiveness of the method are ideal for supporting in vitro studies, including drug-drug interaction investigations, especially in bioanalytical labs.

Ground-Based Remote Standoff Laser Spectroscopies and Reflectance Spectral Imaging for Multimodal Analysis of Wall Painting Stratigraphy.

This paper presents a novel multimodal remote sensing setup to analyze the complex stratigraphy of historical wall paintings at distances of order 10 m. The proposed method enables comprehensive investigation of the chemical composition of multilayer paint stratigraphy by combining standoff laser-induced breakdown spectroscopy for elemental profiling with noninvasive standoff Raman spectroscopy and visible and near-infrared (400-900 nm) reflectance spectral imaging for depth-resolved complementary material characterization from a range of distances with instruments and operators located on stable ground. Following proof-of-concept laboratory tests, the feasibility and effectiveness of this standoff analytical approach is demonstrated through field analysis of a whitewashed historical wall painting, successfully identifying at least seven distinct layers from a distance of 7 m. The remote sensing method presented here can also be applied to other scientific and industrial domains to characterize the chemical composition of layered materials at a distance.

Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series

Nonstationary count time series characterized by features such as abrupt changes and fluctuations about the trend arise in many scientific domains including biophysics, ecology, energy, epidemiology, and social science domains. Current approaches for integer-valued time series lack the flexibility to capture local transient features while more flexible models for continuous data types are inadequate for universal applications to integer-valued responses such as settings with small counts. We present a modeling framework, the negative binomial Bayesian trend filter (NB-BTF), that offers an adaptive model-based solution to capturing multiscale features with valid integer-valued inference for trend filtering. The framework is a hierarchical Bayesian model with a dynamic global-local shrinkage process. The flexibility of the global-local process allows for the necessary local regularization while the temporal dependence induces a locally smooth trend. In simulation, the NB-BTF outperforms a number of alternative trend filtering methods. Then, we demonstrate the method on weekly power outage frequency in Massachusetts townships. Power outage frequency is characterized by a persistent low level trend with occasional spikes. These illustrations show the estimation of a smooth, nonstationary trend with adequate uncertainty quantification.

Representing Geospatial Knowledge in Narratives

This paper explores the representation of geospatial knowledge within narratives through a Semantic Web approach. We introduce the NOnt+Space (NOnt+S) model, an extension of the CIDOC CRM-based Narrative Ontology, which allows the representation of narratives and their geospatial aspects. By leveraging standards such as CRMgeo and GeoSPARQL, NOnt+S ensures systematic and interoperable geospatial representation in narratives, enabling geospatial queries on knowledge graphs. We present an assessment of NOnt+S utilising data from the H2020 MOVING European project (2021-2024), which collected knowledge about European mountain value chains intended as Cultural Heritage. We have represented this knowledge as geospatial narratives using NOnt+S. GeoSPARQL queries and semantic reasoning applied to the created KG reveal the ontology ability to infer new geospatial knowledge. Our work contributes to the ongoing efforts in the Semantic Web community to integrate and represent geospatial information within narratives, promoting collaboration and interoperability across various scientific domains.

Researcher identities and values in the impact agenda: the case of artificial intelligence academics

AbstractA major shift in the research sector has been the increased expectation from policymakers and funders that academic research should yield some socioeconomic benefits or ‘impacts’ rather than merely new knowledge. In this paper, we explore the role that impact has in academics’ motivations and values and how impact is being integrated into academics’ core functions of research and education. We do this through in-depth interviews (n = 60) with scientists who work on the development or application of artificial intelligence (AI), broadly defined. This AI’s focus situates our participants within a strategically important, high-priority area of research for all three national contexts included in our study—Australia, the UK and the USA. Our findings reveal that the impact mission has become central to understanding the motivations and values of academics, but unevenly. We identify divergence between those who work on AI from a foundational computer science perspective and those who develop and apply AI within other scientific domains. The two groups have different understandings of key notions such as ‘impact’ and ‘applied research’, as well as different ways of integrating the impact agenda into their research and education activities. The study highlights the importance of flexible approaches to research policy and governance that are based on a deeper understanding of what motivates researchers, and that take into account academics’ educational role. Greater holistic understanding of how academic identities and practices are accommodating the impact agenda is essential to maximise synergy across activities and avoid unintended consequences.

Exploring the Role of Tools and Technologies in Enhancing Human Knowledge Acquisition: The Flora of Cambodia as a Pilot Project.

The globalization of the world profoundly impacts the academic and scientific domains. Technological advances have enabled an immeasurable progress in many scientific fields, for example structural or functional genomics, bioinformatics or even the development of artificial intelligence, which is becoming a catalyst for disciplines such as taxonomy. However, we are witnessing an unprecedented paradox. We have entered a period described as the 6th mass extinction (e.g., Ceballos et al. 2015, Ceballos et al. 2020, Cowie et al. 2022), where biodiversity has been in decline for many years because of anthropogenic activity (Myers et al. 2000, Sachs et al. 2009, Butchart et al. 2010) and yet, techniques have never been so advanced (e.g., online databases, metabarcording and eDNA identification methods). They produce a deluge of results and data that must be managed to facilitate their access and re-use and benefit to all users, i.e., scientists and stake-holders or policy makers on a global scale. Currently, the flora of Cambodia is part of the Flora of Cambodia, Laos and Viêt-nam*1 and is jointly edited by the Royal Botanic Garden of Edinburgh and the Muséum national d’Histoire naturelle in Paris since 2013. Given Cambodia's historical ties to French Indochina, France's long-term presence has significantly contributed to the documentation of Cambodia's biodiversity. The largest collection of Cambodian specimens is housed at the Paris Herbarium. The Flora of Cambodia project (Ung 2023) started in 2023 as a pilot project for testing the effectiveness of different open tools developped for taxonomists, e.g., the echinopscis platform*2 developped at the Royal Botanical Garden Kew; Bionomia*3; data made available online through platforms such as the Global Biodiversity Information Facility, Atlas of Living Australia, and World Flora Online. Other sources will also be used, like publications (Joyce et al. 2020) and "les herbonautes"*4, a citizen science platform. The short-term result is to produce a checklist of the vascular plants of Cambodia in multiple languages including Khmer. This project aligns with the FAIR (Findable, Accessible, Interoperable, Reusable) (Wilkinson et al. 2016) and CARE (Collective benefit, Authority to control, Responsability, Ethics) (Carroll et al. 2020) principles.

Mathematical Information Retrieval: A Review

Mathematical formulas are commonly used to demonstrate theories and basic fundamentals in the Science, Technology, Engineering, and Mathematics (STEM) domain. The burgeoning research in the STEM domain results in the mass production of scientific documents that contain both textual and mathematical terms. In scientific information, the definition of mathematical formulas is expressed through context and symbolic structure that adheres to strong domain-specific notions. Whereas the retrieval of textual information is well-researched, and numerous text-based search engines are present. However, textual information retrieval systems are inadequate for searching scientific information containing mathematical formulas, including simple symbols to complicated mathematical structures. The retrieval of mathematical information is in its infancy, and it requires the inclusion of new technologies and tools to promote the retrieval of scientific information and the management of digital libraries. This article provides a comprehensive study of mathematical information retrieval and highlights their challenges and future opportunities.