Combining Data Analysis Research Articles

Combined localization and clustering approach for reduced energy presumption in heterogeneous IoT

The field of H-IoT is emerging with enormous potential to empower various technologies. Smart cities and advanced manufacturing are a few of the fields where H-IoT is currently used. The issue with H-IoT is its heavy energy consumption while transmitting data, which makes scaling difficult. To overcome such issues, a hybrid approach of Crayfish Optimization (CFO) with FCM and Restricted Boltzmann Machine (RBM) with Soft Sign Activation (SSA) has been proposed. Initially, Node initialization lays the foundation by configuring individual sensor nodes for network participation. After initialization, Fuzzy C Means clustering optimizes data aggregation by categorizing nodes into clusters based on similarity. Gathering Neighbor Node Traffic Data (NNTD) provides insights into communication patterns. Based on the threshold of NNTD, node localization is performed that enhances network accuracy by pinpointing sensor node locations. Integration of CFO into clustering, along with localization further improves cluster head selection for optimal data routing. Classification through the RBM with SSA function enhances anomaly detection, combining data analysis for optimizing energy utilization in heterogeneous IoT environments. The ‘combined CFO-FCM and SSA-RBM’ has been implemented in MATLAB and achieved an accuracy of 94.50%. As a result, the overall performance of the system is improved.

Securing brand trust and reputation in an era of political polarisation and AI-driven disinformation

This paper analyses the seismic reputational risks now facing brands as two major sociotechnical forces converge — the rise of ideological polarisation and the emergence of AI’s ability to generate synthetic media impersonating organisations. It examines how brands have embraced polarised stances on social issues to build deeper connections with values-driven consumers, especially younger demographics like millennials and Gen Z. By adopting partisan positions. Brands also become targets for ideological attacks seeking to erode trust. Meanwhile, rapid advances in generative AI empower malicious actors to disseminate disinformation, attacking brands with harmful narratives, misinformation and disinformation on an unprecedented scale and realism to do financial and reputational harm. The paper provides an in-depth examination of both dynamics. It explores how younger consumers increasingly demand corporate advocacy on divisive issues, forcing brands to navigate complex sociopolitical fault lines. Taking progressive stances risks a backlash from ideological opponents ready to instigate reputation-damaging retaliation. The paper also details the exponential threats introduced by AI synthesis technologies like generative adversarial networks. It explains how even modestly resourced attackers could soon produce unlimited volumes of fake content, misinformation and disinformation to target brands across all digital channels and media types. To address these converging risks, the paper recommends that brands invest in capabilities including monitoring sociocultural tensions, technologically authenticating communications, establishing early disinformation warning systems, educating consumers on resisting manipulation, crafting proactive narratives, practicing radical transparency and testing defences. It emphasises the need for holistic ‘narrative intelligence’ combining data analysis, cultural discernment and human wisdom. With deliberate strategies focused on verification, transparency and continuous learning, brands can foster resilience and enduring trust despite inevitable disruptions. The paper concludes that competence in navigating partisan debates and AI deception will become a competitive advantage in determining which brands lead in this complex future.

A survey of sustainable development of intelligent transportation system based on urban travel demand

<p>This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development.<strong></strong></p>

Fast tracking tool selection for sustainability decisions

Abstract Non-technical Summary In decision-making, especially for sustainability, choosing the right assessment tools is crucial but challenging due to the abundance of options. A new method is introduced to streamline this process, aiding policymakers and managers. This method involves four phases: scoping, cataloging, selection, and validation, combining data analysis with stakeholder engagement. Using the food system as an example, the approach demonstrates how practitioners can select tools effectively based on input variables and desired outcomes to address sustainability risks. This method can be applied across various sectors, offering a systematic way to enhance decision-making and manage sustainability effectively. Technical Summary Decision making frequently entails the selection and application of assessment tools. For sustainability decisions there are a plethora of tools available for environmental assessment, yet no established and clear approach to determine which tools are appropriate and resource efficient for application. Here we present an extensive inventory of tools and a novel taxonomic method which enables efficient, effective tool selection to improve decision making for policymakers and managers. The tool selection methodology follows four main phases based on the divergence-convergence logic; a scoping phase, cataloging phase, selection phase and validation phase. This approach combines elements of data-driven analysis with participatory techniques for stakeholder engagement to achieve buy-in and to ensure efficient management of progress and agile course correction when needed. It builds on the current limited range and scope of approaches to tool selection, and is flexible and Artificial Intelligence-ready in order to facilitate more rapid integration and uptake. Using the food system as a case study, we demonstrate how practitioners can use available input variables and desired output metrics to select the most appropriate tools to manage sustainability risks, with the approach having wide applicability to other sectors. Social Media Summary New method simplifies tool selection for sustainable decisions, aiding policymakers & managers. #Sustainability #DecisionMaking

Robustness of apparent diffusion coefficient–based lymph node classification for diagnosis of prostate cancer metastasis

ObjectivesThe aim of this proof-of-principle study combining data analysis and computer simulation was to evaluate the robustness of apparent diffusion coefficient (ADC) values for lymph node classification in prostate cancer under conditions comparable to clinical practice.Materials and methodsTo assess differences in ADC and inter-rater variability, ADC values of 359 lymph nodes in 101 patients undergoing simultaneous prostate-specific membrane antigen (PSMA)-PET/MRI were retrospectively measured by two blinded readers and compared in a node-by-node analysis with respect to lymph node status. In addition, a phantom and 13 patients with 86 lymph nodes were prospectively measured on two different MRI scanners to analyze inter-scanner agreement. To estimate the diagnostic quality of the ADC in real-world application, a computer simulation was used to emulate the blurring caused by scanner and reader variability. To account for intra-individual correlation, the statistical analyses and simulations were based on linear mixed models.ResultsThe mean ADC of lymph nodes showing PSMA signals in PET was markedly lower (0.77 × 10−3 mm2/s) compared to inconspicuous nodes (1.46 × 10−3 mm2/s, p < 0.001). High inter-reader agreement was observed for ADC measurements (ICC 0.93, 95%CI [0.92, 0.95]). Good inter-scanner agreement was observed in the phantom study and confirmed in vivo (ICC 0.89, 95%CI [0.84, 0.93]). With a median AUC of 0.95 (95%CI [0.92, 0.97]), the simulation study confirmed the diagnostic potential of ADC for lymph node classification in prostate cancer.ConclusionOur model-based simulation approach implicates a high potential of ADC for lymph node classification in prostate cancer, even when inter-rater and inter-scanner variability are considered.Clinical relevance statementThe ADC value shows a high diagnostic potential for lymph node classification in prostate cancer. The robustness to scanner and reader variability implicates that this easy to measure and widely available method could be readily integrated into clinical routine.Key Points• The diagnostic value of the apparent diffusion coefficient (ADC) for lymph node classification in prostate cancer is unclear in the light of inter-rater and inter-scanner variability.• Metastatic and inconspicuous lymph nodes differ significantly in ADC, resulting in a high diagnostic potential that is robust to inter-scanner and inter-rater variability.• ADC has a high potential for lymph node classification in prostate cancer that is maintained under conditions comparable to clinical practice.Graphical abstract

Business Analytics Using Predictive Algorithms

In today's data-driven business landscape, organizations strive to extract actionable insights and make informed decisions using their vast data. Business analytics, combining data analysis, statistical modeling, and predictive algorithms, is crucial for transforming raw data into meaningful information. However, there are gaps in the field, such as limited industry focus, algorithm comparison, and data quality challenges. This work aims to address these gaps by demonstrating how predictive algorithms can be applied across business domains for pattern identification, trend forecasting, and accurate predictions. The report focuses on sales forecasting and topic modeling, comparing the performance of various algorithms including Linear Regression, Random Forest Regression, XGBoost, LSTMs, and ARIMA. It emphasizes the importance of data preprocessing, feature selection, and model evaluation for reliable sales forecasts, while utilizing S-BERT, UMAP, and HDBScan unsupervised algorithms for extracting valuable insights from unstructured textual data.

The preventive effect of PNF stretching exercise on sports injuries in physical education based on IoT data monitoring

In physical education, in order to prevent sports injuries of students, it is necessary to study and build a set of physical education Internet of Things data monitoring training system to prevent sports injuries of students. This system is mainly composed of sensors, smart phones and cloud servers. Wearable devices equipped with sensors are used to complete data acquisition and transmission by means of the Internet of Things system, and relevant parameters are sorted and monitored by combining data analysis technology. The system makes a more in-depth, comprehensive and accurate analysis and processing of the collected data, so as to better evaluate the status and quality of students' sports, find out the existing problems in time, and put forward the corresponding solutions. By analyzing students' sports data and health data, the system generates personalized training programs, including training intensity, training time, training frequency and other parameters, so as to meet the needs and actual conditions of different students and avoid sports injuries caused by overtraining. This system can better analyze and process the collected data, provide teachers with more comprehensive and in-depth assessment and monitoring of students' sports status, and provide students with more personalized and scientific training programs, so as to effectively prevent the occurrence of students' sports injuries.

Untargeted screening of plant metabolites based on data-independent and data-dependent acquisition modes using LC-ESI-QTOF-MS: Tribulus terrestris L. as a case study

Metabolomics has been used as a powerful tool for the analysis, and drug-lead identification in medicinal plants and herbal medicines. For the coverage of a broader range of plant-based metabolites using LC-MS, one of the important parameters is the selection of analysis mode and data processing for identification. This paper describes the utility of two distinctive acquisition modes in combination, a classic data-dependent acquisition (DDA) mode and a data-independent acquisition (DIA) mode for obtaining the mass spectrometry data of plant extracts using LC-ESI-QTOF/MS. Tribulus terrestris plant was used as a case study. We have applied three-step data analysis pipeline 1-annotation and putative identification of metabolites, 2-validation, and relative quantification, and 3-multivariate analysis using open-access MS-DIAL, Skyline, and Perseus software. A total of four samples of T. terrestris (aqueous extracts), two fruits, and two whole plant samples, from two different regions, were used. By combining data analysis results of plant fruit samples from two different regions, a total of 95 and 77 metabolites were identified in positive and negative ionization modes, respectively. Similarly, in the analysis of the whole plant from two different regions, 75 and 76 metabolites were identified in positive and negative ionization modes, respectively. We suggested the use of DDA mode for annotation, identification of metabolites, and generation of transition lists in MS-DIAL, furthermore, the use of DIA acquisition mode for enhancing metabolites sensitivity in complex samples, deconvolute MS1/MS2 spectra in Skyline for the quantitative performance and analytical reliability. The developed protocol can be used for the broader coverage of plant-based metabolites.

기계 번역과 자연어처리를 활용한 튜터링 활동 분석

This is a follow-up study by Yoon, Kim, Choi, and Kim (2021). In the current study, the same text as the previous study was machine-translated into English, and then the topics derived through a statistical model, which were compared with the categories derived from the Korean text of the previous study. By doing this, rather than simply evaluating the quality of machine translation, the present study examined 1) the natural language processing model applied to text, 2) the role of human researchers in the process of interpreting the results after fitting the model, and 3) the harmony between human and machine translation, The texts to be analyzed were collected from the liberal arts education tutoring program of D university. A theoretical description of the Latent Dirichlet Allocation model, which is often used for topic modeling, and an explanation of the use of the R library ‘topicmodels’ were added. The analyses revealed a somewhat different pattern of topics from the previous study, and the authors’ interpretation was presented. Finally, while acknowledging the explosive growth of natural language processing and machine translation as a branch of it that cannot be reversed, the room for human translators to complement machine translation was suggested. This study presents a new approach to liberal arts education research by combining data analysis techniques using natural language processing and machine translation discussions.

Federated Learning on Tamperproof Data for PHM on Marine Vessels Using a Docker Based Infrastructure

Prognostics and health management (PHM) on systems such as vehicles and marine vessels are sometimes held back by complexities relating to data ownership and intellectual property rights. This is particularly true when multiple Original Equipment Manufacturers (OEMs) deliver components or sub-systems to a customer while having an interest in monitoring and maintenance of said components or sub-systems. Further, the collection of PHM data from a fleet which may be non-uniform and spread across the globe with varying degrees of connectivity can be challenging from a bandwidth and cybersecurity point of view. Federated learning may address some of these challenges and open up new opportunities for how to approach PHM on a global and non-uniform fleet of components or systems. In this article, we will discuss how a Docker based infrastructure for data collection, storage and analysis in combination with a methodology for tamperproofing PHM data can be a powerful substrate for bringing trust and transparency to federated learning implementations of PHM algorithms. We also demonstrate a basic blockchain enabled federated learning experiment using the proposed Docker based infrastructure in combination with centralized data centers.

Development of comprehensive decision support tools in distance learning quality management processes

The object of this study is the quality of distance learning. The need for procedures to assess the quality of this form of education was manifested most clearly in connection with the COVID-19 pandemic, wars, and other global problems, which predetermine the relevance of the study. The study considers the construction of a decision support model for assessing the quality of distance learning. Underlying the method is a combination of the method of expert assessments and the criterion model of data analysis, the basic method for analyzing the data obtained is the method of hierarchy analysis. Structural and functional schemes of the quality management system for distance learning are proposed. During the study, 10 criteria and 52 indicators were selected, and the weight of each indicator was calculated. Based on the weight values obtained, a scheme of the criteria model of decision support was built to assess the quality of distance learning. During the expert evaluation of the criteria and indicators, it was determined that the weight of indicators within the criterion ranges from 0.09953 to 0.34262. Such a difference in weight values indicates the optimality of the set of indicators within the criterion. Due to the combination of a criteria-based approach to data analysis in combination with the method of expert assessments, the model can be easily adapted for a point assessment of individual components and finding problem areas in the implementation of distance learning and management decision-making. The results of the study reported here may be of interest to both heads of educational institutions and employees of services involved in processing information about the organization and reporting for strategic decision-making.

Discovering temporal scientometric knowledge in COVID-19 scholarly production.

The mapping and analysis of scientific knowledge makes it possible to identify the dynamics and/or growth of a particular field of research or to support strategic decisions related to different research entities, based on bibliometric and/or scientometric indicators. However, with the exponential growth of scientific production, a systematic and data-oriented approach to the analysis of this large set of productions becomes increasingly essential. Thus, in this work, a data-oriented methodology was proposed, combining Data Analysis, Machine Learning and Complex Network Analysis techniques, and Data Version Control (DVC) tool, for the extraction of implicit knowledge in scientific production bases. In addition, the approach was validated through a case study in a COVID-19 manuscripts dataset, which had 199,895 articles published on arXiv, bioRxiv, medRxiv, PubMed and Scopus databases. The results suggest the feasibility of the proposed methodology, indicating the most active countries and the most explored themes in each period of the pandemic. Therefore, this study has the potential to instrument and expand strategic decisions by the scientific community, aiming at extracting knowledge that supports the fight against the COVID-19 pandemic.

A Network Meta-Analysis of the Clinical Efficacy and Safety of Commonly Used Chinese Patent Medicines in the Auxiliary Treatment of Poststroke Depression.

Background Poststroke depression (PSD) is a serious complication of clinical cerebrovascular disease. Patients not only have depression-related emotional symptoms but also have physical symptoms, such as autonomic dysfunction. At the same time, patients with varying degrees of depression will delay the neurological function of stroke patients. The recovery time of cognitive function and limb function will increase the risk of accidental death and even aggravate the mortality of cerebrovascular disease. Through combining data analysis and related literature, seven types of Chinese patent medicines (CPMs) widely used in the clinical treatment of PSD have been screened out. These herbs exhibit some clinical comparability under the conditions that the syndrome type and dosage form are relatively uniform. Therefore, in this study, the network meta-analysis method was used to evaluate the safety and efficacy of the seven CPMs screened out, and the probability ranking was performed to screen the best clinical auxiliary treatment plan of CPM. Methods We searched the Chinese databases, including CNKI, WANFANG, and VIP, as well as the English databases, including the Cochrane Library, EMBASE, and PubMed, from inception to May 31, 2020, to identify randomized controlled trials (RCTs) on seven kinds of CPMs that were the subjects of the clinical research. The bias risk and quality of the included studies were analyzed with the Cochrane Handbook (version 5.1), ADDIS, and R software, and the results were compared in a network meta-analysis (NMA). Results In terms of clinical effectiveness, the seven kinds of CPMs all improved clinical curative effects, with Jieyu Anshen capsule adjuvant treatment having the most significant effect [odds ratio (OR) = 5.00, 95% CI (1.72–9.48)]. Wuling capsule AT can effectively reduce the score index of scale factors for the HAMD score, NIHSS score, and TESS score [mean difference (MD) = −3.95, 95% CI (−4.88–3.00); OR = −3.25, 95% CI (−5.46)–1.05); OR = 0.22, 95% CI (0.05–0.79), resp.]. Conclusion The mechanisms of seven CPMs in the adjuvant treatment of PSD have advantages. In terms of safety and efficacy, the CPMs had better clinical adjuvant treatment performance. Although this study concluded that the Jieyu Anshen capsule is the preferred drug for clinical treatment, a clear conclusion still needs to be verified in a high-quality randomized controlled study. In clinical practice, accurate selection and application can be carried out according to the specific characteristics of patients.

Potential for combining semantics and data analysis in the context of digital twins.

Modern production systems can benefit greatly from integrated and up-to-date digital representations. Their applications range from consistency checks during the design phase to smart manufacturing to maintenance support. Such digital twins not only require data, information and knowledge as inputs but can also be considered integrated models themselves. This paper provides an overview of data, information and knowledge typically available throughout the lifecycle of production systems and the variety of applications driven by data analysis, expert knowledge and knowledge-based systems. On this basis, we describe the potential for combining data analysis and knowledge-based systems in the context of production systems and describe two feasibility studies that demonstrate how knowledge-based systems can be created using data analysis.This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

Identifying and responding to outlier demand in revenue management

Revenue management strongly relies on accurate forecasts. Thus, when extraordinary events cause outlier demand, revenue management systems need to recognise this and adapt both forecast and controls. Many passenger transport service providers, such as railways and airlines, control the sale of tickets through revenue management. State-of-the-art systems in these industries rely on analyst expertise to identify outlier demand both online (within the booking horizon) and offline (in hindsight). So far, little research focuses on automating and evaluating the detection of outlier demand in this context. To remedy this, we propose a novel approach, which detects outliers using functional data analysis in combination with time series extrapolation. We evaluate the approach in a simulation framework, which generates outliers by varying the demand model. The results show that functional outlier detection yields better detection rates than alternative approaches for both online and offline analyses. Depending on the category of outliers, extrapolation further increases online detection performance. We also apply the procedure to a set of empirical data to demonstrate its practical implications. By evaluating the full feedback-driven system of forecast and optimisation, we generate insight on the asymmetric effects of positive and negative demand outliers. We show that identifying instances of outlier demand and adjusting the forecast in a timely fashion substantially increases revenue compared to what is earned when ignoring outliers.

On assessing grindability of recycled and ore-based crankshaft steel: an approach combining data analysis with material science

Material-related grindability variations when grinding recycled and ore-based steel can significantly impair the process efficiency during finishing of automotive crankshafts. To address this problem and to achieve more robust grinding processes, the underlying causes of variation need to be understood. The present work investigates the feasibility of using quality data obtained during production to study grindability variations and identify material-related effects. Analysis of non-destructive inspection protocols indicates steel supplier-dependent differences in grindability. However, no systematic grindability differences between recycled and ore-based steel could be identified. Possible correlations between grindability and material characteristics obtained from supplied steel certificates are discussed.

High-resolution analysis of the physicochemical characteristics of sandstone media at the lithofacies scale

Abstract. The prediction of physicochemical rock properties in subsurface models regularly suffers from uncertainty observed at the submeter scale. Although at this scale – which is commonly termed the lithofacies scale – the physicochemical variability plays a critical role for various types of subsurface utilization, its dependence on syndepositional and postdepositional processes is still subject to investigation. The impact of syndepositional and postdepositional geological processes, including depositional dynamics, diagenetic compaction and chemical mass transfer, onto the spatial distribution of physicochemical properties in siliciclastic media at the lithofacies scale is investigated in this study. We propose a new workflow using two cubic rock samples where eight representative geochemical, thermophysical, elastic and hydraulic properties are measured on the cubes' faces and on samples taken from the inside. The scalar fields of the properties are then constructed by means of spatial interpolation. The rock cubes represent the structurally most homogeneous and most heterogeneous lithofacies types observed in a Permian lacustrine-deltaic formation that deposited in an intermontane basin. The spatiotemporal controlling factors are identified by exploratory data analysis and geostatistical modeling in combination with thin section and environmental scanning electron microscopy analyses. Sedimentary structures are well preserved in the spatial patterns of the negatively correlated permeability and mass fraction of Fe2O3. The Fe-rich mud fraction, which builds large amounts of the intergranular rock matrix and of the pseudomatrix, has a degrading effect on the hydraulic properties. This relationship is underlined by a zonal anisotropy that is connected to the observed stratification. Feldspar alteration produced secondary pore space that is filled with authigenic products, including illite, kaolinite and opaque phases. The local enrichment of clay minerals implies a nonpervasive alteration process that is expressed by the network-like spatial patterns of the positively correlated mass fractions of Al2O3 and K2O. Those patterns are spatially decoupled from primary sedimentary structures. The elastic properties, namely P-wave and S-wave velocity, indicate a weak anisotropy that is not strictly perpendicularly oriented to the sedimentary structures. The multifarious patterns observed in this study emphasize the importance of high-resolution sampling in order to properly model the variability present in a lithofacies-scale system. Following this, the physicochemical variability observed at the lithofacies scale might nearly cover the global variability in a formation. Hence, if the local variability is not considered in full-field projects – where the sampling density is usually low – statistical correlations and, thus, conclusions about causal relationships among physicochemical properties might be feigned inadvertently.

Refracturing candidate selection for MFHWs in tight oil and gas reservoirs using hybrid method with data analysis techniques and fuzzy clustering

The selection of refracturing candidate is one of the most important jobs faced by oilfield engineers. However, due to the complicated multi-parameter relationships and their comprehensive influence, the selection of refracturing candidate is often very difficult. In this paper, a novel approach combining data analysis techniques and fuzzy clustering was proposed to select refracturing candidate. First, the analysis techniques were used to quantitatively calculate the weight coefficient and determine the key factors. Then, the idealized refracturing well was established by considering the main factors. Fuzzy clustering was applied to evaluate refracturing potential. Finally, reservoirs numerical simulation was used to further evaluate reservoirs energy and material basis of the optimum refracturing candidates. The hybrid method has been successfully applied to a tight oil reservoir in China. The average steady production was 15.8 t/d after refracturing treatment, increasing significantly compared with previous status. The research results can guide the development of tight oil and gas reservoirs effectively.

Two folate-derived analogues from an aqueous decoction of Uncaria rhynchophylla.

Two new folate-derived analogues, named uncarophyllofolic acids A (1) and B (2), respectively, were isolated from the Uncaria rhynchophylla hook bearing stem (Gouteng in Chinese). The distinct stereochemical structures of 1 and 2 were determined by spectroscopic data analysis in combination with acidic hydrolysis and Marfey's derivatization, along with comparison of their specific rotation and Cotton effect (CE) data with those of the biogenetically related known derivatives as well as theoretical calculations of electronic circular dichroism (ECD) spectra. A plausible biosynthetic pathway of 1 and 2, associating to folate metabolism and the previously reported orychophragines A-C from Orychophragmus violaceus, is discussed.

Early experiences on Summit: Data analytics and AI applications

Oak Ridge National Laboratory (ORNL) installed the Summit supercomputer in 2018. Summit is an accelerated-node architecture with 4,608 nodes, each with two IBM P9 and six NVIDIA Volta V100 GPU processors, significant DRAM footprint, robust HBM quantities supporting the GPUs, nonvolatile memory, and fast NVLink and Infiniband interconnects. This machine was designed to deliver over 200 peak double-precision petaflops for scientific modeling and simulation applications and over 3 peak reduced-precision ExaOps. Summit features impact application performance depending on whether the codes are simulation-oriented, write-intensive, data-analysis-oriented, read-intensive, or communication-intensive codes. In the context of artificial intelligence (AI) and machine learning (ML), these features support data-intensive applications that infer and predict statistical relationships in complex datasets. This article presents recent experiences at ORNL using Summit for applications in AI and ML and describes example code and algorithmic changes necessary to use Summit effectively. Finally, this article discusses research directions in scalable ML, including, algorithms research and combining data analysis with modeling and simulation in an accelerated-node, exascale environment.