Scientific Data Research Articles

Analysis of barriers to Maintenance 4.0 in manufacturing industries

PurposeAsset maintenance in manufacturing industries is a critical issue as organizations are highly sensitive towards maximizing asset uptime. In the advent of Industry 4.0, maintenance is increasingly becoming technology driven and is being termed as Maintenance 4.0. Several barriers impede the implementation of Maintenance 4.0. This article aims at - exploring the barriers to implementation of Maintenance 4.0 in manufacturing industries, categorizing them, analysing them to prioritize and suggesting the digital technologies to overcome them.Design/methodology/approachTwenty barriers to the implementation of Maintenance 4.0 were identified through literature survey and discussion with the industry experts. The identified barriers were divided into five categories based on their source of occurrence and prioritized using fuzzy-technique for order preference by similarity to ideal solution (TOPSIS), sensitivity analysis was carried out to check the robustness of the solution.Findings“Data security issues” has been ranked as the most influencing barrier towards the implementation of Maintenance 4.0, whereas “lack of skilled engineers and data scientists” is the least influencing barrier that impacts the implementation of Maintenance 4.0 in the manufacwturing industries.Practical implicationsThe outcomes of this research will help manufacturing industries, maintenance engineers/managers, policymakers, and industry professionals for detailed understanding of barriers and identify easy pickings while implementing Maintenance 4.0.Originality/valueManufacturing industries are witnessing a paradigm shift due to digitization and maintenance 4.0 forms the cornerstone. Little research has been carried in Maintenance 4.0 and its implementation; this article will help in bridging the gap. The prioritization of the barriers and digital course of actions to overcome those is a unique contribution of this article.

Lessons from a data scientist during COVID-19.

Lessons from a data scientist during COVID-19.

Optimal Control of Underdamped Systems: An Analytic Approach

Optimal control theory deals with finding protocols to steer a system between assigned initial and final states, such that a trajectory-dependent cost function is minimized. The application of optimal control to stochastic systems is an open and challenging research frontier, with a spectrum of applications ranging from stochastic thermodynamics to biophysics and data science. Among these, the design of nanoscale electronic components motivates the study of underdamped dynamics, leading to practical and conceptual difficulties. In this work, we develop analytic techniques to determine protocols steering finite time transitions at a minimum thermodynamic cost for stochastic underdamped dynamics. As cost functions, we consider two paradigmatic thermodynamic indicators. The first is the Kullback–Leibler divergence between the probability measure of the controlled process and that of a reference process. The corresponding optimization problem is the underdamped version of the Schrödinger diffusion problem that has been widely studied in the overdamped regime. The second is the mean entropy production during the transition, corresponding to the second law of modern stochastic thermodynamics. For transitions between Gaussian states, we show that optimal protocols satisfy a Lyapunov equation, a central tool in stability analysis of dynamical systems. For transitions between states described by general Maxwell-Boltzmann distributions, we introduce an infinite-dimensional version of the Poincaré-Lindstedt multiscale perturbation theory around the overdamped limit. This technique fundamentally improves the standard multiscale expansion. Indeed, it enables the explicit computation of momentum cumulants, whose variation in time is a distinctive trait of underdamped dynamics and is directly accessible to experimental observation. Our results allow us to numerically study cost asymmetries in expansion and compression processes and make predictions for inertial corrections to optimal protocols in the Landauer erasure problem at the nanoscale.

New Metrics for Analysing Multiple-choice Questions

While the ideal of constructive alignment in curriculum is well established, and the importance of evaluating learning and teaching is well known, evaluating assessments remains a complex task and gaps can arise between learning outcomes, learning activities and assessments. Our study outlines an innovative way of analysing multiple-choice question (MCQ) examinations, which reveals possible weaknesses in the examination design and gaps in the alignment of curriculum. Individual examination questions are analysed through traditional metrics of the Discrimination Index (DI) and Difficulty Index (DIFF), combined with two novel metrics called the Grade Inversion Score (GIS) and Association with Total Score (ATS). Focusing on examination marks from a data science examination from The University of Sydney, we perform two investigations. First, we identify poorly designed questions using DI, DIFF, GIS and ATS. Second, as multiple questions arise as deficient in these metrics, we explore specific areas in the curriculum where there may be misalignment of course material with the learning outcomes. Our analysis provides a simple visual way for examiners to inspect the validity of an examination design and encourages an evidence-based approach to exploring curriculum alignment using multiple-choice assessments.

Forecasting Urban Traffic States with Sparse Data Using Hankel Temporal Matrix Factorization

Forecasting urban traffic states is crucial to transportation network monitoring and management, playing an important role in the decision-making process. Despite the substantial progress that has been made in developing accurate, efficient, and reliable algorithms for traffic forecasting, most existing approaches fail to handle sparsity, high-dimensionality, and nonstationarity in traffic time series and seldom consider the temporal dependence between traffic states. To address these issues, this work presents a Hankel temporal matrix factorization (HTMF) model using the Hankel matrix in the lower dimensional spaces under a matrix factorization framework. In particular, we consider an alternating minimization scheme to optimize the factor matrices in matrix factorization and the Hankel matrix in the lower dimensional spaces simultaneously. To perform traffic state forecasting, we introduce two efficient estimation processes on real-time incremental data, including an online imputation (i.e., reconstruct missing values) and an online forecasting (i.e., estimate future data points). Through extensive experiments on the real-world Uber movement speed data set in Seattle, Washington, we empirically demonstrate the superior forecasting performance of HTMF over several baseline models and highlight the advantages of HTMF for addressing sparsity, nonstationarity, and short time series. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This research was supported by the Institute for Data Valorisation, the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, the National Natural Science Foundation of China [Grants 12371456, 72101049, 72232001], the Sichuan Science and Technology Program [Grant 2024NSFJQ0038], and the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045].

The Gromov–Wasserstein Distance Between Spheres

AbstractThe Gromov–Wasserstein distance—a generalization of the usual Wasserstein distance—permits comparing probability measures defined on possibly different metric spaces. Recently, this notion of distance has found several applications in Data Science and in Machine Learning. With the goal of aiding both the interpretability of dissimilarity measures computed through the Gromov–Wasserstein distance and the assessment of the approximation quality of computational techniques designed to estimate the Gromov–Wasserstein distance, we determine the precise value of a certain variant of the Gromov–Wasserstein distance between unit spheres of different dimensions. Indeed, we consider a two-parameter family $$\{d_{{{\text {GW}}}p,q}\}_{p,q=1}^{\infty }$$ { d GW p , q } p , q = 1 ∞ of Gromov–Wasserstein distances between metric measure spaces. By exploiting a suitable interaction between specific values of the parameters p and q and the metric of the underlying spaces, we are able to determine the exact value of the distance $$d_{{{\text {GW}}}4,2}$$ d GW 4 , 2 between all pairs of unit spheres of different dimensions endowed with their Euclidean distance and their uniform measure.

Frank–Wolfe and friends: a journey into projection-free first-order optimization methods

AbstractInvented some 65 years ago in a seminal paper by Marguerite Straus-Frank and Philip Wolfe, the Frank–Wolfe method recently enjoys a remarkable revival, fuelled by the need of fast and reliable first-order optimization methods in Data Science and other relevant application areas. This review tries to explain the success of this approach by illustrating versatility and applicability in a wide range of contexts, combined with an account on recent progress in variants, both improving on the speed and efficiency of this surprisingly simple principle of first-order optimization.

Texas Water Observatory: A Distributed Network for Monitoring Water, Energy, and Carbon Cycles Under Variable Climate and Land Use on Gulf Coast Plains

Abstract In the Gulf Coastal Plains of Texas, a state-of-the-art distributed network of field observatories, known as the Texas Water Observatory (TWO), is developed to better understand the water, energy, and carbon cycles across the critical zone (encompassing aquifers, soils, plants, and atmosphere) at different spatiotemporal scales. Using more than 300 advanced real-time / near-real-time sensors, this observatory monitors high-frequency water, energy, and carbon storage and fluxes in the Brazos River corridor, which are critical for coupled hydrologic, biogeochemical, and land-atmosphere process understanding in the region. TWO provides a regional resource for better understanding and/or managing agriculture, water resources, ecosystems, biodiversity, disasters, health, energy, and weather/climate. TWO infrastructure spans common land uses in this region, including (traditional/aspirational cultivated agriculture, rangelands, native prairie, bottomland hardwood forest, and coastal wetlands). Sites represent landforms from low-relief erosional uplands to depositional lowlands across climatic and geologic gradients of central Texas. We present the overarching vision of TWO and describe site design, instrumentation specifications, data collection, and quality control protocols. We also provide a comparison of water, energy, and carbon budget across sites, including evapotranspiration, carbon fluxes, radiation budget, weather, profile soil moisture and soil temperature, soil hydraulic properties, hydrogeophysical surveys, groundwater levels and groundwater quality reported at TWO primary sites for 2018-2020 (with certain data gaps). In conjunction with various earth-observing remote sensing and legacy databases, TWO provides a master testbed to evaluate process-driven or data-driven critical zone science, leading to improved natural resource management and decision support at different spatiotemporal scales.

GENERATIVE AI: A TOOL FOR ADDRESSING DATA SCARCITY IN SCIENTIFIC RESEARCH

Generative AI, a pivotal advancement in data science, addresses data scarcity by producing high-quality synthetic data that mirrors real-world data. This article explores Generative AI's capabilities, including data augmentation, privacy-preserving anonymization, simulation of rare events, and cost-efficient data collection. Techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are discussed, highlighting their role in creating realistic and diverse synthetic data. The practical applications span healthcare, finance, and climate science, demonstrating Generative AI's transformative potential in enhancing research across various scientific disciplines.

The Psychedelic Renaissance: A Catholic Perspective

After being outlawed in 1970, psychedelics have reemerged in the consciousness of Western society in the form of the so-called psychedelic renaissance. This has led to widespread interest in psychedelic compounds being used for recreation, treatment of mental illness and addiction, and even the so-called “enhancement” of individuals and society. This renewed interest in psychedelics has resulted in seemingly endless publications in both the popular and the academic press, including authors from fields as diverse as philosophy, theology, pharmacology, neuroscience, and clinical medicine. A common thread in this developing literature is the claim that psychedelic compounds mediate their reported benefits by inducing the so-called psychedelic mystical experience (PME). The nature of PME is hotly debated, with some claiming that it is entirely psychological and others proposing that it involves contact with extramental reality. This raises a number of important questions for Catholics, especially whether PME should be considered properly “mystical,” how PME compares with traditionally understood Catholic mysticism, and whether, and if so under what conditions, psychedelics could be licitly used by the Catholic faithful. This paper is an attempt to begin the process of reconciling empiric scientific data regarding psychedelics generally, and PME specifically, with Catholic philosophical and theological considerations, with the goal of both providing recommendations regarding the licitness of the use of psychedelic compounds and inviting conversation about this important and challenging topic. Summary Over the past 20 years, there has been a renewed interest in of psychedelics. Many articles have been published extoling the benefits of psychedelics, including for the treatment of mental illness and addiction, recreation, and “enhancement” of individuals and society. A common claim is that the benefits of psychedelics are a result of the psychedelic mystical experience (PME). This paper considers both the use of psychedelics and PME from the perspective of Catholic theology, provides recommendations about their use for the Catholic faithful, and invites further conversation about this important and challenging topic. Short Summary This paper considers challenges posed by psychedelics, considers licitness of use, and calls for further discussion.

Fundamentals and Applications Related to Data Science, Machine Learning, and Statistical Processing

Fundamentals and Applications Related to Data Science, Machine Learning, and Statistical Processing

Optimal fasting duration for mice as assessed by metabolic status

In the study of obesity and diabetes, mice are widely used for experimental research, and fasting is a common procedure used to reset metabolism in mouse models. The fasting duration for experimental mice varies greatly in nutritional and metabolic studies, ranging from 2 to 48 h. This study aims to assess the optimal fasting duration for mice fed low- and high-fat diets over a short period of time. C57BL/6J mice were fed a low-fat diet (LFD) or high-fat diet (HFD) and fasted for 4, 6, 8, 10, 12, or 24 h. The effects of different conditions after fasting on the metabolic level of mice were explored, and the data were collected for analysis. Our data indicate that fasting has inconsistent effects on mice fed a low-fat or high-fat diet. To compare the metabolic differences between mice in different dietary levels and thereby secure better scientific data, mice should fast for 6 h in animal experiments. Fasting for 6 h is also recommended when comparing glucose tolerance with insulin tolerance.

Regional Differences in Carbon Budgets and Inter-Regional Compensation Zoning: A Case Study of Chongqing, China

Carbon compensation can guide human activities in reducing carbon emissions or increasing carbon sequestration and also represents an important approach for coordinating regional development. In this paper, Chongqing Municipality, whose internal development is varied, was selected as a case study. The annual carbon emissions, carbon sequestration, carbon deficits, and inter-regional carbon compensation costs from 2000 to 2021 were continuously estimated via local optimization methods, and a carbon compensation zoning scheme was proposed that integrates the present situation and trend analysis. The results show that (1) Chongqing’s total carbon emissions were greater than the total carbon sequestration, and the carbon deficit was approximately 556.24 × 104 t~3621.58 × 104 t. (2) County-level carbon budgets have large regional differences; the counties that should always receive carbon compensation are from the southeast and northeast regions, and the counties that should always pay carbon compensation are from central urban areas and the surrounding new urban areas. (3) All the counties were zoned into key payment areas, basic payment areas, key recipient areas, and basic recipient areas. The key payment areas, which account for 39.47%, maintain and grow payment status and are the main sources of carbon compensation costs, while the key recipient areas, which account for 44.74%, maintained a negative compensation status and a continuous downward trend, meaning that they may receive increasing carbon compensation costs. This paper revealed inequities in carbon compensation and proposed a novel zoning solution, which can provide scientific reference and data support for further establishing inter-regional carbon compensation mechanisms.

Recent scientific data on the treatment of female infertility of tubal origin

In 1976 in the United States about 10-15% of couples failed to conceive after at least one year of marriage without the use of contraceptive methods. At the same time, the number of visits to private doctors for infertility problems increased from about 600,000 in 1968 to about 900,000 in 1980. This increased concern about infertility problems appears to be concentrated in that cohort of the American female population born after World War II, currently in their thirties or early forties.

Quantum Tensor DBMS and Quantum Gantt Charts: Towards Exponentially Faster Earth Data Engineering

Earth data is essential for global environmental studies. Many Earth data types are naturally modeled by multidimensional arrays (tensors). Array (Tensor) DBMSs strive to be the best systems for tensor-related workloads and can be especially helpful for Earth data engineering, which takes up to 80% of Earth data science. We present a new quantum Array (Tensor) DBMS data model and new quantum approaches that rely on the upcoming quantum memory and demonstrate exponential speedups when applied to many toughest Array (Tensor) DBMS challenges stipulated by classical computing and real-world Earth data use-cases. We also propose new types of charts: Quantum Gantt (QGantt) Charts and Quantum Network Diagrams (QND). QGantt charts clearly illustrate how multiple operations occur simultaneously across different data items and what are the input/output data dependencies between these operations. Unlike traditional Gantt charts, which typically track project timelines and resources, QGantt charts integrate specific data items and operations over time. A Quantum Network Diagram combines several QGantt charts to show dependencies between multistage operations, including their inputs/outputs. By using a static format, QGantt charts and Quantum Network Diagrams allow users to explore complex processes at their own pace, which can be beneficial for educational and R&D purposes.

A Data Science and Sports Analytics Approach to Decode Clutch Dynamics in the Last Minutes of NBA Games

A Data Science and Sports Analytics Approach to Decode Clutch Dynamics in the Last Minutes of NBA Games

Saving Millions in Government Procurement Through Data Science and Market Design

Saving Millions in Government Procurement Through Data Science and Market Design

Impact of Robotic Surgery Objective Performance Indicators: A Systematic Review.

Objective intra-operative performance metrics are an new resource for the field of surgical data science. In this study, we conducted a comprehensive review to analyze the existing literature on robotic surgical objective performance indicators (OPIs). OPIs have been used to assess surgical skill and provide automated, objective feedback in multiple settings. Clinically, OPIs have been utilized to predict post-operative patient outcomes and measure intra-operative efficiency. OPIs have shown promise in preliminary surgical education and patient outcomes related studies, and further multi-center collaborative research will be imperative to validate the use of OPIs in the field of surgery.

Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality

Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relies on the quality and quantity of the training data. Most of such works employed small-scale public datasets, without proving the efficacy of generalization to external datasets. This study explored the optimization of pancreas segmentation accuracy by pinpointing the ideal dataset size, understanding resource implications, examining manual refinement impact, and assessing the influence of anatomical subregions. We present the AIMS-1300 dataset encompassing 1,300 CT scans. Its manual annotation by medical experts required 938 h. A 2.5D UNet was implemented to assess the impact of training sample size on segmentation accuracy by partitioning the original AIMS-1300 dataset into 11 smaller subsets of progressively increasing numerosity. The findings revealed that training sets exceeding 440 CTs did not lead to better segmentation performance. In contrast, nnU-Net and UNet with Attention Gate reached a plateau for 585 CTs. Tests on generalization on the publicly available AMOS-CT dataset confirmed this outcome. As the size of the partition of the AIMS-1300 training set increases, the number of error slices decreases, reaching a minimum with 730 and 440 CTs, for AIMS-1300 and AMOS-CT datasets, respectively. Segmentation metrics on the AIMS-1300 and AMOS-CT datasets improved more on the head than the body and tail of the pancreas as the dataset size increased. By carefully considering the task and the characteristics of the available data, researchers can develop deep learning models without sacrificing performance even with limited data. This could accelerate developing and deploying artificial intelligence tools for pancreas surgery and other surgical data science applications.

Frequentist Prediction Sets for Species Abundance using Indirect Information

Abstract Citizen science databases that consist of volunteer-led sampling efforts of species communities are relied on as essential sources of data in ecology. Summarising such data across counties with frequentist-valid prediction sets for each county provides an interpretable comparison across counties of varying size or composition. As citizen science data often feature unequal sampling efforts across a spatial domain, prediction sets constructed with indirect methods that share information across counties may be used to improve precision. In this article, we present a nonparametric framework to obtain precise prediction sets for a multinomial random sample based on indirect information that maintain frequentist coverage guarantees for each county. We detail a simple algorithm to obtain prediction sets for each county using indirect information where the computation time does not depend on the sample size and scales nicely with the number of species considered. The indirect information may be estimated by a proposed empirical Bayes procedure based on information from auxiliary data. Our approach makes inference for under-sampled counties more precise, while maintaining area-specific frequentist validity for each county. Our method is used to provide a useful description of avian species abundance in North Carolina, USA based on citizen science data from the eBird database.