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Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help with the management of patients with BD. We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023. Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7–92.8 %, and 59.0–95.2 % for relapse/recurrence prediction (4 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78–0.88, 21.4–100 %, and 77.0–99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71–0.99, 44.4–97.9 %, and 38.9–95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, BD type I, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic functional connectivity in corticostriatal circuitry. ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results. • Adverse outcome prediction aids in the management of patients with BD. • We reviewed studies assessing ML models in predicting relapses/recurrences, hospitalizations, and suicide-related events in BD. • ML predicts adverse outcomes of BD with various performance measures across different models. • Future research is needed to integrate this knowledge into the clinical management of in BD. [ABSTRACT FROM AUTHOR]

• Deep auto-encoder model is used to handle the non-linear and redundant information in dam key parameters. • A hybrid system based on intelligent optimization algorithms and deep learning for predicting tailings dam parameters. • The uncertainty analysis provides more comprehensive and precise interval predictions. • The prediction accuracy of monitoring parameters of tailings dam is significantly improved. Tailings dams, as critical infrastructure, play a vital role in ensuring the safety and reliability of tailings pond systems. Predicting the trend of tailings dam monitoring parameters helps monitor its operational status and support safety management and decision-making. The traditional time series prediction model focuses on point prediction while neglecting the nonlinear feature of data and the uncertainty of prediction results, which is far from meeting the requirements for risk monitoring and safety management of tailings dam systems. To address this, Deep Auto-Encoder is used to extract and optimize features of multi-dimensional time series; Whale Optimization Algorithm, Long Short Term Memory, and Kernel Density Estimation are used to build a monitoring parameters trend interval prediction model. It can effectively predict and analyze the uncertainty of tailings dam monitoring parameters. Time series data of monitoring parameters are collected from a tailings dam in Anhui, China and modeled to verify the effectiveness of the model. The results show that the hybrid prediction model performs the best in monitoring parameter prediction accuracy and trend. The Root Mean Square Error values of the Whale Optimization Algorithm and Long Short Term Memory prediction for reservoir water level, surface displacement, internal displacement, phreatic line, and beach width are 0.0160, 1.0405, 0.0389, 0.0176, and 0.7525, with errors reduced by 0.37 % to 17.92 % compared to other models. The uncertainty analysis confirms the high reliability of the model. The Kernel Density Estimation effectively forecasts the fluctuation range of predicted values, significantly improving the prediction effectiveness of the monitoring parameters. [ABSTRACT FROM AUTHOR]

There has been a growing interest in venous thromboembolism following spinal surgery over the past few years. However, there currently needs to be a bibliometric report on this field. This study aims to construct the knowledge structure of venous thromboembolism after spinal surgery and explores the current status of research productivity, research directions, hotspots, and trends. All articles related to venous thromboembolism after spinal surgery from the Web of Science Core Collection database for 1990 to 2023 were retrieved. For bibliometric analysis, information extraction involves country/region, institutions, journals, authors, references, and keyword-related data. In this study, a total of 814 articles were identified. Studies related to venous thromboembolism after spinal surgery are showing an increasing trend, with the United States contributing the most. JOHNS HOPKINS UNIVERSITY is a high-productivity institution. The journal "SPINE" is highly productive. Research directions cover venous thromboembolism and bleeding, risk factors and prevention, complications, and perioperative blood protection strategies. Current research hotspots are risk factors, surgical location and approach, and perioperative blood protection strategies. Future research trends include establishing a predictive system for venous thromboembolism after spinal surgery to guide personalized prevention and treatment. This study constructed the knowledge structure of venous thromboembolism after spinal surgery, revealing current research hotspots and future trends. Future research trends include personalized prevention and treatment strategies for venous thromboembolism after spinal surgery, especially safe and effective chemical prophylaxis. It is hoped that this study can lay the foundation for subsequent research. [Display omitted] [ABSTRACT FROM AUTHOR]

The manufacturing process is the last opportunity to build an ideal design reliability index into a product. With the advancement of intelligent manufacturing technology, the concept of quality evolves from conformance to fitness for use, which emphasizes that reliability should be built into product with quality control. To effectively implement reliability assurance in the manufacturing process, it is necessary to accurately identify the vital few characteristics that are critical to reliability. Thus, a heuristic key reliability characteristic (KRC) analysis in manufacturing model fusing big quality data is proposed. First, on the basis of the fusion big quality data in manufacturing-by-manufacturing system Reliability-operational process Quality- output product Reliability (RQR) chain, a data driven KRC analysis model is proposed, and a reliability proactive control framework in manufacturing driven by KRC is expounded. Second, considering mass quality and reliability data, an effective KRC identification method based on data mining using multi-objectives genetic algorithm (MOGA) is established. Third, considering manufacturing data and product failure risk, an extended risk priority number (RPN) for KRC ranking is proposed. Finally, an example of an insulating base of subway locomotive is provided to verify the proposed approach. • A novel KRC conceptual model is proposed. • A heuristic KRC identification approach based on MOGA is presented. • A KRC ranking approach based on extended RPN is proposed. • The proposed method is illustrated using an insulating base of subway locomotive example. [ABSTRACT FROM AUTHOR]

The Triple Ionosophere PhotoMeter (TRIPM) is designed to make the disk observations of the Earth airglow emissions at OI 135.6 nm and N 2 Lyman-Birge-Hopfield (LBH) band that flew aboard the newly launched early morning satellite Fengyun-3E (FY-3E) which was launched on July 5th, 2021. This is the first attempt to obtain the global twilight airglow data in the far ultraviolet band using Ionosophere PhotoMeter. The seasonal behavior of the twilight airglow at the OI 135.6 nm and N 2 LBH band is exhibited and interpreted in the paper. Validity of the TRIPM data is analyzed by comparison with the simulation results of the Global Airglow (GLOW) and the simulation results reveal good consistency with the observational results. Furthermore, based on the ionospheric contributions calculated by GLOW model, we extract the ionospheric information from the TRIPM 135.6 nm emissions. This means the ionospheric signature in the 135.6 nm radiance at the dawn and dusk times can be provided. Comparison between the ionospheric 135.6 nm radiance due to O+ and electron radiative recombination with GPS TEC data shows a similar morphology of equatorial arcs and seasonal variation of the ionosphere. [ABSTRACT FROM AUTHOR]

We study a setting where a receiver must design a questionnaire to recover a sequence of symbols known to a strategic sender, whose utility may not be incentive compatible. We allow the receiver the possibility of selecting the alternatives presented in the questionnaire, and thereby linking decisions across the components of the sequence. We show that, despite the strategic sender and the noise in the channel, the receiver can recover exponentially many sequences, but also that exponentially many sequences are unrecoverable even by the best strategy. We define the growth rate of the number of recovered sequences as the information extraction capacity. A generalization of the Shannon capacity, it characterizes the optimal amount of communication resources required while communicating with a strategic sender. We derive bounds leading to an exact evaluation of the information extraction capacity in many cases. Our results form the building blocks of a novel, non-cooperative regime of communication involving a strategic sender. • An uninformed receiver aims to recover information from an informed strategic sender • No assumption of incentive compatibility or external incentives • Information-theoretic analysis shows a fundamental limit on recovered information • Real-world setting of screening of travellers using questionnaires can be studied • Novel theory combining Shannon's information theory and Myerson's mechanism design [ABSTRACT FROM AUTHOR]

This paper explores the utilization of randomized SVD (rSVD) in the context of kernel matrices arising from radial basis functions (RBFs) for the purpose of solving interpolation and Poisson problems. We propose a truncated version of rSVD, called trSVD, which yields a stable solution with a reduced condition number in comparison to the non-truncated variant, particularly when manipulating the scale or shape parameter of RBFs. Notably, trSVD exhibits exceptional proficiency in capturing the most significant singular values, enabling the extraction of critical information from the data. When compared to the conventional truncated SVD (tSVD), trSVD achieves comparable accuracy while demonstrating improved efficiency. Furthermore, we explore the potential of trSVD by employing scale parameter strategies, such as leave-one-out cross-validation and effective condition number. Then, we apply trSVD to solve a 2D Poisson equation, thereby showcasing its efficacy in handling partial differential equations. In summary, this study offers an efficient and accurate solver for RBF problems, demonstrating its practical applicability. The code implementation is provided to the scientific community for their access and reference. [ABSTRACT FROM AUTHOR]

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy. [ABSTRACT FROM AUTHOR]

Mechanical engineering drawings (MEDs) accompany a product lifecycle from conceptional design to final production. The digitisation of MEDs has become increasingly important due to demands for data authenticity, intellectual property protection, efficient data storage and communication, and compliance with data integrity and security regulations. Unlike CAD-based engineering design software, legacy MEDs are often manually drawn or contain manually labeled specifications on blueprints. A notable gap exists in the automated process pipeline of modern Computer-Aided Tolerance (CAT) software, particularly in integrating Geometrical Tolerance Specification Callouts (GTSC) on MEDs. This study proposes an integrated model based on digital image processing and deep learning, which combines character (symbol, text and number) localization, segmentation, and recognition to intelligently identify and read GTSCs on MEDs. The focus of this work is on image filtering, GTSC block localization and tilt correction, multiple lines and character segmentation, and semantic recognition. Experiment results demonstrate that this innovative technique effectively automates the labor-intensive process of reading and registering GTSC with a precision performance that meets industry benchmarks. [ABSTRACT FROM AUTHOR]

The area of data science knowledge responsible for dealing with this new reality is diffuse, including mathematics, statistics, computing, engineering, psychology, and administration, among many other areas that make up a new scenario that is still changing. Different models have emerged over the years to systematize the procedures to be followed. Among them, CRISP-DM (Cross Industry Standard Process for Data Mining) has become one of the most widespread in the industry. However, the lack of detailed instructions means the framework is often incorrectly used. Therefore, this research aims to present a utilitarian and didactic model based on the latest advances in the literature and through the lens of production engineering. In order to achieve this objective, exploratory research was carried out based on a systematic review and subsequent categorization of each of the CRISP-DM steps, detailing the authors' contributions to each stage. In addition, it is proposed that guidelines from the areas of Quality Management and Risk Management be added to the subject, consolidating a useful and didactic model of relevance. [ABSTRACT FROM AUTHOR]

• Using the FDA adverse event reporting system database, ETI and DILI reports were found to be significantly associated (p < 0.05) for all disproportionality measures (PRR, ROR, IC, EGBM, chi-squared). • The ROR for ETI-DILI is greater than that of ∼10 % of the "Most-DILI concern" drugs in the FDA DILIRank dataset. • ETI-DILI reports for people with cystic fibrosis were predominately male, in contrast to patient reports for other drugs and DILI. • "Hospitalization" was the second most common patient outcome for ETI-DILI after "other serious outcomes". • Most people with cystic fibrosis experienced onset times within 3 months of initiation; however, several occurred prior to 3 months or beyond 1 year, indicating additional monitoring may be prudent. The efficacy and safety of elexacaftor/tezacaftor/ivacaftor (ETI) have been established in prospective clinical trials. Liver function test elevations were observed in a greater proportion of patients receiving ETI compared with placebo; however, the relatively small number of patients and short duration of study preclude detection of rare but clinically significant associations with drug-induced liver injury (DILI). To address this gap, we assessed the real-world risk of DILI associated with ETI through data mining of the FDA adverse event reporting system (FAERS). Disproportionality analyses were conducted on FAERS data from the fourth quarter of 2019 through the third quarter of 2022. Comparative patient demographics, onset time and outcomes for ETI-DILI were also obtained. 452 reports of DILI associated with ETI were found, representing 2.1 % of all adverse event reports for ETI. All disproportionality measures were significant for ETI-DILI at p < 0.05; the reporting odds ratio (ROR) (2.82) was comparable to that of drugs classified by FDA as "Most-DILI concern". The most notable demographic finding was a male majority (5:4 male to female ratio) for ETI-DILI compared to a female majority (4:5 male to female ratio) for non ETI-DILI. Median ETI-DILI onset time was 50.5 days, and hospitalization was the second most common complication. Using FAERS data, ETI was found to be disproportionately associated with DILI. Future research is needed to investigate the hepatotoxic mechanisms and assess potential mitigation strategies for ETI-induced hepatotoxicity. [ABSTRACT FROM AUTHOR]

Comprehensive adverse event (AE) surveillance programs in interventional radiology (IR) are rare. Our aim was to develop and validate a retrospective electronic surveillance model to identify outpatient IR procedures that are likely to have an AE, to support patient safety and quality improvement. We identified outpatient IR procedures performed in the period from October 2017 to September 2019 from the Veterans Health Administration (n = 135,283) and applied electronic triggers based on posyprocedure care to flag cases with a potential AE. From the trigger-flagged cases, we randomly sampled n = 1,500 for chart review to identify AEs. We also randomly sampled n = 600 from the unflagged cases. Chart-reviewed cases were merged with patient, procedure, and facility factors to estimate a mixed-effects logistic regression model designed to predict whether an AE occurred. Using model fit and criterion validity, we determined the best predicted probability threshold to identify cases with a likely AE. We reviewed a random sample of 200 cases above the threshold and 100 cases from below the threshold from October 2019 to March 2020 (n = 20,849) for model validation. In our development sample of mostly trigger-flagged cases, 444 of 2,096 cases (21.8%) had an AE. The optimal predicted probability threshold for a likely AE from our surveillance model was >50%, with positive predictive value of 68.9%, sensitivity of 38.3%, and specificity of 95.3%. In validation, chart-reviewed cases with AE probability >50% had a positive predictive value of 63% (n = 203). For the period from October 2017 to March 2020, the model identified approximately 70 IR cases per month that were likely to have an AE. This electronic trigger-based approach to AE surveillance could be used for patient-safety reporting and quality review. [ABSTRACT FROM AUTHOR]

The domain of Social Collaboration Analytics (SCA) is gathering momentum due to the increasing importance of Enterprise Collaboration Systems (ECS) that are used to build performant digital workplaces. ECS collect and store large amounts of digital trace data, which reflect the collaborative work of employees. Collaborative work activity is usually supported by multiple systems, which complicates the analysis of the digital traces. The cross-system analysis of collaborative work is challenging because of the nature of the log files that are stored in heterogeneous formats and at different levels of log granularity. These challenges cannot easily be overcome with the help of existing approaches for data preprocessing. In this paper, approaches and frameworks for data mining, data preprocessing and SCA are adapted to design a special method for Social Process Mining (SPM). The DaProXSA approach was developed to obtain interpretable, high-quality event logs of multiple ECS and builds on existing literature as well as on findings from an analysis of leading ECS. DaProXSA contributes to the aim of discovering and interpreting patterns and processes that occur in computer-mediated collaborative work. [ABSTRACT FROM AUTHOR]

Today, higher education institutions are focused on understanding which factors are associated with the failure or success of students to, early on, be able to implement measures that can reduce the low performance of students and even dropout. The retention rate is positively and negatively influenced by factors belonging to several dimensions (personal, environmental, and institutional). We aim to use information from those dimensions to identify students enrolled in a Computer Networks course at risk of failing the subject. Besides, this needs to happen as early as possible, to be able to provide the students, for example, with extra support or resources to try to prevent that negative outcome. For predicting the grade level on the first test, the best accuracy obtained was 55%. However, most C-level grades were correctly classified, with 63% accuracy in predicting the students that are most at risk of failing, which is one of our main objectives. As for the prediction of the second test's grade level, the best accuracy obtained was 89% and concerned data regarding the students' interaction with the LMS together with students' grades history. All the C-level grades were correctly classified (100% accuracy) and so we were able to correctly predict every student at a high risk of failing. Using the procedure described in this paper, we are able to anticipate the students needing extra support, and provide them with different resources, to try to prevent their negative outcome. [ABSTRACT FROM AUTHOR]

Knowledge management represents for organizations the possibility of having information aimed at developing strategies and obtaining competitive advantages. In the last decade, the volume of data generated has increased exponentially. For organizations, it is important to use technologies and techniques to process data quickly and automatically to assist organizations in the decision-making process. The purpose of this article is to present a Data Mining model for knowledge management in which technology is used to find patterns, connections, correlations, or anomalies in a large amount of data, allowing to find problems, hypotheses, and opportunities more easily, as well as generate insights that result in competitive advantages for organizations. [ABSTRACT FROM AUTHOR]

Biomaterials designed to interact with living systems should be evaluated for their biocompatibility; however, current definitions of biocompatibility are ambiguous and not well delimited. The need for a consensus on the definition of biocompatibility complicates the understanding of its practical requirements, rendering data extraction difficult. A working definition of biocompatibility will enable the use of computational tools to extract relevant information and perform reasoning. Analyzing the international standards allowed us to include relevant specifications in the working definition of biocompatibility and identify useful vocabularies for text mining. Charting the key elements and gaps in existing biocompatibility definitions enabled us to narrow down a unified and implementable working definition matching automated data extraction requirement. The surge in 'Big data' has significantly influenced biomaterials research and development, with vast data volumes emerging from clinical trials, scientific literature, electronic health records, and other sources. Biocompatibility is essential in developing safe medical devices and biomaterials to perform as intended without provoking adverse reactions. Therefore, establishing an artificial intelligence (AI)-driven biocompatibility definition has become decisive for automating data extraction and profiling safety effectiveness. This definition should both reflect the attributes related to biocompatibility and be compatible with computational data-mining methods. Here, we discuss the need for a comprehensive and contemporary definition of biocompatibility and the challenges in developing one. We also identify the key elements that comprise biocompatibility, and propose an integrated biocompatibility definition that enables data-mining approaches. [ABSTRACT FROM AUTHOR]

The current research focused on enhancing the extraction and purification processes of triterpenoids found in persimmon leaves, as well as assessing their biological effects. The extraction yield of triterpenoids was 8.26%. Activated carbon was used to purify the extracts, resulting in an increase in the content of triterpenoids in the final product from 24.43% to 79.66%. The recovery yield of triterpenoids from the ethanol extract was 73.57%. Ethanol was recovered and reused as a solvent. The total triterpenoids exhibited cytotoxic activities against several cancer cell lines, with IC 50 values of approximately 32.49–55.02 μg/mL, as well as anti-inflammatory activities (IC 50 = 29.55 ± 1.67 μg/mL). The total extract and total triterpenoids demonstrated significant inhibition against α-glucosidase, having IC 50 values of 0.18 ± 0.05 μg/mL and 2.12 ± 0.15 μg/mL, respectively. Additionally, for antioxidant activity, the IC 50 values of the total extract and total triterpenoids were 12.53 ± 0.20 and 13.92 ± 0.18 μg/mL, respectively. The triterpenoids showed higher inhibition of acetylcholinesterase compared with the total extract (IC 50 : 40.07 ± 2.54 and 112.22 ± 9.68 μg/mL, respectively). However, they did not show inhibitory activity against bacteria or fungi. In conclusion, information about the extraction, purification and biological activities of total triterpenoids from persimmon leaves was fully provided for the first time. [Display omitted] • Triterpenoids of persimmon leaves were extracted, purified, and tested of bioactivities. • Extraction yield of total triterpenoids was determined to be 8.26%. • Total triterpenoids were purified from 24.43% to 79.66%. • Ethanol was recovered and reused at least 3 times to extract triterpenoids. • Bioactivities of triterpenoids show potentials in drug development. [ABSTRACT FROM AUTHOR]

Extracting knowledge from data streams requires fast incremental algorithms that are able to handle unlimited processing and ever-changing data with finite memory. A strategy for this challenge is the use of ensembles owing to their ability to tackle concept drift and achieve highly accurate predictions. However, ensembles often require a lot of computational resources. In this study, we propose a novel ensemble-based classification algorithm for data streams, Adaptive Regularized Ensemble (ARE), with low demand for computational resources. The algorithm combines strategies that contribute to high prediction accuracy using only incorrectly classified instances into the training step, random-sized feature subspace for each ensemble element and classifier selection for final ensemble voting. After an extensive experimental study, ARE exhibited high predictive performance and outperformed state-of-the-art ensembles on data streams for real and synthetic datasets while requiring a low processing time and memory consumption. • Classification algorithm for data streams fast and with low memory consumption. • Ensemble that incorporates only instances incorrectly classified in the training step. • Ensemble with random feature subspace size. • Classifier selection for final ensemble voting. [ABSTRACT FROM AUTHOR]

The global plastic crisis is a significant concern. Addressing it requires a multi-faceted approach involving legislative, social, and technological measures to reduce post-consumer plastic waste and increase the recycling rate and material circularity. The EU Packaging Directive mandates a 50% recycling rate for plastics by the end of 2025 and 55% by 2030. Machine learning techniques are increasingly being explored to improve the efficiency of waste sorting, thereby contributing to achieving the aforementioned recycling rate goals. However, their effectiveness relies on the availability of high-quality and comprehensive datasets. One of the challenges in this field is the need for suitable datasets for training and testing machine learning models. Furthermore, access to detailed and real-world data, such as information from recycling plants, is often restricted due to paywalls, limiting the accessibility of critical data for research and development. This review sheds light on the situation of RGB (red, green, blue) image datasets and NIR (near-infrared) data availability to identify current dataset gaps and advocate for open access to relevant data sources. The target is to promote collaboration and innovation to combat the plastic crisis and promote sustainable practices collaboratively. [ABSTRACT FROM AUTHOR]

Hepatic cirrhosis represents an advanced stage of fibrosis in the liver, resulting from various conditions such as hepatitis and chronic alcoholism. This article uses data from the Mayo Clinic's clinical trial on primary bile cirrhosis carried out between 1974 and 1984. Rapid and accurate identification of the condition is crucial for the implementation of effective interventions, capable of mitigating liver damage and preventing complications, especially in the early stages of the disease. The focus of the research is the evaluation of Data Mining (DM) techniques, using clinical data, to predict survival outcomes in patients with cirrhosis. The study uses the CRISP-DM methodology, analysing different classification algorithms, including k-Nearest Neighbours, Random Forest, Decision Trees, Gradient Boosting and Naive Bayes. The effectiveness of the model is evaluated, highlighting the Random Forest with Holdout Sampling and a ratio of 0.7, whose performance averages around 80%. The nominal attributes were also analysed in order to discover possible patterns of association. [ABSTRACT FROM AUTHOR]

[Display omitted] Small RNAs (sRNAs), found extensively in plants, play an essential role in plant growth and development. Although various sRNA analysis tools have been developed for plants, the use of most of them depends on programming and command-line environments, which is a challenge for many wet-lab biologists. Furthermore, current sRNA analysis tools mostly focus on the analysis of certain type of sRNAs and are resource-intensive, normally demanding an immense amount of time and effort to learn the use of numerous tools or scripts and assemble them into a workable pipeline to get the final results. Here, we present sRNAminer, a powerful stand-alone toolkit with a user-friendly interface that integrates all common functions for the analysis of three major types of plant sRNAs: microRNAs (miRNAs), phased small interfering RNAs (phasiRNAs), and heterochromatic siRNAs (hc-siRNAs). We constructed a curated or "golden" set of MIRNA and PHAS loci, which was used to assess the performance of sRNAminer in comparison to other existing tools. The results showed that sRNAminer outperformed these tools in multiple aspects, highlighting its functionality. In addition, to enable an efficient evaluation of sRNA annotation results, we developed Integrative Genomics Viewer (IGV)-sRNA, a modified genome browser optimized from IGV and we incorporated it as a functional module in sRNAminer. IGV-sRNA can display a wealth of sRNA-specific features, enabling a more comprehensive understanding of sRNA data. sRNAminer and IGV-sRNA are both platform-independent software that can be run under all operating systems. They are now freely available at https://github.com/kli28/sRNAminer and https://gitee.com/CJchen/IGV-sRNA. [ABSTRACT FROM AUTHOR]

Exploiting the capability of deep learning-based techniques, this research addresses an important and relevant problem on how to cost effectively detect thyroid cancer. In recent decades, there has been a significant increase in the incidence of thyroid cancer, prompting the need to understand its epidemiology. Existing studies have primarily employed qualitative techniques to investigate a single risk factor correlated with the disease development at a time. However, such an approach is inefficient and tends to ignore the interwoven connections among factors, resulting in a considerable disagreement with the identified risk factors among scholars. Additionally, the use of deep learning techniques in conjunction with medical imaging for computer-aided diagnostic (CAD) systems design has shown promises in detecting the disease, while there are research gaps regarding the detection of subtype and their co-existence. More importantly, existing CAD systems have shortcomings in adapting to different sample groups. To address these challenges, this research aims to shed light on the pathogenesis of thyroid cancer, enhance diagnostic performance, and improve generalisation of deep learning-based decision support systems. By harnessing the power of machine learning, specifically data mining and deep learning techniques, we seek to improve our understanding of the underlying mechanisms of the disease and develop innovative, robust, accurate, and efficient diagnostic tools. Extensive experiments indicate superior performance of the proposed methods than existing works. The systems proposed in this study have great impact to the wider society and contribute to the advancement of thyroid cancer research while enhancing clinical practice in related detection and subsequent management. [ABSTRACT FROM AUTHOR]

Big data and its analysis are crucial for other applications like the Internet of Things (IoT), data mining, cloud computing, etc. The volume of data is expanding along with the internet and other digital technologies. Traditional systems, however, cannot process this volume of data. Analyzing this much data will take a lot of work. However, there are several difficulties in assuring data security and privacy, such as safeguarding against data leakage and security breaches, flaws in open databases, and third-party data sharing, among others. This paper's goal is to analyze the benefits of blockchain in big data as well as its drawbacks and identify potential future approaches. [ABSTRACT FROM AUTHOR]

Access to large amounts of data, provided by continuous technological progress, becomes increasingly important for organisations. The access to data becomes even more useful when managers consult structured information in real time. This requires the adoption of data analysis techniques that, at a strategic level, allow the definition of business goals according to pre-defined objectives, and which help companies in their functional activities. With Data Science, Data Mining and Machine Learning platforms allow the development of decision support systems, capable of establishing patterns between data and making truly useful predictions for decision support. The methodology used in this work aims to combine Data Science methods, such as Data Mining and Machine Learning, in order to demonstrate the importance of transforming data into organised and oriented information to support decision making. [ABSTRACT FROM AUTHOR]

Educational Data Mining has been growing and many institutions have started to use it to become more competitive. In recent years, many studies have been done on Educational Data Mining, applied to different study topics, and using distinct methods and algorithms. With the growing popularity of Educational Data Mining, it would be beneficial to have a summary of the most used techniques and approaches. To this end, a review was conducted to identify the most important methods, and algorithms in the context of Educational Data Mining. Fifteen studies were analysed, and the results showed the most used topics, methods, algorithms, and the relationship between them. These results can help new studies define their techniques and approaches. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive exploration of the development of an AI-driven Gen-Alpha education guidance indicator for Oman, employing cutting-edge machine learning techniques. The primary objective is to deliver a highly personalized support system and guidance mechanism that accompanies students throughout their academic journey. The research places a spotlight on the pivotal role of Artificial Intelligence (AI) within the realm of education. It skillfully demonstrates AI's robust capabilities in accurately forecasting student performance through the effective use of data mining and advanced machine learning techniques. An integral aspect of this research is the application of AI in aiding students to navigate the involved process of subject selection for their higher education pursuits. This multidimensional process encompasses exhaustive data collection, the detailed creation of a sophisticated machine learning model, and the application of a diverse array of state-of-the-art algorithms. Among these algorithms, K-Nearest Neighbors (KNN), Decision Trees, Random Forest, and Support Vector Machines (SVM) stand out prominently. Notably, the SVM algorithm emerges as the outright winner, delivering an extraordinary accuracy rate of 77%. This remarkable achievement underscores the model's unwavering robustness and its potential to redefine the educational landscape in Oman. In essence, this paper transcends the boundaries of conventional research and offers conclusive validation of AI's revolutionary potential in reshaping educational paradigms. By facilitating data-driven decision-making, the AI-driven Gen-Alpha education guidance indicator empowers students to embark on their educational journeys well-informed and confident, thereby playing a pivotal role in advancing Oman's education system. [ABSTRACT FROM AUTHOR]

Missing data imputation is a critical task in the data pre-processing stage to ensure the quality, stability, and reliability of machine learning models. If missing values are imputed incorrectly, it can result in erroneous predictions and inconsistent model performance. Traditional imputation methods often struggle with complex data patterns having non-linearity and uncertainties. By integrating soft clustering principles, the proposed work provides a flexible framework for imputing missing data by taking into account the underlying inherent data pattern. To address the missing data challenges, this paper presents a novel imputation technique called Radial Basis Kernel Intuitionistic Fuzzy Ⅽ-Means Imputation (KIFCMI), which builds upon the standard Intuitionistic Fuzzy Ⅽ-Means (IFCM) technique. KIFCMI explores the application of centroid-based imputation using IFCM by incorporating the RBF kernel-induced metric in the data space as a replacement for the original Euclidean norm metric. By employing a kernel function, KIFCMI enables the clustering of data that lacks linear separability within the original space, enabling the formation of homogeneous clusters in a space of higher dimensionality. The effectiveness of the proposed imputation technique is validated on ten diverse real-world datasets obtained from the Public Library UCI with 10% and 20% missing data. The comparative analyses of the proposed technique KIFCMI, is carried out with three conventional techniques, namely fuzzy c-means imputation (FCMI), kernelized fuzzy c-means imputation (KFCMI), and intuitionistic fuzzy c-means imputation (IFCMI). The experimental results using two performance measures, namely RMSE and MAE, showcase the robustness and versatility of the proposed technique across other imputation outcomes. This research paper contributes to the evolving landscape of missing data imputation, offering insights into the practical applications of fuzzy clustering techniques. [ABSTRACT FROM AUTHOR]

The progression of biotechnological and health science fields has instigated a substantial proliferation of data, encompassing high-throughput genetic information and extensive clinical data derived from expansive electronic health record repositories. In the domain of biosciences, the indispensability of machine learning and data mining methodologies has surged, attaining heightened significance for the purpose of converting extant information into usable knowledge. Diabetes mellitus is a metabolic condition that affects human health. Extensive research on diabetes (diagnosis, therapy, etc.) has generated vast volumes of data. Manual diagnosis of diabetes has always been a time-consuming task. Therefore, automatic detection and diagnosis of diabetes using artificial intelligence and machine learning is gaining prominence. In our work, we have devised a novel architecture using machine learning for the automatic diagnosis of diabetes. We implemented our model using many algorithms and found that random forest is the most optimized and accurate one for classification purposes. It produced the highest accuracy of 98.07%, precision, recall, F1-score of 98%, and logarithmic loss of 0.03 using the mRMR feature selection method and 0.2 test split. It produced a recall of 97%, the precision of 97%, an F1-score of 97%, an accuracy of 96.79%, and a log loss of 0.11 on 0.3 test split with mRMR feature selection. Also, the proposed model has performed better than most state-of-the-art models. [ABSTRACT FROM AUTHOR]

One of the important application of natural language processing (NLP) is Name Entity Recognition (NER). It automatically recognise and categorise named entities in a document. Named Entities can be the name of an individual, group, place, etc. It is crucial to the success of NER applications including text summarization, machine translation and information extraction and retrieval. It is one of the most useful application tools for a variety of topics and languages. Despite its widespread use and effectiveness in English, this field is currently under investigation for other Indian languages, such as Bodo. Due to the lack of resources and a high-quality dataset, NER in Bodo is a difficult task. In this research, a deep learning-based NER tagger is investigated for the Bodo language and NER tagged dataset is generated for Bodo language using Docanno and enlarge the dataset size by employing a data augmentation technique. As there is no Bodo NER baseline model to compared with, we employed several deep learning techniques for Bodo NER System and compared their results. We achieved an accuracy of 99.62%, precision of 99.75%, recall of 98.74% and F-score of 99.35% when employed with LSTM and character based. This study also highlights GRU and CNN based models performance in Bodo NER task. [ABSTRACT FROM AUTHOR]

The paper presents advancements in healthcare data capture through the application of image-based extraction techniques, which include sophisticated image processing techniques such as resizing and adaptive thresholding, for prescription information. With the increasing digitization of medical records, automating the extraction of relevant data from prescription documents has become crucial. This research explores the utilization of image processing and optical character recognition (OCR) methodologies to extract prescription information accurately. By converting prescription documents into image format and employing OCR algorithms, the text content is extracted and parsed for critical details such as medication names, dosages, and patient instructions. Notably, our methodology excels in overcoming limitations associated with handwritten documents, achieving an impressive accuracy rate of 98%. This image-based approach offers a streamlined and efficient method for capturing prescription data, reducing manual data entry efforts, and minimizing potential errors. Experimental evaluations demonstrate the effectiveness and accuracy of the proposed approach, highlighting its potential to enhance healthcare data capture and improve patient care. [ABSTRACT FROM AUTHOR]

Financial anomalies must be detected in order for financial institutions and regulatory bodies to manage risks and avoid fraudulent behavior. Financial anomaly detection is the practice of identifying unexpected or irregular financial transactions or patterns that may indicate fraudulent behavior or errors. It is crucial in today's digital era to prevent fraud, limit financial losses, and maintain secure financial systems. Various types of financial anomalies, such as credit card fraud, money laundering, financial statement fraud, and cryptocurrency fraud, pose significant risks to individuals and organizations. This review critically evaluates a selected research article on the use of blockchain technology in conjunction with data mining techniques to detect financial anomalies. The paper employs the case study method to demonstrate how well the suggested integrated system works in spotting financial anomalies. This review evaluates the article's methodology and conclusions and discusses its implications for practice. The report claims that merging data mining methods with blockchain technology can increase the precision and effectiveness of financial anomaly identification. This research advances knowledge about how block-chain technology and data mining techniques can be used to find financial abnormalities while also offering suggestions for further study and use. [ABSTRACT FROM AUTHOR]

Techniques for classifying data using data mining are now a day prevalent in agriculture. The method of classifying seeds involves grouping various seed varieties according to their morphological characteristics. To accomplish categorization of the typical Charotar region (generally comprising Anand and Kheda districts of the Gujarat State of India) Gujarat Wheat (GW) varieties (TRITICUM – AESTIVUM) viz. GW 273, GW 496, GW 322, LOK-1, and GDW 1255 (TRITICUM – DURUM), Weka Explorer was used. The features used are area, perimeter, solidity, aspect ratio, major and minor axis of seed kernel, Hue, Saturation, Value, and SF1 (empirical). Features reduction was done using Information Gain (IG) and its modified version Gain Ratio (GR). This paper compares performance of Tree based data mining algorithms in classifying wheat varieties. For classification we used purely tree-based machine learning algorithms viz. J48, Random Forest, Hoeffding Tree, Logistic Model Tree (LMT), and REPTree. LMT- logistics regression method gives higher accuracy 96.4% compared to other classifiers. Hoeffding Tree classifiers stood second with 96% accuracy. For validation 10-fold cross validation was used. By reducing the number of folds in cross validation performance of most algorithms decreased except J48. The percentage of correctly classified instance increased for all algorithms when features were selected by GR except for J48. [ABSTRACT FROM AUTHOR]

Geographic spaces is a vast and complex system involving multiple elements and nonlinear interactions of these elements, and rich in geographical phenomena, processes and patterns. Artificial intelligence methods (AI) are increasingly utilized to extract information of interest, patterns and insights from massive remote sensing (RS) images. Among them, two representative paradigms for RS interpretation are knowledge-driven symbolism and data-driven connectionism. Knowledge-driven approaches are certain and theoretically sound, which consider the regularities, formulae with accompanying theorems, and expert knowledge. However, it is difficult to address huge-scale RS data and complex nonlinear problems. The data-driven paradigm has stronger data learning and representation ability, yet poor interpretability, low trustworthiness of interpretation results, and reliance on massive labeled data. To address these limitations, we argue that current RS intelligent interpretation requires the guidance of geographic ideas and a unified theoretical framework rather than the independent use of remotely sensed data, duplicative development of interpretation models, or isolated applications of geographic information extraction. Thus, we introduce the concepts of trustworthy RS interpretation (TRSI) with multi-granularity of space, time, and attribute to facilitate the understanding of the morphology, types, and indicators (visual perception) and the perspective of the structure, evolution, and trends (geographical cognition) of complex geographical space. Second, integrated technologies of the TRSI with multiple scales and stages are developed, including a pixel-level visual perception, a geo-parcel-level quantitative inversion, and a scene-level geographical cognition. Finally, a RS big data interpretation system with quantitative semantic parsing is designed to support precise applications with cloud–edge-end collaboration. Rather than a simple overview and accumulation of traditional RS intelligent interpretation methods, this paper analyzes current challenges in RS interpretation and proposes new concepts, technologies, and applications of TRSI. It will empower us to perceive the complex geographical space comprehensibly and reveal its structural composition and evolutionary mechanism, serving geospatial decision applications such as evaluation, planning, and forecasting. [ABSTRACT FROM AUTHOR]

Stroke is a significant cause of mortality and morbidity worldwide, making it essential to identify individuals at risk of experiencing a stroke. The aim of this research article is to develop a predictive model to determine the stroke risk of individuals based on their medical history and compare the effectiveness of preprocessing techniques on the model's performance. The methodology involves two streams of analysis - with and without data preprocessing - utilizing classification models to predict stroke risk (K-Nearest Neighbor, Decision Tree and Support Vector Machine). The results indicate that data preprocessing improves the performance of all models, with KNN and SVM showing high precision and recall values, making them effective models for predicting strokes. Conversely, the decision tree model performs well with data preprocessing despite slightly lower accuracy and recall values. These findings suggest that preprocessing is a crucial stage in machine learning and can enhance the performance of classification models in predicting stroke risk. [ABSTRACT FROM AUTHOR]

General Election is the most prominent political activity in the election of people representatives in democratic countries. In the Information era, the veracity and volume of available information opportunities to inform the decision-making of election candidates. This research outlines the development of a Business Analytics Electoral Recommender System. The system aims to provide recommendations for political candidates based on various factors such as demographics, political preferences, and socio-economic status. We also propose an election candidate's recommender system using a hybrid of collaborative filtering (CF) and a knowledge-based (KB) model. The KB approach used a web crawler to scrape and process relevant information on various internet sources and social media to nominate the representative candidates based on the voter's preferences. We used a hybrid approach of The Cross Industry Standard Process for Data Mining (CRISP-DM) to the web scraping process of unstructured and semi-structured static and dynamic information of representative candidates. The recommendation uses the CF approach based on criteria for informed electoral decision-making. The results from user acceptance test showed the score are 86% for ease of use and 78.5% for the perceived usefulness of the proposed system's key features. The average percentage of behavioral intentions, and attitudes towards the actual use and utilization of the system, was 88.5%, which concludes that the respondents were generally satisfied with the proposed system. [ABSTRACT FROM AUTHOR]

The role of solute carrier family 25 member 15 (SLC25A15), a critical component of the urea cycle, in hepatocellular carcinoma (HCC) progression remains poorly understood. This study investigated the impact of SLC25A15 on HCC progression and its mechanisms. We systematically investigated the function of SLC25A15 in HCC progression using large-scale data mining and cell, animal, and organoid models. Furthermore, we analyzed its involvement in reprogramming glutamine metabolism. SLC25A15 expression was significantly decreased in HCC tissues, and patients with low SLC25A15 levels had a poorer prognosis. Hypoxia-exposed HCC cells or tissues had lower SLC25A15 expression. A positive correlation between HNF4A, a transcription factor suppressed by hypoxia, and SLC25A15 was observed in both HCC tissues and cells. Modulating HNF4A levels altered SLC25A1 5 mRNA levels. SLC25A15 upregulated SLC1A5, increasing glutamine uptake. The reactive metabolic pathway of glutamine was increased in SLC25A15-deficient HCC cells, providing energy for HCC progression through additional lipid synthesis. Ammonia accumulation due to low SLC25A15 levels suppressed the expression of OGDHL (oxoglutarate dehydrogenase L), a switch gene that mediates SLC25A15 deficiency-induced reprogramming of glutamine metabolism. SLC25A15-deficient HCC cells were more susceptible to glutamine deprivation and glutaminase inhibitors. Intervening in glutamine metabolism increased SLC25A15-deficient HCC cells' response to anti-PD-L1 treatment. SLC25A15 is hypoxia-responsive in HCC, and low SLC25A15 levels result in glutamine reprogramming through SLC1A5 and OGDHL regulation, promoting HCC progression and regulating cell sensitivity to anti-PD-L1. Interrupting the glutamine-derived energy supply is a potential therapeutic strategy for treating SLC25A15-deficient HCC. We first demonstrated the tumor suppressor role of solute carrier family 25 member 15 (SLC25A15) in hepatocellular carcinoma (HCC) and showed that its deficiency leads to reprogramming of glutamine metabolism to promote HCC development. SLC25A15 can serve as a potential biomarker to guide the development of precision therapeutic strategies aimed at targeting glutamine deprivation. Furthermore, we highlight that the use of an inhibitor of glutamine utilization can enhance the sensitivity of low SLC25A15 HCC to anti-PD-L1 therapy. [Display omitted] • SLC25A15 expression is significantly reduced in HCC tissues, and patients with low levels have a worse prognosis. • Hypoxia exposure reduces SLC25A15 expression by inhibiting HNF4A. • Low SLC25A15 levels result in glutamine reprogramming by inhibiting OGDHL, promoting HCC progression. • Low SLC25A15 reduced PD-L1 expression, thus interfering with the response to anti-PD-L1 therapy. • Interrupting the glutamine-derived energy supply may benefit patients with HCC and SLC25A15 deficiency. [ABSTRACT FROM AUTHOR]

To evaluate a natural language processing (NLP) system for extracting structured information from the free-form text of rectal cancer magnetic resonance imaging (MRI) reports written in Chinese. A rule-based NLP model that could extract 11 key image features of rectal cancer was constructed using 358 MRI reports of rectal cancer written between 2015 and 2021. Fifty reports written before 2015 and 50 written after 2021 were used as test datasets, and the reference standard was determined by manual extraction of information by two radiologists. The length and reporting rate of image features in pre-2015 and post-2021 datasets, as well as the accuracy, precision, recall, and F1 score of feature extraction by the NLP system, were compared. The time required for the NLP to extract data was compared with that required by the radiologists. Reports written after 2021 had longer diagnostic impression sections than reports written before 2015. The reporting rate of key imaging features of rectal cancer was 36.55% before 2015 and 79.82% after 2021. The accuracy, precision, recall, and F1 score of NLP for correct extraction of values from reports were 93.82%, 95.63%, 87.06%, and 91.15%, respectively, for pre-2015 reports, and 92.55%, 98.53%, 94.15%, and 96.29%, respectively, for post-2021 reports. NLP generated all the structured information in <1 second. The NLP system with rule-based pattern matching achieved rapid and accurate structured processing of rectal cancer MRI reports. MRI reports with structured templates are more suitable for NLP-based extraction of information. • The study developed a NLP model to extract 11 key features from MRI reports. • The F1 score of NLP was 91% for pre-2015 reports, and 96% for post-2021 reports. • The NLP system with rule-based pattern realized the structured processing. • The method would be transferable to other diseases and radiological modalities. [ABSTRACT FROM AUTHOR]

Neural entity-linking models excel at bridging the lexical gap of multiple facets of facts, such as entity-related claims or evidence documents. Despite advancements in self-supervised learning and pretrained language models, challenges persist in entity linking, particularly in interpretability and transferability. Moreover, these models need many aligned documents to adapt to emerging entities, which may not be available due to data scarcity. In this work, we propose a novel Demonstrative Self-TrAining fRamework (D-STAR) that leverages multi-perspective thought navigation. D-STAR iteratively optimizes a question generator and an entity retriever by navigating thoughts on a dynamic graph reasoning across multiple perspectives for question generation. The generated question–answer pairs, along with hard negatives shared in the graph, enable adaptation with minimal computational overhead. Additionally, we introduce a new task, source-free entity linking, focusing on unsupervised transfer learning without direct access to original domain data. To demonstrate the feasibility of this task, we provide a generated question–answering dataset, FandomWiki , for novel entities. Our experiments show that D-STAR significantly improves baselines on SciFact, Zeshel, and FandomWiki. • Adapting a pretrained entity linking model for entity queries of emerging domains. • Dynamically prompting a large language model with multiperspective thought navigation algorithm. • Harnessing topological data to mine hard negatives in contrastive learning. • Conceptualizing source-free entity linking. [ABSTRACT FROM AUTHOR]

Nowadays, the utilization of social media platforms continues to rise, as is the dissemination of misleading data. This fake information is damaging to both society and individuals. Hence, it is essential to prevent and detect the dissemination of fake news. Furthermore, the main challenge is the lack of an effective technique for distinguishing between truthful and false reviews; even individuals are usually unable to recognize the difference. In contract to numerous machine learning-based methods, the deep learning-based methods exhibit a superior capacity for accurately detecting fake news. Earlier studies focused on machine learning based and data mining techniques, with limited exploration of deep learning-based methods for automated detecting fake news task. This study attempts to introduce a method based on the GRU deep model in order to detect automatically fake news. Experimental evaluation using a benchmark fake news dataset indicates extremely encouraging results and enhanced performances. [ABSTRACT FROM AUTHOR]

Software architects and developers face challenges while trying to explain the complex functional relationships between actors and systems in the process of system design. The field of natural language processing (NLP) and information extraction is always growing. One of the biggest problems that comes up time and time again is finding relationships between entities and actions in textual data. Actor-action relation extraction (AARE) is a hybrid model that combines NLP with rule-based and machine-learning models. It effectively analyzes unstructured data and takes into account different contextual factors, saving time and reducing errors. The proposed approach extracts Actors, actions, entities, and relationships (AARE) from natural language text more accurately and completely using machine learning techniques and rule-based systems. The hybrid model handles unstructured input and adapts to changing linguistic signals using machine learning. The hybrid approach uses Named Entity Recognition, rule-based extraction, and machine learning principles to convert unstructured data into structured format. It uses tokenization, part-of-speech tagging, nlp, and semantic role labeling for relationship categorization. The model has a 93% accuracy rate and is effective in extracting actor- action relations. Future research should focus on improving rule-based techniques, semantic learning, and addressing complexities in UML diagrams. [ABSTRACT FROM AUTHOR]

The two-phase Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) campaign over India has generated datasets of numerous natural and artificial surroundings with high signal-to-noise ratios and higher spatial and spectral consistencies. However, the first phase AVIRIS-NG datasets of urban areas have 8.1 m spatial resolution, which is insufficient for producing level-4 urban material and natural cover maps required to model Earth's processes at the micro-scale or constantly monitor urban processes. The current work addresses this drawback by demonstrating a part of the proposed proof-of-concept approach on the AVIRIS-NG dataset of Ahmedabad, India, wherein super-resolution (SR) is proposed to enhance the hyperspectral image's spatial resolution and preserve its rich spectral content at the targeted spatial scale before utilizing it for producing detailed urban land cover maps. A step-wise account of the SR process has been provided herein to aid the reader in reproducing these experiments. Super-resolved products generated using four SR algorithms have been compared and validated using a composite framework based on processing time, qualitative visual investigation of selected image patches and spectral profiles, re-interpretation of Wald's reduced resolution protocol and full-reference image quality metrics. Results suggest the AVIRIS-NG dataset's successful spectral and spatial fidelity at a larger spatial scale, albeit varying in each super-resolved output. Sparse regression and natural prior (SRP), and anchored neighbourhood regression (ANR) produce the best and poorest super-resolved outputs among the tested SR algorithms. Although gradient profile prior (GPP) generated super-resolved product is the second best, it is also the most computationally expensive algorithm. An additional yardstick in the form of a spectral similarity analysis of randomly selected unknown spectral signatures on super-resolved output against a reference spectral library has been proposed to ascertain spectral content preservation further before utilizing the super-resolved product for urban information extraction. This exercise's findings reveal the presence of the target urban land cover feature at the unknown pixels with matching scores of greater than 0.5 in both best and worst super-resolved products, thereby advocating the extension of the proposed proof-of-concept to recently launched spaceborne hyperspectral missions' datasets. [ABSTRACT FROM AUTHOR]

Condition monitoring and predictive maintenance applications receive ongoing scientific attention in production technology. Larger companies, especially machine and component manufacturers, already offer related products. Small and medium-sized enterprises (SMEs) in particular show interest in developing and offering solutions in this market themselves to gain economic advantages, to improve resource utilization of their machines or to be able to offer these advantages to their own customers. In the development process, however, they often encounter problems already in the digitization of the machines. The first hurdle is to obtain an analysis-capable data set. This is due to the fact that common and established general data mining development process models, such as CRISP-DM, do not focus on production technology, causing difficulties for engineers during deployment. A problem with existing process models is the limited practicality in the engineering domain due to restricted adaptability. In a previous paper, a guideline for engineers for data mining suitable digitization of production machines was developed in order to solve these problems. The related results were provided in the context of a project for condition monitoring of mixing machines. In this paper, the proposed method is applied to components of a 5-axis CNC milling machine in three different monitoring use cases. A complete workflow is presented, including effect analysis, sensor selection, formulation of predictive scenarios, data preparation, training of machine learning algorithms and vizualization. Data and documentation are provided alongside this publication. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of a fully automated machine learning (ML) pipeline on time series data from the domain of production engineering. The workflow aims to streamline the machine learning (ML) process and reduce manual effort by incorporating automated machine learning (AutoML) and automated featurization techniques. A comparative study is created to evaluate the proposed workflow against existing state-of-the-art methods using six datasets from production engineering, five widely used ones and a newly presented one. The study compares the performance of the automated workflow with the manual data mining process described in the literature. The results lead to two main conclusions: first, an automated featurization process cannot match the performance of manual feature generation, but still delivers good results without requiring manual work. Second, if the manually generated features are used, AutoML can outperform the manual process in solving the combined problem of algorithm selection and hyperparameter optimization, leading to better prediction results. The presented approach serves as a useful reference for other methods, showing the practicality of baseline workflow without human bias. Overall, the proposed data processing pipeline demonstrates the potential of AutoML in production engineering and provides valuable insights for practitioners in this domain. [ABSTRACT FROM AUTHOR]

As bike-share systems proliferate across the US, a deeper understanding of current bike-share users could enable an expansion of these services and their benefits to a larger population. To grow bike-share demand sustainably and equitably, cities must understand how different segments of the population use this service. Understanding the different segments within a market enable operators to develop strategies that are tailored toward the needs of each segment. This study uses data from bike-share user surveys in the Sacramento region to perform market segmentation based on perceptions of the bike-share service, mode use patterns, and bike-share use. We focus on the market segments with more limited means of transportation: low-income individuals, zero-car households, students, and transit and car-share users. The results show that individuals with low incomes, those without cars, and students use the service frequently for commuting and a variety of non-commuting purposes. Bike-share is generally adopted by all mode user groups but is used at a higher rate by the super multimodal and active multimodal groups. The occasional users of the bike-share service are mainly those with higher incomes and individuals who have access to a personal car. The segments consisting of non- and infrequent-personal bike users use the bike-share service at a greater rate for different purposes than regular bicyclists, suggesting that bike-share may act as a lever for increasing bike travel for some users. Results from data mining suggest that bike-share operators should target low-income and zero-car owners for new recruitment as these groups are more likely to use bike-share frequently. The results provide insights that may be helpful to cities as they consider strategies to increase bike-share demand in a way that enhances social equity. • We perfom market segmentation based on the perception of the bike-share service, mode use patterns, and bike-share use. • Bike-share is generally adopted by all types of modality groups. • Individuals with low incomes, students, and non-auto owners use the service frequently for variety of travel purposes. • Bike-share may act as a lever for increasing bike travel for some users. [ABSTRACT FROM AUTHOR]

Hierarchies of sets, and most multilevel clustering models, have been characterized as convexities induced by interval functions satisfying specific properties, thus giving rise to a unifying framework for characterizing multilevel clustering models. Here, we show that this unifying framework can be relevant to data mining practice. First, we provide a flexible characterization of hierarchies and weak hierarchies as interval convexities. Second, we investigate the Apresjan hierarchy and the Bandelt and Dress weak hierarchy, and characterize them as interval convexities. Third, we propose a method for computing recursively a sequence of path-based dissimilarities which decreases from an arbitrary dissimilarity downto its subdominant ultrametric. We prove that these path-based dissimilarities define two sequences of nested families of interval convexities. One sequence increases from the Apresjan hierarchy to the single-link hierarchy, and the other from a subset of the single-link hierarchy to the Bandelt and Dress weak hierarchy. Applications to the simplification and validation of the single-link hierarchy of an arbitrary dissimilarity are discussed. [ABSTRACT FROM AUTHOR]

To summarize the rules of acupoint selection in treatment of cancer with moxibustion by analyzing the articles of relevant clinical trials in recent years in China, so as to provide the references for acupoint selection of moxibustion for cancer. The articles of clinical trials for treatment of cancer with moxibustion were retrieved from China National knowledge Infrastructure (CNKI), VIP Chinese Journal Service Platform (VIP) and Wanfang Data Knowledge Service Platform (Wanfang) from 1st January 2015 to 30th June 2021. Using EpiData software, the data were extracted and exported in Excel. The analysis was performed with IBM SPSS Statistics 23 software. A total of 516 articles were collected, including 533 prescriptions with 164 acupoints involved. The high-frequency acupoints were similar among 7 types of cancers and these acupoints mostly acted on supporting the antipathogenic qi , replenishing qi and regulating the spleen and stomach. Regarding meridian tropism, the acupoints from the conception vessel, the governor vessel and bladder meridian were dominant in treatment of cancer with moxibustion. In terms of location distribution, the selected acupoints were generally distributed on the chest, abdomen, lumbar region and the upper back of the body. Based on cancer related symtomes in different systems, the use frequency of Zusanli (ST36) was the highest for the symptoms of the immune, digestive, circulatory and motor systems; that of Feishu (BL13) was the highest for the respiratory system symptoms and that of Guanyuan (CV4) was the highest for the urinary system symptoms. The cluster analysis of high-frequency acupoints showed that the acupoints of moxibustion for cancer generally work on cultivating yang, supporting the antipathogenic qi , strengthening the spleen and harmonizing the stomach, and were selected in association with the clinical symptoms of different cancers. In treatment of cancer with moxibustion, the acupoints of the conception vessel and the governor vessel, as well as the back- shu points of the bladder meridian were dominated. All of these acupoints acted on supporting the antipathogenic qi , replenishing qi and regulating the spleen and stomach. It emphasized the etiology of "the deficiency in the root" of cancer, and the characteristics of acupoint selection based on syndrome differentiation in treatment with moxibustion. [ABSTRACT FROM AUTHOR]

The aim of the study was to explore the regularity of acupoints combination in the treatment of functional dyspepsia (FD) by means of data mining technology. We conducted a systematic search of nine databases from their inception to December 2022 to identify eligible randomized controlled trials (RCTs). The acupoints prescriptions in the treatment of FD were extracted and analyzed based on association rule mining and network analysis. Furthermore, the acupoints prescribed for irritability symptom of FD were quantified by forward inference, reverse inference and Bayes factor (BF) correction. A total of 45 RCTs on 30 acupoints were included, involving 172 times the total application frequency. Analysis of acupoints prescriptions covered 29 meridian acupoints and one extra-ordinary point, disturbing in 10 meridians. The top three acupoints in application frequency were Zusanli (ST36, 19.77%), Neiguan (PC6, 16.86%), Zhongwan (CV12, 12.21%). The most involved meridian was the foot yangming stomach meridian (34 times). The most frequently used acupoints combinations were PC6 and ST36. The core prescriptions were ST36, PC6, Tianshu(ST25), CV12. The specificities of acupoints selection for FD with dysphoria as follows: using the forward inference, PC6, Taichong(LR3), Qimen(LR14), CV12, Danzhong(CV17), ST25, ST36 were selected. Using the reverse inference and BF correction, PC6, CV12, CV17, Sanyinjiao(SP6), ST25, ST36 were selected. ST36, PC6, ST25, CV12 could be used as the main prescriptions in treating FD. For FD with dysphoria the acupoints prescription is PC6, CV12, CV17, ST25 and ST36. [ABSTRACT FROM AUTHOR]

Nosocomial infections represent an ongoing challenge to healthcare quality and patient safety, negatively impacting clinical outcomes and increasing the burden on healthcare systems. Thus, controlling this type of infection plays a very important role in ensuring a better quality of life for patients. Although the control and prevention measures for these infections are well defined, their signaling and detection is carried out manually and sometimes late, which compromises the health status of patients and everyone around them. In this context, this study emerged with the aim of exploring the potential of data mining techniques to predict the occurrence of nosocomial infections, with a specific focus on infections associated with surgical interventions. Using datasets for the period between 2018 and 2022, sourced from a Portuguese hospital and duly anonymized to protect patient privacy, several classification algorithms and data balancing techniques were analyzed to deal with the uneven nature of the data and the presence of minority classes. Among the algorithms and balancing techniques used, it was found that the Random Forest algorithm combined with the Oversampling technique showed superior performance in identifying cases of nosocomial infections associated with surgical interventions. The results of this study highlight the importance of collaboration between medicine and technology, indicating that the integration of data mining techniques can prove to be valuable tools to improve clinical decision-making and infection management in surgical context. [ABSTRACT FROM AUTHOR]