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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.
New generation sequencing platforms are producing data with significantly higher throughput and lower cost. A portion of this capacity is devoted to individual and community scientific projects. As these projects reach publication, raw sequencing datasets are submitted into the primary next-generation sequence data archive, the Sequence Read Archive (SRA). Archiving experimental data is the key to the progress of reproducible science. The SRA was established as a public repository for next-generation sequence data as a part of the International Nucleotide Sequence Database Collaboration (INSDC). INSDC is composed of the National Center for Biotechnology Information (NCBI), the European Bioinformatics Institute (EBI) and the DNA Data Bank of Japan (DDBJ). The SRA is accessible at www.ncbi.nlm.nih.gov/sra from NCBI, at www.ebi.ac.uk/ena from EBI and at trace.ddbj.nig.ac.jp from DDBJ. In this article, we present the content and structure of the SRA and report on updated metadata structures, submission file formats and supported sequencing platforms. We also briefly outline our various responses to the challenge of explosive data growth.
In recent years, two communities have grown around a joint interest on how big data can be exploited to benefit education and the science of learning: Educational Data Mining and Learning Analytics. This article discusses the relationship between these two communities, and the key methods and approaches of educational data mining. The article discusses how these methods emerged in the early days of research in this area, which methods have seen particular interest in the EDM and learning analytics communities, and how this has changed as the field matures and has moved to making significant contributions to both educational research and practice.
Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows. (C) 2015 Elsevier B.V. All rights reserved.
Machine learning systems deployed in real-world environments frequently encounter data imperfections such as noise, missing values, class imbalance, and distribution shifts. Despite substantial progress in model development, most evaluation protocols rely on clean benchmark datasets, creating a gap between laboratory performance and operational reliability. Existing robustness studies often focus on isolated perturbation types or single model families, lacking a unified benchmarking framework. This study proposes a structured and reproducible benchmarking methodology to systematically evaluate model robustness under controlled data degradation scenarios. Multiple classical machine learning algorithms and deep learning models were assessed across diverse benchmark datasets. Controlled perturbations—including feature noise, label corruption, missingness mechanisms, imbalance ratios, and covariate shifts—were introduced at progressive levels. Performance was evaluated using predictive metrics, robustness degradation rate (RDR), and computational efficiency, with statistical validation across repeated experimental runs. Results indicate that ensemble-based methods consistently achieved the strongest robustness, maintaining degradation rates below 10% under moderate noise and imbalance conditions. Deep neural networks demonstrated superior clean-data accuracy but experienced sharper degradation under structured corruption and distribution shifts. Mitigation strategies such as regularization and resampling reduced degradation by 5–12% under moderate perturbations but showed limited effectiveness under extreme conditions. The findings demonstrate that robustness is multidimensional and dependent on alignment between model inductive bias and data imperfection type. The proposed benchmarking framework provides practical guidance for selecting machine learning models suited to imperfect data environments, advancing reliable and deployment-ready AI systems
Abstract Urban development faces multifaceted challenges, including climate change, mobility transitions, resource depletion, social inequality, and demographic shifts. To address these, cities must become responsive and generative , placing citizens at the center of transformation. Traditional top-down planning often fails to leverage citizen participation and overlooks their growing familiarity with digital and AI tools. This study introduces Citizen Design Science , a collaborative methodology that integrates citizen engagement with advances in data science, AI, and design science. By combining participatory design, computational instruments, geospatial analytics, simulation, and real-time data, the approach empowers both experts and non-experts to shape resilient and livable human settlements. Case examples from education, research, culture, and urban planning demonstrate how Citizen Design Science democratizes development and fosters inclusive, scientifically grounded processes. The methodology emphasizes citizen empowerment, technological integration, and collaborative governance across scales, from villages to megacities. Key challenges remain, including time-intensive engagement, digital accessibility, shared human-AI governance, data quality, and the digital divide. Overcoming these obstacles is essential for scaling impact and ensuring resilient, livable settlements.
One of the main goals of today's technology is to create a connected environment between humans and technological devices to perform daily physical activities. However, users with speech disorders cannot use this application. Loss of verbal communication can be caused by injuries and neurodegenerative diseases that affect motor production, speech articulation, and language comprehension. To overcome this problem, Brain-Computer Interfaces (BCI) use EEG signals as assistive technology to provide a new communication channel for individuals who cannot communicate due to loss of motor control. Of the several BCI studies that use EEG signals, no studies have studied Balinese characters. As a first step, this study examines the acquisition of EEG signal data for Balinese character recognition. There are several stages in obtaining EEG signal data for Balinese character spelling imagination in this study: preparation of research documents, preparation of stimulus media, submission of ethical permits, determination of participants, recording process, data presentation, and publication of datasets. The result datasets from this study are in the form of raw data, and data was analyzed for 18 Balinese and 6 vowel characters, both spelling and imagined.
Computer applications to medicine. Medical informatics, Science (General)
Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki
et al.
Abstract This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. Specifically, a neural network is applied as a base learner for predicting antenna parameters, resulting in increased predictive performance, achieving R², EVS, MSE, RMSE, and MAE values of 0.96, 0.998, 0.00842, 0.00453, and 0.00999, respectively. Utilizing regression-based machine learning, antenna parameters are optimized to attain dual-band resonance with bandwidths of 3.34 THz and 1 THz across two bands, ensuring robust data throughput and communication stability. The antenna, designed with dimensions of 70 × 280 μm², demonstrates a maximum gain of 15.82 dB, excellent isolation exceeding − 32.9 dB, and remarkable efficiency of 99.8%, underscoring its suitability for high-density, high-speed 6G environments. The design methodology integrates CST simulations and an RLC equivalent circuit model, substantiated by ADS simulations, with comparable reflection coefficients validating the accuracy of the models. With its compact footprint, broad bandwidth, and optimized isolation and efficiency, the proposed MIMO antenna is positioned as an ideal candidate for future 6G communication applications.
Arkadiusz Kacała, Mateusz Dorochowicz, Jędrzej Fischer
et al.
<i>Background and Objectives:</i> Deep venous thrombosis (DVT) is associated with pulmonary embolism and long-term complications such as post-thrombotic syndrome (PTS). Anticoagulation prevents thrombus extension but does not actively remove clot. Interventional techniques, including catheter-directed thrombolysis, mechanical and pharmacomechanical thrombectomy, and venous stenting, have been introduced to restore venous patency and reduce complications. This systematic review summarizes current evidence on outcomes, safety, and patient selection for these procedures. <i>Materials and Methods:</i> A systematic search of PubMed, EMBASE, Cochrane Library, and Web of Science was conducted for studies published between January 2000 and February 2024. Eligible studies included randomized controlled trials, systematic reviews, meta-analyses, and observational studies with ≥20 patients. Extracted outcomes were technical success, thrombus clearance, venous patency, PTS, quality of life, and complications. Risk of bias was assessed using the Cochrane Risk of Bias Tool, Newcastle–Ottawa Scale, and AMSTAR-2. <i>Results:</i> Of 456 records screened, 35 studies were included. Randomized trials (CaVenT, ATTRACT, CAVA) showed that catheter-directed and pharmacomechanical approaches improved venous patency and reduced moderate-to-severe PTS in selected patients with iliofemoral DVT, though overall benefit was variable. Mechanical thrombectomy devices (e.g., AngioJet, ClotTriever, FlowTriever) achieved high thrombus clearance and shorter procedural times, with device-specific complication profiles. Observational data demonstrated venous stenting patency rates of 74–89% at 12 months. Study heterogeneity limited direct comparisons. <i>Conclusions:</i> Interventional procedures can reduce PTS and improve outcomes in carefully selected patients, particularly those with acute iliofemoral DVT. Modern mechanical and pharmacomechanical techniques enhance efficiency and safety, while venous stenting addresses underlying obstructions. Further high-quality trials with long-term follow-up are needed to define optimal patient selection and comparative effectiveness.
Purpose: Although magnesium sulfate (MgSO4) is widely used as an analgesic adjuvant to peripheral analgesic cocktails, its efficacy in total knee arthroplasty (TKA) is still controversial. Therefore, we systematically reviewed and meta-analyzed the literature to assess the analgesic efficacy of MgSO4 as an adjuvant to the analgesic cocktail in TKA.
Methods: The PubMed, EMBASE, Web of Science, and Cochrane Library databases were searched. The meta-analysis was performed according to the PRISMA guidelines. Data were qualitatively synthesized or meta-analyzed using a random-effects model.
Results: Five randomized controlled trials involving 432 patients were included. Meta-analyses detected significant differences between the MgSO4 and control groups in the visual analog scale (VAS) pain scores (rest) at 6, 12, and 24 h postoperatively; VAS pain scores (motion) at 12, 24, and 48 h postoperatively; morphine consumption within 24 h, 24–48 h, and during the total hospitalization period; time to first rescue analgesia after TKA; and length of hospital stay. Regarding the functional recovery, the meta-analysis demonstrated significant differences between groups in terms of knee range of motion on postoperative day 1; daily mobilization distance on postoperative day 1; and daily mobilization distance. There was no significant intergroup difference in surgical complications.
Conclusion: The findings suggest that MgSO4 is a promising adjunct to the analgesic cocktail, achieving significant improvements in pain scores and total opioid consumption during the early postoperative period after TKA.
The primary objective of this study was to assess the influence of exercise interventions on cancer-related fatigue (CRF), specifically in breast cancer patients, with the ultimate goal of establishing an optimal exercise prescription for breast cancer patients. A comprehensive search was undertaken across multiple databases, including Embase, PubMed, Cochrane Library, Web of Science, and Scopus, covering data published up to 1 September 2023. A meta-analysis was conducted to calculate the standardized mean difference (SMD) along with its corresponding 95% confidence interval (CI), thereby quantifying the effectiveness of exercise in alleviating CRF in the breast cancer patient population. Twenty-six studies met the inclusion criteria. Aerobic exercise (SMD, −0.17, <i>p</i> = 0.02), resistance exercise (SMD, −0.37, <i>p</i> = 0.0009), and combined exercise (SMD, −0.53, <i>p</i> < 0.0001) significantly improved CRF in breast cancer patients. In addition, exercise intervention conducted ≥3 times per week (SMD, −0.47, <i>p</i> = 0.0001) for >60 min per session (SMD, −0.63, <i>p</i> < 0.0001) and ≥180 min per week (SMD, −0.79, <i>p</i> < 0.0001) had greater effects on improving CRF in breast cancer patients, especially middle-aged patients (SMD, −0.42, <i>p</i> < 0.0001). Exercise is an effective approach to improving CRF in breast cancer patients. When devising an exercise program, the primary consideration should be the incorporation of combined exercise as the principal intervention. This entails ensuring that participants engage in the program at least three times weekly, with each session lasting for more than 60 min. The ultimate aim is to achieve a total weekly exercise duration of 180 min by progressively increasing the frequency of exercise sessions.
One of the learning models that is currently strongly advised to be used in the learning process is Project Based Learning (PJBL). The purpose of this study is to ascertain how PJBL affects students' learning outcomes in the digestive system. Students (n=61) from Muara Enim, Indonesia's class XI Science public senior high school served as the research subjects. A quasi-experimental design with a non-equivalent control group is the study methodology employed. Project assignments, surveys, and observation sheets served as the tools. The independent sample t-test in the statistical program for social science 28 (SPSS version 28) was used to assess learning outcomes data. With a gain of 33.6 and n-gain of 0.63, the study's findings show that using the PjBL learning paradigm enhances students' learning outcomes. PjBl can increase motivation, and as it uses real-world challenges, strong curiosity will impact learning outcomes.
Silvia Diz-de Almeida, Raquel Cruz, Andre D Luchessi
et al.
The genetic basis of severe COVID-19 has been thoroughly studied, and many genetic risk factors shared between populations have been identified. However, reduced sample sizes from non-European groups have limited the discovery of population-specific common risk loci. In this second study nested in the SCOURGE consortium, we conducted a genome-wide association study (GWAS) for COVID-19 hospitalization in admixed Americans, comprising a total of 4702 hospitalized cases recruited by SCOURGE and seven other participating studies in the COVID-19 Host Genetic Initiative. We identified four genome-wide significant associations, two of which constitute novel loci and were first discovered in Latin American populations (BAZ2B and DDIAS). A trans-ethnic meta-analysis revealed another novel cross-population risk locus in CREBBP. Finally, we assessed the performance of a cross-ancestry polygenic risk score in the SCOURGE admixed American cohort. This study constitutes the largest GWAS for COVID-19 hospitalization in admixed Latin Americans conducted to date. This allowed to reveal novel risk loci and emphasize the need of considering the diversity of populations in genomic research.