Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown
Published: Jan. 1, 2024
Language: Английский
Management Decision, Journal Year: 2025, Volume and Issue: unknown
Published: March 14, 2025
Purpose This research aims to investigate the interaction between artificial intelligence (AI) capability, big data capabilities, sustainability design and organizational effectiveness in context of furniture industry. It explore how investments AI technologies can spur sustainability-focused innovation ultimately increase corporate performance. Design/methodology/approach Based on collected from businesses operating industry, this uses a quantitative approach analyze relationships independent variables (AI capability features), mediating variable (sustainability design) dependent (organizational effectiveness). The structural equation modeling (SEM) technique was used test proposed theoretical model hypotheses. SmartPLS program for analysis. Findings Analysis results show significant positive relationship Moreover, demonstrates its important role translating technological advances into tangible performance by capabilities effectiveness. Research limitations/implications Although contributes valuable insights, it also has limitations. would not be appropriate make general assessment generalizability findings due focus industry fact that were furniture-producing companies Istanbul. Future could additional industries incorporate qualitative methods provide deeper understanding underlying mechanisms driving observed relationships. Practical implications offer insights practitioners seeking leverage potential sustainable include strategic recommendations integrating principles strategies, leveraging data-driven decision-making processes encouraging through investments. Originality/value originality lies comprehensive examination intertwined dynamics effectiveness, especially By combining knowledge multiple disciplines, offers new perspective business practices.
Language: Английский
Citations
0
Ethik in der Medizin, Journal Year: 2024, Volume and Issue: unknown
Published: March 28, 2024
Abstract Definition of the problem Biomedical research based on big data offers immense benefits. Large multisite that integrates large amounts personal health data, especially genomic and genetic might contribute to a more personalized medicine. This type requires transfer storage highly sensitive which raises question how protect subjects against harm, such as privacy breach, disempowerment, disenfranchisement, exploitation. As result, there is trade-off between reaping benefits big-data-based biomedical protecting subjects’ right informational privacy. Arguments Blockchain technologies are often discussed technical fix for abovementioned due their specific features, namely provenance, decentralization, immutability, access governance system. However, implementing blockchain in also questions regarding consent, legal frameworks, workflow integration. Hence, accompanying measures, I call enablers, necessary unleash potential technologies. These enablers innovative models ownership models, regulatory models. Conclusion alone insufficient resolve aforementioned trade-off. Combining this with outlined above be best way perform at same time subjects.
Language: Английский
Citations
3
Lab on a Chip, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 1, 2025
Created in BioRender. Menon, N. (2025). https://www.BioRender.com/l48m487.
Language: Английский
Citations
0
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 24, 2025
The healthcare sector is experiencing a digital transformation propelled by the Internet of Medical Things (IOMT), real-time patient monitoring, robotic surgery, Electronic Health Records (EHR), medical imaging, and wearable technologies. This proliferation tools generates vast quantities data. Efficient timely analysis this data critical for enhancing outcomes optimizing care delivery. Real-time processing Healthcare Big Data (HBD) offers significant potential improved diagnostics, continuous effective surgical interventions. However, conventional cloud-based systems face challenges due to sheer volume time-sensitive nature migration large datasets centralized cloud infrastructures often results in latency, which impedes applications. Furthermore, network congestion exacerbates these challenges, delaying access vital insights necessary informed decision-making. Such limitations hinder professionals from fully leveraging capabilities emerging technologies big analytics. To mitigate issues, paper proposes Regional Computing (RC) paradigm management HBD. RC framework establishes strategically positioned regional servers capable regionally collecting, processing, storing data, thereby reducing dependence on resources, especially during peak usage periods. innovative approach effectively addresses constraints traditional facilitating at level. Ultimately, it empowers providers with information required deliver data-driven, personalized optimize treatment strategies.
Language: Английский
Citations
0
Biomedical Engineering and Computational Biology, Journal Year: 2025, Volume and Issue: 16
Published: Feb. 1, 2025
This work presents an enhanced identification procedure utilising bioinformatics data, employing optimisation techniques to tackle crucial difficulties in healthcare operations. A system model is designed essential by analysing major contributions, including risk factors, data integration and interpretation, error rates wastage gain. Furthermore, all aspects are integrated with deep learning optimisation, encompassing normalisation hybrid methodologies efficiently manage large-scale resulting personalised solutions. The implementation of the suggested technology real time addresses significant disparity between data-driven applications, hence facilitating seamless genetic insights. contributions illustrated time, results presented through simulation experiments 4 scenarios 2 case studies. Consequently, comparison research reveals that efficacy for enhancing routes stands at 7%, while complexity diminish 1%, thereby indicating operations can be transformed computational biology.
Language: Английский
Citations
0
Neurotrauma Reports, Journal Year: 2025, Volume and Issue: 6(1), P. 391 - 401
Published: Jan. 1, 2025
Language: Английский
Citations
0
Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 288 - 304
Published: Feb. 8, 2024
Over 10 million people worldwide suffer with Parkinson's disease (PD). The ecumenical medical community continues to rely on physical examination, scales, signs, and symptoms, but there is no evaluation for this obscenely common disease. As technology advances, machine learning healthcare are becoming more entwined. One of the methods known as functional gradient boosting (FGB) has shown promise a diagnostic tool PD patients, helping speeding up their recovery. FGB helps practitioners create tailored therapeutics that improve treatment results patient's quality life. Additionally, it providers track development diseases, foresee describe treatments. research positioned significantly transform Parkinsonism rehabilitation patient outcomes.
Language: Английский
Citations
2
Bioinformatics, Journal Year: 2024, Volume and Issue: 40(2)
Published: Feb. 1, 2024
Abstract Motivation The volume of biomedical data generated each year is growing exponentially as high-throughput molecular, imaging and mHealth technologies expand. This rise in has contributed to an increasing reliance on demand for computational methods, consequently increased attention software quality integrity. Results To simplify verification diverse data-processing pipelines, we created PipeVal, a light-weight, easy-to-use, extensible tool file validation. It open-source, easy integrate with complex workflows, modularized extensibility new formats. PipeVal can be rapidly inserted into existing methods pipelines automatically validate verify inputs outputs. reduce wasted compute time attributed corruption or invalid paths, significantly improve the data-intensive software. Availability implementation open-source Python package under GPLv2 license it freely available at https://github.com/uclahs-cds/package-PipeVal. docker image at: https://github.com/uclahs-cds/package-PipeVal/pkgs/container/pipeval.
Language: Английский
Citations
2
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 7, 2024
DNA sequencing continues to get cheaper and faster. In parallel, algorithmic innovations have allowed inference of a wide range nuclear, mitochondrial, somatic evolutionary from data. To make automated, high-quality more readily available, we created an extensible Nextflow meta-pipeline called metapipeline-DNA. Metapipeline-DNA supports processing raw reads through alignment, variant detection, quality control subclonal reconstruction. Each step quality-control, data-visualization multiple algorithms. is cloud-compatible highly configurable, with options subsect, optimize analyses, including automated failure-recovery. enables high-scale, fault-tolerant, comprehensive analysis genome sequencing. Availability: open-source pipeline under the GPLv2 license available at https://github.com/uclahs-cds/metapipeline-DNA.
Language: Английский
Citations
2
Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 12 - 46
Published: March 4, 2024
Artificial intelligence (AI) systems are designed by humans that, given a complex goal, act in the physical or digital dimension perceiving their environment through data acquisition, interpreting collected structured unstructured data, reasoning on knowledge, processing information, derived from this and deciding best action(s) to take achieve goal. It is precisely AI's ability carry out speedy analysis of datasets that one its key strengths. The recent renaissance AI largely has been driven successful application deep learning — which involves training an artificial neural network with many layers (that is, ‘deep' network) huge datasets. rise dissemination clinical medicine will refine our diagnostic accuracy rule-out capabilities. In Book Chapter, we focus applications could augment change practice, identify impact arising development suggest future research directions.
Language: Английский
Citations
1