Search Results (1,523)

Aquaculture industry is a major contributor to the world’s food supply and therefore provides food security globally. However, many problems exist for the development of the industry; they include poor disease control, lack of sufficient workers, and inefficient use of resources. The advent of big data and Artificial Intelligence (AI) technologies presents an opportunity for the aquaculture industry to utilize these technologies to improve operational practices in the entire industry. A proposed framework for a decision-making support system, utilizing a multi-source data hub will be established to enhance current operational practices in aquaculture. This framework will be designed to collect and integrate multiple types of big data from aquaculture (environmental data, trading data, and data from Internet sources), and then create a common platform for accessing and processing the collected data. Four main modules will be created: monitoring environmental factors for shrimp farming, sharing shrimp farming equipment, calculating costs/profits associated with shrimp farming, and predicting price changes. Experimental prototypes of web-based systems will be built and tested to evaluate their performance. Ultimately, the proposed framework will provide users with a user-friendly platform to access the analysis results and recommendations provided to support decisions made during shrimp aquaculture production. Therefore, our work will serve as a reference point for the adoption of leading-edge technologies into aquaculture production management and contribute to sustainable growth of the aquaculture industry. Full article

As an important component of the global fishery economy, the crab breeding and processing industry faces the dual challenges of sustainable development and technological upgrading. This paper first systematically analyzes the regional distribution and core biological characteristics of major global economic crab species, laying a foundation for the targeted design of processing technologies and equipment. Secondly, based on advances in crab processing technology, the industry is categorized into two systems: live crab processing and dead crab processing. Live crab processing has formed a full-chain technological system of “fishing–temporary rearing–depuration–grading–packaging”. Dead crab processing focuses on high-value utilization: high-pressure processing enhances the quality of crab meat; liquid nitrogen quick-freezing combined with modified atmosphere packaging extends shelf life; and biological fermentation and enzymatic hydrolysis facilitate the green extraction of chitin from crab shells. In terms of intelligent equipment application, sensor technology enables full coverage of aquaculture water quality monitoring, precise classification during processing, and vitality monitoring during transportation. Automation technology reduces labor costs, while fuzzy logic algorithms ensure the process stability of crab meat products. The integration of the Internet of Things (IoT) and big data analytics, combined with blockchain technology, enables full-link traceability of the “breeding–processing–transportation” chain. In the future, cross-domain technological integration and multi-equipment collaboration will be the key to promoting the sustainable development of the industry. Additionally, with the support of big data and artificial intelligence, precision management of breeding, processing, logistics, and other links will realize a more efficient and environmentally friendly crab industry model. Full article

In this age of consumerism, most companies are doing their utmost to convince their customers of their products and to attract new customers. The IT development we see today is a perfect solution for strengthening the relationship between companies and their customers, giving them the opportunity to expand their customer base. The Internet of Things refers to an inter-connected system of smart devices that communicate and exchange data and big data analytics over the internet. As this involves the process of the treating data to unlock hidden information, patterns, and insights, the combination of both tools creates a revolution in customer relations and gives us the opportunity to understand our customers’ needs before they do themselves. This article presents an exploratory literature review of studies analyzing the relationship between IOT and big data in marketing. It provides a deep analysis of various scholars’ works that examine the methodology used by these tools to reinforce customer relations and acquire new ones. This review provides an overview of the most interesting research on this topic and the methods and techniques employed as well as an analysis of the obstacles and challenges involved. The results of this research show that IOT and big data analytics are key factors for an efficient analysis of clients’ needs. Full article

The integration and emerging adoption of machine learning (ML) algorithms in healthcare big data has revolutionized clinical decision-making, predictive analytics, and real-time medical diagnostics. However, the application of machine learning in healthcare big data faces computational challenges, particularly in efficiently processing and training on large-scale, high-velocity data generated by healthcare organizations worldwide. In response to these issues, this study critically reviews and examines current state-of-the-art advancements in machine learning algorithms and big data frameworks within healthcare analytics, with a particular emphasis on solutions addressing data volume and velocity. The reviewed literature is categorized into three key areas: (1) efficient techniques, arithmetic operations, and dimensionality reduction; (2) advanced and specialized processing hardware; and (3) clustering and parallel processing methods. Key research gaps and open challenges are identified based on the evaluation of the literature across these categories, and important future research directions are discussed in detail. Among the several proposed solutions are the utilization of federated learning and decentralized data processing, as well as efficient parallel processing through big data frameworks such as Apache Spark, neuromorphic computing, and multi-swarm large-scale optimization algorithms; these highlight the importance of interdisciplinary innovations in algorithm design, hardware efficiency, and distributed computing frameworks, which collectively contribute to faster, more accurate, and resource-efficient AI-driven healthcare big data analytics and applications. This research supports the UNSDG 3 (Good Health and Well-Being) and UNSDG 9 (Industry, Innovation and Infrastructure) by integration of machine learning in healthcare big data and promoting product innovation in the healthcare industry, respectively. Full article

This article reviews the state of the art, implementation barriers, and emerging trends in industrial digitalization, drawing on studies published between 2020 and July 2025. It analyzes how classical Industry 4.0 technologies, simulation and modeling, and Industry 5.0 priorities are transforming production processes in smart factories, yielding higher productivity, reduced downtime, and improved quality. At the same time, the literature documents persistent obstacles, including system integration and interoperability, security and data-privacy risk, and financial constraints, especially for SMEs. Looking ahead, future directions point to a gradual shift towards sustainable intelligent manufacturing with human–robot collaboration and data-centric operations. In addition, the article proposes and validates a conceptual framework for the digitalization of manufacturing companies and provides practical recommendations for stakeholders seeking to leverage digital technologies for operational excellence and sustainable value creation. Full article

The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communication. We propose H-RT-IDPS, a hierarchical real-time intrusion detection and prevention system targeting two high-priority IoV security pillars: availability (traffic overload) and integrity/authenticity (spoofing), with spoofing evaluated across multiple subclasses (GAS, RPM, SPEED, and steering wheel). In the offline phase, deep learning and hybrid models were benchmarked on the vehicular CAN bus dataset CICIoV2024, with the BiLSTM-XGBoost hybrid chosen for its balance between accuracy and inference speed. Real-time deployment uses a TinyML-distilled CNN on vehicles for ultra-lightweight, low-latency detection, while RSU-level BiLSTM-XGBoost performs a deeper temporal analysis. A Kafka–Spark Streaming pipeline supports localized classification, prevention, and dashboard-based monitoring. In baseline, stealth, and coordinated modes, the evaluation achieved accuracy, precision, recall, and F1-scores all above 97%. The mean end-to-end inference latency was 148.67 ms, and the resource usage was stable. The framework remains robust in both high-traffic and low-frequency attack scenarios, enhancing operator situational awareness through real-time visualizations. These results demonstrate a scalable, explainable, and operator-focused IDPS well suited for securing SC-IoV deployments against evolving threats. Full article

Background/Objectives: Big data analysis has revolutionized medical research, making it possible to analyze vast amounts of data and gain valuable insights that were previously impossible to obtain. Our knowledge of the characteristics of vitamin D sufficiency is primarily based on data from a limited number of observations, generally spanning a few years at most. Methods: Here at the Medical Faculty of the University of Debrecen, the big data approach has allowed us to analyze trends in vitamin D status using nearly 60,000 25-hydroxyvitamin D (25(OH)D) concentration results from 2000 onwards. Results: Apart from analyzing the well-known phenomenon of seasonality in 25(OH)D concentration, we observed a trend in test requests, which increased from a few hundred in 2000 to almost 10,000 in 2020. Of particular interest is the change in the gender gap in test requests. In previous years, test requests were primarily from women, but by the end of the analysis period, a significant number of requests were from men as well. Since the data set includes all age groups, we analyzed 25(OH)D concentration for incremental age sets of five years, from a few months to 100 years old. The prevalence of vitamin D insufficiency (<75 nmol/L) was clearly demarcated among various years of observation, age groups, sexes, and seasons. Our data was particularly valuable for analyzing the effect of the methodology used for 25(OH)D determination. Three different methodologies were used during the study period, and clear, statistically significant bias was observed. Conclusions: Our results clearly demonstrate the effect of the methodology used to determine 25(OH)D concentrations on vitamin D status, explicitly highlighting the urgent need to standardize the various platforms used to measure this important analyte and its consequences for public health. Full article

Ensuring product quality and safety is fundamental to sustainable production and consumption. With the rapid advancement of digital technologies such as blockchain and big data, quality and safety traceability systems have become essential tools to enhance transparency, accountability, and governance efficiency across supply chains. The sustainable functioning of these systems, however, depends on the coordinated actions of multiple stakeholders—including governments, enterprises, consumers, and industry associations—making the study of technological and institutional interactions particularly significant. This paper extends evolutionary game theory to the context of technology-enabled sustainable governance by constructing a tripartite game model involving government regulators, traceability enterprises, and consumers from both technological and institutional perspectives. Unlike existing studies, which focused solely on government regulation, this research explicitly incorporates the role of industry associations in shaping stakeholder behavior and integrates consumer rights protection mechanisms as well as the adoption of emerging technologies such as blockchain into the model. Analytical derivations and MATLAB-based simulations reveal that strengthening reward–penalty mechanisms and improving digital maturity significantly enhance enterprises’ incentives for truthful information disclosure; consumers’ verification and reporting behaviors generate bottom-up pressure that encourages stricter governmental supervision; and active participation of industry associations helps share regulatory costs and stabilize cooperative equilibria. These findings suggest that combining technological innovation with institutional collaboration not only improves transparency and strengthens consumer trust but also reshapes the incentive structures underlying traceability governance. The study provides new insights into how multi-stakeholder coordination and technological adoption jointly foster transparent, credible, and resilient traceability systems, offering practical implications for advancing digital transformation and co-governance in sustainable supply chains. Full article

Heavy metal contamination in foodstuff poses a serious threat to food safety and human health; therefore, the development of toxic heavy metal detection methods is crucial. However, lots of these methods, based on traditional nanomaterials, have unavoidable limitations, such as high instrument cost, complicated operation procedures, or a long analysis time, which restrict their wide application in heavy metal detection. This review aims to conduct a systematic overview of major analytical methods using novel upconversion nanoparticles (UCNPs) for assessing heavy metal ions in complex food matrices in the context of food safety and show their potential application prospects when combined with big data and artificial intelligence. Due to their unique optical properties, good bio-compatibility, and tunable interfacial chemistry, UCNPs have shown significant detection advantages in the field of food heavy metal analysis. The review summarizes the progress of the application of UCNPs in heavy metal detection in food. Despite the development of new technologies such as artificial intelligence, and the continuous optimization and improvement of its own design, the wide application of UCNPs in food safety detection still has great potential for further development. Full article

China is rich in medicinal–ornamental plants with multifunctional uses, making a significant contribution to global landscaping, environmental beautification, and the health industry. In the post-pandemic era, there is an increasing focus on improving living environments and enhancing immune health, leading to a growing demand for the development and utilization of these plant resources. Resource evaluation is fundamental to their widespread application in landscaping, commercial production, germplasm innovation, and sustainable utilization. However, current research is limited, and there is an absence of a comprehensive evaluation system. The evaluation of these plants, particularly endangered wild species, is vital for biodiversity conservation, rational resource utilization, and breeding. This study proposes a resource evaluation model based on three key aspects: ecological adaptability, medicinal value, and ornamental value. It also reviews commonly employed research methods, such as the scoring method, analytic hierarchy process (AHP), and fuzzy mathematics. Looking forward, we highlight the importance of establishing fundamental evaluation indicators, integrating new technologies, leveraging big data, and strengthening evaluations for germplasm innovation and the protection of these multifunctional medicinal–ornamental plant resources in China. Full article

Mental disorders affect nearly one billion persons worldwide, having a substantial burden on individuals, families, and healthcare systems. Current diagnostic and therapeutic approaches could fail to reach optimal outcomes, highlighting the need for more effective and personalized interventions. Precision psychiatry aims to address this challenge by integrating multidimensional data, ranging from genomics and epigenomics to neuroimaging and psychometric assessments, through advanced computational tools such as machine learning and artificial intelligence. This transdisciplinary approach could allow the study of biologically informed endophenotypes, improve diagnostic accuracy, and support individualized treatment strategies. Emerging technologies, including pharmaco-neuroimaging, virtual histology, and large-scale consortia, are advancing the field by elucidating the molecular and circuit-level correlates of mental disorders. Although significant progress has been made, the translational gap between research and clinical practice remains a critical issue. Effective implementation will require the systematic integration of bioinformatic tools, big data analytics, and clinician-guided interpretation, in a context in which the evolving landscape of precision psychiatry continues to prioritize therapeutic alliance and individualized patient care. Full article

This study examines the synergistic interaction between technology-driven digitalization and circular economy principles in enhancing sustainable supply chain performance among small and medium-sized enterprises (SMEs). Rather than examining digital technologies in isolation, we adopt an integrative systems perspective that conceptualizes digitalization as a multi-layered ecosystem comprising sensing (Internet of Things), intelligence (Artificial Intelligence and Big Data Analytics), verification (Blockchain), and coordination (Digital Collaboration Capability) layers. Through empirical analysis of 168 Tunisian SMEs across manufacturing and service sectors, this paper investigates the indirect impact of these complementary digital capabilities on sustainable supply chain performance, mediated by three dimensions of circular economy integration: waste reduction, resource efficiency, and sustainable design. The results indicate that digitalization has a positive influence on both environmental and economic performance, operating indirectly through the adoption of circular economy practices. By enhancing transparency, traceability, and operational efficiency, digital innovations reinforce circular economy practices, which consequently promote greater resilience and sustainability in supply chains. Sub-dimensional analyses reveal technology-specific mechanisms: IoT most strongly enables resource efficiency, AI and BDA drive waste reduction, Blockchain facilitates sustainable design, and Digital Collaboration Capability exhibits balanced effects across all circular dimensions. These findings underscore the critical role of integrated technological ecosystems, rather than isolated technology adoptions, in advancing sustainable supply chain management, particularly in resource-constrained SME contexts. Full article

Background: Digital supply chain transformation research exhibits a critical gap, examining technologies in isolation rather than as integrated ecosystems. Methods: This study addresses this limitation by developing a comprehensive orchestration frame-work through PRISMA-guided systematic review of 96 publications (2012–2024) using bibliometric analysis, structural topic modeling, and thematic synthesis across Scopus and Web of Science databases. Results: Analysis revealed three distinct research clusters: Supply Chain Management (centrality: 14.95), Digital Transformation (centrality: 9.50, density: 101.05), and Big Data Analytics (density: 113.22), with substantial negative correlations (−0.48 to −0.54) indicating organizational evolution from fragmented adoption toward integration. Conclusions: Publications increased 78% year-over-year during 2021–2022, while Supply Chain Management dominated topic prevalence (41%) and Big Data Analytics declined from 0.9 to 0.15 as practices normalized. The Digital Supply Chain Orchestration Framework conceptualizes transformation as multi-layered with hierarchical relationships between foundational domains, technological enablers, integration mechanisms, and value creation dimensions. This framework provides structured approaches for organizations to assess digital maturity, identify technological gaps, and develop strategic roadmaps aligned with Sustainable Development Goals, bridging theory and practice for integrated, value-driven digital transformation. Full article

Understanding how different population groups interact with urban environments is essential for analyzing spatial dynamics and informing urban planning, especially in cities experiencing high visitor pressure. This study presents a methodological framework for the spatial and temporal delineation of urban areas based on user-generated location-based data. By collecting nearly 1 million Google Maps reviews in the municipality of Donostia-San Sebastián, we identify and classify user profiles based on their spatiotemporal behavior. First, we collect points of interest (POIs) and associated reviews, including profile identifiers and timestamps. Then, we perform user-level webscraping to reconstruct review histories, enabling us to infer the predominant geographical origin of each user. Users are classified as residents or tourists using both spatial prevalence and temporal activity patterns. The resulting data is aggregated onto a hexagonal grid for geostatistical analysis. Using the Getis-Ord Gi* statistic and Mann-Kendall trend tests, we identify hotspots and long-term trends of activity for different population segments. Additionally, we propose novel indicators such as predominant periods of activity and diversity of geographical origin per cell to characterize heterogeneous patterns of urban use. Our results reveal distinct behavioral patterns, highlighting a more evenly distributed use of urban space by residents, with spatially overlapping yet temporally offset activities across central areas where tourists tend to concentrate their interactions. This spatiotemporal concentration is intensified as the tourists’ origin becomes more distant, suggesting that proximity shapes urban engagement. The proposed methodology offers a replicable strategy for urban analysis using publicly accessible user-generated data and contributes to the understanding of sociospatial dynamics in tourism-intensive cities. Full article

Funded pensions have become a key focus in strategies to ensure supplementary income during retirement. This paper explores two distinct approaches for estimating replacement rates: a deep learning model and a Mamdani Fuzzy Inference System (FIS). Using synthetic datasets for training, the deep learning model delivered accurate replacement rate predictions when benchmarked against exact solutions. On the other hand, the FIS approach, which leverages expert insights and practical experience, produced encouraging results but revealed opportunities for refining the definitions of intervals and linguistic categories. To bridge the strengths of both approaches, we introduce a conceptual integration using the Analytic Hierarchy Process (AHP), providing a multi-criteria decision-support framework that combines predictive accuracy from neural networks with the interpretability of fuzzy systems. The findings emphasize the potential of artificial intelligence (AI) methods, including neural networks and fuzzy logic, in advancing pension planning. While these techniques remain underutilized in this area, they hold significant promise for developing decision-support systems, particularly in big data contexts. Such systems can offer initial replacement rate estimates, serving as valuable inputs for experts during the decision-making process. Additionally, the paper suggests future research into multi-criteria decision analysis to improve decision-making within multi-pillar pension frameworks. Full article