Search Results (17,487)

Multi-view clustering (MVC) is a fundamental unsupervised learning task for exploring latent structures from heterogeneous multi-view data. Existing MVC methods face critical challenges including the high computational cost of full-graph tensor models, neglect of high-order interactions between diversity and consistency information, and anchor misalignment across different views. In this paper, we propose an efficient anchor-guided MVC framework (EAG-DCT) via diversity–consistency learning and low-rank tensor recovery. The proposed method jointly learns consensus anchors, view-specific diversity graphs, and a global consistency graph in a unified model that integrates all graphs into a high-order tensor to capture rich cross-view correlations. By imposing a nonconvex low-rank constraint on the tensor, we effectively enhance the synergy between diversity and consistency learning. Our framework achieves high computational efficiency and scalability for large-scale data. Comprehensive experimental results on benchmark datasets validate that EAG-DCT outperforms state-of-the-art MVC methods in both clustering effectiveness and efficiency. Full article

The advent of drone technology has led to groundbreaking advancements across various industries, including warehousing operations. In recent years, warehouse drones have garnered significant attention due to their potential to revolutionize traditional inventory management and order fulfillment processes. This paper presents a comprehensive review that synthesizes findings from more than 120 research papers on drone-enabled practices in warehouses. The review systematically considers multiple parameters, including drone function (inventory counting, mapping, surveillance, inspection, and intralogistics support), robot platforms used (UAV, UAV-AGV), deployment architecture (single and multi-drone system), validation approach (real-time and simulation), technology and methodology used (modern electronic devices, AI, and IOT), and environmental context (dynamic and static). Furthermore, the paper explores the diverse applications of warehouse drones in inventory management, maintenance and inspection, picking and packaging, goods transportation, security and surveillance, and warehouse layout optimization. The review highlights that most studies still rely on single-UAV systems tested mainly in simulations, with only a few real-time demonstrations of fully autonomous performance inside real warehouses. Although multi-drone approaches are emerging to improve scalability, they continue to struggle with coordination and safety. Research remains largely focused on static environments, with dynamic warehouse conditions receiving far less attention despite their practical importance. The findings of the review are presented with the tabulated results and a comparative table to provide a better understanding of the review work, which helps to identify the existing literature gap. The review presents its findings through clear tables and comparisons, making it easier to understand existing studies and pinpoint the gaps in the current literature. Full article

This study addresses the problem of optimal resource allocation in nonlinear multi-module dynamic systems arising in complex computational and techno-economic processes, where numerical stability and strict enforcement of structural constraints are critical. The objective is to develop a computationally efficient optimal control algorithm capable of handling bilateral control constraints and external balance conditions without resorting to large-scale nonlinear programming or boundary-value shooting. The proposed method is based on a modified Lagrangian formulation, in which bilateral Karush–Kuhn–Tucker (KKT) conditions are analytically embedded into the optimality system. The resulting computational scheme consists of a coupled system of matrix and vector differential equations solved through a non-iterative backward–forward integration procedure. Numerical experiments conducted on a nonlinear model with Cobb–Douglas-type operators demonstrate the stable convergence of the trajectories toward a stationary regime, strict satisfaction of bilateral constraints, and consistent enforcement of balance relations throughout the planning horizon. Empirical scalability analysis indicates approximately cubic computational complexity with respect to the state dimension, while sensitivity tests confirm the numerical robustness across different integration tolerances and ODE solvers. These results demonstrate that the proposed structure-preserving framework provides a computationally stable and practically implementable approach to constrained optimal control in nonlinear multi-module systems. Full article

5-aminolevulinic acid (5-ALA) affords various positive health effects, including benefits for conditions such as diabetes. Biological fermentation holds potential for efficiently mass-producing biomolecules, including 5-ALA, yet it generally results in a mixture of target molecules and impurities, including byproducts. Pyrazine-2,5-dipropionic acid (PY), a dimer of 5-ALA, can easily form in 5-ALA production and is one of its major impurities. In this study, we developed an integrated purification process for 5-aminolevulinic acid phosphate (5-ALAP) produced via biological fermentation. The process consists of 16 stages, including impurity removal (ion-exchange resins) and crystallization. Three types of ion-exchange resin (IER) columns were combined to remove impurities such as byproducts and pigment. Comprehensive condition setting for crystallization was carried out to reduce the amount of residual poor solvent in the 5-ALAP crystals. The obtained crystals contained significantly fewer impurities (<0.05% vs. 5-ALAP), such as PY, compared with their commercially available counterparts. The residual poor solvent in the 5-ALAP crystals was reduced to below 1000 ppm under the crystallization conditions. We confirmed the high scalability of the purification method developed in this study. Therefore, this article provides an industrially applicable purification process for fermentatively produced 5-ALA. Full article

Cardiac telerehabilitation has become a promising alternative to traditional programmes for preventing acute coronary syndrome (ACS) in the secondary phase. However, current implementations are still reactive and standardised, lacking personalisation and flexibility in clinical settings. By integrating artificial intelligence (AI), it may be possible to overcome these limitations and provide intelligent, scalable, and patient-centred care. Methods: We conducted a structured literature review across PubMed, Scopus, the Cochrane Library, and Web of Science, targeting English-language studies published from January 2015 to May 2025. Inclusion criteria included adult populations with a history of ACS or high cardiovascular risk, assessing interventions based on AI, telerehabilitation, or their combination. Studies are needed to report clinical, functional, behavioural, or technological outcomes. A thematic narrative synthesis was utilised. Results: AI-enhanced telerehabilitation demonstrates potential advantages over conventional digital care in selected domains, including adaptive risk prediction, personalised exercise modulation, and adherence support. Several systems report real-time adjustment of exercise protocols, early dropout detection, and predictive analytics for rehospitalisation. AI integration may also contribute to personalised behavioural feedback and psychosocial monitoring. Nevertheless, the overall level of evidence remains preliminary and heterogeneous, with most AI-based interventions evaluated in pilot, feasibility, or modelling studies rather than large-scale randomized trials. Conclusions: The integration of AI into telerehabilitation represents a promising evolution in post-ACS care, shifting from predominantly reactive monitoring toward more adaptive and data-driven support models. While early-phase studies suggest feasibility and potential clinical benefit, robust multicentre randomized controlled trials and cost-effectiveness analyses are required before definitive conclusions regarding superiority or widespread implementation can be drawn. Full article

Accurate and reliable vehicle detection, tracking, and counting across different surveillance platforms are fundamental requirements for developing smart Traffic Management Systems (TMS) and promoting sustainable urban mobility. Recent advances in both ground-level surveillance and remote sensing using deep learning have opened new opportunities for extracting detailed vehicular information from high-resolution aerial and surveillance video data. Our research reported here aims to present a unified, real-time vehicle analysis framework that integrates lightweight deep learning–based detection, robust multi-object tracking, and trajectory-driven counting within a single modular pipeline. The proposed framework employs a “You Only Look Once” system, YOLOv10-S as the detection backbone and enhances its robustness through supervision-level knowledge distillation without introducing any architectural modifications. Temporal consistency is enforced using an observation-centric multi-object tracking algorithm (OC-SORT), enabling stable identity preservation under camera motion and dense traffic conditions. Vehicle counting is performed using a trajectory-based virtual gate strategy, reducing duplicate counts and improving counting reliability. Comprehensive experiments conducted on the UA-DETRAC and VisDrone benchmarks show that the proposed framework effectively balances detection performance, tracking robustness, counting accuracy, and real-time efficiency in both ground-based and aerial surveillance settings. Furthermore, cross-dataset evaluations under direct train–test transfer highlight the inherent challenges of domain shift while showing that knowledge distillation consistently improves robustness in detection, tracking identity consistency, and vehicle counting. Overall, this framework enables effective real-world traffic monitoring by adopting a scalable and practical system design, where reliability is prioritized over architectural complexity. Full article

Knowledge graphs represent real-world facts as structured triplets and underpin a wide range of applications, including question answering, recommendation, and retrieval-augmented generation. Automatically extracting such triplets from unstructured text is essential for scalable knowledge base construction. Traditional extraction methods require task-specific training data and struggle to generalize across domains. Large language models (LLMs) offer an alternative through in-context learning, enabling flexible extraction without fine-tuning. However, LLMs frequently hallucinate—generating plausible triplets unsupported by the source text. The root cause is the lack of provenance: existing methods produce triplets without explicit links to their textual origins, making faithfulness unverifiable. This paper presents Anchor-Extraction-Verification-Supplement (AEVS), a framework that grounds every triplet element to the source text. AEVS operates in three stages: (1) anchor discovery identifies entities, relation phrases, and attribute values with precise positions, forming a constrained extraction vocabulary; (2) grounded extraction generates triplets linked to discovered anchors; and (3) restoration-based verification validates triplets through hierarchical matching, with a coverage-aware supplement ensuring comprehensive extraction. Experiments on WebNLG, REBEL, and Wiki-NRE demonstrate consistent improvements over both trained models and LLM-based baselines. Ablation studies confirm that anchor-based constraints are the primary mechanism for hallucination reduction. Dedicated analyses of anchor discovery quality, computational cost (2.83–4.28 LLM calls per sample), and hallucination rates (0.23–20.23% across model–dataset configurations) provide insights into the framework’s practical applicability and limitations. . Full article

Multi-view clustering (MVC) is crucial for exploiting complementary information from multi-view data. Anchor-based MVC methods are efficient for large-scale tasks but lack the ability to balance view-specific local complementarity and cross-view global consistency. To address this issue, we propose GL4-MVC, a dual-level anchor graph learning framework. It constructs anchor graphs with integrated adaptive learning of view-specific local anchors and concatenated a priori cross-view global anchor guidance, with an orthogonal mapping matrix enabling cross-level alignment to ensure effective guidance of global information for local learning. GL4-MVC is scalable and suitable for large-scale data. Extensive experimental results confirm the effectiveness and efficiency of GL4-MVC. Full article

Goats are important livestock species in most rural households and were amongst the first species to be domesticated. Despite this, their production is based on extensive systems, exposing them to numerous challenges affecting their productivity. This review examines the applications of precision livestock farming (PLF) and AI-driven technologies in goat management, focusing on their impacts on productivity, welfare, genetic potential, health monitoring, feeding efficiency and sustainability outcomes and identifying challenges for their adoption in smallholder and extensive systems. Unlike previous reviews that focus mainly on cattle raised under intensive systems, this review synthesizes their use in goat production and highlights technological, socio-economic and infrastructural constraints. A conventional literature review approach is used, with studies retrieved from major databases using relevant keywords. The selected studies are evaluated to assess technological applications, benefits and adoption challenges, followed by a SWOT analysis. Engineering aspects of precision livestock farming—including sensors, data connectivity, system integration, automation and scalability—are also discussed. Ideally, these technologies operate as integrated decision-support systems that jointly improve productivity, animal welfare and sustainability, rather than performing isolated tasks. However, many PLF solutions remain at low technology-readiness levels and are constrained by infrastructure gaps, sensor reliability and compatibility issues, which collectively limit adoption in smallholder systems. Future research should focus on the development of cost-effective, reliable PLF systems for smallholder producers, while policy and capacity-building initiatives are needed to enhance infrastructure, training and technology adoption for scalable implementation. Full article

Developed by Marais and Rabinowitz, the A²/O process is a pivotal biotechnology for biological nitrogen and phosphorus removal, developed by optimizing the five-stage Phoredox protocol. Renowned for its efficient configuration and straightforward operation, it has been extensively adopted in municipal and industrial wastewater treatment projects globally, including numerous facilities in China. However, the conventional A²/O process faces inherent operational challenges: the conflicting SRT requirements between autotrophic nitrifying bacteria (needing long SRT for stable nitrification) and PAOs, intense competition for carbon sources among PAOs and denitrifying bacteria, and the inhibitory effects of residual nitrate and DO on phosphorus release and denitrification. To address these issues, a range of optimization strategies has been developed, including SRT adjustment, carbon source distribution optimization, the integration of biofilm carriers, the addition of external carbon sources, and innovative modified configurations such as the Reversed A²/O, JHB, UCT, and MUCT. These approaches synergistically mitigate nitrate interference and enhance nutrient removal efficiency by decoupling microbial SRT demands, supplementing readily biodegradable carbon sources, and optimizing hydraulic flow paths. Future research should focus on deepening the understanding of the metabolic mechanisms underlying nitrogen and phosphorus removal, developing sustainable and efficient external carbon source systems, refining multi-mode reactor design for engineering scalability, optimizing combined processes for ultra-low C/N ratio wastewater treatment, and advancing low-temperature adaptation technologies. These efforts aim to further improve the process’s efficacy, stability, and sustainability, enabling it to meet increasingly stringent environmental discharge standards. Full article

Arteriosclerosis obliterans (ASO) is associated with impaired walking function and claudication. However, the effects of information and communication technology (ICT)-based wearable interventions on objectively measured gait outcomes in this population have not been determined. In this 12-week intervention, 52 patients with ASO were randomly assigned to an ICT-based wearable-assisted exercise intervention (n = 30) or a control (n = 22) group. All participants wore a triaxial accelerometer–based device on the non-dominant wrist to monitor moderate-to-vigorous physical activity (MVPA), expressed as average min/day. The intervention group received structured exercise guidance, including walking and lower-limb strengthening exercises, and weekly feedback based on device data; the control group received no exercise instruction or feedback. Primary outcomes were gait speed and 6 min walk test (6MWT) distance; secondary outcomes included MVPA and cognitive function. The intervention group showed significant improvements in gait speed and 6MWT distance compared with those in the control group (p < 0.05), indicating enhanced ambulatory function. An exploratory machine learning analysis suggested that gait speed and 6MWT distance are informative variables for functional-status characterization. ICT-based wearable interventions may serve as scalable approaches for functional rehabilitation in ASO; larger, longer-term studies should confirm these effects and clarify the underlying mechanisms. Full article

The rapid growth of Internet of Medical Things (IoMT) devices has revolutionized diagnostics and patient care within smart healthcare networks. However, this progress has also expanded the attack surface due to the heterogeneity and interconnectivity of medical devices. To overcome the limitations of traditional batch-trained security models, this study proposes an adaptive online intrusion detection framework designed for real-time operation in dynamic healthcare environments. The system combines Leveraging Bagging with Hoeffding Tree classifiers for incremental learning while integrating the Page–Hinkley test to detect and adapt to concept drift in evolving attack patterns. A modular and scalable network architecture supports centralized monitoring and ensures seamless interoperability across various IoMT protocols. Implemented within a low-latency, high-throughput stream-processing pipeline, the framework meets the stringent clinical requirements for responsiveness and reliability. To simulate streaming conditions, we evaluated the model using the CICIoMT2024 dataset, presenting one instance at a time in random order to reflect dynamic, real-time traffic in IoMT networks. Experimental results demonstrate exceptional performance, achieving accuracies of 0.9963 for binary classification, 0.9949 for six-class detection, and 0.9860 for nineteen-class categorization. These results underscore the framework’s practical efficacy in protecting modern healthcare infrastructures from evolving cyber threats. Full article

Green-synthesis routes for producing CuO nanoparticles offer a simplified, sustainable, and low-cost replacement for conventional chemical methods, eliminating the need for harsh chemicals and providing an easily scalable process for industrial-level production. Although numerous studies have investigated synthesizing CuO nanoparticles from single plant extracts, comparative assessments of multi-plant-mediated CuO nanoparticles alongside synthetic CuO remain limited. In this work, CuO nanoparticles were green-synthesized from three different plant sources, namely ginger, red onion peels, and garlic, and their physicochemical and biological properties were tested against the synthetic CuO. All plant extracts produced pure-phased monoclinic CuO nanoparticles as confirmed by UV–Vis, XRD, FTIR, and SEM/EDX analyses. SEM showed distinct nanoparticle morphologies, with CuO from ginger extract exhibiting uniform nanocubes, while nanoparticles from red onion and garlic extracts exhibited more aggregated and irregular structures. Their crystallite sizes were 8–9 nm lower than the ~11 nm observed for the synthetic CuO, highlighting the phytochemical role in shaping the nanoparticles’ morphology. The antibacterial efficacy against S. aureus and E. coli showed that ginger-derived and synthetic CuO had the strongest bacterial inhibition and bactericidal potency compared to onion- and garlic-derived CuO samples. However, synthetic CuO had the highest cytotoxicity risk, hindering its suitability for biological uses, while CuO-ginger maintained good cell viability at moderate concentrations. CuO-onion and CuO-garlic gave lower antibacterial cytocompatibility performance due to their thicker capping layers, which led to decreased Cu²⁺ release and ROS production. Ginger-derived CuO achieved an optimal trade-off between antibacterial and cytotoxic efficiency, highlighting its prospects as a candidate for biomedical applications. Full article

Quantum computing exploits the principles of quantum mechanics to perform computation. Information is stored in qubits and processed with a sequence of quantum gates arranged as circuits. Verifying the correctness of quantum circuits is becoming essential as hardware scales in qubit count and architectural complexity. Traditional testing and naive simulation do not scale and quickly become computationally infeasible because the state space grows exponentially. This creates a strong need for more powerful and scalable verification techniques. Formal methods offer a viable solution by providing mathematically rigorous and scalable verification techniques that address these scalability challenges through abstraction, symbolic reasoning, and probabilistic guarantees. This study examines how formal methods are applied to quantum-circuit verification. Specifically, four families of formal techniques: barrier certificates, abstract interpretation, model checking, and theorem proving are examined, along with the theoretical foundations and practical applications of these techniques. Finally, the study highlights open challenges and identifies promising directions for future research. An extensive set of references is included to support further study and exploration. Full article

The construction industry faces persistent challenges in productivity, efficiency, and sustainability. Digital twin (DT) technology has emerged as a promising pathway for lifecycle optimisation, yet its construction adoption remains limited. Key barriers include fragmentation across project phases, weak data continuity at handover, and conceptual ambiguity between DT and Building Information Modelling (BIM). This systematic literature review analyses 160 peer-reviewed studies (2018–2026) selected from 463 Scopus records using a PRISMA-guided process and inter-rater reliability testing (Cohen’s κ = 0.83). The review clarifies that DTs extend beyond BIM in three ways: they enable bidirectional, automated physical-digital data exchange; integrate heterogeneous real-time sources such as IoT sensors and operational systems; and maintain lifecycle continuity from design through to end-of-life. Select advanced implementations report notable performance gains. These include rework and logistics reductions of up to 80%, cost savings of approximately 5%, schedule acceleration of around two months, energy reductions of 15–30%, and maintenance cost reductions of 10–25%. These figures reflect case-level outcomes from high-performing pilots and should not be read as typical industry benchmarks. Broader adoption remains constrained by interoperability gaps, data quality challenges, digital maturity deficits, misaligned stakeholder incentives, and paper-based regulatory environments. DTs represent a socio-technical transformation, not a standalone technology upgrade. Realising their potential requires coordinated progress in standards development, governance frameworks, collaborative delivery models, and workforce capability. Future research should focus on scalable interoperability, longitudinal lifecycle value validation, human-centred adoption strategies, and sustainability assessment methods to support evidence-based diffusion of DTs in the built environment. Full article