Search Results (292)

Floodplains sustain fisheries, water supply, and climate regulation, and their services depend on the water regime—the extent, depth, frequency, duration, and connectivity of inundation—which dams, drought, and land-use change are altering. Remote sensing and GIS can supply evidence for managing these systems sustainably, yet the methods remain dispersed, and their fit to management needs has not been assessed. Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) and a publicly posted protocol, we retrieved peer-reviewed studies from Dimensions and OpenAlex (2004–2026), searched on 13 July 2026 and updated on 14 August 2026, and screened them in two stages with two reviewers. We charted data by study area, sensors, methods, variables, and drivers, then mapped them onto the decisions and Sustainable Development Goal targets they inform. Of 137 studies, 53% appeared since 2021; 2026 is only partially covered. Inundation extent dominates (83%), mapped mainly with Landsat (36%) and radar, whereas water level (28%), connectivity (21%), inundation frequency (14%), storage (13%), hydroperiod (12%), and depth (8%) remain scarce, as does evidence from data-scarce transboundary basins, including Central Asia. The attributes most needed for environmental-flow, allocation, and restoration decisions thus appear to be the least observed—a decision–observation mismatch that shapes monitoring priorities. Full article

Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems. Full article

Product-quality complaints are usually handled as pharmaceutical quality-system events, yet defects in sterility, potency, identity, packaging, labelling, storage, distribution or delivery-device function may affect medication use, treatment continuity and patient outcomes. This structured narrative review examines product-quality complaints (PQCs) as a patient-safety interface linking pharmaceutical quality systems, pharmacovigilance, medication-error prevention, recall action and pharmacoepidemiology. Existing pharmacovigilance, quality-management and recall systems address different components of this pathway, but no integrated framework was identified in the sources reviewed that specifies how product-quality complaints should be linked to exposure evidence and patient outcomes. Sources were identified through targeted PubMed searches and purposive retrieval of official regulatory, pharmacovigilance and public-health documents up to 5 June 2026; the review was not registered and included no quantitative synthesis. The sources identified concentrate on regulatory architecture, sentinel contamination events and shortage-associated harms; routine complaints are studied comparatively little, and no source identified reported the complaint-to-defect-to-exposure-to-outcome cascade with a complaint-level denominator. We propose the term patient-relevant complaint status (PRCS) for a complaint warranting patient-safety triage because the reported defect could plausibly affect exposure, sterility, potency, identity, delivery, medication use or treatment continuity, together with a TRACE workflow, a six-level clinical-consequence classification and separately graded certainty for defect confirmation, patient exposure and outcome attribution. These tools are proposed, unvalidated and hypothesis-generating; they do not convert complaints into adverse reactions, recalls into causality or spontaneous reports into incidence. Their intended role is to structure patient-safety triage, batch-aware linkage, exposure reconstruction, clinical follow-up and proportionate mitigation while causal attribution remains incomplete; this role requires prospective validation before routine implementation. Full article

Industrial operations increasingly face high-stakes decisions that involve people, data streams, simulations, and control systems. Urgent sessions often require external expertise, retrieval of documents and live telemetry, running what-if simulations, and verifying safety constraints. These scenarios highlight the need for secure interoperability, explainable decision support, and human-in-the-loop control. This paper presents a proposal of a technology-agnostic reference architecture that builds on Industry 4.0 frameworks by incorporating the human-centric, resilient, and sustainable principles of Industry 5.0. Its intelligent layer enables the new approach to human involvement in the process, facilitating meaningful human–machine collaboration. The proposed research provides a practical and conceptual framework for systems engineers, industrial software architects, and operations managers seeking to transition legacy operational plants into human-aligned ecosystems. Its feasibility is evaluated through a simulation-based underground mining testbed, where heterogeneous data sources and communication protocols are integrated into a common operational environment. The proof of concept shows how telemetry, data storage, machine learning models, and operator feedback can be combined to support auditable, explainable, and human-contestable industrial decisions, demonstrating the classification accuracy, remaining useful life forecasting capabilities, and enhanced recommendation precision enabled by iterative operator feedback loops. Full article

This paper proposes a structured methodology for constructing a digital twin (DT) of a planned flexible manufacturing system (FMS) for gear wheel production, integrating value stream mapping (VSM), discrete-event simulation (DES), production scheduling, and CAD/CAM modeling into a hierarchical, iterative design framework. The methodology was validated on an industrial case study involving a Polish manufacturer of gear transmissions undergoing modernization from conventional machining to a fully automated system incorporating CNC machining centers, industrial robots, automated guided vehicles (AGVs), and an automated storage and retrieval system (ASRS). Production scheduling was performed using eight algorithms including a random-search method and an ant colony optimization (ACO) algorithm, applied to a representative of 1072 parts across 10 gear wheel types. Simulation models of both conventional and automated systems were developed in FlexSim 2024. The best random instance achieved a makespan of 72,317 s in the automated model, compared to 75,634 s for the list rule—a 4.4% improvement that corresponds to approximately 55 min of production time per batch. AGV fleet sizing experiments identified four vehicles as the optimal configuration, beyond which marginal gains fall below 2%. The proposed digital twin framework enables virtual commissioning, continuous production planning, and what-if analysis prior to and during physical system implementation. Full article

Conventional nitrogen fertilizers are essential to food production but impose substantial energy, greenhouse-gas, and reactive-nitrogen losses. This review compares Haber–Bosch-derived urea, ammonium nitrate, calcium nitrate, green ammonia, and fertigation with plasma-activated water (PAW), in which non-thermal plasma fixes atmospheric nitrogen directly into water as NO₃⁻/NO₂⁻ and, in some systems, NH₄⁺. A PRISMA-adapted Scopus screening retrieved 765 records. Automated screening excluded 312 records; all 453 provisionally retained records were then manually audited, removing 88 additional false positives and yielding 365 plasma nitrogen-fixation studies, including 157 PAW/plasma-in-liquid records. The comparison uses explicit system boundaries for energy, carbon intensity, nitrogen-use efficiency, and technology readiness. The lowest verified directly measured in-water system reports 1.14 MJ mol⁻¹ N for total soluble nitrogen, whereas lower values near 0.4–0.5 MJ mol⁻¹ N refer mainly to gas-phase or modeled plasma fixation and are not directly interchangeable with PAW. Controlled-environment studies report improved germination or vegetative growth in several crops and, in one full-cycle controlled horticultural study with a nitrate-equivalent control, fruit performance comparable with conventional nitrate fertilization. Nevertheless, PAW is not a general replacement for synthetic fertilizer. Its most credible near-term role is as a decentralized complement in fertigation, protected cultivation, hydroponics, and remote or supply-constrained systems supplied by low-carbon electricity. Major barriers are dilute and variable nitrogen concentration, reactor durability, storage stability, incomplete techno-economic accounting, and the absence of replicated multi-season field validation. Minimum reporting requirements and research priorities are proposed. Full article

Dynamic aeronautical information is still widely disseminated through textual Notice to Air Missions (NOTAMs), while message-oriented storage cannot directly maintain the evolving states of affected objects. The objective of this study is to determine whether NOTAM-derived object events can be organized into traceable bitemporal states that support accurate and efficient current and historical access. To this end, this paper proposes BiTE, a bitemporal event-centered database framework that connects object-level event evidence, historical state versions, and materialized current-state projections. By integrating business and system time with NOTAM-specific lifecycle rules, BiTE supports state maintenance, historical reconstruction, and source traceability. A MongoDB-based prototype was evaluated using 44,591 NOTAMs from five major U.S. aerodromes. Independent manual validation showed 96.14–100% agreement across object identification and lifecycle-maintenance tasks. Across 1500 manually verified queries, BiTE achieved F1 scores of 99.43% and 98.66% for current-state and airport-overview retrieval, respectively, and a historical hit rate of 95.80%, outperforming representative message-oriented, relational-bitemporal, and RDF-based implementations. Mean query latency remained below 3.7 ms, while functionally equivalent ablations confirmed the performance contribution of the layered architecture. These results demonstrate that BiTE enables accurate, traceable, and efficient object-state management for dynamic aeronautical information. Full article

Cover song recognition typically relies on computationally expensive raw audio analysis, which limits applicability in resource-constrained or privacy-preserving scenarios. Existing audio-free alternatives use metadata or lyrics to augment rather than replace audio analysis, and the performance level achievable from compact pre-computed audio descriptors alone has not been systematically established. This paper is an empirical study of that constrained regime: how far pre-computed audio descriptors go without raw audio, and which engineering choices matter. We propose a lightweight approach based on a Siamese retrieval model operating on Million Song Dataset features and evaluated on the SecondHandSongs benchmark. The method combines temporal slope features extracted from pitch and timbre time series, confidence-weighted feature multiplication, and a hybrid delta-gradient optimization framework designed for low-dimensional feature spaces. Each component contributes measurably: temporal slope features raise the unweighted baseline from 0.370 to 0.400 MAP@10, normalization to 0.420, heuristic feature weighting to 0.520, and delta-gradient refinement to the final 0.553, to our knowledge, the best result reported under this constraint. This remains below audio-based state-of-the-art systems, which exploit fine-grained spectral detail that compact descriptors discard; in exchange, the proposed system requires much less storage and computation, enabling privacy-preserving, bandwidth-constrained, and audio-unavailable deployment scenarios. Full article

This paper presents a systematic literature review examining how blockchain technologies can enhance the security and performance of multi-cloud systems. Multi-cloud architectures offer resilience, scalability, and flexibility; however, they also pose complex security challenges related to APIs, service-level agreements (SLAs), orchestration, and authentication. The promise of blockchain technology to improve the security and transparency of numerous applications, including cloud storage systems, has attracted considerable attention in recent years. Much research has focused on decentralized storage in cloud environments, spanning supply chains, FinTech, healthcare, and education. Still, the integration of blockchain with the cloud and its potential to enhance security and performance warrant an in-depth study. Using the PRISMA methodology, a structured search was conducted across six major scientific databases, including IEEE, ACM Digital Library, ScienceDirect, Scopus, Web of Science, and IJIMAI. Twenty-four primary papers published between 2019 and 2025 were selected for analysis after clear inclusion and exclusion criteria were applied. This review examines the security dimensions in multi-cloud environments—architectural vulnerabilities, API security, authentication, orchestration and automation vulnerabilities, SLAs, and cybersecurity compliance issues—in relation to blockchain technology. Based on the identified gaps, we propose a SOC-LLM-augmented security architecture that integrates blockchain-based evidence integrity, statistical anomaly detection, machine learning, large language models, and autonomous AI agents to enable intelligent attack detection and response. The proposed framework introduces specialized agents for detection, correlation, threat intelligence retrieval, blockchain evidence validation, explanation generation, and response planning. The analysis shows that integrating SOC-LLM capabilities with blockchain can move multi-cloud security from passive auditability toward proactive, explainable, and human-in-the-loop cyber defense. Finally, this paper discusses open challenges, including LLM hallucination, data scarcity, real-time scalability, evaluation standardization, and trustworthy deployment in critical multi-cloud infrastructures. The study’s conclusion highlights research gaps and suggests future lines of inquiry concerning scalable blockchain architectures and the incorporation of AI for proactive cloud security monitoring. Full article

In recent years, owing to the extensive application of lithium-ion batteries (LIBs) in large-scale energy storage, transportation systems, and portable electronics, the LIB market has expanded rapidly. Proper recycling of spent LIBs can significantly alleviate environmental and economic burdens. Bioleaching, as an environmentally friendly and cost-effective approach for metal recovery from primary and secondary resources, is particularly suitable for the processing of spent LIBs. However, its efficiency significantly decreases under high-pulp-density conditions. Therefore, improving metal recovery performance under such conditions remains a critical challenge. This review systematically summarizes the microorganisms and leaching strategies employed for LIB bioleaching in the related peer-reviewed publications from 2015 to 2025, which were retrieved from the Web of Science, Scopus, and ScienceDirect databases. Based on this, this review analyzes the mechanistic limitations under high-pulp-density conditions and elucidates the key factors responsible for reduced efficiency. Furthermore, several process-intensification strategies are discussed, along with future perspectives for industrial-scale application. The increasing market demand and rapid technological development in LIB recycling highlight the strong potential of bioleaching technologies. This review provides mechanistic insights into microbial recovery processes and offers guidance for future research on high-pulp-density bioleaching systems. Full article

The advent of cloud–fog–edge computing has transformed distributed data processing by performing computation closer to end devices. Due to resource constraints at edge nodes and the dynamic nature of fog-assisted communication, secure and efficient group key distribution and batch verification in such decentralized systems remain a major challenge. Many existing protocols based on Chinese remainder theorem (CRT) use a straightforward scalar product to mask the group key and hence fail in multifactor security. Others suffer from architectural overhead since they require distinct and independent sets of moduli equations with multiple mathematical structures for different network layers, which increases computing overhead, limits scalability and delays synchronization during frequent node leave/join. To mitigate these challenges, this paper proposes a unified distributed CRT-based protocol for cloud–fog–edge environments. Our protocol introduces a two-factor modular key masking mechanism by incorporating a unique secret parameter for every edge node to strengthen group key protection and enhance the overall robustness of the key distribution mechanism. Additionally, our protocol uses a single set of moduli equations across cloud–fog–edge networks, which drastically reduces computation and storage costs at the fog layer. Our protocol achieves $\mathcal{O}$ (1) efficiency for rekeying. Formal security analysis using ProVerif and the ROR model demonstrates that our protocol has considerable security advantages. To prove its practicality, an ESP32-based simulation on Wokwi is used to verify the correctness of group key distribution, retrieval, and batch message verification. The performance analysis findings show that our protocol outperforms others in computation cost, communication cost, security and applicability for resource-constrained cloud–fog–edge computing networks. Full article

Background: Four-way shuttle-based storage and retrieval systems (FSS/RSs) have recently emerged as flexible and scalable solutions for high-density warehousing, enabling shuttle movement in four directions and supporting multi-deep dual-access storage configurations. However, these features increase the complexity of vehicle coordination and collision management. This study proposes a multi-agent path-planning methodology for FSS/RSs with multi-deep dual-access lanes hosting homogeneous items. Methods: An A*-based path-planning framework was developed and integrated with a dynamic collision-management strategy comprising collision detection, priority assignment, and collision avoidance. The methodology was evaluated through a multi-scenario analysis considering different fleet sizes, priority-assignment strategies, safety-area extensions, transaction-entry patterns, and collision-management policies. Results: The results show that fleet size is the most influential operational parameter, significantly affecting throughput, waiting times, and collision frequency. Increasing the number of vehicles improves system productivity but also increases traffic interactions and congestion. The analyses further highlight the effects of safety-area size, priority rules, and transaction-entry patterns on operational performance and system robustness. Conclusions: The proposed methodology effectively combines path planning and collision management in four-way shuttle systems, providing a decision-support tool for evaluating operational trade-offs among throughput, congestion control, and system stability in multi-deep dual-access warehouse environments. Full article

Colorimetric sensor array (CSA) technology has been increasingly applied in food quality and safety analysis due to its advantages of low cost, visual readout, high sensitivity and suitability for on-site monitoring. However, a dedicated review that systematically integrates advances in sensing materials, application scenarios, analytical strategies and practical implementation of CSA technology for real food matrices is still lacking. This review summarizes the fundamental characteristics of CSA technology from the relevant studies published during the past 5 years. The literature was retrieved using keywords such as “colorimetric sensor array” and “food quality detection,” and was filtered by predefined criteria prioritizing original research with verified sensing performance and practical validation in food applications. Emphasis is placed on the design and performance of sensitive materials. Furthermore, using a task-oriented framework, representative studies on CSA applications in food spoilage detection, adulteration identification, food composition analysis, contamination monitoring, food storage monitoring and volatile compound detection are discussed. In addition, the integration of CSA systems with portable devices, machine learning algorithms and intelligent detection platforms is critically analyzed. Finally, the detection characteristics, current challenges and future development prospects of CSA technology in food quality and safety analysis are highlighted. This review is expected to provide valuable insights for further development, optimization and practical implementation of intelligent CSA-based sensing systems in the food industry. Full article

Nowadays, the management of digital medical images faces increasing challenges due to the volume, diversity, and need for interoperability between systems. The DICOM standard has become the main format for storing and transmitting medical images, enabling the integration of data from studies such as MRI, CT, and USG. However, its complex structure and the increasing volume of data generated by interconnected devices, including IoT sensors, demand new strategies for efficient storage and retrieval. Clinical databases must support large volumes of heterogeneous data while ensuring fast access, availability, and secure information exchange. This review explores the integration of the DICOM standard with medical database systems, emphasizing the role of sensors as a primary source in clinical data management. The findings aim to support the development of more effective strategies for data retrieval and exchange, such as database fragmentation, to reduce query response times and improve information systems used by healthcare professionals and patients. Additionally, various data sets or benchmarks used in the analyzed studies are described. As a result, two approaches are identified as particularly noteworthy among the reviewed works, serving as a reference for future applications and technological developments in healthcare. Full article

Cube-shaped automated storage and retrieval systems (AS/RS) enhance storage density by organizing inventory in a three-dimensional grid. However, they face two operational bottlenecks: (1) digging—the temporary removal and restacking of upper bins to access a target bin—and (2) inefficient idle staging and return policies in multi-AGV operations. We proposed a demand-based digging and bin-placement strategy and a waiting-point (staging) selection policy that considers AGV positions and remaining task times. These control policies are implemented in both rule-based and multi-agent reinforcement learning (MARL) variants. Their performance is evaluated using a Discrete Event System Specification (DEVS) simulation framework. In a 30 × 30 × 4 grid, three experiments demonstrated that deploying five AGVs achieved the best performance within the tested configuration; the demand-based digging and placement strategy achieved a 6.2% reduction in makespan, and the rule-based and MARL staging policies achieved additional reductions of 2.5% and 1.1%, respectively. These results highlight the benefits of jointly optimizing digging and multi-AGV staging and provide practical guidance for control-policy design in cube-shaped AS/RS. Full article