A data-driven process control strategy aligned with quality by design using a local linear modelling method and fault detection & diagnosis for twin-screw granulation.

Precise control of drying endpoint and duration in fluidized bed drying via proportional feedback: ensuring granule quality and process robustness.

Pain is a multidimensional experience emerging from the coordinated activity of distributed neural circuits that integrate sensory, emotional, and cognitive information. The purpose of this review is to highlight and synthesise current understanding of the circuit mechanisms underlying ascending nociception and descending modulation of pain, with particular emphasis on brainstem areas and their involvement in shaping pain perception. We discuss how dysregulation of these control systems may contribute to persistent pain states. It is consistently demonstrated that pain is both a dynamic and context-dependent process, shaped by extensive bidirectional interactions within the central nervous system. In humans, nociceptive signals transmitted through spinal and trigeminal pathways engage brainstem and forebrain networks that generate protective behaviours and aversive subjective states, while descending modulatory systems exert powerful control over signal gain at early relay sites. Recent evidence asserts that this reciprocal organisation is what allows pain to be flexibly adjusted according to internal state, environmental threat, learning history, and behavioural priorities. Understanding the coordinated neural and behavioural responses that lead to typical pain processing, and which components of these myriad systems become maladaptive in clinical pain settings, is critical for optimising treatment selection to individual symptom profiles.

X-ray diffraction (XRD) combined with in situ mechanical testing is a powerful, non-destructive technique that provides valuable information on structural evolution and defect formation during deformation. Unlike many other characterization methods, XRD does not impose constraints on sample dimensions, does not require a vacuum environment, and can be applied to a wide range of materials with periodic structures. However, such experiments present significant challenges due to the need for precise synchronization between independently controlled systems: one managing mechanical loading and the other handling X-ray measurements. In this work, we report on the successful software-level integration of a nanoindenter control system directly into the beamline control system. This integration enables full control of both mechanical and diffraction measurements from a single user interface, allowing real-time synchronization and automation of complex in situ experiments. We demonstrate the capabilities of this setup through uniaxial compression tests on α-Ti micropillars. Mechanical data from the nanoindenter and XRD patterns were collected simultaneously in an automated mode. Raster scans were performed on the micropillar in its pristine state, at two intermediate deformation stages, and post-mortem. This approach enabled detailed analysis of mechanical behavior and structural evolution under load, illustrating the effectiveness of the integrated system for advanced in situ studies.

Enhancing prediabetes and diabetes detection through a machine learning-enabled self-assessment approach.

• Adaptive cascaded control applied to grid-forming inverter. • Voltage direct-tracking using a model reference adaptive controller. • Controller validated through extensive tests with wide parameter variations. • Comparison between adaptive and fixed-gain control techniques. • Model-free adaptive control applied to microgrid synchronization. This paper presents a cascaded adaptive control strategy for the inner loop of a grid-forming inverter. The control structure combines an inner proportional regulator for current limiting with an outer Model Reference Adaptive Controller (MRAC) for voltage regulation, tracking references from the primary control layer. The primary control utilizes a droop mechanism with virtual impedance to enhance active and reactive power decoupling. For grid-reconnection scenarios, the secondary control implements an adaptive proportional-integral-derivative (PID) controller in the synchronization loop, effectively compensating for non-deterministic communication delays while maintaining a simple design framework. The control system is validated through hardware-in-the-loop (HIL) simulations, evaluating five critical scenarios: steady-state operation (islanded and grid-connected), reconnection transients, unplanned islanding, and a microgrid fault. A comparative analysis with a fixed-gain controller demonstrates the superior performance of the proposed adaptive approach in managing uncertainties and dynamic grid conditions, with consistent improvements across all scenarios and error reductions of up to 90% in key performance indices.

Traditional computer vision-based quality perception lacks depth information, limiting its application to reliable food quality management in the post-harvest supply chain. Three-dimensional (3D) reconstruction technology captures detailed surface geometry and internal structural information for reliable non-destructive quality inspection. Combined with emerging artificial intelligence (AI) technologies, 3D data-driven adaptive management brings potential for next-generation post-harvest quality management. This review analyzed 90 major related studies during 2015–2025, covering high-throughput 3D in-line inspection, high-resolution tomography, portable 3D sensing, and AI-driven 3D reconstruction technologies. Post-harvest supply chain application scenarios are mainly distributed in post-harvest processing (33 studies), manufacturing (29 studies), distribution (15 studies), and consumption (13 studies). Among them, fruit and vegetable products are the most intensively researched, highlighting the suitability and potential benefits of 3D reconstruction for these types of products. Besides, multiple 3D reconstruction technologies have been validated for postharvest evaluation, with X-ray CT dominating postharvest processing, manufacturing, and distribution, and portable RGB imaging devices dominating application in consumption. Besides, relevant 3D reconstruction analysis is evolving from geometry-driven to AI-enhanced analysis and management, highlighting the growing role of 3D reconstruction technology in intelligent, traceable, and sustainable post-harvest supply chain quality management. In the future, by integrating digital twins, IoT, and blockchain technologies, it is expected to build a transparent, tamper-proof quality traceability and control system across the global food supply chain. • 3D reconstruction significantly improves non-destructive food quality inspection. • AI integration expands 3D reconstruction applications in food supply chains. • Applications span post-harvest, processing, logistics, and consumer evaluation. • Enables automation in grading, manufacturing, cold chain, and dietary assessment. • Future trends include sensor fusion, XR, and digital twin for food quality control.

On-road evaluation of sickness-less motion planning in automated vehicles: 'Head motion first' helps!

Self-adaptive elastic flaps with bending and torsion for 3D blunt body drag reduction

This paper presents the design of an IoT-based flood pump control system utilizing currently developed IoT devices and technologies. The system employs a smart switch (DIT AI-SM01) to control and monitor flood pumps. Experimental results confirm the effectiveness of the proposed system in automating flood pump operations. Equipment status can be monitored and controlled remotely via a smartphone or PC with the appropriate IoT application, simplifying the control system and significantly reducing response time within the flood pump control range.

Fourth-generation synchrotron radiation sources based on diffraction-limited storage rings impose stringent requirements on beamline alignment precision, diagnostic reliability, and real-time monitoring under high-brightness operating conditions. To address these requirements, an integrated fluorescent-target imaging and real-time beam diagnostic platform has been developed at the High Energy Photon Source (HEPS). The system incorporates a radiation-tolerant and ultra-high-vacuum-compatible mechanical assembly with diamond and YAG:Ce scintillators for different heat-load conditions. A distributed multi-camera architecture implemented within the Experimental Physics and Industrial Control System (EPICS) areaDetector framework enables synchronized multi-angle monitoring of beam profiles. Key beam parameters are extracted through online image processing and published as EPICS process variables for control-system integration. To support high-throughput diagnostic data handling, the platform further integrates the Mamba Data Worker framework, enabling coordinated multi-target acquisition, HDF5 storage, and automated metadata ingestion into a dedicated HEPS beamline alignment database. Representative deployment demonstrated stable synchronized operation of 13 cameras, with online analysis completed in less than 20 ms and end-to-end latency below 50 ms. These results establish a practical and scalable framework for beamline diagnostics, alignment support, and data-driven optimization atHEPS.

Agricultural data from the EU’s Integrated Administration and Control System (IACS) offer a rich, spatially explicit source for land-use analysis. However, cross-border reuse is limited by heterogeneous national implementations, institutional fragmentation, and governance constraints. This paper examines how EU data sovereignty can be operationalized in geospatial infrastructures to enable analytical reuse of IACS data. We present the Spatial Agricultural Information System (SAIS), a pan-European platform developed at the European Commission’s Joint Research Centre to harmonize national IACS datasets while preserving Member State data ownership and control. Using a multi-dimensional evaluation framework encompassing governance, technical architecture, data lifecycle management, and access policies, we assess how SAIS operationalizes sovereignty without impeding analytical usability. Illustrative examples demonstrate how SAIS integrates IACS data with soil and Earth Observation datasets to support cross-border land-use research. The case study shows that governance-aware geospatial platforms can advance EU land-use science while maintaining sovereign control over sensitive agricultural data.

Polygalacturonic acid-functionalized pH/enzyme dual-responsive zeolitic imidazolate framework-8 nanopesticide system for prevention and control of rapeseed sclerotinia stem rot.

Comparative evaluation of embedded platforms for real-time acquisition in plasma control and data acquisition systems

This study developed a taxonomic approach based on 30 technical failure occurrences between 2002 and 2020. This framework extends the author's previous work on human-machine-environment-procedure (HMEP) taxonomy through the application of Rasmussen's Risk Management Framework (RRMF), which can serve as a common framework to aggregate or compare datasets collected by different organizations. This RRMF-based HMEP taxonomy was used to categorize the contributing factors of 30 technical failure occurrences into seven mutually exclusive categories and respective subcategories for statistical analysis and other risk management analysis. The RRMF provided a hierarchical structure for these first-layer categories, including government oversight, manufacturer deficiency, company management, company procedure and documentation, people and activity management, technical failure, and environment. The functional block diagram and failure modes and effects analysis (FMEA) were applied to translate the technical failures of 12 environment control system failure cases into FMEA tabular statements. Several failure modes had been identified for environmental control system failure occurrences, e.g., sense line leakage, flawed sensor input, duct crack and leakage, broken part (pin and spring), seal degradation, contamination, tube rupture, mechanical joining defect, poor contact, and unanticipated failure instances for electronic centralized aircraft monitor (ECAM). Each failure mode can provide diagnostic information to identify and fix the environmental control system failure problem more effectively. In conclusion, this RRMF-based HMEP taxonomy provided a mental model to guide the data collection during an accident investigation and subsequently derive accident patterns as the core for Safety Management System (SMS) implementation.

Numerical describing function analysis of closed-loop discrete-time reset control systems

Mudan granules (MDs) are a widely used Chinese patent medicine for treating diabetic peripheral neuropathy (DPN). TCM quality is the basis to ensure its efficacy; however, the current quality standard for MD relies on the control of only two constituents derived from the minister drugs. Therefore, it is necessary to establish a more comprehensive quality evaluation system based on rational quality markers (Q-markers). In this study, Q-markers of MD were discovered through systematic evaluations. Thirty-seven compounds were newly characterized in MD by UPLC-QTOF-MS. Among all the 173 constituents in MD, 64 were found to be absorbed into the bloodstream and/or sciatic nerve of rats in prototype. The contents of 24 candidate Q-markers were determined, and the pharmacokinetics features of nine ones were analyzed by two developed UPLC-MS methods. Six candidate Q-markers were first found to exhibit protective effects on Schwann cells against the injury of high glucose/lipid. Finally, nine Q-markers were screened, and network pharmacology analysis showed that the enriched pathways for these nine Q-markers covered the major DPN-related pathways for the full set of 173 constituents. The results provide important evidence for improving the quality control system of MD and reference for discovering Q-markers of other TCM prescriptions.

Unraveling the link between brain injury and enhanced artistic skills.

This study examines emissions from the rapidly growing nautical tourism sector in Croatia, a major hub for charter and leisure boat tourism, focusing on the significant emissions caused by vessels that lack advanced emission control systems. Despite intensive petrol and diesel consumption, emissions from this sector remain under-researched; this study addresses that gap using data from the Green Sail Association’s Ecological Footprint Calculation Platform, collected during a 2023-24 pilot project in selected marinas in the Šibenik-Knin and Split-Dalmatia counties. The sample includes sailboats, yachts, and catamarans 10 to 20 m in length. Operational data (engine hours, fuel consumption, technical specifications) were gathered from charter companies and skippers. Emissions of CO2, NOX, CO, and PM were calculated using EMEP/EEA Tier 1 methodology based on fuel consumption and emission factors. Emissions rise with vessel length and are notably higher for catamarans due to greater fuel consumption. Though only 35 % of the sample, catamarans contributed nearly 60 % of total emissions. Based on the findings on the sample of 160 vessels, extrapolated to Croatia’s fleet (~4,500 vessels), seasonal emissions are estimated at 30,000 to 40,000 t CO2, 300 to 400 t NOX, 100 to 150 t CO, and 15 to 25 t PM. This study represents the first large-scale emissions estimate for charter vessels and recommends further research with a more representative sample, broader geographical coverage and advanced methods such as Tier 3 calculations, collection of generator fuel consumption data and real-world emissions measurements to improve emission factors.

Reachabilty sequences of minimal length for discrete-time switched linear control systems