Search Results (38)

Multimodal sensor fusion is increasingly used to improve observability for fault diagnosis and prognostics, enabling Remaining Useful Life estimation in complex mechatronic and robotic systems. Yet, real-world deployments remain vulnerable to sensor faults and data integrity issues—including bias and drift, miscalibration, dropouts, saturation, cross-talk, time desynchronization, and domain shift—which can propagate through fusion pipelines and lead to optimistic validation and poor generalization. These challenges are particularly consequential in safety- and health-adjacent applications such as collaborative robots, wearable/rehabilitation devices, and human-centric mechatronic systems where decisions based on faulty sensing may affect both reliability and user safety. This review synthesizes the state of the art on (i) sensor fault taxonomies and fault models relevant to multimodal fusion, (ii) fault-aware fusion strategies spanning data-, feature-, and decision-level integration, and (iii) how sensor faults and uncertainty impact diagnosis and remaining-life estimators. We will conduct a systematic scoping review of peer-reviewed literature, extracting sensor modalities, fault characterization or injection protocols, fusion architectures, validation settings (simulation, hardware-in-the-loop, bench, and in-field/on-body studies), and reporting completeness. Beyond summarizing methods, we provide practical reporting standards for sensor-fusion-based diagnosis and prognostics, including a minimum disclosure set covering synchronization, fault ground truth, missingness handling, leakage controls, uncertainty calibration, and task-relevant metrics. Reusable checklists and evidence tables are included to support more comparable, reproducible, and deployment-ready research. Full article

Post-processing remains a major bottleneck in the fabrication of microfluidic devices using Digital Light Processing Stereolithography (DLP-SLA) 3D printing, where unpolymerized resin trapped within internal structures must be removed without damaging delicate features such as thin membranes, valves, and pumps. Manual flushing is slow, inconsistent, and prone to structural failure, especially as device complexity and port counts increase. Here, we present the first fully automated flushing system for DLP-SLA microfluidic devices, enabled by a standardized chip-to-chip (C2C) interconnect architecture and an electronically controlled pneumatic routing platform. A reusable 32-port flushing interface chip provides alignment, sealing, and modular coupling to arbitrary device chips through integrated microgaskets, while a network of electronic pressure controllers, differential pressure sensors, and multi-port rotary valves enable precise, programmable application of pressure, vacuum, and solvent conditions. We introduce a fluidic-circuit model of the system that relates applied pressure to the pressure drop across device structures and experimentally validate this model using channels with varying fluidic resistances. Using this platform, we demonstrate robust flushing of both passive (straight and serpentine channels) and active (valves, pumps) microfluidic elements, as well as application-specific devices including mixers and concentration-gradient generators. Our system eliminates manual handling, improves valve membrane survival, and provides repeatable flushing across a broad range of device geometries. This work establishes a scalable foundation for automated post-processing in 3D-printed microfluidics and significantly advances the practicality of DLP-SLA fabrication for complex, multi-layered microfluidic devices. Full article

Objective: Functional training exercises involve complex multi-joint movements that challenge traditional rule-based or data-driven recognition systems. This paper introduces a Movement Description Language (MDL) designed to formally represent, analyze, and evaluate such exercises using camera-based pose estimation and interpretable, composable structures. Methods: The proposed MDL models each exercise as a finite-state machine defined by pose-derived angle proxy transitions, allowing movements to be described in a modular and reusable way. Demonstrated with MediaPipe landmark extraction from monocular video, while the MDL remains compatible with any pose estimation algorithm, the framework focuses on exercise phase detection and repetition counting. Experimental validation was conducted on a dataset of 1513 videos of 12 functional exercises (squats, deadlifts, lunges, shoulder presses, planks, push-ups, pull-ups, bent-over rows, box jumps, thrusters, overhead squats, and burpees) obtained from public pose datasets, competition footage, and recordings of 9 participants in real-world environments. Results: Automated repetition counts were compared against manually annotated ground truth, showing an overall repetition-counting accuracy of 97.2%, with a mean per-exercise accuracy of 98.8% (range 95–100%). The MDL successfully handled both simple and compound exercises, maintaining reliable phase detection despite variations in execution speed, camera perspective, and environmental conditions. Conclusions: The system was implemented using real-time pose estimation to demonstrate the practical execution of the MDL framework. The proposed MDL provides a transparent, extensible, and computationally efficient framework for functional exercise analysis. By bridging human-readable movement semantics with executable motion logic, it enables interpretable automatic repetition counting and phase detection, offering an alternative to black-box recognition approaches. The results support its potential for scalable deployment in training, monitoring and movement analysis applications. The proposed system is not intended for biomechanical measurement or clinical-grade kinematic analysis, but rather for interpretable modeling of exercise structure and repetition detection using approximate pose-derived signals. Full article

Direct collocation transcription is a dominant technique for solving complex optimal control problems, converting continuous dynamics into large-scale, sparse nonlinear programming problems. The computational efficiency of this approach is fundamentally limited by the evaluation of first- and second-order derivatives required by modern optimization algorithms. While general-purpose automatic differentiation tools exist, they often fail to fully exploit the repetitive substructure inherent in trajectory discretization. This paper presents a vectorized, sparse, second-order forward automatic differentiation framework specifically tailored for direct collocation methods. By explicitly distinguishing between scalar and vector nodes within the expression graph, the proposed method leverages the independence of mesh point evaluations to enable Single Instruction, Multiple Data (SIMD) execution and optimize memory access patterns. This structure-aware approach ensures linear time complexity with respect to the number of discretization nodes while maintaining the flexibility to handle complex dependencies. The methodology is implemented in the open-source software package pockit and is validated through three distinct engineering case studies: the aggressive stabilization of a nano-quadrotor, the powered descent guidance of a reusable launch vehicle, and a low-thrust heliocentric orbital transfer. These applications demonstrate the framework’s capability to deliver high-performance derivative computation for large-scale, nonlinear dynamical systems. Full article

This work introduces Hybrid Membrane–Neural P systems (HMN P systems), a computational model that integrates principles from membrane computing and spiking neural P systems. The resulting framework offers a versatile foundation for the development of bio-inspired arithmetic architectures. Within this setting, we propose a compact family of arithmetic kernels capable of executing signed addition, subtraction, multiplication, and division in both modular and non-modular arithmetic domains. By leveraging intrinsic spike aggregation, spike–anti-spike annihilation, and exhaustive rule application, the proposed designs achieve efficient and reliable arithmetic computation in a constant number of simulation steps under exhaustive semantics and assuming synchronized input, independent of operand values. Addition and subtraction are executed intrinsically upon spike arrival, requiring no internal computation steps, while multiplication and division are completed in a single simulation step by one neuron. Furthermore, we introduce a modular-reduction kernel that operates in two simulation steps with a single neuron, and leverage its modular structure to construct modular multiplication and division through composition with non-modular arithmetic modules. Comparative evaluations against representative SNP and SNQ arithmetic designs demonstrate that HMN kernels achieve operand-independent execution time while requiring fewer neurons. Distinct from most existing approaches, the HMN framework natively supports signed operands through a dual-spike representation, thereby eliminating the need for auxiliary sign-handling mechanisms. Asynchronous spike arrivals can be managed by an optional synchronization membrane; since this mechanism is decoupled from the arithmetic kernels, its overhead is excluded from kernel performance and reported separately. Collectively, these results establish HMN systems as an efficient and modular platform for constant-time arithmetic computation, offering reusable arithmetic kernels that serve as a foundation for higher-level constructions, including those arising in elliptic-curve and modular arithmetic. Full article

Metaheuristic algorithms have become essential tools for solving complex, high-dimensional, and constrained optimization problems. This paper introduces an adaptive R implementation of the parameter-free Jaya algorithm, enhanced with methodological innovations for both single-objective and multi-objective settings. The proposed framework integrates adaptive population management, dynamic constraint-handling, diversity-preserving perturbations, and Pareto-based archiving, while retaining Jaya’s parameter-free simplicity. These extensions are further supported by parallel computation and visualization tools, enabling scalable and reproducible applications. Benchmark evaluations on standard test functions demonstrate improved convergence accuracy, solution diversity, and robustness compared to the classical Jaya and other baseline algorithms. To highlight real-world applicability, the method is applied to a renewable energy planning problem, where trade-offs among cost, emissions, and reliability are explored. The results confirm that the adaptive Jaya approach can generate well-distributed Pareto fronts and provide practical decision support for energy system design. The main contributions of this work are threefold: (i) the development of an adaptive multi-objective extension of the Jaya algorithm that preserves its parameter-free philosophy while incorporating diversity preservation, dynamic constraint handling, and Pareto-based selection; (ii) a unified and openly available R implementation that integrates methodological advances with parallel computation and visualization, addressing the lack of transparent and reusable MO-Jaya tools in the existing literature; and (iii) a systematic evaluation on benchmark test functions and a renewable energy planning case study, demonstrating competitive convergence, robust Pareto diversity, and practical decision-making insights compared to established methods. By openly releasing the software in R (≥3.5.0), this work contributes both a methodological advance in multi-objective metaheuristics and a transparent tool for applied optimization in engineering and environmental domains. Full article

In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article

The ongoing advance of urbanization has increased the need for accurate monitoring of urban green space (UGS). However, existing remote-sensing UGS mapping still struggles with inconsistent data quality, diverse urban forms, and limited cross-city generalization. This study focuses on China’s Guangdong-Hong Kong-Macao Greater Bay Area as its research region, establishing a fully automated UGS mapping framework based on Sentinel-2 time-series imagery and standardized OpenStreetMap (OSM) data. This process achieves UGS mapping at 10 m resolution for 16 cities within the metropolitan area through a dynamic standardized OSM tagging system, a Sentinel-2 satellite image sample generation mechanism integrating spectral and textural features, multidimensional sample quality assessment and weighting strategies, as well as balanced cross-city sampling and weighted SVM classification. The results demonstrate that this method exhibits stable performance across multiple urban environments, achieving an average overall accuracy of approximately 0.83 and an average F1 score of approximately 0.82. The highest recorded F1 score reaches 0.96, highlighting the method’s strong generalization capability under diverse urban conditions. The mapping results reveal significant disparities in UGS distribution within the Guangdong-Hong Kong-Macao Greater Bay Area, reflecting the combined effects of varying urban development patterns and ecological contexts. The unified workflow proposed in this study demonstrates strong applicability in handling heterogeneous urban structures and enhancing cross-regional comparability. It provides consistent, transparent, and reusable foundational data for regional eco-urban planning, urban green infrastructure development, and policy evaluation. Full article

Digital twin technology is emerging as a critical enabler for the lifecycle management of complex engineering equipment, yet its implementation faces significant hurdles. Generic, one-size-fits-all digital twin platforms often fail to address the unique characteristics of this domain—such as tightly coupled multi-physics, high-fidelity modeling requirements, and the need for real-time model execution under harsh operating conditions. This creates a critical need for a structured, reusable blueprint. However, a dedicated reference architecture that systematically guides the development of such specialized digital twins is notably absent. To bridge this gap, this paper proposes MetaD-DT, a reference architecture designed to enable and streamline the development of digital twins specifically for complex engineering equipment. We detail its comprehensive four-layer architecture, core functional modules, and streamlined graphical development workflow. The MetaD-DT’s efficacy and practical value are validated through two distinct industrial case studies: a health management system for diesel engine Diesel Particulate Filter (DPF) and an intelligent control optimization system for Indirect Air-Cooled (IAC) towers. These applications validate the framework’s ability to support the creation of robust digital twins that can effectively handle complex industrial dynamics and improve O&M (Operation And Maintenance) efficiency. This work provides a systematic architectural blueprint for the future development of specialized and efficient digital twins in the engineering equipment domain. Full article

Efficient interaction between PHP (Hypertext Preprocessor) applications and MySQL databases is essential for the performance of modern web systems. This study systematically compares the two most widely used PHP APIs for working with MySQL databases—MySQLi (MySQL Improved extension) and PDO (PHP Data Objects)—under identical experimental conditions. The analysis covers execution time, memory consumption, and the stability and variability of results across different types of SQL (Structured Query Language) queries (simple queries, complex JOIN, GROUP BY/HAVING). A specialized benchmarking tool was developed to collect detailed metrics over several hundred repetitions and to enable graphical and statistical evaluation. Across the full benchmark suite, MySQLi exhibits the lowest mean wall-clock execution time on average (≈15% overall). However, under higher query complexity and in certain connection-handling regimes, PDO prepared statement modes provide competitive latency with improved predictability. These results should be interpreted as context-aware rankings for the tested single-host environment and workload design, and as a reusable benchmarking framework intended for replication under alternative deployment models. Statistical analysis (Kruskal–Wallis and Mann–Whitney tests) confirms significant differences between the methods, while Box-plots and histograms visualize deviations and the presence of outliers. Unlike earlier studies, this work provides a controlled and replicable benchmarking environment that tests both MySQLi and PDO across multiple API modes and isolates the impact of native versus emulated prepared statements. It also evaluates performance under complex-query workloads that reflect typical reporting and analytics patterns on the ClassicModels schema. To our knowledge, no previous study has analyzed these factors jointly or provided a reusable tool enabling transparent comparison across PHP–MySQL access layers. The findings provide empirical evidence and practical guidelines for choosing the optimal API depending on the application scenario, as well as a tool that can be applied for further testing in various web environments. Full article

Climate change intensifies agricultural risks, requiring an integrated analysis of climatic, hydrological, and crop data to support resilient farming. Despite advances in remote sensing, in-field sensors, and artificial intelligence, fragmented data silos hinder spatiotemporal risk assessments by requiring labor-intensive data handling. We present agriclima, a federated, cloud-native, FAIR-by-design platform that unifies heterogeneous agricultural and environmental datasets under consistent identity, policy, and metadata governance. Its scalable open-source architecture, compliance with INSPIRE and RNDT standards, and privacy-preserving access enable researchers and decision-makers to perform comprehensive analyses with minimal coding, accelerating data-driven agricultural risk management. Developed and tested in a research project by a consortium of stakeholders in agricultural risk management, the platform was evaluated via: (1) FAIR assessment of 26 datasets using F-UJI, (2) system performance monitoring on Kubernetes, and (3) a demonstrative spatiotemporal aggregation use case. It achieved 80% average FAIR compliance, with perfect accessibility (7.00/7.00), while findability and reusability remain key areas for improvement. Performance showed stable operation (CPU 17.24%, memory 49.89%) with capacity headroom. The demonstrative use case validated that researchers can conduct spatiotemporal analyses with minimal coding effort through the abstracted data access components. Beyond technical evaluation, we share lessons learned to guide future platform development and metadata standardization, highlighting the platform’s effectiveness as a foundation for data-driven agricultural decision-making. Full article

Shaft sinking in soft, water-rich strata frequently suffers from low cutting efficiency, cycle-time mismatches between excavation and muck removal, and weak system-level coordination. To elucidate the synergistic mechanisms governing excavation–muck removal interactions and to realize end-to-end performance gains, we investigate the East Ventilation Shaft of the Xinjie Taigemiao mining district as a representative artificial ground freezing (AGF) project. First, drawing on the mechanics of frozen ground and field monitoring, we establish a relationship model linking advance rate, drum rotational speed, cutting depth, and muck production, thereby clarifying why lower rotational speeds, moderate cutting depths, and rational traction reduce energy consumption and mitigate disturbances to the frozen wall. Next, for muck handling, we build a full-process discrete element method (DEM) model, integrate design-of-experiments with response-surface optimization to identify key factors, calibrate contact models, and select collection geometries. The results show that a graded-angle collecting structure improves pile concentration and discharge compliance; combined with a tiered chain-bucket–vertical belt–twin-skip configuration, it delivers matched cycle times and stable “gather–convey–hoist” operation. Finally, two-stage full-scale tests jointly validate excavation and muck removal, demonstrating that the proposed synergy model and optimized parameters sustain continuous, efficient performance across operating conditions. The study provides a reusable mechanistic framework and parameterization blueprint for AGF shaft design and construction. Full article

Key drivetrain components in wind turbines are prone to misalignment faults due to long-term operation under fluctuating loads and harsh environments. Because misalignment develops gradually rather than occurring instantly, reliable evaluation of structural designs and surface treatments requires long-duration, multi-sensor, and multi-condition experiments that generate massive heterogeneous datasets. Traditional data management relying on manual folders and USB drives is inefficient, redundant, and lacks traceability. To address these challenges, this study presents a dedicated misalignment experimental data management platform specifically designed for wind power applications. The innovation lies in its ability to synchronize vibration, electrostatic, and laser alignment data streams in long-term tests, establish a traceable and reusable data structure linking experimental conditions with sensor outputs, and integrate laboratory results with field SCADA data. Built on Laboratory Information Management System (LIMS) principles and implemented with an MVC + Spring Boot + B/S architecture, the platform supports end-to-end functions including multi-sensor data acquisition, structured storage, automated processing, visualization, secure sharing, and cross-role collaboration. Validation on drivetrain shaft assemblies confirmed its ability to handle multi-terabyte datasets, reduce manual processing time by more than 80%, and directly integrate processed results into fault identification models. Overall, the platform establishes a scalable digital backbone for wind turbine misalignment research, supporting structural reliability evaluation, predictive maintenance, and intelligent operation and maintenance. Full article

The growing adoption of the Internet of Things (IoT) demands scalable, energy-efficient communication for autonomous devices. Narrowband IoT (NB-IoT), as a low-power wide-area technology, offers reliable connectivity but remains difficult to integrate in MicroPython systems due to the absence of high-level GSM libraries. This paper introduces a modular, object-oriented MicroPython library that abstracts AT command handling, automates network configuration, and supports protocols such as MQTT and Blynk. The architecture features a layered, hardware-agnostic core and device-specific adapters, enhancing portability and extensibility. The library includes structured exception handling and automated retries to improve system reliability. Empirical validation using a Raspberry Pi Pico and SIM7020E module in a typical IoT scenario demonstrated an up to 81% reduction in implementation time. By providing a reusable and extensible framework, this work improves developer productivity, enhances error resilience, and establishes a solid foundation for rapid NB-IoT application development. Future directions include cross-hardware validation and AI-assisted code and test generation. Full article

This work demonstrates the potential of polydimethylsiloxane sponges for removing organic UV filter compounds such as octinoxate from aqueous solutions. The sponges were fabricated using simple templates made of hydrophilic fused or pressed particles (sugar or NaCl salt) with an approximate particle size of 0.4 mm. Among the prepared sponges, those templated with sugar cubes or coarse salt exhibited the highest adsorption capacity, effectively adsorbing up to 0.6% of their own mass in octinoxate. The PDMS sponges were fully regenerable, allowing for the complete removal of octinoxate without any detectable changes in their adsorption properties or dry weight. Due to their simple fabrication, ease of handling, ability to float, and reusability, PDMS sponges present an environmentally friendly and low-maintenance alternative to conventional filtration systems for the removal of octinoxate and potentially other UV filter compounds from environmental surface waters and recreational water bodies. Full article