Search Results (279)

This scoping review synthesizes 35 studies on sensor technologies and artificial intelligence for fruit detection, classification, and quality assessment, contrasting Latin American and international research traditions. The analysis suggests notable differences in technological approaches between the included Latin American and international studies: Latin American research tends to emphasize in developing accessible, practical solutions using classical computer vision and low-cost hardware, while international studies more frequently employ through deep learning architectures, multi-modal sensing, and complete robotic automation systems. Across the included studies, relatively limited attention was given to AI-assisted decision support for agricultural practitioners, insufficient consideration of inclusivity, and the scarce integration of environmental sustainability into intelligent sensing system design. The review identifies that only 8 of 35 studies originate from Latin America, suggesting an uneven geographical distribution of the available evidence. The included studies generally reported high accuracy values, yet these findings must be interpreted with caution given the reliance on curated datasets that may not represent real-world variability. The reviewed evidence suggests that future research may benefit not from one approach dominating the other, but from a thoughtful integration of complementary strategies, including knowledge transfer, edge computing democratization, and human-centered design. Overall, this review suggests that the ultimate goal extends beyond accuracy metrics to the transformation of agricultural practices that enhance food security, economic development, and environmental sustainability across the global agricultural landscape. It is important to note that this work does not propose or validate a new fruit detection algorithm but rather synthesizes and critically evaluates existing scientific evidence regarding sensor technologies and artificial intelligence applied to fruit detection and quality assessment. Full article

This paper addresses the urgent scientific and applied problem of automatic tracking of highly maneuverable Unmanned Aerial Vehicles (UAVs) using systems based on platforms with limited computing power (Edge Computing). The paper analyzes the shortcomings of classical correlation trackers and detectors based on deep neural networks when tracking targets with non-linear trajectories. A hybrid tracking method is proposed, combining the speed of a Kernelized Correlation Filter (KCF) and the accuracy of a neural network detector (YOLO11s). A key feature of the method is the developed algorithm for adaptive Kalman Filter correction, which utilizes a dynamic, scale-invariant Prediction Error metric as a trigger for motion anomaly detection. This allows the system to distinguish between measurement noise and sharp target maneuvers, executing an adaptive state reset using finite differences only at critical moments. Experimental validation on edge hardware (Raspberry Pi 5) using highly dynamic video sequences from the UAV123 and VisDrone datasets demonstrated that the proposed approach maintains an average processing speed of 18.89 FPS. By limiting deep neural network invocations to merely 2.71% of total frames, the algorithm successfully curtails thermal throttling while achieving a global Mean Root Square Error (RMSE) of 259.10 pixels across highly erratic trajectories. The method ensures high tracking reliability without a critical increase in computational load, making it highly suitable for use in autonomous embedded systems. Full article

The transition toward Agriculture 4.0 aims to improve farm performance and sustainability; however, existing macroeconomic indicators do not fully capture the depth of farm-level digital adoption. This study proposes the Digital Farm Index (DFI) as a tool for assessing digital maturity and regional disparities in European agriculture. The research combines bibliometric mapping of the scientific literature with an empirical DFI assessment for 18 European Union Member States using Eurostat data. The empirical assessment utilizes multiple linear regression, log-linear scale modeling, and hierarchical clustering to analyze adoption patterns and structural determinants. The index integrates four dimensions: connectivity, precision agriculture, robotics, and farm management information systems (FMIS). Results indicate that adoption is concentrated in larger farms, as area-weighted digital maturity (DFI-Hectares) consistently exceeds farm-level adoption (DFI-Farms). CAPEX-based cost modeling suggests the existence of a technological indivisibility threshold, whereby digitalization may become an entry barrier for fragmented farms with limited economies of scale. Multiple linear regression suggests an East–West structural trend: while farm size influences the territorial diffusion of technology, a more consolidated regional innovation ecosystem appears more relevant for farm-level adoption. Findings also highlight a hardware–software imbalance and limited use of data-driven managerial tools. Support policies should therefore move beyond equipment subsidies and include technology transfer networks, digital skills, and managerial data-integration tools. Full article

Certain physics-based technologies (quantum computing, fusion energy, advanced materials, brain–computer interfaces) have remained “five years away” for decades. This paper argues that this perpetual horizon is not a forecasting failure but the temporal signature of Perpetual Five-Year Technologies (PFYTs): technologies of atoms, not bits, whose technical object and enabling ecosystem must co-develop. Drawing on philosophy of technology and sociology of scientific practice, and grounded in quantitative analysis of cross-platform performance data and high-impact research across hardware architectures, the paper shows that PFYT temporality is endogenous, produced by concretization dynamics and recursive constraint discovery rather than by market failures or insufficient funding. Quantum computing provides the paradigmatic case. Treating the quantum processor as a technical individual in active individuation explains why multiple computing paradigms persist and why each breakthrough resets rather than collapses the horizon. The convergence of machine learning and quantum hardware into pipelines for drug and materials discovery shows how Physical AI reshapes PFYT dynamics, compressing some cycles while introducing new forms of co-individuation between intelligence and matter. The framework generates diagnostics for physics-based frontiers where technical objects and milieus co-produce developmental time. Full article

Partial differential equations (PDEs) play a central role in scientific and engineering analysis, with applications spanning fluid dynamics, heat and mass transfer, electromagnetism, quantum mechanics, and financial modeling, where they are used to describe diffusion processes, wave propagation, and the evolution of complex systems over space and time. Solving multidimensional partial differential equations (PDEs) is a computationally challenging problem, even for the most advanced classical systems. Over the past decade, quantum computing has attracted significant interest as a potential approach for solving complex computational problems, including multidimensional PDEs. Although a variety of approaches have been proposed for solving PDEs, most of the existing techniques are based on variational quantum algorithms (VQAs). Despite being promising, these VQA-based approaches suffer from low accuracy, long execution times, and limited scalability. In this work, we propose a scalable and efficient quantum algorithm for solving multidimensional PDEs. Our algorithm has two variants. One variant is based on the finite difference method (FDM), classical-to-quantum (C2Q) encoding, and numerical instantiation, whereas the other is based on FDM, C2Q, and column-by-column decomposition (CCD). We have also evaluated our algorithm using several practical case studies; namely, Poisson, heat, Black–Scholes, and Navier–Stokes equations. The results show that our proposed approach achieves higher accuracy, greater scalability, and faster execution time than the VQA-based approaches. We validated these findings on both noise-free and noisy simulators, as well as on a hardware emulator and real IBM quantum hardware. Full article

Background and Purpose: Contemporary digital government initiatives in China face a well-documented real-world paradox: massive fiscal outlays on digital governance coexist with marked disequilibrium in regional innovation returns. Two structural mismatches define this paradox. First, local governments overwhelmingly prioritize high-visibility hardware investments such as data centers and large AI models, while neglecting deep-seated institutional reforms including cross-departmental business process reengineering and factor market liberalization. The pervasive phenomenon of “aggregated but non-interoperable data, and interoperable data left unused” reflects a severe asynchrony between rapid technological deployment and lagging institutional restructuring. Second, comparable digital investments yield vastly divergent innovation dividends across eastern, central, and western regions, with regional divergence entrenching into a rigid “higher in the east lower in the west, higher in the south lower in the north” pattern. Extant literature, largely confined to the lens of “instrumental rationality,” reduces digital government to an exogenous technological variable, leaving it unable to explain this core practical puzzle of “homogeneous inputs generating heterogeneous returns.” Moving beyond the narrow “technology-enabled governance” narrative, this study draws on the Digital-Era Governance (DEG) paradigm to investigate the actual impact of institutional restructuring on regional scientific and technological innovation capacity, aiming to provide empirical evidence to unlock the inefficiency lock-in prevalent in digital governance practices. Research Methods: This study uses 280 prefecture-level cities and above in China from 2018 to 2023 as the research sample and constructs a two-way fixed-effects model for benchmark regression analysis. To address endogeneity, the average level of digital government development in other cities within the same province is used as an instrumental variable, and the 2SLS method is employed to identify the causal effect. On this basis, a series of robustness checks are conducted, including excluding the special impact of the COVID-19 pandemic, substituting core variable measures, and decomposing the dimensions of the core explanatory variables, to ensure the reliability of the research conclusions. For mechanism identification, the Bootstrap sampling method is used to test the dual mediating effects of “digital industry agglomeration” and “resource misallocation alleviation”; furthermore, moderating effects and heterogeneity analysis models are introduced to reveal the boundary constraints of regional economic development levels and city types on the empowerment effect. Main Findings: Empirical results show that: (1) Digital government construction significantly improves regional scientific and technological innovation capacity, and this conclusion remains valid after endogeneity treatment and robustness checks. (2) Mechanism analysis demonstrates that digital government drives innovation through the dual paths of “promoting digital industry agglomeration” and “alleviating resource misallocation,” with the marginal contribution of alleviating resource misallocation being significantly higher than that of industrial agglomeration. This suggests that, in transitional economies, eliminating institutional frictions in factor mobility brings greater innovation dividends than simply building physical spatial clusters. (3) Moderating effects indicate that the higher the level of regional economic development, the stronger the innovation empowerment effect of digital government. (4) Heterogeneity analysis further reveals that the innovation dividends of digital government are significant only in non-resource-based cities, non-central cities, and large and medium-sized cities, while in resource-based cities, central cities, and small cities, the effects are systematically absorbed and not significant. Research Conclusions and Contributions: This study breaks through the ontological limitations of existing research that views digital government as a technological tool, grounding it within the DEG theoretical framework and confirming that digital government is an institutional force in the reconstruction of regional innovation ecosystems. The findings suggest to policymakers that digital government construction should promote a shift from a “technology-oriented” to an “institution-oriented” approach. The policy focus should shift from mere infrastructure expansion to the elimination of deep-seated institutional frictions, the improvement of factor allocation efficiency, and the advancement of gradients and the implementation of classified governance, all guided by regional economic foundations and heterogeneity characteristics. Full article

The co-circulation of respiratory viruses, including SARS-CoV-2, influenza A/B, and respiratory syncytial virus (RSV), represents a significant global health challenge that requires rapid, accurate, and differential diagnosis to support infection control and appropriate clinical decision-making. This narrative review summarizes emerging multimodal point-of-care testing (POCT) strategies for the detection and management of these respiratory viruses. Relevant studies were identified through literature searches of major scientific databases, including PubMed, Scopus, and Web of Science, focusing on recent advances in molecular diagnostics, biosensors, microfluidics, and digital health technologies. To improve clinical interpretation and comparative assessment, current POCT platforms were organized into four operational tiers based on infrastructure dependence, degree of portability, and level of decentralization of testing. Tier 1 (Professional Clinical Systems) includes fully integrated automated molecular diagnostic platforms designed for use in hospital and emergency care settings. Tier 2 (Field-Deployable Systems) comprises portable molecular and isothermal amplification technologies designed for use in decentralized or resource-limited environments. Tier 3 (Hardware-Lite Assays) includes simplified diagnostic approaches that minimize instrument requirements and are suitable for near-patient or low-infrastructure settings. Tier 4 (Consumer-Digital Diagnostics) encompasses emerging smartphone- and IoT-integrated diagnostic platforms that support user-driven testing and digital health connectivity. This tier-based framework reflects a proposed stratification of POCT technologies along a decentralization continuum and aims to facilitate comparison and selection of diagnostic strategies across diverse healthcare settings. Full article

Context: Modern quantum-computing experimentation generates heterogeneous, context-dependent execution data whose scientific value depends on preserving calibration state, compilation decisions, and run outcomes in a traceable and repository-ready form. In the NISQ era, probabilistic outputs, time-varying hardware conditions, and opaque transpilation pipelines create a data-management problem that directly affects reproducibility, traceability, and long-term reuse of experimental records. Goal: This paper aims to address this gap by proposing a specialized metadata and schema model for managing quantum-circuit execution data as governed, machine-interpretable, and evolvable repository artifacts. Proposal: We propose QC-MM, a platform-agnostic metadata model for capturing, validating, and relating contextual evidence of quantum-circuit experiments. The model integrates time-indexed calibration binding, transpilation traceability, lightweight provenance links, validation rules, and controlled schema evolution through a JSON Schema specification. Results: The evaluation follows a multi-scenario protocol and shows that QC-MM captures dynamic calibration context in IBM Quantum Cloud, remains interoperable through a local SpinQ NMR device, and makes transpilation effects traceable through structured records. It also supports repeated-run statistical reporting and links compilation decisions to execution outcomes, including circuit-depth reductions and changes in an estimated fidelity proxy under different optimization settings. Conclusions: QC-MM provides a specialized data-modeling and schema-governance foundation for traceable quantum-experiment repositories. Beyond improving reproducibility-oriented reporting, the proposal contributes to metadata validation, controlled schema evolution, and repository-oriented management of contextual experimental data. Full article

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

Smart warehouses require autonomous mobile robots to complete lifelong tasks while avoiding conflicts, respecting battery constraints, and sharing charging stations. Existing MAPF methods provide strong conflict resolution, but energy, charging, and online action repair are commonly evaluated separately. We present ECR-HR, an energy-constrained hybrid repair framework that combines a normalized energy model, charging-aware goals, risk-informed priorities, and bounded local conflict repair. The scientific contribution is a coupled execution and evaluation interface rather than a new complete MAPF solver or a claim of dominance over MAPF-LNS2. In reproducible simulation, we compare ECR-HR with classical, repair-based, lazy-search, conflict-based, and learning-based baselines. In 40-seed nominal evaluation, ECR-HR reduces candidate conflict rate relative to WHCA* from 0.0479 to 0.0255 ($p=3.89\times {10}^{-6}$) while MAPF-LNS2 achieves the strongest raw success. A 30-seed study using MovingAI map geometry, priority and repair comparisons, module-level runtime profiling, simulated disturbance tests, 25-seed energy coefficient sensitivity, and preference weight sensitivity further define the framework’s operating boundary. Enhanced GNN-PPO-HR increases held-out success from the original 0.188 to $0.753\pm 0.174$ but remains below mature search baselines. All evidence is simulation-based, the energy coefficients are normalized rather than hardware-calibrated, and real-robot validation remains necessary. Full article

In ultra-cold narrow-window drilling, pipe connection causes flow-path switching as the main circulation is interrupted and bypass circulation is established, breaking the initial relative pressure balance of the whole wellbore and inducing asymmetric transient variations in flow distribution, annular friction, and bottomhole pressure response, thereby increasing the risks of wellbore instability, lost circulation, and kicks. To address the poor pressure-control accuracy, long non-productive time, and inadequate low-temperature adaptability of conventional drilling technologies in the Irkutsk block of Russia, this study developed and field-tested an integrated all-electric managed pressure drilling (MPD) and cold-resistant continuous circulation system (CCS). Existing conventional technologies often suffer from high communication latency and hydraulic freezing in extreme cold environments, leading to uncoordinated pressure compensation. To overcome these limitations, the scientific novelty of this work lies in proposing a transient pressure rebalancing mechanism that effectively suppresses the asymmetric pressure disturbances induced by topological flow path switching. Methodologically, the proposed system was validated through a comprehensive industrial field test. An improved Herschel–Bulkley temperature–pressure coupled model was established to dynamically calculate full wellbore annular pressure loss. Furthermore, a dedicated hardware adapter module utilizing multi-protocol conversion was integrated to achieve a communication delay of less than 8 ms, enabling high frequency coordinated pressure regulation. Field results demonstrate that compared to the delayed responses of conventional systems, the proposed integrated approach successfully maintained a dynamic backpressure tracking error within ±0.069 MPa under extreme conditions of −38 °C and a narrow pressure window of 0.08 g/cm³. The rapid suppression of asymmetric transient responses prevented any lost circulation, kicks, or wellbore collapse. These findings highlight the significant advantages of the integrated system in maintaining pressure field stability, thereby providing a robust and innovative engineering solution for complex well interventions. Full article

To address the challenges of difficult value quantification, lack of market benchmarks, and scarcity of historical data for embedded software amidst the intelligent transformation of equipment systems, this study develops a scientific price estimation method based on functional capability contribution. A nonlinear pricing model is constructed to accurately characterize the two-stage evolution of software price: diminishing marginal utility during the mature technology accumulation stage and exponential growth during the technical bottleneck breakthrough stage. To ensure the consistency of pricing logic between hardware and software, a penalty function is innovatively designed to modify the standard likelihood function, effectively transforming practical business logic into a model regularization term. Parameter estimation is achieved by employing a Bayesian inference framework integrated with operational constraints, utilizing Markov Chain Monte Carlo (MCMC) sampling to realize robust posterior inference under small-sample constraints. Empirical analysis demonstrates that the proposed method achieves superior cross-domain data transfer performance compared to traditional baseline models, with a Leave-One-Out Cross-Validation (LOOCV) Mean Absolute Percentage Error (MAPE) of 21.2%. This research provides a practical value-oriented price estimation method for embedded equipment software pricing. Full article

Different types of Virtual Power Plants (VPPs) play distinct roles within power systems. To scientifically evaluate the operational benefits of VPPs, this paper constructs a comprehensive evaluation framework based on combined weighting and the Evaluation based on Distance from Average Solution (EDAS) method. First, an evaluation index system is established encompassing four dimensions: economic, environmental, social, and technical. Subsequently, a hybrid model integrating DEMATEL, CRITIC, Game Theory, and EDAS is proposed. Specifically, the DEMATEL method is employed to analyze the causal relationships among indicators and determine subjective weights, while the CRITIC method is used to calculate objective weights. Game Theory is then applied to optimize the combination of weights, and the EDAS method is utilized to rank the alternatives. Empirical analysis of five VPP scenarios indicates that the renewable energy accommodation rate and hardware investment costs are the core driving factors affecting operational benefits. Specifically, the renewable-energy accommodation rate exhibits the highest combined weight of 0.08, and the hardware investment cost reaches 0.07. Among the scenarios, a wind-solar-storage hybrid VPP demonstrates the optimal comprehensive performance. The results are consistent with comparative methods such as TOPSIS, verifying the reliability of the proposed framework and providing a scientific reference for VPP investment decision-making. Full article

The rapid evolution of diffusion models has shifted visual synthesis from text-only inputs to precisely controlled generation driven by multi-source heterogeneous sensor signals (e.g., audio, 3D, and physiological data). This paper presents a systematic review of cross-modal mapping and controllable generation under multi-source collaboration. More precisely, we propose a unified “cross-modal mapping and injection” taxonomy by abstracting the intervention logic of heterogeneous signals. Fundamentally, we analyze these mechanisms in a backbone-agnostic manner, delineating the architectural transition from legacy U-Net dependencies to scalable architectures like Diffusion Transformers (DiTs) and tracing the technical evolution from single-source atomic driving to complex multi-source collaborative paradigms. Our mechanistic analysis reveals that seamless feature fusion heavily relies on gradient conflict resolution, rigorous arbitration, and dynamic disentanglement under multi-constraint scenarios. Furthermore, by systematizing current evaluation metrics, we identify intrinsic quality-controllability trade-offs through performance game analysis (e.g., Pareto optimization), yielding a scientifically grounded technical selection guide. The study concludes that overcoming current generation limitations necessitates integrating Hardware-in-the-Loop (HIL) deployment, PDE-driven physical constraints, and causal inference, laying the foundation for next-generation robust and real-time generative models. Full article

Ship propulsion electrification is an important enabler towards a sustainable shipping industry. Ship power systems are turning into modern microgrids integrating different generation/storage resources, converter technologies and electric propulsion, utilizing different control levels and communication systems. The definition of comprehensive test requirements, set-ups and procedures is critical to ensure that the equipment will behave as expected in the ship system context. Comprehensive testing is becoming increasingly challenging due to complex interactions at the system level, attributed to electrical, mechanical/hydrodynamic, control, protection, and information and communication systems present in modern and future ships. Standardization has addressed the testing of several individual components, as well as specific system tests for marine applications; however, a holistic testing approach is missing. This paper reviews the generic and maritime standards for testing ship electric power propulsion systems and equipment, focusing on generators/motors, power electronic drives and onshore power supply systems. A review of the scientific literature is performed, classifying the publications according to the testing method, such as pure hardware tests, co-simulation and hardware in the loop simulation (HIL). The need for holistic testing of shipboard microgrids is explained. A holistic HIL testing approach is proposed, which integrates hardware controllers and power equipment of different manufacturers and functions, in order to reduce the complexity and cost of sea trials. The proposed approach is accompanied by example implementation and application guidelines. Full article