Search Results (52)

In the knowledge economy era, optimizing R&D team size is crucial for breakthrough innovation. Breakthrough technologies rely more on knowledge restructuring and technological leaps than general technologies do. However, it remains unclear whether breakthrough technology formation follows a simple “more people, more power” logic within technological systems. This work examines 35,955 patents in recommendation system technology to propose a relationship model between collaboration scale and breakthrough technological innovation based on patent data from the recommendation system field. It aims to elucidate how collaboration scale influences breakthrough technological innovation through knowledge restructuring, thereby providing theoretical support and practical guidance for enterprises, institutions, and governments in innovation activities to advance technological innovation. The findings reveal three key points: (1) The relationship between collaboration scale and breakthrough innovation is not linear but follows an inverted U-shaped curve; (2) Knowledge recombination significantly mediates this relationship, also exhibiting an inverted U-shaped pattern with collaboration scale; (3) The inverted U-shaped effect of collaboration scale on breakthrough innovation varies by country. The optimal thresholds are 14.058 entities for China, 57.151 entities for the United States, and 4.801 entities for Russia. This work breaks through the limitations of the traditional theoretical framework and constructs a three-dimensional analysis framework of “collaboration scale → knowledge recombination → breakthrough technological innovation”. By introducing the mediating variable of knowledge recombination, this paper reveals the mechanism of R&D team size on radical innovation. It provides a theoretical basis for the construction of an innovation team and provides a theoretical basis for enterprises, governments, and institutions. Full article

Ultrathin oxide semiconductors are promising channel materials for next-generation thin-film transistors (TFTs), but their performance is severely limited by bulk and interface defects as the channel thickness approaches a few nanometers. In this study, we show that high-pressure hydrogen annealing (HPHA) effectively mitigates these limitations in 3.6 nm thick ZnO TFTs. HPHA-treated devices exhibit a nearly four-fold increase in on-current, a steeper subthreshold swing, and a negative shift in threshold voltage compared to reference groups. X-ray photoelectron spectroscopy reveals a marked reduction in oxygen vacancies and hydroxyl groups, while capacitance–voltage measurements confirm more than a three-fold decrease in interface trap density. Low-frequency noise analysis further demonstrates noise suppression and a transition in the dominant noise mechanism from carrier number fluctuation to mobility fluctuation. These results establish HPHA as a robust strategy for defect passivation in ultrathin oxide semiconductor channels and provide critical insights for their integration into future low-power, high-density electronic systems. Full article

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article

This paper aims to unveil the symmetry–asymmetry transition mechanisms in transient fault waveforms of offshore wind power AC/DC transmission systems, addressing the critical limitation of traditional simulation methods of the fact that they cannot characterize the dynamic evolution of system symmetry, such as static impedance adjustment failing to capture transient asymmetry caused by parameter imbalance or converter control. It proposes a fault waveform simulation approach integrating mechanism analysis, scenario extraction, and model optimization. Key contributions include clarifying the quantitative links between key system parameters like submarine cable capacitance and inductance and symmetry–asymmetry characteristics, defining the transient decay rate oscillation frequency and voltage peak as core indicators to quantify symmetry breaking intensity; classifying typical fault scenarios into a symmetry-breaking type with synchronous three-phase imbalance and a persistent asymmetry type with zero-sequence and negative-sequence distortion based on symmetry evolution dynamics and revising grid-connection test indices such as lowering the low-voltage ride-through threshold and specifying the voltage type for different test objectives; and constructing a simplified embedded RLC second-order model with symmetry–asymmetry constraints to reproduce the whole process of symmetric steady state–fault symmetry breaking–recovery symmetry reconstruction. Simulation results verify the method’s effectiveness, with symmetry indicator reproduction errors ≤ 5% and asymmetric feature fitting goodness R² ≥ 0.92, which confirms that the method can effectively reveal the symmetry–asymmetry mechanisms of offshore wind power fault transients and provides reliable technical support for improving offshore wind power fault simulation accuracy and grid-connection test reliability, laying a theoretical basis for the grid-connection testing of offshore wind turbines and promoting the stable operation of offshore wind power systems. Full article

The large size and metal-enclosed casings of oil-immersed power transformers present significant challenges for patrol robots attempting to accurately locate their position within the transformer. Therefore, this paper proposes a three-dimensional spatial localization method for transformer patrol robots using a nine-element ultrasonic array. This method is based on wavelet decomposition and weighted filter beamforming (WD-WFB) algorithms. To address the issue of strong noise interference in the field, the ultrasonic localization signals are adaptively decomposed into wavelet coefficients at different frequencies and scales. An improved semi-soft thresholding function is applied to the decomposed wavelet coefficients to reduce noise and reconstruct the localization signals, resulting in localization signals with low distortion and a high signal-to-noise ratio(SNR). To overcome the limitations of traditional beamforming algorithms regarding interference resistance and signal resolution, this paper presents an improved WFB algorithm. By obtaining the energy distribution of the scanning area and determining the position of the maximum energy point, the spatial position of the transformer patrol robot can be determined. The test results show that the proposed improved semi-soft threshold function demonstrates superior denoising performance compared to traditional threshold functions. When compared to the soft threshold function, it achieves improvements of 15.32% in SNR and 15.57% in normalized correlation coefficient (NCC), along with a 48.91% reduction in root mean square error (RMSE). Compared with the hard threshold function, the improvement is even more significant: the SNR is improved by 60.55%, the NCC is improved by 24.90%, and the RMSE is reduced by 58.77%. The denoising effect was significantly improved compared to the traditional threshold function. In a 1200 mm × 1000 mm × 1000 mm transformer test box, the improved WFB algorithm in this paper was used to perform multiple localizations of the transformer patrol robot at different positions after denoising the field signals using the semi-soft threshold function. The maximum relative localization error was 3.47%, and the absolute error was within 2.6 cm, meeting engineering application requirements. Full article

Cascading link failures continue to imperil power grids, transport networks, and cyber-physical systems, yet the relationship between a network’s robustness at the moment of attack and its subsequent resiliency remains poorly understood. We introduce a dynamic framework in which connectivity-based cascades and distributed self-healing act concurrently within each time-step. Failure is triggered when a node’s active-neighbor ratio falls below a threshold $\phi$; healing activates once the global fraction of inactive nodes exceeds trigger $T$ and is limited by budget $B$. Two real data sets—a 332-node U.S. airport graph and a 1133-node university e-mail graph—serve as testbeds. For each graph we sweep the parameter quartet ($\phi ,\hspace{0.17em}B,\hspace{0.17em}T,attackmode$) and record (i) immediate robustness $R$, (ii) 90% recovery time T₉₀, and (iii) cumulative average damage. Results show that targeted hub removal is up to three times more damaging than random failure, but that prompt healing with $B\ge 0.12$ can halve T₉₀. Scatter-plot analysis reveals a non-monotonic correlation: high-$R$ states recover quickly only when $B$ and $T$ are favorable, whereas low-$R$ states can rebound rapidly under ample budgets. A multiplicative fit $T_{90}\propto B^{-\beta }g\left(T\right)h\left(R\right)$ (with $\beta \approx 1$) captures these interactions. The findings demonstrate that structural hardening alone cannot guarantee fast recovery; resource-aware, early-triggered self-healing is the decisive factor. The proposed model and data-driven insights provide a quantitative basis for designing infrastructure that is both robust to failure and resilient in restoration. Full article

Background: Chronic obstructive pulmonary disease (COPD) is a progressive condition leading to declining lung function, dyspnea, and reduced quality of life. Pulmonary rehabilitation (PR) remains a cornerstone in COPD management; however, access remains limited, with less than 3% of eligible patients participating. Inspiratory muscle training (IMT), especially through novel methods like the Test of Incremental Respiratory Endurance (TIRE), offers a potential home-based alternative to traditional rehabilitation services. Despite growing interest, a key knowledge gap persists: few randomized trials have directly compared TIRE with threshold loading IMT over extended, largely unsupervised home-based periods while concurrently evaluating inspiratory muscle endurance and adherence. This randomized controlled trial aimed to evaluate the long-term efficacy of TIRE IMT compared to traditional threshold IMT and sham training in COPD patients. The study also assessed adherence to these home-based interventions, focusing on unsupervised periods without additional motivational support. Methods: A total of 52 COPD patients were randomly assigned to one of three groups: TIRE IMT, Threshold IMT, or Sham IMT. The study consisted of an 8-week supervised Phase I followed by a 24-week unsupervised Phase II. Training details: TIRE—session template set to 50% of the day’s maximal sustained effort; 6 levels × 6 inspirations (total 36) with preset inter-breath recoveries decreasing from 60 s to 10 s. Threshold IMT—spring-loaded valve set to 50% MIP (re-set at week 4); 36 inspirations completed within ≤30 min. Sham—valve set to minimal resistance (9 cmH₂O); 36 inspirations within ≤30 min. Primary outcomes included changes in maximal inspiratory pressure (MIP) and sustained maximal inspiratory pressure. Secondary outcomes focused on adherence rates and correlations with functional capacity. Results: Of the 52 participants, 36 completed the study. Participant details: TIRE n = 12 (mean age 60.9 ± 12.9 years), Threshold n = 12 (67.4 ± 6.9 years), Sham n = 12 (67.3 ± 8.7 years); overall 21/36 (58%) men; mean BMI 30.0 ± 7.5 kg/m². The TIRE IMT group demonstrated significantly greater improvements in MIP (31.7%) and SMIP compared to both the Threshold and Sham groups at 24 weeks (p < 0.05). Despite a decline in adherence during the unsupervised phase, the TIRE group maintained superior outcomes. No adverse events were reported during the intervention period. Conclusions: In this randomized trial, TIRE IMT was associated with greater improvements in inspiratory muscle performance than threshold and sham IMT. While adherence was higher in the TIRE group, it declined during the unsupervised phase. The clinical interpretation of these findings should consider the relatively wide confidence intervals and modest sample size. Nevertheless, the mean change in MIP in the TIRE arm exceeded a recently proposed minimal important difference for COPD, suggesting potential clinical relevance; however, no universally accepted minimal important difference exists yet for SMIP. Further adequately powered trials are warranted. Full article

Despite being designed considering infinite fatigue-life, failures of motor crankshafts forged from DIN 34CrNiMo6 steels have been reported in Brazilian power plants. As such, the present work aims to discuss the failure of a crankshaft within this context, with the purpose of verifying whether the stresses developed in critical locations of the component were in accordance with the steel’s fatigue limits, as well as if the material exhibits an adequate resistance to crack propagation. Taking into consideration a set of critical-plane stress-based multiaxial fatigue criteria, namely Findley, Matake, McDiarmid and Susmel and Lazzarin, the fatigue behaviour of the material is analysed and discussed. Furthermore, $da/dN$ versus $\mathsf{\Delta }K$ experiments were carried out with the purpose of determining the DIN 34CrNiMo6 steel’s crack growth threshold $\mathsf{\Delta }{K}_{th}$ and comparing it to the $\mathsf{\Delta }{K}_{th}$ of three other commercially available steels (DIN 42CrMo4, SAE 4140 and SAE 4340). The selected multiaxial fatigue criteria indicated that the stresses developed throughout the component were not sufficient to drive the crankshaft to failure, thus indicating safety. On the other hand, the DIN 34CrNiMo6 steel presented the lowest $\mathsf{\Delta }{K}_{th}$ ($6.6\mathrm{M}\mathrm{P}\mathrm{a}{\mathrm{m}}^{1/2}$) among all the considered steels ($10.86$, $12.38$ and $7.22\mathrm{M}\mathrm{P}\mathrm{a}{\mathrm{m}}^{1/2}$ for the DIN 42CrMo4, SAE 4140 and SAE 4340, respectively), therefore being susceptible to shorter fatigue lives in comparison to the other materials. Full article

The South China Sea (SCS) is a critical fishery region facing sustainability challenges due to overexploitation, geopolitical tensions, and inadequate monitoring. Traditional monitoring methods, such as AIS and VMS, have limitations due to data gaps and vessel deactivation. We developed an improved remote sensing algorithm using VIIRS nighttime light observations (2013–2022) to detect and classify lit fishing boats in the SCS. The study introduces a Two-Dimensional Constant False Alarm Rate (2D-CFAR) algorithm integrated with morphological analysis, which enhances boats’ detection accuracy. The classification of fishing boat types was based on light power thresholds derived from spatial entropy analysis, where distinct clustering patterns indicated three operational categories: small interfering lights (<1.2–3.7 kW), small-to-medium-sized lit fishing boats (1.2–3.7 to 28.6–43.2 kW), and large lit fishing boats (>28.6–43.2 kW). Our findings reveal a 4.4-fold dominance of small-to-medium-sized lit fishing boats over large lit fishing boats. China’s summer fishing moratorium effectively reduces large lit fishing boats activity by 85%, yet small-to-medium-sized lit fishing boats, primarily from neighboring countries like Vietnam, persist, exploiting this period illegally. Spatially, small-to-medium-sized lit fishing boats concentrate in the central SCS, southeast Vietnam, and Nansha Islands, while large lit fishing boats target upwelling zones near Hainan and Guangdong. Moreover, a new fishing hotspot emerged in eastern SCS, reflecting intensified resource and geopolitical competition. Light intensity analysis reveals rapid growth in contested areas (10% annually, p < 0.01), underscoring ecological risks. These findings highlight the limitations of unilateral policies and the urgent need for regional cooperation to curb illegal, unreported, and unregulated (IUU) fishing. Our algorithm offers a robust tool for monitoring fishing dynamics, providing quantitative insights into vessel distribution, policy impacts, and resource-driven patterns. This supports evidence-based fisheries management and biodiversity conservation in the SCS, adaptable to other marine regions facing similar challenges. Full article

Background: Most patients with hypertension exhibit elevated and detectable levels of natriuretic peptides, particularly BNP and NT-proBNP, as well as troponin concentrations. However, the prognostic relevance of this finding has not been clearly established in patients who have hypertension without heart failure (HF). In this review, we aimed to evaluate the prognostic utility of BNP/NT-proBNP alongside troponin T/I for risk stratification in hypertensive patients, excluding those with HF. Methods: This systematic review was registered in PROSPERO (CRD42024552031). A systematic literature search was conducted using two online databases, Ovid Medline and Web of Science, to identify studies. Data retrieved from articles were used in line with the PRISMA statement guidelines. Participants were aged ≥ 18 years with hypertension. The primary end point was a major adverse cardiac event (MACE) and its individual components. Descriptive synthesis was performed, and data are presented in tabular form. Results: Seventeen studies (70,021 participants) were retrieved for analysis comprising eight prospective cohort studies, six randomized controlled trials, and three retrospective studies. The review evaluated cardiac biomarkers: BNP (n = 6), NT proBNP (n=9), troponin T (n = 4), and troponin I (n = 7). Studies predicted composite MACE (n = 8), all-cause mortality (n = 7), HF (n = 6), and atrial fibrillation (n = 3) outcomes. Cardiac biomarkers showed a strong association with reported outcomes. However, heterogeneity in biomarker thresholds and methodologies limited comparability. Conclusions: The obtained results suggest that elevated cardiac biomarkers BNP/NT-proBNP and troponin I are associated with significantly higher risk of MACE and are powerful predictors in clinical setting. However, large-scale studies are required to validate the robustness and prognostic utility of these biomarkers Full article

Wheat disease detection is a crucial component of intelligent agricultural systems in modern agriculture. However, at present, its detection accuracy still has certain limitations. The existing models hardly capture the irregular and fine-grained texture features of the lesions, and the results of spatial information reconstruction caused by standard upsampling operations are inaccuracy. In this work, the GDFC-YOLO method is proposed to address these limitations and enhance the accuracy of detection. This method is based on YOLOv11 and encompasses three key aspects of improvement: (1) a newly designed Ghost Dynamic Feature Core (GDFC) in the backbone, which improves the efficiency of disease feature extraction and enhances the model’s ability to capture informative representations; (2) a redesigned neck structure, Disease-Focused Neck (DF-Neck), which further strengthens feature expressiveness, to improve multi-scale fusion and refine feature processing pipelines; and (3) the integration of the Powerful Intersection over Union v2 (PIoUv2) loss function to optimize the regression accuracy and convergence speed. The results showed that GDFC-YOLO improved the average accuracy from 0.86 to 0.90 when the cross-overmerge threshold was 0.5 (mAP@0.5), its accuracy reached 0.899, its recall rate reached 0.821, and it still maintained a structure with only 9.27 M parameters. From these results, it can be known that GDFC-YOLO has a good detection performance and stronger practicability relatively. It is a solution that can accurately and efficiently detect crop diseases in real agricultural scenarios. Full article

Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. Full article

Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO₂), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO₂ measurement is activated when the specified T and $RH$ change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and $RH$ signals, the number of CO₂ measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO₂ measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO₂ concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations. Full article

Islanding detection remains a critical challenge for grid-connected distributed generation systems, as passive techniques suffer from inherent non-detection zones (NDZ), and active methods often degrade power quality. This paper introduces a hybrid detection strategy based on monitoring inherent grid harmonics via a Digital Lock-In Amplifier. By comparing real-time 5th and 7th harmonic amplitudes against their three-cycle-delayed values, the passive stage adaptively identifies potential islanding without fixed thresholds. Upon detecting significant relative variation, a brief injection of a non-characteristic 10th harmonic (limited to under 3% distortion for three line cycles) serves as active verification, ensuring robust discrimination between islanding and normal disturbances. Case studies demonstrate detection within 140 ms—faster than typical reclosing delays and well below the 2 s limit of IEEE std. 1547—while preserving current zero-crossings and enabling grid impedance estimation. The method’s resilience to grid disturbances and stiffness is validated through PSIM simulations and laboratory experiments, meeting IEEE 1547 and UL 1741 requirements. Comparative analysis shows superior accuracy and minimal power-quality impact relative to existing passive, active, and intelligent approaches. Full article

This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or CO₂ emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios. Full article