Search Results (152,750)

Artificial Intelligence (AI) adoption has emerged as a critical priority for governments globally, driven by its transformative potential in improving public service delivery, governance efficiency, and innovation ecosystems. Despite this, substantial disparities exist in AI readiness and adoption levels across countries, necessitating an in-depth exploration of the factors influencing AI adoption. This study leverages data from the Oxford Insights Government AI Readiness Index to model cross-country patterns of government AI readiness through clustering, regression, classification, and explainable machine learning. A Random Forest regression model was first used to estimate the 2024 AI Government Readiness score using lagged 2023 indicators. However, because the dependent variable is a composite index constructed from conceptually related dimensions, this model is interpreted as a lagged score-approximation and benchmarking exercise rather than as an independent forecasting model. The main analytical contribution lies in the clustering-classification framework, which identifies four country-level AI readiness profiles and evaluates the indicators that most strongly distinguish countries across low, moderate-low, intermediate, and high readiness groups. SHAP and permutation-based interpretation methods are used to examine feature contributions, while recognizing that these results indicate model contribution rather than causal effects. The findings underscore the multifaceted nature of AI readiness, emphasizing the interplay between governance, digital infrastructure, and technological investment. Full article

Objectives: Tandem spinal stenosis (TSS) is often underdiagnosed and traditionally managed with multi-stage surgery (MSS). Single-stage surgery (SSS) is an alternative, but prior studies largely emphasize younger, healthier patients. This study evaluated perioperative and functional outcomes after SSS for TSS in a surgically diverse cohort. Methods: A retrospective chart review included 20 patients who underwent SSS for TSS at a single academic institution. Mean age was 63.75 years, and median modified frailty index was 2. Etiologies included degenerative, traumatic, and neoplastic disease across cervical, thoracic, and lumbar regions. Outcomes included operative characteristics, complications, readmissions, and functional recovery measured by Visual Analog Scale (VAS) pain and modified Japanese Orthopaedic Association (mJOA) scores. Results: The mean number of operated levels was 5.2, mean operative time was 232.4 min, total OR time was 355.1 min, and length of stay was 6.9 days. Surgical complications occurred in 15% of patients, medical complications in 25%, and 90-day readmission in 15%, with no 30-day mortality. Mean mJOA improved from 12.86 at baseline to 16.08 at first follow-up and 16.46 at 3 months; REML mixed-effects modeling showed a significant timepoint effect (F (4, 34.55) = 9.15, p < 0.001), with significant Sidak-adjusted improvement at both timepoints. VAS pain showed no significant longitudinal effect. Conclusions: SSS for TSS appears feasible in a real-world, surgically diverse cohort including older and moderately frail patients. These findings support individualized SSS candidacy assessment. Full article

Existing component-based models have difficulty interpreting the field response of earth pressure balance (EPB) shield tunneling parameters when the excavation face gradually changes from hard rock to sandy strata. To address this problem, this study proposes a mechanism-informed semi-empirical analysis based on a continuous face sand fraction function, $\eta \left(z\right)$, which represents the evolution of the sand-bearing area fraction at the excavation face along the shield advance direction. The function is constructed from the geological profile and is used as a continuous reformulation of existing face-composition descriptors such as rock/sand ratio and composite ratio. Based on $\eta \left(z\right)$, engineering-equivalent models for total thrust and cutterhead torque are developed and evaluated using field tunneling data from Qingdao Metro Line 15 and Line 5. The Dandan right-line data are used for parameter calibration, while the Basi right-line data from Rings 590–650 are used for independent validation without further parameter tuning. The results show that the η-related correction term improves the independent validation performance of the total thrust model, reducing the MAPE from 22.56% to 14.25%. In contrast, cutterhead torque exhibits stronger operational variability and interval-specific baseline offset. After applying a baseline correction determined from the stable hard-rock section of the Basi interval, the ring-scale torque MAPE decreases from 44.07% to 17.25%, but the additional $\eta$-related torque contribution remains limited. These results indicate that total thrust is more sensitive to the gradual increase in face sand fraction, whereas cutterhead torque is more strongly influenced by machine condition, cutter wear, and operational control. The proposed approach provides a field-based semi-empirical reference for interpreting tunneling parameter responses in similar hard-rock-to-sandy-strata transition zones. Full article

Background/Objectives: Parental stress during neonatal intensive care unit (NICU) admission affects parental well-being, bonding, and neonatal outcomes. Reliable assessment requires instruments adapted to the linguistic and cultural context of each population. This study aimed to adapt the Parental Stressor Scale: NICU (PSS:NICU) for use in Spain and evaluate its psychometric properties. Methods: The adaptation comprised forward translation, back-translation, expert review, pilot testing with cognitive debriefing (n = 15), and psychometric evaluation. The adapted scale was administered to 160 parents (80 mothers, 80 fathers) of NICU-admitted neonates; 159 cases were retained for analysis. Internal consistency was assessed with Cronbach’s alpha. Exploratory factor analysis (EFA; maximum likelihood, Promax rotation) with parallel analysis examined the factor structure. Confirmatory factor analysis (CFA) tested the original four-factor model, and a second-order model with five first-order factors was subsequently evaluated. Results: Internal consistency was excellent (total α = 0.968; subscales α = 0.866–0.961). Parallel analysis supported a five-factor EFA solution over a four-factor solution. CFA of the original four-factor model yielded poor fit (CFI = 0.755, TLI = 0.737, RMSEA = 0.125). A second-order model with five first-order factors and one general stress factor improved fit (CFI = 0.819, RMSEA = 0.107), though indices remained below conventional thresholds. Standardised factor loadings were moderate to high (0.57–0.96). Conclusions: The Spanish PSS:NICU demonstrates excellent reliability and provides preliminary evidence supporting its use for research and clinical assessment. The original four-factor structure was not replicated; the data suggest a five-factor organisation with evidence of a higher-order stress construct. Structural validity requires confirmation in larger, independent samples. Full article

In wind tunnel testing, an active vibration suppression system based on piezoelectric actuators is an effective means to ensure stable operation. However, in a cryogenic wind tunnel testing environment, the performance of piezoelectric actuators degrades significantly when they are exposed to cold temperatures and subjected to uneven cooling. This is particularly problematic during real-time changes in the attack angle of a test model. To ensure the reliable operation of wind tunnel tests, an active–passive hybrid thermal control method is proposed in this paper. First, the insulation and heating structure was designed based on the thermal analysis results. Then, combining simulation and measured data, the temperature field was reconstructed in real time using a recurrent neural network algorithm. Next, considering the non-uniform heat dissipation of the system, a thermal allocation module was designed based on digital–physical integration to actively control the overall and localized heat. Finally, a heat preservation performance test platform was established to conduct cooling experiments in a small-scale cryogenic wind tunnel. The results indicated that the proposed thermal control method reduced the average cooling rate of the system by 97% and improved the overall temperature uniformity by approximately 94.23%. Full article

Morel detection in agroforestry cultivation scenes remains challenging because soil-background camouflage, illumination variation, and dense clustered growth can lead to missed small targets and false positives in background regions. This study proposes Morel-YOLO, a lightweight morel detection method based on YOLOv13n for agricultural perception. The model retains the original multi-scale feature-fusion framework and introduces three targeted modifications: a StarNet backbone for reducing redundant computation, a DSC3k2_DWRSeg module in the shallow P${}_3$ branch for strengthening fine-grained texture and small-target representation, and a Detect_MBConv head for reducing prediction-branch overhead while preserving detection accuracy. On the test set, Morel-YOLO achieves 91.9% precision, 86.6% recall, 93.6% mAP${}_{50}$, and 70.8% mAP${}_{50--95}$, improving mAP${}_{50--95}$ by 1.3 percentage points over YOLOv13n. The model contains 1.48 M parameters, has a model size of 3.31 MB, and requires 6.2 GFLOPs. On the Small-hard and Dense-hard subsets, mAP${}_{50--95}$ reaches 69.1% and 66.8%, respectively, corresponding to gains of 1.5 and 1.3 percentage points over the baseline. Under IoU = 0.75, both false positives and false negatives are also reduced on the two hard subsets. These results suggest that Morel-YOLO improves the balance among detection accuracy, robustness, and model compactness on the evaluated dataset; however, its practical deployment on embedded agricultural platforms still requires dedicated on-device validation. Full article

Adding inspection trains to existing railway timetables is a complex task that must balance operational efficiency and service reliability, which are essential for the sustainable operation and maintenance of high-speed railway infrastructure. To address this challenge, a feasible region-based space–time network modeling approach is proposed for incorporating Comprehensive Inspection Trains (CITs) into existing railway schedules, aiming to enhance inspection efficiency while minimizing operational disruptions. Firstly, the constraints that need to be considered when scheduling for CIT are comprehensively analysed and modelled, and a mixed-integer nonlinear model with the objective of minimizing the total number of stops is constructed. In order to eliminate the difficulty of solving this model, based on the original space–time network method, more kinds of train event arcs are introduced to accurately portray the train operation process; in particular, the extra time consumed due to the acceleration and deceleration process is also reflected in the network construction process. The feasibility of various event arcs is evaluated with time windows, and the original problem finally transforms into the equivalent shortest path problem on a feasible event arc network. The processing procedure includes key stages, such as station space–time discretization, interval operation event processing, station capacity handling, and network simplification. The experimental results indicate that the approach effectively resolves all station capacity conflicts, compresses inspection durations, and optimizes the number of stops. Remarkably, the number of non-full-speed inspection sections is reduced by 43.16%, demonstrating the model’s efficiency. Additionally, the proposed approach is computationally efficient, improves timetable capacity utilization for infrastructure inspection, and supports the sustainable operation of high-speed railway systems. Full article

This paper investigates the effects of disturbance observers on a long-travel precision positioning platform. We propose a multi-layer control architecture, including a disturbance observer, a feedforward compensator, gain-scheduling, and control switching. The platform consists of motor and piezoelectric transducer (PZT) stages to enable nanometre-level accuracy within 10 cm travel ranges. We identified the dynamic models of the stages through experiments and applied them to develop control designs. The PZT stage was equipped with feedforward compensators, a disturbance observer and real-time switching control schemes to achieve robust and precise tracking. On the other hand, we applied gain-scheduling and feedforward compensation to the motor stages to track large displacements. The control effects of the integrated platform were validated through simulations and experiments and demonstrated significant improvements in accuracy and robustness. Finally, the platform was incorporated with two-photon polymerisation to fabricate micro-lenses. This work evaluates the lenses’ optical properties to highlight the advantages provided by the multiple control structure for improving precision and microfabrication applications. Full article

Horqin Sandy Land suffers from desertification, drought, and low fertility, limiting soybean production. Agrivoltaics provides a promising integrated model; however, the effects of agrivoltaics combined with water–fertilizer management on crop productivity remain unclear. A 2-year field experiment was conducted in a semi-arid area with three treatments, open-field control (Open), shaded area under panels (Under), and light-exposed area inter-panels (Gap). Results showed that photovoltaic systems combined with integrated water–fertilizer management improved soybean yield, soil water, and nutrient conditions. Soybean grain yield was 60.7% and 38.2% higher in the Gap and Under treatments, respectively, than in the Open. The highest yield in the Gap treatment resulted from both enhanced photosynthesis and improved root development. The Under endured light stress but exhibited morphological plasticity (plant height and leaf area increased by 43.1%, 48.2%), and shading alleviated water stress since soil water content was increased by 81.6–119.0% during growing seasons, transpiration rate (Tr) decreased by 55.1%, and leaf water use efficiency (WUE) increased by 48.8%. The Open suffered from soil degradation and water and fertilizer loss, resulting in severely limited yield. Agrivoltaics increased net income by 1466 CNY·ha⁻¹ and improved soil nutrients, demonstrating economic and ecological benefits. Thus, it is a suitable technical model for semi-arid sandy regions. Full article

Accurate cultivar identification of commercial dried goji berries is essential for raw material sorting, batch consistency assessment, and quality control during processing and distribution. Conventional approaches based on manual judgment or physicochemical analysis are often subjective, labor-intensive, time-consuming, and costly, making them unsuitable for rapid commercial sorting and quality inspection. To develop a rapid, low-cost, and nondestructive method for dried goji berry cultivar identification, this study proposes a visual recognition framework that integrates RGB imaging with lightweight deep learning. A dataset comprising 25,899 RGB images from five cultivars of commercial dried goji berry samples, namely Ningqi No. 7, Linqi No. 5, Ningqi No. 1, Keqi 6082, and Jingqi No. 1, was constructed. Given the pronounced surface shrinkage, complex texture, and subtle inter-cultivar appearance differences of dried goji berries, an image quality enhancement method was designed to strengthen the representation of color gradation, textural details, and edge information. For model development, CoAtNet was selected as the baseline network and redesigned for lightweight deployment. By integrating an improved feature extraction module and an information-preserving downsampling module, the proposed LSH-CoAtNet model enhances fine-grained feature representation while reducing computational cost. On the quality-enhanced image dataset, the proposed method achieved an accuracy of 98.80%, a precision of 98.81%, a recall of 98.80%, and an F1-score of 98.80%. The model contained only 6.41 M parameters and required 1.60 GFLOPs, outperforming the baseline model in both classification performance and computational efficiency. Ablation experiments and five-fold cross-validation further confirmed the effectiveness of the image quality enhancement strategy, the contribution of each improved module, and the stability of the model. Overall, the proposed method, which combines RGB image quality enhancement with LSH-CoAtNet, provides a low-cost, nondestructive, and efficient technical solution for rapid cultivar identification, raw material sorting, batch consistency assessment, and quality control of commercial dried goji berries during processing and distribution. It may also serve as a reference for intelligent classification and quality inspection of other specialty dried horticultural products. Full article

Water level monitoring is closely linked to the safety of production and daily activities along riverbanks, making real-time and high-precision water level measurement an urgent technical demand. The feature extraction backbone of the Unet model is modified, and the lightweight MobileNet V2 network is adopted in this paper. The constructed network achieves significantly higher computational efficiency than standard convolutions, effectively overcoming the limited real-time performance of conventional water level measurement methods. Furthermore, the coordinate attention (CA) mechanism is integrated into the skip connections of Unet to strengthen the network’s capability to extract key features for water level segmentation, thereby further improving the accuracy of water level detection. A novel piecewise linear fitting method for water level line measurement based on monocular vision is proposed, and field-measured water level data are adopted to verify the calculation results. The main achievements of the improved model include the following: (1) Compared with the baseline model, the improved model MCUnet (MobileNet V2 + CA + Unet) achieves a 5.77% increase in accuracy and a 25.71% improvement in inference speed on the experimental water surface recognition dataset. (2) Taking the field-observed water level as the reference, the mean absolute error of the proposed image-based water level monitoring method reaches approximately 1.69 cm. (3) In comparison with DeepLab, U2net and Unet, the MCUnet model gains accuracy improvements of 4.47%, 2.81% and 5.77% respectively, with the detection frame rate increased by 12 FPS, 15 FPS and 11 FPS correspondingly. Through this work, the paper can provide some theoretical support and technical references for overcoming the limitations of conventional water level measuring devices, including strict installation requirements, limited measurement precision, high deployment and maintenance costs, and cumbersome data processing. Full article

Photoacoustic spectroscopy (PAS), quartz-enhanced photoacoustic spectroscopy (QEPAS), and light-induced thermoelastic spectroscopy (LITES) represent indirect absorption spectroscopy techniques for trace gas sensing, whose performance has long been advanced through hardware-oriented enhancement strategies. However, as hardware technologies continue to advance, conventional hardware-based enhancements are increasingly bottlenecked by weak responses, complex cross-interferences, and coupled multiphysics parameters. To transcend these limitations, algorithm-assisted methods, including traditional algorithms, machine learning, deep learning, and intelligent optimization, are being systematically integrated into these spectroscopic systems. This review summarizes recent progress in intelligent indirect absorption spectroscopy from three interconnected dimensions. First, we outline advanced signal processing and spectral reconstruction strategies designed to achieve weak-signal recovery and background noise suppression. Second, the focus shifts to data-driven parameter inversion, showing how multidimensional artificial intelligence models contribute to concentration retrieval, environmental compensation, multicomponent recognition, spectral-overlap decoupling, and front–back-end collaborative waveform coding and demultiplexing. Third, intelligent system optimization is examined, in which surrogate modeling, swarm-intelligence search, physics-guided topology optimization and multi-objective algorithms are employed to improve the design efficiency of the key elements such as photoacoustic resonators and multipass cells (MPCs). Additionally, prospects for future technological developments are also discussed in the concluding section. Full article

The primary objective of this article is to examine the organizational, economic, and sustainability-related implications of implementing artificial intelligence (AI) systems in small and medium-sized enterprises (SMEs) in Poland. The study combines a survey of 112 SMEs in the Kuyavian–Pomeranian region, including 70 AI-using firms, with 13 in-depth interviews with managers. The quantitative analysis applies logit models to identify determinants of perceived AI effects on internal processes: working time and workload reduction, automation, cost effects, and creativity. The qualitative component explains how AI is adopted and embedded in business practice. The results show that AI adoption in SMEs is increasingly common but remains uneven and mostly operational. The strongest effects concern workload reduction and time efficiency, particularly in service firms and where AI is used intensively. Advanced AI adoption increases the probability of perceiving workload and cost-related effects. However, these effects should not be interpreted simply as direct cost reduction. Rather, AI improves productivity and work capacity while creating new costs related to paid tools, data preparation, integration, output verification, and governance. The interviews show that AI implementation follows a staged path: from curiosity-driven experimentation, through cognitive work augmentation, to workflow integration and, in selected cases, AI-enabled business model innovation. The transition from ad hoc use to strategic implementation depends less on firm size alone and more on process maturity, capabilities, and data readiness. Barriers also change with maturity: early-stage firms face a lack of knowledge, time, and clear use cases, whereas advanced users encounter data quality, hallucinations, security, integration, and governance problems. The study finds that sustainability considerations, particularly environmental impacts and ESG-related implications of AI, remain largely unperceived in SME decision-making. Entrepreneurs primarily interpret sustainability through the lenses of organizational resilience, long-term competitiveness, adaptability, and responsible digital transformation rather than through formal environmental metrics. The findings suggest that SME managers should implement AI gradually, link adoption to measurable process-level outcomes, and invest in AI literacy and governance. They should also integrate responsible AI principles into organizational strategy to support sustainable digital transformation. The study contributes to the literature by showing that AI adoption in SMEs should be understood not only as a productivity-enhancing process but also as a broader organizational transition shaping long-term sustainability and resilience. Full article

Conventional depletion development and waterflooding are often ineffective in tight oil reservoirs because of their ultra-low permeability, complex fracture–matrix architecture, and limited fluid mobility. Although inter-fracture CO₂ flooding has demonstrated considerable potential for enhanced oil recovery (EOR), the coupled effects of key operational parameters on reservoir pressure evolution, fracture–matrix mass transfer, and oil mobilization remain inadequately understood. In this study, a multi-component compositional simulation model, constrained by detailed geological characterization and calibrated through production history matching of the Yuan 284 block in the Changqing Oilfield, was developed to systematically evaluate the effects of CO₂ injection rate, injection–production time ratio, and shut-in duration on recovery performance and reservoir response. The results show that increasing the CO₂ injection rate from 1000 to 50,000 m³/d improves the recovery factor from 40.49% to 49.90%; however, the incremental recovery gain decreases markedly beyond 30,000 m³/d, which is aggravated by enhanced gas channeling through high-conductivity fracture pathways. Analysis of the injection–production time ratio indicates that an optimal ratio of 0.50 provides the best balance between reservoir energy replenishment and oil displacement efficiency, whereas excessively small ratios result in insufficient pressure support and reduced recovery. In contrast, extending the shut-in duration consistently lowers recovery performance by weakening fracture–matrix mass transfer and promoting pressure dissipation, demonstrating that immediate production following injection is more effective than prolonged soaking under the investigated conditions. The optimized operating scheme yields a recovery factor of 48.87%, substantially exceeding the representative waterflooding recovery level of 35.20%. These findings clarify the mechanisms controlling pressure maintenance, CO₂ utilization efficiency, and volumetric sweep during inter-fracture asynchronous CO₂ flooding, and provide both theoretical insights and practical guidance for the efficient development of ultra-low-permeability fractured tight oil reservoirs. Full article

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder driven by complex interactions between protein aggregation, oxidative stress, neuroinflammation, and cellular dysfunction. Among plant-derived compounds, curcumin has emerged as one of the most extensively studied polyphenols due to its broad spectrum of biological activities. This review provides a critical synthesis of the mechanistic, preclinical, and clinical evidence on curcumin in AD. Experimental studies consistently demonstrate that curcumin modulates key pathogenic processes, including neuroinflammatory signaling, oxidative stress, and amyloid-β aggregation, with more limited evidence for effects on tau pathology. While in vitro studies offer detailed mechanistic insights, in vivo models provide more integrated evidence, including improvements in cognitive performance and reductions in pathological markers. Despite this strong preclinical foundation, the clinical evidence remains limited and inconsistent. Randomized controlled trials have not demonstrated clear therapeutic efficacy, with outcomes strongly influenced by formulation, bioavailability, and study design. Poor solubility, rapid metabolism, and limited brain exposure remain key translational barriers. In response, increasing attention has been directed toward formulation strategies and structurally related compounds. Emerging curcuminoids, such as bisdemethoxycurcumin (BDMC), are discussed as potential next-generation candidates. Preliminary evidence suggests that BDMC may modulate oxidative stress, autophagy, astrocyte senescence, and amyloid-related processes, although the data remain largely preclinical. Overall, curcumin represents a mechanistically rich and preclinically promising multi-target compound but with unresolved translational limitations. Future research should prioritize pharmacokinetic optimization, formulation-dependent validation, and exploration of novel curcuminoid strategies to bridge the gap between experimental findings and clinical application in AD. Full article