Search Results (1,008)

Background: Tobacco use is disproportionately high in rural areas, contributing to elevated cancer mortality, yet it often goes untreated due to limited access to care, high poverty and uninsured rates, and co-occurring substance use disorders (SUDs). This study explored the utility of using rapid qualitative analysis (RQA) to guide the adaptation of a tobacco-free workplace program (TFWP) in Texas healthcare centers serving adults with SUDs in medically underserved areas. Methods: From September–December 2023 and May–July 2024, we conducted 11 pre-implementation, virtual semi-structured group interviews focused on adapting the TFWP to local contexts (N = 69); 7 with providers (n = 34) and managers (n = 12) and 4 with patients (n = 23) in 6 healthcare centers. Two qualified analysts independently summarized transcripts, using RQA templates of key domains drawn from interview guides to summarize and organize data in matrices, enabling systematic comparison. Results: The main themes identified were minimal organizational tobacco cessation support and practices, and attitudinal barriers, as follows: (1) the need for program materials tailored to local populations; (2) limited tobacco cessation practices and partial policies—staff requested guidance on enhancing tobacco screenings and cessation delivery, and integrating new interventions; (3) contradictory views on treating tobacco use that can inhibit implementation (e.g., wanting to quit yet anxious that quitting would cause SUD relapse); and (4) inadequate environmental supports—staff requested treating tobacco-use training, patients group cessation counseling; both requested nicotine replacement therapy. Conclusions: RQA identified key areas requiring capacity development through participants’ willingness to adopt the following adaptations: program content (e.g., trainings and tailored educational materials), delivery methods/systems (e.g., adopting additional tobacco care interventions) and implementation strategies (e.g., integrating tobacco cessation practices into routine care) critical to optimizing TFWP fit and implementation. The study findings can inform timely formative evaluation processes to design and tailor similar intervention efforts by addressing site-specific needs and implementation barriers to enhance program uptake. Full article

Thylakoid rhodanese-like protein (TROL) serves as a thylakoid membrane hinge linking photosynthetic electron transport chain (PETC) complexes to nicotinamide adenine dinucleotide phosphate (NADPH) synthesis. TROL is the docking site for the flavoenzyme ferredoxin-NADP⁺ oxidoreductase (FNR). Our prior work indicates that the TROL-FNR complex maintains redox equilibrium in chloroplasts and systemically in plant cells. Improvement in the knowledge of redox regulation mechanisms is critical for engineering stress-tolerant plants in times of elevated global drought intensity. To further test this hypothesis and confirm our previous results, we monitored light-independent ROS propagation in the leaves of Arabidopsis wild type (WT), TROL knock-out (KO), and TROL ΔRHO (RHO-domain deletion mutant) mutant plants in situ by using confocal laser scanning microscopy with specific fluorescent probes for the three different ROS: O₂^·−, H₂O₂, and ¹O₂. Plants were grown under the conditions of normal substrate moisture and under drought stress conditions. Under the drought stress conditions, the TROL KO line showed ≈32% less O₂^·− while the TROL ΔRHO line showed ≈49% less H₂O₂ in comparison with the WT. This research confirms the role of dynamical TROL-FNR complex formation in redox equilibrium maintenance by redirecting electrons in alternative sinks under stress and also points it out as promising target for stress-tolerant plant engineering. Full article

Mechanical failures frequently occur in On-Load Tap Changers (OLTCs) during operation, potentially compromising the reliability and stability of power systems. The goal of this study is to develop an intelligent and accurate diagnostic approach for OLTC mechanical fault identification, particularly under the challenge of non-stationary vibration signals. To achieve this, a novel hybrid method is proposed that integrates the Gazelle Optimization Algorithm (GOA), Feature Mode Decomposition (FMD), and a Transformer-based classification model. Specifically, GOA is employed to automatically optimize key FMD parameters, including the number of filters (K), filter length (L), and number of decomposition modes (N), enabling high-resolution signal decomposition. From the resulting intrinsic mode functions (IMFs), statistical time domain features—peak factor, impulse factor, waveform factor, and clearance factor—are extracted to form feature vectors. After feature extraction, the resulting vectors are utilized by a Transformer to classify fault types. Benchmark comparisons with other decomposition and learning approaches highlight the enhanced performance of the proposed framework. The model achieves a 95.83% classification accuracy on the test set and an average of 96.7% under five-fold cross-validation, demonstrating excellent accuracy and generalization. What distinguishes this research is its incorporation of a GOA–FMD and a Transformer-based attention mechanism for pattern recognition into a unified and efficient diagnostic framework. With its high effectiveness and adaptability, the proposed framework shows great promise for real-world applications in the smart fault monitoring of power systems. Full article

The ability to perform instantaneous frequency measurement (IFM) of unknown microwave signals holds significant importance across various application domains. This paper presents a power-independent microwave photonic IFM system. The proposed system implements frequency measurement through the construction of an amplitude comparison function (ACF) curve, achieved by introducing a frequency-dependent time delay via an optical tunable delay line (OTDL) for the signal under test (SUT). System simulation demonstrates the measurement capability across a wide bandwidth of 0.1–40 GHz with high precision, exhibiting frequency errors ranging from −0.03 to 0.04 GHz. The scheme also maintains consistent performance under varying input power levels. Key implementation aspects, including single-sideband modulation selection and system extension methods, are analyzed in detail to optimize measurement accuracy. Notably, the proposed architecture features a simple and compact design with excellent integration potential. These characteristics, combined with its wide operational bandwidth and high measurement precision, make this approach particularly suitable for demanding applications in electronic reconnaissance and communication. Full article

To achieve rapid detection of stainless steel passivation quality, a time-domain impedance method was investigated based on a potential step transient with a portable three-electrode probe. A comparison of the effects of signal analysis and transient parameters was conducted, and the results were compared with those obtained in a bulk solution with a general three-electrode system. The measured transient current with the probe offered a higher signal-to-noise ratio, with minimal deviation from the frequency-domain impedance observed at a step amplitude of 0–100 mV. Measurements using the three-electrode probe under different stabilization times indicated that, after 30 s of stabilization, the measurement deviation was less than 1%, enabling a rapid assessment. Comparative testing of surfaces with varying passivation quality revealed that the pitting potential increases with increasing time-domain impedance, demonstrating the method’s capability to distinguish passivated surfaces with different corrosion resistances. Full article

The present study implements novel innovative geostatistical imaging using precision agriculture (PA) under sugarbeet field conditions. Two driplines layout designs (d.l.d.) and soil water content (SWC)–irrigation treatments (A: d.l.d. = 1.00 m driplines spacing × 0.50 m emitters inline spacing; B: d.l.d. = 1.50 m driplines spacing × 0.50 m emitters inline spacing) were applied, with two subfactors of clay loam and clay soils (laboratory soil analysis) for modeling (evaluation of seven models) TDR multi-sensor network measurements. Different sensor calibration methods [method 1(M1) = according to factory; method 2 (M2) = according to Hook and Livingston] were applied for the geospatial two-dimensional (2D) imaging of accurate GIS maps of rootzone soil moisture profiles, soil apparent dielectric Ka profiles, and granular and hydraulic parameters profiles, in multiple soil layers (0–75 cm depth). The modeling results revealed that the best-fitted geostatistical model for soil apparent dielectric Ka was the Gaussian model, while spherical and exponential models were identified to be the most appropriate for kriging modelling, and spatial and temporal imaging was used for accurate profile SWC ${\theta v}_{TDR}$(m³·m⁻³) M1 and M2 maps using TDR sensors. The resulting PA profile map images depict the spatio-temporal soil water and apparent dielectric Ka variability at very high resolutions on a centimeter scale. The best geostatistical validation measures for the PA profile SWC ${\theta v}_{TDR}$ maps obtained were MPE = −0.00248 (m³·m⁻³), RMSE = 0.0395 (m³·m⁻³), MSPE = −0.0288, RMSSE = 2.5424, ASE = 0.0433, Nash–Sutcliffe model efficiency NSE = 0.6229, and MSDR = 0.9937. Based on the results, we recommend d.l.d. A and sensor calibration method 2 for the geospatial 2D imaging of PA GIS maps because these were found to be more accurate, with the lowest statistical and geostatistical errors, and the best validation measures for accurate profile SWC imaging were obtained for clay loam over clay soils. Visualizing sensors’ soil moisture results via geostatistical maps of rootzone profiles have practical implications that assist farmers and scientists in making informed, better and timely environmental irrigation engineering decisions, to save irrigation water, increase water use efficiency and crop production, optimize energy, reduce crop costs, and manage water resources sustainably. Full article

To address the challenge of physically realizing fractional-order electrical networks, this study proposes an implementation method for a mechatronic inerter–spring–damper (ISD) suspension based on a fractional-order biquadratic transfer function. Building upon a previously established model of a mechatronic ISD suspension, the influence of parameter perturbations on the suspension’s dynamic performance characteristics was systematically investigated. Positive real synthesis was employed to determine the optimal five-element passive network structure for the fractional-order biquadratic electrical network. Subsequently, the Oustaloup filter approximation algorithm was utilized to realize the integer-order equivalents of the fractional-order electrical components, and the approximation effectiveness was analyzed through frequency-domain and time-domain simulations. Bench testing validated the effectiveness of the proposed method: under random road excitation at 20 m/s, the root mean square (RMS) values of the vehicle body acceleration, suspension working space, and dynamic tire load were reduced by 7.86%, 17.45%, and 2.26%, respectively, in comparison with those of the traditional passive suspension. This research provides both theoretical foundations and practical engineering solutions for implementing fractional-order transfer functions in vehicle suspensions, establishing a novel technical pathway for comprehensively enhancing suspension performance. Full article

The sound speed profile (SSP) is an important feature in the field of ocean acoustics. The accurate estimation of SSP is significant for the development of underwater position, communication, and associated fundamental marine research. The Res-SACNN model is proposed for SSP inversion based on the convolutional neural network (CNN) embedded with the residual network and self-attention mechanism. It combines the spatiotemporal characteristics of sea level anomaly (SLA) and sea surface temperature anomaly (SSTA) data and establishes a nonlinear relationship between satellite remote sensing data and sound speed field by deep learning. The single empirical orthogonal function regression (sEOF-r) method is used in a comparative experiment to confirm the model’s performance in both the time domain and the region. Experimental results demonstrate that the proposed model outperforms sEOF-r regarding both spatiotemporal generalization ability and inversion accuracy. The average root mean square error (RMSE) is decreased by 0.92 m/s in the time-domain experiment in the South China Sea, and the inversion results for each month are more consistent. The optimization ratio hits 71.8% and the average RMSE decreases by 7.39 m/s in the six-region experiment. The Res-SACNN model not only shows more superior inversion ability in the comparison with other deep-learning models, but also achieves strong generalization and real-time performance while maintaining low complexity, providing an improved technical tool for SSP estimation and sound field perception. Full article

Background: Large language models (LLMs) like ChatGPT are increasingly being explored for medical applications. However, their reliability in providing medication advice for patients with complex clinical situations, particularly those with multiple comorbidities, remains uncertain and under-investigated. This study aimed to systematically evaluate the performance, consistency, and safety of ChatGPT in generating medication recommendations for complex cardiovascular disease (CVD) scenarios. Methods: In this simulation-based study (21 January–1 February 2024), ChatGPT 3.5 and 4.0 were prompted 10 times for each of 25 scenarios, representing five common CVDs paired with five major comorbidities. A panel of five cardiologists independently classified each unique drug recommendation as “high priority” or “low priority”. Key metrics included physician approval rates, the proportion of high-priority recommendations, response consistency (Jaccard similarity index), and error pattern analysis. Statistical comparisons were made using Z-tests, chi-square tests, and Wilcoxon Signed-Rank tests. Results: The overall physician approval rate for GPT-4 (86.90%) was modestly but significantly higher than that for GPT-3.5 (85.06%; p = 0.0476) based on aggregated data. However, a more rigorous paired-scenario analysis of high-priority recommendations revealed no statistically significant difference between the models (p = 0.407), indicating the advantage is not systematic. A chi-square test confirmed significant differences in error patterns (p < 0.001); notably, GPT-4 more frequently recommended contraindicated drugs in high-risk scenarios. Inter-model consistency was low (mean Jaccard index = 0.42), showing the models often provide different advice. Conclusions: While demonstrating high overall physician approval rates, current LLMs exhibit inconsistent performance and pose significant safety risks when providing medication advice for complex CVD cases. Their reliability does not yet meet the standards for autonomous clinical application. Future work must focus on leveraging real-world data for validation and developing domain-specific, fine-tuned models to enhance safety and accuracy. Until then, vigilant professional oversight is indispensable. Full article

The quantitative characterization of near-surface heterogeneity using ground-penetrating radar (GPR) is an important but challenging task. The estimation of subsurface geostatistical parameters from surface-based common-offset GPR reflection data has so far relied upon a Monte-Carlo-type inversion approach. This allows for a comprehensive exploration of the parameter space and provides some measure of uncertainty with regard to the inferred results. However, the associated computational costs are inherently high. To alleviate this problem, we present an alternative deep-learning-based technique, that, once trained in a supervised context, allows us to perform the same task in a highly efficient manner. The proposed approach uses a convolutional neural network (CNN), which is trained on a vast database of autocorrelations obtained from synthetic GPR images for a comprehensive range of stochastic subsurface models. An important aspect of the training process is that the synthetic GPR data are generated using a computationally efficient approximate solution of the underlying physical problem. This strategy effectively addresses the notorious challenge of insufficient training data, which frequently impedes the application of deep-learning-based methods in applied geophysics. Tests on a wide range of realistic synthetic GPR data generated using a finite-difference time-domain (FDTD) solution of Maxwell’s equations, as well as a comparison with the results of the traditional Monte Carlo approach on a pertinent field dataset, confirm the viability of the proposed method, even in the presence of significant levels of data noise. Our results also demonstrate that typical mismatches between the dominant frequencies of the analyzed and training data can be readily alleviated through simple spectral shifting. Full article

Icing on transmission lines in cold regions can cause asymmetry in the conductor cross-section. This asymmetry can lead to low-frequency, large-amplitude oscillations, posing a serious threat to the stability and safety of power transmission systems. In this study, the aerodynamic characteristics of crescent-shaped and sector-shaped iced eight-bundled conductors were systematically investigated over an angle of attack range from 0° to 180°. A combined approach involving wind tunnel tests and high-precision computational fluid dynamics (CFD) simulations was adopted. In the wind tunnel tests, static aerodynamic coefficients and dynamic time series data were obtained using a high-precision aerodynamic balance and a turbulence grid. In the CFD simulations, transient flow structures and vortex shedding mechanisms were analyzed based on the Reynolds-averaged Navier–Stokes (RANS) equations with the SST k-ω turbulence model. A comprehensive comparison between the two ice accretion geometries was conducted. The results revealed distinct aerodynamic instability mechanisms and frequency-domain characteristics. The analysis was supported by Fourier’s fourth-order harmonic decomposition and energy spectrum analysis. It was found that crescent-shaped ice, due to its streamlined leading edge, induced a dominant single vortex shedding. In this case, the first-order harmonic accounted for 67.7% of the total energy. In contrast, the prismatic shape of sector-shaped ice caused migration of the separation point and introduced broadband energy input. Stability thresholds were determined using the Den Hartog criterion. Sector-shaped iced conductors exhibited significant negative aerodynamic damping under ten distinct operating conditions. Compared to the crescent-shaped case, the instability risk range increased by 60%. The strong agreement between simulation and experimental results validated the reliability of the numerical approach. This study establishes a multiscale analytical framework for understanding galloping mechanisms of iced conductors. It also identifies early warning indicators in the frequency domain and provides essential guidance for the design of more effective anti-galloping control strategies in resilient power transmission systems. Full article

Introduction: Genetic plasticity and adaptive camouflage in critical pathogens have contributed to the global surge in multidrug-resistant (MDR) infections, posing a serious threat to public health and therapeutic efficacy. Antimicrobial resistance, now a leading cause of global mortality, demands urgent action through diagnostics, vaccines, and therapeutics. In India, the Indian Council of Medical Research’s surveillance network identifies Escherichia coli as a major cause of urinary tract infections, with increasing prevalence in human gut microbiomes, highlighting its significance across One Health domains. Methods: Whole-genome sequencing of E. coli strain ECG015, isolated from a human gut sample, was performed using the Illumina NextSeq platform. Results: Genomic analysis revealed multiple antibiotic resistance genes, virulence factors, and efflux pump components. Phylogenomic comparisons showed close relatedness to pathovars from both human and animal origins. Notably the genome encoded protein tyrosine kinases (Etk/Ptk and Wzc) and displayed variations in the envelope stress-responsive CpxAR two-component system. Promoter analysis identified putative CpxR-binding sites upstream of genes involved in resistance, efflux, protein kinases, and the MazEF toxin–antitoxin module, suggesting a potential regulatory role of CpxAR in stress response and persistence. Conclusions: This study presents a comprehensive genomic profile of E. coli ECG015, a gut-derived isolate exhibiting clinically significant resistance traits. For the first time, it implicates the CpxAR two-component system as a potential central regulator coordinating antimicrobial resistance, stress kinase signaling, and programmed cell death. These findings lay the groundwork for future functional studies aimed at targeting stress-response pathways as novel intervention strategies against antimicrobial resistance. Full article

In recent years, the continuous advancement of deep learning technology and its integration into the domain of low-light image enhancement have led to a steady improvement in enhancement effects. However, this progress has been accompanied by an increase in model complexity, imposing significant constraints on applications that demand high real-time performance. To address this challenge, inspired by the state-of-the-art Zero-DCE approach, we introduce a novel method that transforms the low-light image enhancement task into a curve estimation task tailored to each individual image, utilizing a lightweight shallow neural network. Specifically, we first design a novel curve formula based on Gamma correction, which we call the Gamma-based light-enhancement (GLE) curve. This curve enables outstanding performance in the enhancement task by directly mapping the input low-light image to the enhanced output at the pixel level, thereby eliminating the need for multiple iterative mappings as required in the Zero-DCE algorithm. As a result, our approach significantly improves inference speed. Additionally, we employ a lightweight network architecture to minimize computational complexity and introduce a novel global channel attention (GCA) module to enhance the nonlinear mapping capability of the neural network. The GCA module assigns distinct weights to each channel, allowing the network to focus more on critical features. Consequently, it enhances the effectiveness of low-light image enhancement while incurring a minimal computational cost. Finally, our method is trained using a set of zero-reference loss functions, akin to the Zero-DCE approach, without relying on paired or unpaired data. This ensures the practicality and applicability of our proposed method. The experimental results of both quantitative and qualitative comparisons demonstrate that, despite its lightweight design, the images enhanced using our method not only exhibit perceptual quality, authenticity, and contrast comparable to those of mainstream state-of-the-art (SOTA) methods but in some cases even surpass them. Furthermore, our model demonstrates very fast inference speed, making it suitable for real-time inference in resource-constrained or mobile environments, with broad application prospects. Full article

Selecting appropriate research methodologies in project management traditionally relies on individual expertise and intuition, leading to variability in study design and reproducibility challenges. To address this gap, we introduce a machine learning-driven recommendation system that objectively matches project management use cases to suitable research methods. Leveraging a curated dataset of 156 instances extracted from over 100 peer-reviewed articles, each example is annotated by one of five application domains (cost estimation, performance analysis, risk assessment, prediction, comparison) and one of seven methodology classes (e.g., regression analysis, time-series analysis, case study). We transformed textual descriptions into TF-IDF features and one-hot-encoded contextual domains, then trained and rigorously tuned three classifiers—random forest, support vector machine, and K-nearest neighbours—using stratified five-fold cross-validation. The random forest model achieved superior performance (93.8% ± 1.9% accuracy, macro-F1 = 0.93, ROC-AUC = 0.94), demonstrating robust generalisability across diverse scenarios, while SVM offered the highest precision on dominant classes. Our framework establishes a transparent, reproducible workflow—from literature extraction and annotation to model evaluation—and promises to standardise methodology selection, enhancing consistency and rigour in project management research design. Full article

Background: Patient satisfaction is a critical outcome in the rehabilitation of edentulous patients. While conventional fabrication methods are widely used, digital workflows are emerging as viable alternatives. However, direct comparative evidence from the patient’s perspective remains limited. Objective: To compare patient satisfaction between conventional complete dentures (C-CD) and digital complete dentures (D-CD) in maxillary edentulous patients, including changes in perceptions over time and final prosthesis preference. Methods: A prospective, randomized crossover clinical trial was conducted in 2023–2024 involving 40 completely maxillary edentulous patients meeting specific inclusion criteria. Participants were randomly allocated into two sequence groups: Group 1 (n = 20) received C-CD first, and Group 2 (n = 20) received D-CD first, each for 6 months (T1), followed by crossover to the alternate denture for another 6 months (T2). Patient satisfaction was measured using a 10-item questionnaire at 6 and 12 months. Statistical analysis: Wilcoxon signed-rank tests were used for within-subject comparisons of denture types, and Mann–Whitney U tests for between-group comparisons, with significance set at p ≤ 0.05. Results: Using the paired crossover analysis, D-CD showed significantly better comfort than C-CD (p < 0.05). D-CD scored significantly higher than C-CD in most satisfaction domains, including comfort, retention, speech, esthetics, and need for adjustments (p ≤ 0.05). Median scores for retention, speech, esthetics, and other domains were slightly higher with D-CD but did not reach statistical significance (p > 0.05). Additionally, the D-CD required fewer post-insertion adjustment visits than the C-CD (p < 0.05). By the end of the trial, 28 patients (70%) preferred the digital denture as their final prosthesis, whereas 12 patients (30%) preferred the conventional denture. Conclusions: Incorporating digital technology in the fabrication of complete dentures significantly enhances patient satisfaction compared to conventional methods. This study highlights the clinical relevance of modern dental prosthesis technology and supports the wider integration of digital workflows. Within the limitations of this pilot study, digitally fabricated complete dentures provided overall patient satisfaction comparable to conventional dentures, with the D-CD offering a notable improvement in comfort. The majority of patients ultimately favored the digital denture, supporting the clinical viability of CAD/CAM workflows. Full article