Search Results (179,428)

In this work, a lightweight framework enabled by the modified YOLOv11s-seg model for tea shoot detection and plucking point localization is proposed. Detecting tea shoots and localizing plucking points with higher accuracy generally require larger model size and more model parameters, making it difficult to balance accuracy and lightweighting. To overcome this limitation, a modified lightweight YOLOv11s-seg model is developed. First, the multi-scale edge information enhancement is introduced into the conventional YOLOv11s-seg to extract edge feature better and improve the detection accuracy of tea shoots. Meanwhile, context anchor attention is utilized to modify the cross stage partial spatial attention module in a backbone network to improve the detection capability for small objects. Moreover, the detail calibration reconstruction feature pyramid network is proposed. It utilizes spatial and contextual semantic information to reconstruct and calibrate features in key regions, enhancing the capability for object fusion and recognition at various scales. Furthermore, with the modified model performing instance segmentation to acquire the contour of each tea shoot, the coordinates of the three lowest pixel points in the contour are captured to localize the plucking point based on the average coordinates. In addition, the layer-adaptive magnitude-based pruning (LAMP) method is used to lighten the model. The experimental results show that the LAMP-pruned modified YOLOv11s-seg model with a speedup ratio of 1.5 achieves a mAP@0.5 of 86.5% for tea shoot detection, exhibiting a 4.7 percentage point improvement over the conventional YOLOv11s-seg model. Moreover, it exhibits an accuracy of 81.9% for plucking point localization on the validation and test subsets with 232 images in total, and its number of parameters, model size and floating point operations (FLOPs) separately achieve reductions of 67.3%, 66.2%, and 24.9% over the conventional model as well. Therefore, the proposed LAMP-pruned modified model shows good balance between lightweighting and detection accuracy. Finally, the modified LAMP-pruned YOLOv11s-seg model is deployed on a Jetson Orin NX edge module and measured in a tea plantation, with the measured results exhibiting a detection speed of 34.1 FPS and verifying its availability in practical applications. Full article

The large-scale integration of distributed photovoltaics (DPVs) into the distribution network exacerbates voltage fluctuations and substantially increases network losses. To improve the voltage quality and economic efficiency of distribution networks, a Volt/Var optimization (VVO) model is established. Coordinating multiple heterogeneous devices, the model aims to minimize the total voltage deviation, the total network losses, and the regulation cost of discrete equipment simultaneously. Considering multi-constraint coupling characteristics, a quantitative method is proposed to evaluate the reactive power regulation potential of DPVs under intricate operating conditions. Then, the multi-strategy integrated rime optimization algorithm (MSIRIME) is utilized for the model solution. Fuch chaotic mapping generates uniformly distributed and ergodic initial populations. A dual-branch search mechanism combining the snow ablation optimizer with the rime optimization significantly enhances global exploration capabilities. The guided learning strategy balances exploration and exploitation for high-dimensional VVO, preventing local optima. Case tests on a modified IEEE 33-bus system demonstrate that the proposed model exhibits excellent effectiveness and robustness. Moreover, MSIRIME exhibits better optimization performance than some classic and recently proposed strategies, reducing the average network losses and voltage deviation over 30 independent runs by at least 5.87% and 52.22%, respectively, relative to those of the compared methods. Full article

Real-time scheduling in large-scale electric vehicle charging stations is challenged by stochastic vehicle arrivals, dynamic departures, limited charging resources, and station-level power constraints. To address this problem, this paper proposes a two-stage Supervised Service Matching and Reinforcement Power Dispatch (SMPD) framework, termed SMPD, which decomposes the original coupled scheduling problem into supervised service matching and reinforcement learning-based power dispatch. In the first stage, a supervised matching network learns EV-charger service suitability from historical charging-session records and determines service access decisions for feasible EV–charger pairs. In the second stage, a Soft Actor-Critic-based controller allocates continuous charging power to connected EVs under EV-side charging limits, charger capacity constraints, and the station-level total power constraint. The proposed framework is evaluated using public charging-session data from the ElaadNL dataset. Experimental results show that SMPD achieves lower average waiting time, higher average revenue, lower composite penalty, and comparable demand satisfaction compared with rule-based, single-stage reinforcement learning, and multi-agent baselines. Sensitivity and robustness analyses further indicate that SMPD maintains favorable scheduling performance and acceptable online decision time under the tested charger-scale settings and operational disturbance scenarios. These results suggest that the proposed two-stage design provides an effective and computationally tractable approach for real-time scheduling in large-scale EV charging stations. Full article

Background/Objectives: This investigation was performed because corticosteroid injections are commonly used for symptomatic relief in patients with meniscal deficiency, yet their effect on graft survivorship and postoperative outcomes following meniscal allograft transplantation (MAT) remains poorly understood, with limited literature specifically addressing this topic. The aim of this study is to evaluate whether preoperative intra-articular corticosteroid injections (ICS) are associated with reoperation after MAT. Secondary aims included comparing reoperation-free survival, patient-reported outcome measures (PROMs), and patient acceptable symptom state (PASS) achievement. Methods: A retrospective review of 130 adults undergoing meniscal allograft transplantation (MAT) between 2011 and 2023 was performed. Patients with documented corticosteroid injection (CSI) status and ≥2 years of follow-up were included. Exclusion criteria included prior meniscal allograft transplantation, receipt of non-corticosteroid injections (e.g., hyaluronic acid or platelet-rich plasma), concomitant osteotomy procedures, multi-ligament knee reconstruction or inadequate follow-up. Propensity score matching (2:1 no steroid: steroid) based on age, sex, body mass index, fixation technique, operative compartment, and concomitant procedures yielded 54 matched patients (35 no steroid, 19 steroid). The primary outcome was ipsilateral knee reoperation, categorized as major reoperation (revision MAT, anterior cruciate ligament reconstruction, osteochondral allograft transplantation, conversion to total knee arthroplasty, meniscectomy and meniscus repair). Minor reoperations included irrigation and debridement, lysis of adhesions or manipulation under anesthesia, hardware removal, chondroplasty, and synovectomy. Reoperation-free survival was assessed using Kaplan–Meier analysis. PROMs and PASS were compared using adjusted regression models. Statistical significance was set at p < 0.05. Results: Baseline characteristics and follow-up were comparable between groups (7.6 ± 3.5 vs. 6.6 ± 3.2 years; p = 0.30). Overall reoperation occurred in 37.1% of patients in the no-steroid group and 31.6% in the steroid group (p = 0.771). Major reoperation rates were similar (17.1% vs. 15.8%; p = 1.000. There was no significant difference in minor reoperations between groups (20.0% vs. 10.5%; p = 0.468). Kaplan–Meier analysis demonstrated no difference in reoperation-free survival (p = 0.903), with comparable survival at the 1-, 2-, and 5-year time points. No individual subtypes differed significantly between groups. PROMs and PASS achievement were also similar, with no statistically significant differences observed. Conclusions: Preoperative corticosteroid injection was not associated with increased reoperation risk, inferior reoperation-free survival, or worse patient-reported outcomes following meniscal allograft transplantation. However, given the study’s limited power, lack of detailed injection characteristics, and the use of a heterogeneous complication outcome, these findings should be interpreted cautiously, as further investigation is warranted. Full article

In the context of an increasingly complex and dynamic digital ecosystem, accurately assessing the value of Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises is crucial for making effective decisions. Traditional valuation methods struggle to effectively address issues such as the high R&D expenditures and significant operational risks associated with these enterprises. This study proposes an interpretable intelligent decision-support framework for valuing SRDI enterprises listed on the Beijing Stock Exchange (BSE), constructing a multidimensional indicator system that encompasses solvency, profitability, and R&D capabilities. Feature importance screening using the XGBoost algorithm was conducted to identify key indicators as input variables for a backpropagation (BP) neural network. Concurrently, the Particle Swarm Optimization (PSO) algorithm was applied to the neural network to optimize initial weights and thresholds, thereby modeling nonlinear valuation relationships. Empirical analysis of 770 SRDI firms listed on the Beijing Stock Exchange from 2020 to 2024 indicates that the XGBoost-PSO-BPNN model achieved a coefficient of determination of 0.8083 on the test set, outperforming traditional linear models and benchmark models such as single-tree models. SHAP explainability analysis further reveals that current asset turnover, return on assets, and equity concentration are the primary value drivers. This study employs various clustering methods to further classify enterprises into three categories and proposes recommendations for differentiated regulatory policies, providing intelligent decision support for enterprises operating within complex digital ecosystems. Full article

Under weak-grid conditions, grid-following (GFL) control of doubly fed induction generators (DFIGs) suffers from reduced stability margins, deteriorated dynamic performance, and intensified oscillations near the stability boundary. To address these issues, a pre-synchronized switching strategy between GFL and grid-forming (GFM) modes, triggered by locally measured operating variables, is proposed. Based on the GFL control model, the evolution of system dynamics with decreasing short-circuit ratio is analyzed, thereby elucidating how reduced grid strength progressively weakens robustness and disturbance rejection and eventually leads to instability. To characterize this deterioration, a set of normalized indices is constructed to quantify the oscillation levels of active power, phase-locked loop frequency, and point of common coupling voltage, enabling reliable identification of control-performance deterioration. A pre-synchronization scheme based on a virtual power closed loop is then developed, allowing the target mode to converge to the current operating point prior to takeover and enabling smooth bidirectional switching between GFL and GFM modes. Hardware-in-the-loop results demonstrate that the proposed strategy accurately detects GFL performance deterioration and effectively suppresses boundary oscillations while mitigating switching transients, thereby enhancing the adaptability of DFIGs to variations in grid strength. Full article

Microplastics (MPs) are increasingly detected in dairy products, raising food-safety concerns. Their behavior in complex food matrices and interactions with probiotic microorganisms remain poorly understood. This exploratory study evaluated storage-dependent changes in operationally defined, digestion-resistant recoverable residues in yogurt containing Bifidobacterium longum subsp. infantis (ATCC 15697). Yogurt samples were prepared with polypropylene (PP), polyethylene (PE), and polystyrene (PS), individually and in combination, and analyzed over 21 days of refrigerated storage. Gravimetric values served as relative, operational indicators of recoverable residues—not validated absolute polymer masses—while polymer identity was qualitatively confirmed by pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS). B. longum subsp. infantis remained viable throughout storage (6.3–8.2 log₁₀ CFU/g). All MP-containing groups showed consistent storage-associated decreases in recoverable residue fractions, greatest in PP, followed by PE and PS; probiotic-free controls remained stable. Polymer-specific Py-GC/MS signals were detectable at all time points. Because polymer identity was retained and the workflow was not validated for absolute recovery, findings are interpreted as storage-associated changes in extractability, filterability, and/or residue recovery—not as polymer degradation, mineralization, or biological removal. These in vitro observations are limited to the yogurt matrix and do not support extrapolation to livestock exposure, human dietary risk, or farm-to-fork transfer. Within these limits, the findings provide a preliminary, hypothesis-generating perspective on probiotic–microplastic interactions in fermented dairy products. Full article

The subcellular localization of long noncoding RNAs (lncRNAs) is a central determinant of their function, yet its molecular determinants remain incompletely defined, and most existing predictors rely on the primary sequence. Because RNA-binding proteins (RBPs) are the proximal effectors of RNA compartmentalization, this study tested whether the composition of RBPs bound to a lncRNA is predictive of its nuclear or cytoplasmic localization. Enhanced crosslinking and immunoprecipitation (eCLIP) occupancy for 139 RBPs in K562 cells was integrated with the cytoplasmic–nuclear relative concentration indices (CN-RCIs) derived from matched subcellular fractionation, and localization was modeled under chromosome-grouped cross-validation with nested regularization. RBP-occupancy composition predicted localization beyond the transcript size and total binding amount (incremental cross-validated coefficient of determination, delta-R-squared = 0.17; receiver-operating-characteristic area under the curve, AUC = 0.73, a moderate-strength association; Freedman–Lane permutation, p = 0.005). This increment persisted (delta-R-squared = 0.12; p = 0.005) against an expanded baseline that additionally absorbed the transcript abundance, intron content and exon number, indicating predictive information that is not reducible to these transcript features, and the classifier was well calibrated (Brier score = 0.10; expected calibration error = 0.02). The signed coefficient profile separated RBP function systematically: factors acting in nuclear processes (splicing, 3′-end processing, and nuclear-matrix association) carried negative, nuclear-direction weights, whereas factors acting in cytoplasmic processes (translation and messenger RNA stability) carried positive, cytoplasmic-direction weights (Mann–Whitney p = 0.013). The profile generalized across cell lines: a K562-trained model predicted HepG2 localization (transfer AUC = 0.71 using 76 shared RBPs), and HepG2 reproduced the association independently (AUC = 0.77). The association is correlational and of moderate strength; it is presented as an interpretable, RBP-occupancy-based complement to sequence-based predictors of lncRNA localization. Full article

Operational labor shortages have become a pressing challenge for hospitality organizations, especially in highly seasonal tourism destinations such as Greece, where service experiences are deeply tied to cultural identity and authentic hospitality. While much of the existing research has examined understaffing from operational or human resource management perspectives, limited attention has been paid to its impact on the organizational capacity to sustain authentic hospitality experiences. Using Service-Dominant Logic (SDL) as an interpretive framework, this study views authentic hospitality as an organizational process shaped by employee interaction, cultural transmission, and service delivery practices. Drawing on survey data from 201 hotel employees in Greece, it investigates the relationship between operational labor shortages, organizational pressures, and perceived threats to authentic hospitality within hotel operations. The findings reveal significant positive relationships between work stress and service quality decline, as well as between cultural knowledge and perceived challenges in maintaining authentic hospitality. Multiple regression analysis further shows that reactive hiring, serious understaffing, and payroll cost pressure are significantly linked to perceived challenges in sustaining authentic hospitality, while service quality decline exhibits a positive but statistically non-significant effect in the final model. The study contributes to hospitality authenticity literature by emphasizing employee perceptions of authenticity as an organizationally supported process rather than merely a guest-centered outcome. The results also highlight the importance of workforce planning, recruitment quality, and cultural onboarding in supporting authentic hospitality within Greek hotel operations. Full article

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

Florida mosquito control districts are increasingly confronted with severe Culicoides biting midge problems in coastal areas. Yet, there is no clear guidance for integrating Culicoides management into mosquito-focused operations. This study describes population abundance and distribution trends for the biting midge Culicoides furens on a residential island in Cedar Key, Florida. We use multi-year adult trapping data to help develop strategies that may be used by mosquito control districts to target C. furens populations where they are nuisance pests. Trap data from 2005 and 2007 identified seasonal peaks, high spatial heterogeneity, and substantial year-to-year variation, with an 88.3% reduction in trap captures between 2005 and 2007. These findings provide a foundation for integrated Culicoides management strategies where legal mandates, emerging pathogen risks, and taxpayer-driven nuisance complaints may justify expanded Culicoides control activities by Florida’s Mosquito Control Districts. Full article

Precise control of sinter reducibility is essential for stable blast furnace operation. Each mineral phase present in sinter, such as hematite, magnetite and calcium ferrite exhibits different reducibility. In XRD analysis, the requirement for sample pulverization leads to the loss of mineralogical texture information. This makes it difficult to quantitatively correlate the complex mineral phases present in the sinter with reducibility. This study introduces a novel quantitative approach using hyperspectral imaging to distinguish specific mineral morphologies. Reduction experiments simulating blast furnace thermal and gas conditions were conducted on several sinters. Multiple regression analysis was applied to correlate mineral fractions and macroporosity with reduction rates across three distinct reduction stages. In the low-temperature stage, hematite, macroporosity and acicular calcium ferrites were identified as the primary drivers of reduction. In the intermediate stage, acicular calcium ferrites continued to enhance reactivity, whereas coarse calcium ferrite showed a significant negative influence. In the high-temperature stage, macroporosity strongly promoted reduction, while coarse calcium ferrite and magnetite hindered it due to the formation of shell-like metallic iron structures which impede gas diffusion. These findings demonstrate that hyperspectral imaging combined with multi-stage regression analysis offers a useful tool for designing optimal sinter mineralogy for blast furnace performance. Full article

Flavor esters are valuable compounds widely used in the food, beverage, and cosmetics industries for their aroma and flavor-enhancing properties. Traditional methods of obtaining these compounds, such as extraction from natural sources or chemical synthesis, present challenges related to cost and toxicity, respectively. Enzymatic synthesis, particularly using immobilized lipases, offers a sustainable and efficient alternative. This study investigates the application of CRL immobilized on Diaion HP-20 for geranyl butyrate synthesis via esterification of geraniol and butanoic acid using Candida rugosa lipase (CRL) immobilized on Diaion HP-20 (CRL-DHP-20). The immobilization process resulted in a protein loading of 29.6 ± 2.2 mg/g support from an initial 40 mg/g, and the immobilized biocatalyst exhibited a hydrolytic activity of 124.0 ± 2.5 U/g using olive oil emulsion. Reaction conditions were optimized through a central composite design, evaluating the influence of biocatalyst concentration, temperature, and agitation on ester conversion. The optimal conditions (13.4% CRL-DHP-20, 48.2 °C, and 220.1 rpm) led to 85.4% conversion in 360 min. Additionally, CRL-DHP-20 retained 84% of its initial activity after six reaction cycles, indicating good operational stability. These findings highlight the potential of CRL-DHP-20 as an effective and reusable biocatalyst for green synthesis of flavor esters. Full article

Small unmanned aerial vehicles are difficult short-range radar targets because their millimeter-wave radar cross-sections often fall between −10 and −25 dBsm. This paper presents a modular analytical and simulation-based benchmark of a cascaded 77 GHz TDM-MIMO FMCW radar with 12 transmitters and 16 receivers, yielding a 192-element virtual ULA over a 40 m instrumented range. The framework is organized around the main counter-UAV sensing functions: range–Doppler processing first evaluates target observability and provides range–Doppler gates; Doppler-dependent TDM phase compensation is then required before virtual-array snapshots are formed for DoA estimation; and a separate long-dwell single-transmitter branch evaluates micro-Doppler separability using handcrafted features and a nearest-centroid Mahalanobis classifier. Four benchmarks are considered: detection under Swerling fluctuation models, residual TDM phase error caused by Doppler quantization, DoA estimation under an idealized far-field snapshot model, and micro-Doppler separability among UAV and bird classes. Under Swerling I, targets with a mean RCS of $-10$ dBsm or larger maintain detection probability above 0.9 throughout the 40 m window, whereas the $-20$ and $-25$ dBsm classes fall below that level at about 28 m and 21 m. In the far-field DoA benchmark, TLS-ESPRIT gives the lowest conditional RMSE and remains about 13–14 dB above the subarray CRLB at moderate SNR; however, these angular results are reference ceilings because the short-range operating region violates the full-aperture far-field condition and because residual TDM phase error can be severe without accurate compensation. In the micro-Doppler benchmark, birds exceed 95% per-class accuracy at 20 dB total SNR, but overall four-class accuracy saturates near 72–75% and UAV-only three-class accuracy near 63%, with most confusion between the micro-quadrotor and fixed-wing classes. This study therefore identifies architecture-specific performance margins and limitations before measured-data field validation, rather than claiming complete deployment-level performance. Full article

Full-duplex acoustic telemetry is important for real-time bidirectional measurement and control in intelligent coiled-tubing operations, but its reliability in deep horizontal wells is limited by long-range dispersion, asymmetric flow-induced noise, and severe near-end self-interference. This study proposes an orthogonal frequency-band planning and synergistic interference suppression method for full-duplex acoustic communication in coiled tubing. A dispersion model and an asymmetric attenuation model were first established for a fluid-filled coiled-tubing cylindrical-shell waveguide to characterize the physical transmission constraints. A multiphysics multi-objective cost function was then formulated by considering dispersion flatness, channel attenuation, asymmetric noise adaptability, and spectral isolation, and an improved simulated annealing algorithm was used to optimize the uplink and downlink frequency bands. In addition, a three-stage suppression architecture integrating mechanical decoupling, physical-layer frequency isolation, and CEEMDAN–wavelet denoising was developed to reduce self-interference and residual nonstationary noise. Full-scale experiments on a 457.2 m coiled-tubing surface circulation system showed that the proposed method improved the output signal-to-interference-plus-noise ratio from −15 dB to 18.5 dB and maintained a bit error rate below 1.2 × 10⁻⁴ at 400 L/min. These results indicate that the proposed approach can enhance the robustness of full-duplex acoustic telemetry under strong flow-induced noise. Full article