Search Results (5,140)

Urinary tract infections (UTIs) are among the most common pathologies, with a high incidence in women and hospitalized patients. Their diagnosis is based on the presence of clinical symptoms and signs in addition to the detection of microorganisms in urine trough urine cultures, a time-consuming and resource-intensive test. The goal was to optimize UTI detection through artificial intelligence (machine learning) using non-microbiological laboratory parameters, thereby reducing unnecessary cultures and expediting diagnosis. A total of 4283 urine cultures from patients with suspected UTIs were analyzed in the Microbiology Laboratory of the University Hospital Virgen de las Nieves (Granada, Spain) between 2016 and 2020. Various machine learning algorithms were applied to predict positive urine cultures and the type of isolated microorganism. Random Forest demonstrated the best performance, achieving an accuracy (percentage of correct positive and negative classifications) of 82.2% and an area under the ROC curve of 87.1%. Moreover, the Tree algorithm successfully predicted the presence of Gram-negative bacilli in urine cultures with an accuracy of 79.0%. Among the most relevant predictive variables were the presence of leukocytes and nitrites in the urine dipstick test, along with elevated white cells count, monocyte count, lymphocyte percentage in blood and creatinine levels. The integration of AI algorithms and non-microbiological parameters within the diagnostic and management pathways of UTI holds considerable promise. However, further validation with clinical data is required for integration into hospital practice. Full article

Synthetic inertia (SI) enables wind turbine generators (WTGs) to support frequency stability by releasing stored kinetic energy during disturbances. Existing grid-code requirements, such as those of Hydro-Québec TransÉnergie (HQT) and ENTSO-E/Nord Pool, improve the first frequency nadir but often aggravate a second frequency dip (SFD) or risk rotor over-deceleration (OD) when the boost magnitude is large. This paper proposes an enhanced SI requirement that retains the stepwise boost-and-hold structure but replaces the time-based ramp-down with a rotor-speed-dependent recovery, followed by a smooth transition back to maximum power point tracking (MPPT). The proposed scheme was validated using an electromagnetic transient model of the Unison U151 wind turbine (4.569 MW, inertia constant 9.68 s), designed for Korea’s low-wind conditions. Five case studies at wind speeds of 5 and 7 m/s with varying boost levels confirmed that all methods yield identical first nadirs for a given boost, but only the proposed approach consistently maintained a higher second nadir, stabilized rotor dynamics, and prevented repeated dips. These results demonstrate that rotor-speed-dependent SI requirements, when combined with high-inertia turbines, can enhance frequency stability while protecting turbine operation, offering practical guidance for future grid-code revisions. Full article

This study proposes a Personalized Deep Learning-Based Sleep Recommendation System Using Lifelog Data (PDSRS-LD). Traditional sleep research primarily relies on bio signals such as EEG and ECG recorded during sleep but often fails to sufficiently reflect the influence of daily activities on sleep quality. To address this limitation, we collect lifelog data such as stress levels, fatigue, and sleep satisfaction via wearable devices and use them to construct individual user profiles. Subsequently, real sleep data obtained from an AI-powered motion bed are incorporated for secondary training to enhance recommendation performance. PDSRS-LD considers comprehensive user data, including gender, age, and physical activity, to analyze the relationships among sleep quality, stress, and fatigue. Based on this analysis, the system provides personalized sleep improvement strategies. Experimental results demonstrate that the proposed system outperforms existing models in terms of F1 score and Average Precision (mAP). These results suggest that PDSRS-LD is effective for real-time, user-centric sleep management and holds significant potential for integration into future smart healthcare systems. Full article

Background/Objectives: Malnutrition, systemic inflammation, and immune dysfunction are key determinants of adverse outcomes in older adults following acute illness. Composite biomarkers integrating these domains could enhance early risk stratification. This study investigates, for the first time in acute geriatric care, the prognostic value of the C-reactive protein–albumin–lymphocyte (CALLY) index—a composite marker of nutritional, inflammatory, and immune status—in predicting short-term survival. Methods: We retrospectively analyzed 264 patients admitted to the acute geriatrics ward of Santa Maria della Misericordia Hospital in Perugia. The CALLY index was calculated as: (Albumin × Lymphocytes)/(CRP × 10⁴). The optimal prognostic cut-off was determined using receiver operating characteristic (ROC) curve analysis. Three-month survival was assessed by Kaplan–Meier analysis. Results: The cohort included 167 women (63.3%) and 97 men (36.7%), with a mean age of 88.0 ± 6.4 years. At 3-month follow-up, 80 patients (30.3%) had died. The CALLY index showed an area under the ROC curve of 0.647 (95% CI: 0.576–0.718; p < 0.001), with a cut-off of 0.055 (sensitivity: 68.5%, specificity: 46.3%). Among deceased patients, 42.5% had a CALLY index <0.055. After multivariable adjustment, a lower CALLY index remained independently associated with increased mortality (B = −0.805; OR = 0.45; 95% CI: 0.215–0.930; p = 0.031). Kaplan–Meier analysis demonstrated significantly higher survival in patients with a CALLY index ≥ 0.055 (Log-rank test: 13.71; p < 0.001). Conclusions: The CALLY index shows a modest but statistically significant discriminative ability for predicting short-term mortality in acutely ill older adults. As a simple, low-cost marker derived from routine laboratory tests, it holds potential for integration into clinical workflows to guide nutritional, metabolic, and prognostic management strategies in geriatric acute care. Full article

In this presentation, we have identified the domain of equivalence amongst the Boltzmann, Gibbs, and thermodynamic entropies. In this domain, ergodicity is followed even for (i) all nonequilibrium steady states and (ii) those time-dependent nonequilibrium states belonging to it. The condition of this domain is either that the rate of entropy change is zero or its magnitude is exceedingly small. Its implication is that, in this domain, Jaynes’ principle of maximum entropy estimate also holds. Outside this domain, the said equivalence among three entropies is not feasible, and the operation of the Jaynes’ principle of maximum entropy estimate does not remain of practical utility. Full article

Exploring materials for the uranium extraction from seawater holds great significance for the sustainable development of the nuclear industry. Though many adsorbents have been investigated to extract uranium, they still suffer from the issues of low adsorption performance and high production cost. In this work, biomass nanofiber adsorbents (PA-CS NFs) were prepared by the electrospinning of chitosan followed by functionalization with phytic acid. Based on the cost analysis, the preparation expense of PA-CS NFs was $16.4 kg⁻¹, lower than those of common synthetic polymer adsorbents. In addition, PA-CS NFs showed fast removal kinetics (equilibrium time = 60 min), high uptake capacity (457.8 mg g⁻¹), and good selectivity (the ratio of uranium/competing ion capacities > 3.8) from uranium spiked solution. PA-CS NFs also exhibited the ability to remove trace uranyl ions (distribution coefficient = 4.7 × 10⁵ mL g⁻¹) and satisfy recycling capacity. The experimental tests and theoretical calculations confirmed that the phosphate groups in the functionalized phytic acid displayed the main contribution to the uranyl ion adsorption, which had higher binding energy than the functional groups in chitosan. Benefiting from the good adsorption ability, low cost, and macroscopical membrane form, PA-CS NFs were applied to natural seawater for uranium extraction, and an extraction capacity of 4.52 mg g⁻¹ could be achieved after 35 days’ testing. On account of the obtained results, this study offers an efficient and low-cost nanofiber adsorbent for uranium extraction. Full article

Glazed components in ancient Chinese architecture hold profound historical and cultural value. However, over time, environmental erosion, physical impacts, and human disturbances gradually lead to various forms of damage, severely impacting the durability and stability of the buildings. Therefore, preventive protection of glazed components is crucial. The key to preventive protection lies in the early detection and repair of damage, thereby extending the component’s service life and preventing significant structural damage. To address this challenge, this study proposes a Restoration-Scale Identification (RSI) method that integrates depth information. By combining RGB-D images acquired from a depth camera with intrinsic camera parameters, and embedding a Convolutional Block Attention Module (CBAM) into the backbone network, the method dynamically enhances critical feature regions. It then employs a scale restoration strategy to accurately identify damage areas and recover the physical dimensions of glazed components from a global perspective. In addition, we constructed a dedicated semantic segmentation dataset for glazed tile damage, focusing on cracks and spalling. Both qualitative and quantitative evaluation results demonstrate that, compared with various high-performance semantic segmentation methods, our approach significantly improves the accuracy and robustness of damage detection in glazed components. The achieved accuracy deviates by only ±10 mm from high-precision laser scanning, a level of precision that is essential for reliably identifying and assessing subtle damages in complex glazed architectural elements. By integrating depth information, real scale information can be effectively obtained during the intelligent recognition process, thereby efficiently and accurately identifying the type of damage and size information of glazed components, and realizing the conversion from two-dimensional (2D) pixel coordinates to local three-dimensional (3D) coordinates, providing a scientific basis for the protection and restoration of ancient buildings, and ensuring the long-term stability of cultural heritage and the inheritance of historical value. Full article

To address the safety challenges for personnel in the complex and hazardous environments of offshore drilling platforms, this paper introduces the Platform Safety Detection YOLO (PSD-YOLO), an enhanced, real-time object detection framework based on YOLOv10s. The framework integrates several key innovations to improve detection robustness: first, the Channel Attention-Aware (CAA) mechanism is incorporated into the backbone network to effectively suppress complex background noise interference; second, a novel C2fCIB_Conv2Former module is designed in the neck to strengthen multi-scale feature fusion for small and occluded targets; finally, the Soft-NMS algorithm is employed in place of traditional NMS to significantly reduce missed detections in dense scenes. Experimental results on a custom offshore platform personnel dataset show that PSD-YOLO achieves a mean Average Precision (mAP@0.5) of 82.5% at an inference speed of 232.56 FPS. The efficient and accurate detection framework proposed in this study provides reliable technical support for automated safety monitoring systems, holds significant practical implications for reducing accident rates and safeguarding personnel by enabling real-time warnings of hazardous situations, fills a critical gap in intelligent sensor monitoring for offshore platforms and makes a significant contribution to advancing their safety monitoring systems. Full article

The dynamic and time-sensitive nature of online food delivery, along with real-world factors like sudden changes in order volumes and the availability of couriers, distinguishes it from traditional vehicle routing scenarios. Apart from the many studies in the literature that handle this problem from specific angles, our solution proposes a new approach that provides real-time routing with the awareness of the expectations of multiple stakeholders in the ecosystem. For this purpose, we develop a Mixed Integer Programming (MIP) model that minimizes unmet demand and workforce requirements simultaneously to meet platform and courier expectations while maintaining the timeliness of the operation to meet restaurant and customer expectations. Since the model requires more time to provide good results for even small-size problems, we develop a multi-step algorithmic approach supported by strategies that hold or dissolve a part of the solutions to create opportunities for better results. A framework for agent-based simulation was created to implement the strategies and the algorithmic steps, accurately mimicking the operations and movements of couriers. The effectiveness of this solution was evaluated through experiments based on a real-world case study. The results indicate that our solution can generate high-quality results in a short time across various configurations, which are defined by different demand and supply patterns and varying problem sizes. Full article

Chicken meat is highly consumed worldwide due to its nutritional value, but its high water content and abundance of polyunsaturated fatty acids make it particularly vulnerable to structural and oxidative damage during freezing and thawing. Slow freezing, in particular, generates large ice crystals that severely impair water-holding capacity (WHC), increase drip loss, promote color deterioration, and intensify protein and lipid oxidation. Innovative thawing strategies are therefore required to mitigate these quality losses. Ultrasound (US) has been successfully applied to accelerate thawing of fast-frozen meat; however, its potential for slowly frozen chicken breast remains poorly understood. This study aimed to evaluate the effects of US-assisted thawing at two frequencies (25 and 130 kHz), two amplitudes (100% and 60%), and three operating modes (normal, sweep, and degas) on the quality of slowly frozen chicken breast. Conventional thawing required 50 min, yielding WHC of 9.87%, drip loss of 4.65%, free sulfhydryls of 16.38 µmol/g, and ∆E of 3.91. In contrast, the optimized US condition (25 kHz, 100% amplitude, sweep mode) thawed samples in only 18 min, with markedly improved WHC (23.14%), reduced drip loss (3.25%), higher preservation of free sulfhydryls (24.69 µmol/g), and minimal color change (∆E = 3.72). Conversely, less effective parameters (e.g., 130 kHz, 60% amplitude, normal mode) prolonged thawing and compromised quality, with WHC dropping to 9.96% and drip loss increasing to 9.05%. Overall, US reduced thawing time under all conditions, but quality responses depended strongly on the applied parameters. The present findings demonstrate the novelty of optimizing US frequency, amplitude, and mode for thawing slowly frozen chicken breast, highlighting sweep mode at 25 kHz and 100% amplitude as the most effective strategy. Future research should explore its scalability and industrial applicability for poultry processing. Full article

Using the “Policy Pressure-Innovation Alignment-Performance Transformation” theory, this paper looks at how ESG ratings, green innovation, and corporate dual-organizational performance are linked. This study uses a multi-period Difference-in-Differences (DID) model in conjunction with a conditional mediation effect model to examine how ESG ratings causally influence substantive green innovation, which in turn improves corporate financial and environmental performance. Regression results show that corporate ESG ratings have a big effect on the performance of both organizations. ESG ratings have a bigger effect on financial performance, while ESG scores have a bigger effect on environmental performance. Looking at the sub-dimensions shows that policy ratings have immediate effects on environmental performance and delayed effects on financial performance. The conclusion that the internalization response of corporate environmental costs is timely, while the market revaluation has a delayed transmission effect, holds true after being tested through parallel trend analysis and synthetic DID testing. More research shows that differences in ESG ratings hurt financial performance but help environmental performance. This means that differences in ESG ratings may lead to more real green innovation activities, which have a direct effect on the environment and, in the end, lead to bigger improvements in environmental performance. The moderating effect test shows that being aware of the environment makes substantive green innovation more focused on quality by making people feel responsible for their actions. Also, environmental management leads to more corporate green patents, which has resource displacement effects and makes green patent innovations less effective. Heterogeneity analysis shows that state-owned businesses use their institutional advantages to improve the “quality-quantity” of substantive green innovation, which helps their corporate green development performance. Declining businesses push for green innovation to fix problems that are already there, but mature businesses don’t like ESG rating policies because they are stuck in their ways, which stops them from making real progress in green innovation. This paper ends with micro-level evidence and theoretical support to solve the “greenwashing” problem of ESG and come up with “harmonious coexistence” policy combinations that work for businesses. Full article

The variational method holds considerable significance within mathematical and theoretical physics. Its importance stems from its capacity to characterise natural systems through physical quantities, irrespective of the chosen frame of reference. This characteristic makes it a powerful tool for understanding the behaviour of diverse physical phenomena. A global and statistical approach originating from the principles of non-equilibrium thermodynamics has been developed. This approach culminates in the principle of maximum entropy generation, specifically tailored for open systems. The principle itself arises as a direct consequence of applying the Lagrangian approach to open systems. The work focuses on a generalised method for deriving the thermodynamic Hamiltonian. This Hamiltonian is essential to the dynamical analysis of open systems, allowing for a detailed examination of their time evolution. The analysis suggests that irreversibility appears to be a fundamental process related to the evolution of states within open systems. Full article

The high-strength aluminum alloy 7B04 used in aircraft structures poses challenges in welding. In this study, 7075 aluminum alloy powder is used as an interlayer to strengthen the vacuum diffusion bonding (DB) joint of 7B04 aluminum alloy. Surface treatments with plasma activation before DB can effectively increase the bonding rate and lap shear strength (LSS) of the joint. The effects of DB temperature, pressure, and holding time on the joint LSS were analyzed by developing a quadratic regression model based on the response surface method (RSM). The model’s determination coefficient reached 99.52%, with a relative error of about 5%, making it suitable for 7B04 aluminum alloy DB process parameters optimization and joint performance prediction. Two sets of process parameters (505 °C-5.7 h-4.5 MPa and 515 °C-7.5 h-4.4 MPa) were acquired using the satisfaction function optimization method. Experimental results confirmed that the error between measured and predicted LSS is approximately 5%, and a higher LSS of 174 MPa was achieved at 515 °C-7.5 h-4.4 MPa. Full article

Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring. Full article

Dysregulated cytokine responses are a hallmark of severe COVID-19; however, the persistence of these responses following hospital discharge remains inadequately understood. This study aimed to characterize the inflammatory profile of hospitalized COVID-19 patients in Mureș County, Romania, at the point of admission and one month post-discharge. We conducted a prospective observational study involving 68 patients with RT-PCR-confirmed SARS-CoV-2 infection, classified according to disease severity. Blood samples were collected at baseline and after one month. Macrophage migration inhibitory factor (MIF) levels were quantified using ELISA, while other cytokines, including MCP-1, IP-10, IFN-γ, IL-4, IL-10, IL-13, IL-17, and TNF-α, were measured via Luminex multiplex assays. Patients with severe disease exhibited significantly elevated levels of MIF, IFN-γ, IL-17, and TNF-α at admission (p < 0.0001). Although cytokine concentrations generally declined over time, patients with severe disease continued to display persistently elevated MIF (mean 31,035 pg/mL), IFN-γ, and TNF-α, indicative of ongoing inflammatory processes. Clinical parameters such as respiratory rate and oxygen saturation correlated with disease severity. These findings suggest that severe COVID-19 induces a prolonged inflammatory response, with MIF and IFN-γ remaining elevated beyond the acute phase. Cytokine profiling holds potential for improving prognostic assessments and identifying patients at risk of long-term immune dysregulation, with MIF emerging as a potential candidate marker for immune recovery and a possible target for therapy. Full article