Search Results (11,294)

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a DIC algorithm. Specifically, the method leverages the inverse compositional Gauss–Newton algorithm combined with a prediction-correction scheme (IC-GN-PC), considering three critical parameters as interval variables. Uncertainty analysis is conducted using a non-probabilistic interval-based multidimensional parallelepiped model, where accuracy and efficiency serve as the reliability indexes. To achieve both high computational accuracy and efficiency, these two reliability indexes are simultaneously improved by optimizing the chosen parameter intervals. The optimized algorithm parameters are subsequently tested and validated through two case studies. The proposed method can be generalized to enhance multiple aspects of an algorithm’s performance by optimizing the relevant parameter intervals. Full article

Object detection systems are used in various fields such as autonomous vehicles and facial recognition. In particular, object detection using deep learning networks enables real-time processing in low-performance edge devices and can maintain high detection rates. However, edge devices that operate far from administrators are vulnerable to various physical attacks by malicious adversaries. In this paper, we implement a function for detecting traffic signs by using You Only Look Once (YOLO) as well as Faster-RCNN, which can be adopted by edge devices of autonomous vehicles. Then, assuming the role of a malicious attacker, we executed adversarial patch attacks with Adv-Patch and Dpatch. Trying to cause misdetection of traffic stop signs by using Adv-Patch and Dpatch, we confirmed the attacks can succeed with a high probability. To defeat these attacks, we propose an image reconstruction method using an autoencoder and the Structural Similarity Index Measure (SSIM). We confirm that the proposed method can sufficiently defend against an attack, attaining a mean Average Precision (mAP) of 91.46% even when two adversarial attacks are launched. Full article

Objectives: The aim of this study was to compare cortisol concentrations, nutritional composition, and the antioxidant status of human milk of women living in Bydgoszcz (Poland), taking into account maternal factors (fertility, area of residence, economic activity, and breastfeeding period). Methods: The basic composition of human milk was evaluated using the MIRIS HMA^TM analyzer. The level of cortisol was determined by the enzyme-linked immunosorbent method. In order to determine the antioxidant activity, the DPPH radical method was used. Results: It was observed that the concentration of cortisol in human milk in the group of women living in the city center was higher compared to the milk of women living on the outskirts of the city. In the group of women breastfeeding from 3 to 5 weeks after childbirth, the concentration of cortisol in milk was higher compared to the group of women breastfeeding less than 12 months of age and compared to the group of women lactating over 12 months of age. The antioxidant status of human milk was highest in the group of professionally active women and in the group of breastfeeding women from 3 to 5 weeks after childbirth. The basic composition and the caloric value of human milk differed statistically significantly in the study groups. Conclusions: Based on this study, it can be concluded that the composition and antioxidant status of human milk depends on maternal factors (fertility, professional activity, area of residence, and breastfeeding period). Higher cortisol concentrations in breast milk are probably determined by the area of residence (city center and associated higher noise/sound and stress levels) and lactation period (hormonal imbalance, fatigue, and postpartum period). Milk from economically active women shows greater protection against reactive oxygen species compared to milk from inactive women, protecting against the occurrence of diseases of civilization. Milk from breastfeeding women over 12 months of age also shows protection against reactive oxygen species, despite the fact that the highest level of antioxidant status of human milk occurs in the initial period of lactation. Full article

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in the DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second-level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes the BPA to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming six fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that there is a 67.5%, 83.5%, 76.8%, 83%, 79.6%, and 84.1% probability of detecting fire when it occurs, respectively, and identifies fire occurrence in approximately 2.4 s, an improvement from 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety. Full article

Background and Objectives: Middle East Respiratory Syndrome (MERS) is a viral respiratory illness caused by a coronavirus called Middle East respiratory syndrome. In the current study, immunoinformatics studies were applied to design an epitope-based vaccine construct against Middle East Respiratory Syndrome. Materials and Methods: In this study, epitopes base vaccine construct was designed against MERS using immunoinformatics approach. Results: In this approach, the targeted proteins were screened, and probable antigenic, non-allergenic, and good water-soluble epitopes were selected for vaccine construction. In vaccine construction, the selected epitopes were joined by GPGPG linkers, and a linear multi-epitope vaccine was constructed. The vaccine construct underwent a physiochemical property analysis. The 3D structure of the vaccine construct was predicted and subjected to refinement. After the refinement, the 3D model was subjected to a molecular docking analysis, TLRs (TLR-3 and TLR-9) were selected as receptors for vaccine construct, and the molecular docking analysis study determined that the vaccine construct has binding ability with the targeted receptor. Conclusions: The docking analysis also unveils that the vaccine construct can properly activate immune system against the target virus however experimental validation is needed to confirm the in silico findings further. Full article

Bayesian optimization due to its flexibility and sample efficiency has become a standard approach for simulation optimization. To reduce this problem, one can resort to cheaper surrogates of the objective function. Examples are ubiquitous, from protein engineering or material science to tuning machine learning algorithms, where one could use a subset of the full training set or even a smaller related dataset. Cheap information sources in the optimization scheme have been studied in the literature as the multi-fidelity optimization problem. Of course, cheaper sources may hold some promise toward tractability, but cheaper models offer an incomplete model inducing unknown bias and epistemic uncertainty. In this manuscript, we are concerned with the discrete case, where $f\left(\mathbf{x},{\mathbf{w}}_i\right)$ is the value of the performance measure associated with the environmental condition ${\mathbf{w}}_i$ and $p\left({\mathbf{w}}_i\right)$ represents the relevance of the condition ${\mathbf{w}}_i$ (i.e., the probability of occurrence or the fraction of time this condition occurs). The main contribution of this paper is the proposal of a Gaussian-based framework, called augmented Gaussian process (AGP), based on sparsification, originally proposed for continuous functions and its generalization in this paper to stochastic optimization using different risk profiles for combinatorial optimization. The AGP leverages sample and cost-efficient Bayesian optimization (BO) of multiple information sources and supports a new acquisition function to select the new source–location pair considering the cost of the source and the (location-dependent) model discrepancy. An extensive set of computational results supports risk-aware optimization based on CVaR (conditional value-at-risk). Computational experiments confirm the actual performance of the MISO-AGP method and the hyperparameter optimization on benchmark functions and real-world problems. Full article

Background/Objectives: Parenteral nutrition (PN) is used when enteral feeding is not possible. It is a complex mixture of nutrients that must meet a patient’s needs but can face stability issues, such as lipid emulsion destabilisation and precipitate formation. Stability studies are complex, and the methodologies used are very varied in the literature. In addition, many studies are outdated and use outdated components. This study conducts a stability analysis of PN solutions using optical microscopy. Methods: Samples were prepared according to clinical practice standards and previous studies. We used a counting chamber for optical microscopic observations and different storage conditions (RT, 4 °C 1–14 days). Results: Precipitates larger than 5 µm were found in 8 out of 14 samples after 14 days of storage at room temperature, and none were observed in refrigerated samples. More lipid globules larger than 5 µm were detected in samples stored at room temperature than in those stored in a refrigerator after 14 days. Additionally, the number of large globules generally increased from day 1 to day 14 in most samples. Conclusions: The observed precipitates were probably calcium oxalate crystals, the formation of which is possible in PN but is not expected under the usual storage conditions in a hospital environment. Prolonged storage time and storage at room temperature increases the formation of these precipitates. These findings highlight the importance of using filters during both the preparation and administration of PN to prevent large particles from reaching patients. Full article

Federated learning is a decentralized privacy-preserving mechanism that allows multiple clients to collaborate without exchanging their datasets. Instead, they jointly train a model by uploading their own gradients. However, recent research has shown that attackers can use clients’ gradients to reconstruct the original training data, compromising the security of federated learning. Thus, there has been an increasing number of studies aiming to protect gradients using different techniques. One common technique is secret sharing. However, it has been shown in previous research that when using secret sharing to protect gradient privacy, the original gradient cannot be reconstructed when one share is lost or a server is damaged, causing federated learning to be interrupted. In this paper, we propose an approach that involves using additive secret sharing for federated learning gradient aggregation, making it difficult for attackers to easily access clients’ original gradients. Additionally, our proposed method ensures that any server damage or loss of gradient shares are unlikely to impact the federated learning operation, within a certain probability. We also added a membership level system, allowing members of varying levels to ultimately obtain models with different accuracy levels. Full article

Background/Objectives: Aging and chronic stress are regarded as the most important risk factors of cognitive decline. Aged spontaneously hypertensive rats (SHRs) represent a suitable model of age-related vascular brain diseases. The aim of this study was to explore the effects of chronic isolation stress in aging SHRs on their cognitive functions and response to acute stress, as well as the influence of the chronic oral intake of N-Pep-Zn, the Zn derivative of N-PEP-12. Methods: Nine-month-old SHRs were subjected to social isolation for 3 months (SHRiso group), and one group received N-pep-Zn orally (SHRisoP, 1.5 mg/100 g BW). SHRs housed in groups served as the control (SHRsoc). The behavioral study included the following tests: sucrose preference, open field, elevated plus maze, three-chamber sociability and social novelty and spatial learning and memory in a Barnes maze. Levels of corticosterone, glucose and proinflammatory cytokines in blood plasma as well as salivary amylase activity were measured. Restraint (60 min) was used to test acute stress response. Results: Isolation negatively affected the SHRs learning and memory in the Barnes maze, while the treatment of isolated rats with N-Pep-Zn improved their long-term memory and working memory impairments, making the SHRisoP comparable to the SHRsoc group. Acute stress induced a decrease in the relative thymus weight in the SHRiso group (but not SHRsoc), whereas treatment with N-Pep-Zn prevented thymus involution. N-pep-Zn mitigated the increment in blood cortisol and glucose levels induced by acute stress. Conclusions: N-pep-Zn enhanced the adaptive capabilities towards chronic (isolation) and acute (immobilization) stress in aged SHRs and prevented cognitive disturbances induced by chronic isolation, probably affecting the hypothalamo–pituitary–adrenal, sympathetic, and immune systems. Full article

Background/Objectives: In Korea’s emergency medical system, when an emergency patient arises, patients receive on-site treatment and care during transport at the pre-hospital stage, followed by inpatient treatment upon hospitalization. From the perspective of emergency patient management, it is critical to identify the high death rate of patients with certain conditions in the emergency room. Therefore, it is necessary to compare and analyze the determinants of the death rate of patients admitted via the emergency room and generally hospitalized patients. In fact, previous studies investigating determinants of survival periods or length of stay (LOS) primarily used multiple or logistic regression analyses as their main research methodology. Although medical data often exhibit censored characteristics, which are crucial for analyzing survival periods, the aforementioned methods of analysis fail to accommodate these characteristics, presenting a significant limitation. Methods:Therefore, in this study, survival analyses were performed to investigate factors affecting the dying risk of general inpatients as well as patients admitted through the emergency room. For this purpose, this study collected and analyzed the sample cohort DB for a total of four years from 2016 to 2019 provided by the Korean National Health Insurance Services (NHIS). After data preprocessing, the survival probability was estimated according to sociodemographic, patient, health checkup records, and institutional features through the Kaplan–Meier survival estimation. Then, the Cox proportional hazards models were additionally utilized for further econometric validation. Results: As a result of the analysis, in terms of the ‘city’ feature among the sociodemographic characteristics, the small and medium-sized cities exert the most influence on the death rate of general inpatients, whereas the metropolitan cities exert the most influence on the death rate of inpatients admitted through the emergency room. In terms of institution characteristics, it was found that there is a difference in determinants affecting the death rate of the two groups of study, such as the number of doctors per 100 hospital beds, the number of nurses per 100 hospital beds, the number of hospital beds, the number of surgical beds, and the number of emergency beds. Conclusions: Based on the study results, it is expected that an efficient plan for distributing limited medical resources can be established based on inpatients’ LOS. Full article

Background/Objectives: Nasal and nasopharyngeal swabs are commonly used for detecting respiratory viruses, including influenza, which significantly alters host cell metabolites. This study aimed to develop a machine learning model to identify biomarkers that differentiate between influenza-positive and -negative cases using clinical metabolomics data. Method: A publicly available dataset of 236 nasopharyngeal samples screened via liquid chromatography–quadrupole time-of-flight (LC/Q-TOF) mass spectrometry was used. Among these, 118 samples tested positive for influenza (40 A H1N1, 39 A H3N2, 39 Influenza B), while 118 were negative controls. A stacking-based model was proposed using the top 20 selected features. Thirteen machine learning models were initially trained, and the top three were combined using predicted probabilities to form a stacking classifier. Results: The ExtraTrees stacking model outperformed other models, achieving 97.08% accuracy. External validation on a prospective cohort of 96 symptomatic individuals (48 positive and 48 negatives for influenza) showed 100% accuracy. SHAP values were used to enhance model explainability. Metabolites such as Pyroglutamic Acid (retention time: 0.81 min, m/z: 84.0447) and its in-source fragment ion (retention time: 0.81 min, m/z: 130.0507) showed minimal impact on influenza-positive cases. On the other hand, metabolites with a retention time of 10.34 min and m/z 106.0865, and a retention time of 8.65 min and m/z 211.1376, demonstrated significant positive contributions. Conclusions: This study highlights the effectiveness of integrating metabolomics data with machine learning for accurate influenza diagnosis. The stacking-based model, combined with SHAP analysis, provided robust performance and insights into key metabolites influencing predictions. Full article

In this study, improvements are made to Inter-SubNet, a state-of-the-art speech enhancement method. Inter-SubNet is a single-channel speech enhancement framework that enhances the sub-band spectral model by integrating global spectral information, such as cross-band relationships and patterns. Despite the success of Inter-SubNet, one crucial aspect probably overlooked by Inter-SubNet is the unequal perceptual weighting of different spectral regions by the human ear, as it employs MSE as its loss function. In addition, MSE loss has a potential convergence concern for model learning due to gradient explosion. Hence, we propose further enhancing Inter-SubNet by either integrating perceptual loss with MSE loss or modifying MSE loss directly in the learning process. Among various types of perceptual loss, we adopt the temporal acoustic parameter (TAP) loss, which provides detailed estimation for low-level acoustic descriptors, thereby offering a comprehensive evaluation of speech signal distortion. In addition, we leverage Huber loss, a combination of L1 and L2 (MSE) loss, to avoid the potential convergence issue for the training of Inter-SubNet. By the evaluation conducted on the VoiceBank-DEMAND database and task, we see that Inter-SubNet with the modified loss function reveals improvements in speech enhancement performance. Specifically, replacing MSE loss with Huber loss results in increases of 0.057 and 0.38 in WB-PESQ and SI-SDR metrics, respectively. Additionally, integrating TAP loss with MSE loss yields improvements of 0.115 and 0.196 in WB-PESQ and CSIG metrics. Full article

Background/Objectives: Implementing model-informed precision dosing (MIPD) strategies guided by population pharmacokinetic/pharmacodynamic (PK/PD) models could enhance the management of inflammatory diseases such as psoriasis. However, the extent of individual experimental data gathered during MIPD significantly influences the uncertainty in estimating individual PK/PD parameters, affecting clinical dose selection decisions. Methods: This study proposes a methodology to individualize ustekinumab (UTK) dosing strategies for 23 Spanish patients with moderate to severe chronic plaque psoriasis., considering the uncertainty of individual parameters within a population PK/PD model. Results: An indirect response model from previous research was used to describe the PK/PD relationship between UTK serum concentrations and the Psoriasis Area and Severity Index (PASI) score. A maximum inhibition drug effect (I_max) model was selected, and a first-order remission constant rate of psoriatic skin lesion (k_out = 0.016 d⁻¹) was estimated. Conclusions: The MIPD approach predicted that 35% and 26% of the patients would need an optimized and intensified dosage regimen, respectively, compared to the regimen typically used in clinical practice. This analysis demonstrated its utility as a tool for selecting personalized UTK dosing regimens in clinical practice in order to optimize the probability of achieving targeted clinical outcomes in patients with psoriasis. Full article

Background and Objectives: Perceived health status (PHS) is associated with various health outcomes in older adults, but its relationship with resilience in the context of events with a major impact on functional status (FS), such as hip fracture, has not been explored. Our objective was to evaluate whether older adults who report good PHS before a hip fracture have a higher probability of returning to their baseline physical performance (PP) and personal independence. Materials and Methods: We analyzed data from waves 1 and 2 of the Survey of Health, Ageing and Retirement in Europe (SHARE) study, enrolling patients ≥ 65 years who experienced a hip fracture between these two waves. As study outcomes, we analyzed changes in PP and functional abilities (FAs). Results: We included 149 participants with a mean age of 75.7 years (SD: 6.5); women comprised 66%. The incidence of loss of PP was 51.7% among participants with good PHS and 59.6% among those with poor PHS. FA worsened in 40% of participants with good PHS and 58.4% in those with poor PHS. Relative risk (RR) for loss of FA in people with good PHS was 0.68 (95% CI: 0.48–0.98), which did not change after an adjustment for age, gender, baseline FA, depression, number of comorbidities, education, income, and social support, despite it not reaching statistical significance. After adjustment, the risk of worsening PP in participants with good PHS was reduced by 34% (95% CI: 0.41–1.06). Conclusions: A simple question on PHS may predict the resilience of older adults after an acute stressor. A systematic evaluation of PHS can help identify patients with a higher probability of regaining function after a hip fracture and thus provide useful information for resource allocation. Full article

Automatic assessment of the operation ability of operators based on computers is an essential approach for improving the effectiveness of equipment operation training and enhancing equipment safety. Present methods primarily focus on the operation results but pay less attention to the operation procedure. One reason is that there is a lack of a model that has the ability to describe all probable paths to accomplish the same task. Therefore, an operations chain model is put forward for the first time to describe the standard operation procedure and relationships among operations based on the decomposition of operational tasks and the relationships among the various operations required to fulfill the task. A specific operation task corresponds to an operations chain, which will form one or multiple standard operation sequences that will allow trainees to complete the same task through different paths. The Needleman–Wunsch sequence alignment algorithm is introduced to match the trainees’ operation sequence with all standard sequences. The maximum alignment result is the score of the trainees’ operations. An example shows that the operations chain model can accurately describe the complex structure of the standard operating procedures. The Needleman–Wunsch sequence alignment algorithm can objectively evaluate the trainee’s operation capabilities. The combination of the operations chain model and sequence alignment algorithm can form a complete operation procedure assessment method that is friendlier to trainees and has more objective evaluation results. The method will help to improve the effectiveness of the competency assessment of equipment operators. Full article