Fingerprint authentication based on deep convolutional descent inversion tomography.

Fingerprint authentication is widely used in various areas. While existing methods effectively extract and match fingerprint features, they encounter difficulties in detecting wet fingers and identifying false minutiae. In this paper, a fast fingerprint inversion and authentication method based on Lamb waves is developed by integrating deep learning and multi-scale fusion. This method speeds up the inversion performance through deep fast inversion tomography (DeepFIT) and uses Mask R-CNN to improve authentication accuracy. DeepFIT utilizes fully connected and convolutional operations to approach the descent gradient, enhancing the efficiency of ultrasonic array reconstruction. This suppresses artifacts and accelerates sub-millimeter-level fingerprint minutia inversion. By identifying the overall morphological relationships of various minutia in fingerprints, meaningful minutia representing individual identities are extracted by the Mask R-CNN method. It segments and matches multi-scale fingerprint features, improving the reliability of authentication results. Results indicate that the proposed method has high accuracy, robustness, and speed, optimizing the entire fingerprint authentication process.

Water hammer detection based on FIV online analysis using a distributed fiber optic sensor.

In long-distance oil pipelines, the abnormal flow state of the fluid in the pipeline can cause continuous water hammer effects, which can damage the pipeline and even cause leakage or collapse. Based on this phenomenon, this paper proposes a new, to the best of our knowledge, detection method based on sensing optical fiber and phase-sensitive optical time-domain reflectometry (φ-OTDR) system to solve the problem of continuous water hammer detection in long-distance pipelines. The method uses a non-invasive, low-cost, real-time approach to monitor pipeline wall deformation characteristics and assess flow-induced vibration (FIV) intensity by statistically distinguishing water hammer signals from normal vibration signals and calculating the peak-to-average ratio (PAR) of these signals. Experimental results show that the FIV status of different types of pipelines can be effectively monitored by calculating the PAR of water hammer signals and vibration signals. The measured PAR based on the φ-OTDR system has high consistency with the PAR variation trend of simulation results, the distributed fiber optic sensor is less affected by environmental factors, and its detection distance and anti-interference ability are better than those of high-precision commercial triaxial acceleration sensors.

Application of lipid and polymeric-based nanoparticles for treatment of inner ear infections via XGBoost.

Taking hearing loss as a prevalent sensory disorder, the restricted permeability of blood flow and the blood-labyrinth barrier in the inner ear pose significant challenges to transporting drugs to the inner ear tissues. The current options for hear loss consist of cochlear surgery, medication, and hearing devices. There are some restrictions to the conventional drug delivery methods to treat inner ear illnesses, however, different smart nanoparticles, including inorganic-based nanoparticles, have been presented to regulate drug administration, enhance the targeting of particular cells, and decrease systemic adverse effects. Zinc oxide nanoparticles possess distinct characteristics that facilitate accurate drug delivery, improved targeting of specific cells, and minimized systemic adverse effects. Zinc oxide nanoparticles was studied for targeted delivery and controlled release of therapeutic drugs within specific cells. XGBoost model is used on the Wideband Absorbance Immittance (WAI) measuring test after cochlear surgery. There were 90 middle ear effusion samples (ages = 1-10 years, mean = 34.9 months) had chronic middle ear effusion for four months and verified effusion for seven weeks. In this research, 400 sets underwent wideband absorbance imaging (WAI) to assess inner ear performance after surgery. Among them, 60 patients had effusion Otitis Media with Effusion (OME), while 30 ones had normal ears (control). OME ears showed significantly lower absorbance at 250, 500, and 1000 Hz than controls (p < 0.001). Absorbance thresholds >0.252 (1000 Hz) and >0.330 (2000 Hz) predicted a favorable prognosis (p < 0.05, odds ratio: 6). It means that cochlear surgery and WAI showed high function in diagnosis and treatment of inner ear infections. Regarding the R2 0.899 and RMSE 1.223, XGBoost shows excellent specificity and sensitivity for categorizing ears as having effusions absent or present or partial or complete flows present, with areas under the curve (1-0.944).

Stress overload, influencing factors, and psychological experiences of nurse managers during early stages of the COVID-19 pandemic: a sequential explanatory mixed method study.

Background: Healthcare systems had an exceptionally difficult time during the early COVID-19 pandemic. Nurse managers in particular made enormous contributions to ensuring the safety of patients and front-line nurses while being under excessive psychological stress. However, little is known about their experiences during this time. Objective: The aim of this study was thus to assess the level of stress overload and psychological feelings of nurse managers during the early COVID-19 pandemic. Methods: A mixed methods sequential explanatory design study with non-random convenience sampling was performed, following the STROBE and COREQ checklists. The study was conducted at the Affiliated Dongyang Hospital, Wenzhou Medical University, with data collected from six provinces in southern China (Zhejiang, Hubei, Shanghai, Jiangsu, Hunan and Jiangxi) during March 2020 and June 2020. A total of 966 nurse managers completed the Stress Overload Scale and Work-Family Support Scale. In addition, a nested sample of nurse managers participated in semi-structured face-to-face interviews. The data were then analyzed using qualitative content analysis, Pearson correlation, and multiple linear regression. Results: The quantitative results showed that nurse managers experienced a moderate level of stress load. There was a significant negative correlation between work-family support and stress load (r = -0.551, p < 0.01). Concerns about protecting front-line nurses and work-family support were the main factors affecting the stress load, which accounted for 34.0% of the total variation. Qualitative analysis identified four main thematic analyses that explained stress load: (1) great responsibility and great stress, (2) unprecedented stress-induced stress response, (3) invisible stress: the unknown was even more frightening, and (4) stress relief from love and support. Taken together these findings indicate that concern about protecting front-line nurses and negative work-family support of nurse managers were the main factors causing stress overload. Conclusion: Implementing measures focused on individual psychological adjustment combined with community and family support and belongingness is one potential strategy to reduce psychological stress among nurse managers.

Nanocatalysts as fast and powerful medical intervention: Bridging cochlear implant therapies and advanced modelling using Hidden Markov Models (HMMs) for effective treatment of inner ear infections.

As human population growth and waste from technologically advanced industries threaten to destabilise our delicate ecological equilibrium, the global spotlight intensifies on environmental contamination and climate-related changes. These challenges extend beyond our external environment and have significant effects on our internal ecosystems. The inner ear, which is responsible for balance and auditory perception, is a prime example. When these sensory mechanisms are impaired, disorders such as deafness can develop. Traditional treatment methods, including systemic antibiotics, are frequently ineffective due to inadequate inner ear penetration. Conventional techniques for administering substances to the inner ear fail to obtain adequate concentrations as well. In this context, cochlear implants laden with nanocatalysts emerge as a promising strategy for the targeted treatment of inner ear infections. Coated with biocompatible nanoparticles containing specific nanocatalysts, these implants can degrade or neutralise contaminants linked to inner ear infections. This method enables the controlled release of nanocatalysts directly at the infection site, thereby maximising therapeutic efficacy and minimising adverse effects. In vivo and in vitro studies have demonstrated that these implants are effective at eliminating infections, reducing inflammation, and fostering tissue regeneration in the ear. This study investigates the application of hidden Markov models (HMMs) to nanocatalyst-loaded cochlear implants. The HMM is trained on surgical phases in order to accurately identify the various phases associated with implant utilisation. This facilitates the precision placement of surgical instruments within the ear, with a location accuracy between 91% and 95% and a standard deviation between 1% and 5% for both sites. In conclusion, nanocatalysts serve as potent medicinal instruments, bridging cochlear implant therapies and advanced modelling utilising hidden Markov models for the effective treatment of inner ear infections. Cochlear implants loaded with nanocatalysts offer a promising method to combat inner ear infections and enhance patient outcomes by addressing the limitations of conventional treatments.

Refining feasibility assessment of endoscopic ear surgery: a radiomics model utilizing machine learning on external auditory canal CT scans.

BACKGROUND: Feasibility assessment of endoscopic ear surgery (EES) relies solely on subjective evaluation by surgeons. OBJECTIVE: Extracting radiomic features from preoperative CT images of the external auditory canal, we aim to classify EES patients into easy and difficult groups and improve accuracy in determining surgery feasibility. METHODS: 85 patients' external auditory canal CT scans were collected and 139 radiomic features were extracted using PyRadiomics. The most relevant features were selected and three machine learning algorithms (logistic regression, support vector machine, and random forest) were compared using K-fold cross-validation (k = 5) to predict surgical feasibility. RESULTS: The best-performing machine learning model, the support vector machine (SVM), was selected to predict the difficulty of EES. The proposed model achieved a high accuracy of 86.5%, and F1 score of 84.6%. The area under the ROC curve was 0.93, indicating good discriminatory power. CONCLUSIONS AND SIGNIFICANCE: The proposed machine learning model provides a reliable and accurate method for classifying patients undergoing otologic surgery based on preoperative imaging data. The model can help clinicians to better prepare for challenging surgical cases and optimize treatment plans for individual patients.

Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability.

Nanoparticles (NPs) are a promising alternative to antibiotics for targeting microorganisms, especially in the case of difficult-to-treat bacterial illnesses. Antibacterial coatings for medical equipment, materials for infection prevention and healing, bacterial detection systems for medical diagnostics, and antibacterial immunizations are potential applications of nanotechnology. Infections in the ear, which can result in hearing loss, are extremely difficult to cure. The use of nanoparticles to enhance the efficacy of antimicrobial medicines is a potential option. Various types of inorganic, lipid-based, and polymeric nanoparticles have been produced and shown beneficial for the controlled administration of medication. This article focuses on the use of polymeric nanoparticles to treat frequent bacterial diseases in the human body. Using machine learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), this 28-day study evaluates the efficacy of nanoparticle therapy. An innovative application of advanced CNNs, such as Dense Net, for the automatic detection of middle ear infections is reported. Three thousand oto-endoscopic images (OEIs) were categorized as normal, chronic otitis media (COM), and otitis media with effusion (OME). Comparing middle ear effusions to OEIs, CNN models achieved a classification accuracy of 95%, indicating great promise for the automated identification of middle ear infections. The hybrid CNN-ANN model attained an overall accuracy of more than 0.90 percent, with a sensitivity of 95 percent and a specificity of 100 percent in distinguishing earwax from illness, and provided nearly perfect measures of 0.99 percent. Nanoparticles are a promising treatment for difficult-to-treat bacterial diseases, such as ear infections. The application of machine learning models, such as ANNs and CNNs, can improve the efficacy of nanoparticle therapy, especially for the automated detection of middle ear infections. Polymeric nanoparticles, in particular, have shown efficacy in treating common bacterial infections in children, indicating great promise for future treatments.

Transcanal endoscopic management of isolated congenital middle ear malformations.

BACKGROUND: A few studies have reported transcanal endoscopic management of isolated congenital middle ear malformations (CMEMs). OBJECTIVE: The purpose of this study is to describe our surgical experience in endoscopic ear surgery for isolated CMEMs and evaluate the surgical effect of hearing reconstruction. METHODS: From January 2017 to January 2022, a retrospective study was performed on 36 patients (37 ears) with isolated CMEMs who all underwent endoscopic surgery. Demographic data, high-resolution computed tomography (HRCT) findings, intraoperative findings, surgical management and audiometric data were recorded. RESULTS: Anomalies were categorized according to the Teunissen and Cremers classification system: 8 ears were categorized as class I, 8 ears as class II, 19 ears as class III and 2 ears as class IV. The air conduction pure tone average (AC-PTA) of 37 cases was 61.5 ± 8.6 dB preoperatively and 29.6 ± 6.9 dB postoperatively (p < 0.001). The mean preoperative air-bone gap (ABG) significantly decreased from 43.1 ± 8.7 dB to 12.8 ± 5.5 dB postoperatively. 36 of 37 cases (97%) met the criteria for successful operation. CONCLUSION: Isolated CMEMs are mainly manifested as aplasia of the stapes' superstructure and dysplasia of the long process of the incus. Transcanal endoscopic surgery seems a safe technique for the management of isolated CMEMs.

Behavioral Intentions and Factors Influencing Nurses' Care of COVID-19 Patients: A Cross-Sectional Study.

Objective: Behavioral intentions to care for patients with infectious diseases are crucial for improving quality of care. However, there have been few studies of the behavioral intentions and factors influencing patient care by clinical nurses during the COVID-19 pandemic. This study aims to explore cognition, attitudes, subjective norms, self-efficacy, and behavioral intentions of clinical nurses while caring for COVID-19 patients and to explore any influencing factors. Method: A cross-sectional survey was conducted of nurses through convenience sampling in southeast China from February 2020 to March 2020. The questionnaire was developed based on the theory of planned behavior and self-efficacy. Results: A total of 774 nurses completed the survey. Of these, 69.12% (535/774) reported positive behavioral intentions, 75.58% (585/774) reported a positive attitude, and 63.82% (494/774) reported having the confidence to care for patients. However, the lack of support from family and friends and special allowance affected their self-confidence. Attitude, self-efficacy, subjective norms, and ethical cognition were significantly positively correlated with behavioral intentions (r = 0.719, 0.690, 0.603, and 0.546, respectively, all P < 0.001). Structural equation model showed that self-efficacy, attitude, ethical cognition, and subjective norms had positive effects on behavioral intentions (ß = 0.402, 0.382, 0.091, and 0.066, respectively, P < 0.01). The total effect of behavioral intentions was influenced by attitude, ethical cognition, self-efficacy, and subjective norms (ß = 0.656, 0.630, 0.402, and 0.157, respectively, P < 0.01). In addition, ethical cognition had a positive mediating effect on behavioral intentions (ß = 0.539, P < 0.001). Conclusion: The study results indicated that attitude, ethical cognition, and self-efficacy were the main factors influencing nurses' behavioral intention. Efforts should be made to improve nurses' attitude and self-efficacy through ethical education and training to increase behavioral intentions to care for patients with infectious diseases, which will improve the quality of nursing care.