False Alarm Rate Research Articles

Entrepreneurial Marketing and Business Sustainability in Bayelsa State

This study investigated entrepreneurial marketing and business sustainability in Bayelsa State. The study formulated three specific objectives, three research questions and three hypotheses. The study adopted a descriptive survey design. The population of this study consists of 900 entrepreneurs and employees of the fifteen selected small scale businesses under investigation in Yenagoa Bayelsa State. Taro Yamane formula was used to arrive at the sample size of 200. A purposive sampling method was adopted. A structural questionnaire consisting of 15-item questions on a 4-point rating scale titled “Entrepreneurial Marketing and Business Sustainability Questionnaire (EMBSQ) was used for data collection. The research instrument was validated by three experts in the Department of Business Education of Ignatius Ajuru University of Education Port Harcourt for face validation. The reliability of the instrument was ascertained using the Cronbach Alpha Reliability method. After the analysis, a reliability coefficient index of 0.78 was obtained which deemed the instrument highly reliable. To analyse the data collected, the mean and standard deviation was used while the hypotheses was tested using the t-test at 0.05 alpha level of significance. Findings revealed that entrepreneurial proactiveness, innovativeness and calculated risk-taking to a high extent relate to business sustainability in Bayelsa State. Hence, entrepreneurial marketing dimensions adopted for entrepreneurial ventures are very important and indispensable for the sustainability of the business. Based on the findings of the study it was recommended that entrepreneurial proactiveness should be propagated and encouraged as it seeks novel ways in creating value for the customers. Entrepreneurs should continue to invest in innovation that yields positive and significant influences toward the sustainability of business in Bayelsa State of Nigeria.

A randomized algorithm based approach to fault detection for uncertain systems subject to deception attack

This paper presents a fault detection strategy for discrete uncertain systems subject to deception attack based on the probabilistic framework. Probabilistic parameter models are utilized for characterizing model uncertainties, multiplicative faults and additive faults. An observer-based fault detection filter is constructed to generate fault information under deception attack. A randomized algorithm based approach is developed to determine the observer gain and threshold for attaining a trade-off between the false alarm rate (FAR) and fault detection rate (FDR). Simulation of an unmanned surface vehicle (USV) system and comparison against existing work are performed to illustrate the effectiveness of the proposed scheme.

End-to-End Learning-Based Study on the Mamba-ECANet Model for Data Security Intrusion Detection

With the rapid development of information technology, network security issues have become increasingly prominent. In particular, data security intrusions pose serious threats to the data privacy and system security of enterprises and individuals. Traditional intrusion detection systems often exhibit low detection accuracy and high false alarm rates when faced with complex and dynamic network environments and diverse attack methods. Therefore, this paper proposes a data security intrusion detection system based on deep learning, which integrates the Mamba model and ECANet model and employs an end-to-end learning approach for training and optimization. First, the Mamba model is introduced for preliminary data feature extraction, whose efficient feature representation capabilities provide a solid foundation for the subsequent detection process. Then, by integrating the ECANet model, feature selection is further optimized through the attention mechanism, enhancing the model’s focus on important features. Finally, an end-to-end learning approach is adopted to train and optimize the entire system, ensuring excellent performance and robustness in practical applications. Experimental results show that the proposed intrusion detection system demonstrates higher detection accuracy on multiple test datasets, improving by approximately 5% compared to traditional methods, providing a new and effective solution for data security.

Data-driven unsupervised anomaly detection of manufacturing processes with multi-scale prototype augmentation and multi-sensor data

Accurate anomaly detection (AD) of machine tools is crucial to ensure the quality and efficiency of the manufacturing processes. Due to the lack of tool anomaly information, it is difficult for AD model to precisely capture the distribution of health states and then obtain a discriminative decision boundary. Current methods try to reconstruct the normal data distribution without restricting the abnormal, resulting in the unacceptable overlap between normal and abnormal regions and finally leading to high false alarm rate. To tackle these issues, a hierarchical augmented autoencoder is proposed for AD of machine tools during manufacturing. First, a skip-connected autoencoder is built to basically learn the normal representations of multi-sensor data in an unsupervised manner. Then, to improve further emphasis the reconstruction on normality and suppress that on anomalies, we propose hierarchical memory modules to store multi-scale normal prototypical patterns, using them as a prior to guide the reconstruction with preference. Finally, A compound metric loss function is designed to measure data similarity considering both distance and angle perspectives, which can restrain noise interference and enhance model robustness. Extensive experiments are conducted on real-world CNC machine tool datasets, the proposed method achieves better performance for unsupervised AD compared with other typical methods.

Patient satisfaction with quality of care at out-patient departments in selected health facilities in Kumasi, Ghana

BackgroundHealth care is an indispensable element for economic growth and development of individuals and nations. Healthcare service quality is associated with patient satisfaction, ensuring the safety and security of patients, reducing mortality and morbidity, and improving the quality of life. Patient satisfaction with health service is linked to increased utilization following contendness with healthcare received from health providers. There is an increasing public perception of poor quality of care among patients visiting public health facilities in Ghana which translates into service dissatisfaction. Meanwhile, patient dissatisfaction will more likely result in poor utilization, disregard for medical advice, and treatment non-adherence. The study was conducted to assess patients’ satisfaction with quality of care at the outpatient departments of selected health facilities in Kumasi, Ghana.MethodsAn institutional-based analytical cross-sectional study was conducted among patients (aged ≥ 18 years) visiting outpatient departments of selected health facilities in Kumasi from October - December, 2019. A systematic sampling technique was adopted to collect quantitative information from 385 respondents using a structured questionnaire. At 95% confidence interval and 5% alpha level, two-level logistic regression models were performed. Model I estimated the crude associations and the effect of covariates was accounted for in Model II. The results were presented in odds ratio with a corresponding 95% confidence interval. All analysis were performed using STATA statistical software version 16.0.ResultsOut of the 385 participants, 90.9% of the participants were satisfied with the services they received. Being married [AOR = 3.06, 95%CI = 1.07–8.74], agreeing that the facility is disability-friendly [AOR = 7.93, 95%CI = 2.07–14.43], facility has directional signs for navigation [AOR = 3.12, 95%=1.92–10.59] and the facility has comfortable and attractive waiting area [AOR = 10.02, 95%CI = 2.35–22.63] were associated with satisfaction with health service among patients. Spending more than 2 h at the health facility [AOR = 0.45, 95%CI = 0.04–0.93] and having perceived rude and irritating provider [AOR = 0.14, 95%CI = 0.04–0.51] had lower odds of satisfaction with health service received.ConclusionThere is a high patient satisfaction with services received at out-patient departments which is influenced by a multiplicity of factors; being married, and agreeing that the facility is disability-friendly, has directional signs for navigation, and the waiting area is comfortable and attractive. The study findings call for the need to develop and implement health delivery interventions and strategies (i.e. patient-centered interventions, disability-friendly facilities, and sustainability and improvement of quality service) to improve and sustain patient satisfaction levels with health care service. These strategies must be directed towards addressing inequalities in infrastructural development and inputs needed for healthcare delivery in the health system.

Transcription repression of estrogen receptor alpha by ghrelin/Gq/11/YAP signaling in granulosa cells promotes polycystic ovary syndrome.

Polycystic ovarian syndrome (PCOS) is a prevalent endocrinological disorder affected by ghrelin. This study aimed to investigate the molecular mechanisms underlying the effects of ghrelin on PCOS manifestations in mice and to assess the therapeutic potential of ghrelin. Female C57BL/6 mice were subcutaneously injected with 6 mg/100 g dehydroepiandrosterone (DHEA) for 20 days to induce PCOS. Alterations in reproductive cycles, ovarian morphology, serum sex hormone levels, and related signaling markers were examined. Furthermore, ghrelin-induced effects on granulosa cells and the role of ghrelin/Gq/11/ Yes-associated protein (YAP) signaling were studied by silencing Gαq/11 or YAP using si-RNAs. Finally, we evaluated the therapeutic potential of anti-ghrelin antibodies in DHEA-induced PCOS mice. DHEA administration led to significant PCOS-associated changes including weight gain, disrupted estrous cycles, ovarian morphological alterations, and hormonal imbalances in mice, with elevated Gαq/11 and acylated ghrelin expression, which was also noted in PCOS patients. However, treatment with anti-ghrelin antibodies effectively managed DHEA-induced damage in PCOS mice. In vitro, ghrelin exposure resulted in granulosa cell injury and modulated estrogen receptors alpha (ERα) and YAP protein levels, whereas silencing YAP and Gαq/11 reversed ghrelin-induced detrimental effects and up-regulated ERα expression. This study revealed that DHEA-induced PCOS traits in mice could be improved by anti-ghrelin antibodies, with the ghrelin/Gq/11/YAP signaling pathway identified as a crucial mediator in granulosa cells, affecting ERα transcription to regulate PCOS. These findings suggest a potential therapeutic strategy for the treatment of PCOS.

Distributed target detection based on gradient test in deterministic subspace interference

This paper investigates the problem of distributed target detection in the presence of interference and Gaussian noise, where the target signal and interference are assumed to lie in different deterministic subspaces. Building upon this assumption, we propose several adaptive detectors resorting to the gradient criterion tailored for homogeneous environment and partially homogeneous environment. Simulation results indicate that the proposed gradient-based detectors outperform their competitors in some scenarios. Furthermore, all of these Gradient-based detectors exhibit the constant false alarm rate (CFAR) property.

High-Level Feature Fusion Deep Learning Model for Fault Detection in Handling Equipment in Dry Bulk Ports

The flexibility of handling equipment in dry bulk ports is poor, and frequent equipment fault induced by the high-load and high-power working conditions greatly impacts the overall port handling operations, making accurate fault detection play an important role in improving the efficiency and stability of dry bulk port operations. However, as we know, most fault detection methods for port handling equipment depend heavily on monitoring sensor data, which is not applicable in the dry bulk port due to high configuration and maintenance cost, as well as the high false alarm rate of monitoring sensors caused by strong background noise. To solve the problem, this study proposes a High-Level Feature Fusion Deep Learning Model, which uses different deep learning sub-models to extract features of structured and unstructured data. It fuses the extracted feature vectors to achieve fault detection in the handling equipment, establishing the mapping relationship between the fault (e.g., waiting for the pre-loading process, equipment fault, and others) and multi-source heterogeneous operation and maintenance data for the handling equipment, including reclaimers, belt conveyors, dumpers, and ship loaders. To verify the effectiveness of the proposed method, the actual data of a coal port in Northern China is employed as an example. The results show the deep learning model can achieve high prediction accuracy (over 86%) with high efficiency (0.5 s for each sample), which provides decision support for the fault detection in dry bulk port handling equipment.

Gender Differences in Usage of Pathao’s Two-Wheeler Services

The study tests the significant difference in usage patterns of Pathao Services between two different groups: male and female. In this context, the Independent Samples t-Test has been used to test equality in the mean usage of Pathao Services for both groups with and without the assumption of equal and unequal variances. We tested the sample of 397 participants, and the variance between these groups was found to be unequal by the Levene's Test showing the significant difference. The results reveal a statistically significant mean difference in the use of Pathao Services between the two groups. Precisely, the average of difference for 0.199, at 95% confidence interval, falls within a range of 0.087 to 0.310 when variances are considered to be equal and within a range of 0.073 to 0.325 when variances are taken to be unequal. As a matter of fact, the p-values for both tests were below the conventional alpha level of 0.05, meaning the differences observed are significant. The results derived from unequal variances tests were considered more reliable because of the significant F-value obtained from Levene's Test. These results therefore indicate that one group is using Pathao Services more than the other. The significant differences of usage would require further examination of demographic, socio-economic, or even behavioral reasons responsible for these differences and might be useful in service provision and intervention. This thus calls for more research to find the root causes of such and perhaps inform strategies to help improve service engagement across diverse user groups.

An enhanced digital twin-driven fault detection and isolation method based on sensor series imaging mechanism for gas turbine engine

Condition based maintenance plays a crucial role in ensuring the safety and reliability of gas turbine engine. Specifically, fault detection and isolation (FDI) can improve efficiency and reduce costs during maintenance. The traditional FDI methods, which developed only using physical engine data, lack a comprehensive consideration of high accuracy and strong applicability across different engine individuals. In this paper, an enhanced digital twin-driven FDI method is proposed, which combines a digital engine, sensor series imaging mechanism and a convolutional neural network. The digital engine is constructed to mirror the operational status of the physical engine. The output residuals between the physical and digital engines are transformed into multi-channel images to train a convolutional neural network for FDI of the physical engine. The network is then fine-tuned using fault-free data from the target engine to further strengthen diagnostic performance across different engine individuals. The effectiveness of the digital engine is verified using actual ground test data, and the performance of the proposed method is demonstrated through comparative simulations. The network, trained on operational data from Engine A and fine-tuned with fault-free data from other engines, exhibits the highest diagnostic accuracy and fault isolation rate. Compared with the physical engine data-based method, the accuracy is increased from 68.25% to 97.95%, the fault isolation rate is improved from 85.91% to 99.26%, and the false alarm rate is decreased from 4.41% to 0.34%. The results show that the proposed method has better FDI capabilities across different engine individuals.

Optimized Intrusion Detection for IoMT Networks with Tree-Based Machine Learning and Filter-Based Feature Selection.

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detection, we are planning to build a multi-classification approach for future work which will be able to not only detect the attacks but also categorize them. Additionally, we will consider using our proposed feature selection technique for different ML classifiers and evaluate the model's performance empirically in real-world IoMT scenarios.

Does The Declaration of The Covid-19 Occurrence in Indonesia Affect Abnormal Return and Trading Volume Activity? Research on Jakarta Islamic Index

The Coronavirus, which causes the disease outbreak known as Covid-19, spreads rapidly over the world and attacks the human respiratory system. The Covid-19 in Indonesia on March 2, 2020 has been confirmed by the Indonesian government. The financial market has been impacted by this situation, and stock markets worldwide have declined. The aim of this investigation is to ascertain whether a noticeable discrepancy exists between abnormal return and trading volume activity on the Jakarta Islamic Index (JII) both prior to and subsequent to the declaration of Covid-19 in Indonesia. The results derived from this examination demonstrate that the statistical significance of the Difference Test, which examines the abnormal return on the Jakarta Islamic Index (JII) both prior to and subsequent the Covid-19 announcement in Indonesia, is observed to be 0.024, which is less than the conventional alpha level of 0.05. Furthermore, The statistical significance level of the Difference Test in relation to the trading volume activity on the Jakarta Islamic Index (JII) prior to and following the Covid-19 announcement in Indonesia has been determined to be 0.009, which is less than the commonly accepted threshold of 0.05. The inference that can be drawn from this investigation is that there exist notable discrepancies in the abnormal return and trading volume activity on the Jakarta Islamic Index (JII) prior to and subsequent to the disclosure of Covid-19 in Indonesia.

Single-cell sequencing analysis and bulk-seq identify IGFBP6 and TNFAIP6 as novel differential diagnosis markers for postburn pathological scarring

BackgroundIf not accurately diagnosed and treated, postburn pathological scars, such as keloids and hypertrophic scars, can lead to negative clinical outcomes. However, differential diagnosis at the molecular level for postburn pathological scars remains limited. Using single-cell sequencing analysis, we investigated the genetic nuances of pathological scars at the cellular level. This study aimed to identify molecular diagnostic biomarkers to distinguish between postburn keloids and hypertrophic scars. MethodsSingle-cell sequencing, differential expression, and weighted co-expression network analyses were performed to identify potential key genes for discriminating between keloids and hypertrophic scars. Postburn clinical samples were collected from our centre to validate the expression levels of the identified key genes. ResultsSingle-cell sequencing analysis unveiled 29 and 30 cell clusters in keloids and hypertrophic scars, respectively, predominantly composed of fibroblasts. Bulk differential gene analysis showed 96 highly expressed genes and 69 lowly expressed genes in keloids compared to hypertrophic scars. By incorporating previous research, Gene Set Enrichment Analysis was conducted to select fibroblasts as the focus of research. According to the single-cell data, 301 genes were stably expressed in fibroblasts from both types of pathological scars. Consistently, Weighted Gene Co-expression Network Analysis revealed that the blue module genes were mostly hub genes associated with fibroblasts. After intersecting fibroblast-related genes in single-cell data, Weighted Gene Co-expression Network Analysis-hub module genes, and bulk differential expression genes, insulin-like growth factor binding protein 6 and tumour necrosis factor alpha-induced protein 6 were identified as key genes to distinguish keloids from hypertrophic scars, resulting in diagnostic accuracies of 1.0 and 0.75, respectively. Immunohistochemical Staining and Quantitative Reverse Transcription PCR revealed that the expression levels of tumour necrosis factor alpha induced protein 6 and insulin-like growth factor binding protein 6 were significantly lower in postburn keloids than in hypertrophic scars- ConclusionsTumour necrosis factor alpha induced protein 6 and insulin-like growth factor binding protein 6, exhibiting high diagnostic accuracy, provide valuable guidance for the differential diagnosis and treatment of postburn pathological scars-

Monocyte derived large extracellular vesicles in polytrauma.

Despite significant progress in the medical field, there is still a pressing need for minimal-invasive tools to assist with decision-making, especially in cases of polytrauma. Our team explored the potential of serum-derived large extracellular vesicles, so called microparticles/microvesicles/ectosomes, to serve as a supportive tool in decision-making in polytrauma situations. We focused on whether monocyte derived large EVs may differentiate between polytrauma patients with internal organ injury (ISS>15) and those without. Thus, we compared our EV data to soluble biomarkers such as tumour necrosis factor alpha (TNF alpha) and Interleukin-8 (IL-8). From the blood of 25 healthy and 26 patients with polytrauma large EVs were isolated, purified, and characterized. TNF alpha and IL-8 levels were quantified. We found that levels of these monocyte derived large EVs were significantly higher in polytrauma patients with internal organ damage and correlated with the ISS. Interestingly, we also observed a decline in AnnV+CD14+ large EVs during normal recovery after trauma. Thus, inflammatory serological markers as TNF alpha and as IL-8 demonstrated an inability to discriminate between polytrauma patients with or without internal organ damage, such as spleen, kidney, or liver lacerations/ruptures. However, TNF and IL-8 levels were elevated in polytrauma cases overall when contrasted with healthy non-traumatic controls. These findings suggest that delving deeper into the potential of AnnV+ large EVs derived from monocytes could highly beneficial in the managment of polytrauma, potentially surpassing the efficacy of commonly used serum markers.

Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.

Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deterioration, attempt to dichotomize a dynamic and progressive phenomenon, and offer poor performance. Can a deep learning deterioration prediction model (Deep Learning Enhanced Triage and Emergency Response for Inpatient Optimization [DETERIO]) based on a consensus definition of deterioration (the Adult Inpatient Decompensation Event [AIDE] criteria) and that approaches deterioration as a state "value-estimation" problem outperform a commercially available deterioration score? The derivation cohort contained retrospective patient data collected from both inpatient services (inpatient) and emergency departments (EDs) of two hospitals within the University of California San Diego Health System. There were 330,729 total patients; 71,735 were inpatient and 258,994 were ED. Of these data, 20% were randomly sampled as a retrospective "testing set." The validation cohort contained temporal patient data. There were 65,898 total patients; 13,750 were inpatient and 52,148 were ED. DETERIO was developed and validated on these data, using the AIDE criteria to generate a composite score. DETERIO's architecture builds upon previous work. DETERIO's prediction performance up to 12 hours before T0 was compared against Epic Deterioration Index (EDI). In the retrospective testing set, DETERIO's area under the receiver operating characteristic curve (AUC) was 0.797 and 0.874 for inpatient and ED subsets, respectively. In the temporal validation cohort, the corresponding AUC were 0.775 and 0.856, respectively. DETERIO outperformed EDI in the inpatient validation cohort (AUC, 0.775 vs. 0.721; p < 0.01) while maintaining superior sensitivity and a comparable rate of false alarms (sensitivity, 45.50% vs. 30.00%; positive predictive value, 20.50% vs. 16.11%). DETERIO demonstrates promise in the viability of a state value-estimation approach for predicting adult physiologic deterioration. It may outperform EDI while offering additional clinical utility in triage and clinician interaction with prediction confidence and explanations. Additional studies are needed to assess generalizability and real-world clinical impact.

Mutation-Based Multivariate Time-Series Anomaly Generation on Latent Space with an Attention-Based Variational Recurrent Neural Network for Robust Anomaly Detection in an Industrial Control System

Anomaly detection involves identifying data that deviates from normal patterns. Two primary strategies are used: one-class classification and binary classification. In Industrial Control Systems (ICS), where anomalies can cause significant damage, timely and accurate detection is essential, often requiring analysis of time-series data. One-class classification is commonly used but tends to have a high false alarm rate. To address this, binary classification is explored, which can better differentiate between normal and anomalous data, though it struggles with class imbalance in ICS datasets. This paper proposes a mutation-based technique for generating ICS time-series anomalies. The method maps ICS time-series data into a latent space using a variational recurrent autoencoder, applies mutation operations, and reconstructs the time-series, introducing plausible anomalies that reflect multivariate correlations. Evaluations of ICS datasets show that these synthetic anomalies are visually and statistically credible. Training a binary classifier on data augmented with these anomalies effectively mitigates the class imbalance problem.

Autonomous radar interference detection and mitigation using neural network and signal decomposition

&lt;p&gt;Autonomous radar interference is a challenging problem in autonomous vehicle systems. Interference signals can decrease the signal-to-interference-noise ratio (SINR), and this condition decreases the performance detection of autonomous radar. This paper exploits a neural network and signal decomposition to detect and mitigate radar interference in autonomous vehicle applications. A neural network (NN) with four inputs, one hidden layer, and one output is trained with various signal-to-noise (SNR), interference radar bandwidth, and sweep time of autonomous radar. Four inputs of NN represent SNR, mean, total harmonic distortion (THD), and root means square (RMS) of the received radar signal. Variational mode decomposition (VMD) and zeroing based on a constant false alarm rate (CFAR-Z) are used to mitigate radar interference. VMD algorithm is applied to decompose interference signals into multi-frequency sub-band. As a result, the proposed neural network can detect radar interference, and NN-VMD-CFAR-Z can increase SINR up to 2dB higher than the NN-CFAR-Z algorithm.&lt;/p&gt;

Research on signal denoising algorithm based on ICEEMDAN eddy current detection

This study addresses the challenges of non-stationarity and significant background noise interference in eddy current detection signals by proposing a noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise (ICEEMDAN). The process commences with the signal being decomposed using Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise into a finite number of Intrinsic Mode Functions (IMFs). Each Intrinsic Mode Function is then evaluated for the presence of high-frequency noise using a Power Spectral Density (PSD) analysis. The high-frequency noise present in the Intrinsic Mode Functions is then reduced using Normalized Least Mean Squares (NLMS) before being reconstructed with the remaining Intrinsic Mode Functions. Subsequently, the reconstructed signals are subjected to another round of decomposition using Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise. The Pearson Product Moment Correlation Coefficient (PPMCC) is utilised to calculate the correlation between the Intrinsic Mode Functions within each layer, retaining those with a strong correlation to further attenuate noise. Ultimately, the local maxima judgement method selectively amplifies defect signals by assessing changes in peak and valley degrees, thereby improving the signal-to-noise ratio of the eddy current detection signal. The experimental results demonstrate that, in comparison to the use of only the conventional Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise and Normalized Least Mean Squares denoising methods, the proposed method increases the Signal-to-Noise Ratio (SNR) by 1.08 dB and 2.31 dB, respectively, and decreases the Mean Square Error (MSE) by 106.9 and 223.9, respectively. The false alarm rate for stainless steel welded tubes with defects is 1.4%, while the false alarm rate for stainless steel welded tubes without defects is 0.4%.

Cook’s balloon versus dinoprostone for Labour induction of term pregnancies with fetal GROWth restriction: study protocol for a randomised controlled trial in tertiary maternity hospitals in Spain (COLIGROW study)

IntroductionFetal growth restriction (FGR) affects about 3%–5% of term pregnancies. If prenatally detected and anterograde umbilical artery flow is preserved (stage I), it is recommended to deliver at term (≥...

Detection of Subclinical Neurotrophic Keratopathy by Noncontact Esthesiometry.

This study aimed to analyze corneal sensitivity with a new noncontact and handheld esthesiometer (Brill Engines, Spain) in patients with dry eye disease (DED) and patients on hypotensive drops, and to compare it with healthy subjects. A total of 31 patients (57 eyes) with DED, 23 patients (46 eyes) with glaucoma, and 21healthy patients (33 eyes) were recruited. In all patients, corneal sensitivity was measured. Subsequently, a keratography test (Keratograph 5M, Oculus) was carried out to measure tear meniscus height (TMH), non-invasive breakup time (NIBUT), bulbar redness (Jenvis scale), and corneal staining (CS, Oxford scale). Both corneal sensitivity and ocular surface parameters were compared between DED, glaucoma, and healthy subjects. Linear mixed models were constructed to utilize data from both eyes of patients. An alpha level of 0.05 was considered statistically significant. The mean age was 56.1 ± 16.1years in the DED group, 69.5 ± 11.7years in the glaucoma group, and 37.190 ± 11.677years in the control group. After adjustment for age and sex, corneal sensitivity was significantly reduced in DED and glaucoma vs control group (P = 0.02 and P = 0.009, respectively). NIBUT was lower in DED and glaucoma groups (P < 0.001 and P = 0.001, respectively). Redness and CS values were higher in the DED group (P = 0.04 and P = 0.001, respectively). TMH was lower in the glaucoma group (P = 0.03). Corneal sensitivity measured with a novel noncontact esthesiometer was reduced in DED and glaucoma groups compared to controls. In clinical practice, this esthesiometer could be an easy-to-use device to screen for patients with subclinical neurotrophic keratopathy.