False Alarm Rate Research Articles

Mechanism research of elastic fixation promoting fracture healing based on proteomics and fracture microenvironment.

This study aimed to demonstrate the promoting effect of elastic fixation on fracture, and further explore its mechanism at the gene and protein expression levels. A closed tibial fracture model was established using 12 male Japanese white rabbits, and divided into elastic and stiff fixation groups based on different fixation methods. Two weeks after the operation, a radiograph and pathological examination of callus tissue were used to evaluate fracture healing. Then, the differentially expressed proteins (DEPs) were examined in the callus using proteomics. Finally, in vitro cell experiments were conducted to investigate hub proteins involved in this process. Mean callus volume was larger in the elastic fixation group (1,755 mm3 (standard error of the mean (SEM) 297)) than in the stiff fixation group (258 mm3 (SEM 65)). Pathological observation found that the expression levels of osterix (OSX), collagen, type I, alpha 1 (COL1α1), and alkaline phosphatase (ALP) in the callus of the elastic fixation group were higher than those of the stiff fixation group. The protein sequence of the callus revealed 199 DEPs, 124 of which were highly expressed in the elastic fixation group. In the in vitro study, it was observed that a stress of 200 g led to upregulation of thrombospondin 1 (THBS1) and osteoglycin (OGN) expression in bone marrow mesenchymal stem cells (BMSCs). Additionally, these genes were found to be upregulated during the osteogenic differentiation process of the BMSCs. Elastic fixation can promote fracture healing and osteoblast differentiation in callus, and the ability of elastic fixation to promote osteogenic differentiation of BMSCs may be achieved by upregulating genes such as THBS1 and OGN.

Intelligent Lost Circulation Monitoring Method Based on Data Augmentation and Temporal Models

Deep and offshore drilling operations face complex geological formations, uncertain formation pressures, and narrow safety density windows, making them susceptible to lost circulation risks. To address these challenges, this paper introduces an innovative, intelligent lost circulation monitoring model that incorporates geological lithology information. This model not only utilizes real-time drilling parameters, but also encodes geological information such as rock type as inputs to the model. By combining these key lithological features, the model can comprehensively assess wellbore stability and reduce the lost circulation risks. In this paper, the Conditional Tabular Generative adversarial network (CTGAN) model is used to enhance the data of small-sample risk data, which can effectively expand the data distribution space and improve the performance of the model. This paper conducts a comparative analysis of intelligent monitoring results using artificial neural networks (ANNs), long short-term memory (LSTM), and temporal convolutional networks (TCNs). The results show that the TCN achieves an identification accuracy of 93.7%. Furthermore, the analysis reveals that the inclusion of lithology information significantly enhances the model’s performance, resulting in a 7.1% increase in accuracy. The false alarm rate of the model can be reduced by 10.2%, considering the fluctuation of the logging curve caused by the on/off condition of the pump. This indicates that the introduction of lithology information and the condition of the pump on−off provide advantages in monitoring and identifying lost circulation risks, enabling a more precise assessment of wellbore stability and a reduction in lost circulation incidents. The method of lost circulation monitoring proposed in this paper provides an important safety guarantee for the oil drilling industry.

Monthly Maximum Magnitude Prediction in the North–South Seismic Belt of China Based on Deep Learning

The North–South Seismic Belt is one of the major regions in China where strong earthquakes frequently occur. Predicting the monthly maximum magnitude is of significant importance for proactive seismic hazard defense. This paper uses seismic catalog data from the North–South Seismic Belt since 1970 to calculate and extract multiple seismic parameters. The monthly maximum magnitude is processed using Variational Mode Decomposition (VMD) with sample segmentation to avoid information leakage. The decomposed multiple modal data and seismic parameters together form a new dataset. Based on these datasets, this paper employs four deep learning models and four time windows to predict the monthly maximum magnitude, using prediction accuracy (PA), False Alarm Rate (FAR), and Missed Alarm Rate (MR) as evaluation metrics. It is found that a time window of 12 generally yields better prediction results, with the PA for Ms 5.0–6.0 earthquakes reaching 77.27% and for earthquakes above Ms 6.0 reaching 12.5%. Compared to data not decomposed using VMD, traditional error metrics show only a slight improvement, but the model can better predict short-term trends in magnitude changes.

Condition assessment and predictive maintenance for contact probe using health index and encoder‐decoder LSTM model

AbstractContact probe is broadly used for the continuous monitoring of microelectronic components in manufacturing industries. False rejection of fine product due to defective contact probe significantly reduces the yield in production. Traditionally, defect detection for contact probes heavily depends on a valid range manually defined by engineers over the measured value of certain parameters. However, the subjective range defined according to engineer experience is prone to trigger a high rate of false alarms due to the inherent noise in the measured parameters. To address this issue, we construct a health index (HI) with the contact resistance‐directly‐related features to help monitor and assess the condition of contact probe. Based on the established HI, we develop Long Short‐Term Memory (LSTM) encoder‐decoder machine learning model to assess the condition of contact probe by forecasting the HI value in the future. Encoders from LSTM and convolutional neural network (CNN) are selected as the encoder‐decoder architecture for the sequence‐to‐sequence prediction due to their advantage in extracting the correlation of features at different scales. An explainable Artificial Intelligence (XAI) technique named Local interpretable model‐agnostic explanations (LIME) is used to quantify the contribution of each feature to the model prediction. The encoder from CNN is found to outperform the LSTM encoder in extracting the inter‐feature correlation. Finally, the predicted HI is used to signal the alarm for the maintenance action of contact probe when its value is below a predefined threshold. Comparison between the action alarm triggered by the developed HI and the actual maintenance records suggests that the proposed approach achieves at least 75% accuracy for the triggered alarm in the next 15 mins.

A Dynamic False Alarm Rate Control Method for Small Target Detection in Non-Stationary Sea Clutter

Sea surface non-stationarity poses significant challenges to sea-surface small target detection, particularly in maintaining a stable false alarm rate (FAR). In dynamic maritime scenarios with non-stationary characteristics, the non-stationarity of sea clutter can easily cause significant changes in the clutter feature space, leading to a notable deviation between the preset FAR and the measured FAR. By analyzing the temporal and spatial variations in sea clutter, we model the relationship between the preset FAR and the measured FAR as a two-parameter linear function. To address the impact of sea surface non-stationarity on FAR, the model parameters are estimated in real time within the environment and used to guide the dynamic adjustment of the decision region. We applied the proposed method to both convex hull and support vector machine (SVM) detectors and conducted experiments using measured X-band sea-detecting datasets. Experiments demonstrate that the proposed method effectively reduces the deviation between the measured mean FAR and the preset FAR. When the preset FAR is 10−2, the proposed method achieves an average FAR of 1.067 × 10−2 with the convex hull detector and 1.043 × 10−2 with the SVM detector.

Antinociceptive and antineuropathic effects of Trifolium resupinatum L. on formalin-induced nociception and cervical spinal cord hemi-contusion: Underlying Mechanisms

Ethnopharmacological relevanceTrifolium resupinatum L. (Fabaceae), known as Persian clover, ethnomedicinally used in Persian folk medicine to treat peritoneal inflammation, rheumatism, and back pain. Aim of the studyTo investigate the antineuropathic and antinociceptive activities of Trifolium resupinatum leaves essential oil (TREO) in male Wistar rats, as well as to explore the potential mechanisms of action. Materials and methodsThe antinociceptive activity of TREO and its main constituents, quercetin (Qc) was assessed using the formalin-induced paw licking test. Moreover, the potential mechanisms of antinociception were evaluated through various competitive and non-competitive antagonisms. Additionally, the antineuropathic potential was investigated using the cervical spinal cord hemi-contusion (CCS) model, and the role of phosphorylated Stat-3 was analyzed using Western blotting. ResultsTREO exerted significant antinociceptive activity (P < 0.01) in both phases of the formalin-induced test; however, its effects were more pronounced in the second phase. Modulators of the NO-cGMP-K+ channel pathway significantly reversed the antinociceptive activity of TREO (P < 0.05). Additionally, antagonists of TRPV1 and TRPV2, as well as CB1 and GABAA receptors, significantly reversed the antinociceptive effects of TREO (P < 0.05). In another study, both TREO and Qc significantly attenuated hyperalgesia and mechanical allodynia (P < 0.01) when evaluated using the CCS-induced nociception model. Notably, TREO also reduced the expression levels of interleukin-1 beta, interleukin-2, and tumor necrosis factor alpha in CCS-induced rats (P < 0.05). ConclusionTREO and Qc exhibit both antinociceptive and anti-neuropathic activities. The antinociceptive effects are partially mediated through the NO-cGMP-K+ channel pathways, along with the activation of TRPV, GABA, and cannabinoid receptors. Furthermore, the anti-neuropathic activity of TREO may be partially regulated through the inhibition of cytokines.

Deep CNN for Parkinson’s Disease Classification Using Line Spectral Frequency Images of Sustained Speech Phonation

It is proposed to use Deep Convolution Neural Network (DCNN) which is a good classifier of natural images to learn speech spectrum images of sustained phonation to detect Parkinson’s Disease (PD) as an alternative to the existing feature-based machine learning method. It is shown that the proposed method yields very high accuracy without the need for separate feature computation stage. The speech spectrum representations proposed are Short Time Fourier Transform (STFT) spectrum of size Nx256 and Line Spectral Frequency (LSF) spectrum of size Nx16. LSF reflects the speech production mechanism and it is a novel idea to use LSF spectrum in DCNN to detect PD speech. The spectrum images look like random patterns and the performance is improved when using an additional deeper hidden layer of tampering pattern in the last stage of a fully connected layer. Using a standard PD-sustained phonation dataset the training accuracies achieved are 98.50% and 92.50% for STFT and LSF method, respectively. The validation accuracies achieved are 84.38% for STFT and 100% for LSF. The STFT method results in a sensitivity of 97.05%, a specificity of 88.63%, a precision of 86.84%, an F1-score of 91.66, a false positive rate (FPR) of 11.36%, and a false alarm rate of 12.82%. The LSF method results in a sensitivity 97.05%, a specificity of 95.45%, a precision of 94.28%, an F1-score of 95.65, an FPR of 4.50%, and a false alarm rate of 5.71%. The LSF based method performs better and the performance comparison with the state-of-the-art methods brings out the merits of the LSF spectrum image-based DCNN learning in PD detection using sustained phonation.

9376 Successful Implementation Of Continuous Glucose Monitoring In An Internal Medicine Residency Clinic

Abstract Disclosure: A.E. Manov: None. A. Donepudi: None. A.S. Dhaliwal: None. J. Tam: None. Y. Badi: None. M. Sharaf: None. R. Haddadin: None. A. Wang: None. K. Mefferd: None. Abstract In this 3-year retrospective observational cohort study, data from fifty-one patients with type 1 or type 2 Diabetes Mellitus (DM), receiving a minimum of 3-to 4 insulin injections per day, and self-monitoring their blood glucose (SMBG) 4-times a day, were derived from our internal medicine residency primary care clinic. The patients were equipped with a Continuous Glucose Monitoring (CGM) device that shared 24-hour glucose data with the clinic. They were assigned to members of our CGM team which included internal medicine or transitional year medical residents who functioned under the supervision of a board-certified endocrinologist. In consultation with our endocrinologist, the resident assessed the patients' glucose management data and adjusted their treatment regimens bi-weekly by calling the patients, and monthly by seeing them in the clinic. The rationale of the study which was confirmatory was to show that in the Internal Medicine Residency clinic in a project governed by the medicine residents, the CGM will lead to improvement in diabetes mellitus control compared to SMBG and this can be done not only in specialized endocrine clinics. The inclusion criteria for selecting the patients were as follows: Age-18-90, diagnosis of Type 1 or type 2 Diabetes Mellitus using 3-4 Injections of Insulin per day +/- other per oral antidiabetic medications and SMBG 4 times a day, HbA1c above 7% while using SMBG, patients who can use the CGM device and receive their primary medical care in our Internal medicine Residency clinic.To assess for differences in A1C, TIR, average blood glucose, percent of time spent in mild hypoglycemia, and percent of time spent in pronounced hypoglycemia, five paired t-tests were conducted. A Bonferroni correction set the new accepted alpha level at 0.01. Significant results from the study include a reduction of HbA1c from 9.9% to 7.6%, average blood glucose decrement from 242 mg/dL to 169 mg/dl, reduction of the incidence of mild hypoglycemia below 70 mg/dl to 54 mg/dl from 4.68% to 0.76% per day, and more pronounced hypoglycemia with glucose less than 54 mg/dl from 3.1% per day to 0.2% per day. We observed a significant increase in the time in the range of blood glucose from 33% to 67% per day. Furthermore, 9.5% of the patients in this study eventually discontinued daily insulin injections and continued treatment with oral diabetic medications with or without the use of injectable GLP-1 receptors once a week.This study affirms that CGM devices significantly improved glycemic control compared to SMBG, supporting its efficacy in optimizing glycemic control in real-world clinical practice. The results imply this can be accomplished in internal medicine residency clinics and not exclusively in specialized endocrine clinics. As far as we know, this is the first study of this kind in a residency clinic in the USA. Presentation: 6/1/2024

12397 Predictors Of Hospital Readmission Within Three Months Among Patients Admitted For Diabetic Ketoacidosis (dka)

Abstract Disclosure: O. Onwudiwe: None. L. Yerashevich: None. C. Onwudiwe: None. B. Baral: None. A. Acharya: None. H. Nguyen: None. Purpose: Diabetic ketoacidosis (DKA) represents a critical, life-threatening complication of diabetes, with higher rates of re-admission noted among individuals from lower socioeconomic backgrounds, those lacking insurance coverage, and those with suboptimal follow-up care. This study seeks to identify predictors of hospital readmission within three months among patients admitted for DKA in a community hospital setting. Methods: Patient data were extracted from electronic medical records from 2019 to 2021 for individuals admitted due to DKA using ICD codes. DKA diagnosis was based on the presence of hyperglycemia, high anion gap metabolic acidosis, and ketonemia. Demographic information, results of routine biochemical tests, insurance status, follow-up with primary care providers (PCP) or endocrinologists, and clinical progression were retrospectively collected from patients' records. Multivariable logistic regression analyses were conducted to identify predictors of readmission. Results: A total of 222 unique DKA admission visits were analyzed. The mean age (standard deviation) of the patients was 48 (16) years, and 58% of them were male. Among these visits, 39% had type 1 diabetes, 43% had type 2 diabetes, and 18% were newly diagnosed diabetes cases. The majority of patients were overweight, with a mean body mass index (BMI) of 27, and the mean glycated hemoglobin level was 10.8. Common triggers for DKA included insulin non-adherence (52%), infection (37.8%), and alcohol use (8.6%). Logistic regression analysis revealed that lack of follow-up significantly increased the odds of readmission within three months (B = 2.025, p = .003, Exp(B) = 7.576, 95% CI [1.952, 29.401]), as did having type 1 diabetes (B = 1.473, p = .019, Exp(B) = 4.364, 95% CI [1.272, 14.978]). However, the association between insulin adherence and readmission was not statistically significant at the conventional alpha level of 0.05 (B = 1.329, p = .112, Exp(B) = 3.777, 95% CI [0.733, 19.458]). Other demographic (age, gender, employment status, insurance status, medication compliance,) and clinical [insulin adherence, presence of infection, blood glucose level at diagnosis of DKA, and level of glycated hemoglobin (Hba1c)] variables did not significantly predict readmission within 3 months. Conclusion: DKA, being preventable with adequate insulin adherence, is also influenced by factors such as infection and lifestyle choices. The findings highlight the critical role of post-discharge follow-up care in reducing readmission rates among DKA patients. Healthcare providers should prioritize ensuring timely and comprehensive follow-up, particularly for patients with type 1 diabetes, to optimize post-hospitalization management and prevent avoidable readmissions. Presentation: 6/3/2024

EVALUATING THE CHARACTERISTICS OF THE VTSM SPECTRUM SENSING METHOD IN COGNITIVE RADIO NETWORKS

The article investigates the complexities associated with spectrum analysis in cognitive radio networks (CRNs). It begins by acknowledging the evolving challenges of spectrum sensing due to the dynamic nature of wireless environments and the increasing demand for efficient spectrum utilization. The study thoroughly examines various traditional spectrum sensing methods, including Energy Detector Based Sensing, Waveform-Based Sensing, Cyclostationarity-Based Sensing, and Matched Filtering. The focus of the research is on the Variable Time Segment Monitoring (VTSM) method, a novel approach that optimizes spectrum sensing by dynamically adjusting the time segments used for analyzing spectral characteristics. The paper highlights the VTSM method's ability to enhance detection accuracy and reduce false alarms by adapting to different signal environments, making it particularly suited for complex and dynamic CRN scenarios. Furthermore, the article compares VTSM with traditional methods across key performance metrics such as detection accuracy, false alarm rate, latency, and computational complexity. The analysis reveals that while traditional methods have their strengths, VTSM offers a balanced approach, combining flexibility, accuracy, and moderate computational demands, thereby providing a versatile solution for modern spectrum sensing challenges. The findings contribute to the broader understanding and advancement of cognitive radio technologies, supporting the development of more robust and efficient spectrum sensing solutions, which are crucial for optimizing network performance and ensuring reliable communication in increasingly congested and complex wireless environments. The article concludes by emphasizing the importance of selecting the appropriate spectrum sensing method based on the specific requirements of the CRN application, considering factors such as accuracy, computational resources, and environmental dynamics. The findings contribute to the broader understanding and advancement of cognitive radio technologies, supporting the development of more robust and efficient spectrum sensing solutions.

Efficacy of Topical Cyclosporine Combined with Punctal Plugs in Treating Dry Eye Disease and Inflammation

Purpose To evaluate the effect of punctal plugs combined with cyclosporine eye drops on dry eye disease (DED) and ocular surface inflammation. Methods In a clinical trial, 73 patients were randomly allocated into three groups: punctal plug group, combination therapy group, and cyclosporine group. At the baseline and four weeks after treatment, the Schirmer I test score, fluorescein tear film break-up time (FBUT), ocular surface staining score and dry eye symptoms were assessed. Tear samples were collected to detect the level of inflammatory factors (interleukins, matrix metalloproteinase 9 (MMP-9) and tumor necrosis factor alpha (TNF-α)). In an animal experiment, a New Zealand rabbit dry eye model was induced. The rabbits were randomly divided into control group, punctal plug group, and combination therapy group (n = 6). Conjunctival goblet cell density, protein level of MMP-9 in conjunctiva and mRNA levels of inflammatory factors in conjunctiva and cornea were measured before and after treatment. Results In combination therapy group of the clinical trial, the following results were observed: significant improvement in Schirmer I test scores and FBUT compared to the cyclosporine group and punctal plug group, respectively; a decrease in the tear levels of IL-6, IL-1, and MMP-9 compared to the punctal plug group; and a decrease in the tear levels of IL-1α, IL-6, and IL-17 compared to the baseline (all p < 0.05). In the animal experiment, rabbits in combination therapy group had a higher goblet cell density (p < 0.01) and lower mRNA levels of IL-16 (p < 0.05), IL-17 (p < 0.05), and MMP-9 (p < 0.01) in conjunctiva and that of MMP-9 (p < 0.01) in cornea compared to punctal plug group. Conclusion Cyclosporine eye drops combined with degradable punctal plugs is a more optimized clinical treatment strategy for DED compared with degradable punctal plugs or cyclosporine eye drops alone, considering the influence of comprehensive clinical efficacy and ocular surface inflammation.

Marine Radar Constant False Alarm Rate Detection in Generalized Extreme Value Distribution Based on Space-Time Adaptive Filtering Clutter Statistical Analysis

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the Pfa is set at 0.0001, the proposed method exhibits an average improvement in PD of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the Pfa is set at 0.001, the proposed method exhibits an average improvement in PD of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR.

The effects of metformin on inflammation and apoptosis in rats with preeclampsia.

Defined clinically by elevated blood pressure along with either proteinuria and/or maternal organ dysfunction, representing a major cause of morbidity and mortality pregnant women and newborns. Metformin (MET), an oral antidiabetic medication, has been shown to prevent preeclampsia (PE) through various mechanisms, including reducing inflammation, improving endothelial dysfunction, improving mitochondrial function, and altering cellular homeostasis and energy metabolism. Herein, we explored the role of MET in PE and its underlying molecular mechanisms using in in vivo experiments. RT-qPCR, Western blot (WB), and immunohistochemistry (IHC) were conducted to assess the mRNA or protein expression of genes related to mitochondrial apoptosis. Additionally, ELISA was conducted to quantify the expression of mitochondrial apoptosis and inflammation-related genes, as well as PE biomarkers. Treatment with MET in PE rats ameliorated hypertension and proteinuria, altered the expression of PE biomarkers, and significantly inhibited L-NAME-induced inflammation and cell apoptosis. MET modulated the levels of inflammatory cytokines tumor necrosis factor alpha (TNF-α), interleukin (IL)-6, and IL-10, mitigating inflammation in PE rats. Furthermore, MET regulated mitochondrial outer membrane permeability (MOMP), thereby reducing cell apoptosis occurring in the mitochondrial pathway of PE rats. This study demonstrates that MET alleviates inflammation and cell apoptosis in PE rats by modulating the expression of inflammatory factors and MOMP. Our results indicate that MET has huge therapeutic potential against PE.

Optimizing the Activation of WWTP Wet-Weather Operation Using Radar-Based Flow and Volume Forecasting with the Relative Economic Value (REV) Approach

Wastewater treatment plants (WWTPs) connected to combined sewer systems must cope with high flows during wet-weather conditions, often leading to bypass and thus pollution of water bodies. Radar rainfall forecasts coupled with a rainfall-runoff model provides flow and volume forecasts that can be used for deciding when to switch from normal to wet-weather operation, which temporarily allows for higher inflow. However, forecasts are by definition uncertain and may lead to potential mismanagement, e.g., false alarms and misses. Our study focused on two years of operational data from the Damhuså sewer catchment and WWTP. We used the Relative Economic Value (REV) framework to optimize the control parameters of a baseline control strategy (thresholds on flow measurements and radar flow prognosis) and to test new control strategies based on volume instead of flow thresholds. We investigated two situations with different objective functions, considering higher negative impact from misses than false alarms and vice versa, and obtained in both cases a reduction of the rate of false alarms, higher flow thresholds and lower bypass compared to the baseline control. We also assess a new control strategy that employs thresholds of predicted accumulated volume instead of predicted flow and achieved even better results.

Effect of lycopene on prostate cancer among native African men: A protocol for an open-label randomized clinical trial in Tanzania

Background Prostate cancer (PCa) is the most common cancer and the fifth leading cause of death in men worldwide. The treatment of PCa depends on the clinical stage of the disease, prostate-specific antigen (PSA) level, and histology. Lycopene is a bright red carotenoid found in tomatoes, which enhances apoptosis in prostate cells, but its effectiveness has not been studied in East African countries. This study aimed to determine the effectiveness of lycopene from tomato extracts in reducing PSA levels, disease progression, and apoptosis in the prostate glands of men with PCa in Tanzania. Methods This study will be a randomized phase III clinical trial of men diagnosed with PCa in Tanzania. In total, 400 men will be randomized in a 1:1 ratio to receive intervention (n=200) and control (n=200) and followed for 12 months. The intervention arm will receive tomato paste for daily use in addition to the standard treatment, whereas the control arm will only follow the standard of care. The primary endpoints will be a reduction in PSA levels, improved clinical status, and apoptosis of the prostate gland. Data analysis was performed based on the intention-to-treat principle. Descriptive statistics were used to compare average lycopene and PSA levels in the intervention group using T-test and Chi-squared tests. Generalized linear mixed models will be used to further assess the effect of lycopene on PCa progression, urinary symptoms, and PSA reduction. All statistical tests were two-sided at an alpha level of &lt;0.05. Discussion The study used a food supplement as a drug/intervention with minimal or no adverse reactions. However, there is a fear that the control group may not adhere to the protocol after learning the benefits of tomato paste. The study findings will promote the consumption of tomato paste in males diagnosed with PCa to improve the clinical status and reduce disease progression. Trial registration The study has been registered at the Pan African Clinical Trial Registry with registration No PACTR202405488763956.

MFT: A novel memory flow transformer efficient intrusion detection method

Intrusion detection is a critical field in network security research that is devoted to detecting malicious traffic or attacks on networks. Even with the advances in today's Internet environment, a lot of intrusion detection techniques still fail to take into account the long-term characteristics present in network data, which results in a high false alarm rate. Some researchers have tried to address this problem by using the traditional transformer model; however, it is not very effective when dealing with complex relationships and the subtle classification requirements of large amounts of sequential data. This work presents a novel solution called the memory flow transformer (MFT) in response to the limitations of the conventional transformer model. By utilizing a carefully designed memory flow structure, MFT transcends traditional limitations and makes it possible to obtain complex long-term features from network traffic. This innovation enables the model to identify deep connections at a finer level between a wide variety of network traffic data. Extensive experiments were carried out on the complex CICIDS 2017 and NSL-KDD datasets to validate the effectiveness of the MFT model. The results were outstanding, demonstrating MFT's powerful detection abilities. With regard to performance metrics like accuracy, F1 score, false alarm rate, and training time, MFT is superior to current state-of-the-art approaches. Network security is greatly strengthened by MFT, which provides practitioners in the intrusion detection field with novel and effective techniques.

A deep decentralized privacy-preservation framework for online social networks

This paper addresses the critical challenge of privacy in Online Social Networks (OSNs), where centralized designs compromise user privacy. We propose a novel privacy -preservation framework that integrates blockchain technology with deep learning to overcome these vulnerabilities. Our methodology employs a two-tier architecture: the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm (ePSOGSA) for optimizing feature selection, while the second tier employs an enhanced Non-symmetric Deep Autoencoder (e-NDAE) for anomaly detection. Additionally, a blockchain network secures users’ data via smart contracts, ensuring robust data protection. When tested on the NSL-KDD dataset, our framework achieves 98.79% accuracy, a 10% false alarm rate, and a 98.99% detection rate, surpassing existing methods. The integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

Real world feasibility of patient-based real time quality control (PBRTQC) using five analytes in a South African laboratory

BackgroundPatient-based real-time quality control (PBRTQC) identifies possible bias in methods by utilising shifts in trend of statistical measures in laboratory results. In this study we aimed to compare and optimize various PBRTQC procedures for serum alanine aminotransferase, albumin, calcium, ferritin and sodium. MethodsIn a bias simulation study, we added artificial bias to intervals of patient data and then evaluated the efficiency with which various PBRTQC procedures were able to detect this bias. PBRTQC procedures used included block size, moving statistic calculation, control limits as well as truncation limits. The number of patients till error detection, the false alarm rate as well as validation charts were utilised to select the optimal PBRTQC procedure for each analyte. ResultsThe optimal PBRTQC procedures identified for each analyte were: ALT − MA T0 50 MaxMin; Albumin − MA T5 100 Perc; Calcium − MM T5 50 Perc; Ferritin − MA T0 100 MaxMin; and Sodium − MA T5 75 MaxMin. (T, Truncation limits; Control limits- MA, Moving Average, MM, Moving median; Perc, percentile) ConclusionsThe use of large, real patient datasets allows for the reliable determination of laboratory-specific PBRTQC procedures. This study demonstrates that the moving average calculation excels in both normal and transformed analyte distributions. Whilst, the benefits of PBRTQC procedures are proven, the complex and time-consuming optimisation process may be a barrier to the rapid implementation in under-resourced countries.

An algorithm for multi-target tracking in low-signal-to-clutter-ratio underwater acoustic scenes

To simplify the computational complexity of the Joint Probabilistic Data Association (JPDA) algorithm for multi-target tracking in low-signal-to-clutter-ratio underwater acoustic scenes, and to solve the problem of unstable tracking caused by poor measurement accuracy of active sonar, a trajectory information-based JPDA optimization algorithm (TJPDA) is designed to meet the real-time and stability requirements of practical engineering. The false alarm rate is reduced by accumulating correlated target trajectories in multiple frames, and the influence factor is introduced to modify the correlation probability and simplify the confirmation matrix splitting process. This effectively solves the problem of information combination explosion and trajectory divergence caused by adjacent target interference in low signal-to-clutter-ratio underwater acoustic scenes. The simulation results show that compared with the JPDA algorithm, the TJPDA algorithm not only improves target-tracking accuracy but also reduces computation time, making it more suitable for practical engineering applications.

Indication of Sharp and Strong Phase Transitions from NICER Observations

In this Letter, we present a new, weakly model-dependent test for “standard” equation-of-state (EoS) models that disfavor sharp and strong phase transitions by using NS mass and radius observations. We show the radii of two NSs observed by NICER (PSR J0740+6620 and PSR 0030+0451) are correlated if these two NSs are built upon standard EoS models. The radii of NSs with different masses are sensitive to the pressures at different densities, and the pressures at different densities are strongly correlated in standard EoS models. We further show that the correlation of the NS radii can be significantly weakened when additional degrees of freedom concerning the first-order phase transitions are added into the EoSs. We propose a new quantity, D L, which measures the extent to which the linear correlation of the radii of two NSs is weakened. Our method gives a 48% identification probability (with a 5% false alarm rate) of finding beyond-standard EoS models in NICER observations. Future observations with higher measurement accuracy can confirm or rule out this identification. Our method is generalizable to any pair of NS masses and can be employed with other sets of observations in the future.