Optimal Voting Rule and Minimization of Total Error Rate in Cooperative Spectrum Sensing for Cognitive Radio Networks, Journal of Telecommunications and Information Technology, 2021, nr 1

In cognitive radio technology, spectrum sensing is essential for detecting spectrum holes which may be allotted to secondary users. In this paper, an optimal voting rule is used for cooperative spectrum sensing while minimizing the total error rate (TER). The proposed spectrum sensing method is more energy-efficient and may be implemented in practice. It is relied upon in an improved energy detector whose utilization depends on the presence or absence of the primary user. Expressions for false alarm and missed detection probabilities are derived in the paper as well. Overall performance is analyzed both for AWGN and Rayleigh fading channels, in the presence of additive white Gaussian noise (AWGN). The optimum voting rule is applied to the cooperative spectrum sensing process in order to identify the optimum number of sensing nodes and the detection threshold. Finally, an energy-efficient spectrum sensing algorithm is proposed, requiring a lower number of cognitive users for a given error bound

Efficacy of nano-hydroxyapatite prepared by an aqueous solution combustion technique in healing bone defects of goat

The present study was undertaken to evaluate porous hydroxyapatite (HAp), the powder of which was prepared by a novel aqueous solution combustion technique, as a bone substitute in healing bone defects in vivo, as assessed by radiologic and histopathologic methods, oxytetracycline labeling, and angiogenic features in Bengal goat. Bone defects were created in the diaphysis of the radius and either not filled (group I) or filled with a HAp strut (group II). The radiologic study in group II showed the presence of unabsorbed implants which acted as a scaffold for new bone growth across the defect, and the quality of healing of the bone defect was almost indistinguishable from the control group, in which the defect was more or less similar, although the newly formed bony tissue was more organized when HAp was used. Histologic methods showed complete normal ossification with development of Haversian canals and well-defined osteoblasts at the periphery in group II, whereas the control group had moderate fibro-collagenization and an adequate amount of marrow material, fat cells, and blood vessels. An oxytetracycline labeling study showed moderate activity of new bone formation with crossing-over of new bone trabeculae along with the presence of resorption cavities in group II, whereas in the control group, the process of new bone formation was active from both ends and the defect site appeared as a homogenous non-fluoroscent area. Angiograms of the animals in the control group showed uniform angiogenesis in the defect site with establishment of trans-transplant angiogenesis, whereas in group II there was complete trans-transplant shunting of blood vessel communication. Porous HAp ceramic prepared by an aqueous combustion technique promoted bone formation over the defect, confirming their biologic osteoconductive property

Acute otitis externa: Consensus definition, diagnostic criteria and core outcome set development.

OBJECTIVE: Evidence for the management of acute otitis externa (AOE) is limited, with unclear diagnostic criteria and variably reported outcome measures that may not reflect key stakeholder priorities. We aimed to develop 1) a definition, 2) diagnostic criteria and 3) a core outcome set (COS) for AOE. STUDY DESIGN: COS development according to Core Outcome Measures in Effectiveness Trials (COMET) methodology and parallel consensus selection of diagnostic criteria/definition. SETTING: Stakeholders from the United Kingdom. SUBJECTS AND METHODS: Comprehensive literature review identified candidate items for the COS, definition and diagnostic criteria. Nine individuals with past AOE generated further patient-centred candidate items. Candidate items were rated for importance by patient and professional (ENT doctors, general practitioners, microbiologists, nurses, audiologists) stakeholders in a three-round online Delphi exercise. Consensus items were grouped to form the COS, diagnostic criteria, and definition. RESULTS: Candidate COS items from patients (n = 28) and literature (n = 25) were deduplicated and amalgamated to a final candidate list (n = 46). Patients emphasised quality-of-life and the impact on daily activities/work. Via the Delphi process, stakeholders agreed on 31 candidate items. The final COS covered six outcomes: pain; disease severity; impact on quality-of-life and daily activities; patient satisfaction; treatment-related outcome; and microbiology. 14 candidate diagnostic criteria were identified, 8 reaching inclusion consensus. The final definition for AOE was 'diffuse inflammation of the ear canal skin of less than 6 weeks duration'. CONCLUSION: The development and adoption of a consensus definition, diagnostic criteria and a COS will help to standardise future research in AOE, facilitating meta-analysis. Consulting former patients throughout development highlighted deficiencies in the outcomes adopted previously, in particular concerning the impact of AOE on daily life

INVESTIGATION OF DISCRETE WAVELET TRANSFORM DOMAIN OPTIMAL PARAMETRIC APPROACH FOR DENOISING OF PHONOCARDIOGRAM SIGNAL

Phonocardiogram (PCG) signals are contaminated with various noise signals, which hinders the accurate diagnostic interpretation of the signal. Discrete wavelet transform (DWT) is a well-known technique used to remove noise from PCG signals and improve signal quality. The performance of DWT-based denoising depends upon several parameters involved in the process, such as mother wavelets used for decomposition, the number of decomposition levels (DLs), thresholding technique used and the threshold estimation rule followed. In this work, an investigative study is carried out to select the optimal parameter values which give the best denoising performance. The metrics such as mean-square error (MSE), normalized-mean-square error (NMSE), root-mean-square error (RMSE), percentage root-mean-square difference (PRD) and signal-to-noise ratio (SNR) are used to evaluate the performance of the denoising in this study. The results obtained show that the fifth-order Coiflet wavelet is best suited for denoising PCG signals when applied with the soft thresholding (ST) function and rigrsure threshold selection rule. Also, the optimum number of DLs resulting in better performance is level 6. SNR value obtained from the studies shows the efficacy of the parameters selected for denoising. The denoised PCG signals provide accurate information to determine various kinds of heart valve-related disorders (HVDs). </jats:p

Deep Learning-Based Hybrid Model with Multi-Head Attention for Multi-Horizon Stock Price Prediction

The prediction of stock prices is challenging due to their volatility, irregular patterns, and complex time-series structure. Reliably forecasting stock market data plays a crucial role in minimizing financial risk and optimizing investment strategies. However, traditional models often struggle to capture temporal dependencies and extract relevant features from noisy inputs, which limits their predictive performance. To improve this, we developed an enhanced recursive feature elimination (RFE) method that blends the importance of impurity-based features from random forest and gradient boosting models with Kendall tau correlation analysis, and we applied SHapley Additive exPlanations (SHAP) analysis to externally validate the reliability of the selected features. This approach leads to more consistent and reliable feature selection for short-term stock prediction over 1-, 3-, and 7-day intervals. The proposed deep learning (DL) architecture integrates a temporal convolutional network (TCN) for long-term pattern recognition, a gated recurrent unit (GRU) for sequence capture, and multi-head attention (MHA) for focusing on critical information, thereby achieving superior predictive performance. We evaluate the proposed approach using daily stock price data from three leading companies&mdash;HDFC Bank, Tata Consultancy Services (TCS), and Tesla&mdash;and two major stock indices: Nifty 50 and S&amp;P 500. The performance of our model is compared against five benchmark models: temporal convolutional network (TCN), long short-term memory (LSTM), GRU, Bidirectional GRU, and a hybrid TCN&ndash;GRU model. Our method consistently shows lower error rates and higher predictive accuracy across all datasets, as measured by four commonly used performance metrics

Localization of transfer RNA genes on the physical map of Vibrio eltor phage e<SUB>4</SUB> genome

Transfer RNAs were isolated from uninfected and phage e<SUB>4</SUB>-infected Vibrio eltor Mak 757 cells. These tRNAs were then aminoacylated with <SUP>3</SUP>H-labeled amino acids and hybridized to DNA isolated from phage e<SUB>4</SUB>. Significant hybridization was observed only with tRNA isolated from phage e<SUB>4</SUB>-infected cells. Restriction enzyme digestion of phage e<SUB>4</SUB> DNA followed by Southern blot using [<SUP>32</SUP>P]tRNA from infected cells revealed that tRNA genes were contained in a 3.4-kb Kpnl fragment. The tRNA genes were located on the physical map of the phage genome 19 kb from one of the termini

Investigation on explainable machine learning models to predict chronic kidney diseases

Abstract Chronic kidney disease (CKD) is a major worldwide health problem, affecting a large proportion of the world’s population and leading to higher morbidity and death rates. The early stages of CKD sometimes present without visible symptoms, causing patients to be unaware. Early detection and treatments are critical in reducing complications and improving the overall quality of life for people afflicted. In this work, we investigate the use of an explainable artificial intelligence (XAI)-based strategy, leveraging clinical characteristics, to predict CKD. This study collected clinical data from 491 patients, comprising 56 with CKD and 435 without CKD, encompassing clinical, laboratory, and demographic variables. To develop the predictive model, five machine learning (ML) methods, namely logistic regression (LR), random forest (RF), decision tree (DT), Naïve Bayes (NB), and extreme gradient boosting (XGBoost), were employed. The optimal model was selected based on accuracy and area under the curve (AUC). Additionally, the SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) algorithms were utilized to demonstrate the influence of the features on the optimal model. Among the five models developed, the XGBoost model achieved the best performance with an AUC of 0.9689 and an accuracy of 93.29%. The analysis of feature importance revealed that creatinine, glycosylated hemoglobin type A1C (HgbA1C), and age were the three most influential features in the XGBoost model. The SHAP force analysis further illustrated the model’s visualization of individualized CKD predictions. For further insights into individual predictions, we also utilized the LIME algorithm. This study presents an interpretable ML-based approach for the early prediction of CKD. The SHAP and LIME methods enhance the interpretability of ML models and help clinicians better understand the rationale behind the predicted outcomes more effectively