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

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

Optimal Voting Rule and Minimization of Total Error Rate in Cooperative Spectrum Sensing for Cognitive Radio Networks

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

Multichannel Multiscale Two-Stage Convolutional Neural Network for the Detection and Localization of Myocardial Infarction Using Vectorcardiogram Signal

Myocardial infarction (MI) occurs due to the decrease in the blood flow into one part of the heart, and it further causes damage to the heart muscle. The 12-channel electrocardiogram (ECG) has been widely used to detect and localize MI pathology in clinical studies. The vectorcardiogram (VCG) is a 3-channel recording system used to measure the heart’s electrical activity in sagittal, transverse, and frontal planes. The VCG signals have advantages over the 12-channel ECG to localize posterior MI pathology. Detection and localization of MI using VCG signals are vital in clinical practice. This paper proposes a multi-channel multi-scale two-stage deep-learning-based approach to detect and localize MI using VCG signals. In the first stage, the multivariate variational mode decomposition (MVMD) decomposes the three-channel-based VCG signal beat into five components along each channel. The multi-channel multi-scale VCG tensor is formulated using the modes of each channel of VCG data, and it is used as the input to the deep convolutional neural network (CNN) to classify MI and normal sinus rhythm (NSR) classes. In the second stage, the multi-class deep CNN is used for the categorization of anterior MI (AMI), anterior-lateral MI (ALMI), anterior-septal MI (ASMI), inferior MI (IMI), inferior-lateral MI (ILMI), inferior-posterior-lateral (IPLMI) classes using MI detected multi-channel multi-scale VCG instances from the first stage. The proposed approach is developed using the VCG data obtained from a public database. The results reveal that the approach has obtained the accuracy, sensitivity, and specificity values of 99.58%, 99.18%, and 99.87%, respectively, for MI detection. Moreover, for MI localization, we have obtained the overall accuracy value of 99.86% in the second stage for our proposed network. The proposed approach has demonstrated superior classification performance compared to the existing VCG signal-based MI detection and localization techniques

Cefuroxime-impregnated calcium phosphates as an implantable delivery system in experimental osteomyelitis

The study aimed to investigate the effectiveness of porous calcium phosphates viz., hydroxyapatite (HAp) and a bi-phasic calcium phosphate (BCP) with predominately beta-tricalcium phosphate (beta-TCP) prepared by aqueous solution combustion method impregnated with cefuroxime axetil for the treatment of experimental osteomyelitis and compared with parenteral treatment. In vitro release of the drug was tested for its sustained elution characteristics for 21 days in PBS (pH 7.2) and measured by HPLC. In the in vivo study, bone infection was induced in tibia of rabbits by inoculation of 1 ml (3 x 10(6)) CFU Staphylococcus aureus. On the 21st day, after the development of osteomyelitis, six animals were treated by filling the cavity with cefuroxime-impregnated HAp blocks (Group II), six animals with the same drug-impregnated beta-TCP (Group III) and in six others, only cefuroxime (15 mg/kg twice daily) was injected parenterally 6 weeks (Group IV). Group I with six animals was kept untreated. Histologically, the signs of infection were found to subside by 3 and 6 weeks. Radiological evaluation with cefuroxime-impregnated HAp and PTCP pointed out the disappearance of sequestrum and existence of newly formed bony specules. Concentration of cefuroxime in bone and serum as estimated by HPLC showed highest value on day 21 itself which reduced marginally by day 42 in both the groups and these values were higher than minimum inhibitor concentration (MIC) against S. aureus. Our findings suggest that bi-phasic calcium phosphates with predominately beta-TCP content is a very efficient carrier material for antibiotic compounds even for refractory infections by S. aureus. (C) 2008 Elsevier Ltd and Techna Group S.r.l. All rights reserved

Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings

The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs