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Hybrid nanofluids (HNFs) are a new breed of nanofluids that possess numerous tempting applications encompassing microfluidics, transportation, defense, medical, etc. The objective of this novel exploration is to inspect the behavior of Hamilton–Crosser (H-C) and Yamada–Ota (Y-O) HNF flow models past a stretching cylinder. The H-C model is also used to gauge which particle shape (blade, platelet, cylinder, brick) is more effective in the improvement of the heat transfer rate. The envisioned flow is influenced by the Hall current, Cattaneo–Christov (C-C) heat flux and variable thermal conductivity (TC). The uniqueness of the projected model is the notion of a heterogeneous reaction sprouting on the surface of the cylinder in the presence of an absorbent medium. Owing to this supposition, the chemical reaction occurs in the least possible time. The proposed model’s novelty lies in the consideration of the surface catalyzed reaction in the HNF flow models past a stretching cylinder amalgamated with the unique impacts of the Hall current, C-C heat flux and variable TC. The thermal performance of the two renowned models H-C and Y-O is also evaluated. The MATLAB software bvp4c technique is used for numerical outcomes of this coupled system. The analysis depicts that the performance of the Y-O HNF flow model is far above the H-C HNF flow model. It is also inferred from the results that blade-shaped nanoparticles possess higher TC than the other nanoparticles. The heat transfer rate for blade-shaped nanoparticles is stronger than the other nanoparticles. The fluid concentration reduces for higher surface-catalyzed reaction parameter. The corroboration of the proposed model is also given in this study. The comparative results disclosed that in the case of the magnetic parameter [Formula: see text] the minimum error percentage is 0.015% for [Formula: see text] and permeability parameter [Formula: see text] the least error percentage is 0.037% for [Formula: see text]

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EEG signals are recordings of electrical activity in the brain, and they can be used to detect epileptic seizures. Cross-spectral analysis involves analyzing the spectral coherence between different frequency bands in EEG signals. Cross-spectral seizure detection is a technique used to detect seizures in electroencephalography (EEG) signals by analyzing the frequency content of the signal in different frequency bands. Spectral coherence is a measure of how correlated two signals are in the frequency domain, and it can be used to identify patterns in EEG signals that are characteristic of seizures. Even if the ECG, which provides a direct measure of heart rate, were polluted by noise or missing altogether, the heart rate computed from such signals would be erroneous. This necessitates the use of an accurate heart rate estimate, which is especially important when the ECG is noisy or missing. To put it another way, fusion combines cardiovascular data with no cardiovascular (NC) signals, which are not connected to cardiac activity but include signs of heartbeats. According to the results of our evaluation of standard datasets, they determined that the SSF-TKE approach is particularly successful at identifying R-peak artefacts in non-cardiovascular signals that ECG anomalies have contaminated. When tested on standard datasets, the beat SQI-based voting fusion technique demonstrated a high degree of accuracy in predicting heart rate from a fusion of multimodal information. Compared to the single signal technique, the fusion methodology out per-forms it when determining heart rate precision. This strategy was evaluated using ECG and ABP signals from a synthetic noise dataset, which was created by adding various forms of calibrated noise to clean signals and then testing the outcomes of the technique on those signals. As a result of our paper, we noticed that merging cardiovascular and no cardiovascular inputs increased the accuracy of physiological parameter assessment.

Over the past decade, the significance of solar photovoltaic (PV) system has played a major role due to the rapid growth in the solar PV industry. Reliability, efficiency and safety of solar. PV system can be enhanced by continuous monitoring of the system and detecting the faults if any as early as possible. Reduced real time power generation and reduced life span of the solar PV system are the results if the fault in solar PV system is found undetected. Therefore, it is mandatory to identify and locate the type of fault occurring in a solar PV system. The faults occurring in the solar PV system are classified as: physical, environmental, and electrical faults which are further classified into different types as described in this paper. Once a fault is located and detected, an appropriate diagnosis method needs to be used to rectify it. In this paper, a comprehensive review of diverse fault diagnosis techniques reported in various literature is listed and described. This paper helps the researchers to get an awareness of the various faults occurring in a solar PV system and enables them to choose a suitable diagnosis technique based on its performance metrics to rectify the fault occurring in solar PV systems.

Background  Pericarditis is a common cardiology presentation, most often due to a viral or idiopathic cause. Listeria as a cause of pericarditis is rare. Listeria is an infection that is readily treatable with antibiotics following accurate identification. Without adequate treatment, Listeria infection has a high mortality rate. Case summary  In this case, a fit and well 59-year-old man complained of headaches and fever to the emergency department (ED). He was provisionally diagnosed with giant cell arteritis (GCA) and commenced on management pathways for GCA. He represented to the ED with chest pain and electrocardiogram (ECG) changes suggestive of a clinical presentation of pericarditis. He received treatment for idiopathic pericarditis with no clinical resolution. Cardiac magnetic resonance imaging (MRI) showed myopericardial inflammation associated with a right atrial mural thrombus. After 2 weeks of poor treatment response, peripheral blood cultures grew Listeria monocytogenes and the patient responded well to antibiotic treatment. Repeat cardiac MRI after an extended course of antibiotics showed resolution of MRI signs. Discussion  This is a case of Listeria myopericarditis. Physicians should consider rarer causes of myopericarditis in treatment resistance cases. Cardiac MRI has utility in atypical or treatment resistant patients to assess myopericardial inflammation and response to treatment.

Treating cancer tumors is a main goal of cancer research. The author of this paper identifies a new manner to treat cancer tumors more effectively using a recommended architecture of a nanorobot called CANBOT. It contains a number of nano-components: an actuator, temperature sensor, chemical sensor, and microcontroller. CANBOT starts its role by moving toward the tumor cells using the actuator. It senses the tumor cell by capturing its image and sensing its chemicals by the chemical sensor. When CANBOT distinguishes the tumor, it verifies the survival of the tumor cells by its temperature sensor. CANBOT increases the temperature of the tumor cell through the warmer. Sensing of the cancer chemicals starts over to detect the remaining existence of cancer cells. The suggested nanorobot injects the cell with the drug from a tiny tank throughout a nano pump with a small pine needle. A nano-microcontroller controls the mechanism of CANBOT formative the role of each one and the appropriate sequences. The position of the proposed nanorobot is simulated with reference to the position of the tumor using an analytical model. The conclusion is drawn that destroying the tumor requires instilling the robot into the cancer tumor directly for effective treatment.

Genomic tiling arrays are able to inspect the genome of haphazard species for which the sequence is known. The plan of proper oligonucleotide probes for such arrays is computationally difficult if features such as oligonucleotide quality and recurring regions are considered. Prior works have developed the minimal tiling path problem for the choice of oligonucleotides using Dijkstra’s shortest path algorithm to compute universal finest tiling paths from millions of candidate oligonucleotides on computers. Although Dijkstra’s algorithm works well, it is complicated and may take a long time for routers to process it and the efficiency of the network fails. In this paper, the author discusses a search approach that can decrease the average complexity time of tilling arrays. This aspiration is realized by searching for the shortest path to the probes using a faster algorithm. This paper enhances A* Algorithm and exploits the enhanced version, called A**, instead of Dijkstra’s algorithm. The enhanced version is more efficient and can decrease the average time complexity, thus increasing the performance of tiling array.