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The purpose of this research was to look at the behavior of two well-known commercial aircraft types in Indonesia (the A320 and the B738) during the take-off phase. This was done to provide new information in the field of aviation, particularly flight safety. Observations were made at Sultan Hasanuddin International Airport by observing aircraft ADS-B data, which defines the behavior of the flight pattern. This ADS-B data is the subject of data analysis, which will subsequently be taught to the machine (computer) so that it can recognize the pattern and construct clusters. The purpose of this study is to utilize unsupervised learning, specifically K-Means clustering, to categorize and identify patterns in unlabeled ADS-B data obtained from AERO-TRACK. To prepare the raw data and create a dataset, data analysis techniques were employed. The machine learning model generates three distinct clusters: cluster 1 represents aircraft take-off on two-thirds of the runway, cluster 2 represents aircraft take-off on the entire runway, and cluster 3 represents aircraft take-off on one-third of the runway. The elbow method is utilized to analyze and interpret the three clusters produced by the model. An interesting observation is that the B738 aircraft dominate in all three clusters, while the A320 aircraft dominate in clusters 1 and 3. Notably, in cluster 2, there is a significant number of commercial planes taking off, accounting for 145 out of 628 flights. Based on the observed data spanning 91 days (September 26 to December 26, 2022), there is a 23% probability of runway excursion (overshooting the runway) in this cluster. Additionally, the research reveals that A320 aircraft demonstrate a safe zone take-off rate of 87%, whereas the B738 aircraft demonstrate a rate of 70.5%. These findings, derived from the analysis of ADS-B data such as GPS-Altitude and Coordinate, are intended to serve as valuable knowledge for aviation authorities, aviation users, and other stakeholders in the aviation industry [ABSTRACT FROM AUTHOR]

The rate coefficients for the reactions of Ar+ and Ar+2 with CH4 and CS2 are obtained as a function of energy by using a selected ion flow tube with an electric drift field (SIFT-DRIFT). A diagnostic analysis of the system and technique has been completed and allows rate coefficients to be reported with a high level of confidence. Rate coefficients and product branching ratios are determined for the methane reactions from thermal (room temperature) to center-of-mass kinetic energies of about 0.15 eV and for the carbon disulfide reactions from thermal to about 0.6 eV. The overall rate coefficients for these reactions do not vary a great deal over the investigated energy range. The greatest dependence is found for the slowest reaction Ar+ with CS2, where a minimum in the rate coefficient is observed near 0.15 eV. The behavior of the branching ratios for S+ and CS+2 suggests curve crossings between a repulsive state and the A, B, and C electronic states of CS+2. [ABSTRACT FROM AUTHOR]

Rates and products of reaction are reported for the thermal energy charge transfer of Ar+ and Ar+2 with N2, H2, Xe, and Kr using a selected ion flow tube (SIFT). New rate coefficients were measured for the reactions Ar++Xe→Xe++Ar, Ar+2+N2 →Ar+·N2+Ar, Ar+2+H2 →ArH++H+Ar, and Ar+2+H2 →ArH+2+Ar; the rates are 4.3×10-13, 2.2×10-10, 3.6×10-10, and 1.1×10-10 cm3/s, respectively. The finding of (Ar·N2)+ as the product of the reaction of Ar+2 with N2 shows the bond energy of Ar+·N2≥1.27 eV in agreement with other techniques. In the case of H2, two reaction products are observed for reaction with Ar+2[ATOTHER]@B:[/ATOTHER]ArH+ (85±10%) and ArH+2, but not Ar2H+ as suggested by earlier studies. Lower limits for the proton affinity of Ar (3.69 eV) and the dissociation energy for H+2·Ar (0.97 eV) are determined from the present work. Considering all the reaction rates, the proximity of the recombination energy of Ar+ and Ar+2 to a band in the photoelectron spectrum of a specific neutral reactant offers an explanation for the observed ordering of the relative rates of the monomer and dimer reactions. [ABSTRACT FROM AUTHOR]

BackgroundThe US Centers for Disease Control and Prevention have identified a lack of encouragement, support, or companionship from family and friends as a major barrier to physical activity. To overcome this barrier, online social networks are now actively leveraging principles of companion social support in novel ways. ObjectiveThe aim was to evaluate the functionality, features, and usability of existing online social networks which seek to increase physical activity and fitness among users by connecting them to physical activity partners, not just online, but also face-to-face. MethodsIn September 2012, we used 3 major databases to identify the website addresses for relevant online social networks. We conducted a Google search using 8 unique keyword combinations: the common keyword “find” coupled with 1 of 4 prefix terms “health,” “fitness,” “workout,” or “physical” coupled with 1 of 2 stem terms “activity partners” or “activity buddies.” We also searched 2 prominent technology start-up news sites, TechCrunch and Y Combinator, using 2 unique keyword combinations: the common keyword “find” coupled with 1 of 2 stem terms “activity partners” and “activity buddies.” Sites were defined as online social health activity networks if they had the ability to (1) actively find physical activity partners or activities for the user, (2) offer dynamic, real-time tracking or sharing of social activities, and (3) provide virtual profiles to users. We excluded from our analysis sites that were not Web-based, publicly available, in English, or free. ResultsOf the 360 initial search results, we identified 13 websites that met our complete criteria of an online social health activity network. Features such as physical activity creation (13/13, 100%) and private messaging (12/13, 92%) appeared almost universally among these websites. However, integration with Web 2.0 technologies such as Facebook and Twitter (9/13, 69%) and the option of direct event joining (8/13, 62%) were not as universally present. Largely absent were more sophisticated features that would enable greater usability, such as interactive engagement prompts (3/13, 23%) and system-created best fit activities (3/13, 23%). ConclusionsSeveral major online social networks that connect users to physical activity partners currently exist and use standardized features to achieve their goals. Future research is needed to better understand how users utilize these features and how helpful they truly are.

Background: The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, the reconstructed signals of ECG can be processed to ConvBiLSTM. The proposed architecture of DAE-ConvBiLSTM is the end-to-end diagnosis of heart abnormality detection., Results: As a result, the performance of DAE-ConvBiLSTM has obtained an average of above 98.59% accuracy, sensitivity, specificity, precision, and F1 score from the existing studies. The DAE-ConvBiLSTM has also experimented with detecting T-wave (due to ventricular repolarisation) morphology abnormalities., Conclusion: The development architecture for detecting heart abnormalities using an unsupervised learning DAE and supervised learning ConvBiLSTM can be proposed for an end-to-end learning algorithm. In the future, the precise accuracy of the ECG main waveform will affect heart abnormalities detection in clinical practice., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Tutuko et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results.

The aim of the present study was to investigate the effect of silver nanoparticles (AgNP) of different sizes toward two primary producer aquatic species. Thalassiosira pseudonana and Synechococcus sp. have been selected as representative models for the lower trophic organisms in marine and freshwater habitats, respectively. Time-dependent cellular growth was measured upon exposure to both AgNP and silver nitrate (AgNO(3)). In addition, AgNP behavior in freshwater and marine waters has been followed by CPS disc centrifuge, in the time frame of AgNP exposure studies, and the kinetic release of silver from AgNP of different sizes was measured by dialysis and inductively coupled plasma mass spectrometry (ICP-MS). The combination and interpretation of all these data suggest that a shared effect of AgNP and released silver was responsible for the toxicity in both organisms. Furthermore, the toxic effects induced by AgNP exposure in the present study seem to result from a mixture of parameters including aggregated state, size of the AgNP, stability of the preparation, and speciation of the released silver.