Occupational Injuries and Health Status Among Rural Tribal Non-Traditional Fishing Communities in the Coastal Region of Tamil Nadu, India.

OBJECTIVES: This study evaluated the occupational injuries and health hazards associated with fishing as an occupation among non-traditional rural tribal fishing communities in the coastal region of Tamil Nadu, India. METHODS: This cross-sectional study included a total of 170 individuals belonging to a fishing community, comprising both male (n = 82) and female (n = 88) participants. The demographic details including occupational history, lifestyle characteristics, socio-economic status, personal habits, and health status were assessed through the questionnaire survey. RESULTS: The fishing community has a low socioeconomic status and poor literacy, lifestyle, and personal habits. The mean age of the participants was 38.8 yrs (male 34.8; female 39.9 yrs). Only 10% reported usage of personal protective equipment (PPE), and the work duration varied from 8 to 24 hrs in a day. While male subjects reported smoking habits (12%) and alcohol consumption (23%), none of the females reported alcohol consumption and smoking habits. The major occupational injuries that occurred were due to catfish (72%) and oysters (48%). A large number of female subjects reported musculoskeletal pains. The body mass index of about 28% of fishermen was above the normal range. Abnormal blood sugar, blood pressure, and respiratory and neurological symptoms were the other major health complaints. The major environmental hazards reported were salinity, solar radiation, tides, and high wind. CONCLUSION: Injuries from handling fish and oysters were observed to be the major occupational burden. Additonally, a high prevalence of musculoskeletal pain and chronic health illness was commonly observed among the fishers. Adequate training and awareness programs are required for effective management of occupational health hazards and health promotion.

A new species of six-gilled hagfish (Myxinidae: Eptatretus) from the Lakshadweep Sea.

A new species of hagfish, Eptatretus wadgensis sp. nov., is described from the Wadge Bank, Lakshadweep Sea, India, obtained from a depth of ~250300 m through deep-sea trawling. It is diagnosed by having six pairs of gill pouches and gill apertures, 3/3 multicusp teeth, total slime pores 6769, six branchial slime pores, and ventral aorta bifurcating at the 4th or between 4th and 5th gill pouch. The new species has significant morphological differences in total dental cusps, total slime pores, body proportions and the absence of the nasal-sinus papilla when compared to congeners and formed a distinct clade in phylogenetic reconstruction and a genetic distance of 3.414.00% when comparing K2P parameters with the nearest species. A key to the Eptatretus species of the Indian Ocean is provided.

Emergency rate-driven control for rotor angle instability in power systems.

Renewable energy sources in modern power systems pose a serious challenge to the power system stability in the presence of stochastic fluctuations. Many efforts have been made to assess power system stability from the viewpoint of the bifurcation theory. However, these studies have not covered the dynamic evolution of renewable energy integrated, non-autonomous power systems. Here, we numerically explore the transition phenomena exhibited by a non-autonomous stochastic bi-stable power system oscillator model. We use additive white Gaussian noise to model the stochasticity in power systems. We observe that the delay in the transition observed for the variation of mechanical power as a function of time shows significant variations in the presence of noise. We identify that if the angular velocity approaches the noise floor before crossing the unstable manifold, the rate at which the parameter evolves has no control over the transition characteristics. In such cases, the response of the system is purely controlled by the noise, and the system undergoes noise-induced transitions to limit-cycle oscillations. Furthermore, we employ an emergency control strategy to maintain the stable non-oscillatory state once the system has crossed the quasi-static bifurcation point. We demonstrate an effective control strategy that opens a possibility of maintaining the stability of electric utility that operates near the physical limits.

Explainable artificial intelligence for heart rate variability in ECG signal.

Electrocardiogram (ECG) signal is one of the most reliable methods to analyse the cardiovascular system. In the literature, there are different deep learning architectures proposed to detect various types of tachycardia diseases, such as atrial fibrillation, ventricular fibrillation, and sinus tachycardia. Even though all types of tachycardia diseases have fast beat rhythm as the common characteristic feature, existing deep learning architectures are trained with the corresponding disease-specific features. Most of the proposed works lack the interpretation and understanding of the results obtained. Hence, the objective of this letter is to explore the features learned by the deep learning models. For the detection of the different types of tachycardia diseases, the authors used a transfer learning approach. In this method, the model is trained with one of the tachycardia diseases called atrial fibrillation and tested with other tachycardia diseases, such as ventricular fibrillation and sinus tachycardia. The analysis was done using different deep learning models, such as RNN, LSTM, GRU, CNN, and RSCNN. RNN achieved an accuracy of 96.47% for atrial fibrillation data set, 90.88% accuracy for CU-ventricular tachycardia data set, and also achieved an accuracy of 94.71, and 94.18% for MIT-BIH malignant ventricular ectopy database for ECG lead I and lead II, respectively. The RNN model could only achieve an accuracy of 23.73% for the sinus tachycardia data set. A similar trend is shown by other models. From the analysis, it was evident that even though tachycardia diseases have fast beat rhythm as their common feature, the model was not able to detect different types of tachycardia diseases. The deep learning model could only detect atrial fibrillation and ventricular fibrillation and failed in the case of sinus tachycardia. From the analysis, they were able to interpret that, along with the fast beat rhythm, the model has learned the absence of P-wave which is a common feature for ventricular fibrillation and atrial fibrillation but sinus tachycardia disease has an upright positive P-wave. The time-based analysis is conducted to find the time complexity of the models. The analysis conveyed that RNN and RSCNN models could achieve better performance with lesser time complexity.

Molecular identification of Bigeyes (Perciformes, Priacanthidae) from Indian waters.

Thirty-five individuals of six priacanthid fish species were sampled from different localities along the coast of India covering the Arabian Sea and Bay of Bengal. The partial sequence of 16S rRNA and cytochrome oxidase subunit I (COI) genes were analyzed for species identification and phylogenetic relationship among the Indian priacanthids (Priacanthus hamrur, P. prolixus, P. blochii, P. sagittarius, Cookeolus japonicus, and Pristigenys refulgens). The intraspecies genetic distance ranged from 0.000 to 0.002, while distances varied from 0.008 to 0.157 interspecies based on 16S sequences. Using COI data analysis, the intraspecies genetic distance ranged from 0.000 to 0.005, while interspecies distances varied from 0.009 to 0.108. Several sequences labeled Priacanthus hamrur in GenBank are shown to be P. prolixus. We also observed cryptic speciation in Heteropriacanthus cruentatus. Partial sequences of 16S rRNA and COI genes provided phylogenetic information to distinguish thirteen species of priacanthids, indicating the usefulness of molecular markers in species identification.