Exploring the Medication Pattern of Chinese Medicine for Peptic Ulcer Based on Data Mining

During the last decades, Chinese medicine has been widely used for curing various diseases in the healthcare domain. Based on the databases of medicine wisdom and modern application of prescriptions, we have explored the medication pattern of ancient and modern prescriptions for the treatment of peptic ulcer in various patients. In this paper, we have proposed a neural network model which is based on the time series decomposition and is able to mine and predict the medication pattern of peptic ulcer treatment in Chinese medicine. For this purpose, cumulative distance level method, Mann–Kendall trend analysis, Hurst exponent, and characteristic point methods are used for the trend analysis. Likewise in the proposed model, the wavelet analysis method is used for the periodicity analysis and Mann–Kendall mutation test method along with Pettitt methods is used for mutability analysis. In addition, autocorrelation and unit root methods are utilized to test the random terms. The Chinese herbal formulas (where the main diseases are peptic ulcer, peptic ulcer, cerebral leakage, and cerebral abscess) are collected from the databases of medicine wisdom and modern application of prescriptions. Furthermore, methods of frequency analysis, association rule analysis, and factor analysis are used to evaluate the grouping pattern of prescriptions for peptic ulcer treatment. The error in the proposed scheme between the predicted and the measured values of 87 prescriptions, which involve five Chinese medicines for peptic ulcer and 160 Chinese medicines, obtained from the neural network was 16.79%.

Multi-Objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones

A communication system based on unmanned aerial vehicles (UAVs) is a viable alternative for meeting the coverage and capacity needs of future wireless networks. However, because of the limitations of UAV-enabled communications in terms of coverage, energy consumption, and flying laws, the number of studies focused on the sustainability element of UAV-assisted networking in the literature was limited thus far. We present a solution to this problem in this study; specifically, we design a Q-learning-based UAV placement strategy for long-term wireless connectivity while taking into account major constraints such as altitude regulations, nonflight zones, and transmit power. The goal is to determine the best location for the UAV base station (BS) while reducing energy consumption and increasing the number of users covered. Furthermore, a weighting method is devised, allowing energy usage and the number of users served to be prioritized based on network/battery circumstances. The suggested Q-learning-based solution is contrasted to the standard k-means clustering method, in which the UAV BS is positioned at the centroid location with the shortest cumulative distance between it and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.

Multi-objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones

Unmanned aerial vehicles (UAVs)-based communication system is a promising solution to meet coverage and capacity requirements of future wireless networks. However, UAV-enabled communications is constrained with its coverage, energy consumption, and flying regulations, and the number of works focusing on the sustainability aspect of UAV-assisted networking has been limited in the literature so far. In this paper, we propose a solution to this limitation; particularly, we design a $Q$-learning-based UAV positioning scheme for sustainable wireless connectivity considering key constraints, that are, altitude regulations, non-flight zones, and transmit power. The objective is to find the optimal position of the UAV base station (BS) and minimize the energy consumption while maximizing the number of users covered. Moreover, a weighting mechanism is developed, where the energy consumption and number of users covered can be prioritized according to network/battery conditions. The proposed Q-learning-based solution is compared to the baseline k-means clustering method, where the UAV BS is positioned at the centroid location that minimizes the cumulative distance between the UAV BS and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.

Cloud shadows drive vertical migrations of deep-dwelling marine life

Many zooplankton and fishes vertically migrate on a diel cycle to avoid predation, moving from their daytime residence in darker, deep waters to prey-rich surface waters to feed at dusk and returning to depth before dawn. Vertical migrations also occur in response to other processes that modify local light intensity, such as storms, eclipses, and full moons. We observed rapid, high-frequency migrations, spanning up to 60 m, of a diel vertically migrating acoustic scattering layer with a daytime depth of 300 m in the subpolar Northeastern Pacific Ocean. The depth of the layer was significantly correlated, with an ∼5-min lag, to cloud-driven variability in surface photosynthetically available radiation. A model of isolume-following swimming behavior reproduces the observed layer depth and suggests that the high-frequency migration is a phototactic response to absolute light level. Overall, the cumulative distance traveled per day in response to clouds was at least 36% of the round-trip diel migration distance. This previously undescribed phenomenon has implications for the metabolic requirements of migrating animals while at depth and highlights the powerful evolutionary adaptation for visual predator avoidance.

Exercises and Skin Physiology During International Space Station Expeditions

BACKGROUND: The first skin physiological pilot experiment (SkinA) on a single astronaut showed a deterioration of the skin. In a follow-up experiment (SkinB) we showed that skin physiological parameters improved on average. However, it is well known that sports have positive effects on the skin, that astronauts prefer special sports devices, and do sports with different intensity. The aim of this study was to analyze the different sports activities of SkinB astronauts and to find out whether they have an influence on the skin physiological parameters.METHODS: The cumulative distance covered on the treadmill and on the cycle ergometer as well as the repetition of arm-related exercises have been calculated and possible correlation between sports activities and skin physiological parameters have been analyzed.RESULTS: The average distance covered for all six astronauts per day is 1364 AU on the treadmill T2, and 11,077 AU on the cycle ergometer CEVIS. In addition, the astronauts performed an average of about 73 repetitions of all arm-related exercises daily. Here, we were able to show very well how differently the astronauts on the ISS train. In addition, a decreasing trend in skin volume can be observed in astronauts with increasing activity on the bicycle and more repetitions on arm-related exercises.CONCLUSION: Increased activity on the cycle ergometer and increased arm-related exercises have a medium negative impact on the parameter skin volume and thus reflects more fluid content in the skin. No correlations between sports activities and skin moisture/skin barrier function could be found.Braun N, Hunsdieck B, Theek C, Ickstadt K, Heinrich U. Exercises and skin physiology during International Space Station expeditions. Aerosp Med Hum Perform. 2021; 92(3):160166.

A model-based quantification of startle reflex habituation in larval zebrafish

Zebrafish is an established animal model for the reproduction and study of neurobiological pathogenesis of human neurological conditions. The ‘startle reflex’ in zebrafish larvae is an evolutionarily preserved defence response, manifesting as a quick body-bend in reaction to sudden sensory stimuli. Changes in startle reflex habituation characterise several neuropsychiatric disorders and hence represent an informative index of neurophysiological health. This study aimed at establishing a simple and reliable experimental protocol for the quantification of startle reflex response and habituation. The fish were stimulated with 20 repeated pulses of specific vibratory frequency, acoustic intensity/power, light-intensity and interstimulus-interval, in three separate studies. The cumulative distance travelled, namely the sum of the distance travelled (mm) during all 20 stimuli, was computed as a group-level description for all the experimental conditions in each study. Additionally, by the use of bootstrapping, the data was fitted to a model of habituation with a first-order exponential representing the decay of locomotor distance travelled over repeated stimulation. Our results suggest that startle habituation is a stereotypic first-order process with a decay constant ranging from 1 to 2 stimuli. Habituation memory lasts no more than 5 min, as manifested by the locomotor activity recovering to baseline levels. We further observed significant effects of vibratory frequency, acoustic intensity/power and interstimulus-interval on the amplitude, offset, decay constant and cumulative distance travelled. Instead, the intensity of the flashed light did not contribute to significant behavioural variations. The findings provide novel insights as to the influence of different stimuli parameters on the startle reflex habituation and constitute a helpful reference framework for further investigation.

Acoustic tracking of a large predatory marine gastropod, Charonia tritonis, on the Great Barrier Reef

Crown-of-thorns starfish Acanthaster planci (COTS) outbreaks are a major cause of coral cover loss on the Great Barrier Reef (GBR), with manual culling having only localised success. The endangered giant triton snail Charonia tritonis is a natural predator of COTS, although aquarium and field observations indicate the intensity of direct predation may be inadequate to significantly mediate outbreaks. However, their mere presence can elicit a chemically induced sensory behavioural response which may suppress COTS populations when in non-outbreak status. While there is increasing knowledge of the sensory biology of both species, little is known regarding giant triton snail numbers on the GBR or about how they move and occupy space, making it difficult to determine their true zone of influence and thus their capacity to disrupt COTS behaviour. We used passive acoustic telemetry to establish short-term activity space and movement patterns of giant triton snails on the GBR. Individuals were tracked for up to 41 d, were observed to travel 234.24 m d-1, with a mean total cumulative distance travelled at night (1923.19 m) nearly double that observed during the day (1014.84 m). These distances encompass those reported for COTS (10.3 m d-1) and align with COTS nocturnal behaviour. Space utilisation distributions (UDs) revealed a mean (±SD) home range of 1179.40 ± 659.40 m2 (95% UD) and a core area of 195.68 ± 141.31 m2 (50% UD). Revealing the short-term movement patterns of this natural COTS predator within a reef environment advances knowledge of its spatial ecology and will provide information for its future conservation and for COTS management efforts.

Population Estimation of Proboscis Monkeys in Mangroves at Kuching Wetland National Park, Sarawak

Boat survey on proboscis monkey (Nasalis larvatus) population in Kuching Wetland National Park (KWNP) was conducted to estimate the current population density and population size of this primate. The survey was conducted on September 2015 and January 2016 covering a cumulative distance of 128.91 km of mangrove riverbank. A cumulative total of 158 individuals comprising 19 groups, including one all-male group and three solitary males were recorded throughout the survey. The population density of proboscis monkeys in mangrove forest at KWNP was estimated at 1.63 individuals/km2 or 0.20 groups/km2. Based on the extrapolation of the estimated population density data, the population size of proboscis monkey in mangrove forest at KWNP was estimated to be 82 individuals. Last published report on the estimation of proboscis monkey population in Sarawak was more than 30 years ago. This study was conducted as a part of the efforts to assess the current population status of proboscis monkey in Sarawak. Keywords: Estimation, population density, population size, proboscis monkey