Real-Time Tracking of Wildlife with IoT Solutions in Movement Ecology

Movement ecology has grown increasingly significant in the backdrop of global environmental changes, emphasizing the importance of understanding animal mobility patterns. The integration of Internet of Things (IoT) technology offers transformative potential for real-time wildlife tracking, addressing limitations of traditional methods like radio telemetry. Through IoT devices, researchers can acquire immediate, high-resolution datasets spanning vast distances, capturing multiple data points such as environmental conditions and physiological parameters. Existing implementations range from monitoring elephant movements in Africa to observing bird migrations. However, while promising, challenges like battery longevity, device weight, data management, and animal safety persist. As technological advances emerge, future prospects include more efficient, integrated solutions combining IoT with other technologies, poised to reshape and enrich our understanding of wildlife movement

Rayleigh LIDAR and satellite (HALOE, SABER, CHAMP and COSMIC) measurements of stratosphere-mesosphere temperature over a southern sub-tropical site, Reunion (20.8° S; 55.5° E): climatology and comparison study

International audienceFor the first time, climatology of the middle atmosphere thermal structure is presented, based on 14 years of LIDAR and satellite (HALOE, SABER, CHAMP and COSMIC) temperature measurements. The data is collected over a southern sub-tropical site, Reunion Island (20.8° S; 55.5° E), for the height range between 30 and 60 km. The overall monthly mean temperature shows a maximum of 265-270 K at the stratopause height region from ~44-52 km and peaks during the months of March and November. Furthermore, the temperature profiles are compared with different satellite datasets (HALOE, CHAMP, COSMIC and SABER) and the results are found to be in reasonable agreement with each other, although a relative difference in temperature of ± 5 to 6 K is noticed. In comparison, LIDAR shows higher/lower temperatures for the lower mesosphere/upper stratosphere height region. The differences in temperature measured by the LIDAR and satellite measurements are analogous with previous results available elsewhere. Long-term temperature measurements are used to further study seasonal oscillations, especially annual, semi-annual and quasi-biennial oscillations. In comparison with SAO, the measured spectral amplitudes of AO shows dominant amplitudes in both the upper stratosphere and lower mesosphere height regions. Using LIDAR and the other satellite measurements, the quasi-biennial oscillation was found to be approximately 26 months. The spectral amplitudes are comparable to the results reported earlier by other researchers

A deep study on real-time atmospheric aerosols and variability over Indian subcontinent environment

Study of atmospheric aerosols is very complex owing to their short life time and, chemical constituents. Aerosol loading is highly regional and there life time is very short. An attempt has been made in this paper to study the variability of atmospheric aerosols over Indian subcontinent using a statistical parameter Coefficient of Variation (COV). The magnitude of variability over this region is studied. The factors influencing the variability are studied to find the reasons for difference in magnitude over different regions. Rainfall naturally influences the variability aerosols due to scavenging, but if it is the only factor influencing the variability, the study is absurd. It is found that the influence of rainfall on COV is not pronounced as it is not a seasonal phenomenon. Influence of other parameters viz. Topography, wind vector, thermal power plants and population on variability of aerosols is clearly found. These results help in classifying aerosol zones on the basis of variability of aerosol optical depth. The study also helps in finding the cause of aerosol loading over a region. Measures can be taken to decrease the loading if it is due to local sources

Lidar observations of sporadic Na layers over Gadanki (13.5° N, 79.2° E)

We studied the characteristics of sporadic sodium layers (SSLs) observed with the sodium (Na) resonance scattering lidar at Gadanki (13.5° N, 79.2° E). The SSLs were observed on a total of 63 occasions during 464 h of Na lidar observations from January 2005 to February 2006. The observations showed that one SSL event occurred, on average, every 7 h. The most prominent sporadic layer, which formed on 12 February 2005, exhibited a peak density of 60 722 Na atoms/cm³ around 92 km and it was nearly twice the peak density reported from elsewhere using ground-based observations. In general, the SSLs exhibited the following characteristics: (1) they developed at heights between 88 and 98 km with an average height around 94 km; (2) maximum density occurred during the early morning hours between 02:00 and 05:00 IST; (3) the ratio of the maximum peak Na density to the average density was normally around 3 to 5 and it exceeded even 10 in some cases; (4) the events lasted from a few minutes to several hours. The formation period of the SSLs was longer compared to the decay period of the SSLs. Most of the SSL events showed downward motions

Role of AI (Artificial Intelligence) and Machine Learning in Transforming Operations in Healthcare Industry: An Empirical Study

Artificial intelligence (AI) represents a dynamic and disruptive field within computer science, composed to revolutionize the healthcare sector by primarily restructuring medical practices and healthcare delivery. With the production of complex healthcare data, AI is set to play an increasingly prominent role in this industry. Healthcare payers, providers, and institutions in the health sciences have already accepted various forms of AI. It is imperious for healthcare experts to grip the current landscape of Artificial intelligence and Machine Learning technologies and their potential to enhance the effectiveness, safety, and accessibility of healthcare services, ultimately achieving value-based care. The incorporation of artificial intelligence into healthcare offers many advantages, including enhanced patient care, more accurate diagnostics, improved operational efficiency, and cost reductions. A sample of 301 respondents was collected from healthcare industry. The factors that determine the Role of AI (Artificial Intelligence) and Machine Learning in Transforming Operations in Healthcare Industry are Early detection and prediction of disease, Drug development and discovery, Personalized treatment, and Remote monitoring and telemedicine

Lidar observations of sodium layer over low latitude, Gadanki (13.5 degrees N, 79.2 degrees E) : seasonal and nocturnal variations

In this paper, we present seasonal and nocturnal variations of mesospheric sodium (Na) layer parameters observed over Gadanki (13.5° N, 79.2° E), based on 166 nights during the period from January 2005 to December 2006, for the first time. The total Na content decreases during the evening and reaches a minimum value around midnight and maximum in the early morning. The year-to-year variations illustrate that Na layers reach the peak value close to 93.5 km for the year 2005 and ~93 km for the year 2006 and falls to near zero value around 110 km. Though, seasonal variation of sodium density illustrate maximum values in September, December and March, we require a larger data base for September months to conclude the statement. The column abundance shows maximum during autumn equinox and minimum during winter. The obtained seasonal and nocturnal variation of sodium layer parameters are compared with mid-latitude observations and further possible mechanisms are discussed

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Not AvailableComputational prediction of potential miRNAs and their target genes was performed to identify the miRNAs and genes associated with temperature response in rice. The data of temperatureresponsive miRNAs of Arabidopsis, and miRNAs and whole genome data of rice were used to predict potential miRNAs in O. sativa involved in temperature response. A total of 55 miRNAs were common in both the species. A total of 27 miRNAs were predicted at the first time in rice. Target genes were searched for these 27 miRNAs in rice genome following stringent criteria. Real time PCR based on expression analysis of nine miRNAs showed that majority of the miRNAs were down regulated under heat stress for rice cultivar Nagina 22. Furthermore, miR169, miR1884 and miR160 showed differential expression in root and shoot tissues of rice.Identification and expression studies of miRNAs duringheat stress will advance the understanding of gene regulation under stress in rice.Not Availabl

Lidar observations of sodium layer over low latitude, Gadanki (13.5° N, 79.2° E): seasonal and nocturnal variations

In this paper, we present seasonal and nocturnal variations of mesospheric sodium (Na) layer parameters observed over Gadanki (13.5° N, 79.2° E), based on 166 nights during the period from January 2005 to December 2006, for the first time. The total Na content decreases during the evening and reaches a minimum value around midnight and maximum in the early morning. The year-to-year variations illustrate that Na layers reach the peak value close to 93.5 km for the year 2005 and ~93 km for the year 2006 and falls to near zero value around 110 km. Though, seasonal variation of sodium density illustrate maximum values in September, December and March, we require a larger data base for September months to conclude the statement. The column abundance shows maximum during autumn equinox and minimum during winter. The obtained seasonal and nocturnal variation of sodium layer parameters are compared with mid-latitude observations and further possible mechanisms are discussed

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Not AvailableMicroRNAs are small noncoding regulatory RNAs which control gene expression by mRNA degradation or translational repression. They are significant molecular players regulating important biological processes such as developmental timing and stress response. We report here the discovery of miRNAs derived from ribosomal DNA using the small RNA datasets of 16 deep sequencing libraries of rice. Twelve putative miRNAs were identified based on highly stringent criteria of novel miRNA prediction. Surprisingly, 10 putative miRNAs (mi_7403, mi_8435, mi_12675, mi_4266, mi_4758, mi_4218, mi_8200, mi_4644, mi_14291, mi_16235) originated from rDNA of rice chromosome 9. Expression analysis of putative miRNAs and their target genes in heat tolerant and susceptible rice cultivars in control and high temperature treated seedlings revealed differential regulation of rDNA derived miRNAs. This is the first report of rDNA derived miRNAs in rice which indicates their role in gene regulation during high temperature stress in plants. Further studies in this area will open new research challenges and opportunities to broaden our knowledge on gene regulation mechanisms.Not Availabl