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Human activities affect wildlife in several ways, ungulates tend to avoid areas of high human use and alter their behavior to avoid human activity. We used remote camera traps to quantify the relative abundance and activity of wild ungulates in high and low human use areas within Nagarjunasagar Srisailam Tiger Reserve (NSTR). Major human activity in NSTR included collection of forest produce and fuel wood, and livestock grazing. Poaching for bush-meat and the use of hunting dogs was also prevalent, but could not be quantified. The relative abundance of wild ungulates was high in low human-use areas except for chital and wild pigs, which require flat terrain and water found in prime areas for settlements. Diurnal ungulates like Chital and Nilgai substantially altered their activity in response to human activity, as did nocturnal species like Sambar and Mouse Deer. The demographic response of ungulates in NSTR has been poor compared to other tiger reserves that have been made free of human use. Our research highlights the importance of having human-free protected areas so as to achieve the desired conservation objectives of harbouring viable populations of large carnivores that require high prey abundance.

Artificial Intelligence (AI) technologies have become a popular application in order to improve the sustainability of smart fisheries. Although the ultimate objective of AI applications is often described as sustainability, there is yet no proof as to how AI contributes to sustainable fisheries. The proper monitoring of the longitudinal delivery of different human impacts on activities such as fishing is a major concern today in aquatic conservation. The term "potential fishing zone" (PFZ) refers to an anticipated area of any given sea where a variety of fish may congregate for some time. The forecast is made based on factors including the sea surface temperature (SST) and the sea superficial chlorophyll attentiveness. Fishing advisories are a by-product of the identification procedure. Normalization and preliminary processing are applied to these unprocessed data. The gathered attributes, together with financial derivatives and geometric features, are then utilised to make projections about IPFZ's Technique are used to get the final determination (CECT). In this study, we offer a technique for identifying and mapping fishing activity. Experimentations are performed to validate the efficacy of the CECT method in comparison to machine learning (ML) and deep learning (DL) methods across a variety of measurable parameters. Results showed that CECT obtained 94% accuracy, while Convolutional neural network only managed 92% accuracy on 80% training data and 20% testing data.

Aims: The objective of this work was to develop and evaluate the performance of an automatic fish feeder- to enhance aquacultural practices and farmponds Place and Duration of Study: The research study was conducted at Madakasira, (Latitude: 13°56ʹ55.96″ N and Longitude: 77°18ʹ45.38″ E) which is located in Anantapur district of Andhra Pradesh, India. The mean annual temperature and annual rainfall of the study area are 27.6°C and 532 mm, respectively. Most of the rainfall (~80%) is received during South-West monsoon. The experiment study was conducted from March‒June, 2018. Methodology: Automatic fish feeder includes both electrical and mechanical mechanism. The materials includes for the development of the feeder was cone shape hopper, top covering lid, base, 12V DC motor, 12V 7Ah Rechargeable Lead Acid Battery, Arduino Uno, Bluetooth HC-05 module, 5v Relay, DC-DC step-down converter. The developed feeder was operated with the help of mobile app by using Bluetooth HC-05 module. The spreading distance and amount of feed spread, opening angles of the feeder were evaluated during the research period. Results: Our results showed that the amount of feed spread was maximum (140 g m-1) with 1.0 m height and was minimum (101 g m-1) at 0.2 m height. This results clearly shows that the fish feeder has to be kept at maximum height so that it covers maximum spreading distance with minimum loss of fish feed by drift. With increase in height of the machine from ground surface, the spreading distance of the fish feed from the feeding machine was also increased. Conclusion: Farmers were satisfied with this new technology which helps to avoid manual feeding and provides the clean environment.

In this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.

Inhibitory potential of plant pigment bixin against UVA induced activation of dermal enzymic pathway related photo skin aging.

In the present study, unani formulations Habb-e-Suranjan, Habb-e-Azaraqui are evaluated for the presence and quantification of phytochemicals, total antioxidant capacity and ferric reducing antioxidant capacity. The total phenolic and flavonoid content were estimated according to folin-ciocalteau and aluminum chloride colorimetric methods respectively. The total antioxidant capacity and ferric reducing antioxidant capacity were conducted according to standard methods. The results of phytochemical screening revealed the presence of phenols, flavonoids, saponins, phytosterols and tannins in Habb-e-Suranjan and Habb-e-Azaraqui. The quantitative study of total phenols and flavonoids in these formulations were expressed in terms of Gallic acid and Quercetin equivalents. The total antioxidant capacities of the formulations were less compared to standard ascorbic acid. Hence concluded that the studied unani formulations shown to possess phyto-therapeutic constituents which may be responsible for antioxidant and reducing power activities. Overall, the formulations are proving the claimed therapeutic activities by antioxidant mechanism.

In the generation of proof-primarily based totally medicine, the randomized scientific trial corresponds to the pinnacle step withinside the qualitative scale of the proof to be had withinside the literature, whilst small collection of instances or the outline of person instances occupy the closing place. However, the latter constitute a critical a part of scientific exercise and feature substantially stimulated the evolution of medicine, contributing substantially to the development of medical knowledge. Vitamin B12 deficiency stocks numerous not unusual place signs that have an effect on numerous tissues and organs with fitness aliments, so its prognosis can be unobvious for the large array of its outcomes and research strategies used. In this review, we centered our interest on a few case reviews associated with the nutrition B12 deficiency related to anemia, neurologic disorders, and hyperhomocysteinemia. B12 deficiency reversal is actually performed with the aid of using spark off therapy, although it isn't the equal for numerous disorders.

The medium range weather forecast issued from Agrometeorological centre (AMC) Amaravathi on rainfall, maximum temperature, minimum temperature, Relative humidity, wind direction, wind speed and cloud cover for the years 2020-2021 has been verified with observed rainfall recorded at Chef Panning Office (CPO) Ananthapuramu district to known its accuracy. Among all the parameters we verified the accuracy of rainfall for the blocks and district of Ananthapuramu. The results revealed that the correct rainfall was higher in winter, summer season compared to south west and north east monsoon. However, winter and summer season, the accuracy of rainfall forecast was >90 per cent with RMSE value of

Although foam concrete was initially intended to be avoid filler and insulating material, interest in its structural qualities has grown again due to its reduced weight, material savings, and potential for extensive use of waste materials like fly ash. This paper's main objective is to review the present state of knowledge on foam concrete, where sand is partially replaced by glass powder, fly ash, and blast furnace slag in various weights and proportions. The review highlights the necessity for creating new pathways by doing in-depth research on materials, mixture design, and methods to maximise the potential of this material for structural applications.

In recent years, Ru(ii) complexes have gained high importance in medicinal chemistry due to their significant anti-cancer activities, which are directly related to their DNA binding ability.

An Indian Information Services in Hyderabad generates and disseminates potential fishing zone (PFZ) alerts for the advantage of the fishing community. Currently, over 50,000 people around the country use these services on a regular basis for their fishing activities. INCOIS also delivers 3-hourly forecasts of ocean state, including ocean wave heights/direction and wind speed/direction for specific places, to protect the safety of fishermen and to help them decide whether to venture into the sea or not. In order to increase the fishing operations, fisherman needs to know the future prediction of PFZ. Therefore, in this research work, online available data are collected to predict day based potential fishing zone forecasting. The data are obtained from the Sea Surface Temperature (SST) and Chlorophyll channels of NOAA-AVHRR (USA) and Eutelsat (ESA) Met-Op series satellites, as well as optical bands of Ocean sat-II (India) and MODIS Aqua (USA) satellites. To determine the PFZ forecasting, Function Fitting Neural Network (FFNN) is used. The pre-processing includes removing the missing data, characters of features are modified into variables and floating format are converted into double for better prediction. The total number of 13 features are used as training data and UPDATED_DATE as target data to predict the PFZ for given future data.

The Almost 1.3 million casualties are reported round a calendar year due to road accidents. Advanced collision avoidance systems play major role in predicting the collision risk to avoid accidents. The existing deep learning algorithms are unable to predict the crash risk efficiently. In the existing system, Long Short Term Memory algorithm is used to predict the crash risk where weights are not optimized. The objective is to predict the rear end collision risk with optimized weight by combining Long Short Term Memory(LSTM) with Levenberg–Marquardt (LM) algorithms. The proposed algorithm predicts the collision risk considering vehicle, driver related factors, and temporal dependencies. Next Generation Simulation Project (NGSIM) dataset is used to evaluate the proposed model. The performance of the proposed system is compared with the performance of Long Short Term Memory and Back Propagation Neural Network. 95.6% of accuracy is achieved by LM-LSTM based Time series Deep Network Model. The prediction accuracy has been improved considerably than the existing algorithms. There is the drastic improvement in minimization of false alarm and missed alarm rate. The main advantage of the proposed system is that it will present warning at the time of high collision risk and it helps drivers to prevent from accident.

We discuss some of the geometric properties, such as the foliated Schwarz symmetry, the monotonicity along the axial and the affine-radial directions, of the first eigenfunctions of the Zaremba problem for the Laplace operator on annular domains. These fine geometric properties, together with the shape calculus, help us to prove that the first eigenvalue is strictly decreasing as the inner ball moves towards the boundary of the outer ball., 21 pages, 3 figures

An upsurge in early onset of photoaging due to repeated skin exposure to environmental stressors such as UV radiation is a challenge for pharmaceutical and cosmeceutical divisions. Current reports indicate severe side effects because of chemical or synthetic inhibitors of matrix metalloproteases (MMPs) in anti-skin aging cosmeceuticals. We evaluated the adequacy of bixin, a well-known FDA certified food additive, as a scavenger of free radicals and its inhibitory mechanism of action on MMP1, collagenase, elastase, and hyaluronidase. The anti-skin aging potential of bixin was evaluated by several biotechnological tools