Analyzing Energy-efficiency and Route-selection of Multi-level Hierarchal Routing Protocols in WSNs

The advent and development in the field of Wireless Sensor Networks (WSNs) in recent years has seen the growth of extremely small and low-cost sensors that possess sensing, signal processing and wireless communication capabilities. These sensors can be expended at a much lower cost and are capable of detecting conditions such as temperature, sound, security or any other system. A good protocol design should be able to scale well both in energy heterogeneous and homogeneous environment, meet the demands of different application scenarios and guarantee reliability. On this basis, we have compared six different protocols of different scenarios which are presenting their own schemes of energy minimizing, clustering and route selection in order to have more effective communication. This research is motivated to have an insight that which of the under consideration protocols suit well in which application and can be a guide-line for the design of a more robust and efficient protocol. MATLAB simulations are performed to analyze and compare the performance of LEACH, multi-level hierarchal LEACH and multihop LEACH.Comment: NGWMN with 7th IEEE Inter- national Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA 2012), Victoria, Canada, 201

In vitro Maturation and Fertilization of Riverine Buffalo Follicular Oocytes in Media Supplemented with Oestrus Buffalo Serum and Hormones

Effects of two maturation media (TCM-199 and Ham's F-12) with and without the addition of oestrus buffalo serum (OBS) and hormones (FSH, LH, E2) on the maturation rate of buffalo follicular oocytes were evaluated. The results revealed a significant (P P 2+ free Tyrode's medium (63.72%) than in TALP (10.9%) and IVF-TL (32.18%). Thus, TCM-199 containing hormones and OBS appeared better for in vitro maturation, whereas modified Ca2+ free tyrode's medium was found to be more suitable for in vitro fertilization of buffalo follicular oocytes

Success of Aquaculture Industry with New Insights of Using Insects as Feed: A Review

Most of world's fish and seafood are produced by aquaculture, which is one of the biggest contributors to the world's food security. The substantial increase in prices of conventional feed ingredients and the over-exploitation of natural resources are some of the biggest constraints to aquaculture production. To overcome this stress, different approaches and techniques are used, among which the use of non-conventional feed ingredients in the aquaculture sector is the most recent approach. Different non-conventional feed ingredients such as plant-based products, algae (both micro and macroalgae), single-cell protein (bacteria and yeast), and insect meal are currently used in aquaculture for sustainable food production. Amongst all these novel ingredients, insects have greater potential to replace fishmeal. The existence of about 1.3 billion tons of food and agriculture waste from the food chain supply poses a serious environmental threat. Insects are tiny creatures that can thrive on organic waste and thus can convert the waste to wealth by the bioconversion and nutritional upcycling of organic waste. Insects have the potential to recover nutrients from waste aquaculture products, and many fish species feed on insects naturally. Therefore, employing insects in the aquaculture sector to replace fishmeal is an eco-friendly approach. The present review briefly highlights emerging non-conventional feed ingredients, with special attention given to insects. The current review also focuses on the nutritional value of insects, factors affecting the nutritional value of insects, potential insects that can be employed in the aquaculture sector, the physiological response of fish when fed with insect meal, techno-functional properties of insect meal, and emerging approaches for addressing possible downsides of employing insect meal in fish diets. Finally, it suggests avenues for further research into these inventive fishmeal replacements

Prediction of Sandstone Dilatancy Point in Different Water Contents Using Infrared Radiation Characteristic: Experimental and Machine Learning Approaches

In rock mechanics, the dilatancy point is always occurring before rock failure during loading process. Water content plays a significant role in the rock physiomechanical properties, which also impact the rock dilatancy point under loading process. This dilatancy point significantly plays a warning role in the rock engineering structures stability. Therefore, it is essential to predict the rock dilatancy point under different water contents to get an early warning for effective monitoring of engineering projects. This study investigates the water contents effects on sandstone dilatancy point under loading in the presence of infrared radiation (IR). Furthermore, this IR was used for the first time as an input parameter for different artificial intelligence (AI) techniques to predict the dilatancy point in the stress-strain curve. The experimental findings show that the stress range in stress-strain curve stages (crack closure and unstable crack propagation) increases with water content. However, this range for deformation and stable crack propagation stages decreases with water content. The dilatancy stress, crack initiation stress, and elastic modulus are negatively linearly correlated, while peak stress and stress level are negatively quadraticaly correlated with a high (R2). The absolute strain energy rate, which gives a sudden increase at the point of dilatancy, is used as the dilatancy point index. The stress level is 0.86 σmax at the dilatancy point for dry rock and decreases with water content. This index is predicted from IR data using three computing techniques: artificial neural network (ANN), random forest regression (RFR), and k-nearest neighbor (KNN). The performance of all techniques was evaluated using R2 and root-means-square error (RMSE). The results of the predicted models show satisfactory performances for all, but KNN is remarkable. The research findings will be helpful and provide guidelines about underground engineering project stability evaluation in water environments. © 2022 Liqiang Ma et al. All Rights Reserved.This paper was supported by the National Natural Science Foundation of China (51874280) and the Fundamental Research Funds for the Central Universities (2021ZDPY0211)

Can retail investors induce corporate green innovation? -Evidence from Baidu Search Index

China's rapid economic development has caused some environmental damage in recent years. The popularity of the Internet has enriched the ways for investors to obtain information, which would exert an impact on corporate environmental behavior. Focusing on micro-enterprise green innovation from the perspective of informal regulation, this paper investigates the impact of investor attention on corporate green innovation. This study takes Chinese A-share listed companies from 2011 to 2018 as samples, constructs panel fixed-effects models and adopts multiple linear, Logistic and Tobit regressions. This article finds that investor attention, measured by the web search index, can significantly improve corporate green innovation. The conclusion is still valid after a series of robust tests. Besides, mechanism tests reveal that investor attention can promote corporate green innovation by improving the implementation efficiency of punitive environmental regulation, the use efficiency of environmental subsidies, and by increasing the reputation cost of enterprises. In additional tests, this paper further clarifies that investors' attention to negative public opinion can play a better role in environmental governance, and reveals the reason why investors are motivated to improve corporate green innovation. This research puts forward a unique perspective, which extends the understanding of informal environmental regulation and enriches research on green innovation at the micro-enterprise level, promoting the cross research of finance and environmental protection. © 2022 The Author(s

The competent sentinel node: an association with an axillary presentation and an occult or a small primary invasive breast carcinoma

The concept of the sentinel node describes a primary or sentinel lymph node (SLN), which exists and through which tumour cells from a primary tumour in a particular location must first travel to spread to a particular regional lymph node group. In this series we present three patients presenting with a pathological axillary node associated with either an occult or very small primary breast cancer. In each case the primary tumour was found to have metastasised to the palpable node, however despite the significant enlargement of this node, no other axillary nodes were found to be affected on axillary node clearance. This has led us to postulate that the SLN in some cases contains unique characteristics that enable it to prevent further spread of the tumour up the lymphatic chain. Hence the term the competent sentinel node

Development of Predictive Models for Determination of the Extent of Damage in Granite Caused by Thermal Treatment and Cooling Conditions Using Artificial Intelligence

Thermal treatment followed by subsequent cooling conditions (slow and rapid) can induce damage to the rock surface and internal structure, which may lead to the instability and failure of the rock. The extent of the damage is measured by the damage factor (DT), which can be quantified in a laboratory by evaluating the changes in porosity, elastic modulus, ultrasonic velocities, acoustic emission signals, etc. However, the execution process for quantifying the damage factor necessitates laborious procedures and sophisticated equipment, which are time-consuming, costly, and may require technical expertise. Therefore, it is essential to quantify the extent of damage to the rock via alternate computer simulations. In this research, a new predictive model is proposed to quantify the damage factor. Three predictive models for quantifying the damage factors were developed based on multilinear regression (MLR), artificial neural networks (ANNs), and the adoptive neural-fuzzy inference system (ANFIS). The temperature (T), porosity (ρ), density (D), and P-waves were used as input variables in the development of predictive models for the damage factor. The performance of each predictive model was evaluated by the coefficient of determination (R2), the A20 index, the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the variance accounted for (VAF). The comparative analysis of predictive models revealed that ANN models used for predicting the rock damage factor based on porosity in slow conditions give an R2 of 0.99, A20 index of 0.99, RMSE of 0.01, MAPE of 0.14, and a VAF of 100%, while rapid cooling gives an R2 of 0.99, A20 index of 0.99, RMSE of 0.02, MAPE of 0.36%, and a VAF of 99.99%. It has been proposed that an ANN-based predictive model is the most efficient model for quantifying the rock damage factor based on porosity compared to other models. The findings of this study will facilitate the rapid quantification of damage factors induced by thermal treatment and cooling conditions for effective and successful engineering project execution in high-temperature rock mechanics environments. © 2022 by the authors