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The purpose of this paper is to investigate the stock market response to dividend announcements in high growth emerging markets of Gulf countries.

The sample includes 1,092 dividend announcements from 299 listed firms over the period 2010-2015.

In the environment where there is an absence of capital gain and income tax, the authors find some evidence for the stock price reaction that partly supports the signaling hypothesis. The findings show that the Gulf Cooperation Council (GCC) market is inefficient because of the leakage information before the announcement in bad news, and the delay of share price adjustment in good news. In addition, the authors report significant trading volume (TV) reaction in all the three announcements clusters, where dividends increase, decrease, and are constant, lending support to the hypothesis that the dividend change announcements have an impact on the TV response due to different investors’ preferences.

This is the first empirical paper on market reaction in share price and TV around dividend announcement using data for the majority of GCC countries.

Although user stickiness has been studied for several years in the field of live e-commerce, little attention has been paid to the effects of streamer attributes on user stickiness in this field. Rooted in the stimulus-organism-response (S-O-R) theory, this study investigated how streamer attributes influence user stickiness.

The authors obtained 496 valid samples from Chinese live e-commerce users and explored the formation of user stickiness using partial least squares-structural equation modeling (PLS-SEM). Artificial neural network (ANN) was used to capture linear and non-linear relationships and analyze the normalized importance ranking of significant variables, supplementing the PLS-SEM results.

The authors found that attractiveness and similarity positively impacted parasocial interaction (PSI). Expertise and trustworthiness positively impacted perceived information quality. Moreover, streamer-brand preference mediated the relationship between PSI and user stickiness, as well as the relationship between perceived information quality and user stickiness. Compared to PLS-SEM, the predictive ability of ANN was more robust. Further, the results of PLS-SEM and ANN both showed that attractiveness was the strongest predictor of user stickiness.

This study explained how streamer attributes affect user stickiness and provided a reference value for future research on user behavior in live e-commerce. The exploration of the linear and non-linear relationships between variables based on ANN supplements existing research. Moreover, the results of this study have implications for practitioners on how to improve user stickiness and contribute to the development of the livestreaming industry.

Much of the discussion surrounding the antivaccine movement focuses on the decision of parents to not vaccinate their children and the resulting danger posed to others. However, the primary risk is borne by the child left unvaccinated. Although living in a developed country with high vaccination rates provides a certain amount of protection through population immunity, the unvaccinated child is still exposed to a considerably greater risk of preventable diseases than one who is vaccinated. I explore the tension between parental choice and the child’s right to be free of preventable diseases. The chapter’s goal is twofold: to advocate for moving from a dyadic framework – considering the interests of the parents against those of the state – to a triadic one, in which the interests of the child are given as much weight as those of the parent and the state; and to discuss which protections are available, and how they can be improved. Specific legal tools available to protect that child are examined, including tort liability of the parents to the child, whether and to what degree criminal law has a role, under what circumstances parental choice should be overridden, and the role of school immunization requirements in protecting the individual child.

The following are the Definitions and Standards for Jams, Jellies, and the like, as laid down by the United States Department of Agriculture, that is to say the Federal Department, and in force at the present time in matters relating to inter‐state commerce. The Definitions and Standards have been closely followed by the various States in Union:—

In modern technology, the wireless sensor networks (WSNs) are generally most promising solutions for better reliability, object tracking, remote monitoring and more, which is directly related to the sensor nodes. Received signal strength indication (RSSI) is main challenges in sensor networks, which is fully depends on distance measurement. The learning algorithm based traditional models are involved in error correction, distance measurement and improve the accuracy of effectiveness. But, most of the existing models are not able to protect the user’s data from the unknown or malicious data during the signal transmission. The simulation outcomes indicate that proposed methodology may reach more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.

This paper present a deep convolutional neural network (DCNN) from the adaptation of machine learning to identify the problems on deep ranging sensor networks and overthrow the problems of unknown sensor nodes localization in WSN networks by using instance parameters of elephant herding optimization (EHO) technique and which is used to optimize the localization problem.

In this proposed method, the signal propagation properties can be extracted automatically because of this image data and RSSI data values. Rest of this manuscript shows that the ECO can find the better performance analysis of distance estimation accuracy, localized nodes and its transmission range than those traditional algorithms. ECO has been proposed as one of the main tools to promote a transformation from unsustainable development to one of sustainable development. It will reduce the material intensity of goods and services.

The proposed technique is compared to existing systems to show the proposed method efficiency. The simulation results indicate that this proposed methodology can achieve more constant and accurate position states of the unknown nodes and the target node in WSNs domain than the existing methods.

The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).

The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.

Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.

Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.

MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.

The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.

Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.

Businesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products.

The multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR).

The findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance.

The study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined.

The main purpose of this controller is to carryout irrigation by the farmers with renewable energy resources.

The proposed design includes the Deep learning based intelligent stand-alone energy management system used for irrigation purpose. The deep algorithm applied here is Radial basis function neural network which tracks the maximum power, maintains the battery as well as load system.

The Radial Basis Function Neural Network algorithm is used for carrying out the training process. In comparison with other conventional algorithms, this algorithm outperforms by higher efficiency and lower tracking time without oscillation.

It is little complex to implement the hardware setup of neural network in terms of training process but the work is under progress.

The practical hardware implementation is under progress.

If controller are implemented in a real-time environment, definitely it helps the human-less farming and irrigation process.

If this system is implemented in real-time environment, every farmer gets benefitted.

This paper analyzes changes in property rights, land uses, and culturally based notions of ownership that have emerged following privatization of communal land in a Samburu pastoralist community in Northern Kenya. The research challenges the strict dichotomy between private and collective rights often found in property rights literature, which does not match empirical findings of overlapping and contested rights.

Part of a long-term ethnographic project investigating the process of land privatization and its outcomes, this paper draws on in-depth interviews and participant observation conducted by the author in Samburu County in 2008, 2009, and 2010. Interviews focused on how land is being used post-privatization as well as emerging social norms regulating its use.

Privatization privileges male household heads with powers including rental, sale, and bequeathal of land. However, informal rights to land extend to women and other household members. Exercise of legal rights is frequently limited due to knowledge and resource gaps. New rules regulating land use have emerged, some represent sharp divergences from past practice while others support shared access to land. These changes challenge Samburu cultural notions of individuality, reciprocity, and shared responsibility.

This research illuminates complex changes following legal shifts in property rights and demonstrates the interactions between formal laws and informal social norms and cultural beliefs about land. The result is that privatization does not have easily predictable outcomes as some theories of property would suggest.

Empirical investigation of the effects of legal changes enables fuller understanding of the implications of policy changes that many governments are pursuing privatization with limited understanding of the likely effects.