SCHOOL OF MANAGEMENT

This article explores the contention that social movements are a significant social force transforming societies through their engagement with new media, such as the Internet, Web 2.0, and digital communications, which are seen as capable of facilitating new power structures. Utilizing della Porta and Diani's framework, it considers how new media technologies may be shaping the structure, identity, opportunity, and protest dimensions of social movements. It concludes by suggesting that new media does offer important opportunities for cost-effective networking, interpretive framing, mobilization, and repertoires of protest action. However, their adoption does not represent the creation of entirely new virtual social movements but rather a new means of providing existing social movement organisations, local activist networks, and street-level protest with a trans-national capacity to collaborate, share information, and communicate with a wider audience. Such new media-enabled social action is both more congruent with a politics of identity but may also increasingly be competing within a media environment saturated by user-generated content.

In this paper, we study a new problem of mining dynamic association rules with comments (DAR-C for short). A DAR-C contains not only rule itself, but also its comments that specify when to apply the rule. In order to formalize this problem, we first present the expression method of candidate effective time slots, and then propose several definitions concerning DAR-C. Subsequently, two algorithms, namely ITS2 and EFP-Growth2, are developed for handling the problem of mining DAR-C. In particular, ITS2 is an improved two-stage dynamic association rule mining algorithm, while EFP-Growth2 is based on the EFP-tree structure and is suitable for mining high-density mass data. Extensive experimental results demonstrate that the efficiency and scalability of our proposed two algorithms (i.e., ITS2 and EFP-Growth2) on DAR-C mining tasks, and their practicability on real retail dataset.

This paper models and analyzes international trade flows using open flow networks (OFNs) with the approaches of flow distances, which provide a novel perspective and effective tools for the study of international trade. We discuss the establishment of OFNs of international trade from two coupled viewpoints: the viewpoint of trading commodity flow and that of money flow. Based on the novel model with flow distance approaches, meaningful insights are gained. First, by introducing the concepts of trade trophic levels and niches, countries' roles and positions in the global supply chains (or value-added chains) can be evaluated quantitatively. We find that the distributions of trading "trophic levels" have the similar clustering pattern for different types of commodities, and summarize some regularities between money flow and commodity flow viewpoints. Second, we find that active and competitive countries trade a wide spectrum of products, while inactive and underdeveloped countries trade a limited variety of products. Besides, some abnormal countries import many types of goods, which the vast majority of countries do not need to import. Third, harmonic node centrality is proposed and we find the phenomenon of centrality stratification. All the results illustrate the usefulness of the model of OFNs with its network approaches for investigating international trade flows.

Most of co-occurrence networks only record co-occurrence relationships between two entities, and ignore the weights of co-occurrence cliques whose size is bigger than two. However, this ignored information may help us to gain insight into the co-occurrence phenomena of systems. In this paper, we analyze co-occurrence networks with clique occurrence information (CNCI) thoroughly. First, we describe the components of CNCIs and discuss the generation of clique occurrence information. And then, to illustrate the importance and usefulness of clique occurrence information, several metrics, i.e. single occurrence rate, average size of maximal co-occurrence cliques and four types of co-occurrence coefficients etc., are given. Moreover, some applications, such as combining co-occurrence frequency with structure-oriented centrality measures, are also discussed.

Forty middle and small rivers in three towns of Fengjing, Songjiang and Zhujiajiao of suburban Shanghai were chosen as sampling sites for water quality research. Measurement results of DO, CODc~, TP, TN and so on show that the rivers are under heavy eutropic conditions, which are several times greater than the critical values of the worst level (Type V) of water. Water pollution situation has different temporal and spatial characteristics. Non-point pollution, such as village domestic sewage, farmland runoff, livestock feces, has become the primary source of pollution of the middle and small rivers in suburban Shanghai.

Astrocytes have been suggested to regulate the extracellular calcium concentration ([Ca2+]o), but this has not been thoroughly investigated. Adult, male Sprague–Dawley rats were used to record changes in [Ca2+]o in the hippocampus during epileptiform activity. Maximal decreases in [Ca2+]o in CA1 were measured in the pyramidal cell layer during 20 Hz, 20 s stimulus trains to the contralateral CA3 region. Maximal decreases in [Ca2+]o in the dentate gyrus were measured when maximal dentate activation had appeared—irrespective of the location, frequency or duration of the stimulation. Maximal decreases were 36% greater in the dentate gyrus than in CA1. During prolonged discharges, [Ca2+]o recovered partially towards the baseline in both hippocampal regions. To investigate the role of astrocytes, local injections of fluorocitrate (FC), a metabolic toxin selectively taken up by astrocytes, were used. FC (0.1, 0.25 or 0.5 mM FC), but not vehicle (2 μl), caused a small, but significant decrease in the maximal changes in CA1, but an increase in the dentate gyrus. The results suggest that maximal decreases in [Ca2+]o occur in the hippocampus in response to burst firing of neurons and that astrocytes play a minimal role in the regulation of [Ca2+]o during epileptiform activity.

Reciprocating exchange with other humans requires individuals to infer the intentions of their partners. Despite the importance of this ability in healthy cognition and its impact in disease, the dimensions employed and computations involved in such inferences are not clear. We used a computational theory-of-mind model to classify styles of interaction in 195 pairs of subjects playing a multi-round economic exchange game. This classification produces an estimate of a subject's depth-of-thought in the game (low, medium, high), a parameter that governs the richness of the models they build of their partner. Subjects in each category showed distinct neural correlates of learning signals associated with different depths-ofthought. The model also detected differences in depth-of-thought between two groups of healthy subjects: one playing patients with psychiatric disease and the other playing healthy controls. The neural response categories identified by this computational characterization of theory-of-mind may yield objective biomarkers useful in the identification and characterization of pathologies that perturb the capacity to model and interact with other humans.

Social norms in humans constrain individual behaviors to establish shared expectations within a social group. Previous work has probed social norm violations and the feelings that such violations engender; however, a computational rendering of the underlying neural and emotional responses has been lacking. We probed norm violations using a two-party, repeated fairness game (ultimatum game) where proposers offer a split of a monetary resource to a responder who either accepts or rejects the offer. Using a norm-training paradigm where subject groups are preadapted to either high or low offers, we demonstrate that unpredictable shifts in expected offers creates a difference in rejection rates exhibited by the two responder groups for otherwise identical offers. We constructed an ideal observer model that identified neural correlates of norm prediction errors in the ventral striatum and anterior insula, regions that also showed strong responses to variance-prediction errors generated by the same model. Subjective feelings about offers correlated with these norm prediction errors, and the two signals displayed overlapping, but not identical, neural correlates in striatum, insula, and medial orbitofrontal cortex. These results provide evidence for the hypothesis that responses in anterior insula can encode information about social norm violations that correlate with changes in overt behavior (changes in rejection rates). Together, these results demonstrate that the brain regions involved in reward prediction and risk prediction are also recruited in signaling social norm violations.

A growing number of studies in exploring empathic modulation have revealed the neural substrates of how social stimuli are represented in the human brain, especially the pain of others. The empathic response of observing other's gains and losses, however, remains not clearly characterized. In the current study, we carried out two experiments with a gamble task to investigate how the effects of interpersonal familiarity and self-participation work on modulating the temporal neural response towards gain and loss of a friend or a stranger using scalp-recorded event-related potentials (ERPs). The electrophysiological data show an increased amplitude of the P300 when observing a friend's performance compared to strangers playing the game in both two experiments. But the distinction of differentiated feedback-related negativity (d-FRN) between friends and strangers was only observed when the player was not involved in the game. These results indicated that the participants exerted more motivational relevance toward their friends than strangers, but the participants' empathic response toward friends was only salient when they were not involved in the gamble directly. Therefore, both familiarity and self-engagement are factors that influence the empathy towards others, complementing the recent research on empathic modulation.

This study examines the importance of natural resources, economic freedom, and sea-access in attracting foreign direct investment (FDI) inflows to the Commonwealth of Independent States (CIS), using panel data from 1998 to 2017. The Prais-Winsten regression with panel-corrected standard errors (PCSEs) is employed for all estimations. Feasible Generalized Least Squares (FGLS), Random Effects with Driscoll-Kraay standard errors (RE (D-K)), and Random Effects of Generalized Least Squares (RE (GLS)) estimators are used to test the sensitivity of PCSEs’ estimates to changes in the underlying empirical model, whereas Instrumental Variables with Two Stage Least Squares (IV (2SLS)), Limited Information Maximum Likelihood (LIML), and Baltagi’s Two-Stage Least-Squares Random-Effects (IV (EC2SLS)) estimators are used to address potential endogeneity concerns. The estimates confirm that natural resources, economic freedom, and sea-access are robust and decisive factors affecting FDI location de...

We examine how women's bargaining power affects child nutritional status using data from rural Senegal. In order to correct for the potential endogeneity of women's empowerment we use information on a mother's ethnicity relative to that of the community she resides in to construct an arguably exogenous instrument. While standard OLS suggests that if a mother has more bargaining power, her children will have a better nutritional status, our IV estimates indicate that the true impact is underestimated if the endogeneity of bargaining power is not taken into account.

Patient satisfaction is a core tool for measuring the performance of the hospitals as well as the service provider and the services that they are providing to the patients. The aim of this research is to evaluate how information received, medical equipment, distance from the hospital, and physical infrastructure influenced patient satisfaction at public hospitals in Southern Punjab, Pakistan. An exploratory research technique was used. We distributed 700 questionnaires through a random method, and 579 provided proper responses. A confirmatory factor analysis (CFA) and a regression analysis were used for the data analysis. The study results illustrated that medical equipment, information received, distance from the hospital, and physical infrastructure had significantly positive impacts (p = 0.001) on patient satisfaction. To promote higher level of satisfaction, there is a need to take appropriate steps for improvement.

Pakistan holds an important geographical status, but extremism and sabotage have severely damaged the tourism industry. In the present study, we examined the impact of destination image (DI) on tourist loyalty (TL) and intension to visit (IV) in Pakistan. Additionally, the study analyzed the mediation effects of electronic word of mouth (eWOM) and tourist satisfaction (TS) on these relationships. Data was collected from 780 tourists in Pakistan. We used confirmatory factor analysis (CFA) and structural equation modeling (SEM) to obtain the results. Our results demonstrated the positive relationships among DI, eWOM, TS, TL, and IV. This study also disclosed that eWOM has a partial mediation effect on the DI–TL relationship, and both eWOM and TS fully mediate the association between DI and IV. Hence, tourist satisfaction can be enhanced by eWOM and destination image, which directly effect tourist loyalty and intention to visit in Pakistan. The proposed framework and research findings ...

The purpose of this study is to analyze early age malnutrition on a gender basis in Pakistan. Pakistan Demographic and Health Survey (PDHS) 2012–2013 data related to households’ characteristics that affect the nutrition of children less than 5 years of age are used for the estimation of results. Gender disparity (measured by girl malnourished in household/boy malnourished in a household) is constructed for the measurement of gender disparity in early age child nutrition. After synthesizing the PDHS data set, 2119 observations are used for regression results of gender disparity. Regression results are analyzed at the level of 5% confidence interval otherwise insignificant. egression results for gender disparity show that households in good socioeconomic status, a greater number of household members, a mother’s higher level of education, mother employment, and the male head of the household, causes a decrease in gender disparity in nutrition intake of children.