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Developing a better understanding of transformations and configurations in global affairs requires researchers in Global Political Economy to take seriously the emergence of forms of technology and their integration into the institutional, social, economic and political fabrics of contemporary life. However, research in key Global Political Economy journals has not been entirely forthcoming in acknowledging and recognising this, over time. Where we see publications that look at areas of technology and the global political economy, what has been consistently missing in Global Political Economy research, however, is the discussion of the direct impact of technology on workers, working conditions, the employment relationship, and social protections, and, as an arguably direct result of digitalisation, new threats for the corporeal and for sustainability of materiality. This debates and commentary piece for the first issue of the Global Political Economy journal argues that the burgeoning literature in these areas can be carried forward by scholars in the field of Global Political Economy.

Color perception is a major guiding factor in the evolutionary process of human civilization, but most of the neurological background of the same are yet unknown. This work attempts to address this area with an EEG based neuro-cognitive study on response of brain to different color stimuli. With respect to a Grey baseline seven colors of the VIBGYOR were shown to 16 participants with normal color vision and corresponding EEG signals from different lobes (Frontal, Occipital & Parietal) were recorded. In an attempt to quantify the brain response while watching these colors, the corresponding EEG signals were analysed using two of the latest state of the art non-linear techniques (MFDFA and MFDXA) of dealing complex time series. MFDFA revealed that for all the participants the spectral width, and hence the complexity of the EEG signals, reaches a maximum while viewing color Blue, followed by colors Red and Green in all the brain lobes. MFDXA, on the other hand, suggests a lower degree of inter and intra lobe correlation while watching the VIBGYOR colors compared to baseline Grey, hinting towards a post processing of visual information. We hope that along with the novelty of methodologies, the unique outcomes of this study may leave a long term impact in the domain of color perception research.

The performance of an up-flow 0.5 m high and 0.076 m diameter fixed-film bioreactor treating lipid-rich synthetic wastewater has been investigated under the present study. The initial porosity of t...

This chapter explores austerity in relation to two major pieces of social policy recently introduced in Australia and Scotland, namely the National Disabilities Insurance Scheme (NDIS) and Personalisation Self-Directed Care. Claiming to promote human rights and consumer choice, both policies introduce cash-for-care packages that significantly change for service users, the female-majority workforce, and the organisations providing services. Consistent with austerity, need for service outstrips supply, labour markets are being rapidly restructured to offer increasingly precarious and degraded employment, including the emergence of gig work in the Australia example, and service organisations in Scotland that are unable to remain solvent in the austerity-led funding regimes. Ideological themes associated with austerity underlie and are reinforced in these programs including a state-led argument that sacrifices, patience and understanding are required of service users while the programs are painstakingly rolled-out and funding packages assessed, reassessed, awarded, and denied. Rather than protecting and empowering service users and workers under these policies, the government has instigated the further privatization of services, and the deepening of insecurity.

Recommender Systems are quite popular and very useful for predictions of various products to consumers by providing recommendations. It deals with the definite type of items and produces the recommendations that are personalized to deliver effective and valuable suggestions to the consumer. The cold-start problem is one of the challenges in recommender systems. The cold-start situation arrives when products added to the collection have either no experiences, or very little. This causes a challenge for collaborative filtering algorithms mainly because they rely on the interactions of the item to make recommendations. In general, it is much harder to ask new user about their personal information (users don’t want to answer too many questions). But it is easier to ask a lot of information about a new item (people who add it are interested in filling in this information to make their products recommended to the customers). The proposed system delivers the recommendation of new items to existing users with high consistency and precision.

The knowledge overload is a big problem in today’s world. In reality, the information overload implies the availability of so much data or knowledge that goes beyond the user’s manageable limits and causes a great difficulty in all kinds of decision taking. The main reason we need a recommender system in modern society is that because of the proliferation of the Internet, people have so many options to choose from. A recommender system refers to a system that can predict a user’s future preference for a set of items and recommends the top items. It is a knowledge retrieval application that enhances accessibility as well as efficiently and effectively suggests relevant items to users by considering the user interests and preferences. A recommendation framework tries to tackle the problem of overloading information. There are so many other instances such as these, where we have plenty of data, but we can't decide what we want. Even though volume of information has increased, a new problem has arisen as people have had difficulty selecting the items they actually want to see. Recommenders systems have the ability to change the way websites interact with users and allow businesses to optimize their Return on Investment (ROI); based on the information, they can collect on the preferences and purchases of each customer. A traditional recommendation system cannot do its work without sufficient information, and big data offers plenty of user data such as past transactions, browsing history, and reviews for recommendation systems in order to provide accurate and efficient recommendations. In short, even the most advanced recommenders without big data can’t be successful. This work primarily discusses and reflects on current issues, challenges, and research gaps in the production of high-quality recommender systems. Such problems and challenges will present new paths for study, and the target can be accomplished for high-quality recommender systems. The entire research is broken into major tasks including the study of state-of-the-art approaches for recommender system and big data applications; overcoming the problem of cold start, scalability, and building a proactive recommender system; this research considers the process of development for a generic intelligent recommender system that can be work on more than one domain; it also expands the basic recommender program definition.

Recent years have revealed an increasing attention and interest in various countries about the problem of dropout of the students in the school and to find out its chief contributing factors. In our model, we attempt to demonstrate how a specific factor can affect students’ academic life, which subsequently produces dropout among school students. In this paper, we propose a methodology and a specific clustering algorithm to identify the factors that results in dropout among the students at different educational levels, such as primary, secondary, and higher secondary and also their percentage of impact among the students. This research will guide the teachers and school administration to improve this dropout scenario of their school. A solution to this problem can be resolved with the use of educational data mining (EDM).

Data mining is the practice of mining valuable information from huge data sets. Data mining allows the users to have perceptions of the data and make convenient decisions out of the information extracted from databases. The purpose of the engineering colleges is to offer greater chances to its students. Education data mining (EDM) is a process for analyzing the student’s performance based on numerous constraints to predict and evaluate whether a student will be placed or not in the campus placement. The idea of predicting the performance of higher education students can help various institutions in improving the quality of education, identifying the pupil’s risk, upgrading the overall accomplishments, and thereby refining the education resource management for better placement opportunities for students. This research proposes a placement prediction model which predicts the chance of an undergrad student getting a job in the placement drive. This self-analysis will assist in identifying the patterns, where a comparative study between two individual methods has been made in order to predict the student’s success and a database has been generated.

Analysis of student performance will help us understand the various factors that affect the overall of a student. Big Data Environment helps in analyzing the various concepts which are inbuilt for better strategies and the choices that are taken for an organization’s overall development. Reduction in cost, time, the development of optimized and novice products, efficient and smart decision-making are some of the fields where it proves to be useful. Considering, the Higher Education System, which is inculpated in predicting the performance of students, this work will help various institutions in not only enhancing the quality of education, but also upgrading the overall accomplishments, identifying the pupil’s at risk, and thereby refining the education resource management. This introspection will aid in identifying the patterns, where a comparative study between two distinct methods has been made in order to predict the student’s success and a database has been generated.

Big Data, as we all know, is becoming a new technological trend in the industries, in science and even businesses. Indefinite data scalability allows organizations to process huge amounts of data in parallel, assisting dramatically decrease the amount of time it takes to manage several amount of work, optimize hardware resource usage and permit the extreme quantity of data per node to be handled. Optimization is to done to attain the finest strategy relative to a set of selected constraints which include maximizing factors such as efficiency, productivity, reliability, strength, and utilization. When the current system becomes insufficient, instead of upgrading it by adding more components to the existing structure you just add more computers to a cluster. This research discusses a hierarchical architecture of Hadoop Nodes namely Name nodes and Data nodes and mainly focuses on the optimization of Data Node by distributing some of its work load to Name Node.

In this study we report on a three-stage design of algal assisted constructed wetland -microbial fuel cell incorporated sand filter for efficient treatment of wastewater and bioelectricity generation, simulating a core of it. It aims for the complete removal of organic matter and other inorganic pollutants such as NO3−, NH4+ and PO43−. The conductive material equipped anode region of constructed wetland (CW) performed better for organic removal, and the system efficiently removed COD up to 96.37 ± 2.6%. The algal biomass passively aerated the cathode region and resulted in efficient removal of 85.14 ± 10.73% of NH4+ and 69.03 ± 10.14% of PO43−. The additional sand filter provided anaerobic situation favorable for removal of NO3− up to 68.41 ± 7.63%. The maximum power and current densities achieved in the system were 33.14 mW m−3 and 235.0 mA m−3, respectively.

The relationship between color and music as part of the complex system consisting of visual and auditory domain has been investigated in this study. As both the stimulus forms are processed in the same part of the human body, i.e., the brain, it will be really interesting to examine whether they share a similarity in perception. Needless to say that color and music both have strong impact on emotion and feelings & also a few studies have been reported in literature to explore causal relationship between color and emotion. This work reports a neuro-cognitive study on response of brain to two different stimulus and their cross-modal associations. In this study the correlation between emotional arousal and the effect of audio and visual stimuli has been studied from a new perspective. 93 participants were asked to hear 6 different music pieces (each of 30 s duration). The type of emotion elicited by different music pieces were identified by the participants from a given collection of possible emotional responses. Then they are asked to assign a color associating the emotion from a given color wheel (structured according to Munsell color system/RGB color space). Each color, associated with a particular music piece, is a mixture of specific Red, Green and Blue values (RGB triplet) and has a specific HEX number (hexadecimal representation), which is recorded for each response. Then, the musical pieces used were further zoomed with the help of fractal technique to identify different emotions related to music in a quantitative approach. Here, to analyze the complexity of the sound signal (which are non-stationary and scale varying in nature), we have used Multifractal detrended fluctuation analysis (MFDFA), which is capable of determining multifractal scaling behavior of non-stationary time series. From the experimental data, it is seen that the visual and emotional response to the auditory stimulus follows a specific trend which is directly related to the stimulus complexity.

The data which are beyond the storage space of the server and beyond to the processing power is called Big Data. It is not manageable by traditional RDBMS or conventional statistical tools. Big data Increases the storage capacities as well as the processing power. Horizontal scaling or sharding is needed to divide the data set and distributes the data over multiple servers. Redundancy and fault tolerance is achieved by horizontal scaling. Optimization of horizontal scaling is an important aspect of Big Data technology. Instead of using vertical scaling that means upgrading to fancier computers when the current system becomes inadequate, we have to add more node (computers) to a cluster. It increases the parallelism, rather than the performance of any one node. This paper presents the fundamentals of big data analytics but directing towards an analysis of various optimization techniques used in the big data environment.

The data which is beyond the storage space of the server and beyond to the processing power is called Big Data. It is not manageable by traditional RDBMS or conventional statistical tools. Big data increases the storage capacities as well as the processing power. Horizontal scaling or sharding is needed to divide the data set and distributes the data over multiple servers. Redundancy and fault tolerance are achieved by horizontal scaling. Optimization of horizontal scaling is an important aspect of Big Data technology. Instead of using vertical scaling that means upgrading to fancier computers when the current system becomes inadequate, we have to add more node (computers) to a cluster. It increases the parallelism, rather than the performance of any one node. This paper presents the fundamentals of big data analytics but directing toward an analysis of various optimization techniques used in the big data environment.

Over the past two decades, offshoring and the relocation of economic activities from the global north to the global south have prompted diverse efforts to describe, map and analyse developments. The global commodity chain (GCC) research of Gereffi and Korzeniewicz (Commodity chains and global capitalism, 1994) stimulated the formulation of related but distinct frameworks (global value chains/GVCs and global production networks/GPNs) to understand the ways in which manufacturing and services are integrated while being geographically dispersed. Taylor (Offshoring and working conditions in remote work, 2010a) applied these GVC and GPN frameworks to the call/contact centre chain which entwined the sites of “remote” service delivery in India with the location of corporate decision-making and final customers in the home countries of the USA, the UK, and elsewhere. This chapter, developing these insights, utilizes the GVC and GPN frameworks to undertake an empirical study that examines work and employment in Indian business process outsourcing (BPO) industry post-recession. The study addresses the lacuna of published work on post-crisis Indian BPO by examining the dynamics of work and employment in three contrasting servicing chain relationships that span the spectrum of offshoring in the Indian BPO industry—an Indian third-party organization, a global service provider and a captive (i.e. in-house) operation. GVC concepts are employed to inform analysis and explain developments and differences. Findings demonstrate the pressures on business strategy and employee experiences of work and employment resulting from these organizations’ respective positioning within global service supply chains during the period 2012–14.

Pre-eclampsia leads to disturbed fetal organ development, including metabolic syndrome, attributed to altered pituitary-adrenal feedback loop. We measured cortisol metabolites in infants born from pre-eclamptic and normotensive women and hypothesised that glucocorticoid exposure would be exaggerated in the former. Twenty-four hour urine was collected from infants at months 3 and 12. Cortisol metabolites and apparent enzyme activities were analysed by gas chromatography-mass spectrometry. From 3 to 12 months, excretion of THS, THF and pregnandiol had risen in both groups; THF also rose in the pre-eclamptic group. No difference was observed with respect to timing of the visit or to hypertensive status for THE or total F metabolites (P>0.05). All apparent enzymes activities, except 17α-hydroxylase, were lower in infants at 12 compared to 3 months in the normotensive group. In the pre-eclamptic group, only 11β-HSD activities were lower at 12 months.17α-hydroxylase and 11β-HSD activities of tetrahydro metabolites were higher in the pre-eclamptic group at 3 months (P