Estimation of computer waste quantities using forecasting techniques

Technology changes rapidly over years providing continuously more options for computer alternatives and making life easier for economic, intra-relation or any other transactions. However, the introduction of new technology “pushes” old Information and Communication Technology (ICT) products to non-use. E-waste is defined as the quantities of ICT products which are not in use and is bivariate function of the sold quantities, and the probability that specific computers quantity will be regarded as obsolete. In this paper, an e-waste generation model is presented, which is applied to the following regions: Western and Eastern Europe, Asia/Pacific, Japan/Australia/New Zealand, North and South America. Furthermore, cumulative computer sales were retrieved for selected countries of the regions so as to compute obsolete computer quantities. In order to provide robust results for the forecasted quantities, a selection of forecasting models, namely (i) Bass, (ii) Gompertz, (iii) Logistic, (iv) Trend model, (v) Level model, (vi) AutoRegressive Moving Average (ARMA), and (vii) Exponential Smoothing were applied, depicting for each country that model which would provide better results in terms of minimum error indices (Mean Absolute Error and Mean Square Error) for the in-sample estimation. As new technology does not diffuse in all the regions of the world with the same speed due to different socio-economic factors, the lifespan distribution, which provides the probability of a certain quantity of computers to be considered as obsolete, is not adequately modeled in the literature. The time horizon for the forecasted quantities is 2014-2030, while the results show a very sharp increase in the USA and United Kingdom, due to the fact of decreasing computer lifespan and increasing sales

Investigating the factors that affect the time of maximum rejection rate of e-waste using survival analysis

This study aims at investigating the factors which influence positively or negatively electronic waste (e-waste) rejection rates. E-waste quantities have been calculated based on historical sales data worldwide and lifespan distribution. The methodology, which is adopted in this paper in order to estimate the effect that economic, cultural, and demographic factors have upon the time at which maximum e-waste rejection is achieved, is a Weibull parametric accelerated failure time model. Considering the event at which the maximum rejection of e-waste takes place as the dependent variable, it is assumed that it is a function of economic (GDP, GINI index, Internet users, exports/imports and prices), demographic (dependency ratio), and cultural covariates (literacy, masculinity, uncertainty avoidance). The variables are fed to the model after transformation into two major constructs derived from Factor Analysis: the first construct is Wealth (exports, imports, and GDP) and the second is Economic Disparity (size of households, literacy, Internet users, and GINI). The results demonstrate that the time of maximum e-waste rejection rate is prolonged by economic disparity and cultural variables (uncertainty avoidance), while wealth causes a shorter time of rejection rate. The proposed methodology is of great value, as its application could provide useful information in order to develop policies for optimal management of e-waste quantities

A financial approach to renewable energy production in Greece using goal programming

Investing in renewable energy production is a high interest venture considering global energy needs and the environmental impact of fossil fuel consumption. Motivated by the goals set by the European Union towards 2020, this study aims at designing a renewable energy map (installing solar power plants) in Greece. Three aspects are considered, namely, social, financial, and power production aspects. A goal programming model is developed under target and structural constraints, and all possible weight combinations are examined. The solutions derived from each iteration are subjected to a financial meta-analysis, considering different tax and return scenarios aligned to the Greek taxation and banking system. The analysis considers Greece and each region separately, taking net present value (NPV) as an objective measure to assess the solutions. From the results, it is concluded that the internal rate of return is approximately 22.5%−25%22.5%−25% for the overall network. In addition, higher NPV values are obtained when the financial and power production aspects are given greater emphasis. The proposed model provides multi-dimensional information for decision makers; investors can determine the optimal budgeting mix, and policy makers can determine the weight on each aspect that guarantees the success of the venture

Determinants of ICT innovation and imitation in the agrifood sector

Diffusion of innovations has gained a lot of attention and concerns different scientific fields. Many studies, which examine the determining factors of technological innovations in the agricultural and agrifood sector, have been conducted assuming the widely-used Technology Accepted Model (TAM), for a random sample of farmers or firms in agricultural sector. In the present study, a holistic examination of the determining factors that affect the propensity of firms to innovate or imitate, is conducted. The diffusion of ICT tools of firms which are engaged in the NACE 02/03 as well as in the NACE 10/11 classifications for 49 heterogeneous national markets is examined, using the Bass model. The innovation parameter is positively associated with rural income, female employment, export activity and education of farmers, while the imitation parameter is increased in societies with large uncertainty avoidance

Environmental Management and Corporate Social Responsibility Practices of Small and Medium-sized Enterprises

The main aim of this paper is to facilitate small and medium sized enterprises (SMEs) to adopt environmental management (EM) and corporate social responsibility (CSR) practices. The study reveals SMEs‟ motivation, pressure, targets and methods for EM and CSR practices. Additionally, the paper investigates how these variables relate to employee number, turnover and geographical locations. The outcomes of the research will add value to SMEs decision-making processes in both strategic and policy levels (e.g. supplier selection) and policymakers‟ initiatives to make SMEs environment and socially friendly. Although there are studies on EM and CSR practices of SMEs, they mainly focus on impact of EM and CSR practices on business performance, and SMEs‟ motivation for adopting EM and CSR practices in specific country. Studies that reveal SMEs‟ motivation, pressure, targets and methods for EM and CSR practices and their relationship with their characteristics (e.g. size, turn over, and geographical location) are scant. This research bridges this gap. Our data originates from 223 carefully selected representative SMEs in the West Midlands, UK (105) and Kolkata, India (118) covering manufacturing and process industries. The relevant data was collected using questionnaires and analysed using Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA) methods. The results reveal that perceptions of SMEs‟ motivation, pressure, targets and methods of EM and CSR practices vary considerably with respect to size, turn over and geographical location. The findings are significant to policymakers, client organizations and individual SME for improving EM and CSR practices

Η προγνωστική αξία των δ-διμερών σε κακοήθεις νεοπλασίες

Ο αιμοστατικός μηχανισμός αλληλεπιδρά με τις κακοήθεις εξεργασίες. Οι ασθενείς με καρκίνο έχουν διαταραχές αιμόστασης χωρίς απαραίτητα κλινική εκδήλωση θρόμβωσης.Τα δ-διμερή είναι βιοδείκτης ενεργοποίησης της αιμόστασης και διερευνάται ο ρόλος τους ως προγνωστικός και προβλεπτικός δείκτης σε κακοήθεις εξεργασίες. Μελετήθηκε η βιβλιογραφία που αφορά στην παθοφυσιολογική συσχέτιση της αιμόστασης με τον καρκίνο και κυρίως τη μετάσταση. Παρουσιάζεται η βιβλιογραφία που αφορά στην προγνωστική και προβλεπτική αξία των δ-διμερών σε διάφορα είδη καρκίνου. Τα δ-διμερή μπορούν να διαδραματίσουν ενδεχομένως κάποιο ρόλο στην πρόγνωση των ασθενών με κακοήθειες, αλλά απαιτούνται περισσότερες εργασίες για να θεμελιωθεί αυτός ο ρόλος.Hemostasis interacts with malignant diseases. Cancer patients have hemostatic disorders without necessarily clinical thrombosis. D-dimer is a biomarker of hemostatic activation and its prognostic and predictive value is under investigation. In this paper, the pathophysiological connection of hemostasis and cancer, mainly metastasis, is presented, along with the literature that studies the prognostic and predictive value of d-dimers in cancer patients mainly without thrombosis. D-dimer may have a possible role in cancer patients' prognosis, but further research is needed for this to be established

Behçet’s Disease, Associated Large Vessel Thrombosis, and Coexistent Thrombophilia: A Distinct Nosological Entity?

Behçet’s disease (BD) represents a multisystemic disorder that combines features of immune-mediated diseases and autoinflammatory disorders. Even though it is recognized that every type or size of vessel can be affected in this disease, there is an inability to describe a coherent model that sufficiently explains the predilection of certain patients with BD for manifesting severe large vessel thrombosis. The inconsistent epidemiologic data and the complex genetic background of BD, along with the controversy of multiple international studies regarding the coexistence of thrombophilia in patients with BD and large vessel thrombosis, make us think that a percentage of these patients may actually suffer from a distinct clinical entity. The stimulus for this concept arose from the clinical observation of three male patients who were admitted to our clinic due to extended vena cava thrombosis. On the occasion of those clinically and laboratory resembling cases, we performed a literature review concerning the epidemiology of BD, associated thrombosis, and coexistent thrombophilic factors, in order to present some evidence, which sustains our hypothesis that certain patients with large vessel thrombosis, who share features of BD and coexistent thrombophilia, should actually be further investigated for the possibility of suffering from a distinct nosological entity

Quantum error-correcting output codes

Quantum machine learning is the aspect of quantum computing concerned with the design of algorithms capable of generalized learning from labeled training data by effectively exploiting quantum effects. Error-correcting output codes (ECOC) are a standard setting in machine learning for efficiently rendering the collective outputs of a binary classifier, such as the support vector machine, as a multi-class decision procedure. Appropriate choice of error-correcting codes further enables incorrect individual classification decisions to be effectively corrected in the composite output. In this paper, we propose an appropriate quantization of the ECOC process, based on the quantum support vector machine. We will show that, in addition to the usual benefits of quantizing machine learning, this technique leads to an exponential reduction in the number of logic gates required for effective correction of classification error

Seen the villains: detecting social engineering attacks using case-based reasoning and deep learning

Social engineering attacks are frequent, well-known and easy-toapply attacks in the cyber domain. Historical evidence of such attacks has shown that the vast majority of malicious attempts against both physical and virtual IT systems were based or been initiated using social engineering methods. By identifying the importance of tackling efficiently cybersecurity threats and using the recent developments in machine learning, case-based reasoning and cybersecurity we propose and demonstrate a two-stage approach that detects social engineering attacks and is based on natural language processing, case-based reasoning and deep learning. Our approach can be applied in offline texts or real time environments and can identify whether a human, chatbot or offline conversation is a potential social engineering attack or not. Initially, the conversation text is parsed and checked for grammatical errors using natural language processing techniques and case-based reasoning and then deep learning is used to identify and isolate possible attacks. Our proposed method is being evaluated using both real and semi-synthetic conversation points with high accuracy results. Comparison benchmarks are also presented for comparisons in both datasets