Search results

The prevalence of cocaine use has been increasing since the mid-1990s in many European countries, Italy included. There is a large variety of patterns of cocaine use in natural settings, but on the whole, the existence of different patterns of cocaine use remains widely unknown to drug professionals, as well as to public opinion. The purpose of this paper is to investigate patterns and trajectories of use, the meaning of use within the context of users’ life styles, the perception of controlled/uncontrolled use, personal strategies to keep drug use “under control”.

This paper illustrates findings from a qualitative study among 115 cocaine users. Participants were recruited using the snow ball sampling (a minimum lifetime experience of 20 instances of cocaine use was required).

The findings confirm the variability of cocaine use trajectories and the prevalent tendency towards more moderate patterns of use. Such variability is in patent contrast to the disease model of addiction and its assumed predetermined linear trajectories. Set, and particularly setting and all the environmental factors, such as life events, appear to be the variables that can better explain the dynamic course of patterns of use.

The main limit concerns the non-randomisation in the selection of the nominees. Participants were recruited in the night entertainment scene of the main Tuscan cities through personal contacts of staff from risk reduction facilities: in spite of the personal and confidential approach, the number of “non institutionalized” users willing to collaborate was too low, therefore the authors decided to omit the randomisation.

The findings bear social implications as they can contribute to a change in the social representation of users so as to reduce the stigma. They can also prompt innovation in the operational models of drug services.

It is the first qualitative research from the “control” perspective ever led in Italy.

The purpose of this paper is to employ stochastic techniques to increase efficiency of the classical algorithms for solving nonlinear optimization problems.

The well-known simulated annealing strategy is employed to search successive neighborhoods of the classical trust region (TR) algorithm.

An adaptive formula for computing the TR radius is suggested based on an eigenvalue analysis conducted on the memoryless Broyden-Fletcher-Goldfarb-Shanno updating formula. Also, a (heuristic) randomized adaptive TR algorithm is developed for solving unconstrained optimization problems. Results of computational experiments on a set of CUTEr test problems show that the proposed randomization scheme can enhance efficiency of the TR methods.

The algorithm can be effectively used for solving the optimization problems which appear in engineering, economics, management, industry and other areas.

The proposed randomization scheme improves computational costs of the classical TR algorithm. Especially, the suggested algorithm avoids resolving the TR subproblems for many times.

We present an approach for pricing American put options with a regime-switching volatility. Our method reveals that the option price can be expressed as the sum of two components: the price of a European put option and the premium associated with the early exercise privilege. Our analysis demonstrates that, under these conditions, the perpetual put option consistently commands a higher price during periods of high volatility compared to those of low volatility. Moreover, we establish that the optimal exercise boundary is lower in high-volatility regimes than in low-volatility regimes. Additionally, we develop an analytical framework to describe American puts with an Erlang-distributed random-time horizon, which allows us to propose a numerical technique for approximating the value of American puts with finite expiry. We also show that a combined approach involving randomization and Richardson extrapolation can be a robust numerical algorithm for estimating American put prices with finite expiry.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.

This paper tests whether employers responded particularly negatively to African American job applicants during the deep U.S. recession that began in 2007. Theories of labor queuing and social closure posit that members of privileged groups will act to minimize labor market competition in times of economic turbulence, which could advantage Whites relative to African Americans. Although social closure should be weakest in the less desirable, low-wage job market, it may extend downward during recessions, pushing minority groups further down the labor queue and exacerbating racial inequalities in hiring. We consider two complementary data sources: (1) a field experiment with a randomized block design and (2) the nationally representative NLSY97 sample. Contrary to expectations, both analyses reveal a comparable recession-based decline in job prospects for White and African American male applicants, implying that hiring managers did not adapt new forms of social closure and demonstrating the durability of inequality even in times of structural change. Despite this proportionate drop, however, the recession left African Americans in an extremely disadvantaged position. Whites during the recession obtained favorable responses from employers at rates similar to African Americans prior to the recession. The combination of experimental methods and nationally representative longitudinal data yields strong evidence on how race and recession affect job prospects in the low-wage labor market.

This chapter reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies – the Direct method, the Inverse Propensity Score (IPS) estimator, and the Doubly Robust (DR) method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.

The K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine.

This study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics.

The performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms.

The KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.

Most manufacturing plants choose the easy way of completely separating human operators from robots to prevent accidents, but as a result, it dramatically affects the overall quality and speed that is expected from human–robot collaboration. It is not an easy task to ensure human safety when he/she has entered a robot’s workspace, and the unstructured nature of those working environments makes it even harder. The purpose of this paper is to propose a real-time robot collision avoidance method to alleviate this problem.

In this paper, a model is trained to learn the direct control commands from the raw depth images through self-supervised reinforcement learning algorithm. To reduce the effect of sample inefficiency and safety during initial training, a virtual reality platform is used to simulate a natural working environment and generate obstacle avoidance data for training. To ensure a smooth transfer to a real robot, the automatic domain randomization technique is used to generate randomly distributed environmental parameters through the obstacle avoidance simulation of virtual robots in the virtual environment, contributing to better performance in the natural environment.

The method has been tested in both simulations with a real UR3 robot for several practical applications. The results of this paper indicate that the proposed approach can effectively make the robot safety-aware and learn how to divert its trajectory to avoid accidents with humans within the workspace.

The method has been tested in both simulations with a real UR3 robot in several practical applications. The results indicate that the proposed approach can effectively make the robot be aware of safety and learn how to change its trajectory to avoid accidents with persons within the workspace.

This paper provides a novel collision avoidance framework that allows robots to work alongside human operators in unstructured and complex environments. The method uses end-to-end policy training to directly extract the optimal path from the visual inputs for the scene.

Addresses the problem of full digital processing of sensor signals at frequencies in the microwave and radio frequency range. Discusses advantages and drawbacks of the emerging digital alias‐free signal processing technology considering it as a new DSP tool prospective for achieving a breakthrough in DSP theory and techniques leading to a stepwise enlarging of the DSP application frequency range.