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Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a direct threat to the security and privacy of the users. While most researches are focused on automatic text classification, in this work we demonstrate the possibility of improving current short messages spam detection systems using a novel method. We combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts. We enrich a publicly available dataset adding these features, first separately and after in combination, of each message to the dataset, creating new datasets. We apply several combinations of the best SMS spam classifiers and filters to each dataset in order to compare the results of each one. Taking into account the experimental results we analyze the real inuence of each feature and the combination of both. At the end, the best results are improved in terms of accuracy, reaching to a 99.01% and the number of false positive is reduced.

Social Media (SM) has become the easiest, cheapest and fastest channel for companies to identify the events that affect their customers. The geo-location capabilities of the SM interactions enable Early Warning Systems to alert not only when the quality of service decays, but also where and how many customers are impacted. In this paper we present a system and a set of supporting metrics that exploit the geo-localized SM stream, quantify the perceived impact of events, incidents, etc. on a particular area over time. Industrial service providers can add this perceptional perspective to their standard monitoring tools to enable a prompt and appropriate reaction, the decision-making in marketing activities and to unveil customer acquisition opportunities applying the system to the competitors’ customers.

The production of miniature parts by the electrochemical discharge micromachining process ($\mu$-ECDM) draws the most of attractions into the industrial field. Parametric influences on machining depth (MD), material removal rate (MRR), and overcut (OC) have been propounded using a mixed electrolyte (NaOH:KOH- 1:1) varying concentrations (wt.%), applied voltage ($V$), pulse on time ($\mu$s), and stand-off distance (SOD) during microchannel cutting on silica glass (SiO${}_2+{\mathrm{\text{NaSiO}}}_3$). Analysis of variances has been analyzed to test the adequacy of the developed mathematical model and multiresponse optimization has been performed to find out maximum MD with higher material removal at lower OC using desirability function analysis as well as neural network (NN)-based Particle Swarm Optimization (PSO). The SEM analysis has been done to find unexpected debris. MD has been improved with better surface quality using a mixed electrolyte at straight polarity using a tungsten carbide (WC) cylindrical tool along with $X$, $Y$, and $Z$ axis movement by computer-aided subsystem and combining with the automated spring feed mechanism. PSO-ANN provides better parametric optimization results for micromachining by the ECDM process.

This paper inspects wetting rules of normal acid, alkanol and alkane on surfaces of stainless steel, platinum sheet and monocrystalline silicon and contrasts the influence on wetting effect by polarity difference. Experimental results show that for alkane not containing functional groups, the increasing of carbon number is beneficial to the enhancement of wetting effect; optimum wetting reagents for different surface present obvious selectivity, and for alkane, the wetting effect of stainless steel, platinum sheet and monocrystalline silicon is from good to bag in sequence. For organic alcohol and organic acid, the sequence is monocrystalline silicon, platinum sheet and stainless steel. Organic alcohol and organic acid are monocrystalline silicon, platinum sheet and stainless steel. Liquid solid interface polarity contrast shows that certain wetting difference is beneficial to the enhancement of wetting effect.