Long-term Product Rating Prediction Based on Users' Short-term Multiple Ratings

Ratings and product reviews could be considered as one of the main features determining the quality of a product in online store systems, especially in deciding whether to place a product as part of an online store's inventory. online vendors are often attracted by product reviews and ratings. However, when the average product rating observed based on a small number of user ratings, the decision maker may not be certain about choosing that product, even if it has a fairly high rate. Long-term rating predictions would help online vendors to identify products and advertise their websites by choosing potential ones. In this paper machine learning approach utilizing linear regression model is used to predict long-term product rate. The model evaluated using the Datasheet of the Amazon Online Store website,1996 to 2014. Keywords: Rating, Long-term Prediction, Machine Learning Algorithm, Linear Regression. DOI: 10.7176/JIEA/9-4-04 Publication date:June 30th 2019

Edge currents as a probe of the strongly spin-polarized topological noncentrosymmetric superconductors

Recently the influence of antisymmetric spin-orbit coupling has been studied in novel topological superconductors such as half-Heuslers and artificial hetero-structures. We investigate the effect of Rashba and/or Dresselhaus spin-orbit couplings on the band structure and topological properties of a two-dimensional noncentrosymetric superconductor. For this goal, the topological helical edge modes are analyzed for different spin-orbit couplings as well as for several superconducting pairing symmetries. To explore the transport properties, we examine the response of the spin-polarized edge states to an exchange field in a superconductor-ferromagnet heterostructure. The broken chiral symmetry causes the uni-directional currents at opposite edges.Comment: 10 pages, 7 figure

Evaluating Attentional Impulsivity: A Biomechatronic Approach

Executive function, also known as executive control, is a multifaceted construct encompassing several cognitive abilities, including working memory, attention, impulse control, and cognitive flexibility. To accurately measure executive functioning skills, it is necessary to develop assessment tools and strategies that can quantify the behaviors associated with cognitive control. Impulsivity, a range of cognitive control deficits, is typically evaluated using conventional neuropsychological tests. However, this study proposes a biomechatronic approach to assess impulsivity as a behavioral construct, in line with traditional neuropsychological assessments. The study involved thirty-four healthy adults who completed the Barratt Impulsiveness Scale (BIS-11) as an initial step. A low-cost biomechatronic system was developed, and an approach based on standard neuropsychological tests, including the trail-making test and serial subtraction-by-seven, was used to evaluate impulsivity. Three tests were conducted: WTMT-A (numbers only), WTMT-B (numbers and letters), and a dual-task of WTMT-A and serial subtraction-by-seven. The preliminary findings suggest that the proposed instrument and experiments successfully generated an attentional impulsivity score and differentiated between participants with high and low attentional impulsivity.Comment: 10 pages, 5 figures, 5 table

A Systematic Review of Consumer Preference for E-Cigarette Attributes: Flavor, Nicotine Strength, and Type

Objective Systematic review of research examining consumer preference for the main electronic cigarette (e-cigarette) attributes namely flavor, nicotine strength, and type. Method A systematic search of peer-reviewed articles resulted in a pool of 12,933 articles. We included only articles that meet all the selection criteria: (1) peer-reviewed, (2) written in English, and (3) addressed consumer preference for one or more of the e-cigarette attributes including flavor, strength, and type. Results 66 articles met the inclusion criteria for this review. Consumers preferred flavored e-cigarettes, and such preference varied with age groups and smoking status. We also found that several flavors were associated with decreased harm perception while tobacco flavor was associated with increased harm perception. In addition, some flavor chemicals and sweeteners used in e-cigarettes could be of toxicological concern. Finally, consumer preference for nicotine strength and types depended on smoking status, e-cigarette use history, and gender. Conclusion Adolescents could consider flavor the most important factor trying e-cigarettes and were more likely to initiate vaping through flavored e-cigarettes. Young adults overall preferred sweet, menthol, and cherry flavors, while non-smokers in particular preferred coffee and menthol flavors. Adults in general also preferred sweet flavors (though smokers like tobacco flavor the most) and disliked flavors that elicit bitterness or harshness. In terms of whether flavored e-cigarettes assisted quitting smoking, we found inconclusive evidence. E-cigarette users likely initiated use with a cigarette like product and transitioned to an advanced system with more features. Non-smokers and inexperienced e-cigarettes users tended to prefer no nicotine or low nicotine e-cigarettes while smokers and experienced e-cigarettes users preferred medium and high nicotine e-cigarettes. Weak evidence exists regarding a positive interaction between menthol flavor and nicotine strength

A Wearable RFID-Based Navigation System for the Visually Impaired

Recent studies have focused on developing advanced assistive devices to help blind or visually impaired people. Navigation is challenging for this community; however, developing a simple yet reliable navigation system is still an unmet need. This study targets the navigation problem and proposes a wearable assistive system. We developed a smart glove and shoe set based on radio-frequency identification technology to assist visually impaired people with navigation and orientation in indoor environments. The system enables the user to find the directions through audio feedback. To evaluate the device's performance, we designed a simple experimental setup. The proposed system has a simple structure and can be personalized according to the user's requirements. The results identified that the platform is reliable, power efficient, and accurate enough for indoor navigation.Comment: 6 pages, 6 figures, 3 table

Designing a security unit to reduce the permeability of anonymous groups and congestion control in wireless sensor networks

Recent developments in the fields of electronics and wireless communication have provided conditions to design and build lower power consumption sensors and use of radio signals for information communications has provided various applications. Design of such networks has their own specific challenges. One of these problems is the probability of congestion due to higher data transmission rate. Using radio signals, the hackers are able to pretend and introduce themselves as a network member through cracking security barriers and in this case the conditions are provided for hacker nodes to access critical information and other destructive activities. Hence, in this paper we will present a new algorithm for the problem of congestion and increase of security in the wireless sensor networks through designing a security unit for the head cluster. The security unit considers a switch for each node by using Linear Feedback Shift Registers (LFSR), till each node encode its data with the key and transfer it to the next node. In this algorithm, security coefficient and sensitive tasks are increased for networks with specific activities and the stepwise algorithm is used instead of end to end algorithm to ensure reliability of package arrival to solve the congestion problem.  In this algorithm, limitations of sensor networks such as limited energy and small size of nodes buffer are taken into account. Results of the evaluations carried out by NS2 simulator have shown that delay in package arrival is reduced, using such algorithm, fewer energy is consumed by the nodes and network lifetime is increased, accordingly

Solvent Effect Modeling in Heterogenous Catalysis

In recent years, the biorefining industry and biofuels have emerged as a major American energy sector. Biofuels are fuels produced from plant and animal material, also referred to as biomass. This includes wood products, manure, and corn, among other materials. Compared to fossil fuels, biofuels are significantly more environmentally friendly and thus pose less of a threat to environmental health. In 2019, the United States consumed around 14.54 billion gallons of ethanol and around 1.81 billion gallons of biodiesel. By 2030, the United States is expected to consume around 95 Mtoe of biofuels. In order to meet current demand and expected growth in demand, it is necessary to advance the efficiency of biomass processing. In this context, special characteristics of biomass feedstock (highly reactive and water soluble, aqueous, and thermally unstable) demands liquid-phase processing technologies for higher product selectivity and lower cost. As a result, to design an efficient liquid-phase process, it is critical to understand the root causes of solvent effects on surface properties and energetics. However, despite recent improvements in studying reactions at gas-solid interfaces, methods capable of investigating reactions occurring at solid-liquid interfaces are less developed; primarily due to the inherent intricacies of a reaction system comprised of both a complex heterogeneous catalyst and a condensed phase. The objectives of this study are to develop and validate a hierarchy of multi-scale methods for computing reaction and activation free energies of elementary processes vi occurring at metal-solvent interfaces and to apply these methods to the rational design of novel heterogeneous catalysts with exceptional activity and selectivity for the liquid-phase conversion of lignocellulosic biomass into transportation fuels or commodity and specialty chemicals. To gain a fundamental understanding of the role of metal identity (catalyst) on the solvent effect, we studied the aqueous-phase effect on the initial C-H and O-H bond cleavages of ethylene glycol (being a commonly studied surrogate molecule of biomass-derived polyols) over the (111) facet of six transition metal surfaces (Ni, Pd, Pt, Cu, Ag, Au) using our explicit solvation method, eSMS. We found a significant metal dependence on aqueous solvation effects that can be traced back to a different amount of charge-transfer between the adsorbed species and metals in the reaction and transition states for the different metal surfaces. In addition, we developed a new hybrid QM/MM approach for computing solvent effects on the free energy of adsorption and desorption processes that enables us to compare our work with experimental findings quantitatively. Consequently, comparison of computational predictions to experimental data of phenol adsorption on the Pt(111) surface indicates that adsorption free energies in the aqueous phase can be determined accurately within an error less than 0.10 eV