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With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

Briefly reviews previous literature by the author before presenting an original 12 step system integration protocol designed to ensure the success of companies or countries in their efforts to develop and market new products. Looks at the issues from different strategic levels such as corporate, international, military and economic. Presents 31 case studies, including the success of Japan in microchips to the failure of Xerox to sell its invention of the Alto personal computer 3 years before Apple: from the success in DNA and Superconductor research to the success of Sunbeam in inventing and marketing food processors: and from the daring invention and production of atomic energy for survival to the successes of sewing machine inventor Howe in co‐operating on patents to compete in markets. Includes 306 questions and answers in order to qualify concepts introduced.

Scholars have primarily focused on how language represents categories. We move beyond this conception to develop a discursive perspective of market categorization focused on how categories are constructed through communicative exchanges. The discursive perspective points to three under-researched mechanisms of category evolution: (1) the interaction between market participants, (2) the power dynamics among market participants and within the discourse, and (3) the cultural and material context in which categories are constructed. In this theoretical paper, we discuss how each of these mechanisms shed light on different phases of category evolution and the methods that could be used to study them.

Nobody concerned with political economy can neglect the history of economic doctrines. Structural changes in the economy and society influence economic thinking and, conversely, innovative thought structures and attitudes have almost always forced economic institutions and modes of behaviour to adjust. We learn from the history of economic doctrines how a particular theory emerged and whether, and in which environment, it could take root. We can see how a school evolves out of a common methodological perception and similar techniques of analysis, and how it has to establish itself. The interaction between unresolved problems on the one hand, and the search for better solutions or explanations on the other, leads to a change in paradigma and to the formation of new lines of reasoning. As long as the real world is subject to progress and change scientific search for explanation must out of necessity continue.

This paper aims to propose a classification method for steel strip surface defects based on a mixed attention mechanism to achieve fast and accurate classification performance. The traditional method of classifying surface defects of hot-rolled steel strips has the problems of low recognition accuracy and low efficiency in the industrial complex production environment.

The authors selected min–max scaling comparison method to filter the training results of multiple network models on the steel strip surface defect data set. Then, the best comprehensive performance model EfficientNet-B0 was refined. Based on this, the authors proposed two mixed attention addition methods, which include squeeze-excitation spatial mixed module and multilayer mixed attention mechanism (MMAM) module, respectively.

With these two methods, the authors achieved 96.72% and 97.70% recognition accuracy on the steel strip data set after data augmentation for adapting to the complex production environment, respectively. Using the transfer learning method, the EfficientNet-B0 based on MMAM obtained 100% recognition accuracy.

This study not only focuses on improving the recognition accuracy of the network model itself but also considers other performance indicators of the network, which are rarely considered by many researchers. The authors further improve the intelligent production technique and address this issue. Both methods proposed in this paper can be applied to embedded equipment, which can effectively improve steel strip factory production efficiency and reduce material and time loss.

Traditionally, organizational theory has been a receptacle of methods and mechanisms from network theory. In this paper, we argue that organizational theory can also be an active contributor to network theory’s conceptual development. To that end, we make explicit a theoretical strategy that has only been used informally by network theorists so far, which – following Vaughan (2002) – we refer to as analogical theorizing. Using the basic correspondence between dyadic relationships as the most minimal form of “organization,” we show that processes and mechanisms extracted from various theoretical strands of organizational theory can be mapped onto the dynamics of social relationships. This allows us to build novel theoretical insight as it pertains to issue of relationship emergence, maintenance, and decay in social networks.

This paper aims to examine how activating an abstract versus concrete construal as a retrieval cue – prior to providing estimates but after exposure to the stimulus – affects retrospective duration estimates of a hedonic experience, the kind of experience one might wish to repeat. Recent research has examined the effect of construal mindsets on prospective time perceptions (Hans and Trope, 2013) as well as the prediction of future durations (Kanten, 2011; Siddiqui et al., 2014).

Two experiments are presented to test four hypotheses. The effect of construal level on time perceptions, confidence in duration judgments and future preferences using two different construal level manipulation techniques and a range of measures for the dependent variables is demonstrated.

This research found that compared to a neutral experience, time perceptions of an enjoyable event are not explained by differences in the level of attention paid to the stimuli; that duration estimates elicited under abstract construals are shorter than those produced by concrete construals; and regardless of construal mindset, memory decay due to time delay appears to be at work. Hence duration estimates shorten. Moreover, abstract construals decrease confidence in duration judgments, but positively affect future preferences compared to a concrete mindset.

This paper expands current knowledge by showing that construal mindsets can be used as retrieval cues to affect evaluations of past experiences and consumers’ experience-based future preferences.

The purpose of this paper is to set up and analyze a formal model for maintenance scheduling for local government purpose buildings.

The authors formulate the maintenance scheduling decision as a dynamic optimization problem, subject to an accelerating decay. This approach offers a formal, yet intuitive, weighting of an important trade-off when deciding a maintenance schedule.

The optimal maintenance schedule reflects a trade-off between the interest rate and the rate at which the decay accelerates. The prior reflects the alternative cost, since the money spent on maintenance could be saved and earn interests, while the latter reflects the cost of postponing maintenance. Importantly, it turns out that it is sub-optimal to have a cyclical maintenance schedule where the building is allowed to decay and then be intensively maintained before decaying again. Rather, local governments should focus the maintenance either early in the building’s life-span and eventually let it decay toward replacement/abandonment or first let it decay to a target level and then keep it there until replacement/abandonment. Which of the two is optimal depends on the trade-off between the alternative cost and the cost of postponing maintenance.

The paper provides a first formal inquiry into important trade-offs that are important for maintenance scheduling of local public purpose buildings.

Applies the analytic‐synthetic dichotomy of hemispheric functioning suggested by Levy‐Agresti and Sperry to explain the chunking theory of Miller. Constructs a theory of cognition, based on cerebral functions which were discovered through hemispheric differences. Shows that all the arguments of Efron against the hemispheric paradigm are merely “puzzles” that can be solved within this paradigm. New findings of Efron and Yund were, in fact, predicted by a component of this theory.

The purpose of this study is to propose a measure for earned attention and a model and procedure for the maximization of earned attention by a company over a period of time.

Utility functions are used as the base of the earned attention measure. An evolutionary algorithm – a memetic algorithm – is applied to identify strategies that aim to maximize earned attention. Computational analysis is performed resorting to simulated data, and the memetic algorithm is assessed through the comparison with a standard steepest ascent heuristic.

The shape of the utility functions considered in the model has a huge impact on the characteristics of the best strategies, with actions focused on increasing a single variable being preferred in case of constant marginal utility, and more balanced strategies having a better performance in the case of decreasing marginal utility. The memetic algorithm is shown to have a much better performance that the steepest ascent procedure.

A new mathematical model for earned attention is proposed, and an approach that has few applications in business problems – a memetic algorithm – is tailored to the model and applied to identify solutions.