Entrepreneurial Skills Reinforcement Model in “Merdeka Belajar Kampus Merdeka” Entrepreneurship at University of Merdeka Malang

In achieving long-term goals and specific targets researchers will develop a model ESRM (entrepreneurial Skill Reinforcement Model) in order to create a strong competitive edge in the face of the complex economic conditions. The existence of limited information regarding training and education that strengthens entrepreneurial skills can affect the quality of an entrepreneur. This study aims to cluster and need analyze the interests of students in entrepreneurship at the University of Merdeka Malang. The method that will be used in this research is using the clustering method, where at this stage the student's entrepreneurial interest will be clustered, then a need assessment is carried out and then the model design is carried out according to the student's needs. In the next stage, a model for strengthening entrepreneurial skills was designed and then improved with focus group discussions and expert tests so that the model became feasible and suitable to be applied. Then it will be tested in a trial class to test the effectiveness of the model. The results of the model's feasibility analysis show that there is a strong agreement between experts and practitioners on the feasibility of the developed model. In addition, based on the effectiveness test, the research product in the form of ESRM is categorized as effective for increasing entrepreneurial skills instudents.

Research on a Trackside Concrete Reinforcement Scheme of a Small Radius Curve at a Junction Section of a Modern Tram

In order to ensure the normal use of a junction section of a modern tram, this paper mainly studied a trackside concrete reinforcement scheme. Firstly, the entire non-reinforcement system model with a small radius curve composed of rail, fastener, fastener cover, flexible material, asphalt layer and track slab was established using the ABAQUS finite element software, and the stress distribution and deformation state of the asphalt layers of the non-reinforcement system model under the social vehicle load were analyzed. Then, the whole system model of the concrete reinforcement scheme was founded, and the stress and deformation of the asphalt layers under the same load were investigated. Finally, the calculation results of the concrete reinforcement model were com-pared with those of the non-reinforcement model, and the reinforcement effect was studied. The results show that the concrete reinforcement scheme significantly reduces the stress and deformation of the asphalt layers and improves the stress distribution and deformation state of the asphalt layers.

Root reinforcement: continuum framework for constitutive modelling

The mechanical contribution of plant roots to soil strength has typically been studied at the ultimate limit state only. Since many geotechnical problems are related to serviceability, such as deformation of infrastructure, a new constitutive modelling framework is introduced. The rooted soil is treated as a composite material with separate constitutive relationships for soil and roots, and a comprehensive stress-strain relationship for the root constituent is presented. The model is compared to direct shear experiments on field soil reinforced with gorse, grass and willow roots, as well as an existing root reinforcement model based on Winkler-spring supported beam theory. The results show that both the newly developed model and the beam-type model yield good predictions for the evolution of root-reinforced shear strength as a function of increasing shear displacements. Both successfully capture the large deformations required to reach peak reinforcement, the reduction in reinforcement due to root breakage and the presence of significant reinforcement even after very large deformations, associated with root slippage. Since both fibre and beam models only require physically meaningful input parameters, they can be useful tools to study the mobilisation of rooted soil strength and simulate the response of rooted soil in continuum-based numerical simulations.

A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy.

Grandparent-child cohesion, friendship quality, and left-behind children’s positive/negative affect in rural China

This study examined the roles of grandparent-child cohesion and friendship quality in left-behind children’s positive and negative affect compared with non-left-behind children. Data from 557 participants indicated that grandparent-child cohesion and friendship quality predicted children’s emotional adaptation. Friend trust and support and intimate exchange had a stronger predictive effect on positive affect among non-left-behind children. Moreover, the interaction effects between grandparent-child cohesion and friendship quality on children’s positive affect supported the reinforcement model, while the interaction effects on negative affect supported the reinforcement model among left-behind children but supported the compensation model among non-left-behind children.

Champ versus Chump: Viewing an Opponent’s Face Engages Attention but Not Reward Systems

When we play competitive games, the opponents that we face act as predictors of the outcome of the game. For instance, if you are an average chess player and you face a Grandmaster, you anticipate a loss. Framed in a reinforcement learning perspective, our opponents can be thought of as predictors of rewards and punishments. The present study investigates whether facing an opponent would be processed as a reward or punishment depending on the level of difficulty the opponent poses. Participants played Rock, Paper, Scissors against three computer opponents while electroencephalographic (EEG) data was recorded. In a key manipulation, one opponent (HARD) was programmed to win most often, another (EASY) was made to lose most often, and the third (AVERAGE) had equiprobable outcomes of wins, losses, and ties. Through practice, participants learned to anticipate the relative challenge of a game based on the opponent they were facing that round. An analysis of our EEG data revealed that winning outcomes elicited a reward positivity relative to losing outcomes. Interestingly, our analysis of the predictive cues (i.e., the opponents’ faces) demonstrated that attentional engagement (P3a) was contextually sensitive to anticipated game difficulty. As such, our results for the predictive cue are contrary to what one might expect for a reinforcement model associated with predicted reward, but rather demonstrate that the neural response to the predictive cue was encoding the level of engagement with the opponent as opposed to value relative to the anticipated outcome.

AN INSTITUTIONAL REINFORCEMENT MODEL FOR THE PROTECTION OF MANGROVES SUSTAINABLE ECOTOURISM IN INDONESIA

This study aims at examining the sustainability of mangrove ecotourism at Cengkrong Mangroves Ecotourism in Indonesia. A quantitative approach was adopted to capture the complexity of the phenomenon. The study was conducted in an area with most mangroves in Indonesia, including Cengkrong Ecotourism in Trenggalek, East Java. Sustainability is achieved when each stakeholder makes a positive contribution to others in ecology, economy, social, institutional and law enforcement, and technology. Using multi-dimensional scaling and Monte Carlo approach, the findings of this study indicate that Cengkrong mangrove ecotourism is classified as “sustainable” (76.20%). The highest dimension is ecology due to the minimum level of pollution in the area. Even Cengkrong beach mangrove is a tourist destination which is potentially polluted by the tourist; however, the area is not densely populated. Nevertheless, amongst the other indicator, social is the lowest (67.95%).

Core Cognitive Mechanisms Underlying Syntactic Priming: A Comparison of Three Alternative Models

Syntactic priming (SP) is the effect by which, in a dialogue, the current speaker tends to re-use the syntactic constructs of the previous speakers. SP has been used as a window into the nature of syntactic representations within and across languages. Because of its importance, it is crucial to understand the mechanisms behind it. Currently, two competing theories exist. According to the transient activation account, SP is driven by the re-activation of declarative memory structures that encode structures. According to the error-based implicit learning account, SP is driven by prediction errors while processing sentences. By integrating both transient activation and associative learning, Reitter et al.'s hybrid model 2011 assumes that SP is achieved by both mechanisms, and predicts a priming enhancement for rare or unusual constructions. Finally, a recently proposed account, the reinforcement learning account, claims that SP driven by the successful application of procedural knowledge will be reversed when the prime sentence includes grammatical errors. These theories make different assumptions about the representation of syntactic rules (declarative vs. procedural) and the nature of the mechanism that drives priming (frequency and repetition, attention, and feedback signals, respectively). To distinguish between these theories, they were all implemented as computational models in the ACT-R cognitive architecture, and their specific predictions were examined through grid-search computer simulations. Two experiments were then carried out to empirically test the central prediction of each theory as well as the individual fits of each participant's responses to different parameterizations of each model. The first experiment produced results that were best explained by the associative account, but could also be accounted for by a modified reinforcement model with a different parsing algorithm. The second experiment, whose stimuli were designed to avoid the parsing ambiguity of the first, produced somewhat weaker effects. Its results, however, were also best predicted by the model implementing the associative account. We conclude that the data overall points to SP being due to prediction violations that direct attentional resources, in turn suggesting a declarative rather than a RL based procedural representation of syntactic rules.