Research on green single machine scheduling based on improved ant colony algorithm

This paper studies the green single-machine scheduling problem that considers the delay cost and the energy consumption of manufacturing equipment and builds its integrated optimization model. The improved ant colony scheduling algorithm based on the Pareto solution set is used to solve this problem. By setting the heuristic information, state transition rules, and other core parameters reasonably, the performance of the algorithm is improved effectively. Finally, the model and the improved algorithm are verified by the simulation experiment of 10 benchmark cases.

Determination of the Shortest Route on the Distribution System using Ant Colony Optimization (ACO) Algorithm (Case Study: Alfamidi Palu Branch – PT. Midi Utama Indonesia)

The distribution system of goods is one of the most important parts for every company. The company certainly has many route options to visit, and this is expected to be conducted efficiently in terms of time. In the distribution of goods by Alfamidi company in Palu City which has 51 outlets include into the category of Traveling Salesman Problem (TSP) because of many route options that can be visited. The problem can be solved by employing the Ant Colony Optimization (ACO) method which is one of the algorithms Ant Colony System (ACS). The ACS acquires principles based on the behavior of ant colonies and applies three characteristics to determine the shortest route namely status transition rules, local pheromone renewal and global pheromones. The result showed that the shortest route of the distribution of goods based on the calculation of selected iterations was ant 1 with the shortest total distance obtained 86.98 km.

Temporal assortment of cooperators in the spatial prisoner’s dilemma

We study a spatial, one-shot prisoner’s dilemma (PD) model in which selection operates on both an organism’s behavioral strategy (cooperate or defect) and its decision of when to implement that strategy, which we depict as an organism’s choice of one point in time, out of a set of discrete time slots, at which to carry out its PD strategy. Results indicate selection for cooperators across various time slots and parameter settings, including parameter settings in which cooperation would not evolve in an exclusively spatial model—as in work investigating exogenously imposed temporal networks. Moreover, in the presence of time slots, cooperators’ portion of the population grows even under different combinations of spatial structure, transition rules, and update dynamics, though rates of cooperator fixation decline under pairwise comparison and synchronous updating. These findings indicate that, under certain evolutionary processes, merely existing in time and space promotes the evolution of cooperation.

Application of Morphosyntactic Cues in Detection of GOAL Semantic Role

This paper analyzes the semantics of verbs with the prefix “do-” and explains the adlativity feature based on the morpho-syntactically annotated corpus hrWaC and handcrafted verb valency frames. The work aims to automatically add all types of adlativity to Croatian verb valency lexicon. As a result, it was revealed that if a language resource encodes “do-” as the adlative prefix in Croatian as a source language, then the adlative meaning in the target language can be assumed as well. Using the valency frame transition rules for language pairs, it is possible to design matching verb valency frames in other languages and consequently describe each verb and its translation by semantic roles (agent, patient, direction-to, and goal) and by selectional restrictions.

Analysis of changes occurring in Codon Positions due to mutations through the cellular automata transition rules

Variation in the nucleotides of a codon may cause variations in the evolutionary patterns of a DNA or amino acid sequence. To address the capability of each position of a codon to have non-synonymous mutations, the concept of degree of mutation has been introduced. The degree of mutation of a particular position of codon defines the number of non-synonymous mutations occurring for the substitution of nucleotides at each position of a codon, when other two positions of that codon remain unaltered. A Cellular Automaton (CA), is used as a tool to model the mutations of any one of the four DNA bases A, C, T and G at a time where the DNA bases correspond to the states of the CA cells. Point mutation (substitution type) of a codon which characterizes changes in the amino acids, have been associated with local transition rules of a CA. Though there can be transitions of a 4-state CA with 3-neighbourhood cells, here it has been possible to represent all possible point mutations of a codon in terms of combinations of 16 local transition functions of the CA. Further these rules are divided into 4 classes of equivalence. Also, according to the nature of mutations, the 16 local CA rules of substitutions are classified into 3 sets namely, ‘No Mutation’, ‘Transition’ and ‘Transversion’. The experiment has been carried out with three sets of single nucleotide variations(SNVs) of three different viruses but the symptoms of the diseases caused by them are to some extent similar to each other. They are SARS-CoV-1, SARS-CoV-2 and H1N1 Type A viruses. The aim is to understand the impact of nucleotide substitutions at different positions of a codon with respect to a particular disease phenotype.

SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging

Sleep staging is fundamental for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively extract salient waves in multimodal sleep data; 2) How to capture the multi-scale transition rules among sleep stages; 3) How to adaptively seize the key role of specific modality for sleep staging. To address these challenges, we propose SalientSleepNet, a multimodal salient wave detection network for sleep staging. Specifically, SalientSleepNet is a temporal fully convolutional network based on the $U^2$-Net architecture that is originally proposed for salient object detection in computer vision. It is mainly composed of two independent $U^2$-like streams to extract the salient features from multimodal data, respectively. Meanwhile, the multi-scale extraction module is designed to capture multi-scale transition rules among sleep stages. Besides, the multimodal attention module is proposed to adaptively capture valuable information from multimodal data for the specific sleep stage. Experiments on the two datasets demonstrate that SalientSleepNet outperforms the state-of-the-art baselines. It is worth noting that this model has the least amount of parameters compared with the existing deep neural network models.

Forest Transition and Its Dynamics in Subtropical Chongqing, China since 1990s

This research aims to advance our understanding towards forest transition, which is about the shift from net deforestation to net reforestation over a given area during certain period, through a case study of a western city in China from 1990 to 2015. Three main contributions are made to the theory and practice of forest land management. First, this research enriches forest transition theory with a meso-level exploration on forest land change in subtropic Chongqing, which echoes the “U” shape transition rules widely observed in the Euro-American context but was found a time lag of the turning point until 2000s. Second, it intentionally identifies the subtype of forests—the artificial plantation, which is considered influential on performance of forest’s carbon sequestration but not paid sufficient attention to. Third, it adds knowledge on forest transition pathway at an intra-urban scale through the identified significance degree of forest transition dynamics, which implies that economic development matters but is less important than topography in a mountainous city like Chongqing, and different dimensions of economy impact differently on forest transition.

Mockingbird Morphing Music: Structured Transitions in a Complex Bird Song

The song of the northern mockingbird, Mimus polyglottos, is notable for its extensive length and inclusion of numerous imitations of several common North American bird species. Because of its complexity, it is not widely studied by birdsong scientists. When they do study it, the specific imitations are often noted, and the total number of varying phrases. What is rarely noted is the systematic way the bird changes from one syllable to the next, often with a subtle transition where one sound is gradually transformed into a related sound, revealing an audible and specific compositional mode. It resembles a common strategy in human composing, which can be described as variation of a theme. In this paper, we present our initial attempts to describe the specific compositional rules behind the mockingbird song, focusing on the way the bird transitions from one syllable type to the next. We find that more often than chance, syllables before and after the transition are spectrally related, i.e., transitions are gradual, which we describe as morphing. In our paper, we categorize four common modes of morphing: timbre change, pitch change, squeeze (shortening in time), and stretch (lengthening in time). This is the first time such transition rules in any complex birdsong have been specifically articulated.