A Power Effective Algorithm for State Encoding

Reducing the area and power dissipation of FSM circuit is of significant importance for EDA technology. Many methods are adopted to achieve an effective and fast transformation of FSMs to binary codes, including Genetic algorithm (GA) and others. In this paper, we propose a GA based state assignment of a FSM circuit to gain the minimization of power consumption and area. We modify the traditional mutation to be an ordered operation, which is also a substitution of the crossover that guarantees every new individual owns better fitness than the old one. We test the proposed algorithm with benchmarks, as well as do the comparison with the published; our method saves both power and area dissipation in reasonable computation time. DOI: http://dx.doi.org/10.11591/telkomnika.v12i6.548

Modular Timing Constraints for Delay-Insensitive Systems

This paper introduces ARCtimer, a framework for modeling, generating, verifying, and enforcing timing constraints for individual self-timed handshake components. The constraints guarantee that the component’s gate-level circuit implementation obeys the component’s handshake protocol specification. Because the handshake protocols are delayinsensitive, self-timed systems built using ARCtimer-verified components are also delay-insensitive. By carefully considering time locally, we can ignore time globally. ARCtimer comes early in the design process as part of building a library of verified components for later system use. The library also stores static timing analysis (STA) code to validate and enforce the component’s constraints in any self-timed system built using the library. The library descriptions of a handshake component’s circuit, protocol, timing constraints, and STA code are robust to circuit modifications applied later in the design process by technology mapping or layout tools. In addition to presenting new work and discussing related work, this paper identifies critical choices and explains what modular timing verification entails and how it works

Approximate Equivalence of the Hybrid Automata with Taylor Theory

Hybrid automaton is a formal model for precisely describing a hybrid system in which the computational processes interact with the physical ones. The reachability analysis of the polynomial hybrid automaton is decidable, which makes the Taylor approximation of a hybrid automaton applicable and valuable. In this paper, we studied the simulation relation among the hybrid automaton and its Taylor approximation, as well as the approximate equivalence relation. We also proved that the Taylor approximation simulates its original hybrid automaton, and similar hybrid automata could be compared quantitatively, for example, the approximate equivalence we proposed in the paper

Automata-Based Analysis of Stage Suspended Boom Systems

A stage suspended boom system is an automatic steeve system orchestrated by the PLC (programmable logic controller). Security and fault-recovering are two important properties. In this paper, we analyze and verify the boom system formally. We adopt the hybrid automaton to model the boom system. The forward reachability is used to verify the properties with the reachable states. We also present a case study to illustrate the feasibility of the proposed verification

Rail surface defect data enhancement method based on improved ACGAN

Rail surface defects present a significant safety concern in railway operations. However, the scarcity of data poses challenges for employing deep learning in defect detection. This study proposes an enhanced ACGAN augmentation method to address these issues. Residual blocks mitigate vanishing gradient problems, while a spectral norm regularization-constrained discriminator improves stability and image quality. Substituting the generator’s deconvolution layer with upsampling and convolution operations enhances computational efficiency. A gradient penalty mechanism based on regret values addresses gradient abnormality concerns. Experimental validation demonstrates superior image clarity and classification accuracy compared to ACGAN, with a 17.6% reduction in FID value. MNIST dataset experiments verify the model’s generalization ability. This approach offers practical value for real-world applications

Fatty Acid Accumulations and Transcriptome Analyses Under Different Treatments in a Model Microalga Euglena gracilis

With the continuous growth of the world’s population and the increasing development of industrialization, the demand for energy by human beings has been expanding, resulting in an increasingly severe energy crisis. Microalgae are considered the most potential alternatives to traditional fossil fuels due to their many advantages, like fast growth rate, strong carbon sequestration capacity, and low growth environment requirements. Euglena can use carbon sources such as glucose, ethanol, and others for heterotrophic growth. Moreover, Euglena is highly adaptable to the environment and has a high tolerance to various environmental stresses, such as salinity, heavy metals, antibiotics, etc. Different treatments of Euglena cells could affect their growth and the accumulation of bioactive substances, especially fatty acids. To expand the industrial application of Euglena as a potential biodiesel candidate, we determine the physiological responses of Euglena against environmental stresses (antibiotics, heavy metals, salinity) or carbon resources (glucose and ethanol), and evaluate the potential for higher quality and yield of fatty acid with a high growth rate. Adding glucose into the culture media increases cell biomass and fatty acid production with high-quality biodiesel characters. The transcriptome analysis helped explore the possible regulation and biosynthesis of fatty acids under different treatments and exploited in the improvement of biodiesel production. This study provides insights for further improvement and various culture treatments for Euglena-based biodiesel and jet fuels

Unraveling the Contribution of High Temperature Stage to Jiang-Flavor Daqu, a Liquor Starter for Production of Chinese Jiang-Flavor Baijiu, With Special Reference to Metatranscriptomics

Jiang-flavor (JF) daqu is a liquor starter used for production of JF baijiu, a well-known distilled liquor in China. Although a high temperature stage (70°C) is necessary for qualifying JF daqu, little is known regarding its active microbial community and functional enzymes, along with its role in generating flavor precursors for JF baijiu aroma. In this investigation, based on metatranscriptomics, fungi, such as Aspergillus and Penicillium, were identified as the most active microbial members and 230 carbohydrate-active enzymes were identified as potential saccharifying enzymes at 70°C of JF daqu. Notably, most of enzymes in identified carbohydrate and energy pathways showed lower expression levels at 70°C of JF daqu than those at the high temperature stage (62°C) of Nong-flavor (NF) daqu, indicating lowering capacities of saccharification and fermentation by high temperature stage. Moreover, many enzymes, especially those related to the degradation of aromatic compounds, were only detected with low expression levels at 70°C of JF daqu albeit not at 62°C of NF daqu, indicating enhancing capacities of generating special trace aroma compounds in JF daqu by high temperature stage. Additionally, most of enzymes related to those capacities were highly expressed at 70°C by fungal genus of Aspergillus, Coccidioides, Paracoccidioides, Penicillium, and Rasamsonia. Therefore, this study not only sheds light on the crucial functions of high temperature stage but also paves the way to improve the quality of JF baijiu and provide active community and functional enzymes for other fermentation industries

Duckweed: a starch-hyperaccumulating plant under cultivation with a combination of nutrient limitation and elevated CO2

IntroductionThe increasing global demand for starch has created an urgent need to identify more efficient and sustainable production methods. However, traditional starch sources, such as crop-based options, experience significant bottlenecks due to limitations in land use, water consumption, and the impacts of climate change. Therefore, there is a pressing need to explore and develop new sources of starch.MethodsWe develop a novel duckweed cultivation technology that combines nutrients limitation and CO2 supplementation to achieve very high starch content. In this study, we integrated whole-genome sequencing, epigenomics, transcriptomics, enzyme activity, and composition variation to elucidate the mechanisms of efficient starch accumulation in duckweed in terms of starch accumulation and carbon partitioning, regulation of the expression of genes in the starch metabolic pathway, and sucrose biosynthesis and transportation.Results and discussionAlthough Landoltia punctata exhibits dramatic gene family contraction, its starch content and productivity reached 72.2% (dry basis) and 10.4 g m-2 d-1, respectively, in 10 days, equivalent to a yield of 38.0 t ha-1 y-1, under nutrient limitation treatment with elevated CO2 levels. We also examined the mechanism of high starch accumulation in duckweed. This phenomenon is associated with the regulation of DNA methylation and transcription factors as well as the significantly upregulated transcription levels and the increased activities of key enzymes involved in starch biosynthesis. Moreover, while nitrogen redistribution was increased, sucrose biosynthesis and transportation and lignocellulose biosynthesis were reduced. These alterations led to a reduction in lignocellulose and protein contents and ultimately an increase in the accumulation of starch in the chloroplasts.ConclusionThis work demonstrates the potential of duckweed as a highly efficient starch producer

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives