Metabolic pathway design is crucial in synthetic biology. Compared to traditional manual methods, evolutionary computation-based approaches greatly reduce time and cost, becoming the dominant trend. Current algorithms fall into two categories: single-objective and multi-objective. Multi-objective methods outperform single-objective ones in diversity and convergence, making them better suited for complex problems by balancing multiple performance metrics. However, existing multi-objective evolutionary algorithms for pathway design remain inefficient. To address this, we propose an efficient method based on a multi-objective shuffled frog leaping algorithm. Experiments show our approach significantly reduces computation time and discovers higher-performing pathways compared to existing methods.

Vocoder-based speech synthesis has become a promising technique to accommodate the demands of high-quality speech analysis, manipulation, and synthesis. However, most existing works focus on how to synthesize normal human voice with high signal-to-noise ratio, neglecting individuals' pathological voice disorder in speech interaction. In this work, we propose a non-linear voice repair vocoder for pathological vowels and sentences, which takes the pathological speech as input and generates high-quality repaired speech. Our approach is specifically designed to enhance the speech quality and intelligibility for individuals with voice disorders. We employ amplitude modulated-frequency modulated (AM-FM) and Teager energy operation techniques to enhance the quality of pitch and spectral envelope. To tackle the instability and fracture problem of pitch, we present spectral tracking algorithm, which not only avoids dramatic change in the edge of voice, but also reduces the errors of half-pitch. Furthermore, we design a spectral reconstruction algorithm, which can effectively rebuild the spectral structure by energy operation to accomplish spectral envelope repair. The proposed PVR-Vocoder shows exceptional performance in pathological voice intelligibility enhancement according to various quality measures including objective indicators, subjective evaluation, and spectrum observations.

Abstract One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination ( R 2 ), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy ( R 2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL.

Recently, metabolic pathway design has attracted considerable attention and become an increasingly important area in metabolic engineering. Manual or computational methods have been introduced to retrieve the metabolic pathway. These methods model metabolic pathway design as a single-objective optimization problem with the weighted sum of a variety of criteria as the final score. While these methods have demonstrated promising results, the majority of current methods do not account for comparisons and competition among criteria. Here, we propose MooSeeker, a metabolic pathway design tool based on the multi-objective optimization algorithm that aims to trade off all the criteria optimally. The metabolic pathway design problem is characterized as a multi-objective optimization problem with three objectives including pathway length, thermodynamic feasibility and theoretical yield. In order to digitize the continuous metabolic pathway, MooSeeker develops the encoding strategy, BioCrossover and BioMutation operators to search for the candidate pathways. Finally, MooSeeker outputs the Pareto optimal solutions of the candidate metabolic pathways with three criterion values. The experiment results show that MooSeeker is capable of constructing the experimentally validated pathways and finding the higher-performance pathway than the single-objective-based methods.

Evolutionary algorithms (EAs) show strong adaptability in solving the complex optimization problems. Fitness evaluation is an important step of EA. In this step, the fitness of individuals is calculated by fitness evaluation methods. However, there are many complex optimization problems in which the existing fitness evaluation methods cannot express solutions’ fitness by crisp values but only fuzzy fitness information. Even if some fitness evaluation methods can predict crisp values to express the individuals’ fitness, these methods must “see” some crisp fitness information in advance. These shortcomings of existing fitness evaluation methods make EAs unable to work effectively in many optimization tasks. In this article, we propose the concept of blind fitness evaluation. A blind fitness evaluation method (BFEM) can output individuals’ crisp fitness information without “seeing” any crisp fitness information in advance. Based on the concept, we propose a BFEM which uses an artificial neural network, named modified fitness comparison network (MFCN) to evaluate the fitness of individuals. The MFCN can be trained by fuzzy fitness information, and the trained MFCN can predict the crisp fitness information of any individual. By applying the BFEM to EAs, the EAs with blind fitness evaluation are obtained and the algorithms can effectively solve the complex optimization problems with only fuzzy fitness information. The proposed BFEM is compared with several popular fitness evaluation methods that rely on crisp fitness information. Experimental results show that the fitness evaluation accuracy of MFCN trained based on only fuzzy fitness information is similar to or higher than that of the fitness evaluation methods relying on crisp fitness information. The EAs with MFCN also show higher performance than the EAs with the popular fitness evaluation methods in search ability and solution quality.

Abstract Lipreading is a visual recognition of speech by using lip movement, which aims to recognise phrases and sentences spoken by a talking face without the audio. However, the existed models for lipreading suffer from slow training speed and insufficient performance. To accelerate the training speed of the model for lipreading, a batch group training algorithm is proposed, which groups all the data of different frames. In addition, a 3D‐MouthNet‐BLSTM‐CTC architecture for lipreading is proposed to improve model performance. It bases on a 3D convolutional neural network, MouthNet, two Bi‐LSTMs, and a CTC objective function. Experiment results in Oulu‐VS2 and self‐built dataset show that 96.2% accuracy rate is achieved on the Oulu‐VS2 dataset, and 93.8% accuracy rate is achieved on the GRID dataset. This article is about lipreading research. It mainly uses deep learning methods to study lip‐reading. A new network architecture and tests on public data sets are proposed to achieve the best results.

This paper proposes an unsupervised high dynamic range (HDR) image reconstruction method based on an over/under-exposed low dynamic range (LDR) image pair. The framework includes two end-to-end branches: transferring an over-exposed image input to under-exposed images and transferring an under-exposed image input to over-exposed images. The LDR images with the same exposure from the two branches are averaged, and then reconstruct an HDR image by merging them. When training the model, we use the L1loss of the same exposure image of the two branches and MEF-SSIM loss function as the objective function to ensure that the two branches get a similar visual effect at the same exposure, and use RGB loss and HSV loss to constrain the brightness and saturation of different exposure images. Experiments demonstrate that our unsupervised framework can generate comparable results with state-of-the-art supervised learning methods.

Individuals, such as voice-related professionals, elderly people and smokers, are increasingly suffering from voice disorder, which implies the importance of pathological voice repair. Previous work on pathological voice repair only concerned about sustained vowel /a/, but multiple vowels repair is still challenging due to the unstable extraction of pitch and the unsatisfactory reconstruction of formant. In this paper, a multiple vowels repair based on pitch extraction and Line Spectrum Pair feature for voice disorder is proposed, which broadened the research subjects of voice repair from only single vowel /a/ to multiple vowels /a/, /i/ and /u/ and achieved the repair of these vowels successfully. Considering deep neural network as a classifier, a voice recognition is performed to classify the normal and pathological voices. Wavelet Transform and Hilbert-Huang Transform are applied for pitch extraction. Based on Line Spectrum Pair (LSP) feature, the formant is reconstructed. The final repaired voice is obtained by synthesizing the pitch and the formant. The proposed method is validated on Saarbrücken Voice Database (SVD) database. The achieved improvements of three metrics, Segmental Signal-to-Noise Ratio, LSP distance measure and Mel cepstral distance measure, are respectively 45.87%, 50.37% and 15.56%. Besides, an intuitive analysis based on spectrogram has been done and a prominent repair effect has been achieved.

The object detection, which has been widely applied in the biomedical field already, is of real significance but technically challenging. In practice, the object detection accuracy is vulnerable to labeling quality, which is usually not a big headache for simple algorithm or model verification since there are a bunch of ideal public available datasets whose classes and tags are all well-marked. However, in real scenarios, image data is often partially or even incorrectly labeled. Particularly, in cell detection, this becomes a thorny issue since the labelling of the dataset is incomplete and inaccurate. To address this issue, we propose a data-augmentation algorithm that can generate full labeled cell image data from incomplete labeled ones. First of all, we randomly extract the labeled objects from raw cell images, and meanwhile, keep their corresponding position information. Next, we employ the framework of cycle-consistent adversarial network, but significantly distinguished from the original one, to generate fully labeled data including both objects and backgrounds. We conduct extensive experiments on a blood cell classification dataset called BCCD to evaluate our model, and experimental results show that our proposed method can successfully address the weak annotation problem and improve the performance of object detection.

Through energy harvesting system, new energy sources are made available immediately for many advanced applications based on environmentally embedded systems. However, the harvested power, such as the solar energy, varies significantly under different ambient conditions, which in turn affects the energy conversion efficiency. In this paper, we propose an approach for designing power-adaptive computing systems to maximize the energy utilization under variable solar power supply. Using the geometric programming technique, the proposed approach can generate a customized parallel computing structure effectively. Then, based on the prediction of the solar energy in the future time slots by a multilayer perceptron neural network, a convex model-based adaptation strategy is used to modulate the power behavior of the real-time computing system. The developed power-adaptive computing system is implemented on the hardware and evaluated by a solar harvesting system simulation framework for five applications. The results show that the developed power-adaptive systems can track the variable power supply better. The harvested solar energy utilization efficiency is 2.46 times better than the conventional static designs and the rule-based adaptation approaches. Taken together, the present thorough design approach for self-powered embedded computing systems has a better utilization of ambient energy sources.

In this paper, the work inis analyzed. An error in its theoretical description part is pointed out and illustrated by a simple example. A modification suggestion is proposed to make the theoretical description of the workmore deliberate and thus being used appropriately.