Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph (DAG) and is more difficult to tackle. In the proposed algorithm, the HMC task is firstly changed into a set of binary classification tasks. Then, two measures are implemented in the algorithm to enhance the HMC performance by considering the hierarchy structure during the learning procedures. Firstly, negative instances selecting policy associated with the SMOTE approach are proposed to alleviate the imbalanced data set problem. Secondly, a nodes interaction method is introduced to combine the results of binary classifiers. It can guarantee that the predictions are consistent with the hierarchy constraint. The experiments on eight benchmark yeast data sets annotated by the Gene Ontology show the promising performance of the proposed algorithm compared with other state-of-the-art algorithms.

Battery state of health (SOH) is a significant metric for evaluating battery life and predicting battery safety. Currently, SOH research is largely based on laboratory data, with a dearth of research on electric vehicle (EV) operating data. Due to the difficulty in obtaining complete charge data under EV operating conditions, this study presents a SOH estimation method utilizing deep network adaptation. First, a data-driven approach is employed to extract voltage, current, state of charge (SOC), and incremental capacity (IC) data features. To compensate for the lack of aging information in the EV operation data domain, transfer learning is employed to construct the SOH estimation model. Additionally, to resolve inconsistent data distribution between the source laboratory battery data domain and the target EV operation data domain, an adaptive layer is added to the network, and adaptation of deep network (ADN) is utilized to enhance the model’s performance. Finally, the model is validated using electric bus operational data. Results indicate that this model’s average Mean Absolute Error (MAE) is less than 3.0%, and, compared to support vector machine (SVM) regression and Gaussian Process Regression (GPR) algorithms, the MAE is reduced by 27.7% and 38.4%, respectively.

Digital microfluidic biochips (DMFBs) are attractive instruments for obtaining modern molecular biology and chemical measurements. Due to the increasingly complex measurements carried out on a DMFB, such chips are more prone to failure. To compensate for the shortcomings of the module-based DMFB, this paper proposes a routing-based fault repair method. The routing-based synthesis methodology ensures a much higher chip utilization factor by removing the virtual modules on the chip, as well as removing the extra electrodes needed as guard cells. In this paper, the routing problem is identified as a dynamic path-planning problem and mixed path design problem under certain constraints, and an improved Dijkstra and improved particle swarm optimization (ID-IPSO) algorithm is proposed. By introducing a cost function into the Dijkstra algorithm, the path-planning problem under dynamic obstacles is solved, and the problem of mixed path design is solved by redefining the position and velocity vectors of the particle swarm optimization. The ID-IPSO routing-based fault repair method is applied to a multibody fluid detection experiment. The proposed design method has a stronger optimization ability than the greedy algorithm. The algorithm is applied to $8\times 9$, $8\times 8$, and $7\times 8$ fault-free chips. The proposed ID-IPSO routing-based chip design method saves 13.9%, 14.3%, and 14.5% of the experiment completion time compared with the greedy algorithm. Compared with a modular fault repair method based on the genetic algorithm, the ID-IPSO routing-based fault repair method has greater advantages and can save 39.3% of the completion time on average in the completion of complex experiments. When the ratio of faulty electrodes is less than 12% and 23%, the modular and ID-IPSO routing-based fault repair methods, respectively, can guarantee a 100% failure repair rate. The utilization rate of the electrodes is 18% higher than that of the modular method, and the average electrode usage time is 17%. Therefore, the ID-IPSO routing-based fault repair method can accommodate more faulty electrodes for the same fault repair rate; the experiment completion time is shorter, the average number of electrodes is lower, and the security performance is better.

Smart Campus is an intelligent and smart environment of teaching, learning and living. It is built on the Internet of Things and application services. Colleges and universities are devoted to the construction and development of the smart campus and have completed some digital campuses of different sizes. However, there are still some problems in the current construction and development process, such as low degree of intelligence, free intelligent functions, energy and resource monitoring is still in the traditional mode of non-intelligent management, the functions of terminal application are still simple, can’t keep up with the intelligent development of the campus. In order to help promote the development of smart campus, we designed a database, smart campus mobile terminal APP. The smart campus APP is based on Android system to monitor and control energy and other nodes in the campus. The monitoring method is real-time, clear and efficient. In addition, smart dormitory, smart classroom and smart map function have also been designed to provide a more convenient and safe living environment for teachers and students in our school. And this APP has laid a solid foundation for establishing a smart, efficient, open, harmonious, humane and safe campus.