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Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service quality of network due to limited energy. To address these problems, this paper proposes a new area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization (IAPSO). For area coverage problem, we set up a multi-objective optimization model in order to improve coverage ratio and reduce redundancy ratio by sensing direction rotation. For energy consumption optimization, we make energy consumption evenly distribute on each sensor node by clustering network. We set up a cluster head selection optimization model which considers the total residual energy ratio and energy consumption balance degree of cluster head candidates. We also propose a cluster formation algorithm in which member nodes choose their cluster heads by weight function. We next utilize an IAPSO to solve two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Extensive simulation results demonstrate the our proposed approach performs better than other ones.

CRISPR diagnostics are effective but suffer from low signal transduction efficiency, limited sensitivity, and poor stability due to their reliance on the trans-cleavage of single-stranded nucleic acid fluorescent reporters. Here, we present CrisprAIE, which integrates CRISPR/Cas reactions with “one to more” aggregation-induced emission luminogen (AIEgen) lighting-up fluorescence generated by the trans-cleavage of Cas proteins to AIEgen-incorporated double-stranded DNA labeled with single-stranded nucleic acid linkers and Black Hole Quencher groups at both ends (Q-dsDNA/AIEgens-Q). CrisprAIE demonstrates superior performance in the clinical nucleic acid detection of norovirus and SARS-CoV-2 regardless of amplification. Moreover, the diagnostic potential of CrisprAIE is further enhanced by integrating it with spherical nucleic acid-modified AIEgens (SNA/AIEgens) and a portable cellphone-based readout device. The improved CrisprAIE system, utilizing Q-dsDNA/AIEgen-Q and SNA/AIEgen reporters, exhibits approximately 80- and 270-fold improvements in sensitivity, respectively, compared to conventional CRISPR-based diagnostics. We believe CrisprAIE can be readily extended as a universal signal generation strategy to significantly enhance the detection efficiency of almost all existing CRISPR-based diagnostics. Current CRISPR diagnostic approaches can be hampered by several limitations, including low signal transduction efficiency and limited sensitivity. Here, the authors present CrisprAIE, an approach based on CRISPR/Cas-mediated “one to more” lighting-up nucleic acid detection using aggregation-induced emission luminogens.

The development of modern communication technologies and smart mobile devices has driven the evolution of mobile crowdsensing (MCS). Optimizing the task assignment process under constrained resources to maximize utility is a key challenge in MCS. However, most existing studies presuppose a sufficient pool of available workers during the task assignment process, overlooking the impact of temporal fluctuations in worker numbers under online scenarios. Additionally, existing studies commonly publish sensing tasks to the MCS platform for immediate assignment upon their arrival. However, the uncertainty in the number of available workers in online scenarios may fail to meet task demands. To address these challenges, this paper proposes a two-stage online task assignment scheme. The first stage introduces an adaptive task pre-assignment strategy based on worker quantity prediction, which determines task acceptance and assigns tasks to suitable subareas. The second stage employs a dynamic online recruitment method to select workers for the assigned tasks, aiming to maximize platform utility. Finally, the simulation experiments conducted on two real-world datasets demonstrate that the proposed methods effectively solve the challenges of online task assignment in MCS.

Wireless sensors deployed in large agricultural areas can monitor and collect data in real time, helping to achieve smart agriculture. But the complexity of the environment and the random deployment method seriously affect the coverage quality. The limited capacity of sensor batteries greatly limits the network lifetime. Therefore, how to extend the network lifetime while ensuring coverage quality is a highly challenging task. This paper proposes a node deployment optimization method to solve the problems of a poor coverage rate and a short network lifetime in hybrid self-powered sensor networks in obstacle environments. This method first optimizes the sensing direction of stationary nodes, expands the coverage range, and repairs coverage holes. Then, an improved bidirectional search A* algorithm is used to plan the obstacle avoidance moving path of mobile nodes, fill the remaining coverage holes, and improve the coverage quality of the network. Finally, a method based on an improved nutcracker optimizer algorithm is proposed to solve the optimal working sequence of nodes, schedule the “sleep or work” state of nodes, and extend the network lifetime. The simulation experiment verified the effectiveness of the proposed method, indicating that its performance in coverage quality, mobile energy consumption, and network lifetime is superior to other compared methods.

Directional sensor networks are a widely used architecture in the sensing layer of the Internet of Things (IoT), which has excellent data collection and transmission capabilities. The coverage hole caused by random deployment of sensors is the main factor restricting the quality of data collection in the IoT sensing layer. Determining how to enhance coverage performance by repairing coverage holes is a very challenging task. To this end, we propose a node deployment optimization method to enhance the coverage performance of the IoT sensing layer. Firstly, with the goal of maximizing the effective coverage area, an improved particle swarm optimization (IPSO) algorithm is used to solve and obtain the optimal set of sensing directions. Secondly, we propose a repair path search method based on the improved sparrow search algorithm (ISSA), using the minimum exposure path (MEP) found as the repair path. Finally, a node scheduling algorithm is designed based on MEP to determine the optimal deployment location of mobile nodes and achieve coverage enhancement. The simulation results show that compared with existing algorithms, the proposed node deployment optimization method can significantly improve the coverage rate of the IoT sensing layer and reduce energy consumption during the redeployment process.

This paper is concerned with auto-focus of microscopes for the surface structure of transparent materials under transmission illumination, where two distinct focus states appear in the focusing process and the focus position is located between the two states with the local minimum of sharpness. Please note that most existing results are derived for one focus state with the global maximum value of sharpness, they cannot provide a feasible solution to this particular problem. In this paper, an auto-focus method is developed for such a specific situation with two focus states. Firstly, a focus state recognition model, which is essentially an image classification model based on a deep convolution neural network, is established to identify the focus states of the microscopy system. Then, an endpoint search algorithm which is an evolutionary algorithm based on differential evolution is designed to obtain the positions of the two endpoints of the region where the real focus position is located, by updating the parameters according to the focus states. At last, a region search algorithm is devised to locate the focus position. The experimental results show that our method can achieve auto-focus rapidly and accurately for such a specific situation with two focus states.

Gold nanoparticle–decorated metal organic frameworks (MOF@AuNPs) with significantly enhanced color signal intensity were synthesized through in situ growth of AuNPs on the MOF skeleton. The resultant MOF@AuNP nanocomposites were characterized with 16.7-fold higher absorbance than conventional 40 nm AuNPs (AuNP 40 ). Thus, for the first time, we applied it as a signal amplification label to improve the immunochromatographic assay (ICA) of human chorionic gonadotropin (HCG). The detection limit of our enhanced ICA was 1.69 mIU/mL, which is ca. 10.6-fold improvement in sensitivity compared to traditional AuNP 40 -ICA. The recoveries of this MOF@AuNPs-ICA ranged from 86.03 to 119.22%, with coefficients of variation of 3.05 to 13.74%. The reliability and practicability were further validated by the clinically used chemiluminescence immunoassay method. Given their excellent signal amplification ability, the proposed MOF@AuNPs could serve as an ideal ICA label for rapid and sensitive detection of disease biomarkers. Graphical abstract

This paper presents a novel hierarchical voltage control framework for distribution networks to mitigate voltage violations by coordinating distributed energy storage systems (DESSs). The framework establishes a two-layer architecture that integrates centralized optimization with distributed execution. In the upper layer, a model predictive control (MPC)-based controller computes optimal power dispatch trajectories for critical buses, effectively decoupling slow-timescale optimization from real-time adjustments. In the lower layer, a broadcast-based controller dispatches parameterized power regulation signals, enabling autonomous active power tracking by the DESS units. This hierarchical design explicitly addresses the scalability limitations of conventional centralized control and the cyber vulnerabilities of peer-to-peer distributed strategies. The effectiveness of the proposed control framework is verified on the modified IEEE 34-bus and 123-bus test feeder. The results show that the proposed method can mitigate the average voltage violation by 93.7% and show control robustness even under 60% communication loss condition.

A major concern in data-driven deep learning (DL) is how to maximize the capability of a model for limited datasets. The lack of high-performance datasets limits intelligent agriculture development. Recent studies have shown that image enhancement techniques can alleviate the limitations of datasets on model performance. Existing image enhancement algorithms mainly perform in the same category and generate highly correlated samples. Directly using authentic images to expand the dataset, the environmental noise in the image will seriously affect the model’s accuracy. Hence, this paper designs an automatic leaf segmentation algorithm (AISG) based on the EISeg segmentation method, separating the leaf information with disease spot characteristics from the background noise in the picture. This algorithm enhances the network model’s ability to extract disease features. In addition, the Cycle-GAN network is used for minor sample data enhancement to realize cross-category image transformation. Then, MobileNet was trained by transfer learning on an enhanced dataset. The experimental results reveal that the proposed method achieves a classification accuracy of 98.61% for the ten types of tomato diseases, surpassing the performance of other existing methods. Our method is beneficial in solving the problems of low accuracy and insufficient training data in tomato disease detection. This method can also provide a reference for the detection of other types of plant diseases.

An important issue in directional sensor networks (DSNs) is how to prolong the network lifetime in Q-coverage scenarios where each target point may have different coverage requirements. When the Q-coverage requirement is met, it is an effective way to maximize the network lifetime by controlling energy consumptions. Unlike the existing results where only the sensing energy consumption is considered, this paper proposes a new hybrid energy consumption pattern, which reflects the reality of energy consumptions more closely. In such a pattern, both sensing and communication energy consumptions are considered. By combining scheduling and clustering technologies to control these two kinds of energy consumptions in each round, a new lifetime-enhancing method (NLEM) is devised to prolong the network lifetime. First, a sensing direction scheduling algorithm for Q-coverage is proposed to make different sensing direction sets meet the coverage requirement of each target point. Then, a new cluster head selection algorithm and an inter-cluster communication algorithm are developed to select an optimal cluster head set and achieve multi-hop communication, respectively. Simulation results demonstrate the effectiveness of the NLEM in prolonging the network lifetime for DSNs in Q-coverage scenarios.